Module Manual
Master
Information and Communication Systems
Cohort: Winter Term 2017
Updated: 28th September 2018
Program description
Content
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.
Career prospects
Learning target
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.
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.
Program structure
Core qualification: 48 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).
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 |
|
Skills |
|
Personal Competence | |
Social Competence |
|
Autonomy |
|
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 M0524: Nontechnical Elective Complementary Courses for Master |
Module Responsible | Dagmar Richter |
Admission Requirements | None |
Recommended Previous Knowledge | None |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The Nontechnical Academic Programms (NTA) imparts skills that, in view of the TUHH’s training profile, professional engineering studies require but are not able to cover fully. Self-reliance, self-management, collaboration and professional and personnel management competences. The department implements these training objectives in its teaching architecture, in its teaching and learning arrangements, in teaching areas and by means of teaching offerings in which students can qualify by opting for specific competences and a competence level at the Bachelor’s or Master’s level. The teaching offerings are pooled in two different catalogues for nontechnical complementary courses. The Learning Architecture consists of a cross-disciplinarily study offering. The centrally designed teaching offering ensures that courses in the nontechnical academic programms follow the specific profiling of TUHH degree courses. The learning architecture demands and trains independent educational planning as regards the individual development of competences. It also provides orientation knowledge in the form of “profiles”. The subjects that can be studied in parallel throughout the student’s entire study program - if need be, it can be studied in one to two semesters. In view of the adaptation problems that individuals commonly face in their first semesters after making the transition from school to university and in order to encourage individually planned semesters abroad, there is no obligation to study these subjects in one or two specific semesters during the course of studies. Teaching and Learning Arrangements provide for students, separated into B.Sc. and M.Sc., to learn with and from each other across semesters. The challenge of dealing with interdisciplinarity and a variety of stages of learning in courses are part of the learning architecture and are deliberately encouraged in specific courses. Fields of Teaching are based on research findings from the academic disciplines cultural studies, social studies, arts, historical studies, communication studies, migration studies and sustainability research, and from engineering didactics. In addition, from the winter semester 2014/15 students on all Bachelor’s courses will have the opportunity to learn about business management and start-ups in a goal-oriented way. The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goal-oriented communication skills, e.g. the skills required by outgoing engineers in international and intercultural situations. The Competence Level of the courses offered in this area is different as regards the basic training objective in the Bachelor’s and Master’s fields. These differences are reflected in the practical examples used, in content topics that refer to different professional application contexts, and in the higher scientific and theoretical level of abstraction in the B.Sc. This is also reflected in the different quality of soft skills, which relate to the different team positions and different group leadership functions of Bachelor’s and Master’s graduates in their future working life. Specialized Competence (Knowledge) Students can
|
Skills |
Professional Competence (Skills) In selected sub-areas students can
|
Personal Competence | |
Social Competence |
Personal Competences (Social Skills) Students will be able
|
Autonomy |
Personal Competences (Self-reliance) Students are able in selected areas
|
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 M1246: Technical Complementary Course for IMPICS (according to Subject Specific Regulations) |
||||
Courses | ||||
|
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 M0673: Information Theory and Coding |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Gerhard Bauch |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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. |
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 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 | DE/EN |
Cycle | SoSe |
Content |
|
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 | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0804: Research Project and Seminar |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Karl-Heinz Zimmermann |
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 372, Study Time in Lecture 168 |
Credit points | 18 |
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 |
Computer Science: Core qualification: Compulsory Computational Science and Engineering: Core qualification: Compulsory Information and Communication Systems: Core qualification: Compulsory |
Course L1761: Project Work |
Typ | Projection Course |
Hrs/wk | 10 |
CP | 15 |
Workload in Hours | Independent Study Time 310, Study Time in Lecture 140 |
Lecturer | Dozenten des SD E |
Language | DE/EN |
Cycle | WiSe |
Content |
Current research topics of the chosen specialization. |
Literature |
Aktuelle Literatur zu Forschungsthemen aus der gewählten Vertiefungsrichtung. |
Course L0817: Seminar |
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 |
Content |
|
Literature | Wird vom Veranstalter bekanntgegeben. |
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 | ||||||||||||||||
|
Module Responsible | Prof. Gerhard Bauch |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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. |
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 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Core qualification: Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 |
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 | DE/EN |
Cycle | WiSe |
Content |
|
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 | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/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 I - Analysis and Structure |
||||||||||||||||
Courses | ||||||||||||||||
|
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 |
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 |
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 |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 Mechatronics: Technical Complementary Course: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory |
Course L0897: Analysis and Structure of 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 |
Language | EN |
Cycle | WiSe |
Content | |
Literature |
Further literature is announced at the beginning of the lecture. |
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 | Dr. Maciej Mühleisen |
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 |
|
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 | Dr. Maciej Mühleisen |
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 |
|
Module M0710: Microwave Engineering |
||||||||||||||||
Courses | ||||||||||||||||
|
Module Responsible | Prof. Arne Jacob |
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 | 6 |
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. Arne Jacob |
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. Arne Jacob |
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. Arne Jacob |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0837: Communication Networks II - Simulation and Modeling |
||||||||
Courses | ||||||||
|
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 |
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 |
Examination | Oral exam |
Examination duration and scale | 45-60 minutes colloquium with two students, therefore about 30 minutes per student. |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 |
Course L0887: Simulation and Modelling 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 |
Further literature is announced at the beginning of the lecture. |
Module M0637: Advanced Concepts of Wireless Communications |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Dr. Rainer Grünheid |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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 (UMTS, LTE) they can put the learnt content into a larger context. |
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 124, Study Time in Lecture 56 |
Credit points | 6 |
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 Computational Science and Engineering: Specialisation Information and Communication Technology: 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 UMTS/HSPA, LTE, LTE Advanced, and WiMAX. |
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. 2nd Edition, Pearson, 2013 Stefani Sesia, Issam Toufik, Matthew Baker: LTE - The UMTS Long Term Evolution. Second Edition, Wiley, 2011 |
Course L0298: Advanced Concepts of Wireless Communications |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Dr. Rainer Grünheid |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0839: Traffic Engineering |
||||||||||||||||
Courses | ||||||||||||||||
|
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 |
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 |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 |
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 |
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 |
|
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 |
Language | EN |
Cycle | WiSe |
Content |
Network Planning and Optimization |
Literature |
Literatur: |
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: |
Focus Signal Processing
Module M0550: Digital Image Analysis |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Rolf-Rainer Grigat |
Admission Requirements | None |
Recommended Previous Knowledge |
System theory of one-dimensional signals (convolution and correlation, sampling theory, interpolation and decimation, Fourier transform, linear time-invariant systems), linear algebra (Eigenvalue decomposition, SVD), basic stochastics and statistics (expectation values, influence of sample size, correlation and covariance, normal distribution and its parameters), basics of Matlab, basics in optics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can
|
Skills |
Students are able to
Students can solve simple arithmetical problems relating to the specification and design of image processing and image analysis systems. Students are able to assess different solution approaches in multidimensional decision-making areas. Students can undertake a prototypical analysis of processes in Matlab. |
Personal Competence | |
Social Competence |
k.A. |
Autonomy |
Students can solve image analysis tasks independently using the relevant literature. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory |
Course L0126: Digital Image Analysis |
Typ | Lecture |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011 |
Module M0677: Digital Signal Processing and Digital Filters |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Gerhard Bauch |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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. |
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 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 Microelectronics Complements: 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 |
|
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 | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Gerhard Bauch |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0738: Digital Audio Signal Processing |
||||||||||||
Courses | ||||||||||||
|
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 |
Examination | Written exam |
Examination duration and scale | 45 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 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 |
|
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 M0556: Computer Graphics |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Tobias Knopp |
Admission Requirements | None |
Recommended Previous Knowledge |
Students are expected to have a solid knowledge of object-oriented programming as well as of linear algebra and geometry. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students have acquired a theoretical basis in computer graphics and have a clear understanding of the process of computer animation. |
Skills |
Students have acquired
|
Personal Competence | |
Social Competence |
Students are trained in communicating abstract ideas and are familiar with planning and conducting projects within a small team. |
Autonomy |
Students are able to direct complex computer animation projects. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: 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 |
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:
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 M0551: Pattern Recognition and Data Compression |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Rolf-Rainer Grigat |
Admission Requirements | None |
Recommended Previous Knowledge |
Linear algebra (including PCA, unitary transforms), stochastics and statistics, binary arithmetics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can name the basic concepts of pattern recognition and data compression. Students are able to discuss logical connections between the concepts covered in the course and to explain them by means of examples. |
Skills |
Students can apply statistical methods to classification problems in pattern recognition and to prediction in data compression. On a sound theoretical and methodical basis they can analyze characteristic value assignments and classifications and describe data compression and video signal coding. They are able to use highly sophisticated methods and processes of the subject area. Students are capable of assessing different solution approaches in multidimensional decision-making areas. |
Personal Competence | |
Social Competence |
k.A. |
Autonomy |
Students are capable of identifying problems independently and of solving them scientifically, using the methods they have learnt. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory |
Course L0128: Pattern Recognition and Data Compression |
Typ | Lecture |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | SoSe |
Content |
Structure of a pattern recognition system, statistical decision theory, classification based on statistical models, polynomial regression, dimension reduction, multilayer perceptron regression, radial basis functions, support vector machines, unsupervised learning and clustering, algorithm-independent machine learning, mixture models and EM, adaptive basis function models and boosting, Markov random fields Information, entropy, redundancy, mutual information, Markov processes, basic coding schemes (code length, run length coding, prefix-free codes), entropy coding (Huffman, arithmetic coding), dictionary coding (LZ77/Deflate/LZMA2, LZ78/LZW), prediction, DPCM, CALIC, quantization (scalar and vector quantization), transform coding, prediction, decorrelation (DPCM, DCT, hybrid DCT, JPEG, JPEG-LS), motion estimation, subband coding, wavelets, HEVC (H.265,MPEG-H) |
Literature |
Schürmann: Pattern Classification, Wiley 1996 Salomon, Data Compression, the Complete Reference, Springer, 2000 |
Module M0552: 3D Computer Vision |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Rolf-Rainer Grigat |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can explain and describe the field of projective geometry. |
Skills |
Students are capable of
With assistance from the teacher students are able to link the contents of the three subject areas (modules)
in practical assignments. |
Personal Competence | |
Social Competence |
Students can collaborate in a small team on the practical realization and testing of a system to reconstruct a three-dimensional scene or to evaluate volume data sets. |
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 |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 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: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory |
Course L0129: 3D Computer Vision |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L0130: 3D Computer Vision |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Focus Software
Module M0753: Software Verification |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Sibylle Schupp |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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 | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: 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 |
|
Literature |
|
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 | ||||||||||||
|
Module Responsible | Prof. Sibylle Schupp |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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 |
Examination | Subject theoretical and practical work |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 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 |
|
Literature |
|
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 M0758: Application Security |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Dieter Gollmann |
Admission Requirements | None |
Recommended Previous Knowledge | Familiarity with Information security, fundamentals of cryptography, Web protocols and the architecture of the Web |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can name current approaches for securing selected applications, in particular of web applications |
Skills |
Students are capable of
|
Personal Competence | |
Social Competence | Students are capable of appreciating the impact of security problems on those affected and of the potential responsibilities for their resolution. |
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 110, Study Time in Lecture 70 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 120 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory Technomathematics: Core qualification: Elective Compulsory |
Course L0726: Application Security |
Typ | Lecture |
Hrs/wk | 3 |
CP | 3 |
Workload in Hours | Independent Study Time 48, Study Time in Lecture 42 |
Lecturer | Prof. Dieter Gollmann |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
Webseiten der OMG, W3C, OASIS, WS-Security, OECD, TCG D. Gollmann: Computer Security, 3rd edition, Wiley (2011) R. Anderson: Security Engineering, 2nd edition, Wiley (2008) U. Lang: CORBA Security, Artech House, 2002 |
Course L0729: Application Security |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Dieter Gollmann |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1301: Software Testing |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Sibylle Schupp |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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. |
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 |
Examination | Subject theoretical and practical work |
Examination duration and scale | Software |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 Software: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: 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 |
|
Literature |
|
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 |
|
Literature |
|
Module M0924: Software for Embedded Systems |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Volker Turau |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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 | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 Software: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: 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. Volker Turau |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
|
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. Volker Turau |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
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 | ||||||||||||
|
Module Responsible | Prof. Sibylle Schupp |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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 | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: 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 |
|
Literature |
|
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 | ||||||||||||
|
Module Responsible | Prof. Dieter Gollmann |
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
|
Skills |
Students are capable of
|
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 |
Examination | Written exam |
Examination duration and scale | 120 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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. Dieter Gollmann |
Language | EN |
Cycle | WiSe |
Content |
|
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. Dieter Gollmann |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0758: Application Security |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Dieter Gollmann |
Admission Requirements | None |
Recommended Previous Knowledge | Familiarity with Information security, fundamentals of cryptography, Web protocols and the architecture of the Web |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can name current approaches for securing selected applications, in particular of web applications |
Skills |
Students are capable of
|
Personal Competence | |
Social Competence | Students are capable of appreciating the impact of security problems on those affected and of the potential responsibilities for their resolution. |
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 110, Study Time in Lecture 70 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 120 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory Technomathematics: Core qualification: Elective Compulsory |
Course L0726: Application Security |
Typ | Lecture |
Hrs/wk | 3 |
CP | 3 |
Workload in Hours | Independent Study Time 48, Study Time in Lecture 42 |
Lecturer | Prof. Dieter Gollmann |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
Webseiten der OMG, W3C, OASIS, WS-Security, OECD, TCG D. Gollmann: Computer Security, 3rd edition, Wiley (2011) R. Anderson: Security Engineering, 2nd edition, Wiley (2008) U. Lang: CORBA Security, Artech House, 2002 |
Course L0729: Application Security |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Dieter Gollmann |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1307: Cryptography |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Chris Brzuska |
Admission Requirements | None |
Recommended Previous Knowledge |
Prerequisites: |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Knowledge of cryptographic primitives such as one-way-functions, digitalen signatures, encryption, key exchange, zero-knowledge proofs as well as implications between the primitives, knowledge of formal security definitions of cryptographic prmitives, connections between cryptography and complexity theory, in particular to the P vs. NP problem. |
Skills | Ability to discuss and devellop security models for cryptographic pimitives. Constructing reductions between cryptographic primitives and ability to say whether small tweaks might harm the security of a cryptographic primitive. |
Personal Competence | |
Social Competence | Ability to critically question schemes and methods that seem intuitively secure. |
Autonomy | |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory |
Course L1806: Cryptography |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Chris Brzuska |
Language | DE/EN |
Cycle | SoSe |
Content |
Content: |
Literature |
Literatur: |
Course L1807: Cryptography |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Chris Brzuska |
Language | DE/EN |
Cycle | SoSe |
Content | |
Literature |
Literatur: |
Module M0943: Network Security |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Dieter Gollmann |
Admission Requirements | None |
Recommended Previous Knowledge | Discrete Mathematics, Computer Networks (TCP/IP) |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can
|
Skills |
Students are capable of
|
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 110, Study Time in Lecture 70 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 120 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory |
Course L1105: Network Security |
Typ | Lecture |
Hrs/wk | 3 |
CP | 3 |
Workload in Hours | Independent Study Time 48, Study Time in Lecture 42 |
Lecturer | Prof. Dieter Gollmann |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
W. Stallings: Cryptography and Network Security: Principles and Practice, 6th edition (2013) A. Menezes, P. van Oorschot, S. Vanstone: Handbook of Applied Cryptography, CRC Press (1997) D. Gollmann: Computer Security, 3rd edition, Wiley (2011) V. Niemi, K. Nyberg: UMTS Security, Wiley (2003) |
Course L1106: Network Security |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Dieter Gollmann |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Focus Networks
Module M0676: Digital Communications |
||||||||||||||||
Courses | ||||||||||||||||
|
Module Responsible | Prof. Gerhard Bauch |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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. |
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 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Core qualification: Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 |
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 | DE/EN |
Cycle | WiSe |
Content |
|
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 | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/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 I - Analysis and Structure |
||||||||||||||||
Courses | ||||||||||||||||
|
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 |
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 |
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 |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 Mechatronics: Technical Complementary Course: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory |
Course L0897: Analysis and Structure of 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 |
Language | EN |
Cycle | WiSe |
Content | |
Literature |
Further literature is announced at the beginning of the lecture. |
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 | Dr. Maciej Mühleisen |
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 |
|
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 | Dr. Maciej Mühleisen |
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 |
|
Module M0837: Communication Networks II - Simulation and Modeling |
||||||||
Courses | ||||||||
|
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 |
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 |
Examination | Oral exam |
Examination duration and scale | 45-60 minutes colloquium with two students, therefore about 30 minutes per student. |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 |
Course L0887: Simulation and Modelling 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 |
Further literature is announced at the beginning of the lecture. |
Module M0839: Traffic Engineering |
||||||||||||||||
Courses | ||||||||||||||||
|
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 |
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 |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 |
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 |
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 |
|
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 |
Language | EN |
Cycle | WiSe |
Content |
Network Planning and Optimization |
Literature |
Literatur: |
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: |
Focus Software and Signal Processing
Module M0550: Digital Image Analysis |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Rolf-Rainer Grigat |
Admission Requirements | None |
Recommended Previous Knowledge |
System theory of one-dimensional signals (convolution and correlation, sampling theory, interpolation and decimation, Fourier transform, linear time-invariant systems), linear algebra (Eigenvalue decomposition, SVD), basic stochastics and statistics (expectation values, influence of sample size, correlation and covariance, normal distribution and its parameters), basics of Matlab, basics in optics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can
|
Skills |
Students are able to
Students can solve simple arithmetical problems relating to the specification and design of image processing and image analysis systems. Students are able to assess different solution approaches in multidimensional decision-making areas. Students can undertake a prototypical analysis of processes in Matlab. |
Personal Competence | |
Social Competence |
k.A. |
Autonomy |
Students can solve image analysis tasks independently using the relevant literature. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory |
Course L0126: Digital Image Analysis |
Typ | Lecture |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011 |
Module M0738: Digital Audio Signal Processing |
||||||||||||
Courses | ||||||||||||
|
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 |
Examination | Written exam |
Examination duration and scale | 45 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 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 |
|
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 | ||||||||||||
|
Module Responsible | Prof. Sibylle Schupp |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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 |
Examination | Subject theoretical and practical work |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 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 |
|
Literature |
|
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 M0551: Pattern Recognition and Data Compression |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Rolf-Rainer Grigat |
Admission Requirements | None |
Recommended Previous Knowledge |
Linear algebra (including PCA, unitary transforms), stochastics and statistics, binary arithmetics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can name the basic concepts of pattern recognition and data compression. Students are able to discuss logical connections between the concepts covered in the course and to explain them by means of examples. |
Skills |
Students can apply statistical methods to classification problems in pattern recognition and to prediction in data compression. On a sound theoretical and methodical basis they can analyze characteristic value assignments and classifications and describe data compression and video signal coding. They are able to use highly sophisticated methods and processes of the subject area. Students are capable of assessing different solution approaches in multidimensional decision-making areas. |
Personal Competence | |
Social Competence |
k.A. |
Autonomy |
Students are capable of identifying problems independently and of solving them scientifically, using the methods they have learnt. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory |
Course L0128: Pattern Recognition and Data Compression |
Typ | Lecture |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | SoSe |
Content |
Structure of a pattern recognition system, statistical decision theory, classification based on statistical models, polynomial regression, dimension reduction, multilayer perceptron regression, radial basis functions, support vector machines, unsupervised learning and clustering, algorithm-independent machine learning, mixture models and EM, adaptive basis function models and boosting, Markov random fields Information, entropy, redundancy, mutual information, Markov processes, basic coding schemes (code length, run length coding, prefix-free codes), entropy coding (Huffman, arithmetic coding), dictionary coding (LZ77/Deflate/LZMA2, LZ78/LZW), prediction, DPCM, CALIC, quantization (scalar and vector quantization), transform coding, prediction, decorrelation (DPCM, DCT, hybrid DCT, JPEG, JPEG-LS), motion estimation, subband coding, wavelets, HEVC (H.265,MPEG-H) |
Literature |
Schürmann: Pattern Classification, Wiley 1996 Salomon, Data Compression, the Complete Reference, Springer, 2000 |
Module M0556: Computer Graphics |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Tobias Knopp |
Admission Requirements | None |
Recommended Previous Knowledge |
Students are expected to have a solid knowledge of object-oriented programming as well as of linear algebra and geometry. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students have acquired a theoretical basis in computer graphics and have a clear understanding of the process of computer animation. |
Skills |
Students have acquired
|
Personal Competence | |
Social Competence |
Students are trained in communicating abstract ideas and are familiar with planning and conducting projects within a small team. |
Autonomy |
Students are able to direct complex computer animation projects. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: 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 |
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:
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 M0924: Software for Embedded Systems |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Volker Turau |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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 | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 Software: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: 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. Volker Turau |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
|
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. Volker Turau |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1301: Software Testing |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Sibylle Schupp |
Admission Requirements | None |
Recommended Previous Knowledge |
|
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. |
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 |
Examination | Subject theoretical and practical work |
Examination duration and scale | Software |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 Software: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: 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 |
|
Literature |
|
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 |
|
Literature |
|
Module M0552: 3D Computer Vision |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Rolf-Rainer Grigat |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can explain and describe the field of projective geometry. |
Skills |
Students are capable of
With assistance from the teacher students are able to link the contents of the three subject areas (modules)
in practical assignments. |
Personal Competence | |
Social Competence |
Students can collaborate in a small team on the practical realization and testing of a system to reconstruct a three-dimensional scene or to evaluate volume data sets. |
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 |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 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: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory |
Course L0129: 3D Computer Vision |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L0130: 3D Computer Vision |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Thesis
Module M-002: Master Thesis |
||||
Courses | ||||
|
Module Responsible | Professoren der TUHH |
Admission Requirements |
|
Recommended Previous Knowledge | |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
The students are able:
|
Personal Competence | |
Social Competence |
Students can
|
Autonomy |
Students are able:
|
Workload in Hours | Independent Study Time 900, Study Time in Lecture 0 |
Credit points | 30 |
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 Electrical Engineering: Thesis: Compulsory Energy and Environmental Engineering: Thesis: Compulsory Energy Systems: Thesis: Compulsory Environmental Engineering: Thesis: Compulsory Aircraft Systems Engineering: Thesis: Compulsory Global Innovation Management: Thesis: Compulsory Computational Science and Engineering: Thesis: Compulsory Computational Science and Engineering: Thesis: Compulsory Information and Communication Systems: Thesis: Compulsory International Production Management: Thesis: Compulsory International Management and Engineering: Thesis: Compulsory Joint European Master in Environmental Studies - Cities and Sustainability: Thesis: Compulsory Logistics, Infrastructure and Mobility: Thesis: Compulsory Materials Science: Thesis: Compulsory Mathematical Modelling in Engineering: Theory, Numerics, Applications: 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 Ship and Offshore Technology: Thesis: Compulsory Theoretical Mechanical Engineering: Thesis: Compulsory Process Engineering: Thesis: Compulsory Water and Environmental Engineering: Thesis: Compulsory |