Program description

Content

Among the industries with the greatest growth rates is the communications industry which, over the years, has achieved in its products the synergy of the classical disciplines of computer science and networking. The International Master Program Information and Communication Systems addresses this rapidly evolving area by laying in-depth foundations for the design and implementation of networking infrastructures, networked Cyber Physical Systems and the applications and services running on them.

The program is organized as a two-year course (four semesters) which starts on 1st of October each year. It includes around two semesters of lectures and practical courses and almost two semesters devoted to work in a research team (project work) and to the preparation of a master’s thesis. The “Master of Science” degree will be awarded. Language of the program is English.

Graduates of the program are provided with the basics and knowledge that are required for a successful engineering activity in the information and communication technology in an international environment. They acquire extensive knowledge in the mathematical, engineering and scientific basic principles of this discipline based on a solid theoretical foundation including all the essential application-oriented aspects. Graduates are qualified to independently resolve problems in the information and communications technology and related disciplines.

The graduates are able to apply methods and procedures required to work on technical issues, as well as critically examine new insights to further develop and incorporate in their work. In this way, they are qualified to carry out their duties for society responsibly.

In addition to the foundational curriculum taught at TUHH, seminars on developing personal skills are integrated into the dual study programme, in the context of transfer between theory and practice. These seminars correspond to the modern professional requirements expected of an engineer, as well as promoting the link between the two places of learning.

The intensive dual courses at TUHH integrating practical experience consist of an academic-oriented and a practice-oriented element, which are completed at two places of learning. The academic-oriented element comprises study at TUHH. The practice-oriented element is coordinated with the study programme in terms of content and time, and consists of practical modules and phases spent in an affiliate company during periods when there are no lectures.

Career prospects

The study of Information and Communication Systems provides the in-depth training in the areas of Information and Communication Technology, Software Systems, IT Security and Signal Processing. This enables excellent career prospects both in the industrial as well as on the academic job market. The Master's degree qualifies graduates for doctoral studies.

In addition, students acquire basic professional and personal skills as part of the dual study programme that enable them to enter professional practice at an early stage and to go on to further study. Students also gain practical work experience through the integrated practical modules. Graduates of the dual course have broad foundational knowledge, fundamental skills for academic work and relevant personal competences.

Learning target

Knowledge

The students gain common knowledge from the core qualification and more specific knowledge depending on the selected specialisation. All students are able to describe information theory and coding basics.

Specialisation Communication Systems:

Students can

show their profound knowledge in digital communications,
describe their specialized knowledge in communication networks,
explain software development principles,
explain signal processing fundamentals.

Specialisation Secure and Dependable IT Systems:

Students can

give an overview of software verification,
describe security principles for information and communication systems,
explain their specialized knowledge in communication networks,
describe software development and signal processing principles.

Skills

The ability to apply knowledge in order to perform tasks and solve problems will be supported in this course. Information and Communication Systems graduates are capable to

solve problems in information and communication systems by applying and adapting techniques, procedures and methods that are required for a successful professional activity and by using engineering systematics,
organize the planning of theoretical and experimental studies in order to develop optimal solutions for complex applications in information and communication technology and evaluate the solutions analyse problems using scientific systematics and solve them most effectively to develop economically viable approaches for products and systematically reflect non-technical implications of engineering activity to responsibly involve them in their actions,
evaluate reliability of developed systems, prepare and review results of practical applications so that they can be used for systems optimization
Investigate, evaluate and integrate new technologies, systems, architecture, services and applications for information and communication systems.

Social skills

The ability of target-oriented work in collaboration with others, communication, and understanding their interests and social situations are goals of this course. The students can

present and argue the results of their work in written and oral form in an comprehensible way,
communicate and collaborate with international professionals, also of other disciplines,
collaborate in challenging projects of information and communications technology in a responsible position,
develop ideas and solutions in team work.

Autonomy

The course helps to improve ability and readiness to act independently and responsibly, reflect own actions and the actions of others, and to develop the own functioning. Information and Communication Systems students are capable to

identify knowledge gaps and propose solutions to overcome these gaps,
expand and deepen their knowledge and skills independently, taking into account ecological and economic demands responsibly,
familiarize themselves with complex tasks, define new tasks and develop the necessary knowledge for solving it and to systematically apply appropriate means.

By continually switching places of learnings throughout the dual study programme, it is possible for theory and practice to be interlinked. Students reflect theoretically on their individual professional practical experience, and apply the results of their reflection to new forms of practice. They also test theoretical elements of the course in a practical setting, and use their findings as a stimulus for theoretical debate.

Program structure

The four-semester program is designed modularly and is based on the university-wide standardized course structure with uniform module sizes (multiples of six credit points (CP)).

Core qualification: 78 CP

Specialization: 42 CP

Master thesis: 30 CP

Total: 120 CP

The core qualification consists of the module Information Theory and Coding (6 CP), technical complementary courses (12 CP), Business & Management (6 CP), nontechnical complementary courses (6 CP) and research project with seminar (18 CP). The research project with seminar consists of a scientific thesis with documentation and accompanying presentations in a seminar among fellow students.

The students choose between two specialisations (42 CP each):

Communication Systems

Containing: Communications, software, and signal processing

Secure and Dependable IT Systems

Containing: IT security, networks, software and signal processing

Students write a master thesis (30 CP).

The structural model of the dual study programme follows a module-differentiating approach. Given the practice-oriented element, the curriculum of the dual study programme is different compared to a standard Bachelor’s course. Five practical modules are completed at the dual students’ partner company as part of corresponding practical terms during lecture-free periods.

Core Qualification

Module M0523: Business & Management

Module Responsible	Prof. Matthias Meyer
Admission Requirements	None
Recommended Previous Knowledge	None
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to find their way around selected special areas of management within the scope of business management. Students are able to explain basic theories, categories, and models in selected special areas of business management. Students are able to interrelate technical and management knowledge.
Skills	Students are able to apply basic methods in selected areas of business management. Students are able to explain and give reasons for decision proposals on practical issues in areas of business management.
Personal Competence
Social Competence	Students are able to communicate in small interdisciplinary groups and to jointly develop solutions for complex problems
Autonomy	Students are capable of acquiring necessary knowledge independently by means of research and preparation of material.
Workload in Hours	Depends on choice of courses
Credit points	6

Courses

Information regarding lectures and courses can be found in the corresponding module handbook published separately.

Module M1759: Linking theory and practice (dual study program, Master's degree)

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	Successful completion of practical modules as part of the dual Bachelor’s course Module "interlinking theory and practice as part of the dual Master’s course"
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students … … can describe and classify selected classic and current theories, concepts and methods related to project management and change and transformation management ... and apply them to specific situations, processes and plans in a personal, professional context.
Skills	Dual students … ... anticipate typical difficulties, positive and negative effects, as well as success and failure factors in the engineering sector, evaluate them and consider promising strategies and courses of action. … develop specialised technical and conceptual skills to solve complex tasks and problems in their professional field of activity/work.
Personal Competence
Social Competence	Dual students … … can responsibly lead interdisciplinary teams within the framework of complex tasks and problems. … engage in sector-specific and cross-sectoral discussions with experts, stakeholders and staff, representing their approaches, points of view and work results.
Autonomy	Dual students … … define, reflect and evaluate goals and measures for complex application-oriented projects and change processes. … shape their professional area of responsibility independently and sustainably. … take responsibility for their actions and for the results of their work.
Workload in Hours	Independent Study Time 96, Study Time in Lecture 84
Credit points	6
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Studienbegleitende und semesterübergreifende Dokumentation: Die Leistungspunkte für das Modul werden durch die Anfertigung eines digitalen Lern- und Entwicklungsberichtes (E-Portfolio) erworben. Dabei handelt es sich um eine fortlaufende Dokumentation und Reflexion der Lernerfahrungen und der Kompetenzentwicklung im Bereich der Personalen Kompetenz.

Course L2890: Responsible Project Management in Engineering (for Dual Study Program)

Typ	Seminar
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Dr. Henning Haschke, Heiko Sieben
Language	DE
Cycle	WiSe/SoSe
Content	Theories and methods of project management Innovation management Agile project management Fundamentals of classic and agile methods Hybrid use of classic and agile methods Roles, perspectives and stakeholders throughout the project Initiating and coordinating complex engineering projects Principles of moderation, team management, team leadership, conflict management Communication structures: in-house, cross-company Public information policy Promoting commitment and empowerment Sharing experience with specialists and managers from the engineering sector Documenting and reflecting on learning experiences
Literature	Seminarapparat

Course L2891: Responsible Change and Transformation Management in Engineering (for Dual Study Program)

Typ	Seminar
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Dr. Henning Haschke, Heiko Sieben
Language	DE
Cycle	WiSe/SoSe
Content	Basic concepts, opportunities and limits of organisational change Models and methods of organisational design and development Strategic orientation and change, and their short-, medium- and long-term consequences for individuals, organisations and society as a whole Roles, perspectives and stakeholders in change processes Initiating and coordinating change measures in engineering Phase models of organisational change (Lewin, Kotter, etc.) Change-oriented information policy and dealing with resistance and uncertainty Promoting commitment and empowerment Successfully handling change and transformation: personally, as an employee, as a manager (personal, professional, organisational) Company-level and globally (systemic) Sharing experience with specialists and managers from the engineering sector Documenting and reflecting on learning experiences
Literature	Seminarapparat

Module M1756: Practical module 1 (dual study program, Master's degree)

Courses

Title	Typ	Hrs/wk	CP
Practical term 1 (dual study program, Master's degree) (L2887)		0	10

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	Successful completion of a compatible dual B.Sc. at TU Hamburg or comparable practical work experience and competences in the area of interlinking theory and practice Course D from the module on interlinking theory and practice as part of the dual Master’s course
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students … … combine their knowledge of facts, principles, theories and methods gained from previous study content with acquired practical knowledge - in particular their knowledge of practical professional procedures and approaches, in the current field of activity in engineering. … have a critical understanding of the practical applications of their engineering subject.
Skills	Dual students … … apply technical theoretical knowledge to complex, interdisciplinary problems within the company, and evaluate the associated work processes and results, taking into account different possible courses of action. … implement the university’s application recommendations with regard to their current tasks. … develop solutions as well as procedures and approaches in their field of activity and area of responsibility.
Personal Competence
Social Competence	Dual students … … work responsibly in project teams within their working area and proactively deal with problems within their team. … represent complex engineering viewpoints, facts, problems and solution approaches in discussions with internal and external stakeholders.
Autonomy	Dual students … … define goals for their own learning and working processes as engineers. … reflect on learning and work processes in their area of responsibility. … reflect on the relevance of subject modules specialisations and specialisation for work as an engineer, and also implement the university’s application recommendations and the associated challenges to positively transfer knowledge between theory and practice.
Workload in Hours	Independent Study Time 300, Study Time in Lecture 0
Credit points	10
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase.
Assignment for the Following Curricula	Civil Engineering: Core Qualification: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Energy Systems: Core Qualification: Compulsory Environmental Engineering: Core Qualification: Compulsory Aircraft Systems Engineering: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Information and Communication Systems: Core Qualification: Compulsory International Management and Engineering: Core Qualification: Compulsory Logistics, Infrastructure and Mobility: Core Qualification: Compulsory Materials Science: Core Qualification: Compulsory Mechanical Engineering and Management: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Biomedical Engineering: Core Qualification: Compulsory Microelectronics and Microsystems: Core Qualification: Compulsory Product Development, Materials and Production: Core Qualification: Compulsory Renewable Energies: Core Qualification: Compulsory Naval Architecture and Ocean Engineering: Core Qualification: Compulsory Theoretical Mechanical Engineering: Core Qualification: Compulsory Process Engineering: Core Qualification: Compulsory Water and Environmental Engineering: Core Qualification: Compulsory

Course L2887: Practical term 1 (dual study program, Master's degree)

Typ
Hrs/wk	0
CP	10
Workload in Hours	Independent Study Time 300, Study Time in Lecture 0
Lecturer	Dr. Henning Haschke
Language	DE
Cycle	WiSe/SoSe
Content	Company onboarding process Assigning a professional field of activity as an engineer (B.Sc.) and associated fields of work Establishing responsibilities and authorisation of the dual student within the company as an engineer (B.Sc.) Working independently in a team and on selected projects - across departments and, if applicable, across companies Scheduling the current practical module with a clear correlation to work structures Scheduling the examination phase/subsequent study semester Operational knowledge and skills Company-specific: Responsibility as an engineer (B.Sc.) in their own area of work, coordinating team and project work, dealing with complex contexts and unsolved problems, developing and implementing innovative solutions Subject specialisation (corresponding to the chosen course [M.Sc.]) in the field of activity Systemic skills Implementing the university’s application recommendations (theory-practice transfer) in corresponding work and task areas across the company Sharing/reflecting on learning Creating an e-portfolio Importance of course contents (M.Sc.) when working as an engineer Importance of development and innovation when working as an engineer
Literature	Studierendenhandbuch Betriebliche Dokumente Hochschulseitige Handlungsempfehlungen zum Theorie-Praxis-Transfer

Module M0673: Information Theory and Coding

Courses

Title	Typ	Hrs/wk	CP
Information Theory and Coding (L0436)	Lecture	3	4
Information Theory and Coding (L0438)	Recitation Section (large)	2	2

Module Responsible	Prof. Gerhard Bauch
Admission Requirements	None
Recommended Previous Knowledge	Mathematics 1-3 Probability theory and random processes Basic knowledge of communications engineering (e.g. from lecture "Fundamentals of Communications and Random Processes")
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students know the basic definitions for quantification of information in the sense of information theory. They know Shannon's source coding theorem and channel coding theorem and are able to determine theoretical limits of data compression and error-free data transmission over noisy channels. They understand the principles of source coding as well as error-detecting and error-correcting channel coding. They are familiar with the principles of decoding, in particular with modern methods of iterative decoding. They know fundamental coding schemes, their properties and decoding algorithms. The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.
Skills	The students are able to determine the limits of data compression as well as of data transmission through noisy channels and based on those limits to design basic parameters of a transmission scheme. They can estimate the parameters of an error-detecting or error-correcting channel coding scheme for achieving certain performance targets. They are able to compare the properties of basic channel coding and decoding schemes regarding error correction capabilities, decoding delay, decoding complexity and to decide for a suitable method. They are capable of implementing basic coding and decoding schemes in software.
Personal Competence
Social Competence	The students can jointly solve specific problems.
Autonomy	The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computer Science in Engineering: Specialisation II. Engineering Science: Elective Compulsory Information and Communication Systems: Core Qualification: Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory

Course L0436: Information Theory and Coding

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	SoSe
Content	Introduction to information theory and coding Definitions of information: Self information, entropy Binary entropy function Source coding theorem Entropy of continuous random variables: Differential entropy, differential entropy of uniformly and Gaussian distributed random variables Source coding Principles of lossless source coding Optimal source codes Prefix codes, prefix-free codes, instantaneous codes Morse code Huffman code Shannon code Bounds on the average codeword length Relative entropy, Kullback-Leibler distance, Kullback-Leibler divergence Cross entropy Lempel-Ziv algorithm Lempel-Ziv-Welch (LZW) algorithm Text compression and image compression using variants of the Lempel-Ziv algorithm Channel models AWGN channel Binary-input AWGN channel Binary symmetric channel (BSC) Relationship between AWGN channel and BSC Binary error and erasure channel (BEEC) Binary erasure channel (BEC) Discrete memoryless channels (DMC) Definitions of information for multiple random variables Mutual information and channel capacity Entropy, conditional entropy Chain rules for entropy and mutual information Channel coding theorem Channel capacity of fundamental channels: BSC, BEC, AWGN channel, binary-input AWGN channel etc. Power-limited vs. bandlimited transmission Capacity of parallel AWGN channels Waterfilling Examples: Multiple input multiple output (MIMO) channels, complex equivalent baseband channels, orthogonal frequency division multiplex (OFDM) Source-channel coding theorem, separation theorem Multiuser information theory Multiple access channel (MAC) Broadcast channel Principles of multiple access, time division multiple access (TDMA), frequency division multiple access (FDMA), non-orthogonal multiple access (NOMA), hybrid multiple access Achievable rate regions of TDMA and FDMA with power constraint, energy constraint, power spectral density constraint, respectively Achievable rate region of the two-user and K-user multiple access channels Achievable rate region of the two-user and K user broadcast channels Multiuser diversity Channel coding Principles and types of channel coding Code rate, data rate, Hamming distance, minimum Hamming distance, Hamming weight, minimum Hamming weight Error detecting and error correcting codes Simple block codes: Repetition codes, single parity check codes, Hamming code, etc. Syndrome decoding Representations of binary data Non-binary symbol alphabets and non-binary codes Code and encoder, systematic and non-systematic encoders Properties of Hamming distance and Hamming weight Decoding spheres Perfect codes Linear codes Decoding principles Syndrome decoding Maximum a posteriori probability (MAP) decoding and maximum likelihood (ML) decoding Hard decision and soft decision decoding Log-likelihood ratios (LLRs), boxplus operation MAP and ML decoding using log-likelihood ratios Soft-in soft-out decoders Error rate performance comparison of codes in terms of SNR per info bit vs. SNR per code bit Linear block codes Generator matrix and parity check matrix, properties of generator matrix and parity check matrix Dual codes Low density parity check (LDPC) codes Sparse parity check matrix Tanner graphs, cycles and girth Degree distributions Code rate and degree distribution Regular and irregular LDPC codes Message passing decoding Message passing decoding in binary erasure channels (BEC) Systematic encoding using erasure message passing decoding Message passing decoding in binary symmetric channels (BSC) Extrinsic information Bit-flipping decoding Effects of short cycles in the Tanner graph Alternative bit-flipping decoding Soft decision message passing decoding: Sum product decoding Bit error rate performance of LDPC codes Repeat accumulate codes and variants of repeat accumulate codes Message passing decoding and turbo decoding of repeat accumulate codes Convolutional codes Encoding using shift registers Trellis representation Hard decision and soft decision Viterbi decoding Bit error rate performance of convolutional codes Asymptotic coding gain Viterbi decoding complexity Free distance and optimum convolutional codes Generator polynomial description and octal description Catastrophic convolutional codes Non-systematic and recursive systematic convolutional (RSC) encoders Rate compatible punctured convolutional (RCPC) codes Hybrid automatic repeat request (HARQ) with incremental redundancy Unequal error protection with punctured convolutional codes Error patterns of convolutional codes Concatenated codes Serial concatenated codes Parallel concatenated codes, Turbo codes Iterative decoding, turbo decoding Bit error rate performance of turbo codes Interleaver design for turbo codes Coded modulation Principle of coded modulation Achievable rates with PSK/QAM modulation Trellis coded modulation (TCM) Set partitioning Ungerböck codes Multilevel coding Bit-interleaved coded modulation
Literature	Bossert, M.: Kanalcodierung. Oldenbourg. Friedrichs, B.: Kanalcodierung. Springer. Lin, S., Costello, D.: Error Control Coding. Prentice Hall. Roth, R.: Introduction to Coding Theory. Johnson, S.: Iterative Error Correction. Cambridge. Richardson, T., Urbanke, R.: Modern Coding Theory. Cambridge University Press. Gallager, R. G.: Information theory and reliable communication. Whiley-VCH Cover, T., Thomas, J.: Elements of information theory. Wiley.

Course L0438: Information Theory and Coding

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1246: Technical Complementary Course for IMPICS (according to Subject Specific Regulations)

Courses

Title

Typ

Hrs/wk

Module Responsible	Prof. Andreas Timm-Giel
Admission Requirements	None
Recommended Previous Knowledge
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours	Depends on choice of courses
Credit points	12
Assignment for the Following Curricula	Information and Communication Systems: Core Qualification: Compulsory

Module M1757: Practical module 2 (dual study program, Master's degree)

Courses

Title	Typ	Hrs/wk	CP
Practical term 2 (dual study program, Master's degree) (L2888)		0	10

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	Successful completion of practical module 1 as part of the dual Master’s course course D from the module on interlinking theory and practice as part of the dual Master’s course
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students … … combine their knowledge of facts, principles, theories and methods gained from previous study content with acquired practical knowledge - in particular their knowledge of practical professional procedures and approaches, in the current field of activity in engineering. … have a critical understanding of the practical applications of their engineering subject.
Skills	Dual students … … apply technical theoretical knowledge to complex, interdisciplinary problems within the company, and evaluate the associated work processes and results, taking into account different possible courses of action. … implement the university’s application recommendations with regard to their current tasks. … develop (new) solutions as well as procedures and approaches in their field of activity and area of responsibility - including in the case of frequently changing requirements (systemic skills).
Personal Competence
Social Competence	Dual students … … work responsibly in cross-departmental and interdisciplinary project teams and proactively deal with problems within their team. … represent complex engineering viewpoints, facts, problems and solution approaches in discussions with internal and external stakeholders and develop these further together.
Autonomy	Dual students … … define goals for their own learning and working processes as engineers. … reflect on learning and work processes in their area of responsibility. … reflect on the relevance of subject modules specialisations and specialisation for work as an engineer, and also implement the university’s application recommendations and the associated challenges to positively transfer knowledge between theory and practice.
Workload in Hours	Independent Study Time 300, Study Time in Lecture 0
Credit points	10
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase.
Assignment for the Following Curricula	Civil Engineering: Core Qualification: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Energy Systems: Core Qualification: Compulsory Environmental Engineering: Core Qualification: Compulsory Aircraft Systems Engineering: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Information and Communication Systems: Core Qualification: Compulsory International Management and Engineering: Core Qualification: Compulsory Logistics, Infrastructure and Mobility: Core Qualification: Compulsory Materials Science: Core Qualification: Compulsory Mechanical Engineering and Management: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Biomedical Engineering: Core Qualification: Compulsory Microelectronics and Microsystems: Core Qualification: Compulsory Product Development, Materials and Production: Core Qualification: Compulsory Renewable Energies: Core Qualification: Compulsory Naval Architecture and Ocean Engineering: Core Qualification: Compulsory Theoretical Mechanical Engineering: Core Qualification: Compulsory Process Engineering: Core Qualification: Compulsory Water and Environmental Engineering: Core Qualification: Compulsory

Course L2888: Practical term 2 (dual study program, Master's degree)

Typ
Hrs/wk	0
CP	10
Workload in Hours	Independent Study Time 300, Study Time in Lecture 0
Lecturer	Dr. Henning Haschke
Language	DE
Cycle	WiSe/SoSe
Content	Company onboarding process Assigning a professional field of activity as an engineer (B.Sc.) and associated fields of work Establishing responsibilities and authorisation of the dual student within the company as an engineer (B.Sc.) Taking personal responsibility within a team and on selected projects - across departments and, if applicable, across companies Scheduling the current practical module with a clear correlation to work structures Scheduling the examination phase/subsequent study semester Operational knowledge and skills Company-specific: Responsibility as an engineer (B.Sc.) in their own area of work, coordinating team and project work, dealing with complex contexts and unsolved problems, developing and implementing innovative solutions Subject specialisation (corresponding to the chosen course [M.Sc.]) in the field of activity Systemic skills Implementing the university’s application recommendations (theory-practice transfer) in corresponding work and task areas across the company Sharing/reflecting on learning Updating their e-portfolio Importance of course contents (M.Sc.) when working as an engineer Importance of development and innovation when working as an engineer
Literature	Studierendenhandbuch Betriebliche Dokumente Hochschulseitige Anwendungsempfehlungen zum Theorie-Praxis-Transfer

Module M1776: Research Project ICS

Courses

Title	Typ	Hrs/wk	CP
Research Project ICS (L2919)	Projection Course	8	12

Module Responsible	Prof. Riccardo Scandariato
Admission Requirements	None
Recommended Previous Knowledge	Basic knowledge and techniques in the chosen field of specialization.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to acquire advanced knowledge in a specific field of Computer Science or a closely related subject.
Skills	Students are able to work self-dependent in a field of Computer Science or a closely related field.
Personal Competence
Social Competence
Autonomy
Workload in Hours	Independent Study Time 248, Study Time in Lecture 112
Credit points	12
Course achievement	None
Examination	Study work
Examination duration and scale	Presentation of a current research topic (25-30 min and 5 min discussion)
Assignment for the Following Curricula	Information and Communication Systems: Core Qualification: Compulsory

Course L2919: Research Project ICS

Typ	Projection Course
Hrs/wk	8
CP	12
Workload in Hours	Independent Study Time 248, Study Time in Lecture 112
Lecturer	Dozenten des SD E
Language	EN
Cycle	WiSe
Content	Current research topics of the chosen specialization.
Literature	Aktuelle Literatur zu Forschungsthemen aus der gewählten Vertiefungsrichtung. / Current literature on research topics of the chosen specialization.

Module M1758: Practical module 3 (dual study program, Master's degree)

Courses

Title	Typ	Hrs/wk	CP
Practical term 3 (dual study program, Master's degree) (L2889)		0	10

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	Successful completion of practical module 2 as part of the dual Master’s course course E from the module on interlinking theory and practice as part of the dual Master’s course
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students … … combine their comprehensive and specialised engineering knowledge acquired from previous study contents with the strategy-oriented practical knowledge gained from their current field of work and area of responsibility. … have a critical understanding of the practical applications of their engineering subject, as well as related fields when implementing innovations.
Skills	Dual students … … apply specialised and conceptual skills to solve complex, sometimes interdisciplinary problems within the company, and evaluate the associated work processes and results, taking into account different possible courses of action. … implement the university’s application recommendations with regard to their current tasks. … develop new solutions as well as procedures and approaches to implement operational projects and assignments - even when facing frequently changing requirements and unpredictable changes (systemic skills). … can use academic methods to develop new ideas and procedures for operational problems and issues, and to assess these with regard to their usability.
Personal Competence
Social Competence	Dual students … … work responsibly in cross-departmental and interdisciplinary project teams and proactively deal with problems within their team. … can promote the professional development of others in a targeted manner. … represent complex and interdisciplinary engineering viewpoints, facts, problems and solution approaches in discussions with internal and external stakeholders and develop these further together.
Autonomy	Dual students … … reflect on learning and work processes in their area of responsibility. … define goals for new application-oriented tasks, projects and innovation plans while reflecting on potential effects on the company and the public. … reflect on the relevance of areas of specialisation and research for work as an engineer, and also implement the university’s application recommendations and the associated challenges to positively transfer knowledge between theory and practice.
Workload in Hours	Independent Study Time 300, Study Time in Lecture 0
Credit points	10
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase.
Assignment for the Following Curricula	Civil Engineering: Core Qualification: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Energy Systems: Core Qualification: Compulsory Environmental Engineering: Core Qualification: Compulsory Aircraft Systems Engineering: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Information and Communication Systems: Core Qualification: Compulsory International Management and Engineering: Core Qualification: Compulsory Logistics, Infrastructure and Mobility: Core Qualification: Compulsory Aeronautics: Core Qualification: Compulsory Materials Science and Engineering: Core Qualification: Compulsory Materials Science: Core Qualification: Compulsory Mechanical Engineering and Management: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Biomedical Engineering: Core Qualification: Compulsory Microelectronics and Microsystems: Core Qualification: Compulsory Product Development, Materials and Production: Core Qualification: Compulsory Renewable Energies: Core Qualification: Compulsory Naval Architecture and Ocean Engineering: Core Qualification: Compulsory Theoretical Mechanical Engineering: Core Qualification: Compulsory Process Engineering: Core Qualification: Compulsory Water and Environmental Engineering: Core Qualification: Compulsory

Course L2889: Practical term 3 (dual study program, Master's degree)

Typ
Hrs/wk	0
CP	10
Workload in Hours	Independent Study Time 300, Study Time in Lecture 0
Lecturer	Dr. Henning Haschke
Language	DE
Cycle	WiSe/SoSe
Content	Company onboarding process Assigning a future professional field of activity as an engineer (M.Sc.) and associated fields of work Extending responsibilities and authorisation of the dual student within the company up to the intended first assignment after completing their studies Working responsibly in a team; project responsibility within own area - as well as across divisions and companies if necessary Scheduling the final practical module with a clear correlation to work structures Internal agreement on a potential topic or innovation project for the Master’s dissertation Planning the Master’s dissertation within the company in cooperation with TU Hamburg Scheduling the examination phase/subsequent study semester Operational knowledge and skills Company-specific: dealing with change, project and team development, responsibility as an engineer in their future field of work (M.Sc.), dealing with complex contexts, frequent and unpredictable changes, developing and implementing innovative solutions Specialising in one field of work (final dissertation) Systemic skills Implementing the university’s application recommendations (theory-practice transfer) in corresponding work and task areas across the company Sharing/reflecting on learning E-portfolio Relevance of study content and personal specialisation when working as an engineer Relevance of research and innovation when working as an engineer
Literature	Studierendenhandbuch betriebliche Dokumente Hochschulseitige Anwendungsempfehlungen zum Theorie-Praxis-Transfer

Specialization Communication Systems

Graduates of the Communication Systems specialisation are qualified to independently resolve problems in communication networks and digital communications. They also have profound knowledge in software development principles and signal processing. Graduates are qualified to independently resolve problems in communication systems technology and related disciplines.

The Communication Systems specialisation is recommended for students who already bring along a good mathematical foundation, basic knowledge in computer science and/or electrical engineering with focus on information and communication technology.

Module M0676: Digital Communications

Courses

Title	Typ	Hrs/wk	CP
Digital Communications (L0444)	Lecture	2	3
Digital Communications (L0445)	Recitation Section (large)	2	2
Laboratory Digital Communications (L0646)	Practical Course	1	1

Module Responsible

Prof. Gerhard Bauch

Admission Requirements

None

Recommended Previous Knowledge

Mathematics 1-3
Signals and Systems
Fundamentals of Communications and Random Processes

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students are able to understand, compare and design modern digital information transmission schemes. They are familiar with the properties of linear and non-linear digital modulation methods. They can describe distortions caused by transmission channels and design and evaluate detectors including channel estimation and equalization. They know the principles of single carrier transmission and multi-carrier transmission as well as the fundamentals of basic multiple access schemes.

The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.

Skills

The students are able to design and analyse a digital information transmission scheme including multiple access. They are able to choose a digital modulation scheme taking into account transmission rate, required bandwidth, error probability, and further signal properties. They can design an appropriate detector including channel estimation and equalization taking into account performance and complexity properties of suboptimum solutions. They are able to set parameters of a single carrier or multi carrier transmission scheme and trade the properties of both approaches against each other.

Personal Competence

Social Competence

The students can jointly solve specific problems.

Autonomy

The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Written elaboration

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Electrical Engineering: Core Qualification: Compulsory
Computer Science in Engineering: Specialisation II. Engineering Science: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems: Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Microelectronics and Microsystems: Core Qualification: Elective Compulsory

Course L0444: Digital Communications

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	WiSe
Content	Repetition: Baseband Transmission Pulse shaping: Non-return to zero (NRZ) rectangular pulses, raised-cosine pulses, square-root raised-cosine pulses Power spectral density (psd) of baseband signals Intersymbol interference (ISI) First and second Nyquist criterion AWGN channel Matched filter Matched-filter receiver and correlation receiver Noise whitening matched filter Discrete-time AWGN channel model Representation of bandpass signals and systems in the equivalent baseband Quadrature amplitude modulation (QAM) Equivalent baseband signal and system Analytical signal Equivalent baseband random process, equivalent baseband white Gaussian noise process Equivalent baseband AWGN channel Equivalent baseband channel model with frequency-offset and phase noise Equivalent baseband Rayleigh fading and Rice fading channel models Equivalent baseband frequency-selective channel model Discrete memoryless channels (DMC) Bandpass transmission via carrier modulation Amplitude modulation, frequency modulation, phase modulation Linear digital modulation methods On-off keying, M-ary amplitude shift keying (M-ASK), M-ary phase shift keying (M-PSK), M-ary quadrature amplitude modulation (M-QAM), offset-QPSK Signal space representation of transmit signal constellations and signals Energy of linear digital modulated signals, average energy per symbol Power spectral density of linear digital modulated signals Bandwidth efficiency Correlation coefficient of elementary signals Error probabilities of linear digital modulation methods Error functions Gray mapping and natural mapping Bit error probabilities, symbol error probabilities, pairwise symbol error probabilities Euclidean distance and Hamming distance Exact and approximate computation of error probabilities Performance comparison of modulation schemes in terms of per bit SNR vs. per symbol SNR Hierarchical modulation, multilevel modulation Effects of carrier phase offset and carrier frequency offset Differential modulation M-ary differential phase shift keying (M-PSK) Coherent and non-coherent detection of DPSK p/M-differential phase shift keying (p/M-DPSK) Differential amplitude and phase shift keying (DAPSK) Non-linear digital modulation methods Frequency shift keying (FSK) Modulation index Minimum shift keying (MSK) Offset-QPSK representation of MSK MSK with differential precoding and rotation Bit error probabilities of MSK Gaussian minimum shift keying (GMSK) Power spectral density of MSK and GMSK Continuous phase modulation (CPM) General description of CPM signals Frequency pulses and phase pulses Coherent and non-coherent detection of FSK Performance comparison of linear and non-linear digital modulation methods Frequency-selective channels, ISI channels Intersymbol interference and frequency-selectivity RMS delay spread Narrowband and broadband channels Equivalent baseband transmission model for frequency-selective channels Receive filter design Equalization Symbol-spaced and fractionally-spaced equalizers Inverse system Non-recursive linear equalizers Linear zero-forcing (ZF) equalizer Linear minimum mean squared error (MMSE) equalizer Non-linear equalization: Decision feedback equalizer (DFE) Tomlinson-Harashima precoding Maximum a posteriori probability (MAP) and maximum likelihood equalizer, Viterbi algorithm Single-carrier vs. multi-carrier transmission Multi-carrier transmission General multicarrier transmission Orthogonal frequency division multiplex (OFDM) OFDM implementation using the Fast Fourier Transform (FFT) Cyclic guard interval Power spectral density of OFDM Peak-to-average power ratio (PAPR) Multiple access Principles of time division multiple access (TDMA), frequency division multiple access (FDMA), code division multiple access (CDMA), non-orthogonal multiple access (NOMA), hybrid multiple access Spread spectrum communications Direct sequence spread spectrum communications Frequency hopping Protection against eavesdropping Protection against narrowband jammers Short vs. long spreading codes Direct sequence spread spectrum communications in frequency-selective channels Rake receiver Code division multiple access (CDMA) Design criteria of spreading sequences, autocorrelation function and crosscorrelation function of spreading sequences Intersymbol interference (ISI) and multiple access interference (MAI) Pseudo noise (PN) sequences, maximum length sequences (m-sequences), Gold codes, Walsh-Hadamard codes, orthogonal variable spreading factor (OVSF) codes Multicode transmission CDMA in uplink and downlink of a wireless communications system Single-user detection vs. multi-user detection
Literature	K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill. S. Haykin: Communication Systems. Wiley R.G. Gallager: Principles of Digital Communication. Cambridge A. Goldsmith: Wireless Communication. Cambridge. D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

Course L0445: Digital Communications

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L0646: Laboratory Digital Communications

Typ	Practical Course
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Gerhard Bauch
Language	DE/EN
Cycle	WiSe
Content	- DSL transmission - Random processes - Digital data transmission
Literature	K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill. S. Haykin: Communication Systems. Wiley R.G. Gallager: Principles of Digital Communication. Cambridge A. Goldsmith: Wireless Communication. Cambridge. D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

Module M0836: Communication Networks

Courses

Title	Typ	Hrs/wk	CP
Selected Topics of Communication Networks (L0899)	Project-/problem-based Learning	2	2
Communication Networks (L0897)	Lecture	2	2
Communication Networks Excercise (L0898)	Project-/problem-based Learning	1	2

Module Responsible	Prof. Andreas Timm-Giel
Admission Requirements	None
Recommended Previous Knowledge	Fundamental stochastics Basic understanding of computer networks and/or communication technologies is beneficial
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to describe the principles and structures of communication networks in detail. They can explain the formal description methods of communication networks and their protocols. They are able to explain how current and complex communication networks work and describe the current research in these examples.
Skills	Students are able to evaluate the performance of communication networks using the learned methods. They are able to work out problems themselves and apply the learned methods. They can apply what they have learned autonomously on further and new communication networks.
Personal Competence
Social Competence	Students are able to define tasks themselves in small teams and solve these problems together using the learned methods. They can present the obtained results. They are able to discuss and critically analyse the solutions.
Autonomy	Students are able to obtain the necessary expert knowledge for understanding the functionality and performance capabilities of new communication networks independently.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Presentation
Examination duration and scale	1.5 hours colloquium with three students, therefore about 30 min per student. Topics of the colloquium are the posters from the previous poster session and the topics of the module.
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory

Course L0899: Selected Topics of Communication Networks

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Andreas Timm-Giel
Language	EN
Cycle	WiSe
Content	Example networks selected by the students will be researched on in a PBL course by the students in groups and will be presented in a poster session at the end of the term.
Literature	see lecture

Course L0897: Communication Networks

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Andreas Timm-Giel, Dr. Koojana Kuladinithi
Language	EN
Cycle	WiSe
Content
Literature	Skript des Instituts für Kommunikationsnetze Tannenbaum, Computernetzwerke, Pearson-Studium Further literature is announced at the beginning of the lecture.

Course L0898: Communication Networks Excercise

Typ	Project-/problem-based Learning
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Andreas Timm-Giel
Language	EN
Cycle	WiSe
Content	Part of the content of the lecture Communication Networks are reflected in computing tasks in groups, others are motivated and addressed in the form of a PBL exercise.
Literature	announced during lecture

Module M0710: Microwave Engineering

Courses

Title	Typ	Hrs/wk	CP
Microwave Engineering (L0573)	Lecture	2	3
Microwave Engineering (L0574)	Recitation Section (large)	2	2
Microwave Engineering (L0575)	Practical Course	1	1

Module Responsible

Prof. Alexander Kölpin

Admission Requirements

None

Recommended Previous Knowledge

Fundamentals of communication engineering, semiconductor devices and circuits. Basics of Wave propagation from transmission line theory and theoretical electrical engineering.

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students can explain the propagation of electromagnetic waves and related phenomena. They can describe transmission systems and components. They can name different types of antennas and describe the main characteristics of antennas. They can explain noise in linear circuits, compare different circuits using characteristic numbers and select the best one for specific scenarios.

Skills

Students are able to calculate the propagation of electromagnetic waves. They can analyze complete transmission systems und configure simple receiver circuits. They can calculate the characteristic of simple antennas and arrays based on the geometry. They can calculate the noise of receivers and the signal-to-noise-ratio of transmission systems. They can apply their theoretical knowledge to the practical courses.

Personal Competence

Social Competence

Students work together in small groups during the practical courses. Together they document, evaluate and discuss their results.

Autonomy

Students are able to relate the knowledge gained in the course to contents of previous lectures. With given instructions they can extract data needed to solve specific problems from external sources. They are able to apply their knowledge to the laboratory courses using the given instructions.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Subject theoretical and practical work

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Electrical Engineering: Core Qualification: Compulsory
Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory

Course L0573: Microwave Engineering

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Alexander Kölpin
Language	DE/EN
Cycle	WiSe
Content	- Antennas: Analysis - Characteristics - Realizations - Radio Wave Propagation - Transmitter: Power Generation with Vacuum Tubes and Transistors - Receiver: Preamplifier - Heterodyning - Noise - Selected System Applications
Literature	H.-G. Unger, „Elektromagnetische Theorie für die Hochfrequenztechnik, Teil I“, Hüthig, Heidelberg, 1988 H.-G. Unger, „Hochfrequenztechnik in Funk und Radar“, Teubner, Stuttgart, 1994 E. Voges, „Hochfrequenztechnik - Teil II: Leistungsröhren, Antennen und Funkübertragung, Funk- und Radartechnik“, Hüthig, Heidelberg, 1991 E. Voges, „Hochfrequenztechnik“, Hüthig, Bonn, 2004 C.A. Balanis, “Antenna Theory”, John Wiley and Sons, 1982 R. E. Collin, “Foundations for Microwave Engineering”, McGraw-Hill, 1992 D. M. Pozar, “Microwave and RF Design of Wireless Systems”, John Wiley and Sons, 2001 D. M. Pozar, “Microwave Engineerin”, John Wiley and Sons, 2005

Course L0574: Microwave Engineering

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Alexander Kölpin
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L0575: Microwave Engineering

Typ	Practical Course
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Alexander Kölpin
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0637: Advanced Concepts of Wireless Communications

Courses

Title	Typ	Hrs/wk	CP
Advanced Concepts of Wireless Communications (L0297)	Lecture	3	4
Advanced Concepts of Wireless Communications (L0298)	Recitation Section (large)	2	2

Module Responsible	Dr. Rainer Grünheid
Admission Requirements	None
Recommended Previous Knowledge	Lecture "Signals and Systems" Lecture "Fundamentals of Telecommunications and Stochastic Processes" Lecture "Digital Communications"
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to explain the general as well as advanced principles and techniques that are applied to wireless communications. They understand the properties of wireless channels and the corresponding mathematical description. Furthermore, students are able to explain the physical layer of wireless transmission systems. In this context, they are proficient in the concepts of multicarrier transmission (OFDM), modulation, error control coding, channel estimation and multi-antenna techniques (MIMO). Students can also explain methods of multiple access. On the example of contemporary communication systems (LTE, 5G) they can put the learnt content into a larger context. The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.
Skills	Using the acquired knowledge, students are able to understand the design of current and future wireless systems. Moreover, given certain constraints, they can choose appropriate parameter settings of communication systems. Students are also able to assess the suitability of technical concepts for a given application.
Personal Competence
Social Competence	Students can jointly elaborate tasks in small groups and present their results in an adequate fashion.
Autonomy	Students are able to extract necessary information from given literature sources and put it into the perspective of the lecture. They can continuously check their level of expertise with the help of accompanying measures (such as online tests, clicker questions, exercise tasks) and, based on that, to steer their learning process accordingly. They can relate their acquired knowledge to topics of other lectures, e.g., "Fundamentals of Communications and Stochastic Processes" and "Digital Communications".
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 minutes; scope: content of lecture and exercise
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory

Course L0297: Advanced Concepts of Wireless Communications

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Dr. Rainer Grünheid
Language	EN
Cycle	SoSe
Content	The lecture deals with technical principles and related concepts of mobile communications. In this context, the main focus is put on the physical and data link layer of the ISO-OSI stack. In the lecture, the transmission medium, i.e., the mobile radio channel, serves as the starting point of all considerations. The characteristics and the mathematical descriptions of the radio channel are discussed in detail. Subsequently, various physical layer aspects of wireless transmission are covered, such as channel coding, modulation/demodulation, channel estimation, synchronization, and equalization. Moreover, the different uses of multiple antennas at the transmitter and receiver, known as MIMO techniques, are described. Besides these physical layer topics, concepts of multiple access schemes in a cellular network are outlined. In order to illustrate the above-mentioned technical solutions, the lecture will also provide a system view, highlighting the basics of some contemporary wireless systems, including LTE, LTE Advanced, and 5G New Radio.
Literature	John G. Proakis, Masoud Salehi: Digital Communications. 5th Edition, Irwin/McGraw Hill, 2007 David Tse, Pramod Viswanath: Fundamentals of Wireless Communication. Cambridge, 2005 Bernard Sklar: Digital Communications: Fundamentals and Applications. Second Edition, Pearson, 2013 Stefani Sesia, Issam Toufik, Matthew Baker: LTE - The UMTS Long Term Evolution. Second Edition, Wiley, 2011 Erik Dahlman, Stefan Parkvall, Johan Sköld: 5G NR - The Next Generation Wireless Access Technology. Second Edition, Academic Press, 2021

Course L0298: Advanced Concepts of Wireless Communications

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Dr. Rainer Grünheid
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0837: Simulation of Communication Networks

Courses

Title	Typ	Hrs/wk	CP
Simulation of Communication Networks (L0887)	Project-/problem-based Learning	5	6

Module Responsible	Prof. Andreas Timm-Giel
Admission Requirements	None
Recommended Previous Knowledge	Knowledge of computer and communication networks Basic programming skills
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to explain the necessary stochastics, the discrete event simulation technology and modelling of networks for performance evaluation.
Skills	Students are able to apply the method of simulation for performance evaluation to different, also not practiced, problems of communication networks. The students can analyse the obtained results and explain the effects observed in the network. They are able to question their own results.
Personal Competence
Social Competence	Students are able to acquire expert knowledge in groups, present the results, and discuss solution approaches and results. They are able to work out solutions for new problems in small teams.
Autonomy	Students are able to transfer independently and in discussion with others the acquired method and expert knowledge to new problems. They can identify missing knowledge and acquire this knowledge independently.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory

Course L0887: Simulation of Communication Networks

Typ	Project-/problem-based Learning
Hrs/wk	5
CP	6
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Lecturer	Prof. Andreas Timm-Giel
Language	EN
Cycle	SoSe
Content	In the course necessary basic stochastics and the discrete event simulation are introduced. Also simulation models for communication networks, for example, traffic models, mobility models and radio channel models are presented in the lecture. Students work with a simulation tool, where they can directly try out the acquired skills, algorithms and models. At the end of the course increasingly complex networks and protocols are considered and their performance is determined by simulation.
Literature	Skript des Instituts für Kommunikationsnetze Further literature is announced at the beginning of the lecture.

Module M1564: Advanced Seminars Computer Science and Communication Technology

Courses

Title	Typ	Hrs/wk	CP
Advanced Seminar Computer Science and Communication Technology I (L2352)	Seminar	2	3
Introductory Seminar Computer Science and Communication Technology II (L2429)	Seminar	2	3

Module Responsible	Dozenten des SD E
Admission Requirements	None
Recommended Previous Knowledge	Basic knowledge of Computer Science and Mathematics at the Master's level.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are able to explicate a specific topic in the field of Computer Science, describe complex issues, present different views and evaluate in a critical way.
Skills	The students are able to familiarize in a specific topic of Computer Science in limited time, realize a literature survey on the specific topic and cite in a correct way, elaborate a presentation and give a lecture to a selected audience, sum up the presentation in 10-15 lines, answer questions in the final discussion.
Personal Competence
Social Competence	The students are able to elaborate and introduce a topic for a certain audience, discuss the topic, content and structure of the presentation with the instructor, discuss certain aspects with the audience, and as the lecturer listen and respond to questions from the audience.
Autonomy	The students are able to define the task in question in an autonomous way, develop the necessary knowledge, use appropriate work equipment, and guided by an instructor critically check the working status.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Presentation
Examination duration and scale	x
Assignment for the Following Curricula	Computer Science: Specialisation IV. Subject Specific Focus: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory

Course L2352: Advanced Seminar Computer Science and Communication Technology I

Typ	Seminar
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des SD E
Language	EN
Cycle	WiSe/SoSe
Content	- Seminar presentations by enrolled students about selected topics of computer science and communication technology - Active participation in discussions
Literature	Wird vom Veranstalter bekanntgegeben.

Course L2429: Introductory Seminar Computer Science and Communication Technology II

Typ	Seminar
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des SD E
Language	DE/EN
Cycle	WiSe/SoSe
Content
Literature

Module M0638: Modern Wireless Systems

Courses

Title	Typ	Hrs/wk	CP
Selected Topics of Modern Wireless Systems (L1982)	Project-/problem-based Learning	2	3
Modern Wireless Systems (L0296)	Lecture	3	3

Module Responsible

Dr. Rainer Grünheid

Admission Requirements

None

Recommended Previous Knowledge

Lecture "Digital Communications"
Lecture "Advanced Concepts of Wireless Communications"

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students have an overview of a variety of contemporary wireless systems of different size and complexity. They understand the technical solutions from the perspective of the physical and data link layer. They have developed a system view and are aware of the technical arguments, considering the respective applications and associated constraints. For several examples (e.g., 5G New Radio), students are able to explain different concepts in a very deep technical detail.

The students are familiar with the contents of lecture and PBL course. They can explain and apply them to new problems.

Skills

Students have developed a system view. They can transfer their knowledge to evaluate other systems, not discussed in the lecture, and to understand the respective technical solutions. Given specific contraints and technical requirements, students are in a position to make proposals for certain design aspects by an appropriate assessment and the consideration of alternatives.

Personal Competence

Social Competence

Students can jointly elaborate tasks in small groups and present their results in an adequate fashion.

Autonomy

Students are able to extract necessary information from given literature sources and put it into the perspective of the lecture. They can continuously check their level of expertise with the help of accompanying measures (such as online tests, clicker questions, exercise tasks) and, based on that, to steer their learning process accordingly. They can relate their acquired knowledge to topics of other lectures, e.g., "Digital Communications" and "Advanced Topics of Wireless Communications".

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Subject theoretical and practical work	PBL-Kurs mit Posterpräsentation

Examination

Oral exam

Examination duration and scale

40 min

Assignment for the Following Curricula

Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory

Course L1982: Selected Topics of Modern Wireless Systems

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Rainer Grünheid
Language	EN
Cycle	WiSe
Content	In this course, selected "hot" topics of modern wireless systems will be covererd. For that purpose, students work in small groups to elaborate a given subject, including a quantitative analysis with provided simulation tools. The results will be presented in a poster session or a talk towards the end of the semester. Possible topics can include various system concepts and related technical principles, such as: WLAN sytems 5G systems Millimeter wave communication Visible light communication Cooperative Multipoint Massive MIMO Massive machine-type communication Interference cancellation Non-orthogonal multiple access Heterogeneous networks ...
Literature	will be provided, depending on the given topics

Course L0296: Modern Wireless Systems

Typ	Lecture
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Dr. Rainer Grünheid
Language	EN
Cycle	WiSe
Content	The lecture gives an overview of contemporary wireless communication concepts and related techniques from a system point of view. For that purpose, different systems, ranging from Wireless Personal to Wide Area Networks, are covered, mainly discussing the physical and data link layer. Systems under consideration include: - Near Field Communication (NFC) - ZigBee / IEEE 802.15.4 - Bluetooth - IEEE 802.11 family - L-band Digital Aeronautical Communication System (LDACS) - Long Term Evolution (LTE) and LTE Advanced - 5G New Radio A special focus is placed on 4th and 5th generation networks; in particular, an in-depth view into the technical principles of the 5G New Radio standard is given.
Literature	John G. Proakis, Masoud Salehi: Digital Communications. 5th Edition, Irwin/McGraw Hill, 2007 Stefani Sesia, Issam Toufik, Matthew Baker: LTE - The UMTS Long Term Evolution. Second Edition, Wiley, 2011 Erik Dahlman, Stefan Parkvall, Johan Sköld: 5G NR - The Next Generation Wireless Access Technology. Second Edition, Academic Press, 2021

Focus Signal Processing

Module M0738: Digital Audio Signal Processing

Courses

Title	Typ	Hrs/wk	CP
Digital Audio Signal Processing (L0650)	Lecture	3	4
Digital Audio Signal Processing (L0651)	Recitation Section (large)	1	2

Module Responsible	Prof. Udo Zölzer
Admission Requirements	None
Recommended Previous Knowledge	Signals and Systems
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Die Studierenden können die grundlegenden Verfahren und Methoden der digitalen Audiosignalverarbeitung erklären. Sie können die wesentlichen physikalischen Effekte bei der Sprach- und Audiosignalverarbeitung erläutern und in Kategorien einordnen. Sie können einen Überblick der numerischen Methoden und messtechnischen Charakterisierung von Algorithmen zur Audiosignalverarbeitung geben. Sie können die erarbeiteten Algorithmen auf weitere Anwendungen im Bereich der Informationstechnik und Informatik abstrahieren.
Skills	The students will be able to apply methods and techniques from audio signal processing in the fields of mobile and internet communication. They can rely on elementary algorithms of audio signal processing in form of Matlab code and interactive JAVA applets. They can study parameter modifications and evaluate the influence on human perception and technical applications in a variety of applications beyond audio signal processing. Students can perform measurements in time and frequency domain in order to give objective and subjective quality measures with respect to the methods and applications.
Personal Competence
Social Competence	The students can work in small groups to study special tasks and problems and will be enforced to present their results with adequate methods during the exercise.
Autonomy	The students will be able to retrieve information out of the relevant literature in the field and putt hem into the context of the lecture. They can relate their gathered knowledge and relate them to other lectures (signals and systems, digital communication systems, image and video processing, and pattern recognition). They will be prepared to understand and communicate problems and effects in the field audio signal processing.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	60 min
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory

Course L0650: Digital Audio Signal Processing

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Udo Zölzer
Language	EN
Cycle	WiSe
Content	Introduction (Studio Technology, Digital Transmission Systems, Storage Media, Audio Components at Home) Quantization (Signal Quantization, Dither, Noise Shaping, Number Representation) AD/DA Conversion (Methods, AD Converters, DA Converters, Audio Processing Systems, Digital Signal Processors, Digital Audio Interfaces, Single-Processor Systems, Multiprocessor Systems) Equalizers (Recursive Audio Filters, Nonrecursive Audio Filters, Multi-Complementary Filter Bank) Room Simulation (Early Reflections, Subsequent Reverberation, Approximation of Room Impulse Responses) Dynamic Range Control (Static Curve, Dynamic Behavior, Implementation, Realization Aspects) Sampling Rate Conversion (Synchronous Conversion, Asynchronous Conversion, Interpolation Methods) Data Compression (Lossless Data Compression, Lossy Data Compression, Psychoacoustics, ISO-MPEG1 Audio Coding)
Literature	- U. Zölzer, Digitale Audiosignalverarbeitung, 3. Aufl., B.G. Teubner, 2005. - U. Zölzer, Digitale Audio Signal Processing, 2nd Edition, J. Wiley & Sons, 2005. - U. Zölzer (Ed), Digital Audio Effects, 2nd Edition, J. Wiley & Sons, 2011.

Course L0651: Digital Audio Signal Processing

Typ	Recitation Section (large)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Udo Zölzer
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0677: Digital Signal Processing and Digital Filters

Courses

Title	Typ	Hrs/wk	CP
Digital Signal Processing and Digital Filters (L0446)	Lecture	3	4
Digital Signal Processing and Digital Filters (L0447)	Recitation Section (large)	2	2

Module Responsible	Prof. Gerhard Bauch
Admission Requirements	None
Recommended Previous Knowledge	Mathematics 1-3 Signals and Systems Fundamentals of signal and system theory as well as random processes. Fundamentals of spectral transforms (Fourier series, Fourier transform, Laplace transform)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students know and understand basic algorithms of digital signal processing. They are familiar with the spectral transforms of discrete-time signals and are able to describe and analyse signals and systems in time and image domain. They know basic structures of digital filters and can identify and assess important properties including stability. They are aware of the effects caused by quantization of filter coefficients and signals. They are familiar with the basics of adaptive filters. They can perform traditional and parametric methods of spectrum estimation, also taking a limited observation window into account. The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.
Skills	The students are able to apply methods of digital signal processing to new problems. They can choose and parameterize suitable filter striuctures. In particular, the can design adaptive filters according to the minimum mean squared error (MMSE) criterion and develop an efficient implementation, e.g. based on the LMS or RLS algorithm. Furthermore, the students are able to apply methods of spectrum estimation and to take the effects of a limited observation window into account.
Personal Competence
Social Competence	The students can jointly solve specific problems.
Autonomy	The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Computer Science in Engineering: Specialisation II. Engineering Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory

Course L0446: Digital Signal Processing and Digital Filters

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	WiSe
Content	Transforms of discrete-time signals: Discrete-time Fourier Transform (DTFT) Discrete Fourier-Transform (DFT), Fast Fourier Transform (FFT) Z-Transform Correspondence of continuous-time and discrete-time signals, sampling, sampling theorem Fast convolution, Overlap-Add-Method, Overlap-Save-Method Fundamental structures and basic types of digital filters Characterization of digital filters using pole-zero plots, important properties of digital filters Quantization effects Design of linear-phase filters Fundamentals of stochastic signal processing and adaptive filters MMSE criterion Wiener Filter LMS- and RLS-algorithm Traditional and parametric methods of spectrum estimation
Literature	K.-D. Kammeyer, K. Kroschel: Digitale Signalverarbeitung. Vieweg Teubner. V. Oppenheim, R. W. Schafer, J. R. Buck: Zeitdiskrete Signalverarbeitung. Pearson StudiumA. V. W. Hess: Digitale Filter. Teubner. Oppenheim, R. W. Schafer: Digital signal processing. Prentice Hall. S. Haykin: Adaptive flter theory. L. B. Jackson: Digital filters and signal processing. Kluwer. T.W. Parks, C.S. Burrus: Digital filter design. Wiley.

Course L0447: Digital Signal Processing and Digital Filters

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0556: Computer Graphics

Courses

Title	Typ	Hrs/wk	CP
Computer Graphics (L0145)	Lecture	2	3
Computer Graphics (L0768)	Recitation Section (small)	2	3

Module Responsible	Prof. Tobias Knopp
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra (in particular matrix/vector computation) Basic programming skills in C/C++
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can explain and describe basic algorithms in 3D computer graphics.
Skills	Students are capable of implementing a basic 3D rendering pipeline. This consists of projecting simple 3D structures (e.g. cube, spheres) onto a 2D surface using a virtual camera. apply geometric transformations (e.g. rotation, scaling) in 2D and 3D computer graphics. using well-known 2D/3D APIs (OpenGL, Cairo) for solving a given problem statement.
Personal Competence
Social Competence	Students can collaborate in a small team on the realization and validation of a 3D computer graphics pipeline.
Autonomy	Students are able to solve simple tasks independently with reference to the contents of the lectures and the exercise sets. Students are able to solve detailed problems independently with the aid of the tutorial’s programming task.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory

Course L0145: Computer Graphics

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	EN
Cycle	SoSe
Content	Computer graphics and animation are leading to an unprecedented visual revolution. The course deals with its technological foundations: Object-oriented Computer Graphics Projections and Transformations Polygonal and Parametric Modelling Illuminating, Shading, Rendering Computer Animation Techniques Kinematics and Dynamics Effects Students will be be working on a series of mini-projects which will eventually evolve into a final project. Learning computer graphics and animation resembles learning a musical instrument. Therefore, doing your projects well and in time is essential for performing well on this course.
Literature	Alan H. Watt: 3D Computer Graphics. Harlow: Pearson (3rd ed., repr., 2009). Dariush Derakhshani: Introducing Autodesk Maya 2014. New York, NY : Wiley (2013).

Course L0768: Computer Graphics

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1700: Satellite Communications and Navigation

Courses

Title	Typ	Hrs/wk	CP
Radio-Based Positioning and Navigation (L2711)	Lecture	2	3
Satellite Communications (L2710)	Lecture	3	3

Module Responsible	Prof. Gerhard Bauch
Admission Requirements	None
Recommended Previous Knowledge	The module is designed for a diverse audience, i.e. students with different background. Basic knowledge of communications engineering and signal processing are of advantage but not required. The course intends to provide the chapters on communications techniques such that on the one hand students with a communications engineering background learn additional concepts and examples (e.g. modulation and coding schemes or signal processing concepts) which have not or in a different way been treated in our other bachelor and master courses. On the other hand, students with other background shall be able to grasp the ideas but may not be able to understand in the same depth. The individual background of the students will be taken into consideration in the oral exam.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are able to understand, compare and analyse digital satellite communications system as well as navigation techniques. They are familiar with principal ideas of the respective communications, signal processing and positioning methods. They can describe distortions and resulting limitations caused by transmission channels and hardware components. They can describe how fundamental communications and navigation techniques are applied in selected practical systems. The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.
Skills	The students are able to describe and analyse digital satellite communications systems and navigation systems. They are able to analyse transmission chains including link budget calculations. They are able to choose appropriate transmission technologies and system parameters for given scenarios.
Personal Competence
Social Competence	The students can jointly solve specific problems.
Autonomy	The students are able to acquire relevant information from appropriate literature sources.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory

Course L2711: Radio-Based Positioning and Navigation

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Gerhard Bauch, Dr. Rico Mendrzik
Language	EN
Cycle	SoSe
Content	Information extraction from communication signals Time-of-arrival principle Ranging in additive white Gaussian noise (AWGN) channel Correlation-based range estimation Effect of multipath propagation on time-of-arrival principle Zero-forcing range estimation in the presence of multipath Optimum range estimation in the presence of multipath Zero-forcing in presence of noise Angle-of-arrival principle Angle-of-arrival estimation in AWGN channel Delay-and-sum estimator Multiple Signal Classifier (MUSIC) MUSIC-based angle-of-arrival estimation Case study: Comparison of estimators in AWGN channels Effect of multipath propagation on angle-of-arrival principle Case study: Comparison of estimators in multipath channels Information fusion of extracted signals Distance-based positioning Principle of time-of-arrival positioning Geometric interpretation Positioning in the absence of noise Linearization of the positioning problem Positioning in the presence of noise Optimality criteria Least squares time-of-arrival positioning Maximum likelihood time-of-arrival positioning Interactive Matlab demo Excursion: gradient descent solvers for nonlinear programs Real-life positioning with embedded development board (Arduino) Linearized least squares time-of-arrival positioning Effect of clock offsets on distance-based positioning Time-difference-of-arrival principle Least squares time-difference-of-arrival positioning Clock offset mitigation via two-way ranging Performance limits of distance-based positioning Fisher information and the Cramér-Rao lower bound Fisher information in the AWGN case Multi-variate Fisher information Cramér-Rao lower bound for synchronized time-of-arrival positioning Case study: Synchronized time-of-arrival positioning Cramér-Rao lower bound for unsynchronized time-of-arrival positioning Case study: Unsynchronized time-of-arrival positioning Angle-based Positioning Angle-of-arrival positioning principle Geometric interpretation angle-of-arrival positioning principle Noise-free angle-of-arrival positioning with known orientation Effect of noise on angle-of-arrival positioning Least squares angle-of-arrival positioning with known orientation Linear least squares angle-of-arrival positioning Effect of orientation uncertainty Angle-difference-of-arrival positioning Geometric interpretation angle difference of arrival positioning Proof of angle-difference-of-arrival locus Inscribed angle lemma Case study: Angle-difference-of-arrival-positioning Performance limits of angle-based positioning Cramér-Rao lower bound for angle-of-arrival positioning with known orientation Case study: Angle-of-arrival positioning with known orientation Information Filtering Bayesian filtering Principle of Bayesian filtering General Problem Formulation Solution to the linear Gaussian case State transition in the linear Gaussian case Proof of predicted posterior distribution of the Kalman filter State update in the linear Gaussian case Proof of marginal posterior distribution of the Kalman filter Working with Gaussian random variables Proof: Affine transformation Proof: Marginalization Proof: Conditioning Kalman filter: Optimum Inference in the linear Gaussian case Modeling of process noise Modeling of measurement noise Case study: Kalman filtering in the linear Gaussian case Interactive Kalman filtering in Matlab Dealing with nonlinearities in Bayesian filtering Nonlinear Gaussian case Extended Kalman filter Proof of predicted posterior distribution of the extended Kalman filter Proof of marginal posterior distribution of the extended Kalman filter Example: Nonlinear state transition Case study: Extended Kalman filtering Practical considerations for filter design Satellite Navigation Overview from positioning perspective Earth-centered earth-fixed (ECEF) coordinate system World geodetic system (WGS) Satellite navigation systems System-receiver clock offsets and pseudo-ranges Unsynchronized time-of-arrival positioning revisited GPS legacy signals and ranging Signal overview Time-of-arrival principle revisited Direct sequence spread spectrum principle Short and long codes Satellite signal generation Carriers and codes Correlation properties of codes Code division multiple access in flat fading channels Navigation message Velocity estimation Hands-on case study: Design of an extended Kalman filter for satellite navigation based on recorded data Robust navigation Multipath-assisted positioning in millimeter wave multiple antenna systems Multi-sensor fusion
Literature

Course L2710: Satellite Communications

Typ	Lecture
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	SoSe
Content	Introduction to satellite communications What is a satellite Overview orbits, Van Allen Belt, components of a satellite Satellite services Frequency bands for satellite services International Telecommunications Union (ITU) Influence of atmospheric impairments Milestones in satellite communications Components of a satellite communications system Ground segment Space segment Control segment Communication links Uplink, downlink Forward link, reverse link Intersatellite links Multiple access Performance measures Effective isotropic radiated power (EIRP), antenna gain, figure of merit, G/T, carrier to noise ratio Signal to noise power ratio vs. carrier to noise ratio Single beam and multibeam satellites Beam coverage Examples for beam coverage of LEO and GEO satellites (Iridium, Viasat) Transparent vs. regenerative payload Orbits Low earth orbot (LEO), medium earth orbit (MEO), geosynchroneous and geostationary orbits (GEO), highly elliptical orbits (HEO Favourable orbits: HEO orbits with 63-64^o inclination, Molnya and Tundra orbits Circular LEO orbits Circular MEO Orbits (Intermediate Circular Orbits (ICO)) Equatorial orbits, geostationary orbit (GEO) Important aspects of LEO, MEO and GEO satellites Kepler’s laws of planetary motion Gravitational force Parameters of ellipses and elliptical orbits Major and minor half axis Foci Eccentricity Eccentric anomaly, mean anomaly, true anomaly Area Orbit period Perigee, apogee Distance of satellite from center of earth Construction of ellipses according to de La Hire Orbital plane in space, inclination, right ascension (longitude) of ascending node, Vernal equinox Newton’s laws of motion Newton’s universal law of gravitation Energy of satellites: Potential energy, kinetic energy, total energy Instantaneous speed of a satellite Kepler’s equation Satellite visibility, elevation Required number of LEO, MEO or GEO satellites for continuous earth coverage Satellite altitude and distance from a point on earth Choice of orbits LEO, HEO, GEO Elliptical orbits with non-zero inclination, Molnya orbits, Tundra orbits Geosynchronous orbits Parameters of geosynchronous orbits Circular geosynchronous orbits Inclined geosynchronous orbits Quasi-zenith satellite systems (QZSS) Syb-synchronous circular equatorial orbits Geostationary orbit Parameters of the geostationary orbit Visibility Propagation delay Applications and system examples Perturbations of orbits Station keeping Station keeping box Estimation of orbit parameters Fundamentals of digital communications techniques Components of a digital communications system Principles of encryption Scrambling Scrambling vs. interleaving for randomization of data sequences Interleaving: Block interleaver, convolutional interleaver, random interleaver Digital modulation methods Linear and non-linear digital modulation methods Linear digital modulation methods QAM modulator and demodulator Pulse shaping, square-root raised-cosine pulses Average power spectral density Signal space constellation Examples: M-ary phase shift keying (M-PSK), M-ary quadrature amplitude shift keying (M-QAM) M-PSK in noisy channels Bit error probabilities of M-PSK and M-QAM M-PSK vs. M-QAM M-ary amplitude and phase shift keying (M-APSK) M-APSK vs. M-QAM Differential phase shift keying (DPSK) Error control coding (channel coding) Error detecting and forward error correcting (FEC) codes Principle of channel coding Data rate, code rate, Baud rate, spectral efficiency of modulation and coding schemes Bandwidth-power trade-off, bandwidth-limited vs. power-limited transmission Coding and modulation for transparent vs. regenerative payload Block codes and convolutional codes Concatenated codes Bit-interleaved coded modulation Convolutional codes Low density parity check (LDPC) codes, principle of message passing decoding, bit error rate performance Cyclic block codes Examples for cyclic block codes Single errors vs. block errors, cyclic block codes for burst errors Generator matrix, generator polynomials Systematic encoding and syndrome determination with shift registers Cyclic redundancy check (CRC) codes Automatic repeat request (ARQ) Principle of ARQ Stop-and-wait ARQ Go-back-N ARQ Selective-repeat ARQ Transmission gains and losses Antenna gain Antenna radiation pattern Maximum antenna gain, 3dB beamwidth Maximum antenna gain of circular aperture Maximum antenna gain of a geostationary satellite with global coverage Effective isotropic radiated power (EIRP) Power flux density Path loss Free space loss, free space loss for geostationary satellites Atmospheric loss Received power Losses in transmit and receive equipment Feeder loss Depointing loss Polarization mismatch loss Combined effect of losses Noise Origins of noise White noise Noise power spectral density and noise power Additive white Gaussian noise (AWGN) channel model Antenna noise temperature Earth brightness temperature Signal to noise ratios Atmospheric distortions Atmosphere of the earth: Troposphere, stratosphere, mesosphere, thermosphere, exosphere Attenuation and depolarization due to rain, fog, rain and ice clouds, sandstorms Scintillation Faraday effect Multipath contributions Link budget calculations GEO clear sky uplink and downlink GEO uplink and downlink under rain conditions Transparent vs. regenerative payload Link availability improvement through site diversity and adaptive transmission Transparent vs. regenerative payload Non-linear amplifiers Saleh model, Rapp model Input and output back-off factor Single carrier and multicarrier operation Dimensioning of transmission parameters Sources of noise: Thermal noise, interference, intermodulation products Signal to noise ratio and bit error probability Robustness against interference and non-linear channels Satellite networks Satellite network reference architectures Network topologies Network connectivity Types of network connectivity On-board connectivity Inter-satellite links Broadcast networks Satellite-based internet Satellite communications systems and standards examples The role of standards in satellite communications The Digital Video Broadcast Satellite Standard: DVB-S, DVB-S2, DVB-S2X Satellites in 3GPP mobile communications networks LEO megaconstellations: SpaceX Starlink, Kuiper, OneWeb Space debris The German Heinrich Hertz mission
Literature

Module M1702: Process Imaging

Courses

Title	Typ	Hrs/wk	CP
Process Imaging (L2723)	Lecture	3	3
Process Imaging (L2724)	Project-/problem-based Learning	3	3

Module Responsible	Prof. Alexander Penn
Admission Requirements	None
Recommended Previous Knowledge	No special prerequisites needed
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Content: The module focuses primarily on discussing established imaging techniques including (a) optical and infrared imaging, (b) magnetic resonance imaging, (c) X-ray imaging and tomography, and (d) ultrasound imaging but also covers a range of more recent imaging modalities. The students will learn: what these imaging techniques can measure (such as sample density or concentration, material transport, chemical composition, temperature), how the measurements work (physical measurement principles, hardware requirements, image reconstruction), and how to determine the most suited imaging methods for a given problem. Learning goals: After the successful completion of the course, the students shall: understand the physical principles and practical aspects of the most common imaging methods, be able to assess the pros and cons of these methods with regard to cost, complexity, expected contrasts, spatial and temporal resolution, and based on this assessment be able to identify the most suited imaging modality for any specific engineering challenge in the field of chemical and bioprocess engineering.
Skills
Personal Competence
Social Competence	In the problem-based interactive course, students work in small teams and set up two process imaging systems and use these systems to measure relevant process parameters in different chemical and bioprocess engineering applications. The teamwork will foster interpersonal communication skills.
Autonomy	Students are guided to work in self-motivation due to the challenge-based character of this module. A final presentation improves presentation skills.
Workload in Hours	Independent Study Time 96, Study Time in Lecture 84
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory Bioprocess Engineering: Specialisation B - Industrial Bioprocess Engineering: Elective Compulsory Bioprocess Engineering: Specialisation C - Bioeconomic Process Engineering, Focus Energy and Bioprocess Technology: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Bioprocess Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Process Engineering and Biotechnology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Process Engineering: Specialisation Environmental Process Engineering: Elective Compulsory Water and Environmental Engineering: Specialisation Environment: Elective Compulsory Water and Environmental Engineering: Specialisation Water: Elective Compulsory

Course L2723: Process Imaging

Typ	Lecture
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Alexander Penn
Language	EN
Cycle	SoSe
Content
Literature	Wang, M. (2015). Industrial Tomography. Cambridge, UK: Woodhead Publishing. Available as e-book in the library of TUHH: https://katalog.tub.tuhh.de/Record/823579395

Course L2724: Process Imaging

Typ	Project-/problem-based Learning
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Alexander Penn, Dr. Stefan Benders
Language	EN
Cycle	SoSe
Content	Content: The module focuses primarily on discussing established imaging techniques including (a) optical and infrared imaging, (b) magnetic resonance imaging, (c) X-ray imaging and tomography, and (d) ultrasound imaging and also covers a range of more recent imaging modalities. The students will learn: what these imaging techniques can measure (such as sample density or concentration, material transport, chemical composition, temperature), how the measurements work (physical measurement principles, hardware requirements, image reconstruction), and how to determine the most suited imaging methods for a given problem. Learning goals: After the successful completion of the course, the students shall: understand the physical principles and practical aspects of the most common imaging methods, be able to assess the pros and cons of these methods with regard to cost, complexity, expected contrasts, spatial and temporal resolution, and based on this assessment be able to identify the most suited imaging modality for any specific engineering challenge in the field of chemical and bioprocess engineering.
Literature	Wang, M. (2015). Industrial Tomography. Cambridge, UK: Woodhead Publishing. Available as e-book in the library of TUHH: https://katalog.tub.tuhh.de/Record/823579395

Module M1598: Image Processing

Courses

Title	Typ	Hrs/wk	CP
Image Processing (L2443)	Lecture	2	4
Image Processing (L2444)	Recitation Section (small)	2	2

Module Responsible	Prof. Tobias Knopp
Admission Requirements	None
Recommended Previous Knowledge	Signal and Systems
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students know about visual perception multidimensional signal processing sampling and sampling theorem filtering image enhancement edge detection multi-resolution procedures: Gauss and Laplace pyramid, wavelets image compression image segmentation morphological image processing
Skills	The students can analyze, process, and improve multidimensional image data implement simple compression algorithms design custom filters for specific applications
Personal Competence
Social Competence	Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.
Autonomy	Students are able to independently investigate a complex problem and assess which competencies are required to solve it.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Data Science: Core Qualification: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Data Science: Specialisation II. Computer Science: Elective Compulsory Data Science: Specialisation IV. Special Focus Area: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Mechatronics: Core Qualification: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory

Course L2443: Image Processing

Typ	Lecture
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	DE/EN
Cycle	WiSe
Content	Visual perception Multidimensional signal processing Sampling and sampling theorem Filtering Image enhancement Edge detection Multi-resolution procedures: Gauss and Laplace pyramid, wavelets Image Compression Segmentation Morphological image processing
Literature	Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011 Pratt, Digital Image Processing, Wiley, 2001 Bernd Jähne: Digitale Bildverarbeitung - Springer, Berlin 2005

Course L2444: Image Processing

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Focus Software

Module M0753: Software Verification

Courses

Title	Typ	Hrs/wk	CP
Software Verification (L0629)	Lecture	2	3
Software Verification (L0630)	Recitation Section (small)	2	3

Module Responsible

Prof. Sibylle Schupp

Admission Requirements

None

Recommended Previous Knowledge

Automata theory and formal languages
Computational logic
Object-oriented programming, algorithms, and data structures
Functional programming or procedural programming
Concurrency

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students apply the major verification techniques in model checking and deductive verification. They explain in formal terms syntax and semantics of the underlying logics, and assess the expressivity of different logics as well as their limitations. They classify formal properties of software systems. They find flaws in formal arguments, arising from modeling artifacts or underspecification.

Skills

Students formulate provable properties of a software system in a formal language. They develop logic-based models that properly abstract from the software under verification and, where necessary, adapt model or property. They construct proofs and property checks by hand or using tools for model checking or deductive verification, and reflect on the scope of the results. Presented with a verification problem in natural language, they select the appropriate verification technique and justify their choice.

Personal Competence

Social Competence

Students discuss relevant topics in class. They defend their solutions orally. They communicate in English.

Autonomy

Using accompanying on-line material for self study, students can assess their level of knowledge continuously and adjust it appropriately. Working on exercise problems, they receive additional feedback. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software verification. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	15 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory

Course L0629: Software Verification

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	Model checking (bounded model checking, CTL, LTL) Real-time model checking (TCTL, timed automata) Deductive verification (Hoare logic) Tool support Recent developments of verification techniques and applications
Literature	C. Baier and J-P. Katoen, Principles of Model Checking, MIT Press 2007. M. Huth and M. Bryan, Logic in Computer Science. Modelling and Reasoning about Systems, 2nd Edition, 2004. Selected Research Papers

Course L0630: Software Verification

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0733: Software Analysis

Courses

Title	Typ	Hrs/wk	CP
Software Analysis (L0631)	Lecture	2	3
Software Analysis (L0632)	Recitation Section (small)	2	3

Module Responsible	Prof. Sibylle Schupp
Admission Requirements	None
Recommended Previous Knowledge	Basic knowledge of software-engineering activities Discrete algebraic structures Object-oriented programming, algorithms, and data structures Functional programming or Procedural programming
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students apply the major approaches to data-flow analysis, control-flow analysis, and type-based analysis, along with their classification schemes, and employ abstract interpretation. They explain the standard forms of internal representations and models, including their mathematical structure and properties, and evaluate their suitability for a particular analysis. They explain and categorize the major analysis algorithms. They distinguish precise solutions from approximative approaches, and show termination and soundness properties.
Skills	Presented with an analytical task for a software artifact, students select appropriate approaches from software analysis, and justify their choice. They design suitable representations by modifying standard representations. They develop customized analyses and devise them as safe overapproximations. They formulate analyses in a formal way and construct arguments for their correctness, behavior, and precision.
Personal Competence
Social Competence	Students discuss relevant topics in class. They defend their solutions orally. They communicate in English.
Autonomy	Using accompanying on-line material for self study, students can assess their level of knowledge continuously and adjust it appropriately. Working on exercise problems, they receive additional feedback. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software analysis. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	software artifacts/mathematical write-ups; short presentation
Assignment for the Following Curricula	Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory

Course L0631: Software Analysis

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	Modeling: Control-Flow Modeling, Data Dependences, Intermediate Languages) Classical Bit-Vector Analyses (Reaching Definition, Very Busy Expressions, Liveness, Available Expressions, May/Must, Forward/Backward) Monotone Frameworks (Lattices, Transfer Functions, Ascending Chain Condition, Distributivity, Constant Propagation) Theory of Data-Flow Analysis (Tarski's Fixed Point Theorem, Data-Flow Equations, MFP Solution, MOP Solution, Worklist Algorithm) Non-Classical Data-Flow Analyses Abstract Interpretation (Galois Connections, Approximating Fixed Points, Construction Techniques) Type Systems (Type Derivation, Inference Trees, Algorithm W, Unification) Recent Developments of Analysis Techniques and Applications
Literature	Flemming Nielsen, Hanne Nielsen, and Chris Hankin. Principles of Program Analysis. Springer, 2nd. ed. 2005. Uday Khedker, Amitabha Sanyal, and Bageshri Karkara. Data Flow Analysis: Theory and Practice. CRC Press, 2009. Benjamin Pierce, Types and Programming Languages, MIT Press. Selected research papers

Course L0632: Software Analysis

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1397: Model Checking - Proof Engines and Algorithms

Courses

Title	Typ	Hrs/wk	CP
Model Checking - Proof Engines and Algorithms (L1979)	Lecture	2	3
Model Checking - Proof Engines and Algorithms (L1980)	Recitation Section (small)	2	3

Module Responsible

Prof. Görschwin Fey

Admission Requirements

None

Recommended Previous Knowledge

Basic knowledge about data structures and algorithms

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students know

algorithms and data structures for model checking,
basics of Boolean reasoning engines and
the impact of specification and modelling on the computational effort for model checking.

Skills

Students can

explain and implement algorithms and data structures for model checking,
decide whether a given problem can be solved using Boolean reasoning or model checking, and
implement the respective algorithms.

Personal Competence

Social Competence

Students

discuss relevant topics in class and
defend their solutions orally.

Autonomy

Using accompanying material students independently learn in-depth relations between concepts explained in the lecture and additional solution strategies.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Subject theoretical and practical work	Die Aufgabe wird im Rahmen von Volresung und Prüfung definiert. Die Lösung der Aufgabe ist Zulassungsvoraussetzung für die Prüfung.

Examination

Oral exam

Examination duration and scale

30 min

Assignment for the Following Curricula

Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory

Course L1979: Model Checking - Proof Engines and Algorithms

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Görschwin Fey
Language	DE/EN
Cycle	SoSe
Content	Correctness is a major concern in embedded systems. Model checking can fully automatically proof formal properties about digital hardware or software. Such properties are given in temporal logic, e.g., to prove "No two orthogonal traffic lights will ever be green." And how do the underlying reasoning algorithms work so effectively in practice despite a computational complexity of NP hardness and beyond? But what are the limitations of model checking? How are the models generated from a given design? The lecture will answer these questions. Open source tools will be used to gather a practical experience. Among other topics, the lecture will consider the following topics: Modelling digital Hardware, Software, and Cyber Physical Systems Data structures, decision procedures and proof engines Binary Decision Diagrams And-Inverter-Graphs Boolean Satisfiability Satisfiability Modulo Theories Specification Languages CTL LTL System Verilog Assertions Algorithms for Reachability Analysis Symbolic CTL Checking Bounded LTL-Model Checking Optimizations, e.g., induction, abstraction Quality assurance
Literature	Edmund M. Clarke, Jr., Orna Grumberg, and Doron A. Peled. 1999. Model Checking. MIT Press, Cambridge, MA, USA. A. Biere, A. Biere, M. Heule, H. van Maaren, and T. Walsh. 2009. Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, The Netherlands, The Netherlands. Selected research papers

Course L1980: Model Checking - Proof Engines and Algorithms

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Görschwin Fey
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1301: Software Testing

Courses

Title	Typ	Hrs/wk	CP
Software Testing (L1791)	Lecture	2	3
Software Testing (L1792)	Project-/problem-based Learning	2	3

Module Responsible	Prof. Sibylle Schupp
Admission Requirements	None
Recommended Previous Knowledge	Software Engineering Higher Programming Languages Object-Oriented Programming Algorithms and Data Structures Experience with (Small) Software Projects Statistics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students explain the different phases of testing, describe fundamental techniques of different types of testing, and paraphrase the basic principles of the corresponding test process. They give examples of software development scenarios and the corresponding test type and technique. They explain algorithms used for particular testing techniques and describe possible advantages and limitations.
Skills	Students identify the appropriate testing type and technique for a given problem. They adapt and execute respective algorithms to execute a concrete test technique properly. They interpret testing results and execute corresponding steps for proper re-test scenarios. They write and analyze test specifications. They apply bug finding techniques for non-trivial problems.
Personal Competence
Social Competence	Students discuss relevant topics in class. They defend their solutions orally. They communicate in English.
Autonomy	Students can assess their level of knowledge continuously and adjust it appropriately, based on feedback and on self-guided studies. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software testing. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	Software
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory

Course L1791: Software Testing

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	SoSe
Content	Fundamentals of software testing Model-based testing Test automation Criteria-based testing
Literature	M. Pezze and M. Young, Software Testing and Analysis, John Wiley 2008. P. Ammann and J. Offutt, "Introduction to Software Testing", 2nd edition 2016. A. Zeller: "Why Programs Fail: A Guide to Systematic Debugging", 2nd edition 2012.

Course L1792: Software Testing

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	SoSe
Content	Fundamentals of software testing Model-based testing Test automation Criteria-based testing
Literature	M. Pezze and M. Young, Software Testing and Analysis, John Wiley 2008. P. Ammann and J. Offutt, "Introduction to Software Testing", 2nd edition 2015.

Module M1682: Secure Software Engineering

Courses

Title	Typ	Hrs/wk	CP
Secure Software Engineering (L2667)	Lecture	2	3
Secure Software Engineering (L2668)	Project-/problem-based Learning	2	3

Module Responsible	Prof. Riccardo Scandariato
Admission Requirements	None
Recommended Previous Knowledge	Familiarity with basic software engineering concepts (e.g., requirements, design) and basic security concepts (e.g., confidentiality, integrity, availability)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can: Elicit security requirements in a software project Model and document security measures in a software design Use threat and risk analysis techniques Understand how security code reviews are performed Understand the core definitions of concepts related to privacy Understand privacy enhancing technologies
Skills	Select appropriate security assurance techniques to be used in a security assurance program
Personal Competence
Social Competence	None
Autonomy	Students can apply the knowledge acquired throughout the course to the resolution of industrial case studies. Students should also be capable to acquire new knowledge independently from academic publications, techical standards, and white papers.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory

Course L2667: Secure Software Engineering

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Riccardo Scandariato
Language	EN
Cycle	SoSe
Content	Secure software development processes and maturity models Techniques to define security requirements Techniques to create, document and analyse the design of secure applications Threat and risk analysis techniques Security code reviews Program repair techniques for security vulnerabilities Privacy engineering
Literature	Sindre, G. and Opdahl, A.L., 2005. Eliciting security requirements with misuse cases. Requirements engineering, 10(1), pp.34-44. Fontaine, P.J., Van Lamsweerde, A., Letier, E. and Darimont, R., 2001. Goal-oriented elaboration of security requirements. Mead, N.R. and Stehney, T., 2005. Security quality requirements engineering (SQUARE) methodology. ACM SIGSOFT Software Engineering Notes, 30(4), pp.1-7. Mirakhorli, M., Shin, Y., Cleland-Huang, J. and Cinar, M., 2012, June. A tactic-centric approach for automating traceability of quality concerns. In 2012 34th international conference on software engineering (ICSE) (pp. 639-649). IEEE. Jürjens, J., UMLsec: Extending UML for secure systems development, International Conference on The Unified Modeling Language, 2002 Lund, M.S., Solhaug, B. and Stølen, K., 2011. A guided tour of the CORAS method. In Model-Driven Risk Analysis (pp. 23-43). Springer, Berlin, Heidelberg. Howard, M.A., 2006. A process for performing security code reviews. IEEE Security & privacy, 4(4), pp.74-79 Diaz, C. and Gürses, S., 2012. Understanding the landscape of privacy technologies. Proceedings of the information security summit, 12, pp.58-63.

Course L2668: Secure Software Engineering

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Riccardo Scandariato
Language	EN
Cycle	SoSe
Content	Secure software development processes and maturity models Techniques to define security requirements Techniques to create, document and analyse the design of secure applications Threat and risk analysis techniques Security code reviews Program repair techniques for security vulnerabilities Privacy engineering
Literature

Module M1794: Applied Cryptography

Courses

Title	Typ	Hrs/wk	CP
Applied Cryptography (L2954)	Lecture	3	4
Applied Cryptography (L2955)	Recitation Section (small)	1	2

Module Responsible

Prof. Sibylle Fröschle

Admission Requirements

None

Recommended Previous Knowledge

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Skills

Personal Competence

Social Competence

Autonomy

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	10 %	Excercises	Die Übungsaufgaben finden semesterbegleitend statt

Examination

Written exam

Examination duration and scale

120 min

Assignment for the Following Curricula

Course L2954: Applied Cryptography

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Sibylle Fröschle
Language	EN
Cycle	SoSe
Content	This module provides a comprehensive knowledge in modern cryptography and how it plays a key role in securing the digital world we live in today. We will thoroughly treat cryptographic primitives such as symmetric and asymmetric encryption schemes, cryptographic hash functions, message authentication codes, and digital signatures. Moreover, we will cover aspects of practical deployment such as key management, public key infrastructures, and secure storage of keys. We will see how everything comes together in applications such as the ubiquitous security protocols of the Internet (e.g. TLS and WPA3) and/or the Internet-of-things. We also discuss current challenges such as the need for post-quantum cryptography.
Literature	Introduction to Modern Cryptography, Third Edition, Jonathan Katz and Jehuda Lindell, Chapman & Hall/CRC, 2021 Sicherheit und Kryptographie im Internet, 5th Edition, Jörg Schwenk, Springer-Verlag, 2020

Course L2955: Applied Cryptography

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Sibylle Fröschle
Language	EN
Cycle	SoSe
Content	See corresponding lecture
Literature	Siehe korrespondierende Vorlesung

Module M1774: Advanced Internet Computing

Courses

Title	Typ	Hrs/wk	CP
Advanced Internet Computing (L2916)	Lecture	2	3
Advanced Internet Computing (L2917)	Project-/problem-based Learning	2	3

Module Responsible	Prof. Stefan Schulte
Admission Requirements	None
Recommended Previous Knowledge	Good programming skills are necessary. Previous knowledge in the field of distributed systems is helpful.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	After successful completion of the course, students are able to: Describe basic concepts of Cloud Computing, the Internet of Things (IoT), and blockchain technologies Discuss and assess critical aspects of Cloud Computing, the IoT, and blockchain technologies Select and apply cloud and IoT technologies for particular application areas Design and develop practical solutions for the integration of smart objects in IoT, Cloud, and blockchain software Implement IoT services
Skills	The students acquire the ability to model Internet-based distributed systems and to work with these systems. This comprises especially the ability to select and utilize fitting technologies for different application areas. Furthermore, students are able to critically assess the chosen technologies.
Personal Competence
Social Competence	Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.
Autonomy	Students are able to independently investigate a complex problem and assess which competencies are required to solve it.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	Group project incl. presentation (50 %), written exam (60 min, 50 %)
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory

Course L2916: Advanced Internet Computing

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Stefan Schulte
Language	EN
Cycle	SoSe
Content	This lecture discusses modern Internet-based distributed systems in three blocks: (i) Cloud computing, (ii) the Internet of Things, and (iii) blockchain technologies. The following topics will be covered in the single lectures: Cloud Computing Elastic Computing Technologies for identification for the IoT: RFID & EPC Communication in the IoT: Standards and protocols Security and trust in the IoT: Concerns and solution approaches Edge and Fog Computing Application areas: Smart factories, smart cities, smart healthcare Blockchain technologies Consensus
Literature	Will be discussed in the lecture

Course L2917: Advanced Internet Computing

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Stefan Schulte
Language	EN
Cycle	SoSe
Content	This project-/problemoriented part of the module augments the theoretical content of the lecture by a concrete technical problem, which needs to be solved by the students in group work during the semester. Possible topics are (blockchain-based) sensor data integration, Big Data processing, Cloud-based redundant data storages, and Cloud-based Onion Routing.
Literature	Will be discussed in the lecture.

Module M0924: Software for Embedded Systems

Courses

Title	Typ	Hrs/wk	CP
Software for Embdedded Systems (L1069)	Lecture	2	3
Software for Embdedded Systems (L1070)	Recitation Section (small)	3	3

Module Responsible

Prof. Bernd-Christian Renner

Admission Requirements

None

Recommended Previous Knowledge

Very Good knowledge and practical experience in programming in the C language
Basic knowledge in software engineering
Basic understanding of assembly language

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students know the basic principles and procedures of software engineering for embedded systems. They are able to describe the usage and pros of event based programming using interrupts. They know the components and functions of a concrete microcontroller. The participants explain requirements of real time systems. They know at least three scheduling algorithms for real time operating systems including their pros and cons.

Skills

Students build interrupt-based programs for a concrete microcontroller. They build and use a preemptive scheduler. They use peripheral components (timer, ADC, EEPROM) to realize complex tasks for embedded systems. To interface with external components they utilize serial protocols.

Personal Competence

Social Competence

Autonomy

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	10 %	Attestation

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory

Course L1069: Software for Embdedded Systems

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Bernd-Christian Renner
Language	DE/EN
Cycle	SoSe
Content	General-Purpose Processors Programming the Atmel AVR Interrupts C for Embedded Systems Standard Single Purpose Processors: Peripherals Finite-State Machines Memory Operating Systems for Embedded Systems Real-Time Embedded Systems Boot loader and Power Management
Literature	Embedded System Design, F. Vahid and T. Givargis, John Wiley Programming Embedded Systems: With C and Gnu Development Tools, M. Barr and A. Massa, O'Reilly C und C++ für Embedded Systems, F. Bollow, M. Homann, K. Köhn, MITP The Art of Designing Embedded Systems, J. Ganssle, Newnses Mikrocomputertechnik mit Controllern der Atmel AVR-RISC-Familie, G. Schmitt, Oldenbourg Making Embedded Systems: Design Patterns for Great Software, E. White, O'Reilly

Course L1070: Software for Embdedded Systems

Typ	Recitation Section (small)
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Bernd-Christian Renner
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1785: Machine Learning in Electrical Engineering and Information Technology

Courses

Title	Typ	Hrs/wk	CP
General Introduction Machine Learning (L3004)	Lecture	1	2
Machine Learning Applications in Electric Power Systems (L3008)	Lecture	1	1
Machine Learning in Electromagnetic Compatibility (EMC) Engineering (L3006)	Lecture	1	1
Machine Learning in High-Frequency Technology and Radar (L3007)	Lecture	1	1
Machine Learning in Wireless Communications (L3005)	Lecture	1	1

Module Responsible	Prof. Gerhard Bauch
Admission Requirements	None
Recommended Previous Knowledge	The module is designed for a diverse audience, i.e. students with different background. It shall be suitable for both students with deeper knowledge in machine learning methods but less knowledge in electrical engineering, e.g. math or computer science students, and students with deeper knowledge in electrical engineering but less knowledge in machine learning methods, e.g. electrical engineering students. Machine learning methods will be explained on a relatively high level indicating mainly principle ideas. The focus is on specific applications in electrical engineering and information technology. The chapters of the course will be understandable in different depth depending on the individual background of the student. The individual background of the students will be taken into consideration in the oral exam.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Microwave Engineering, Optics, and Electromagnetic Compatibility: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Computer Science in Engineering: Specialisation II. Engineering Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory

Course L3004: General Introduction Machine Learning

Typ	Lecture
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Dr. Maximilian Stark
Language	EN
Cycle	SoSe
Content	From Rule-Based Systems to Machine Learning Brief overview recent advances in ML in various domain Outline and expected learning outcomes Basics statistical inference and statistics Basics of information theory The Notions of Learning in Machine Learning Unsupervised and supervised machine learning Model-based and data-driven machine learning Hybrid modelling Online/offline/meta/transfer learning General loss functions Introduction to Deep Learning Variants of neural networks MLP Conv. neural networks Recurrent neural networks Training neural networks (Stochastic) Gradient Descent Regression vs. Classification Classification as supervised learning problem Hands-On Session Representation Learning and Generative Models AutoEncoders Directed Generative Models Undirected Generative Models Generative Adversarial Neural Networks Probabilistic Graphical Models Bayesian Networks Variational inference (variational autoencoder)
Literature

Course L3008: Machine Learning Applications in Electric Power Systems

Typ	Lecture
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Christian Becker, Dr. Davood Babazadeh
Language	EN
Cycle	SoSe
Content
Literature

Course L3006: Machine Learning in Electromagnetic Compatibility (EMC) Engineering

Typ	Lecture
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Christian Schuster, Dr. Cheng Yang
Language	EN
Cycle	SoSe
Content	Electromagnetic Compatibility (EMC) Engineering deals with design, simulation, measurement, and certification of electronic and electric components and systems in such a way that their operation is safe, reliable, and efficient in any possible application. Safety is hereby understood as safe with respect to parasitic effects of electromagnetic fields on humans as well as on the operation of other components and systems nearby. Examples for components and systems range from the wiring in aircraft and ships to high-speed interconnects in server systems and wirless interfaces for brain implants. In this part of the course we will give an introduction to the physical basics of EMC engineering and then show how methods of Machine Learning (ML) can be applied to expand todays physcis-based approaches in EMC Engineering.
Literature

Course L3007: Machine Learning in High-Frequency Technology and Radar

Typ	Lecture
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Alexander Kölpin, Dr. Fabian Lurz
Language	EN
Cycle	SoSe
Content
Literature

Course L3005: Machine Learning in Wireless Communications

Typ	Lecture
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Dr. Maximilian Stark
Language	EN
Cycle	SoSe
Content	Supervised Learning Application - Channel Coding Recap channel coding and block codes Block codes as trainable neural networks Tanner graph with trainable weights Hands-on session Supervised Learning Application - Modulation Detection Recap wireless modulation schemes Convolutional neuronal networks for blind detection of modulation schemes Hands-on session Autoencoder Application - Constellation Shaping I Recap channel capacity and constellation shaping, Capacity achieving machine learning systems Information theoretical explanation of the autoencoder training Hands-on session Autoencoder Application - Constellation Shaping II Training without a channel model Mutual information neural estimator Hands-on session Generative Adversarial Network Application - Channel Modelling Recap realistic channels with non-linear hardware impairments Training a digital twin of a realistic channel with insufficient training data Hands-on session Recurrent Neural Network Application - Channel prediction Recap time-varying channel models Recurrent neural networks for temporal prediction Hands-on session
Literature

Module M1780: Massively Parallel Systems: Architecture and Programming

Courses

Title	Typ	Hrs/wk	CP
Massively Parallel Systems: Architecture and Programming (L2936)	Lecture	2	3
Massively Parallel Systems: Architecture and Programming (L2937)	Project-/problem-based Learning	2	3

Module Responsible

Prof. Sohan Lal

Admission Requirements

None

Recommended Previous Knowledge

An introductory module on computer Engineering or computer architecture, good programming skills in C/C++.

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The course starts with parallel computers classification, multithreading, and covers the architecture of centralized and distributed shared-memory parallel systems, multiprocessor cache coherence, snooping / directory-based cache coherence protocols, implementation, and limitations. Next, students study interconnection networks and routing in parallel systems. To ensure the correctness of shared-memory multithreaded programs, independent of the speed of execution of their individual threads, the important topics of memory consistency and synchronization will be covered in detail. As a case study, the architecture of a few accelerators such as GPUs will also be discussed in detail. Besides understanding the architecture and organization of parallel systems, programming them is also very challenging. The course will also cover how to program massively parallel systems using API/libraries such as CUDA/OpenCL/MPI/OpenMP.

Skills

After completing this course, students will be able to understand the architecture and organization of parallel systems. They will be able to evaluate different design choices and make decisions while designing a parallel system. In addition, they will be able to program parallel systems (ranging from an embedded system to a supercomputer) using CUDA/OpenCL/MPI/OpenMP.

Personal Competence

Social Competence

The course will encourage students to work in small groups to solve complex problems, thus, inculcating the importance of teamwork.

Autonomy

Today, parallel computers are present everywhere. Students will be able to not only program parallel computers independently, but also understand their underlying organization and architecture. This will further help to understand the performance issues of parallel applications and provide insights to improve them.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	20 %	Subject theoretical and practical work

Examination

Oral exam

Examination duration and scale

25 min

Assignment for the Following Curricula

Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Data Science: Specialisation II. Computer Science: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory

Course L2936: Massively Parallel Systems: Architecture and Programming

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sohan Lal
Language	EN
Cycle	WiSe
Content	Brief outline: Parallel computers and their classification Centralized and distributed shared-memory architectures: snooping vs directory-based cache coherence protocols, implementation, and limitations Chip multiprocessors: software-based, block (coarse-grain), interleaved (fine-grain), simultaneous multithreading Synchronization: high-level primitives and implementation, memory consistency models: sequential and weaker memory consistency models Interconnection networks: topologies (direct and indirect networks) and routing techniques Graphics Processing Units (GPUs) architecture and programming using CUDA/OpenCL Parallel programming with message passing interface (MPI), OpenMP
Literature	Michel Dubois, Murali Annavaram, and Per Stenström, Parallel Computer Organization and Design (Book) David A Patterson and John L. Hennessy, Computer Architecture: A Quantitative Approach, Elsevier (Book) David B. Kirk, Wen-mei W. Hwu, Programming Massivley Parallel Processors, Elsevier (Book)

Course L2937: Massively Parallel Systems: Architecture and Programming

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sohan Lal
Language	EN
Cycle	WiSe
Content	There will be 3-4 assignments for project-based learning consisting of the following: Implement and compare different cache coherence protocols using a simulator or a high-level, event-driven simulation interface such as SystemC Programming massively parallel systems to solve computationally intensive problems such as password cracking using CUDA/OpenCL/MPI/OpenMP
Literature	The following literature will be useful for project-based learning. The further required resources will be discussed during the course. David B. Kirk, Wen-mei W. Hwu, Programming Massivley Parallel Processors, Elsevier (Book) MPI Forum, https://www.mpi-forum.org/ SystemC, https://www.accellera.org/community/systemc

Specialization Secure and Dependable IT Systems

Graduates of the Secure and Dependable IT Systems specialisation acquire extensive knowledge in software verification and IT security. They also have knowledge in communication networks and signal processing. They are able to apply methods and procedures required to work on secure and dependable IT systems, as well as critically examine new insights to further develop and incorporate in their work.

The Secure and Dependable IT Systems specialisation is recommended for students who already have a good mathematical foundation and basic knowledge in computer science and software development.

Module M0753: Software Verification

Courses

Title	Typ	Hrs/wk	CP
Software Verification (L0629)	Lecture	2	3
Software Verification (L0630)	Recitation Section (small)	2	3

Module Responsible

Prof. Sibylle Schupp

Admission Requirements

None

Recommended Previous Knowledge

Automata theory and formal languages
Computational logic
Object-oriented programming, algorithms, and data structures
Functional programming or procedural programming
Concurrency

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Skills

Personal Competence

Social Competence

Students discuss relevant topics in class. They defend their solutions orally. They communicate in English.

Autonomy

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	15 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Course L0629: Software Verification

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	Model checking (bounded model checking, CTL, LTL) Real-time model checking (TCTL, timed automata) Deductive verification (Hoare logic) Tool support Recent developments of verification techniques and applications
Literature	C. Baier and J-P. Katoen, Principles of Model Checking, MIT Press 2007. M. Huth and M. Bryan, Logic in Computer Science. Modelling and Reasoning about Systems, 2nd Edition, 2004. Selected Research Papers

Course L0630: Software Verification

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0942: Software Security

Courses

Title	Typ	Hrs/wk	CP
Software Security (L1103)	Lecture	2	3
Software Security (L1104)	Recitation Section (small)	2	3

Module Responsible	Prof. Riccardo Scandariato
Admission Requirements	None
Recommended Previous Knowledge	Familiarity with C/C++, web programming
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can name the main causes for security vulnerabilities in software explain current methods for identifying and avoiding security vulnerabilities explain the fundamental concepts of code-based access control
Skills	Students are capable of performing a software vulnerability analysis developing secure code
Personal Competence
Social Competence	None
Autonomy	Students are capable of acquiring knowledge independently from professional publications, technical standards, and other sources, and are capable of applying newly acquired knowledge to new problems.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 minutes
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory

Course L1103: Software Security

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Riccardo Scandariato
Language	EN
Cycle	WiSe
Content	Reliabilty and Software Security Attacks exploiting character and integer representations Buffer overruns Vulnerabilities in memory managemet: double free attacks Race conditions SQL injection Cross-site scripting and cross-site request forgery Testing for security; taint analysis Type safe languages Development proceses for secure software Code-based access control
Literature	M. Howard, D. LeBlanc: Writing Secure Code, 2nd edition, Microsoft Press (2002) G. Hoglund, G. McGraw: Exploiting Software, Addison-Wesley (2004) L. Gong, G. Ellison, M. Dageforde: Inside Java 2 Platform Security, 2nd edition, Addison-Wesley (2003) B. LaMacchia, S. Lange, M. Lyons, R. Martin, K. T. Price: .NET Framework Security, Addison-Wesley Professional (2002) D. Gollmann: Computer Security, 3rd edition (2011)

Course L1104: Software Security

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Riccardo Scandariato
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1397: Model Checking - Proof Engines and Algorithms

Courses

Title	Typ	Hrs/wk	CP
Model Checking - Proof Engines and Algorithms (L1979)	Lecture	2	3
Model Checking - Proof Engines and Algorithms (L1980)	Recitation Section (small)	2	3

Module Responsible

Prof. Görschwin Fey

Admission Requirements

None

Recommended Previous Knowledge

Basic knowledge about data structures and algorithms

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students know

algorithms and data structures for model checking,
basics of Boolean reasoning engines and
the impact of specification and modelling on the computational effort for model checking.

Skills

Students can

explain and implement algorithms and data structures for model checking,
decide whether a given problem can be solved using Boolean reasoning or model checking, and
implement the respective algorithms.

Personal Competence

Social Competence

Students

discuss relevant topics in class and
defend their solutions orally.

Autonomy

Using accompanying material students independently learn in-depth relations between concepts explained in the lecture and additional solution strategies.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Subject theoretical and practical work	Die Aufgabe wird im Rahmen von Volresung und Prüfung definiert. Die Lösung der Aufgabe ist Zulassungsvoraussetzung für die Prüfung.

Examination

Oral exam

Examination duration and scale

30 min

Assignment for the Following Curricula

Course L1979: Model Checking - Proof Engines and Algorithms

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Görschwin Fey
Language	DE/EN
Cycle	SoSe
Content	Correctness is a major concern in embedded systems. Model checking can fully automatically proof formal properties about digital hardware or software. Such properties are given in temporal logic, e.g., to prove "No two orthogonal traffic lights will ever be green." And how do the underlying reasoning algorithms work so effectively in practice despite a computational complexity of NP hardness and beyond? But what are the limitations of model checking? How are the models generated from a given design? The lecture will answer these questions. Open source tools will be used to gather a practical experience. Among other topics, the lecture will consider the following topics: Modelling digital Hardware, Software, and Cyber Physical Systems Data structures, decision procedures and proof engines Binary Decision Diagrams And-Inverter-Graphs Boolean Satisfiability Satisfiability Modulo Theories Specification Languages CTL LTL System Verilog Assertions Algorithms for Reachability Analysis Symbolic CTL Checking Bounded LTL-Model Checking Optimizations, e.g., induction, abstraction Quality assurance
Literature	Edmund M. Clarke, Jr., Orna Grumberg, and Doron A. Peled. 1999. Model Checking. MIT Press, Cambridge, MA, USA. A. Biere, A. Biere, M. Heule, H. van Maaren, and T. Walsh. 2009. Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, The Netherlands, The Netherlands. Selected research papers

Course L1980: Model Checking - Proof Engines and Algorithms

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Görschwin Fey
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1773: Cybersecurity Data Science

Courses

Title	Typ	Hrs/wk	CP
Cybersecurity Data Science (L2914)	Lecture	2	3
Exercise Cybersecurity Data Science (L2915)	Project-/problem-based Learning	2	3

Module Responsible	Prof. Riccardo Scandariato
Admission Requirements	None
Recommended Previous Knowledge	Basic knowledge of probabilities and statistics. Familiarity with object oriented programming.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can: Apply data science methods to the resolution of complex cybersecurity problems. Use of data science methods to quantify risks and optimize cybersecurity operations. Identify strengths and limitations of state-of-the-art methods Select the performance indicators of data-oriented cybersecurity solutions. Understand cybersecurity threats in data science methods.
Skills	Implement and evaluate data-driven models for the identification, treatment, and mitigation of cybersecurity risks
Personal Competence
Social Competence	None
Autonomy	Students can apply the knowledge acquired throughout the course to the resolution of industrial case studies. Students should also be capable to acquire new knowledge independently from academic publications, techical standards, and white papers.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory

Course L2914: Cybersecurity Data Science

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Riccardo Scandariato
Language	EN
Cycle	SoSe
Content	Theoretical Foundations: Introduction to data science Supervised and unsupervised learning Data science methods (e.g., clustering, decision trees, artificial neural networks) Performance metrics Cybersecutrity Applications: Spam detection Phishing detection Intrusion detection Access-control prediction Denial of Service (DoS) prediction Vulnerability/malware prediction Adversarial machine learning
Literature	[1] Sarker, I.H., Kayes, A.S.M., Badsha, S., Alqahtani, H., Watters, P. and Ng, A., 2020. Cybersecurity data science: an overview from machine learning perspective. Journal of Big data, 7(1), pp.1-29. [2] Truong, T.C., Zelinka, I., Plucar, J., Čandík, M. and Šulc, V., 2020. Artificial intelligence and cybersecurity: Past, presence, and future. In Artificial intelligence and evolutionary computations in engineering systems (pp. 351-363). Springer, Singapore. [3] Dua, S. and Du, X., 2016. Data mining and machine learning in cybersecurity. CRC press. [4] Arp, D., Quiring, E., Pendlebury, F., Warnecke, A., Pierazzi, F., Wressnegger, C., Cavallaro, L. and Rieck, K., Dos and Don'ts of Machine Learning in Computer Security. [5] Torres, J.M., Comesaña, C.I. and Garcia-Nieto, P.J., 2019. Machine learning techniques applied to cybersecurity. International Journal of Machine Learning and Cybernetics, 10(10), pp.2823-2836. [6] Russell, S. and Norvig, P., 2010. Artificial Intelligence: A Modern Approach, Prentice Hall.

Course L2915: Exercise Cybersecurity Data Science

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Riccardo Scandariato
Language	EN
Cycle	SoSe
Content	Theoretical Foundations: Introduction to data science Supervised and unsupervised learning Data science methods (e.g., clustering, decision trees, artificial neural networks) Performance metrics Cybersecutrity Applications: Spam detection Phishing detection Intrusion detection Access-control prediction Denial of Service (DoS) prediction Vulnerability/malware prediction Adversarial machine learning
Literature	[1] Sarker, I.H., Kayes, A.S.M., Badsha, S., Alqahtani, H., Watters, P. and Ng, A., 2020. Cybersecurity data science: an overview from machine learning perspective. Journal of Big data, 7(1), pp.1-29. [2] Truong, T.C., Zelinka, I., Plucar, J., Čandík, M. and Šulc, V., 2020. Artificial intelligence and cybersecurity: Past, presence, and future. In Artificial intelligence and evolutionary computations in engineering systems (pp. 351-363). Springer, Singapore. [3] Dua, S. and Du, X., 2016. Data mining and machine learning in cybersecurity. CRC press. [4] Arp, D., Quiring, E., Pendlebury, F., Warnecke, A., Pierazzi, F., Wressnegger, C., Cavallaro, L. and Rieck, K., Dos and Don'ts of Machine Learning in Computer Security. [5] Torres, J.M., Comesaña, C.I. and Garcia-Nieto, P.J., 2019. Machine learning techniques applied to cybersecurity. International Journal of Machine Learning and Cybernetics, 10(10), pp.2823-2836. [6] Russell, S. and Norvig, P., 2010. Artificial Intelligence: A Modern Approach, Prentice Hall.

Module M1400: Design of Dependable Systems

Courses

Title	Typ	Hrs/wk	CP
Designing Dependable Systems (L2000)	Lecture	2	3
Designing Dependable Systems (L2001)	Recitation Section (small)	2	3

Module Responsible

Prof. Görschwin Fey

Admission Requirements

None

Recommended Previous Knowledge

Basic knowledge about data structures and algorithms

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

In the following "dependable" summarizes the concepts Reliability, Availability, Maintainability, Safety and Security.

Knowledge about approaches for designing dependable systems, e.g.,

Structural solutions like modular redundancy
Algorithmic solutions like handling byzantine faults or checkpointing

Knowledge about methods for the analysis of dependable systems

Skills

Ability to implement dependable systems using the above approaches.

Ability to analyzs the dependability of systems using the above methods for analysis.

Personal Competence

Social Competence

Students

discuss relevant topics in class and
present their solutions orally.

Autonomy

Using accompanying material students independently learn in-depth relations between concepts explained in the lecture and additional solution strategies.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Subject theoretical and practical work	Die Lösung einer Aufgabe ist Zuslassungsvoraussetzung für die Prüfung. Die Aufgabe wird in Vorlesung und Übung definiert.

Examination

Oral exam

Examination duration and scale

30 min

Assignment for the Following Curricula

Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory

Course L2000: Designing Dependable Systems

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Görschwin Fey
Language	DE/EN
Cycle	SoSe
Content	Description The term dependability comprises various aspects of a system. These are typically: Reliability Availability Maintainability Safety Security This makes dependability a core aspect that has to be considered early in system design, no matter whether software, embedded systems or full scale cyber-physical systems are considered. Contents The module introduces the basic concepts for the design and the analysis of dependable systems. Design examples for getting practical hands-on-experience in dependable design techniques. The module focuses towards embedded systems. The following topics are covered: Modelling Fault Tolerance Design Concepts Analysis Techniques
Literature

Course L2001: Designing Dependable Systems

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Görschwin Fey
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1564: Advanced Seminars Computer Science and Communication Technology

Courses

Title	Typ	Hrs/wk	CP
Advanced Seminar Computer Science and Communication Technology I (L2352)	Seminar	2	3
Introductory Seminar Computer Science and Communication Technology II (L2429)	Seminar	2	3

Module Responsible	Dozenten des SD E
Admission Requirements	None
Recommended Previous Knowledge	Basic knowledge of Computer Science and Mathematics at the Master's level.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are able to explicate a specific topic in the field of Computer Science, describe complex issues, present different views and evaluate in a critical way.
Skills	The students are able to familiarize in a specific topic of Computer Science in limited time, realize a literature survey on the specific topic and cite in a correct way, elaborate a presentation and give a lecture to a selected audience, sum up the presentation in 10-15 lines, answer questions in the final discussion.
Personal Competence
Social Competence	The students are able to elaborate and introduce a topic for a certain audience, discuss the topic, content and structure of the presentation with the instructor, discuss certain aspects with the audience, and as the lecturer listen and respond to questions from the audience.
Autonomy	The students are able to define the task in question in an autonomous way, develop the necessary knowledge, use appropriate work equipment, and guided by an instructor critically check the working status.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Presentation
Examination duration and scale	x
Assignment for the Following Curricula	Computer Science: Specialisation IV. Subject Specific Focus: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory

Course L2352: Advanced Seminar Computer Science and Communication Technology I

Typ	Seminar
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des SD E
Language	EN
Cycle	WiSe/SoSe
Content	- Seminar presentations by enrolled students about selected topics of computer science and communication technology - Active participation in discussions
Literature	Wird vom Veranstalter bekanntgegeben.

Course L2429: Introductory Seminar Computer Science and Communication Technology II

Typ	Seminar
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des SD E
Language	DE/EN
Cycle	WiSe/SoSe
Content
Literature

Focus Networks

Module M0836: Communication Networks

Courses

Title	Typ	Hrs/wk	CP
Selected Topics of Communication Networks (L0899)	Project-/problem-based Learning	2	2
Communication Networks (L0897)	Lecture	2	2
Communication Networks Excercise (L0898)	Project-/problem-based Learning	1	2

Module Responsible	Prof. Andreas Timm-Giel
Admission Requirements	None
Recommended Previous Knowledge	Fundamental stochastics Basic understanding of computer networks and/or communication technologies is beneficial
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to describe the principles and structures of communication networks in detail. They can explain the formal description methods of communication networks and their protocols. They are able to explain how current and complex communication networks work and describe the current research in these examples.
Skills	Students are able to evaluate the performance of communication networks using the learned methods. They are able to work out problems themselves and apply the learned methods. They can apply what they have learned autonomously on further and new communication networks.
Personal Competence
Social Competence	Students are able to define tasks themselves in small teams and solve these problems together using the learned methods. They can present the obtained results. They are able to discuss and critically analyse the solutions.
Autonomy	Students are able to obtain the necessary expert knowledge for understanding the functionality and performance capabilities of new communication networks independently.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Presentation
Examination duration and scale	1.5 hours colloquium with three students, therefore about 30 min per student. Topics of the colloquium are the posters from the previous poster session and the topics of the module.
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory

Course L0899: Selected Topics of Communication Networks

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Andreas Timm-Giel
Language	EN
Cycle	WiSe
Content	Example networks selected by the students will be researched on in a PBL course by the students in groups and will be presented in a poster session at the end of the term.
Literature	see lecture

Course L0897: Communication Networks

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Andreas Timm-Giel, Dr. Koojana Kuladinithi
Language	EN
Cycle	WiSe
Content
Literature	Skript des Instituts für Kommunikationsnetze Tannenbaum, Computernetzwerke, Pearson-Studium Further literature is announced at the beginning of the lecture.

Course L0898: Communication Networks Excercise

Typ	Project-/problem-based Learning
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Andreas Timm-Giel
Language	EN
Cycle	WiSe
Content	Part of the content of the lecture Communication Networks are reflected in computing tasks in groups, others are motivated and addressed in the form of a PBL exercise.
Literature	announced during lecture

Module M0676: Digital Communications

Courses

Title	Typ	Hrs/wk	CP
Digital Communications (L0444)	Lecture	2	3
Digital Communications (L0445)	Recitation Section (large)	2	2
Laboratory Digital Communications (L0646)	Practical Course	1	1

Module Responsible

Prof. Gerhard Bauch

Admission Requirements

None

Recommended Previous Knowledge

Mathematics 1-3
Signals and Systems
Fundamentals of Communications and Random Processes

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.

Skills

Personal Competence

Social Competence

The students can jointly solve specific problems.

Autonomy

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Written elaboration

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Course L0444: Digital Communications

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	WiSe
Content	Repetition: Baseband Transmission Pulse shaping: Non-return to zero (NRZ) rectangular pulses, raised-cosine pulses, square-root raised-cosine pulses Power spectral density (psd) of baseband signals Intersymbol interference (ISI) First and second Nyquist criterion AWGN channel Matched filter Matched-filter receiver and correlation receiver Noise whitening matched filter Discrete-time AWGN channel model Representation of bandpass signals and systems in the equivalent baseband Quadrature amplitude modulation (QAM) Equivalent baseband signal and system Analytical signal Equivalent baseband random process, equivalent baseband white Gaussian noise process Equivalent baseband AWGN channel Equivalent baseband channel model with frequency-offset and phase noise Equivalent baseband Rayleigh fading and Rice fading channel models Equivalent baseband frequency-selective channel model Discrete memoryless channels (DMC) Bandpass transmission via carrier modulation Amplitude modulation, frequency modulation, phase modulation Linear digital modulation methods On-off keying, M-ary amplitude shift keying (M-ASK), M-ary phase shift keying (M-PSK), M-ary quadrature amplitude modulation (M-QAM), offset-QPSK Signal space representation of transmit signal constellations and signals Energy of linear digital modulated signals, average energy per symbol Power spectral density of linear digital modulated signals Bandwidth efficiency Correlation coefficient of elementary signals Error probabilities of linear digital modulation methods Error functions Gray mapping and natural mapping Bit error probabilities, symbol error probabilities, pairwise symbol error probabilities Euclidean distance and Hamming distance Exact and approximate computation of error probabilities Performance comparison of modulation schemes in terms of per bit SNR vs. per symbol SNR Hierarchical modulation, multilevel modulation Effects of carrier phase offset and carrier frequency offset Differential modulation M-ary differential phase shift keying (M-PSK) Coherent and non-coherent detection of DPSK p/M-differential phase shift keying (p/M-DPSK) Differential amplitude and phase shift keying (DAPSK) Non-linear digital modulation methods Frequency shift keying (FSK) Modulation index Minimum shift keying (MSK) Offset-QPSK representation of MSK MSK with differential precoding and rotation Bit error probabilities of MSK Gaussian minimum shift keying (GMSK) Power spectral density of MSK and GMSK Continuous phase modulation (CPM) General description of CPM signals Frequency pulses and phase pulses Coherent and non-coherent detection of FSK Performance comparison of linear and non-linear digital modulation methods Frequency-selective channels, ISI channels Intersymbol interference and frequency-selectivity RMS delay spread Narrowband and broadband channels Equivalent baseband transmission model for frequency-selective channels Receive filter design Equalization Symbol-spaced and fractionally-spaced equalizers Inverse system Non-recursive linear equalizers Linear zero-forcing (ZF) equalizer Linear minimum mean squared error (MMSE) equalizer Non-linear equalization: Decision feedback equalizer (DFE) Tomlinson-Harashima precoding Maximum a posteriori probability (MAP) and maximum likelihood equalizer, Viterbi algorithm Single-carrier vs. multi-carrier transmission Multi-carrier transmission General multicarrier transmission Orthogonal frequency division multiplex (OFDM) OFDM implementation using the Fast Fourier Transform (FFT) Cyclic guard interval Power spectral density of OFDM Peak-to-average power ratio (PAPR) Multiple access Principles of time division multiple access (TDMA), frequency division multiple access (FDMA), code division multiple access (CDMA), non-orthogonal multiple access (NOMA), hybrid multiple access Spread spectrum communications Direct sequence spread spectrum communications Frequency hopping Protection against eavesdropping Protection against narrowband jammers Short vs. long spreading codes Direct sequence spread spectrum communications in frequency-selective channels Rake receiver Code division multiple access (CDMA) Design criteria of spreading sequences, autocorrelation function and crosscorrelation function of spreading sequences Intersymbol interference (ISI) and multiple access interference (MAI) Pseudo noise (PN) sequences, maximum length sequences (m-sequences), Gold codes, Walsh-Hadamard codes, orthogonal variable spreading factor (OVSF) codes Multicode transmission CDMA in uplink and downlink of a wireless communications system Single-user detection vs. multi-user detection
Literature	K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill. S. Haykin: Communication Systems. Wiley R.G. Gallager: Principles of Digital Communication. Cambridge A. Goldsmith: Wireless Communication. Cambridge. D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

Course L0445: Digital Communications

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L0646: Laboratory Digital Communications

Typ	Practical Course
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Gerhard Bauch
Language	DE/EN
Cycle	WiSe
Content	- DSL transmission - Random processes - Digital data transmission
Literature	K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill. S. Haykin: Communication Systems. Wiley R.G. Gallager: Principles of Digital Communication. Cambridge A. Goldsmith: Wireless Communication. Cambridge. D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

Module M0837: Simulation of Communication Networks

Courses

Title	Typ	Hrs/wk	CP
Simulation of Communication Networks (L0887)	Project-/problem-based Learning	5	6

Module Responsible	Prof. Andreas Timm-Giel
Admission Requirements	None
Recommended Previous Knowledge	Knowledge of computer and communication networks Basic programming skills
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to explain the necessary stochastics, the discrete event simulation technology and modelling of networks for performance evaluation.
Skills	Students are able to apply the method of simulation for performance evaluation to different, also not practiced, problems of communication networks. The students can analyse the obtained results and explain the effects observed in the network. They are able to question their own results.
Personal Competence
Social Competence	Students are able to acquire expert knowledge in groups, present the results, and discuss solution approaches and results. They are able to work out solutions for new problems in small teams.
Autonomy	Students are able to transfer independently and in discussion with others the acquired method and expert knowledge to new problems. They can identify missing knowledge and acquire this knowledge independently.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory

Course L0887: Simulation of Communication Networks

Typ	Project-/problem-based Learning
Hrs/wk	5
CP	6
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Lecturer	Prof. Andreas Timm-Giel
Language	EN
Cycle	SoSe
Content	In the course necessary basic stochastics and the discrete event simulation are introduced. Also simulation models for communication networks, for example, traffic models, mobility models and radio channel models are presented in the lecture. Students work with a simulation tool, where they can directly try out the acquired skills, algorithms and models. At the end of the course increasingly complex networks and protocols are considered and their performance is determined by simulation.
Literature	Skript des Instituts für Kommunikationsnetze Further literature is announced at the beginning of the lecture.

Module M1774: Advanced Internet Computing

Courses

Title	Typ	Hrs/wk	CP
Advanced Internet Computing (L2916)	Lecture	2	3
Advanced Internet Computing (L2917)	Project-/problem-based Learning	2	3

Module Responsible	Prof. Stefan Schulte
Admission Requirements	None
Recommended Previous Knowledge	Good programming skills are necessary. Previous knowledge in the field of distributed systems is helpful.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	After successful completion of the course, students are able to: Describe basic concepts of Cloud Computing, the Internet of Things (IoT), and blockchain technologies Discuss and assess critical aspects of Cloud Computing, the IoT, and blockchain technologies Select and apply cloud and IoT technologies for particular application areas Design and develop practical solutions for the integration of smart objects in IoT, Cloud, and blockchain software Implement IoT services
Skills	The students acquire the ability to model Internet-based distributed systems and to work with these systems. This comprises especially the ability to select and utilize fitting technologies for different application areas. Furthermore, students are able to critically assess the chosen technologies.
Personal Competence
Social Competence	Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.
Autonomy	Students are able to independently investigate a complex problem and assess which competencies are required to solve it.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	Group project incl. presentation (50 %), written exam (60 min, 50 %)
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory

Course L2916: Advanced Internet Computing

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Stefan Schulte
Language	EN
Cycle	SoSe
Content	This lecture discusses modern Internet-based distributed systems in three blocks: (i) Cloud computing, (ii) the Internet of Things, and (iii) blockchain technologies. The following topics will be covered in the single lectures: Cloud Computing Elastic Computing Technologies for identification for the IoT: RFID & EPC Communication in the IoT: Standards and protocols Security and trust in the IoT: Concerns and solution approaches Edge and Fog Computing Application areas: Smart factories, smart cities, smart healthcare Blockchain technologies Consensus
Literature	Will be discussed in the lecture

Course L2917: Advanced Internet Computing

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Stefan Schulte
Language	EN
Cycle	SoSe
Content	This project-/problemoriented part of the module augments the theoretical content of the lecture by a concrete technical problem, which needs to be solved by the students in group work during the semester. Possible topics are (blockchain-based) sensor data integration, Big Data processing, Cloud-based redundant data storages, and Cloud-based Onion Routing.
Literature	Will be discussed in the lecture.

Module M0839: Traffic Engineering

Courses

Title	Typ	Hrs/wk	CP
Seminar Traffic Engineering (L0902)	Seminar	2	2
Traffic Engineering (L0900)	Lecture	2	2
Traffic Engineering Exercises (L0901)	Recitation Section (small)	1	2

Module Responsible	Prof. Andreas Timm-Giel
Admission Requirements	None
Recommended Previous Knowledge	Fundamentals of communication or computer networks Stochastics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to describe methods for planning, optimisation and performance evaluation of communication networks.
Skills	Students are able to solve typical planning and optimisation tasks for communication networks. Furthermore they are able to evaluate the network performance using queuing theory. Students are able to apply independently what they have learned to other and new problems. They can present their results in front of experts and discuss them.
Personal Competence
Social Competence
Autonomy	Students are able to acquire the necessary expert knowledge to understand the functionality and performance of new communication networks independently.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory

Course L0902: Seminar Traffic Engineering

Typ	Seminar
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Andreas Timm-Giel, Dr. Phuong Nga Tran
Language	EN
Cycle	WiSe
Content	Selected applications of methods for planning, optimization, and performance evaluation of communication networks, which have been introduced in the traffic engineering lecture are prepared by the students and presented in a seminar.
Literature	U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Vieweg + Teubner further literature announced in the lecture

Course L0900: Traffic Engineering

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Andreas Timm-Giel, Dr. Phuong Nga Tran
Language	EN
Cycle	WiSe
Content	Network Planning and Optimization • Linear Programming (LP) • Network planning with LP solvers • Planning of communication networks Queueing Theory for Communication Networks • Stochastic processes • Queueing systems • Switches (circuit- and packet switching) • Network of queues
Literature	Literatur: U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer Weitere Literatur wird in der Lehrveranstaltung bekanntgegeben / Literature: U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer further literature announced in the lecture

Course L0901: Traffic Engineering Exercises

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Andreas Timm-Giel
Language	EN
Cycle	WiSe
Content	Accompanying exercise for the traffic engineering course
Literature	Literatur: U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer Weitere Literatur wird in der Lehrveranstaltung bekanntgegeben / Literature: U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer further literature announced in the lecture

Focus Software and Signal Processing

Module M0738: Digital Audio Signal Processing

Courses

Title	Typ	Hrs/wk	CP
Digital Audio Signal Processing (L0650)	Lecture	3	4
Digital Audio Signal Processing (L0651)	Recitation Section (large)	1	2

Module Responsible	Prof. Udo Zölzer
Admission Requirements	None
Recommended Previous Knowledge	Signals and Systems
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Die Studierenden können die grundlegenden Verfahren und Methoden der digitalen Audiosignalverarbeitung erklären. Sie können die wesentlichen physikalischen Effekte bei der Sprach- und Audiosignalverarbeitung erläutern und in Kategorien einordnen. Sie können einen Überblick der numerischen Methoden und messtechnischen Charakterisierung von Algorithmen zur Audiosignalverarbeitung geben. Sie können die erarbeiteten Algorithmen auf weitere Anwendungen im Bereich der Informationstechnik und Informatik abstrahieren.
Skills	The students will be able to apply methods and techniques from audio signal processing in the fields of mobile and internet communication. They can rely on elementary algorithms of audio signal processing in form of Matlab code and interactive JAVA applets. They can study parameter modifications and evaluate the influence on human perception and technical applications in a variety of applications beyond audio signal processing. Students can perform measurements in time and frequency domain in order to give objective and subjective quality measures with respect to the methods and applications.
Personal Competence
Social Competence	The students can work in small groups to study special tasks and problems and will be enforced to present their results with adequate methods during the exercise.
Autonomy	The students will be able to retrieve information out of the relevant literature in the field and putt hem into the context of the lecture. They can relate their gathered knowledge and relate them to other lectures (signals and systems, digital communication systems, image and video processing, and pattern recognition). They will be prepared to understand and communicate problems and effects in the field audio signal processing.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	60 min
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory

Course L0650: Digital Audio Signal Processing

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Udo Zölzer
Language	EN
Cycle	WiSe
Content	Introduction (Studio Technology, Digital Transmission Systems, Storage Media, Audio Components at Home) Quantization (Signal Quantization, Dither, Noise Shaping, Number Representation) AD/DA Conversion (Methods, AD Converters, DA Converters, Audio Processing Systems, Digital Signal Processors, Digital Audio Interfaces, Single-Processor Systems, Multiprocessor Systems) Equalizers (Recursive Audio Filters, Nonrecursive Audio Filters, Multi-Complementary Filter Bank) Room Simulation (Early Reflections, Subsequent Reverberation, Approximation of Room Impulse Responses) Dynamic Range Control (Static Curve, Dynamic Behavior, Implementation, Realization Aspects) Sampling Rate Conversion (Synchronous Conversion, Asynchronous Conversion, Interpolation Methods) Data Compression (Lossless Data Compression, Lossy Data Compression, Psychoacoustics, ISO-MPEG1 Audio Coding)
Literature	- U. Zölzer, Digitale Audiosignalverarbeitung, 3. Aufl., B.G. Teubner, 2005. - U. Zölzer, Digitale Audio Signal Processing, 2nd Edition, J. Wiley & Sons, 2005. - U. Zölzer (Ed), Digital Audio Effects, 2nd Edition, J. Wiley & Sons, 2011.

Course L0651: Digital Audio Signal Processing

Typ	Recitation Section (large)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Udo Zölzer
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0733: Software Analysis

Courses

Title	Typ	Hrs/wk	CP
Software Analysis (L0631)	Lecture	2	3
Software Analysis (L0632)	Recitation Section (small)	2	3

Module Responsible	Prof. Sibylle Schupp
Admission Requirements	None
Recommended Previous Knowledge	Basic knowledge of software-engineering activities Discrete algebraic structures Object-oriented programming, algorithms, and data structures Functional programming or Procedural programming
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students apply the major approaches to data-flow analysis, control-flow analysis, and type-based analysis, along with their classification schemes, and employ abstract interpretation. They explain the standard forms of internal representations and models, including their mathematical structure and properties, and evaluate their suitability for a particular analysis. They explain and categorize the major analysis algorithms. They distinguish precise solutions from approximative approaches, and show termination and soundness properties.
Skills	Presented with an analytical task for a software artifact, students select appropriate approaches from software analysis, and justify their choice. They design suitable representations by modifying standard representations. They develop customized analyses and devise them as safe overapproximations. They formulate analyses in a formal way and construct arguments for their correctness, behavior, and precision.
Personal Competence
Social Competence	Students discuss relevant topics in class. They defend their solutions orally. They communicate in English.
Autonomy	Using accompanying on-line material for self study, students can assess their level of knowledge continuously and adjust it appropriately. Working on exercise problems, they receive additional feedback. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software analysis. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	software artifacts/mathematical write-ups; short presentation
Assignment for the Following Curricula	Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory

Course L0631: Software Analysis

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	Modeling: Control-Flow Modeling, Data Dependences, Intermediate Languages) Classical Bit-Vector Analyses (Reaching Definition, Very Busy Expressions, Liveness, Available Expressions, May/Must, Forward/Backward) Monotone Frameworks (Lattices, Transfer Functions, Ascending Chain Condition, Distributivity, Constant Propagation) Theory of Data-Flow Analysis (Tarski's Fixed Point Theorem, Data-Flow Equations, MFP Solution, MOP Solution, Worklist Algorithm) Non-Classical Data-Flow Analyses Abstract Interpretation (Galois Connections, Approximating Fixed Points, Construction Techniques) Type Systems (Type Derivation, Inference Trees, Algorithm W, Unification) Recent Developments of Analysis Techniques and Applications
Literature	Flemming Nielsen, Hanne Nielsen, and Chris Hankin. Principles of Program Analysis. Springer, 2nd. ed. 2005. Uday Khedker, Amitabha Sanyal, and Bageshri Karkara. Data Flow Analysis: Theory and Practice. CRC Press, 2009. Benjamin Pierce, Types and Programming Languages, MIT Press. Selected research papers

Course L0632: Software Analysis

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0556: Computer Graphics

Courses

Title	Typ	Hrs/wk	CP
Computer Graphics (L0145)	Lecture	2	3
Computer Graphics (L0768)	Recitation Section (small)	2	3

Module Responsible	Prof. Tobias Knopp
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra (in particular matrix/vector computation) Basic programming skills in C/C++
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can explain and describe basic algorithms in 3D computer graphics.
Skills	Students are capable of implementing a basic 3D rendering pipeline. This consists of projecting simple 3D structures (e.g. cube, spheres) onto a 2D surface using a virtual camera. apply geometric transformations (e.g. rotation, scaling) in 2D and 3D computer graphics. using well-known 2D/3D APIs (OpenGL, Cairo) for solving a given problem statement.
Personal Competence
Social Competence	Students can collaborate in a small team on the realization and validation of a 3D computer graphics pipeline.
Autonomy	Students are able to solve simple tasks independently with reference to the contents of the lectures and the exercise sets. Students are able to solve detailed problems independently with the aid of the tutorial’s programming task.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory

Course L0145: Computer Graphics

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	EN
Cycle	SoSe
Content	Computer graphics and animation are leading to an unprecedented visual revolution. The course deals with its technological foundations: Object-oriented Computer Graphics Projections and Transformations Polygonal and Parametric Modelling Illuminating, Shading, Rendering Computer Animation Techniques Kinematics and Dynamics Effects Students will be be working on a series of mini-projects which will eventually evolve into a final project. Learning computer graphics and animation resembles learning a musical instrument. Therefore, doing your projects well and in time is essential for performing well on this course.
Literature	Alan H. Watt: 3D Computer Graphics. Harlow: Pearson (3rd ed., repr., 2009). Dariush Derakhshani: Introducing Autodesk Maya 2014. New York, NY : Wiley (2013).

Course L0768: Computer Graphics

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1682: Secure Software Engineering

Courses

Title	Typ	Hrs/wk	CP
Secure Software Engineering (L2667)	Lecture	2	3
Secure Software Engineering (L2668)	Project-/problem-based Learning	2	3

Module Responsible	Prof. Riccardo Scandariato
Admission Requirements	None
Recommended Previous Knowledge	Familiarity with basic software engineering concepts (e.g., requirements, design) and basic security concepts (e.g., confidentiality, integrity, availability)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can: Elicit security requirements in a software project Model and document security measures in a software design Use threat and risk analysis techniques Understand how security code reviews are performed Understand the core definitions of concepts related to privacy Understand privacy enhancing technologies
Skills	Select appropriate security assurance techniques to be used in a security assurance program
Personal Competence
Social Competence	None
Autonomy	Students can apply the knowledge acquired throughout the course to the resolution of industrial case studies. Students should also be capable to acquire new knowledge independently from academic publications, techical standards, and white papers.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory

Course L2667: Secure Software Engineering

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Riccardo Scandariato
Language	EN
Cycle	SoSe
Content	Secure software development processes and maturity models Techniques to define security requirements Techniques to create, document and analyse the design of secure applications Threat and risk analysis techniques Security code reviews Program repair techniques for security vulnerabilities Privacy engineering
Literature	Sindre, G. and Opdahl, A.L., 2005. Eliciting security requirements with misuse cases. Requirements engineering, 10(1), pp.34-44. Fontaine, P.J., Van Lamsweerde, A., Letier, E. and Darimont, R., 2001. Goal-oriented elaboration of security requirements. Mead, N.R. and Stehney, T., 2005. Security quality requirements engineering (SQUARE) methodology. ACM SIGSOFT Software Engineering Notes, 30(4), pp.1-7. Mirakhorli, M., Shin, Y., Cleland-Huang, J. and Cinar, M., 2012, June. A tactic-centric approach for automating traceability of quality concerns. In 2012 34th international conference on software engineering (ICSE) (pp. 639-649). IEEE. Jürjens, J., UMLsec: Extending UML for secure systems development, International Conference on The Unified Modeling Language, 2002 Lund, M.S., Solhaug, B. and Stølen, K., 2011. A guided tour of the CORAS method. In Model-Driven Risk Analysis (pp. 23-43). Springer, Berlin, Heidelberg. Howard, M.A., 2006. A process for performing security code reviews. IEEE Security & privacy, 4(4), pp.74-79 Diaz, C. and Gürses, S., 2012. Understanding the landscape of privacy technologies. Proceedings of the information security summit, 12, pp.58-63.

Course L2668: Secure Software Engineering

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Riccardo Scandariato
Language	EN
Cycle	SoSe
Content	Secure software development processes and maturity models Techniques to define security requirements Techniques to create, document and analyse the design of secure applications Threat and risk analysis techniques Security code reviews Program repair techniques for security vulnerabilities Privacy engineering
Literature

Module M1700: Satellite Communications and Navigation

Courses

Title	Typ	Hrs/wk	CP
Radio-Based Positioning and Navigation (L2711)	Lecture	2	3
Satellite Communications (L2710)	Lecture	3	3

Module Responsible	Prof. Gerhard Bauch
Admission Requirements	None
Recommended Previous Knowledge	The module is designed for a diverse audience, i.e. students with different background. Basic knowledge of communications engineering and signal processing are of advantage but not required. The course intends to provide the chapters on communications techniques such that on the one hand students with a communications engineering background learn additional concepts and examples (e.g. modulation and coding schemes or signal processing concepts) which have not or in a different way been treated in our other bachelor and master courses. On the other hand, students with other background shall be able to grasp the ideas but may not be able to understand in the same depth. The individual background of the students will be taken into consideration in the oral exam.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are able to understand, compare and analyse digital satellite communications system as well as navigation techniques. They are familiar with principal ideas of the respective communications, signal processing and positioning methods. They can describe distortions and resulting limitations caused by transmission channels and hardware components. They can describe how fundamental communications and navigation techniques are applied in selected practical systems. The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.
Skills	The students are able to describe and analyse digital satellite communications systems and navigation systems. They are able to analyse transmission chains including link budget calculations. They are able to choose appropriate transmission technologies and system parameters for given scenarios.
Personal Competence
Social Competence	The students can jointly solve specific problems.
Autonomy	The students are able to acquire relevant information from appropriate literature sources.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory

Course L2711: Radio-Based Positioning and Navigation

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Gerhard Bauch, Dr. Rico Mendrzik
Language	EN
Cycle	SoSe
Content	Information extraction from communication signals Time-of-arrival principle Ranging in additive white Gaussian noise (AWGN) channel Correlation-based range estimation Effect of multipath propagation on time-of-arrival principle Zero-forcing range estimation in the presence of multipath Optimum range estimation in the presence of multipath Zero-forcing in presence of noise Angle-of-arrival principle Angle-of-arrival estimation in AWGN channel Delay-and-sum estimator Multiple Signal Classifier (MUSIC) MUSIC-based angle-of-arrival estimation Case study: Comparison of estimators in AWGN channels Effect of multipath propagation on angle-of-arrival principle Case study: Comparison of estimators in multipath channels Information fusion of extracted signals Distance-based positioning Principle of time-of-arrival positioning Geometric interpretation Positioning in the absence of noise Linearization of the positioning problem Positioning in the presence of noise Optimality criteria Least squares time-of-arrival positioning Maximum likelihood time-of-arrival positioning Interactive Matlab demo Excursion: gradient descent solvers for nonlinear programs Real-life positioning with embedded development board (Arduino) Linearized least squares time-of-arrival positioning Effect of clock offsets on distance-based positioning Time-difference-of-arrival principle Least squares time-difference-of-arrival positioning Clock offset mitigation via two-way ranging Performance limits of distance-based positioning Fisher information and the Cramér-Rao lower bound Fisher information in the AWGN case Multi-variate Fisher information Cramér-Rao lower bound for synchronized time-of-arrival positioning Case study: Synchronized time-of-arrival positioning Cramér-Rao lower bound for unsynchronized time-of-arrival positioning Case study: Unsynchronized time-of-arrival positioning Angle-based Positioning Angle-of-arrival positioning principle Geometric interpretation angle-of-arrival positioning principle Noise-free angle-of-arrival positioning with known orientation Effect of noise on angle-of-arrival positioning Least squares angle-of-arrival positioning with known orientation Linear least squares angle-of-arrival positioning Effect of orientation uncertainty Angle-difference-of-arrival positioning Geometric interpretation angle difference of arrival positioning Proof of angle-difference-of-arrival locus Inscribed angle lemma Case study: Angle-difference-of-arrival-positioning Performance limits of angle-based positioning Cramér-Rao lower bound for angle-of-arrival positioning with known orientation Case study: Angle-of-arrival positioning with known orientation Information Filtering Bayesian filtering Principle of Bayesian filtering General Problem Formulation Solution to the linear Gaussian case State transition in the linear Gaussian case Proof of predicted posterior distribution of the Kalman filter State update in the linear Gaussian case Proof of marginal posterior distribution of the Kalman filter Working with Gaussian random variables Proof: Affine transformation Proof: Marginalization Proof: Conditioning Kalman filter: Optimum Inference in the linear Gaussian case Modeling of process noise Modeling of measurement noise Case study: Kalman filtering in the linear Gaussian case Interactive Kalman filtering in Matlab Dealing with nonlinearities in Bayesian filtering Nonlinear Gaussian case Extended Kalman filter Proof of predicted posterior distribution of the extended Kalman filter Proof of marginal posterior distribution of the extended Kalman filter Example: Nonlinear state transition Case study: Extended Kalman filtering Practical considerations for filter design Satellite Navigation Overview from positioning perspective Earth-centered earth-fixed (ECEF) coordinate system World geodetic system (WGS) Satellite navigation systems System-receiver clock offsets and pseudo-ranges Unsynchronized time-of-arrival positioning revisited GPS legacy signals and ranging Signal overview Time-of-arrival principle revisited Direct sequence spread spectrum principle Short and long codes Satellite signal generation Carriers and codes Correlation properties of codes Code division multiple access in flat fading channels Navigation message Velocity estimation Hands-on case study: Design of an extended Kalman filter for satellite navigation based on recorded data Robust navigation Multipath-assisted positioning in millimeter wave multiple antenna systems Multi-sensor fusion
Literature

Course L2710: Satellite Communications

Typ	Lecture
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Gerhard Bauch
Language	EN
Cycle	SoSe
Content	Introduction to satellite communications What is a satellite Overview orbits, Van Allen Belt, components of a satellite Satellite services Frequency bands for satellite services International Telecommunications Union (ITU) Influence of atmospheric impairments Milestones in satellite communications Components of a satellite communications system Ground segment Space segment Control segment Communication links Uplink, downlink Forward link, reverse link Intersatellite links Multiple access Performance measures Effective isotropic radiated power (EIRP), antenna gain, figure of merit, G/T, carrier to noise ratio Signal to noise power ratio vs. carrier to noise ratio Single beam and multibeam satellites Beam coverage Examples for beam coverage of LEO and GEO satellites (Iridium, Viasat) Transparent vs. regenerative payload Orbits Low earth orbot (LEO), medium earth orbit (MEO), geosynchroneous and geostationary orbits (GEO), highly elliptical orbits (HEO Favourable orbits: HEO orbits with 63-64^o inclination, Molnya and Tundra orbits Circular LEO orbits Circular MEO Orbits (Intermediate Circular Orbits (ICO)) Equatorial orbits, geostationary orbit (GEO) Important aspects of LEO, MEO and GEO satellites Kepler’s laws of planetary motion Gravitational force Parameters of ellipses and elliptical orbits Major and minor half axis Foci Eccentricity Eccentric anomaly, mean anomaly, true anomaly Area Orbit period Perigee, apogee Distance of satellite from center of earth Construction of ellipses according to de La Hire Orbital plane in space, inclination, right ascension (longitude) of ascending node, Vernal equinox Newton’s laws of motion Newton’s universal law of gravitation Energy of satellites: Potential energy, kinetic energy, total energy Instantaneous speed of a satellite Kepler’s equation Satellite visibility, elevation Required number of LEO, MEO or GEO satellites for continuous earth coverage Satellite altitude and distance from a point on earth Choice of orbits LEO, HEO, GEO Elliptical orbits with non-zero inclination, Molnya orbits, Tundra orbits Geosynchronous orbits Parameters of geosynchronous orbits Circular geosynchronous orbits Inclined geosynchronous orbits Quasi-zenith satellite systems (QZSS) Syb-synchronous circular equatorial orbits Geostationary orbit Parameters of the geostationary orbit Visibility Propagation delay Applications and system examples Perturbations of orbits Station keeping Station keeping box Estimation of orbit parameters Fundamentals of digital communications techniques Components of a digital communications system Principles of encryption Scrambling Scrambling vs. interleaving for randomization of data sequences Interleaving: Block interleaver, convolutional interleaver, random interleaver Digital modulation methods Linear and non-linear digital modulation methods Linear digital modulation methods QAM modulator and demodulator Pulse shaping, square-root raised-cosine pulses Average power spectral density Signal space constellation Examples: M-ary phase shift keying (M-PSK), M-ary quadrature amplitude shift keying (M-QAM) M-PSK in noisy channels Bit error probabilities of M-PSK and M-QAM M-PSK vs. M-QAM M-ary amplitude and phase shift keying (M-APSK) M-APSK vs. M-QAM Differential phase shift keying (DPSK) Error control coding (channel coding) Error detecting and forward error correcting (FEC) codes Principle of channel coding Data rate, code rate, Baud rate, spectral efficiency of modulation and coding schemes Bandwidth-power trade-off, bandwidth-limited vs. power-limited transmission Coding and modulation for transparent vs. regenerative payload Block codes and convolutional codes Concatenated codes Bit-interleaved coded modulation Convolutional codes Low density parity check (LDPC) codes, principle of message passing decoding, bit error rate performance Cyclic block codes Examples for cyclic block codes Single errors vs. block errors, cyclic block codes for burst errors Generator matrix, generator polynomials Systematic encoding and syndrome determination with shift registers Cyclic redundancy check (CRC) codes Automatic repeat request (ARQ) Principle of ARQ Stop-and-wait ARQ Go-back-N ARQ Selective-repeat ARQ Transmission gains and losses Antenna gain Antenna radiation pattern Maximum antenna gain, 3dB beamwidth Maximum antenna gain of circular aperture Maximum antenna gain of a geostationary satellite with global coverage Effective isotropic radiated power (EIRP) Power flux density Path loss Free space loss, free space loss for geostationary satellites Atmospheric loss Received power Losses in transmit and receive equipment Feeder loss Depointing loss Polarization mismatch loss Combined effect of losses Noise Origins of noise White noise Noise power spectral density and noise power Additive white Gaussian noise (AWGN) channel model Antenna noise temperature Earth brightness temperature Signal to noise ratios Atmospheric distortions Atmosphere of the earth: Troposphere, stratosphere, mesosphere, thermosphere, exosphere Attenuation and depolarization due to rain, fog, rain and ice clouds, sandstorms Scintillation Faraday effect Multipath contributions Link budget calculations GEO clear sky uplink and downlink GEO uplink and downlink under rain conditions Transparent vs. regenerative payload Link availability improvement through site diversity and adaptive transmission Transparent vs. regenerative payload Non-linear amplifiers Saleh model, Rapp model Input and output back-off factor Single carrier and multicarrier operation Dimensioning of transmission parameters Sources of noise: Thermal noise, interference, intermodulation products Signal to noise ratio and bit error probability Robustness against interference and non-linear channels Satellite networks Satellite network reference architectures Network topologies Network connectivity Types of network connectivity On-board connectivity Inter-satellite links Broadcast networks Satellite-based internet Satellite communications systems and standards examples The role of standards in satellite communications The Digital Video Broadcast Satellite Standard: DVB-S, DVB-S2, DVB-S2X Satellites in 3GPP mobile communications networks LEO megaconstellations: SpaceX Starlink, Kuiper, OneWeb Space debris The German Heinrich Hertz mission
Literature

Module M1842: GPU Architectures

Courses

Title	Typ	Hrs/wk	CP
GPU Architecture (L3039)	Lecture	3	4
GPU Architecture (L3040)	Project-/problem-based Learning	1	2

Module Responsible	Prof. Sohan Lal
Admission Requirements	None
Recommended Previous Knowledge	An introductory module on computer engineering or computer architecture, and good programming skills in C/C++.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory

Course L3039: GPU Architecture

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Sohan Lal
Language	EN
Cycle	SoSe
Content	- Review of computer architecture basics - measuring performance, benchmarks, five-stage RISC pipeline, caches - GPU basics - evolution of GPU computing, a high-level overview of a GPU architecture - GPU programming with CUDA - program structure, CUDA threads organization, warp/thread-block scheduling - GPU (micro) architecture - streaming multiprocessors, single instruction multiple threads (SIMT) core design, tensor/RT cores, mixed-precision support - GPU memory hierarchy - banked register file and operand collectors, shared memory, GPU caches (differences w.r.t. CPU caches), global memory - Branch and memory divergence - branch handling, stack-based reconvergence, memory coalescing, coalescer design - Barriers and synchronization - Temporal and spatial locality exploitation challenges in GPU caches - Global memory- high throughput requirements, GDDR/HBM, memory bandwidth optimization techniques - GPU research issues - performance bottlenecks, GPU power modeling, high-power consumption/energy efficiency, GPU security - Application case study - deep learning - Cycle accurate simulators for GPUs The learning in the lectures will be augmented by a semester-long problem-based project.
Literature

Course L3040: GPU Architecture

Typ	Project-/problem-based Learning
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Sohan Lal
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1301: Software Testing

Courses

Title	Typ	Hrs/wk	CP
Software Testing (L1791)	Lecture	2	3
Software Testing (L1792)	Project-/problem-based Learning	2	3

Module Responsible	Prof. Sibylle Schupp
Admission Requirements	None
Recommended Previous Knowledge	Software Engineering Higher Programming Languages Object-Oriented Programming Algorithms and Data Structures Experience with (Small) Software Projects Statistics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students explain the different phases of testing, describe fundamental techniques of different types of testing, and paraphrase the basic principles of the corresponding test process. They give examples of software development scenarios and the corresponding test type and technique. They explain algorithms used for particular testing techniques and describe possible advantages and limitations.
Skills	Students identify the appropriate testing type and technique for a given problem. They adapt and execute respective algorithms to execute a concrete test technique properly. They interpret testing results and execute corresponding steps for proper re-test scenarios. They write and analyze test specifications. They apply bug finding techniques for non-trivial problems.
Personal Competence
Social Competence	Students discuss relevant topics in class. They defend their solutions orally. They communicate in English.
Autonomy	Students can assess their level of knowledge continuously and adjust it appropriately, based on feedback and on self-guided studies. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software testing. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	Software
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory

Course L1791: Software Testing

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	SoSe
Content	Fundamentals of software testing Model-based testing Test automation Criteria-based testing
Literature	M. Pezze and M. Young, Software Testing and Analysis, John Wiley 2008. P. Ammann and J. Offutt, "Introduction to Software Testing", 2nd edition 2016. A. Zeller: "Why Programs Fail: A Guide to Systematic Debugging", 2nd edition 2012.

Course L1792: Software Testing

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	SoSe
Content	Fundamentals of software testing Model-based testing Test automation Criteria-based testing
Literature	M. Pezze and M. Young, Software Testing and Analysis, John Wiley 2008. P. Ammann and J. Offutt, "Introduction to Software Testing", 2nd edition 2015.

Module M1810: Autonomous Cyber-Physical Systems

Courses

Title	Typ	Hrs/wk	CP
Autonomous Cyber-Physical Systems (L3000)	Lecture	2	3
Autonomous Cyber-Physical Systems (L3001)	Recitation Section (small)	2	3

Module Responsible

Prof. Bernd-Christian Renner

Admission Requirements

None

Recommended Previous Knowledge

Very Good knowledge and practical experience in programming in the C language (Module: Procedural Programming)
Basic knowledge in software engineering
Basic knowledge in wired and wireless communication protocols
Principal understanding of simple electronic circuits

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Skills

Personal Competence

Social Competence

Autonomy

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	10 %	Attestation

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Course L3000: Autonomous Cyber-Physical Systems

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Bernd-Christian Renner
Language	EN
Cycle	SoSe
Content
Literature

Course L3001: Autonomous Cyber-Physical Systems

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Bernd-Christian Renner
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1694: Security of Cyber-Physical Systems

Courses

Title	Typ	Hrs/wk	CP
Security of Cyber-Physical Systems (L2691)	Lecture	2	3
Security of Cyber-Physical Systems (L2692)	Recitation Section (small)	2	3

Module Responsible

Prof. Sibylle Fröschle

Admission Requirements

None

Recommended Previous Knowledge

IT security, programming skills, statistics

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students know and can explain

- the threats posed by cyber attacks to cyber-physical systems (CPS)

- concrete attacks at a technical level, e.g. on bus systems

- security solutions specific to CPS with their capabilities and limitations

- examples of security architectures for CPS and the requirements they guarantee

- standard security engineering processes for CPS

Skills

The students are able to

- identify security threats and assess the risks for a given CPS

- apply attack toolkits to analyse a networked control system, and detect attacks beyond those taught in class

- identify and apply security solutions suitable to the requirements

- follow security engineering processes to develop a security architecture for a given CPS

- recognize challenges and limitations, e.g. posed by novel types of attack

Personal Competence

Social Competence

The students are able to

- expertly discuss security risks and incidents of CPS and their mitigation in a solution-oriented fashion with experts and non-experts

- foster a security culture with respect to CPS and the corresponding critical infrastructures

Autonomy

The students are able to

- follow up and critically assess current developments in the security of CPS including relevant security incidents

- master a new topic within the area by self-study and self-initiated interaction with experts and peers.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	10 %	Excercises	Die Übungsaufgaben finden semesterbegleitend statt.

Examination

Written exam

Examination duration and scale

120 min

Assignment for the Following Curricula

Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Data Science: Specialisation II. Computer Science: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory

Course L2691: Security of Cyber-Physical Systems

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Fröschle
Language	EN
Cycle	WiSe
Content	Embedded systems in energy, production, and transportation are currently undergoing a technological transition to highly networked automated cyber-physical systems (CPS). Such systems are potentially vulnerable to cyber attacks, and these can have physical impact. In this course we investigate security threats, solutions and architectures that are specific to CPS. The topics are as follows: Fundamentals and motivating examples Networked and embedded control systems Bus system level attacks Intruder detection systems (IDS), in particular physics-based IDS System security architectures, including cryptographic solutions Adversarial machine learning attacks in the physical world Aspects of Location and Localization Wireless networks and infrastructures for critical applications Communication security architectures and remaining threats Intruder detection systems (IDS), in particular data-centric IDS Resilience against multi-instance attacks Security Engineering of CPS: Process and Norms
Literature	Recent scientific papers and reports in the public domain.

Course L2692: Security of Cyber-Physical Systems

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Fröschle
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1598: Image Processing

Courses

Title	Typ	Hrs/wk	CP
Image Processing (L2443)	Lecture	2	4
Image Processing (L2444)	Recitation Section (small)	2	2

Module Responsible	Prof. Tobias Knopp
Admission Requirements	None
Recommended Previous Knowledge	Signal and Systems
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students know about visual perception multidimensional signal processing sampling and sampling theorem filtering image enhancement edge detection multi-resolution procedures: Gauss and Laplace pyramid, wavelets image compression image segmentation morphological image processing
Skills	The students can analyze, process, and improve multidimensional image data implement simple compression algorithms design custom filters for specific applications
Personal Competence
Social Competence	Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.
Autonomy	Students are able to independently investigate a complex problem and assess which competencies are required to solve it.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Data Science: Core Qualification: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Data Science: Specialisation II. Computer Science: Elective Compulsory Data Science: Specialisation IV. Special Focus Area: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Mechatronics: Core Qualification: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory

Course L2443: Image Processing

Typ	Lecture
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	DE/EN
Cycle	WiSe
Content	Visual perception Multidimensional signal processing Sampling and sampling theorem Filtering Image enhancement Edge detection Multi-resolution procedures: Gauss and Laplace pyramid, wavelets Image Compression Segmentation Morphological image processing
Literature	Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011 Pratt, Digital Image Processing, Wiley, 2001 Bernd Jähne: Digitale Bildverarbeitung - Springer, Berlin 2005

Course L2444: Image Processing

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Thesis

Module M1801: Master thesis (dual study program)

Courses

Title

Typ

Hrs/wk

Module Responsible	Professoren der TUHH
Admission Requirements	None
Recommended Previous Knowledge
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students ... ... use the specialised knowledge (facts, theories and methods) from their field of study and the acquired professional knowledge confidently to deal with technical and practical professional issues. ... can explain the relevant approaches and terminologies in depth in one or more of their subject’s specialist areas, describe current developments and take a critical stance. ... formulate their own research assignment to tackle a professional problem and contextualise it within their subject area. They ascertain the current state of research and critically assess it.
Skills	Dual students ... ... can select suitable methods for the respective subject-related professional problem, apply them and develop them further as required. ... assess knowledge and methods acquired during their studies (including practical phases) and apply their expertise to complex and/or incompletely defined problems in a solution- and application-oriented manner. ... acquire new academic knowledge in their subject area and critically evaluate it.
Personal Competence
Social Competence	Dual students ... ... can present a professional problem in the form of an academic question in a structured, comprehensible and factually correct manner, both in writing and orally, for a specialist audience and for professional stakeholders. ... answer questions as part of a professional discussion in an expert, appropriate manner. They represent their own points of view and assessments convincingly.
Autonomy	Dual students ... ... can structure their own project into work packages, work through them at an academic level and reflect on them with regard to feasible courses of action for professional practice. ... work in-depth in a partially unknown area within the discipline and acquire the information required to do so. ... apply the techniques of academic work comprehensively in their own research work when dealing with an operational problem and question.
Workload in Hours	Independent Study Time 900, Study Time in Lecture 0
Credit points	30
Course achievement	None
Examination	Thesis
Examination duration and scale	According to General Regulations
Assignment for the Following Curricula	Civil Engineering: Thesis: Compulsory Bioprocess Engineering: Thesis: Compulsory Chemical and Bioprocess Engineering: Thesis: Compulsory Computer Science: Thesis: Compulsory Data Science: Thesis: Compulsory Electrical Engineering: Thesis: Compulsory Energy Systems: Thesis: Compulsory Environmental Engineering: Thesis: Compulsory Aircraft Systems Engineering: Thesis: Compulsory Computer Science in Engineering: Thesis: Compulsory Information and Communication Systems: Thesis: Compulsory International Management and Engineering: Thesis: Compulsory Logistics, Infrastructure and Mobility: Thesis: Compulsory Aeronautics: Thesis: Compulsory Materials Science and Engineering: Thesis: Compulsory Materials Science: Thesis: Compulsory Mechanical Engineering and Management: Thesis: Compulsory Mechatronics: Thesis: Compulsory Biomedical Engineering: Thesis: Compulsory Microelectronics and Microsystems: Thesis: Compulsory Product Development, Materials and Production: Thesis: Compulsory Renewable Energies: Thesis: Compulsory Naval Architecture and Ocean Engineering: Thesis: Compulsory Theoretical Mechanical Engineering: Thesis: Compulsory Process Engineering: Thesis: Compulsory Water and Environmental Engineering: Thesis: Compulsory