Program Description🔗

Digitalization affects all areas of life: From toys and smartphones to cars and homes, there is hardware and software. Everyday processes such as shopping or pure communication are digitally permeated. A deep understanding of basic technology in all its breadth and depth, including theoretical models, communication and interaction possibilities as well as hardware and software architectures and intelligent, self-learning systems, is therefore becoming increasingly important.

Orientation of the degree program

The application-oriented Master's degree course in Computer Engineering is designed for a standard period of study of 3 semesters. The course builds consecutively on the Bachelor's degree in Computer Engineering. Computer Engineering. However, it is also suitable as a second course of study to a Bachelor's degree in Computer Science, Electrical Engineering or Media Technology. Students gain in-depth knowledge and understanding of advanced understanding of advanced concepts, methods and technologies in computer science and IT-related communication technology. They will advance to the current state of the art. A high proportion of practical work and projects ensures that students can relate what they have learned in the individual subjects to each other and use it to solve challenging practical and theoretical problems. theoretical problems. theoretischer Probleme einsetzen können.

Occupational fields

The career prospects and future opportunities for computer engineering graduates are very good. In this respect, students with a good degree lay a promising foundation for their career. career. Graduates of the course are particularly predestined for planning, development and management tasks. They have access to a wide range of fields of activity both in companies in the information and communication technology companies, in related fields such as the automotive industry and automation technology as well as with service providers such as banks and insurance companies. Graduates with a Master of Science degree can also go on to study for a doctorate or take up a position in the senior civil service of public institutions. public institutions. Course of study The 3-semester course initially teaches in-depth specialist knowledge in theoretical computer science, computer engineering, mathematics and interdisciplinary skills and soft skills. competencies and soft skills. Specialization is achieved through the individual combination of nine elective modules from various disciplines of Computer Engineering and other computer science and communication technology disciplines. These subjects provide an in-depth knowledge and understanding of the advanced concepts, methods and technologies of the chosen disciplines. chosen disciplines. In this way, students can shape the subject focus of their studies according to their personal interests. Active participation in a current research project in a current research project at the institute is firmly anchored in the course. Here, students practice the scientifically sound analysis and solution of novel problems. The degree program concludes with the completion of a Master's thesis: methods and techniques of scientific work are applied independently to a challenging task.

Alongside the course, professional further training is offered to become a Cisco Certified Network Associate Security (CCNA Security).

Graduate Profile🔗

Graduates of the M. Sc. Computer Science and Engineering degree program have in-depth scientific and methodological skills in the design, analysis and development of complex IT systems. In contrast to the Bachelor's degree program, the focus is on research, innovation, leadership and management skills as well as the cross-system application of IT technologies. You will be able to independently take responsibility for challenging projects in development, research or management and actively shape technological transformation processes.

The Master's degree program in Computer Science and Engineering is aimed at graduates with a sound first degree in computer science and deepens their knowledge in the field of software, hardware, distributed systems, AI, signal processing and communication technology.

Compared to the Bachelor's degree course, which focuses on the broad teaching of technical fundamentals and applications, the Master's degree focuses on:

Scientific work and research-related project development
Individual specialization through elective modules
Interdisciplinary systems thinking
Leadership and innovation skills

Graduates of the Master's degree program develop an individual profile in the following areas:

You are proficient in the development, analysis and evaluation of complex IT systems, taking into account technical, social, legal and ecological framework conditions.
You will be qualified for management tasks in research and development projects, including the management of interdisciplinary and international teams.
Through intensive project work and the research project, you will acquire the ability to work independently on challenging issues with scientific depth.
They can independently develop technical innovations in areas such as distributed systems, AI, embedded systems, network technologies and multimedia communication and transfer them into industrial or scientific applications.
You will be able to contribute to the advancement of knowledge in computer science through scientific work and qualify for a doctorate.
In addition to technical skills, the course also promotes self-organization, ethical judgement and intercultural communication skills - key requirements in modern technology professions.
There are a wide range of professional fields: from technology development in industry, IT strategy and consulting to research at universities, institutes and public institutions.

Fields of Action🔗

Central fields of activity in the degree program are development and design, research and innovation, leadership and management as well as quality assurance and testing. The profile module matrix shows which fields of activity are addressed by which modules.

Development and Design

Interdisciplinary development and testing of algorithms, circuits, software, devices, communication and media technology systems as well as complex computer, communication and embedded systems.

Research and innovation

Perform scientific research work and apply and expand scientific knowledge, from basic research to industrial research, with the qualification for doctoral studies.

Leadership and management

Assume technical leadership and project responsibility, including the coordination and management of working groups and internationally distributed teams, as well as the management of planning and manufacturing processes, project controlling and product management.

Quality assurance and testing

Carrying out quality controls and tests for products and processes, using measurement and testing technologies and coordinating certification processes.

Competencies🔗

The modules of the degree program train students in different competencies, which are described below. The [Profile Module Matrix](#Module Matrix) shows which competences are competencies are addressed by which modules.

Development and design of complex systems

Ability to design and implement large systems, taking into account electrical, software, mechanical and optical aspects, based on a thorough requirements analysis from a technical, economic, ecological and social point of view.

Testing and evaluation of complex systems

Plan, perform and analyze tests to verify and validate these systems, including consideration of user perspectives and technical-economic aspects.

Scientific work and research

Mastery and application of scientific methods, including the ability to research, evaluate and cite relevant literature, and to formulate and present results.

Project management and teamwork

Skills in organizing, managing and supervising projects and teams, even under uncertain conditions, and in making professional and organizational decisions.

Self-organization and self-taught skills

Identification of personal skills, efficient time management and the ability for self-directed learning.

Communication and intercultural competence

Ability to present and defend scientific and technical results convincingly in both German and English, including international and interdisciplinary contexts.

Technical and scientific fundamentals

Comprehensive and in-depth STEM subject knowledge and its application to real-world and theoretical problems.

Sustainability and social responsibility

Evaluate and develop sustainable and socially responsible technologies, including consideration of ethical values.

Analysis, simulation and abstraction

Ability to analyze complex systems, abstract key features and solve problems based on models.

Leadership and decision-making responsibility

Assume responsibility in technical management tasks, develop solution strategies for complex tasks.

Applying ethical values and principles in practice

Incorporating social and ethical considerations into technical decisions and design processes.

Integrative thinking and acting in interdisciplinary teams

Coordinating and integrating contributions from different disciplines to solve complex tasks.

Innovation and creativity

Developing new solutions and concepts to overcome technical challenges.

Study Plans🔗

The following are studyable study plans. Other study plans are also possible. Please note, however, that each module is usually only offered once a year. Please also note that several modules may have to be selected in a particular semester and elective catalogs in order to obtain the total ECTS credit points shown.

Regelstudium, Bsp. Beginn Sommersemester 🔗

Sem.	Abbr.	Name	Mandatory (PF) Elective Catalog (WB)	ECTS
1	THI	Theoretische Informatik	PF	5
	VMT	Vertiefung Mathematik	WB	5
	VTI	Vertiefung Technische Informatik	WB	5
	W	Allgemeiner Wahlbereich	WB	10
	X	Fachübergreifende Kompetenzen und Soft Skills	WB	5
2	FP	Forschungsprojekt	PF	10
	VTI	Vertiefung Technische Informatik	WB	10
	W	Allgemeiner Wahlbereich	WB	10
3	MAA	Masterarbeit	PF	27
3	KOLL	Kolloquium zur Masterarbeit	PF	3

Studienverlauf mit vermindertem Workload; Bsp. Beginn Sommersemester 🔗

Sem.	Abbr.	Name	Mandatory (PF) Elective Catalog (WB)	ECTS
1	THI	Theoretische Informatik	PF	5
	VMT	Vertiefung Mathematik	WB	5
	VTI	Vertiefung Technische Informatik	WB	5
2	VTI	Vertiefung Technische Informatik	WB	5
	W	Allgemeiner Wahlbereich	WB	5
	X	Fachübergreifende Kompetenzen und Soft Skills	WB	5
3	VTI	Vertiefung Technische Informatik	WB	5
3	W	Allgemeiner Wahlbereich	WB	10
4	FP	Forschungsprojekt	PF	10
4	W	Allgemeiner Wahlbereich	WB	5
5	MAA	Masterarbeit	PF	27
6	MAA	Masterarbeit	PF	27
6	KOLL	Kolloquium zur Masterarbeit	PF	3

Modules🔗

The modules of the degree program are described below in alphabetical order.

ACC - Advanced Channel Coding 🔗

Module ID	ACC
Module Name	Advanced Channel Coding
Type of Module	Elective Modules
Recognized Course	ACC - Advanced Channel Coding
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Uwe Dettmar/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Uwe Dettmar/Professor Fakultät IME
Learning Outcome(s) What? Designing and rating of systems for the reliable transmission of data over distorted channels and storage of data for data at rest and data in motion How? By applying results from information theory and applying methods and algorithms for error correcting codes using existing simulations tools, self written programms, and studying existing systems. What for? To be able to design, select, use and apply actual and future digital communication systems for reliable data transmission, and to rate their performance.
Module Contents Lecture / Exercises The underlying concept of this module is a combination from lecture and tutorial. After a lecture block of approximately 20 minutes) the subjects taught are actively trained using Matlab/Octave and Python programs. Syllabus: - Introduction - Basic terms and definitions - short history of channel coding - System and channel models - Review of binary error correcting block and convolutional Codes - Generator and Parity check matrices, - decoding principles, Trellis and Viterbi Algorithm - Some principles on Information Theory - Channel coding theorem - Channel capacity and example calculations - Cyclic Codes, Reed Solomon Codes - Encoding and Decoding, Euklidean and Berlekamp-Massey -Algorithm for Decoding - Basics on LDPC, Polar, and TURBO Codes - iterative decoding, Sum Product Algorithm - Recursive Convolutional Codes - Performance comparison - Basics on Space Time Coding - Channel Model, Capacity improvement, Alamouti Scheme, STBC and STTC and their decoding These subjects are presented during the lecture. Students shall deepen their knowledge by self-study of literature and internet ressources and discuss their results in small learning groups as a teamwork. By the help of small exercises and programs during the presence time, students are able to actively train their knowledge. More extensive problems are solved and discussed in the second part of the course to activate the student's capabilities to solve relevant problems. Students further learn - to analyze communication systems and to estimate their performance - to compare and rate algorithms and methods - to apply their knowledge to technical problems Lab Existing simulation Tools like, e.g., the Matlab Communications Toolbox or AFF3CT (aff3ct.github.io) are used to: - test theoretical results from lecture and tutorial - implement algorithms for error control coding - simulate BER and rate the performance, compare schemes - adapt programs to solve equivalent problems - become familiar with standard simulations tools - train cooperation in small teams Students learn to generate, check, present, and discuss performance results for FEC codes. They need to search for and to study scientific literature as background sources for their simulations. Teams of students get different code families to study. Results are presented to the whole group.
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	Modul HIM: Grundkenntnisse zur linearen Algebra, der Algebra in endlichen Zahlenkörpern, der Stochastik und der digitalen Kommunikationstechnik aus den vorangegangenen Bachelorstudiengängen. Da das Fach im ersten Fachsemster des Masters gewählt werden kann, können keine belastbaren Kenntnisse aus dem Fach HIM verpflichtend vorausgesetzt werden, auch wenn sie hilfreich wären. basics in linear algebra basics in stochastics good programming skills
Mandatory Prerequisites	Lab requires attendance in the amount of: 2 Praktikumstermine und 1 Präsentation Participation in final examination only after successful participation in Lab
Recommended Literature	R. E. Blahut. Algebraic Codes for Data Transmission. Cambridge University Press, Cambridge, 2003. S. Lin and D. J. Costello. Error Control Coding. ISBN 0-13-042672-5. Prentice-Hall, 2004 T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley, New Jersey, 2006 A. Neubauer. Kanalcodierung. Schlembach, Wilburgstetten, 2006. R. Roth. Introduction to Coding Theory. Cambridge, second edition, 2006 B. Sklar. Digital Communications. Prentice Hall PTR, Upper Saddle River, New Jersey, 2001
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	ACC in Master Communication Systems and Networks PO3 ACC in Master Communication Systems and Networks PO4 ACC in Master Elektrotechnik und Informationstechnik PO1 ACC in Master Informatik und Systems-Engineering PO1
Permanent Links to Organization	ILU course page
Specifics and Notes
Last Update	19.7.2025, 14:32:16

AMC - Advanced Multimedia Communications 🔗

Module ID	AMC
Module Name	Advanced Multimedia Communications
Type of Module	Elective Modules
Recognized Course	AMC - Advanced Multimedia Communications
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Andreas Grebe/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Andreas Grebe/Professor Fakultät IME
Learning Outcome(s) What? Understanding service requirements, driven by heterogeneous services, in All-IP networks, and how to design, implemnt and evaluate quality-of-service (QoS) and quality-of-experience (QoE) mechanisms. Competences to evaluate, analyze, design, implement and test multiservice Ip networks with heterogeneous service requirements. How? Based on Bachelor-level competences on IP networking and services, students learn different application (service) requirements from filetransfer to streaming and how to separate and fulfill these requirements in IP networks. In a small team and organized as semester project, students develop their own multiservices networks, optionally based and on existing systems, and learn how to design, implementnt and anlysze their own multiservice network solution. What for? To be able to design, analyze, select, use and apply actual and future network technologies, based on All-IP networks concepts for enterprise networks, telecommunication networks and mobile networks.
Module Contents Lecture / Exercises Content for multimedia applications, encoding of multimedia data, integration of data, audio and video, multimedia traffic requirements, multimedia transport protocols, RTP and MPEG-TS, traffic modeling burst silence model, quality of service (QoS), multiservice networks, IntServ, RSVP, DiffServ, ToS and DSCP, Traffic Classification, Traffic Measurement, Traffic Shaping, Network Scheduling, Queueing (FIFO, RR, WRR, WFQ, CB-WFQ, PQ, LLQ), Congestion Avoidance (RED, WRED, CB-WRED), Quality-of-Exiperience (QoE), MOS Scale, Error Detection, Error Correction, FEC, Interleaving, Jitter Buffer. Students evaluate technologies and network architectures of multiservice networks; they analyse requirements of Multimedia services and systems, design architectures for multiservice networks, implement multiservice networks, and analyze Multimedia communication protocols and their performance metrics. Lab Fundamental knowledge of multiservice networks or multimedia applications in All-IP networks including planning, implementation and evaluation of services. Protocol analysis for functional analysis, performance analysis and troubleshooting. Students evaluate requirements of Multimedia services, and necessary methods for QoS in multiservice networks. They plan and implement IP Multimedia environments as team project, and test QoS performance measures. They are competent in functional analysis and troubleshooting by deep packet inspection (DPI) protocol analysis. They evaluate the performance of the Multimedia network or services in terms of timing, throughput, latency and delays, jitter, robustness in case of packet errors, and security aspects. Individual project proposals by students are wellcome.
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	Modul NGN: Fundamentals of Networks and Protocols (typically Bachelor Level, like prerequisistes in NGN) Layered Communications and Protocol Stacks (ISO/OSI, IETF TCP/IP, IEEE), LAN, MAN, WAN, Fixed Line and Mobile Network Fundamentals, Data Link-Technologies (Ethernet, WiFi), IP-Networking (IPv4, IPv6), IP Routing Protocols (static Routes, RIP, OSPF, BGP), Transport Protocols (TCP (incl. Flow Control / Congestion Control), UDP) and Port Numbers, Application Protocols (HTTP, Request-Response Pattern, Publish-Subscribe Pattern). Bachelor-level knowledge of protocols and layer models, Internet protocols (UDP, TCP, IP, HTTP, FTP), IP addressing (IPv4, IPv6), routing techniques (IP routing, functionality of a router, routing protocols, RIP, OSPF), transmission systems and layer 2 protocols, Ethernet. Understanding distributed systems and applications, sockets and client/server programming, request-response patterns, publishg-subscribe patterns.
Mandatory Prerequisites	Lab requires attendance in the amount of: 6 Meilensteintermine und Projektvorstellungen Participation in final examination only after successful participation in Lab
Recommended Literature	J. Kurose, K. Ross: Computer Networking: A Top-Down Approach, Global Edition, Prentice Hall, 7th ed., 2016 A. S. Tanenbaum, D. J. Wetherall: Computer Networks, Pearson , 5th ed., 2013 W. Stallings: Foundations of Modern Networking, Pearson Education, 2016 H. W. Barz, G. A. Bassett: Multimedia Networks, John Wiley & Sons, 2016 T. Szigeti, C. Hattingh, R. Barton, B. Kenneth: End-to-End QoS Network Design: Quality of Service for Rich-Media & Cloud Networks (2nd Edition) End-to-End QoS Network Design: Quality of Service for Rich-Media & Cloud Networks, Cisco Press, 2nd Ed. 2013 R. Steinmetz, K. Nahrstedt: „Multimedia Systems“, Springer 2004 R. Steinmetz, „Multimedia-Technologie“, Springer 2000
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	AMC in Master Communication Systems and Networks PO3 AMC in Master Communication Systems and Networks PO4 AMC in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

AMS - Special Aspects of Mobile Autonomous Systems 🔗

Module ID	AMS
Module Name	Special Aspects of Mobile Autonomous Systems
Type of Module	Elective Modules
Recognized Course	AMS - Special Aspects of Mobile Autonomous Systems
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Chunrong Yuan/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Chunrong Yuan/Professor Fakultät IME
Learning Outcome(s) Was: Das Modul vermittelt Kompetenzen zur Entwicklung von mobilen autonomen Systemen, insbesondere im Themenbereich der räumlichen Interpretation und Kognition für die sichere Navigation von unbemannten Roboter- und Fahrzeugsystemen sowie intelligente Interaktion und Kollaboration unter Menschen und Robotern. Womit: Die Dozentin vermittelt Wissen und Basisfertigkeiten in einem Vorlesungsteil und betreut parallel dazu praktische Projekte, wobei die Studierenden mittels forschenden Lernens technische Ansätze studieren und erproben, Prototypen aufbauen und testen, Ergebnisse präsentieren, sowohl technische als auch ethische und soziale Aspekte diskutieren, und das Ganze schriftlich dokumentieren. Wozu: Kompetenzen in der Entwicklung von mobilen autonomen Systemen sind essentiell für technische Informatiker*innen und Nachwuchs in verwandten Ingenieurberufen. Derartige Kompetenzen sind unentbehrlich für die Forschung, Entwicklung sowie technische Innovation. Das projektbasierte und forschende Lernen im Team hilft den Studierenden außerdem, sich mit relevanten ethischen und sozialan Aspekten zu beschäftigen, welche im Zusammenhang mit autonomen Systemen stehen.
Module Contents Lecture Mobile autonomous systems Cognitive and behaviour-based robotics Environmental modelling and spatial cognition Interaction and navigation Project Teamwork: Development of autonomous systems with cognitive capabilities and intelligent behaviours. Such cognitive capabilities include e.g.: Recognize objects with sensors, estimate their locations or movements, make 3D representations and interpretations or build a map of the environment etc. Intelligent behaviours can be demonstrated among others by such actions: Move and navigate autonomously without collision in unknown environments, fetch or transport objects for a special application, nature interactions and collaborations among human and robots.
Teaching and Learning Methods	Lecture Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	34 Hours ≙ 3 SWS
Self-Study	116 Hours
Recommended Prerequisites	Capability of software and project development Knowledge of signal processing and mathematics
Mandatory Prerequisites	Project requires attendance in the amount of: 1 Präsentation
Recommended Literature	Siegwart et.al.: Introduction to autonomous mobile robots, MIT Press, 2010
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	AMS in Master Communication Systems and Networks PO3 AMS in Master Communication Systems and Networks PO4 AMS in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

ARP - Alternative Rechnerarchitekturen und Programmiersprachen 🔗

Module ID	ARP
Module Name	Alternative Rechnerarchitekturen und Programmiersprachen
Type of Module	Elective Modules
Recognized Course	ARP - Alternative Computer Architectures and Programming Languages
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. René Wörzberger/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Georg Hartung/Professor Fakultät IME im Ruhestand
Learning Outcome(s) Die Studierenden lernen kennen, wenden an und analysieren verschiedene wichtige Konzepte von Rechnerarchitekturen und Programmiersprachen. Dazu wenden sie für jedes ausgewählte Konzept nach einer kurzen Vorstellung es auf ein selbstgewähltes Beispiel an, wozu sie sich weiteres Wissen über das Konzept erwerben müssen, und analysieden die Vor- und Nachteile des Konzepts in einem Bericht. Damit erlangen sie einen größeren Überblick über verfügbare Architekturen und Programmiersprachen für ihre spätere Tätigkeit als IT-Spezialist, Manager oder in der Forschung.
Module Contents Lecture / Exercises Knowledge of the respective modeling method, programming procedure or architecture and its programming ("Topics"); practice of initial Topic skills in exercises. Project Application of the topic to a self-selected task, analysis of the features of the topic on a concrete example, synthesis with own experiences, teamwork (processing in a small group)
Teaching and Learning Methods	Lecture / Exercises Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	- Experience in imperative programming languages, esp. C - Basic knowledge and experience in operating systems, esp. Linux - Basic knowledge and experience in software engineering - Basic knowledge of computer design and operation, including operation of important digital components - Basic knowledge of formal languages and automata theory
Mandatory Prerequisites	Project requires attendance in the amount of: 4 Termine
Recommended Literature	Jensen, K., Kristensen, L.M.: Coloured Petri Nets Nilsson, U.; Maluszynski, J.: Logic, Programming and Prolog T. Eiter, G. Ianni, T. Krennwallner: 'Answer Set Programming: A Primer' in: Reasoning WEB Semantic Technologies for Information Systems Steve Klabnik and Carol Nichols: The Rust Programming Language William Gropp et al.: Using Advanced MPI / Modern Features of the Message Passing Interface, MIT Press Gerassimos Barlas Multicore and GPU Programming - An Integrated Approach Morgan Kaufmann Publ., Inc.
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	ARP in Master Communication Systems and Networks PO3 ARP in Master Communication Systems and Networks PO4 ARP in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

AVT - Audio- und Videotechnologien 🔗

Module ID	AVT
Module Name	Audio- und Videotechnologien
Type of Module	Elective Modules
Recognized Course	AVT - Audio and Video Technologies
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr.-Ing. Klaus Ruelberg/Professor Fakultät IME
Lecturer(s)	Prof. Dr.-Ing. Klaus Ruelberg/Professor Fakultät IME
Learning Outcome(s) Was: Audio- und Videotechnologien kommen in vielfältiger Weise in der Medienindustrie zum Einsatz. Die Mediendistributionskette, die im Rahmen der LV als exemplarische Anwendung herangezogen und analysiert wird, umfasst verschiedene Technologien wie Datenkompression, Audio- und Videosignalverarbeitung Fehlerschutzmechanismen, digitale Modaluationsverfahren. Womit: Studierende durchdringen eigenständig ausgewählte Themengebiete der Audio- und Videotechnologien, bereiten diese auf und halten einen Fachvortrag. In einem in die LV integrierter Übungsblock entwickeln die Studierende eigenständig algorithmische Lösungskonzepte und setzen diese programmtechnsich um. Wozu: Die Studierenden können akuelle Verfahren zur Audio- und Videocodierung entwickeln und in Hard- und Software implementieren. Sie können Mediendistributionsketten planen, beurteilen und umsetzen sowie fachliche Führungs- und Projektverantwortung übernehmen
Module Contents Lecture / Exercises Audio and video source coding Channel models, channel coding (FEC & digital modulation principles) Broadcast transmission systems (DVB - Digtal Video Broadcasting) implement audio and video coding methods in hard and software develope algorithms and methods for audio and videocoding delvelope and implement digital broadcasting systems Exercises / Lab
Teaching and Learning Methods	Lecture / Exercises Exercises / Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	57 Hours ≙ 5 SWS
Self-Study	93 Hours
Recommended Prerequisites	none
Mandatory Prerequisites	Exercises / Lab requires attendance in the amount of: 1 Termin
Recommended Literature	Proakis, J. Salehi, M. (2007) Digital Communications. McGraw-Hill. ISBN 978-0072957167 Reimers, U. (2001) Digital Video Broadcasting. Springer Verlag. ISBN 978-3-662-04562-6
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	AVT in Master Communication Systems and Networks PO3 AVT in Master Communication Systems and Networks PO4 AVT in Master Medientechnologie PO3 AVT in Master Medientechnologie PO4 AVT in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

AVV - Algorithmen der Videosignalverarbeitung 🔗

Module ID	AVV
Module Name	Algorithmen der Videosignalverarbeitung
Type of Module	Elective Modules
Recognized Course	AVV - Algorithms for video signal processing
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr.-Ing. Klaus Ruelberg/Professor Fakultät IME
Lecturer(s)	Prof. Dr.-Ing. Klaus Ruelberg/Professor Fakultät IME
Learning Outcome(s) WAS: Studierende formulieren gemeinsam mit dem Dozenten eine Aufgabenstellung/Forschungsfrage im Bereich der Videosignalverarbeitung. Unter Anwendung wissenschaftlicher Methoden analysieren sie die Aufgaben- bzw. Fragestellung eigenständig und entwickeln algorithmische Lösungsansätze. WOMIT: Eine Recherche der wissenschaftlichen Literatur bildet die Basis für die Studierenden, um die Aufgabenstellung inhaltlich zu durchdringen und einordnen zu können. Verschiedene, als geignet erscheinende Lösungsansätze werden entwickelt und gegenübergestellt. Mithilfe geeigneter Entwicklungstools (z.B. Matlab) werden die entwickleten Algorithmen umgesetzt und bzgl. der Aufgabenstellung beurteilt. Die erzielten Ergebnisse des Projektes werden in einem Bericht zusammengefasst und im Rahmen eines Vortrages präsentiert. WOZU: Studierenden erhalten die Möglichkeit, sich tiefergehend mit einer wissenschaftlich/entwicklerischen Aufgabenstellung zu befassen.
Module Contents Project The students are introduced to different algorithmic approaches of video signal processing and get an overview of current applications and questions. Analyzing, developing, implementing and evaluating algorithms for video signal processing
Teaching and Learning Methods	Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	12 Hours ≙ 1 SWS
Self-Study	138 Hours
Recommended Prerequisites	keine
Mandatory Prerequisites	Project requires attendance in the amount of: 70% der Praktikumstermine und 1 Präsentation (typischerweise 5 Termine)
Recommended Literature	Signal, Image and Video Processing (Journal), Springer Verlag, Electronic ISSN 1863-1711 Machine Learning for Audio, Image and Video Analysis, Francesco Camastra, Alessandro Vinciarelli, Springer London, 2016, ISBN978-1-4471-6840-9
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	AVV in Master Communication Systems and Networks PO3 AVV in Master Communication Systems and Networks PO4 AVV in Master Medientechnologie PO3 AVV in Master Medientechnologie PO4 AVV in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

CI - Computational Intelligence 🔗

Module ID	CI
Module Name	Computational Intelligence
Type of Module	Elective Modules
Recognized Course	CI - Computational Intelligence
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Studiengangsleiter(in) Master Technische Informatik / Informatik und Systems-Engineering
Lecturer(s)	Prof. Dr. Rainer Bartz/Professor Fakultät IME
Learning Outcome(s) Die Studierenden erarbeiten sich grundlegende Kenntnisse zur Theorie und Anwendung von Methoden der Computational Intelligence. Die Studierenden kennen die gängigen Typen von Optimierungsaufgaben und können konkrete Aufgaben einordnen. Die Studierenden kennen das Prinzip des Simplex-Algorithmus und können eine Problemstellung in die für ihn geeignete Standardform überführen und eine Lösung erarbeiten. Sie können lineare Probleme mit einem Simplex-Algorithmus lösen. Die Studierenden können neuronale Netze einordnen und ihre Anwendbarkeit auf Problemstellungen bewerten. Sie können Lernverfahren klassifizieren und ihre Arbeitsweise beschreiben. Sie können nichtlineare Probleme der Modellbildung und Klassifizierung mit einem neuronalen Netz lösen. Sie kennen die Methodik der Fuzzy Logik und können eine Problemstellung darauf abbilden und das resultierende Systemverhalten begründen. Sie können unscharf definierte Aufgaben mit Hilfe von Fuzzy Logik lösen. Die Studierenden kennen die Arbeitsweise evolutionärer Algorithmen und können ihre Varianten einordnen. Sie können reale Problemstellungen in geeignete Repräsentationen umsetzen. Sie können Selektionsverfahren bewerten und geeignete Selektionsalgorithmen entwerfen. Sie können schwierige Probleme mit Heuristiken der evolutionären Algorithmen lösen. Die Studierenden können mit üblichen Werkzeugen der Computational Intelligence umgehen. Die Studierenden können Aufgaben in einem kleinen Team lösen. Die Studierenden können Systemparameter variieren, Messreihen durchführen und Ergebnisse darstellen, bewerten und diskutieren. Sie können das Verhalten eines Systems bewerten und durch geeignete Modifikationen verbessern. Die Studierenden können internationale wissenschaftliche Literatur analysieren, einordnen, in ihren Kontext stellen und präsentieren.
Module Contents Lecture / Exercises Optimization strategies - classification of problems - gradient algorithms - simplex algorithm - multiobjective optimization and Pareto approach Artificial neural networks - artificial neurons - neural network structures - training algorithms Fuzzy logic - fuzzification - inference - defuzzification Evolutionary algorithms - genome representations - selection mechanisms - recombination operators - mutation operators The students acquire fundamental knowledge on theory and applications of computational intelligence The students know about typical classes of optimization tasks and how to map a specific problem to those classes They know the simplex algorithm and can transform problems into the standard form to find the solutions The students can classify artificial neural networks and determine their applicability for specific tasks They can vary the parameters of neural networks and rate their impact on the results They can classify training algorithms and understand the backpropagation algorithm They know about the fuzzy logic approach, can apply it to specific problems and justify the resulting system behavior The students know how evolutionary algorithms work and can distinguish the variants They can transform a problem specification into a representation appropriate for an evolutionary algorithm They can rate selection strategies and define suitable algorithms The students can solve linear problems with the use of the simplex algorithm They can apply artificial neural networks to solve problems of modeling and classification They can define fuzzy logic systems to solve imprecise and vague tasks They can solve difficult problems heuristically using evolutionary algorithms Lab Application of artificial neural networks to a classification task Variation and multiobjective optimization of neural network parameters Fuzzy-based closed loop control of a system with two inputs The students are familiar with tools supporting computational intelligence The students can vary system parameters, perform test series, and evaluate, present and discuss the results The students are able to understand, present, analyze and discuss scientific publications The students are able to solve problems in small teams They can tackle optimization tasks in a structured and systematic way They can rate the behavior of a system with regard to objectives and study and improve the behavior through parameter variations They are able to cope with international scientific publications, understanding, presenting and discussing them in their context
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	vector functions, gradient
Mandatory Prerequisites	Lab requires attendance in the amount of: 2 Termine
Recommended Literature	Domschke W., Drexl A.; Einführung in Operations Research; Springer Zell, A.: Simulation Neuronaler Netze; Oldenbourg Nauck, D. et al.: Neuronale Netze und Fuzzy-Systeme; Vieweg Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing; Springer Gerdes, I. et al.: Evolutionäre Algorithmen; Vieweg Grosse et al.: Taschenbuch der praktischen Regelungstechnik, Fachbuchverlag Leipzig
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	CI in Master Communication Systems and Networks PO3 CI in Master Communication Systems and Networks PO4 CI in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

CSO - Computersimulation in der Optik 🔗

Module ID	CSO
Module Name	Computersimulation in der Optik
Type of Module	Elective Modules
Recognized Course	CSO - Computersimulation in der Optik
ECTS credits	5
Language	deutsch und englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Holger Weigand/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Holger Weigand/Professor Fakultät IME
Learning Outcome(s) Kompetenz zum Aufbau, zur Analyse, zur Optimierung und Auslegung beleuchtungsoptischer Systeme unter Zuhilfenahme von Software basierend auf nicht-sequentiellem Raytrace. Kompetenz für Software-Entwicklung im Umfeld der Computersimulation (Makro-Programmierung mit Skript-Sprachen, z.B. zum Steuern des In- oder Outputs von Simulationen). Kompetenz zum Erwerb vertiefter Fertigkeiten im Bereich nicht-sequentieller Raytrace-Simulation durch eigenständiges Durcharbeiten von Literatur und Software-Dokumentation, sowie der Einbeziehung des technischen Supports der Software zu einer speziellen Thematik.
Module Contents Lecture / Exercises Lab Project
Teaching and Learning Methods	Lecture / Exercises Lab Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	57 Hours ≙ 5 SWS
Self-Study	93 Hours
Recommended Prerequisites
Mandatory Prerequisites	Participation in final examination only after successful participation in Lecture / Exercises Participation in final examination only after successful participation in Lab
Recommended Literature	W. T. Welford, R. Winston: High Collection Nonimaging Optics, Academic Press, 1989; G. Kloos: Entwurf und Auslegung optischer Reflektoren, Expert, 2007; Deutsche und US-Amerikanische Patentschriften; Datenblätter optischer und opto-elektronischer Komponenten; MIT Scheme Reference, Edition 1.62, 1996 (https://groups.csail.mit.edu/mac/ftpdir/scheme-7.4/doc-html/scheme_toc.html); H. Ramchandran, A. S. Nair: Scilab (a Free Software to Matlab), S. Chand, 2012; F. Thuselt, F. P. Gennrich: Praktische Mathematik mit MATLAB, Scilab und Octave, Springer 2013; T. Sheth: SCILAB: A Practical Introduction to Programming and Problem Solving, CreateSpace, 2016; C. Gomez: Engineering and Scientific Computing with Scilab, Birkhäuser, 1999;
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	CSO in Master Communication Systems and Networks PO3 CSO in Master Communication Systems and Networks PO4 CSO in Master Elektrotechnik PO3 CSO in Master Elektrotechnik und Informationstechnik PO1 CSO in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	5.9.2025, 17:36:59

DLO - Deep Learning und Objekterkennung 🔗

Module ID	DLO
Module Name	Deep Learning und Objekterkennung
Type of Module	Elective Modules
Recognized Course	DLO - image processing master
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Jan Salmen/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Jan Salmen/Professor Fakultät IME
Learning Outcome(s) Die Teilnehmer*innen können selbständig entscheiden, in welchen Situationen sich der Einsatz von Verfahren aus dem Bereich Deep Learning anbietet. Sie können eine entsprechende Lösung entwerfen, iterativ verbessern und praktisch umsetzen. Mögliche Probleme auf dem Weg dahin (z.B. beim Erstellen eines Datensatzes oder beim Training) können sie qualifiziert analysieren und passende Ideen zur Bewältigung entwickeln. Da sie einen guten Überblick über die langjährigen Entwicklungen in Forschung und Technik haben, können sie qualifiziert auf aktuelle Herausforderungen und offene Fragen im Zusammenhang mit Deep Learning schauen. Die Studierenden werden so in die Lage versetzt, sich sowohl im weiteren Studienverlauf als auch im Berufsleben kompetent mit Ansätzen zu beschäftigen, die auf Deep Learning beruhen.
Module Contents Lecture Deep learning algorithms and their application for object recognition in images. Lab training of a neural network evaluation of the performance of a neural network
Teaching and Learning Methods	Lecture Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	34 Hours ≙ 3 SWS
Self-Study	116 Hours
Recommended Prerequisites	The students should have some basic knowledge about image processing and pattern recognition
Mandatory Prerequisites	Lab requires attendance in the amount of: 4 Termine
Recommended Literature	I. Goodfellow, Y. Bengio und A. Courville. Deep Learning. MIT Press, 2016 C. C. Aggarwal. Neural Networks and Deep Learning: A Textbook. Springer, 2018 C. Bishop und H. Bishop. Deep Learning: Foundations and Concepts. Springer, 2024 D. V. Godoy. Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide. Fundamentals. 2022 D. V. Godoy. Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide. Computer Vision. 2022
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	DLO in Master Communication Systems and Networks PO3 DLO in Master Communication Systems and Networks PO4 DLO in Master Elektrotechnik PO3 DLO in Master Elektrotechnik und Informationstechnik PO1 DLO in Master Medientechnologie PO3 DLO in Master Medientechnologie PO4 DLO in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

DMC - Digital Motion Control 🔗

Module ID	DMC
Module Name	Digital Motion Control
Type of Module	Elective Modules
Recognized Course	DMC - Digital Motion Control
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Jens Onno Krah/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Jens Onno Krah/Professor Fakultät IME
Learning Outcome(s) Servomotoren kennenlernen und betreiben Servoumrichter kennenlernen und verwenden Digitale Regelalgorithmen nutzen Prozessidentifikation und Parameterestimation Auslegung von Antriebssystemen
Module Contents Lecture / Exercises Structure of servo motors Structure of servo inverters Digital control algorithms Process identification Design of drive systems Lab Direct Digital Control Quasi-continuous control Predictor / Observer Parameterization of a control system Evaluation of Bode diagrams Demonstrate action competence Commissioning of a servocontroller Minimization of following errors
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	RT, DSS
Mandatory Prerequisites	Lab requires attendance in the amount of: 3 Termine
Recommended Literature	Krah, Jens Onno, Vorlesungsskript MC Krah, Jens Onno: Vorlesungsskript RT (Download) Handbuch ServoStar 300: www.danahermotion.net Schultz, G.: Regelungstechnik, Oldenbourg Verlag, München-Wien Lutz, Wendt: Taschenbuch der Regelungstechnik, Verlag Harri Deutsch
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	DMC in Master Communication Systems and Networks PO3 DMC in Master Communication Systems and Networks PO4 DMC in Master Elektrotechnik PO3 DMC in Master Elektrotechnik und Informationstechnik PO1 DMC in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

DSP - Digital Signal Processing 🔗

Module ID	DSP
Module Name	Digital Signal Processing
Type of Module	Elective Modules
Recognized Course	DSP - Digital Signal Processing
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Harald Elders-Boll/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Harald Elders-Boll/Professor Fakultät IME
Learning Outcome(s) Design, analyse and implement DSP systems in soft and hardware considering computational complexity and hardware resource limitation, by a thorough understanding of the theoretical concepts, especially frequency domain analysis, and practical implementation of DSP systems in software using Python and on microprocessors, to be able to design, select, use and apply actual and future DSP systems for various signal processing application in commercial products.
Module Contents Lecture / Exercises Signals, Systems and Digital Signal Processing Basic Elements of DSP Systems Classification of Signals Continuous-Time and Discrete-Time Signals Deterministic and Random Signals Even and Odd Signals Periodic and Aperiodic Signals Energy and Power of Signals Some Fundamental Signals Discrete-Time Linear Time-Invariant Systems Difference Equations Discrete-Time Convolution Unit-Pulse and Impulse Response Basic Systems Properties: Causality, Stability, Memory Ideal Sampling and Reconstruction Ideal Sampling and the Sampling Theorem Aliasing Fourier-Transform of Discrete-Time Signals Eigenfunctions of Discrete-Time LTI Systems Frequency response of Discrete-Time LTI Systems The Fourier-Transform of Discrete-Time Signals Ideal Continuous-Time Filters Discrete Fourier-Transform Sampling the DTFT The DFT and the Inverse DFT The Fast Fourier Transform Radix-2 FFT Algorithms Linear Convolution Using the FFT Overlap-And-Add Random Signals Review of Probablity and Random Variables Ensemble Averages Correlation Functions Stationary and Ergodic Processes Power Spectral Density Transmission of Random Signals over LTI Systems Advanced Sampling Techniques Quantization and Encoding Sampling of Bandpass Signals Sampling of Random Signals Sample Rate Conversion Sample Rate Reduction by an Integer Factor Sample Rate Increase by an Integer Factor Sample Rate Conversion by a Rational Factor Oversampling and Noise Shaping Students understand the fundamentals of discrete-time signals and systems Students can analyse the frequency content of a given signal using the appropriate Fourier-Transform and methods for spectrum estimation Analysis of discrete-time LTI Systems Students can calculate the output signal via convolution Students can determine the frequency response of a given system Students can characterize a given system in the frequency domain and in the z-domain Implementation of discrete-time LTI systems Students can implement the convolution sum in software Students can implement different structures for IIR systems in software Sudents can use the FFT to implement an FIR system Lab Review of Probablity and Random Variables Moments, Averages and Distribution Functions Random Signals Ensemble Averages Correlation Functions Stationary and Ergodic Processes Power Spectral Density Transmission of Random Signals over LTI Systems Combatting noise Remove or suppress high-frequency noise from low-pass signals Abilty to trade-off different methods for digital coding of speech and audio signals
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	No formal requirements, but students will be expected to be familiar with: Basic Knowledge of Signals and Systems: Continuous-Time LTI-Systems and Convolution, Fourier-Transform Basic Knowledge of Probability and Random Variables
Mandatory Prerequisites	Lab requires attendance in the amount of: 8 Termine Participation in final examination only after successful participation in Lab
Recommended Literature	John G. Proakis and Dimitris K. Manolakis. Digital Signal Processing (4th Edition). Prentice Hall, 2006. Alan V. Oppenheim, Ronald W. Schafer. Discrete-Time Signal Processing (3rd Edition). Prentice Hall, 2007. Vinay Ingle and John Proakis. Digital Signal Processing using MATLAB. Cengage Learning Engineering, 2011.
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	DSP in Master Communication Systems and Networks PO3 DSP in Master Communication Systems and Networks PO4 DSP in Master Elektrotechnik und Informationstechnik PO1 DSP in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

ERMK - Entrepreneurship, Gewerblicher Rechtsschutz, Market Knowledge 🔗

Module ID	ERMK
Module Name	Entrepreneurship, Gewerblicher Rechtsschutz, Market Knowledge
Type of Module	Elective Modules
Recognized Course	GER - Industrial property protection
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every term
Module Coordinator	Prof. Dr. Holger Weigand/Professor Fakultät IME
Lecturer(s)	Ladrière
Learning Outcome(s) Befähigung zum unternehmerischen Denken Einschätzung des Innovationspotentials neuer technischer Entwicklungen Verständnis der Mechanismen des Marktes im Hinblick auf neue technische Innovationen
Module Contents Lecture Types of industrial property rights, significance for companies and inventors, significance of employee invention law and inventor personality law, prerequisites for protection, term of industrial property rights, structure of an application, life cycle from application to patent, subsequent applications, examination and opposition procedures, national, European and international applications, utility models, trademarks, design, law on the protection of secrets, professional field of patent engineer. Carry out a patent search; determine the relevant type of protective right for a given case; be able to correctly file an application with regard to its formal structure; weigh up the advantages and disadvantages of national, European and international applications in a specific application; check the validity of a patent; develop a basic IP strategy. Seminar Types of industrial property rights, significance for companies and inventors, significance of employee invention law and inventor personality law, prerequisites for protection, term of industrial property rights, structure of an application, life cycle from application to patent, subsequent applications, examination and opposition procedures, national, European and international applications, utility models, trademarks, design, law on the protection of secrets, professional field of patent engineer. Carry out a patent search; determine the relevant type of protective right for a given case; be able to correctly file an application with regard to its formal structure; weigh up the advantages and disadvantages of national, European and international applications in a specific application; check the validity of a patent; develop a basic IP strategy.
Teaching and Learning Methods	Lecture Seminar
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	34 Hours ≙ 3 SWS
Self-Study	116 Hours
Recommended Prerequisites
Mandatory Prerequisites
Recommended Literature
Included in Elective Catalog	X - Fachübergreifende Kompetenzen und Soft Skills
Use of the Module in Other Study Programs	ERMK in Master Elektrotechnik PO3 ERMK in Master Elektrotechnik und Informationstechnik PO1 ERMK in Master Medientechnologie PO4 ERMK in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

ESD - Embedded Systems Design 🔗

Module ID	ESD
Module Name	Embedded Systems Design
Type of Module	Elective Modules
Recognized Course	ESD - Embedded Systems Design
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Markus Cremer/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Markus Cremer/Professor Fakultät IME
Learning Outcome(s) Die Studierenden können die Machbarkeit der Entwicklung einer Produktidee im Bereich der Embedded Systems in Bezug auf praktische Realisierbarkeit, Aufwand, Zeit und Kosten und mit vorausschauendem Blick auf den gesamten Entwicklungsprozess sicher beurteilen. Hierzu setzen sie, ausgehend von einer eigenen Produktidee, Methoden und Hilfsmittel (z.B. Software-Tools, Konzepte, Best-Practices, v.a. auch Hardwareentwicklung) eines typischen industriellen Entwicklungsprozesses für Embedded Systems eigenständig praktisch um. Später sind die Studierenden in der Lage, diesen gesamten Entwicklungsprozess in der Industrie oder in Forschungsprojekten autonom zu bewerten und umzusetzen.
Module Contents Lecture / Exercises The exact content will not be determined until the summer semester 2025. Project
Teaching and Learning Methods	Lecture / Exercises Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	Basic knowledge of electrical engineering (simple analog and digital circuits) Basic knowledge of embedded systems (basics of microcontrollers incl. implementation of firmware)
Mandatory Prerequisites
Recommended Literature	Murti, K. (2022). Design Principles for Embedded Systems. Springer Singapore. https://doi.org/10.1007/978-981-16-3293-8 Schmidt, R., Hauschild, D., & Kluge, I. (2024). Elektronik Design: Theorie und Praxis. Elektronik Design: Theorie Und Praxis. https://doi.org/10.1007/978-3-662-68676-8 Ünsalan, C., Gürhan, H. D., & Yücel, M. E. (2022). Embedded system design with ARM Cortex-M microcontrollers: Applications with C, C++ and MicroPython. Embedded System Design with ARM Cortex-M Microcontrollers: Applications with C, C++ and MicroPython, 1–569. https://doi.org/10.1007/978-3-030-88439-0 Morshed, B. I. (2021). Embedded systems - A hardware-software co-design approach: Unleash the power of arduino! In Embedded Systems - A Hardware-Software Co-Design Approach: Unleash the Power of Arduino! Springer International Publishing. https://doi.org/10.1007/978-3-030-66808-2 Marwedel, P. (2021). Embedded System Design. https://doi.org/10.1007/978-3-030-60910-8 Lienig, J., & Scheible, J. (2020). Fundamentals of Layout Design for Electronic Circuits. Fundamentals of Layout Design for Electronic Circuits. https://doi.org/10.1007/978-3-030-39284-0
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	ESD in Master Communication Systems and Networks PO4 ESD in Master Elektrotechnik und Informationstechnik PO1 ESD in Master Medientechnologie PO4 ESD in Master Informatik und Systems-Engineering PO1
Permanent Links to Organization	ILU course
Specifics and Notes
Last Update	19.7.2025, 14:32:16

ETH - Ethik 🔗

Module ID	ETH
Module Name	Ethik
Type of Module	Elective Modules
Recognized Course	ETH - Ethics
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every term
Module Coordinator	Studiengangsleiter(in) Master Technische Informatik / Informatik und Systems-Engineering
Lecturer(s)	NN/Lehrbeauftragter
Learning Outcome(s) Die Studierenden sind in der Lage, ein digitales Produkt gemäß aktueller Anforderungen an Sicherheit und Privatheit zu gestalten. Sie können indem sie -die technischen, rechtlichen und ethischen Grundlagen von Datenschutz, Datensicherheit und Privacy verstehen, - Basistechnologien zu Authentifizierung und Autorisierung kennen und anwenden können, - Zielkonflikte zwischen funktionalen Anforderungen und ELSI (ethical / legal / social implications) erkennen und mit Hilfe von Analysemethoden bewerten, sowie daraus Schlussfolgerungen für die Hard- und Softwarearchitektur ziehen, indem etwa Sicherheits- / Privatsphärenaspekte in das Produkt integriert werden, so dass sie marktfähige und ethisch-rechtliche einwandfreie digitale Produkte erstellen können. An ausgewählten Problemen zur Fragestellung “Ethische Fragestellungen im Coding”, die in der wissenschaftlichen Literatur beschrieben sind, führen die Studierenden Untersuchungen durch, z.B. durch eigene weitere Literaturrecherche, Interviews mit Akteuren im C&C-Umfeld usw. Sie erarbeiten sich damit die Fähigkeit, ethische Problemstellungen in ihrer beruflichen Praxis zu erkennen und sie diskutieren zu können. Dazu betreiben sie - Literaturrecherche, - Diskussion digitaler Produkte unter ethischen Aspekten, - Interviews mit Akteuren im technisch-informatischen-Umfeld usw. Sie erarbeiten sich damit die Fähigkeit, eine auf ethischen Prinzipien beruhende Folgenabschätzung in ihrer beruflichen Praxis vorzunehmen und damit als “Global Citizen” verantwortungsvoll handeln zu können.
Module Contents Lecture / Exercises - technical, regulatory and ethical foundations of data protection, security and privacy - analytical methods for solving conflicting interests between system requirements and ELSi (ehtical / legal / social implications) - ethics' basic concepts Seminar - application of ethics on concrete scenarios
Teaching and Learning Methods	Lecture / Exercises Seminar
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	none
Mandatory Prerequisites
Recommended Literature
Included in Elective Catalog	X - Fachübergreifende Kompetenzen und Soft Skills
Use of the Module in Other Study Programs	ETH in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

FP - Forschungsprojekt 🔗

Module ID	FP
Module Name	Forschungsprojekt
Type of Module	Mandatory Module
Recognized Course	FP - Forschungsprojekt
ECTS credits	10
Language	deutsch und englisch
Duration of Module	1 Semester
Recommended Semester	2
Frequency of Course	every term
Module Coordinator	Studiengangsleiter(in) Master Technische Informatik / Informatik und Systems-Engineering
Lecturer(s)	verschiedene Dozenten*innen / diverse lecturers
Learning Outcome(s) Studierende untersuchen und lösen eine wissenschaftliche Problemstellung, indem sie selbständig den aktuellen Stand der Wissenschaft auf einem Fachgebiet durch Literaturrecherche erarbeiten, ein eigenes Projekt in Abstimmung mit Kollegen planen, durchführen und kontrollieren, das gegebene Problem selbständig (oder im Team) mit wissenschaftlichen Methoden untersuchen und lösen, im Studium erworbenes Fachwissen auf Problemstellung anwenden und hierbei vertiefen, eigene Lösung mit alternativen Lösungsmöglichkeiten vergleichen, erstellte Lösung in Gesamtzusammenhang einordnen und aus fachlicher und gesellschaftlicher Sicht kritisch bewerten und den Stand der Wissenschaft, die fachlichen Grundlagen, die gewählte Lösung und ihre Bewertung gegenüber den weiteren möglichen Lösungsalternativen klar und nachvollziehbar in schriftlicher auf Englisch Form darstellen sowie mündlich auf Englisch präsentieren und verteidigen. um wissenschaftliche Methoden in folgenden Modulen, insbesondere der Masterarbeit, und späteren Berufsleben anwenden zu können.
Module Contents Research Project
Teaching and Learning Methods	Research Project
Examination Types with Weights	cf. exam regulations
Workload	300 Hours
Contact Hours	12 Hours ≙ 1 SWS
Self-Study	288 Hours
Recommended Prerequisites
Mandatory Prerequisites
Recommended Literature
Included in Elective Catalog
Use of the Module in Other Study Programs	FP in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

HIM - Advanced Mathematics 🔗

Module ID	HIM
Module Name	Advanced Mathematics
Type of Module	Elective Modules
Recognized Course	HIM - Advanced Mathematics
ECTS credits	5
Language	deutsch und englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every term
Module Coordinator	Prof. Dr. Heiko Knospe/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Heiko Knospe/Professor Fakultät IME Prof. Dr. Hubert Randerath/Professor Fakultät IME Prof. Dr. Beate Rhein/Professor Fakultät IME
Learning Outcome(s) Was: Das Modul vermittelt grundlegende Konzepte und Methoden der Mathematik, die in den Ingenieurwissenschaften benötigt werden (K. 8). Die Abstraktion und mathematischen Formalisierung von Problemen soll erlernt und angewendet werden (K. 2). Die Studierenden lernen die Anwendung mathematischer Methoden (K. 16). Es soll die Anwendung statistischer Verfahren und die Begründung wissenschaftlicher Aussagen erlernt werden (K. 17). Womit: Der Dozent/die Dozentin vermittelt Wissen und Basisfertigkeiten in der Vorlesung. In der Übung bearbeiten die Studierenden unter Anleitung Aufgaben. Die Übung wird durch Hausaufgaben und Online-Aufgaben (E-Learning) ergänzt. Wozu: Fortgeschrittene Mathematik-Kenntnisse (beispielweise in Vetoranalysis, Statistik und Optimierung) werden in mehreren Moduln des Studiengangs benötigt. Mathematische Methoden sind essentiell für Ingenieure, die wissenschaftlch arbeiten und wissenschaftliche Erkenntnisse anwenden und erweitern (HF2).
Module Contents Lecture / Exercises A combination of: - Vector Analysis - Probability Theory, Statistics and Multivariate Statistics - Stochastic processes - Optimization Vector Analysis - Vector Spaces - Scalar and Vector Functions - Differential Operators - Line Integrals - Double Integrals - Triple Integrals - Change of Variables - Surface Integrals - Divergence Theorem - Theorem of Stokes - Maxwell Equations Probability and Statistics - Descriptive Statistics - Two-dimensional Data - Simple Linear Regression - Probability Spaces - Random Variables - Expectation, Variance, Moments - Jointly Distributed Random Variables - Independent Random Variables - Covariance - Binomial Random Variable - Poisson Random Variable - Uniform Random Variable - Normal Random Variable - Chi-Square Distribution - t-Distribution - Central Limit Theorem - Distributions of Sampling Statistics - Confidence Intervals - Hypothesis Testing - t-Test, f-Test, Chi-Square Test - Overview of various Tests Multivariate Statistics - Analysis of multidimensional data - Multivariate Random Variables - Matrix decompositions, Singular Value Decomposition (SVD) - Factor analysis, Principal Component Analysis (PCA) - Multiple Linear Regression Stochastic Processes - Discrete and continuous time processes - Random walk - Markov chain - Poisson process - Queuing theory Optimization - Linear Programming - Unconstrained Optimization: Gradient method, Newton's method, Trust Region method - Constrained Optimization: Karush–Kuhn–Tucker (KKT) conditions, Lagrange multipliers, Penalty and Barrier functions - Special optimization problems: Mixed Integer Nonlinear Programming, Nonlinear Stochastic Optimization -
Teaching and Learning Methods	Lecture / Exercises
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	34 Hours ≙ 3 SWS
Self-Study	116 Hours
Recommended Prerequisites	Differential and integral calculus and linear algebra (Bachelor-level mathematics)
Mandatory Prerequisites
Recommended Literature	K. Burg, H. Haf, F. Wille, A. Meister, Vektoranalysis - Höhere Mathematik für Ingenieure, Naturwissenschaftler und Mathematiker, Springer Vieweg E. Kreyszig, Advanced Engineering Mathematics, John Wiley & Sons L. Papula, Mathematik für Ingenieure und Naturwissenschaftler Band 3, Springer Vieweg R. E. Walpole, R. H. Myers, S. L. Myers, K. Ye, Probability & Statistics for Engineers & Scientists, Prentice Hall S. M. Ross, Probability and Statistics for Engineers and Scientists, Elsevier S. M. Ross, Stochastic Processes, John Wiley & Sons U. Krengel, Einführung in die Wahrscheinlichkeitstheorie und Statistik A. Koop, H. Moock, Lineare Optimierung, Springer R. Reinhardt, A. Hoffmann, T. Gerlach, Nichtlineare Optimierung, Springer M. Ulbrich, S. Ulbrich, Nichtlineare Optimierung, Birkhäuser
Included in Elective Catalog	VMT - Vertiefung Mathematik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	HIM in Master Communication Systems and Networks PO3 HIM in Master Communication Systems and Networks PO4 HIM in Master Elektrotechnik PO3 HIM in Master Elektrotechnik und Informationstechnik PO1 HIM in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

IBD - InnoBioDiv 🔗

Module ID	IBD
Module Name	InnoBioDiv
Type of Module	Elective Modules
Recognized Course	IBD - InnoBioDiv student projects
ECTS credits	5
Language	englisch
Duration of Module	0.5 Semester
Recommended Semester	1-2
Frequency of Course	every term
Module Coordinator	Prof. Dr. Uwe Dettmar/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Uwe Dettmar/Professor Fakultät IME
Learning Outcome(s) Die Studierenden können in einer Forschungsgruppe ein Experiment teamorientiert planen, durchführen, auswerten und dokumentieren, indem sie auf biologisches und technisches Basiswissen und auf die zur Verfügung gestellten Ressourcen (ein IoT basiertes Mess- und Steuersystem inklusive FarmBot, Sensorik und Aktorik, Materialien und Geräte im Gewächshaus des Instituts für Pflanzenwissenschaften, Checklisten) sowie weitere frei verfügbare Informationsquellen zugreifen, um die Auswirkungen des Klimawandels auf die Wachstumsleistung von Pflanzen und die Biodiversität im Boden erfahrbar zu machen und dadurch Erkenntnisse zu generieren, die für die Gesellschaft im Rahmen des Klimawandels von Relevanz sind.
Module Contents Seminar Development of project ideas, discussion and further development of the projects Project The students acquire... - the ability to develop and implement concepts for the adaptation of plants to climate change. - the ability to plan, conduct and analyze experiments in the fields of plant physiology, soil biology and technology. - the ability to statistically evaluate and present experimental data. - the ability to present and communicate scientific results. - the ability to collaborate interdisciplinary and interculturally and to exchange ideas with students from differentMINT research areas. - Experience in planning and implementing projects and in teamwork At the end, students will have - a deep understanding of the interactions between climate parameters, plant growth and soil biodiversity. - basic knowledge of modern technologies such as robotics, sensor technology and the Internet of Things in the context of plant research. - an awareness of the importance of sustainability, resource conservation and security of supply in the context of population growth and climate change.
Teaching and Learning Methods	Seminar Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	23 Hours ≙ 2 SWS
Self-Study	127 Hours
Recommended Prerequisites	- fluent English since working intercultural and interdisciplinary teams. - basic knowledge in IoT and robotics desireable - ability to work in a team - basic knowledge in plant bilology are not mandatory
Mandatory Prerequisites	Seminar requires attendance in the amount of: 8 Stunden Project requires attendance in the amount of: 5 meetings for project discussions
Recommended Literature	https://farm.bot/ Arif, Tarik M.: Deep Learning on Embedded Systems: A Hands-On Approach Using Jetson Nano and Raspberry Pi, Wiley, 2025, ISBN:978-1-394-26927-3 Agrawal, D. P. (2017). Embedded Sensor Systems. Springer. Marwedel, Peter: Embedded System Design: Embedded Systems Foundations of Cyber-Physical Systems, and the Internet of Things, Springer, 2021, ISBN 978-3-030-60910-8 L. Urry, S. Wassermann: Campbell Biology AP Edition (12th Edition), Pearsson, ISBN-13: 978-0-13-648687-9 Taiz, L., Møller, I. M., Murphy, A., & Zeiger, E. (2022). Plant Physiology and Development. Oxford University Press.
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	IBD in Master Communication Systems and Networks PO3 IBD in Master Communication Systems and Networks PO4 IBD in Master Elektrotechnik und Informationstechnik PO1 IBD in Master Medientechnologie PO4 IBD in Master Informatik und Systems-Engineering PO1
Permanent Links to Organization	Course description in the learning platform
Specifics and Notes	Block course from the beginning of October to mid-November (7 weeks), optional preparation time to build up basic knowledge in the last week of September
Last Update	19.7.2025, 14:32:16

IIS - Intelligent Information Systems 🔗

Module ID	IIS
Module Name	Intelligent Information Systems
Type of Module	Elective Modules
Recognized Course	IIS - Intelligent Information Systems
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Andreas Behrend/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Andreas Behrend/Professor Fakultät IME
Learning Outcome(s) Die Studierenden kennen die verschiedenen Möglichkeiten zur Darstellung von Wissen und können die Vor – und Nachteile einer Darstellungsform bewerten. Die Studierenden erarbeiten sich grundlegende Kenntnisse zur Theorie und Anwendung von deklarativen Programmiersprachen bzw. Regelsystemen. Die Studierenden kennen gängige Typen von Optimierungs- bzw. Suchproblemen und können geeignete deklarative Lösungsansätze identifizieren. Die Studierenden kennen die wichtigsten Inferenzmethoden und können diese einordnen bzw. bewerten. Die Studierenden kennen die Resolutionsmethode und das Verfahren der Unifikation und können diese für eine Problemstellung anwenden. Die Studierenden kennen die wichtigsten Formen der Operationalisierung deklarativer Ausdrücke und können diese bzgl. ihrer Effizienz bei einem Lösungsansatz bewerten. Die Studierenden können für reale Problemstellungen eine geeignete Wissensrepräsentation wählen und eine Lösung mit einem deklarativen Programm erarbeiten. Die Studierenden können aktuelle deklarative Anfragesprachen klassifizieren und hinsichtlich ihrer Ausdrucksmächtigkeit bewerten. Die Studierenden können mit gängigen deklarativen Programmiersprachen umgehen. Die Studierenden können Aufgaben in einem kleinen Team lösen. Die Studierenden können Programmcode verstehen und um Funktionalität erweitern. Sie können das Verhalten einer programmierten Lösung bewerten und durch geeignete Modifikationen verbessern. Die Studierenden können internationale wissenschaftliche Literatur analysieren, einordnen, in ihren Kontext stellen und präsentieren.
Module Contents Lecture / Exercises Foundations of Knowledge Representation - First-order logic - relational, functional, tree-based, graph-oriented fact representation (semantic networks, ontologies) - rule-based systems Automatic reasoning and inference methods - resolution principle (incl. unification) - forward and backward chaining - fixpoint semantics Declarative Programming languages - Functional programming - relational (logical) programming , e.g., Prolog, Datalog, SQL and SPARQL Outlock on current research issues, e.g., query languages, parallel algorithms, distributed systems, combinatorial optimization and language processing. Students have acquired basic knowledge about methods for representing knowledge, automatic reasoning as well as declarative programming languages. They understand the various ways of operationalizing declarative expressions and are able to realize suitable programming solutions for given problems. Lab Representing knowledge by sets of tuples, relations, semantic networks as well as logic-based systems Implementing calculation problems with a functional programming language (e.g. Haskell) using expressions, algebraic data types, infinite data structures and higher-order functions Solving search problems with a logical programming language and recursive expressions Formulating relational queries over knowledge bases (e.g. using SPQAQL or Datalog)
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	programming skills, knowledge about data structures and algorithms
Mandatory Prerequisites
Recommended Literature	G. Hutton: Programming in Haskell, 2nd Ed., Cambridge University Press, 2016 L. Sterling, E. Shapiro: The Art of Prolog, 2nd Ed., MIT Press, 1994 Uwe Schöning. Logik für Informatiker. 5. Auflage, Spektrum Akademischer Verlag, 2000 Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph. Foundations of Semantic Web Technologies. CRC Press 2009. S.J. Russell, P. Norvig: Artificial Intelligence. A Modern Approach, 2. Aufl. Prentice Hall, 2003
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	IIS in Master Communication Systems and Networks PO3 IIS in Master Communication Systems and Networks PO4 IIS in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

ITF - IT-Forensik 🔗

Module ID	ITF
Module Name	IT-Forensik
Type of Module	Elective Modules
Recognized Course	ITF - IT forensics
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Studiengangsleiter(in) Master Technische Informatik / Informatik und Systems-Engineering
Lecturer(s)	Jürgen Bornemann/Lehrbeauftragter
Learning Outcome(s) WAS Studierende spüren digitale Beweise auf und stellen Sie zwecks Verwertbarkeit für weiterführende Analysen sicher, WOMIT indem sie anhand fallbezogener Aufgabenstellungen und mittels forensischer IT-Tools Schwachstellen entdecken und Beweise in Dateisystemen und IT-Infrastrukturen sichern, WOZU um im Berufsleben Gefahren vermeiden, erkennen und abwehren können und ggf. gutachterlich tätig zu werden.
Module Contents Lecture / Exercises Basic concepts of cyber security and digital forensics Typical vulnerabilities, threats and risks Dangers with mobile systems, home office, WLANs Basics and working methods of IT forensics Forensic documentation creation Common tools for forensic investigations Recognize and secure digital evidence Open source forensics File system forensics Forensic analysis of mobile systems Vulnerabilities, threats, attacks on network structures KALI Linux - Operating System for Vulnerability and Pentesting Project Students can work on case-related forensic tasks and incidents independently or in working groups using the knowledge they have acquired. They show how to secure, analyze and document digital evidence.
Teaching and Learning Methods	Lecture / Exercises Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites
Mandatory Prerequisites
Recommended Literature
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	ITF in Master Communication Systems and Networks PO3 ITF in Master Communication Systems and Networks PO4 ITF in Master Elektrotechnik PO3 ITF in Master Elektrotechnik und Informationstechnik PO1 ITF in Master Medientechnologie PO3 ITF in Master Medientechnologie PO4 ITF in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

KOGA - Kombinatorische Optimierung und Graphenalgorithmen 🔗

Module ID	KOGA
Module Name	Kombinatorische Optimierung und Graphenalgorithmen
Type of Module	Elective Modules
Recognized Course	KOGA - Combinatorial Optimization and Graph Algorithms
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Hubert Randerath/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Hubert Randerath/Professor Fakultät IME
Learning Outcome(s) Die Studierenden sind in der Lage Verfahren und Konzepte der Graphentheorie und der Kombinatorischen Optimierung zur Beschreibung und algorithmischen Lösung von Problemstellungen der Informatik, der Technik und des täglichen Lebens anzuwenden. Sie haben die Fertigkeit Verfahren und Konzepte der Graphentheorie und der Kombinatorischen Optimierung zur Beschreibung und algorithmischen Lösung von Problemstellungen der Informatik, der Technik und des täglichen Lebens anzupassen. Sie können algorithmische Denk- und Arbeitweisen wie Komplexität von Problemklassen, Effizienz von Algorithmen und Approximation, die sie induktiv an Optimierungsaufgaben in Netzwerken und gewichteten Graphen erlernt haben, anwenden.
Module Contents Lecture / Exercises - Basics of Graph Theory und Combinatorial Optimization - Minimal Spanning Trees: algorithms of Kruskal, Prim und Tarjan, Greedy algorithms, matroids, Steiner trees, network design - Linear Programs: structure, modelling, normalization, Simplex algorithm, Theory of Duality - Weighted Matchings and the Routhe Inspection Problem: Weighted Matchings in Bipartite Graphs and non-bipartite Graphs, algorithms of Floyd-Warshall and Fleury - Network Flows: Network Theory Basics, Dinic's algorithms, cost-optimial flows - selected discreet and combinatorial optimization problems: Travelling Salesman, Channel Assignment Problem, scheduling problems, routing problems Exercises / Lab
Teaching and Learning Methods	Lecture / Exercises Exercises / Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	57 Hours ≙ 5 SWS
Self-Study	93 Hours
Recommended Prerequisites	Basic knowledge in graph theory Basic knowledge in algorithmics
Mandatory Prerequisites	Lecture / Exercises requires attendance in the amount of: 1 Vortragstermin Exercises / Lab requires attendance in the amount of: 1 Termin
Recommended Literature	Mathematik zum Studienbeginn, Arnfried Kemnitz, Springer Spektrum Verlag Algorithmische Graphentheorie, Volker Turau und Christian Weyer, De Gruyter Verlag Graphentheoretische Konzepte und Algorithmen, Sven Krumke und Harald Noltemeier, Springer Vieweg Verlag Einführung in die angewandte Wirtschaftsmathematik, Jürgen Tietze, Springer Spektrum Verlag Graph Algorithms - Practical Examples in Apache Spark & Neo4j, Mark Needham and Amy Hodler, O'Reilly Verlag
Included in Elective Catalog	VMT - Vertiefung Mathematik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	KOGA in Master Communication Systems and Networks PO3 KOGA in Master Communication Systems and Networks PO4 KOGA in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

KOLL - Kolloquium zur Masterarbeit 🔗

Module ID	KOLL
Module Name	Kolloquium zur Masterarbeit
Type of Module	Mandatory Module
Recognized Course	MAKOLL - Colloquium
ECTS credits	3
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	3
Frequency of Course	every term
Module Coordinator	Studiengangsleiter(in) Master Technische Informatik / Informatik und Systems-Engineering
Lecturer(s)	verschiedene Dozenten*innen / diverse lecturers
Learning Outcome(s) - Darstellung von Forschungsergebnissen in einer Präsentation in vorgegebenem engen zeitlichen Rahmen - Fachliche und außerfachliche Bezüge der eigenen Arbeit darstellen und begründen - Eigene Lösungswege und gewonnene Erkenntnisse darstellen und diskutieren
Module Contents Colloquium The colloquium serves to determine whether the student is able to present the results of the Master's thesis, its technical and methodological foundations, interdisciplinary contexts and extracurricular references orally, to justify them independently and to assess their significance for practice
Teaching and Learning Methods	Colloquium
Examination Types with Weights	cf. exam regulations
Workload	90 Hours
Contact Hours	0 Hours ≙ 0 SWS
Self-Study	90 Hours
Recommended Prerequisites
Mandatory Prerequisites	Module MAA: Die Masterarbeit muss abgeschlossen sein, damit sie im Kolloquium ganzheitlich und abschließend präsentiert werden kann. See exam regulations §29, paragraph 2
Recommended Literature
Included in Elective Catalog
Use of the Module in Other Study Programs	KOLL in Master Communication Systems and Networks PO3 KOLL in Master Communication Systems and Networks PO4 KOLL in Master Elektrotechnik PO3 KOLL in Master Elektrotechnik und Informationstechnik PO1 KOLL in Master Medientechnologie PO3 KOLL in Master Medientechnologie PO4 KOLL in Master Informatik und Systems-Engineering PO1
Specifics and Notes	See also examination regulations §29.
Last Update	19.7.2025, 14:32:16

KRY - Cryptography 🔗

Module ID	KRY
Module Name	Cryptography
Type of Module	Elective Modules
Recognized Course	KRY - Cryptography
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Heiko Knospe/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Heiko Knospe/Professor Fakultät IME
Learning Outcome(s) Was: Die Studierenden lernen die mathematischen Grundlagen der Kryptographie kennen. Es werden Kenntnisse der wichtigsten kryptographischen Methoden und Algorithmen vermittelt (HF 1). Die Studierenden verstehen verschiedene Arten von SIcherheitsanforderungen und analysieren die Sicherheit von kryptographischen Verfahren. Womit: Der Dozent/die Dozentin vermittelt Wissen und Basisfertigkeiten in der Vorlesung. In der Übung bearbeiten die Studierenden unter Anleitung Aufgaben. Im Praktikum werden konkrete Probleme und Fragestellungen der Kryptographie bearbeitet. Wozu: Kryptographie wird eingesetzt um die grundlegenden Ziele der Informationssicherheit zu erreichen. Die Studierenden lernen die Implemenierung und Anwendung von kryptographischen Algorithmen und entwickeln Konzepte um Systeme, Netzwerke und Anwendungen gegen Angriffe zu sichern (HF 2).
Module Contents Lecture / Exercises * Mathematical Fundamentals * Encryption Schemes and Definitions of Security * Elementary Number Theory * Algebraic Structures * Block Ciphers * Stream Ciphers * Hash Functions * Message Authentication Codes * Public-Key Encryption and the RSA Cryptosystem * Key Establishment * Digital Signatures * Elliptic Curve Cryptography * Outlook: Post-quantum cryptography Lab - Solve mathematical and cryptographical problems in Python / SageMath: working with large integers and residue classes, factorization, primality and prime density, RSA key generation and encryption / decryption, Diffie-Hellman key exchange. - Write code to encrypt and decrypt files using the AES block cipher and different operation modes. Analyze the statistical properies of AES ciphertext. - Write code for RSA key generation, key encapsulation / decapsulation and hybrid encryption / decryption.
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	Mathematics (Bachelor level) and programming skills.
Mandatory Prerequisites	Lab requires attendance in the amount of: 3 Termine Participation in final examination only after successful participation in Lab
Recommended Literature	M. Bellare, P. Rogaway, Introduction to Modern Cryptography, UCSD CSE H. Delfs, H. Knebl, Introduction to Cryptography, Springer S. Goldwasser, M. Bellare, Lecture Notes on Cryptography, MIT J. Hoffstein, J. Pipher, J.H. Silverman, An Introduction to Mathematical Cryptography, Springer J. Katz, Y. Lindell, Introduction to Modern Cryptography, CRC Press H. Knospe, A Course in Cryptography, American Mathematical Society C. Paar, J. Pelz, Understanding Cryptography. Springer N.P. Smart, Cryptography Made Simple, Springer K. H. Rosen, Discrete Mathematics and its Applications, McGraw-Hill V. Shoup, A Computational Introduction to Number Theory and Algebra, Cambridge University Press
Included in Elective Catalog	VMT - Vertiefung Mathematik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	KRY in Master Communication Systems and Networks PO3 KRY in Master Communication Systems and Networks PO4 KRY in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

LCSS - Large and Cloud-based Software-Systems 🔗

Module ID	LCSS
Module Name	Large and Cloud-based Software-Systems
Type of Module	Elective Modules
Recognized Course	LCSS - Large and Cloud-based Software-Systems
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. René Wörzberger/Professor Fakultät IME
Lecturer(s)	Prof. Dr. René Wörzberger/Professor Fakultät IME
Learning Outcome(s) Students are capable of designing architectures for complex and mission critical enterprise software systems, implementing these systems and operate them in the Cloud by knowing and trading conflicting interests and concerns of stakeholders, knowing quality attributes and their trade-offs, specifying architecturally significant requirements in quality attribute scenarios, analysing design decisions with respect to their effects on quality attributes and stake-holder interests and concerns, presenting and documenting architectures by means of suitable views, notations and tools, applying methods (like RESTful API design) and tools in order to implement design deci-sions, using cloud resources like virtual machines, containers and storages in order to operate a system in the cloud, in order to be able to produce long-term usable software systems in subsequent lectures and pro-jects and to be able to act as an IT architect, e.g. in an IT department of a larger enterprise.
Module Contents Lecture / Exercises Formally sound handling of quality requirements for availability, performance, capacity and cost efficiency Advantages and disadvantages of basic system architecture styles, such as microservice architectures Scaling of systems and individual tiers, also with regard to possible deployment strategies such as canary or AB deployment, as well as associated load balancing strategies (e.g. consisten hashing) Advanced applications of virtualization, in particular container virtualization and orchestration, for example with Docker and Kubernetes Selection of suitable communication patterns and protocols, in particular HTTP and derivatives such as websockets, server-sent events and gRPC Selection of appropriate API technologies and design philosophies such as REST and GraphQL Use of basic security protocols such as TLS, OAuth2, JWT and OpenID Connect Asynchronous, event-driven communication via messaging and streaming platforms such as Apache Kafka Selection of suitable database models (relational, key-value-, graph-, document-oriented), necessary consistency level, as well as sharding using the example of PostgreSQL, Neo4J, Apache Cassandra and Redis Strategies for caching data, in particular HTTP responses (web caching). Project Be able to formulate and present a research question in the topic area of the course. Design an application prototype that serves to investigate the research question. Develop the application prototype and run it in the cloud Design and conduct test scenarios and experiments with the application prototype to answer the research question.
Teaching and Learning Methods	Lecture / Exercises Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	- advanced programming skills - basic knowledge of web technologies - basic knowledge of databases - basic knowledge in software architectures - basic knowledge of Unified Modeling Language (UML)
Mandatory Prerequisites	Project requires attendance in the amount of: 4 Termine
Recommended Literature	Lecture Notes Large and Cloud-based Software Systems H. Adkins et al.: Building Secure and Reliable Systems, O'Reilly Media, 2020 I. Gregorik: High Performance Browser Networking, O'Reilly Media, 2013 M. Kleppmann: Designing Data-Intensive Applications, O'Reilly Media, 2017
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	LCSS in Master Communication Systems and Networks PO3 LCSS in Master Communication Systems and Networks PO4 LCSS in Master Elektrotechnik und Informationstechnik PO1 LCSS in Master Medientechnologie PO4 LCSS in Master Informatik und Systems-Engineering PO1
Intro Video
Permanent Links to Organization	Ilu course
Specifics and Notes
Last Update	19.7.2025, 14:32:16

MAA - Masterarbeit 🔗

Module ID	MAA
Module Name	Masterarbeit
Type of Module	Mandatory Module
Recognized Course	MAA - Master thesis
ECTS credits	27
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	3
Frequency of Course	every term
Module Coordinator	Studiengangsleiter(in) Master Technische Informatik / Informatik und Systems-Engineering
Lecturer(s)	verschiedene Dozenten*innen / diverse lecturers
Learning Outcome(s) Das Modul vermittelt folgende Kenntnisse und Fertigkeiten: - Komplexe Aufgabenstellungen beurteilen - Selbständiges Verfassen eines längeren wissenschaftlichen Textes - Gute Praxis des wissenschaftlichen Arbeitens anwenden - Darstellung von Forschungsergebnissen in Form eines wissenschaftlichen Artikels nach den Vorgaben gängiger Fachzeitschriften bzw. Konferenzen - Selbstständiges und systematisches Bearbeiten einer komplexen ingenieurwissenschaftlichen Aufgabenstellung unter Verwendung wissenschaftlicher Methoden - Lösungsstrategien entwickeln und umsetzen - Wissenschaftliche Literatur recherchieren und auswerten - Eigene Arbeit bewerten und einordnen Individuelle Vereinbarung des Studierenden mit einem Dozenten der MT bzw. F07 über eine qualifizierte Ingenieurtätigkeit mit einer studiengangsbezogenen Aufgabenstellung mit wissenschaftlichem Anspruch. Die Masterarbeit kann auch extern in einer Forschungsorganisation, einem Wirtschaftsunternehmen o.ä. durchgeführt werden. Die Betreuung erfolgt durch den Dozenten. Die Masterarbeit addressiert die Entwicklung komplexer Medientechnologien unter interdisziplinären Bedingungen (HF1) und das wissenschaftliche Arbeiten um wissenschaftliche Erkenntnisse zu erweitern (HF2)."
Module Contents Thesis The Master's thesis is a written assignment. It should show that the student is capable of independently working on a topic from his or her subject area within a specified period of time, both in its technical details and in its interdisciplinary contexts, using scientific and practical methods. Interdisciplinary cooperation can also be taken into account in the final thesis.
Teaching and Learning Methods	Thesis
Examination Types with Weights	cf. exam regulations
Workload	810 Hours
Contact Hours	0 Hours ≙ 0 SWS
Self-Study	810 Hours
Recommended Prerequisites	See examination regulations §26
Mandatory Prerequisites	see exam regulations §26 paragraph 1
Recommended Literature
Included in Elective Catalog
Use of the Module in Other Study Programs	MAA in Master Communication Systems and Networks PO3 MAA in Master Communication Systems and Networks PO4 MAA in Master Elektrotechnik PO3 MAA in Master Elektrotechnik und Informationstechnik PO1 MAA in Master Medientechnologie PO3 MAA in Master Medientechnologie PO4 MAA in Master Informatik und Systems-Engineering PO1
Specifics and Notes	See also examination regulations §24ff. Contact a professor of the faculty early on for the initial supervision of the thesis.
Last Update	19.7.2025, 14:32:16

MCI - Mensch-Computer-Interaktion 🔗

Module ID	MCI
Module Name	Mensch-Computer-Interaktion
Type of Module	Elective Modules
Recognized Course	MCI - Human Computer Interaction
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Jonas Schild/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Jonas Schild/Professor Fakultät IME
Learning Outcome(s) WAS: Das Modul vermittelt folgende Kenntnisse und Fertigkeiten: - Grundlagen der Mensch-Computer-Interaktion: Definitionen, Normen, Modelle, Prinzipien - Interaktive Systeme aus Hard- und Software konzipieren, implementieren und analysieren - User Experience verstehen und Prinzipien des UX Engineerings anwenden - Wiss. Fragestellungen vor einem Forschungshintergrund der HCI entwickeln - Geeignete Nutzerstudien nach wiss. und ethischen Kriterien konzipieren, planen und durchführen - statistische und deskriptive Daten wissenschaftlich analysieren, veranschaulichen und diskutieren - in heterogenen Teams zusammenarbeiten, sich koordinieren und präsentieren WOMIT: Die Kompetenzen werden zunächst über eine Vorlesung durch die Dozenten vermittelt und danach im Praktikum anhand konkreter Aufgabenstellung von den Studierenden vertieft. Im seminaristischen Teil der Lehrveranstaltung recherieren die Studierenden zu vorgegebenen Themen anhand von Fachartikeln und weiteren Informationsquellen über neue Konzepte der Mensch-Computer Interaktion und stelle diese dar in einer Präsentation dar. WOZU: Die Studierenden erlernen das eigenständige Durchführen von Forschungsprozesse auf dem Gebiet der Mensch-Computer-Interaktion, um im interdisziplinären Team auf Grundlage von selbst entwickelten komplexen, interaktiven Systemen (HF1) aktuelle Fragestellungen aus dem Bereich der Mensch-Computer-Interaktion wissenschaftlich untersuchen (HF2) und dabei die Effektivität und Wirkung von interaktiven Systemen auf Nutzende testen und einschätzen zu können (HF4).
Module Contents Lecture Models and design principles of interactive systems Principles of context-, task- and user-oriented development of interactive systems Basics of barrier-free access to interactive systems Relevant standards and guidelines: EN ISO 9241, ISO 14915, HHS Control options: Dedicated input/output devices, voice control, gesture control Best Practices and Style Guides: Desktop / Web / Mobile / Hybrid Applications Usability evaluation (analytical/empirical, heuristics, expert interviews, focus groups, user studies) Evaluation methods (thinking aloud, eye-tracking, (semi-)structured interviews) Experimental Research: Research Question, Hypotheses, Errors of 1st and 2nd Kind Experiment Design: Between Group, Within Group, Split-Plot, Reliability of Experimental Results Statistical analysis: scale levels, descriptive statistics, T-tests, ANOVA, regression, correlation Surveys: sampling and sample selection, sources of error, questionnaires, evaluation of surveys Lab Capturing and understanding textual tasks Recording tasks and creating models from them Implementing UI components on the basis of the models created Testing and securing developments Checking and evaluating work results of comolitons Applying MCI research methods and terminology Seminar
Teaching and Learning Methods	Lecture Lab Seminar
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	none
Mandatory Prerequisites	Lab requires attendance in the amount of: 2 Termine Seminar requires attendance in the amount of: Vorträge und Schlusspräsentation
Recommended Literature	A. M. Heinecke: Mensch-Computer-Interaktion, Basiswissen für Entwickler und Gestalter, 2. Auflage, Springer, 2011 B. Shneiderman, C. Plaisant: Designing the User Interface: Strategies for Effective Human-Computer Interaction, Addison Wesley, 2009 S. Swink: Game Feel: A Game Designer's Guide to Virtual Sensation, Morgan Kaufmann Game Design Books, 2008 T. Sylvester: Designing Games: A Guide to Engineering Experiences, O'Reilly, 2013 J. Lazar, J.H. Feng, H. Hochheiser, Research Methods in Human-Computer-Interaction, Wiley, 2012
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	MCI in Master Communication Systems and Networks PO3 MCI in Master Communication Systems and Networks PO4 MCI in Master Medientechnologie PO3 MCI in Master Medientechnologie PO4 MCI in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

MLWR - Maschinelles Lernen und wissenschaftliches Rechnen 🔗

Module ID	MLWR
Module Name	Maschinelles Lernen und wissenschaftliches Rechnen
Type of Module	Elective Modules
Recognized Course	MLWR - Machine Learning and Scientific Computing
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Beate Rhein/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Beate Rhein/Professor Fakultät IME
Learning Outcome(s) Was: fortgeschrittene Methoden des maschinellen Lernens auf typische Datensätze der technischen Informatik anwenden Fallstricke des Maschinellen Lernens in der Vorgehensweise erkennen für eine Aufgabenstellung das geeignete Verfahren bestimmen und anwenden können Qualität von Datensätzen beurteilen und verbessern Datenschutzgesetze kennen weit verbreitete Software des maschinellen Lernens anwenden Womit: Die Methoden werden anhand eines Vortrags oder per Lernvideos vermittelt und in Vorlesung und Übung direkt angewendet. Jeder Student wird ein Projekt durchführen (je nach Anzahl der Studierenden in Gruppenarbeit), bei der er sich Teile des Stoffes selber erarbeitet. Wozu: Maschinelles Lernen wird bei den späteren Arbeitgebern immer mehr eingeführt, etwa in der Robotik, aber auch zur Überwachung und Steuerung von Produktionsprozessen oder Energiesystemen und zur Auswertung von Kundendaten, hier ist ein verantwortlicher Einsatz von Daten wichtig.
Module Contents Lecture / Exercises Approximation methods metamodeling regression Multi-criteria optimization formulation Pareto front algorithms visualization Advanced Cluster Analysis Association Pattern Mining Outlier Detection Advanced classification procedures possibly text recognition, web mining, time series analysis Lab Apply and program methods of approximation, multicriteria optimization or machine learning efficiently implement numerical methods Evaluate the complexity of algorithms
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	Basic knowledge of probability theory and machine learning
Mandatory Prerequisites	Lecture / Exercises requires attendance in the amount of: 6 Stunden Lab requires attendance in the amount of: 2 Termine Participation in final examination only after successful participation in Lab
Recommended Literature	A. Geron: Hand-on Machine Learning, O'Reilly Verlag J. Alammar: Hands-on Large Language Models, O'Reilly Verlag
Included in Elective Catalog	VMT - Vertiefung Mathematik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	MLWR in Master Elektrotechnik PO3 MLWR in Master Elektrotechnik und Informationstechnik PO1 MLWR in Master Medientechnologie PO4 MLWR in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

NGN - Next Generation Networks 🔗

Module ID	NGN
Module Name	Next Generation Networks
Type of Module	Elective Modules
Recognized Course	NGN - Next Generation Networks
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Andreas Grebe/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Andreas Grebe/Professor Fakultät IME
Learning Outcome(s) What? Understanding architectures and service signalling in Next Generation Networks (All-IP Networks). Competences to evaluate, analyze, design, implement and test NGN components and service areas with heterogeneous service requirements. How? Based on Bachelor-level competences on IP networking and services, students learn standards, design principles, architectures and sample implementations of Next Generation Networks and serivces in lectures and exercises. In a small team and organized as semester project, students develop their own NGN service solution, optionally based and on existing systems, and learn how to design, implementnt and anlysze their own service solution. What for? To be able to design, analyze, select, use and apply actual and future network servces, based on All-IP networks for enterprise networks, telecommunication networks and mobile networks.
Module Contents Lecture / Exercises Achive basic understanding and implementation knowledge on Next Generation Network (NGN) defintion by ITU-T, IP Multimedia Subsystem by 3GPP, and ETSI, and Next Generation Internet (NGI) definition by IETF, ITU-T standards, Multimedia Services in NGN, VoIP, Video-over-IP, RTP encaplsulation, Service Signaling, SIP protocol, SIP Digest Authentication, SDP service description and capabilities, SIP servers, Session Border Controller (SBC), SIP Gateway Technologies, SIP routing, NAT Gateways, NAT solution, SRR, STUN , TURN, IMS in mobile networks, IMS in fixed-line networks, VoIP in enterprise networks. IMS in virtualized core network. Students evaluate requirements for NGN services and plan, implement and analyze NGN services based on SIP signalling or alternative signalling protocols. They are competent in functional analysis and troubleshooting by deep packet inspection (DPI) protocol analysis. They evaluate the performance of NGN services in terms of timing, throughput, latency and delays, jitter, robustness in case of packet errors, and security aspects. Lab Naming, structuring and classifying concepts and technologies for NGNs or NGIs. Demonstrate network analysis techniques and tools, know methods for NGN services and network planning. Working on a small project in a tiny team (2-3 team members) on actual technologies in the area of NGN services and NGI services. Set-up an NGN/NGI environment and NGN service, including planning, implementation and evaluation of security aspects and protocol anlaysis plus performance evaluation. The results are reviewed during the course period, summarised in a report and presented to the class. Individual project proposals by students are wellcome.
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	Modul BSN: Bachelor Level Networking Knowledge and Skills like teached in BSN. Fundamentals of Networks and Protocols (typically Bachelor Level) Layered Communications and Protocol Stacks (ISO/OSI, IETF TCP/IP, IEEE), LAN, MAN, WAN, Fixed Line and Mobile Network Fundamentals, Data Link-Technologies (Ethernet, WiFi), IP-Networking (IPv4, IPv6), IP Routing Protocols (static Routes, RIP, OSPF, BGP), Transport Protocols (TCP (incl. Flow Control / Congestion Control), UDP) and Port Numbers, Application Protocols (HTTP, Request-Response Pattern, Publish-Subscribe Pattern). Bachelor-level knowledge of protocols and layer models, Internet protocols (UDP, TCP, IP, HTTP, FTP), IP addressing (IPv4, IPv6), routing techniques (IP routing, functionality of a router, routing protocols, RIP, OSPF), transmission systems and layer 2 protocols, Ethernet. Understanding distributed systems and applications, sockets and client/server programming, request-response patterns, publishg-subscribe patterns.
Mandatory Prerequisites	Lab requires attendance in the amount of: 6 Meilensteintermine und Projektvorstellungen Participation in final examination only after successful participation in Lab
Recommended Literature	J. Kurose, K. Ross: Computer Networking: A Top-Down Approach, Global Edition, Prentice Hall, 7th ed., 2016 A. S. Tanenbaum, D. J. Wetherall: Computer Networks, Pearson , 5th ed., 2013 U.Trick, F. Weber: SIP und Telekommunikationsnetze: Next Generation Networks und Multimedia over IP – konkret, De Gruyter Oldenbourg Verlag, 4. Auflage 2015 J. F. Durkin: Voice-enabling the Data Network,Cisco Press 2010 G. Camarillo, M.A. García-Martín: The 3G IP Multimedia Subsystem (IMS), John Wiley Verlag, 2006 W. Stallings: Foundations of Modern Networking, Pearson Education, 2016 J. Doherty: SDN and NFV Simplified, Pearson Education, 2016 J. Edelman: Network Programmability and Automation, O'Reilly 2018 J. van Meggelen, R. Bryant, L. Madsen: Asterisk: The Definitive Guide: Open Source Telephony for the Enterprise, O'Reilly Media, 5th Ed. 2019
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	NGN in Master Communication Systems and Networks PO3 NGN in Master Communication Systems and Networks PO4 NGN in Master Elektrotechnik und Informationstechnik PO1 NGN in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

PAP - Parallele Programmierung 🔗

Module ID	PAP
Module Name	Parallele Programmierung
Type of Module	Elective Modules
Recognized Course	PAP - Parallel Programming
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr.-Ing. Arnulph Fuhrmann/Professor Fakultät IME
Lecturer(s)	Prof. Dr.-Ing. Arnulph Fuhrmann/Professor Fakultät IME
Learning Outcome(s) Medientechnische und interaktive Systeme beinhalten rechenintensive Berechnungen. Um Anforderungen an die Verarbeitung in Echtzeit erfüllen zu können, sind daher Kompetenzen und Wissen über die Grundlagen für die Analyse (HF1, HF2), den Entwurf (HF1, HF2), die Implementierung (HF1, HF2) und die Bewertung (HF1, HF2) paralleler Computerprogramme erforderlich. Folgende Kenntnisse und Kompetenzen werden im Detail vermittelt: - Grundlegende Konzepte, Modelle und Technologien der parallel Verarbeitung benennen, strukturieren, einordnen und abgrenzen - Aufgabenstellungen in Bezug auf die Programmierung paralleler Programme analysieren und strukturieren, einschlägige parallele Hardwarearchitektur zuordnen und auf Paralleldesign übertragen - Parallele Programme unter Einsatz geeigneter Tools analysieren und Ergebnisse nachvollziehbar darstellen - Leistungsfähigkeit paralleler Programme abschätzen und analysieren - Information aus englischen Originalquellen und Standards ableiten Kenntnisse und Basisfertigkeiten werden in der Vorlesung vermittelt. Begleitend dazu werden in den Übungen Kompetenzen und Fertigkeiten ausgebaut und inhaltliche Themen vertieft.
Module Contents Lecture - Basic concepts, models and technologies of parallel processing (parallelism, concurrency, SISD, SIMD, MISD, MIMD, loose- and closely coupled systems, distributed systems) - Parallel performance measures (speedup, efficiency) - Architecture of GPUs - Parallel Algorithms for GPUs Exercises / Lab - Analyze and structure tasks related to programming parallel programs, assign relevant parallel hardware architecture and transfer to parallel design - Implement parallel programs (multicore hardware with threads and GPUs) - Analyze parallel programs using suitable tools and present results in a comprehensible way - Estimate and analyze performance of parallel programs - Derive information from original English sources and standards
Teaching and Learning Methods	Lecture Exercises / Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	The exercises require programming knowledge and the use of console-oriented programs in Linux-based operating systems.
Mandatory Prerequisites	Exercises / Lab requires attendance in the amount of: 2 Termine
Recommended Literature	Wen-mei W. Hwu, David B. Kirk, Izzat El Hajj: Programming Massively Parallel Processors A Hands-on Approach - 4th Edition, 2022 Andrew S. Tanenbaum, Herbert Bos: Modern Operating Systems, 4th Edition, 2015 Jason Sanders: CUDA by Example: An Introduction to General-Purpose GPU Programming, Addison-Wesley Longman, 2010 R. Oechsle: Parallele und verteilte Anwendungen in Java, Hanser, 2011 P. Pacheco: An Introduction to Parallel Programming, Morgan Kaufmann, 2011
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	PAP in Master Communication Systems and Networks PO3 PAP in Master Communication Systems and Networks PO4 PAP in Master Medientechnologie PO3 PAP in Master Medientechnologie PO4 PAP in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

PHTM - Philosophische Handlungstheorie Master 🔗

Module ID	PHTM
Module Name	Philosophische Handlungstheorie Master
Type of Module	Elective Modules
Recognized Course	PHTM - Philosophical theory of action
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Gregor Büchel/Professor Fakultät IME im Ruhestand
Lecturer(s)	Prof. Dr. Gregor Büchel/Professor Fakultät IME im Ruhestand
Learning Outcome(s) (WAS) Studierende wenden philosophischen Theorien auf Probleme des Handelns in der heutigen Gesellschaft an, (WOMIT) indem Sie zentrale philosophische Texte studieren, seminaristisch aufarbeiten und präsentieren, (WOZU) um ihr späteres gesellschaftliches und berufliches Handeln auf philosophisch und ethisch durchdachten Grundlagen aufbauen zu können.
Module Contents Lecture The background of philosophical theories of action is "illuminated" in the lecture Seminar The seminar will focus on the following five texts by Immanuel Kant: "Answering the Question: What is Enlightenment?" 1 "Ideas for a General History with a Cosmopolitan Intention", 1 "Groundwork for the Metaphysics of Morals", the antinomy of freedom and natural necessity in the "Critique of Pure Reason", 1 "On Perpetual Peace". Aspects of the philosophical theory of action given in these texts are to be applied to problems of action in today's society.
Teaching and Learning Methods	Lecture Seminar
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	34 Hours ≙ 3 SWS
Self-Study	116 Hours
Recommended Prerequisites
Mandatory Prerequisites	Seminar requires attendance in the amount of: 6 Termine Participation in final examination only after successful participation in Seminar
Recommended Literature	Immanuel Kant: „Beantwortung der Frage: Was ist Aufklärung? Und andere kleine Schriften“, Berlin (Sammlung Hoffenberg), 2016, ISBN: 978-3-8430-9208-1 Immanuel Kant: „Schriften zur Geschichtsphilosophie“, Stuttgart (Reclam), 2013, ISBN: 978-3-15-009694-9 Immanuel Kant: „Grundlegung zur Metaphysik der Sitten“, Stuttgart (Reclam), 2016, ISBN: 978-3-15-004507-7 Immanuel Kant: „Kritik der reinen Vernunft“, Stuttgart (Reclam), 1966, ISBN: 978-3-15-006461-0 Immanuel Kant: „Zum ewigen Frieden“, Stuttgart (Reclam), 2012, ISBN: 978-3-15-001501-8
Included in Elective Catalog	X - Fachübergreifende Kompetenzen und Soft Skills
Use of the Module in Other Study Programs	PHTM in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

PLET - Projektleitung 🔗

Module ID	PLET
Module Name	Projektleitung
Type of Module	Elective Modules
Recognized Course	PLET - Project management
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Michael Gartz/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Michael Gartz/Professor Fakultät IME Prof. Dr. Uwe Oberheide/Professor Fakultät IME
Learning Outcome(s) Was: Die Studierenden haben organisatorische Kompetenz erworben und können Projekt planen, durchführen, dokumentieren, Produktanforderungen analysieren, Machbarkeit bewerten und Produktqualität planen. Sie können Projektstrukturpläne und Projektzeitpläne erstellen, Projektmeilensteine planen, Projektrisiken erkennen und mildern. Sie können den Einsatz von Personal und Sachressource planen, Reviews planen, Produktverifikation planen. Die Studierenden haben Projektführungskompetenz erworben und können die Projektsteuerung mit agilen, evolutionären Vorgehensmodellen und dem Timeboxmodell durchführen. Sie können Projektmanagementwerkzeuge einsetzen, den Projektfortschritt überwachen / steuern und Projektergebnisse freigeben. Sie können den Entwicklungsprozess fortlaufend optimieren in unklaren Situationen entscheiden. Sie können den Entwicklungsverlauf dokumentieren, Projektberichte verfassen und verteidigen. Die Studierenden haben Personalführungskompetenz erworben und können Aufgaben auf Teammitglieder nach individuellen Qualifikationen und Neigungen verteilen. Sie können die Teambildung fördern, das Team koordinieren und zielorientiert und respektvoll kommunizieren und verbindliche Absprachen treffen und einfordern. Sie können Teamprozesse moderieren, potenzielle Konfliktsituationen erkennen und auflösen und Handlungsalternativen abwägen. Womit: indem sie die in dem Teamleiter Seminar erlernten Kompetenzen und Fertigkeiten und die in dem Projektleiter-Workshop erlernten Projektleitungs-Tools und Kompetenzen anwenden. Wozu: um später in den verschiedensten Industriebereichen Projekte mittels agilen, evolutionären Vorgehensmodellen, wie z.B. SCRUM, zu planen, durchzuführen, zu managen und zum Erfolg zu bringen.
Module Contents Seminar Classifying and delimiting terms explain characteristic properties of development projects Goal orientation and innovation Risk of failure Special organisational form (teamwork) Limited resources Limited realization time abstractly define technical and economic goals in development projects abstractly define, explain and justify project management tasks identify and explain basic success and failure factors in project management unexpected technical problems insufficient staff qualification unclear or conflicting requirements poor project management Insufficient support from senior management identify extended challenges arising from a division of labour in project processing explain selected process models linear models for business project management phase model V-model agile process models for technical project management SCRUM timebox model classify and compare process models with regard to development duration, organizational aspects, quality and cost aspects professional quality control Cost and schedule control in business management Legal requirements for documentation and traceability of project decisions characterize basic tasks and expected results in development projects Planning and control of product quality Planning and controlling the quality of the development process overarching legal requirements industry-specific specifications company-internal specifications project risk management resource management Documentation of the development process Specification of the requirements for the product to be developed Specification of the product design Product development and manufacturing product documentation Verification and validation of the developed product Product release and product monitoring Characterize instruments for controlling team processes plan essential management tasks, milestones and project documents with regard to the course element "Project carry out essential management tasks mentally and identify project risks with foresight handle essential project management tools in a target-oriented manner for project (time) planning for requirement specifications Planning team building procedures, deriving expected challenges and meaningful measures identify potential conflict situations in the team and discuss alternative actions Project Lead team explain to the team members the basic procedure in the project Capturing and classifying the competencies of team members agree on goals in terms of content and deadlines Project management derive requirements specification in the team from the project order and prioritize requirements Create and maintain project plan Identify project risks and plan meaningful mitigation measures, e.g. early feasibility studies Create and maintain project schedule Rough planning of tasks Plan process Planning effort, appointments, rooms Plan Project Reviews apply agile process model in conjunction with Timebox model to ensure a minimal project success Define a minimum goal that can be achieved by the team. define extended goals for fast teams Drafting a final project report Document and evaluate results Document and evaluate the project process Lead team Monitoring and controlling goal achievement Coordinate collaboration between team members Recognizing and resolving conflict situations within the team Project management Planning and managing project sections plan tasks for the next phase of the project in detail and assign them meaningfully to the team members Plan and moderate content reviews with team members Evaluate project results in the team Modify the project section plan and, if necessary, the project plan according to the project procedure. evaluate the approach of the current project phase retrospectively and, if necessary, modify it for the next project phase. Document project sections plan access to shared laboratory resources computer tools special workstations and measuring stations special test environments Prepare project decisions in the team
Teaching and Learning Methods	Seminar Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	23 Hours ≙ 2 SWS
Self-Study	127 Hours
Recommended Prerequisites	basic knowledge of project management basic experience as a member of project teams
Mandatory Prerequisites	Project requires attendance in the amount of: 8 Termine Participation in final examination only after successful participation in Project
Recommended Literature	Hans-D. Litke, „Projektmanagement, Methoden, Techniken, Verhaltensweisen, Evolutionäres Projektmanagement“, Hanser Ken Schwaber: Agiles Projektmanagement mit Scrum (Microsoft Press) Litke, Kunow, Schulz-Wimmer, „Projekt-Management“, Taschenguide , Haufe Stefan Kreiser, Skripte der Vorlesung Software Engineering f.d. Automatisierungstechnik: „Projektmanagement, Vorgehensmodelle“, ILIAS Stanley E.Portny, „Projektmanagement für Dummies“, Wiley Marcus Heidbrink, „Das Projektteam“, Haufe Video Tutorial für SCRUM: http://www.video2brain.com/de/videotraining/agile-softwareentwicklung-mit-scrum
Included in Elective Catalog	X - Fachübergreifende Kompetenzen und Soft Skills
Use of the Module in Other Study Programs	PLET in Master Elektrotechnik PO3 PLET in Master Elektrotechnik und Informationstechnik PO1 PLET in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	22.8.2025, 10:17:37

PLSYP - Projektleitung Systementwicklungs-Projekt 🔗

Module ID	PLSYP
Module Name	Projektleitung Systementwicklungs-Projekt
Type of Module	Elective Modules
Recognized Course	PLSYP - Project Management for System Design Lab
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. René Wörzberger/Professor Fakultät IME
Lecturer(s)	Prof. Dr. René Wörzberger/Professor Fakultät IME
Learning Outcome(s) Studierende leiten ein Team von Systementwicklungs-Projekt-Teilnehmenden (SYP) an indem sie ein interessantes und geeignetes Entwicklungs-Thema ausarbeiten, die Fortschritte des Teams über den gesamten Entwicklungsprozess überwachen, regelmäßige Status-Meetings mit den Teammitgliedern abhalten der Gesamtprojektleitung regelmäßig in weiteren Status-Meetings berichten um (später) in einem (beruflichen) Umfeld Verantwortung für die (technische) Leitung eines Entwicklungsprojekts übernehmen zu können.
Module Contents Project PLSYP participants (Master students) outline a project assignment as part of a project catalog suitable to be worked on by Bachelor students in the course "System Design Practicum". The assignment is to be formulated as a Power Point one-pager according to a provided template. PLSYP participants demonstrate that they can present the requirements and objectives of a complex project in an appealing way in a very limited space. PLSYP participants offer at least one remote information session between mid-July and mid-September so that interested SYP participants can learn about the exact task at hand. PLSYP participants thus demonstrate that they can explain and sell their project ideas in detail. PLSYP participants arrange regular meetings with assigned SYP participants to discuss requirements and progress. PLSYP participants are able to respond comprehensively to detailed questions about requirements, but without prejudging the SYP participants' own technical decisions. PLSYP participants discuss advantages and disadvantages of different technical designs and implementations with SYP participants. They are able to reach consensus with SYP participants on the desired implementation variant. PLSYP participants keep an eye on the schedule created by SYP participants, proactively address SYP participants in the event of major deviations, and attempt to identify and eliminate causes (unrealistic planning, unclear requirements, problems with the technical basis, etc.). In this way, they demonstrate that they can reliably perform even repetitive activities in project management. PLSYP participants attend SYP milestone meetings, especially those in which SYP participants demonstrate their progress. They follow up on the presentations and ask specific questions. PLSYP participants thoroughly review SYP participants' deliverables (documents and source code). They provide written feedback to the SYP participants, discuss it and check its implementation. In this way, they demonstrate how they can influence the quality of the product without working directly on it. PLSYP participants provide the SYP module supervisor with an assessment of their SYP team's performance and can justify and defend it. (The actual assessment of the SYP team is ultimately done by the SYP module supervisor for reasons of examination law).
Teaching and Learning Methods	Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	12 Hours ≙ 1 SWS
Self-Study	138 Hours
Recommended Prerequisites	Previous participation in the systems design practicum or equivalent.
Mandatory Prerequisites	Project requires attendance in the amount of: 3 Fachgespräche
Recommended Literature
Included in Elective Catalog	X - Fachübergreifende Kompetenzen und Soft Skills
Use of the Module in Other Study Programs	PLSYP in Master Informatik und Systems-Engineering PO1
Permanent Links to Organization	Ilu
Specifics and Notes	The course is offered throughout the winter semester. Participation in PLSYP depends in particular on whether sufficient SYP participants are interested and can therefore not be guaranteed.
Last Update	3.9.2025, 17:07:49

QC - Quantum Computing 🔗

Module ID	QC
Module Name	Quantum Computing
Type of Module	Elective Modules
Recognized Course	QC - Quantum Computing
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every term
Module Coordinator	Prof. Dr. Heiko Knospe/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Pascal Cerfontaine/Professor Fakultät IME Prof. Dr. Heiko Knospe/Professor Fakultät IME
Learning Outcome(s) Was: Die Studierenden lernen die Grundlagen des Quantum Computing kennen. Es werden die mathematischen Grundlagen des Quantum Computing und Kenntnisse über Quanten-Schaltkreise und wichtige Quanten-Algorithmen vermittelt (HF 1). Womit: Der Dozent/die Dozentin vermittelt Wissen und Basisfertigkeiten in der Vorlesung. In der Übung bearbeiten die Studierenden unter Anleitung Aufgaben und entwickeln Quantenalgorithmen (HF 1). Wozu: Quantencomputer können mit Hilfe von verschränkten Qubits eine große Zahl von Eingangswerten gleichzeitig verarbeiten und bestimmte schwere Probleme lösen (HF 2).
Module Contents Lecture / Exercises - Fundamental concepts - Single Quantum Bits - Bloch Sphere - Quantum Key Distribution - Hilbert spaces - Bra-Ket notation - Inner product, Outer product, Tensor product - Hermitian and unitary operators - Multiple Qubit Systems - Entangled states - Measurement - Quantum Gates and Circuits - Realizing unitary transformations as Quantum Circuits - Deutsch and Deutsch-Josza algorithms - Discrete Fourier Transform and Quantum Fourier Transform - Quantum Algorithms: Shor, Grover, HHL (optional) - Quantum complexity classes - Quantum Error Correction (optional) Practical quantum computing and coding using the Qiskit SDK.
Teaching and Learning Methods	Lecture / Exercises
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	34 Hours ≙ 3 SWS
Self-Study	116 Hours
Recommended Prerequisites
Mandatory Prerequisites
Recommended Literature	M.A. Nielsen, I.L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press. E. Rieffel, W. Polak, Quantum Computing, MIT Press. B. Zygelman, A First Introduction to Quantum Computing and Information, Springer. H.Y. Wong, Introduction to Quantum Computing, Springer. Matthias Homeister, Quantum Computer verstehen, Springer.
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	QC in Master Communication Systems and Networks PO4 QC in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

QEKS - Qualitätsgesteuerter Entwurf komplexer Softwaresysteme 🔗

Module ID	QEKS
Module Name	Qualitätsgesteuerter Entwurf komplexer Softwaresysteme
Type of Module	Elective Modules
Recognized Course	SEKM - Software Engineering by Components and Pattern
ECTS credits	5
Language	deutsch und englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Stefan Kreiser/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Stefan Kreiser/Professor Fakultät IME
Learning Outcome(s) Studierende sind im Hinblick auf die Qualität eines Softwaresystems in der Lage: - zur vorhersagbaren, effizienten Entwicklung eines Softwaresystems bzw. einer Softwarearchitektur zielgerichtet angemessene Wiederverwendungsstrategien und professionelle Modellierungs- und Entwicklungswerkzeuge sowie den Rahmenbedingungen insgesamt angemessene Projektstrukturen einzusetzen. - die Softwarearchitektur für komplexe, verteilte Automatisierungssysteme unter Berücksichtigung der spezifischen Anforderungen hinsichtlich der besonderen Zielsetzung des jeweiligen Automatisierungssystems zu analysieren, zu konzipieren, zu entwerfen, zu implementieren, zu prüfen und zu bewerten. - die besonderen Anforderungen an die Servicequalität, an die Einsatzumgebung und die organisatorischen Rahmenbedingungen für die Entwicklung, die sich aus dem Entwicklungsprozess und einem angemessenen Lebenszyklusmanagement ergeben, zu erkennen und im Hinblick auf ihre Relevanz für die Softwarearchitektur des Automatisierungssystems zu analysieren und zu bewerten.
Module Contents Lecture / Exercises Terminology value vs. cost of a technical software distributed software system, concurrency software quality, quality of service, refactoring complexity (algorithmic, structural), emergence re-use, symmetry and symmetry operations, abstraction, invariants quality controlled re-use, methodical approaches variants of white box re-use black box re-use grey box re-use (hierarchical approach to re-use) re-use in automation control software systems determinism benefits and challenges tailoring process models and personnel structures in projects meet requirements in development projects predictably (product quality, cost, deadlines) distributed development, maintenance and support of software systems software pattern pattern description using UML essential architectural pattern construction pattern structural pattern behavioural pattern class based (static) vs. object based (dynamic) pattern essential pattern for concurrent and networked real time systems encapsulation and role based extension of layered architectures concurrency structures to optimize throughput and system response latency distributed event processing process synchronisation construction and use of pattern catalogues, pattern languages pattern based design of complex software systems components and frameworks design principles interface architectur active and passive system elements design, programming and test quality configuration and use using middleware systems to develop architectures of technical software systems ORB architectures, e.g. CORBA and TAO integrated system plattforms, e.g. MS .NET multi agent systems (MAS) agent architectural models collaboration between agents agent languages considering cases for MAS application use pattern to design complex software systems extract and discuss purpose, limitation of use, invariant and configurable parts of pattern from english and german literature sources understand implementation skeletons of pattern and map them to problem settings with limited technical focus discuss benefits of using object oriented programming languages derive recurrent settings in the development of complex software systems implement pattern on exemplary settings and test resulting implementations reasonably combine pattern to solve recurring problem settings with a broader technical focus use UML2 notations use professional UML2 IDE for round-trip-engineering integrate software system based on exemplary implementations of the pattern to combine conduct integration test, assess software quality and optimize software system construct black-box-components based on pattern analyse component based software architectures derive suitable scope from architectural specs understand and discuss development process to construct software systems find active and passive system elements and derive system run time behaviour understand abstract system interfaces to interconnect, configure and activate components understand abstract system interfaces to exchange applicational run time data understand system extension points (functional and structural system configuration layer) analyse distribution architectures understand basic system services (describe and reason service usage, relate to system tasks) relate pattern to structure making architectural software artefacts derive suitable range of appications for a given distribution architecture understand engineering process to construct user applications (application layer) discuss attributes and limitation of usage of interconnection protocols find designated system extension points compare MAS to conventional distribution architectures agent vs. component architectural models activation of agents deployment of agents protocols for interconnection and collaboration range of appications and and limitation of usage Seminar challenging seminar topics can be defined e.g. from the following or related subject areas - reusable artifacts for building the architecture of distributed software systems, - professional distribution architectures, - Multiagent systems, - special economic, liability and ethical requirements for software systems with (distributed) artificial intelligence and their effects on the design of software architectures present personal work results and work results of the team in a compact and target-group-oriented way, both orally and in writing Project Develop software artifact of a distribution architecture for complex software systems Carry out project planning in distributed teams with an agile process model Perform extensive system analysis with respect to the role of the artifact in the distribution architecture Determine design input requirements for the development of the artifact Specify and model the software artifact based on the design input requirements Select and justify design principles and patterns to achieve defined quality objectives Derive interfaces, behavioral and structural models iterativly based on patterns in UML2 notations Use professional UML2 design tool purposefully Verify and evaluate models, correct model errors and optimize models Programming software artifacts in C++ define meaningful test scenarios and verify software artifacts Evaluate the quality of the software artifact Present the team's project results to a professional audience in a compact and target-group-oriented way
Teaching and Learning Methods	Lecture / Exercises Seminar Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	57 Hours ≙ 5 SWS
Self-Study	93 Hours
Recommended Prerequisites	Modul PLET: oder aus einem (naturwissenschaftlich-technischen) Bachelorstudium: - grundlegende Kenntnisse in (agilem) Projektmanagement - programming skills in an object-oriented programming language, preferably C++ - knowledge of software modeling using Unified Modeling Language (UML) or other (formal) languages that support modeling of interfaces, behavior and structures - basic knowledge in (agile) project management, SCRUM oder XP - basic knowledge of essential softare architectural models - basic knowledge of interconnection models in software systems (OSI, TCPIP, Messaging)
Mandatory Prerequisites	Project requires attendance in the amount of: 3 Termine Participation in final examination only after successful participation in Project
Recommended Literature	D. Schmidt et.al.: Pattern-Oriented Software Architecture. Patterns for Concurrent and Networked Objects (Wiley) Gamma et.al.: Design Patterns, (Addison-Wesley) Martin Fowler: Refactoring, Engl. ed. (Addison-Wesley Professional) U. Hammerschall: Verteilte Systeme und Anwendungen (Pearson Studium) Andreas Andresen: Komponentenbasierte Softwareentwicklung m. MDA, UML2, XML (Hanser Verlag) T. Ritter et. al.: CORBA Komponenten. Effektives Software-Design u. Progr. (Springer) Bernd Oestereich: Analyse und Design mit UML 2.5 (Oldenbourg) OMG Unified Modeling Language Spec., www.omg.org/um I. Sommerville: Software Engineering (Addison-Wesley / Pearson Studium) K. Beck: eXtreme Programming (Addison-Wesley Professional) Ken Schwaber: Agiles Projektmanagement mit Scrum (Microsoft Press)
Included in Elective Catalog	VTI - Vertiefung Technische Informatik W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	QEKS in Master Communication Systems and Networks PO3 QEKS in Master Communication Systems and Networks PO4 QEKS in Master Elektrotechnik PO3 QEKS in Master Elektrotechnik und Informationstechnik PO1 QEKS in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

QM - Quantenmechanik 🔗

Module ID	QM
Module Name	Quantenmechanik
Type of Module	Elective Modules
Recognized Course	QM - Quantum mechanics
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Uwe Oberheide/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Uwe Oberheide/Professor Fakultät IME
Learning Outcome(s) Die Studierenden besitzen ein Verständnis der Grundlagen quantenmechanischer Prozesse, indem sie anhand klassisch nicht erklärbarer Experimente die Entwicklung der Quantentheorie nachvollziehen und einfache, analytisch auswertbare Anwendungsfälle mathematisch beschreiben und auf reale Anwendungen der Elektrotechnik und Optik überführen, um in zukünftigen technischen Entwicklungen und Technologiefeldern Herausforderungen und Grenzen der Systeme einschätzen sowie wesentliche Strukturen im interdisziplinären Diskurs verstehen zu können.
Module Contents Lecture The failure of classical physics (black spot, photoelectric effect, Compton effect, Stern-Gerlach experiment, Bohr's atom model, matter waves) Quantum behaviour (experiments with spheres, waves and electrons; basic principles of quantum mechanics; principle of indeterminacy; laws of combination of amplitudes; identical particles) Schrödinger equation (development of the wave equation; stationary, time-dependent) simple potential problems (infinitely deep potential pot, finitely deep potential pot, potential stage, potential barrier, harmonic oscillator, hydrogen atom) Basic principles of quantum computers and quantum cryptography Description of given physical problems mathematically by listing the Schrödinger equation and applying of methods to solve the differential equations (separation approaches, limit value considerations) To evaluate physical solutions and select them by analogy Analyzing quantum effects and transferring them to technical applications Seminar Discourse on quantum mechanical processes (uncertainty principle, wave-particle dualism, wave functions/packages) and their applications in real systems in the context of the course
Teaching and Learning Methods	Lecture Seminar
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	34 Hours ≙ 3 SWS
Self-Study	116 Hours
Recommended Prerequisites	In-depth knowledge of mathematics (integral calculus, differential calculus, vector geometry) Basic knowledge of physics (oscillations and waves, double slit, interference, thermodynamics, potential / kinetic energy) Basic knowledge of electrical engineering (magnetic and electric fields, components)
Mandatory Prerequisites
Recommended Literature	Harris – Moderne Physik, Pearson Verlag Feynman - Vorlesungen über Physik Band III:Quantenmechanik, Oldenbourg Verlag
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	QM in Master Elektrotechnik PO3 QM in Master Elektrotechnik und Informationstechnik PO1 QM in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

RFSD - RF System Design 🔗

Module ID	RFSD
Module Name	RF System Design
Type of Module	Elective Modules
Recognized Course	RFSD - RF System Design
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Rainer Kronberger/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Rainer Kronberger/Professor Fakultät IME
Learning Outcome(s) In general: Students will learn how high frequency components of wireless communication systems work Module-specific: students will get a general introduction in rf systems they will learn in detail how transmitters and receivers in wireless communication systems work they will learn in detail how the components of such systems (LNA, mixer, amplifier, oscillator, etc.) work they will learn about limitation effects and noise in such systems they will learn how to adapt the components to each other and how to plan and design the complete system (transmitter and / or receiver)
Module Contents Lecture / Exercises RF System, Applications Noise in RF systems noise classification and characterization noise calculation noise figure noise matching Linear and nonlinear circuit behaviour theory nonlinearities with mixers nonlinearities with amplifiers RF system components receiver componenets transmitter components frequency generation Lab
Teaching and Learning Methods	Lecture / Exercises Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	No formal requirements, but students should have knowledge in High Frequency and Microwave Topics
Mandatory Prerequisites	Participation in final examination only after successful participation in Lecture / Exercises Lab requires attendance in the amount of: 3 Labortermine und 1 Präsentationstermin Participation in final examination only after successful participation in Lab
Recommended Literature	Kraus & Carver Eletromagnetics, McGraw Hilll, 2006. Michale Steer, Microwave and RF Design
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	RFSD in Master Communication Systems and Networks PO3 RFSD in Master Communication Systems and Networks PO4 RFSD in Master Elektrotechnik und Informationstechnik PO1 RFSD in Master Medientechnologie PO4 RFSD in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

SIM - Simulation in der Ingenieurswissenschaft 🔗

Module ID	SIM
Module Name	Simulation in der Ingenieurswissenschaft
Type of Module	Elective Modules
Recognized Course	FEM - Finite element method in electrical engineering
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Wolfgang Evers/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Wolfgang Evers/Professor Fakultät IME
Learning Outcome(s) Die Studierenden können technische Systeme mit Hilfe von rechnergestützten, numerischen Simulationen berechnen, indem sie Modelle der realen Systeme bilden, diese als Modelle in einem Simualtionsprogramm erstellen und unter den gewünschten Randbedingungen die Berechnungen durchführen und auswerten um später bei Entwicklungsaufgaben das Verhalten von zu entwickelnden Produkten im Voraus bestimmen und optimieren können.
Module Contents Lecture / Exercises Discretisation of physical problems using the example of an electrostatic arrangement - One-dimensional model - Two-dimensional model - Replacement of partial derivatives by finite differences - Boundary conditions - Setting up the linear system of equations - Different methods for solving the system of equations - Result representation with interpolation - Use of boundary-fitted grids - Solving a two-dimensional electrostatic problem with FEM software - Exploiting symmetries in the simulation - Solving a two-dimensional magnetic problem with FEM software - Extending the magnetic problem to include non-linear material properties - Extension of the simulation by program-controlled variation of parameters and automatic output of characteristic diagrams with Python Carry out and critically evaluate FEM simulations on various physical effects Project
Teaching and Learning Methods	Lecture / Exercises Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	- Electrostatic: field strength, flux density, dielectrics - Electromagnetism: field strength, flux density, flux, magnetic circuits, induced voltage
Mandatory Prerequisites
Recommended Literature	Thomas Westermann, Modellbildung und Simulation Thomas Westermann: Mathematik für Ingenieure
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	SIM in Master Communication Systems and Networks PO3 SIM in Master Communication Systems and Networks PO4 SIM in Master Elektrotechnik PO3 SIM in Master Elektrotechnik und Informationstechnik PO1 SIM in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

THI - Theoretische Informatik 🔗

Module ID	THI
Module Name	Theoretische Informatik
Type of Module	Mandatory Module
Recognized Course	THI - Theoretical Computer Science
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Hubert Randerath/Professor Fakultät IME
Lecturer(s)	Prof. Dr. Hubert Randerath/Professor Fakultät IME
Learning Outcome(s) (WAS) Die Studierenden erlernen formale Grundlagen der Informatik (WOMIT) indem Sie - den Umgang mit Typ2, Typ1 und Typ0-Sprachen erlernen und formale Maschinen konstruieren, die Sprachen des jeweileigen Typs erkennen, - mit formalen Modellen der Informatik arbeiten, - Kenntnisse der Berechenbarkeits, Entscheidbarkeits- und Komplexitätstheorie auf praktische Probleme anwenden, - einen präzisen Algorithmenbegriff verwenden, um die Tragweite von Algorithmen zu beschreiben und die Komplexität von Algorithmen zu bestimmen, - die prinzipielle Lösbarkeit algorithmischer Probleme untersuchen, (WOZU) um in Forschungsergebnisse in späteren Lehrveranstaltungen und Abschlussarbeiten auf ein solides theoretisches Fundament stellen zu können.
Module Contents Lecture / Exercises An algorithm's complexity can be determined by analyzing its input and the algorithmic core, e.g., by means of the O-notation. The analysis might consist of a polynomial reduction of a known hard problem like the satisfiability problem in propositional logic of the unknown problem.
Teaching and Learning Methods	Lecture / Exercises
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	34 Hours ≙ 3 SWS
Self-Study	116 Hours
Recommended Prerequisites	Basics in automata theory and formal languages
Mandatory Prerequisites
Recommended Literature	Theoretische Grundlagen der Informatik, Rolf Socher, Hanser Verlag Theoretische Informatik, Juraj Hromkovic, Teubner-Verlag Grundkurs Theoretische Informatik, Gottfried Vossen und Kurt-Ulrich Witt,Vieweg-Verlag Theoretische Informatik - kurzgefasst, Uwe Schöning, Spektrum Akademischer Verlag
Included in Elective Catalog
Use of the Module in Other Study Programs	THI in Master Communication Systems and Networks PO3 THI in Master Communication Systems and Networks PO4 THI in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

VAE - Virtual Acoustic Environments 🔗

Module ID	VAE
Module Name	Virtual Acoustic Environments
Type of Module	Elective Modules
Recognized Course	VAE - Virtual Acoustic Environments (VAE)
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr.-Ing. Christoph Pörschmann/Professor Fakultät IME
Lecturer(s)	Prof. Dr.-Ing. Christoph Pörschmann/Professor Fakultät IME
Learning Outcome(s) What: The students learn the basic concepts , the technology and perception-related aspects of cirtual acoustic environemtns. The course will be strongly related to research aspects and projects How: The students apply their knowledge on Signal Processing, Audio, and in the field of VR on different aspects of Virtual Acoustic Environements. Actual trends in reseach and state of the art applications will integrated, tested, analyzed and evaluated. Aim: The students shall be able to work on research topics which consider topics whic are scientifically new and relevant. Apects of scalability and commercialization play a role
Module Contents Lecture The basic concepts of headphone-based or loudspeaker-based VR-systems are introduced Project In one specific topic of Virtual Acoustic Environments the students shall get deep know-how on the technology and apply it to a practical problem and present the results Lab
Teaching and Learning Methods	Lecture Project Lab
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	Know-how on Acoustics and audio signal processing
Mandatory Prerequisites
Recommended Literature	Rozinska, A. "Immersive Sound" Blauert, J. "Spatial Hearing" Zotter, F., Frank, M. "Ambisonics: A Practical 3D Audio Theory for Recording, Studio Production, Sound Reinforcement, and Virtual Reality"
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	VAE in Master Communication Systems and Networks PO3 VAE in Master Communication Systems and Networks PO4 VAE in Master Elektrotechnik und Informationstechnik PO1 VAE in Master Medientechnologie PO3 VAE in Master Medientechnologie PO4 VAE in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

VER - Virtuelle und erweiterte Realität 🔗

Module ID	VER
Module Name	Virtuelle und erweiterte Realität
Type of Module	Elective Modules
Recognized Course	VER - Virtual and Augmented Reality
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr.-Ing. Arnulph Fuhrmann/Professor Fakultät IME
Lecturer(s)	Prof. Dr.-Ing. Arnulph Fuhrmann/Professor Fakultät IME Prof. Dr. Stefan Grünvogel/Professor Fakultät IME
Learning Outcome(s) WAS: Das Modul vermittelt folgende Kenntnisse und Fertigkeiten: - Virtual- und Augmented-Reality-Anwendungen konzipieren, aufbauen und bewerten - Interaktions und Navigationsverfahren erstellen - Basistechnologien der virtuellen und erweiterten Reality weiterentwickeln - Werkzeuge und Methoden zur Entwirklickung von VR/AR-Anwendungen verwenden - Algorithmische und mathematische Grundlagen von VR/AR anwenden WOMIT: Die Kompetenzen werden zunächst über eine Vorlesung durch die Dozenten vermittelt und danach im Praktikum anhand konkreter Aufgabenstellung von den Studierenden vertieft. Im seminaristischen Teil der Lehrveranstaltung recherieren die Studierenden zu vorgegebenen Themen anhand von Fachartikeln und weiteren Informationsquellen über neue Konzepte der virtuellen und erweiterten Realität und stelle diese dar in einer Präsentation dar. WOZU: Die sichere Anwendung der Grundlagen der virtuellen und erweiterten Realität ist Voraussetzung für die Entwicklug komplexer interaktiver medientechnischer Systeme (HF1). Weiterhin erlaubt das Grundlagenwissen die Bewertung bestehender Systeme und das wissenschaftliche Arbeiten in diesem Gebiet (HF2).
Module Contents Lecture Explain terms from the field of virtual and augmented reality Explain and compare data structures and algorithms for VR/AR applications 3D data formats Spatial data structures Describing Multimodal User Interfaces Selection of 3D objects Manipulation of 3D objects Navigation in virtual scenes system control Describe input and output devices and specific virtual and augmented reality hardware display technologies Stereo Displays Autostereoscopic Displays projection solutions Wearable Displays Head Mounted Displays Handheld Displays See-through Displays Workbench Cave Tiled Displays 3D-Audio Force Feedback Devices Haptic feedback input devices controller data gloves locomotion devices Explain algorithmic and mathematical basics stereoscopy tracking capture of position and orientation: Degrees of freedom tracking technologies Mechanical Optical Electromagnetic ultrasound interial eye tracking head tracking object tracking Markerless Tracking Marker-Based Tracking rendering management of large 3D scenes haptic rendering stereo rendering real-time rendering collision detection intersections between primitives Discrete and continuous collision detection acceleration data structures collision response Lab - Design, build and evaluate virtual environments and augmented reality applications - Creating Interaction and Navigation Procedures - Further develop fundamental technologies of virtual and augmented reality - Use tools and methods to implement VR/AR applications - Apply algorithmic and mathematical principles of VR/AR - understand and grasp textual tasks - Testing and debugging your own application Seminar Apply Algorithmic and Mathematical fundamentals Check interaction and navigation procedures Independently obtaining and summarizing scientific literature Present and discuss new concepts of virtual and augmented reality
Teaching and Learning Methods	Lecture Lab Seminar
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	45 Hours ≙ 4 SWS
Self-Study	105 Hours
Recommended Prerequisites	Computer Graphics Computer Animation
Mandatory Prerequisites	Lab requires attendance in the amount of: 2 Termine
Recommended Literature	R. Dörner et al., Virtual und Augmented Reality (VR/AR): Grundlagen und Methoden der Virtuellen und Augmentierten Realität, Springer Vieweg, 2019 Schmalstieg und Höllerer, Augmented Reality – Principles and Practice, Addison Wesley, 2016 T. Akenine-Möller, et al., Real-Time Rendering Fourth Edition, Taylor & Francis Ltd., 2018 J. Jerald, The VR Book: Human-Centered Design for Virtual Reality, Acm Books, 2015
Included in Elective Catalog	W - Allgemeiner Wahlbereich
Use of the Module in Other Study Programs	VER in Master Communication Systems and Networks PO3 VER in Master Communication Systems and Networks PO4 VER in Master Medientechnologie PO3 VER in Master Medientechnologie PO4 VER in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

XGA - Gremienarbeit 🔗

Module ID	XGA
Module Name	Gremienarbeit
Type of Module	Elective Modules
Recognized Course	XGA - Participation in appointment committees
ECTS credits	0
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every term
Module Coordinator	Studiengangsleiter(in) Master Technische Informatik / Informatik und Systems-Engineering
Lecturer(s)
Learning Outcome(s) Anerkennbar ist die Mitarbeit in Berufungskommissionen als studentisches Mitglied. Die Anzahl der anerkannten ECTS-Punkte richtet sich nach der Anzahl der nachgewiesenen Stunden in der Gremientätigkeit. Es wird 1ECTS-Punkt pro 25 Stunden Gremienarbeit angerechnet. Der/die Vorsitzende der Berufungskommission vergibt die ECTS und bescheinigt diese. Es wird erwartet, dass der/die Studierende sich aktiv in die Arbeit einbringt.
Module Contents Project
Teaching and Learning Methods	Project
Examination Types with Weights	cf. exam regulations
Workload	0 Hours
Contact Hours	12 Hours ≙ 1 SWS
Self-Study	-12 Hours
Recommended Prerequisites
Mandatory Prerequisites	Project requires attendance in the amount of: 5 Termine
Recommended Literature
Included in Elective Catalog	X - Fachübergreifende Kompetenzen und Soft Skills
Use of the Module in Other Study Programs	XIB in Bachelor Elektrotechnik PO3 XGA in Bachelor Elektrotechnik und Informationstechnik PO1 XIB in Bachelor Technische Informatik PO3 XIB2 in Bachelor Technische Informatik PO3 XGA in Bachelor Informatik und Systems-Engineering PO1 XGA in Master Informatik und Systems-Engineering PO1
Specifics and Notes	Participation in appointment committees as a student member is recognized. The number of ECTS points recognized depends on the number of hours of committee work demonstrated. One ECTS point is recognized for every 25 hours of committee work. The chairperson of the appointment committee awards the ECTS and certifies them. The student is expected to be actively involved in the work.
Last Update	6.9.2025, 14:51:29

XIM - Fachübergreifende Kompetenzen und Soft Skills 🔗

Module ID	XIM
Module Name	Fachübergreifende Kompetenzen und Soft Skills
Type of Module	Elective Modules
Recognized Course	XPSS - Practically based Summer School
ECTS credits	5
Language	englisch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every summer term
Module Coordinator	Prof. Dr. Rainer Bartz/Professor Fakultät IME
Lecturer(s)
Learning Outcome(s) Die Studierenden lernen, über die fachbezogenen Grenzen ihres Studiums hinweg zu schauen. Sie sind in der Lage, internationale, inter/transdisziplinäre und/oder interkulturelle Aspekte ihres zukünftigen Berufs zu erkennen, einzuordnen, ihr Verhalten darauf einzustellen und auch in fremdem Kontext sicher zu agieren. Das konkrete Lehrangebot wird in der Regel erst kurzfristig zu Beginn des jeweiligen Semesters festgelegt. Es kann unterschiedlichste Themen behandeln; ene Zusammenarbeit mit anderen Fakultäten oder Instutionen ist vorgesehen. Je nach konkret gewähltem Lehrangebot werden die u.a. Kompetenzen unterschiedlich intensiv vermittelt. Das Modul kann auch durch Teilnahme an mehreren verschiedenen kleineren Lehrveranstaltungen erfüllt werden, sofern diese zu den den Modulzielen beitragen und die erforderlichen ECTS-Punkte in Summe erreicht sind.
Module Contents Project Working in small teams, self organisation, project planing, project realisation, presentation
Teaching and Learning Methods	Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	12 Hours ≙ 1 SWS
Self-Study	138 Hours
Recommended Prerequisites	Good knowledge in programming microcontroller. Good understanding of electronic elements and electronic components. Experience or skill in developing electronic circuits.
Mandatory Prerequisites
Recommended Literature
Included in Elective Catalog
Use of the Module in Other Study Programs	XPSS in Bachelor Elektrotechnik PO3 XPSS in Bachelor Elektrotechnik und Informationstechnik PO1 XPSS in Bachelor Technische Informatik PO3 XPSS in Bachelor Informatik und Systems-Engineering PO1
Specifics and Notes	The Summer School will be offered together with collaborating universities. A fixed date can not be guaranteed. Informations to the start of the Summer School will be announced early.
Last Update	19.7.2025, 14:32:16

Additional module variant with same learning outcomes

Module ID	XIM
Module Name	Fachübergreifende Kompetenzen und Soft Skills
Type of Module	Elective Modules
Recognized Course	GER - Industrial property protection
ECTS credits	5
Language	deutsch
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every term
Module Coordinator	Prof. Dr. Rainer Bartz/Professor Fakultät IME
Lecturer(s)	Ladrière
Learning Outcome(s) Die Studierenden lernen, über die fachbezogenen Grenzen ihres Studiums hinweg zu schauen. Sie sind in der Lage, internationale, inter/transdisziplinäre und/oder interkulturelle Aspekte ihres zukünftigen Berufs zu erkennen, einzuordnen, ihr Verhalten darauf einzustellen und auch in fremdem Kontext sicher zu agieren. Das konkrete Lehrangebot wird in der Regel erst kurzfristig zu Beginn des jeweiligen Semesters festgelegt. Es kann unterschiedlichste Themen behandeln; ene Zusammenarbeit mit anderen Fakultäten oder Instutionen ist vorgesehen. Je nach konkret gewähltem Lehrangebot werden die u.a. Kompetenzen unterschiedlich intensiv vermittelt. Das Modul kann auch durch Teilnahme an mehreren verschiedenen kleineren Lehrveranstaltungen erfüllt werden, sofern diese zu den den Modulzielen beitragen und die erforderlichen ECTS-Punkte in Summe erreicht sind.
Module Contents Lecture Types of industrial property rights, significance for companies and inventors, significance of employee invention law and inventor personality law, prerequisites for protection, term of industrial property rights, structure of an application, life cycle from application to patent, subsequent applications, examination and opposition procedures, national, European and international applications, utility models, trademarks, design, law on the protection of secrets, professional field of patent engineer. Carry out a patent search; determine the relevant type of protective right for a given case; be able to correctly file an application with regard to its formal structure; weigh up the advantages and disadvantages of national, European and international applications in a specific application; check the validity of a patent; develop a basic IP strategy. Seminar Types of industrial property rights, significance for companies and inventors, significance of employee invention law and inventor personality law, prerequisites for protection, term of industrial property rights, structure of an application, life cycle from application to patent, subsequent applications, examination and opposition procedures, national, European and international applications, utility models, trademarks, design, law on the protection of secrets, professional field of patent engineer. Carry out a patent search; determine the relevant type of protective right for a given case; be able to correctly file an application with regard to its formal structure; weigh up the advantages and disadvantages of national, European and international applications in a specific application; check the validity of a patent; develop a basic IP strategy.
Teaching and Learning Methods	Lecture Seminar
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	34 Hours ≙ 3 SWS
Self-Study	116 Hours
Recommended Prerequisites
Mandatory Prerequisites
Recommended Literature
Included in Elective Catalog
Use of the Module in Other Study Programs	ERMK in Master Elektrotechnik PO3 ERMK in Master Elektrotechnik und Informationstechnik PO1 ERMK in Master Medientechnologie PO4 ERMK in Master Informatik und Systems-Engineering PO1
Specifics and Notes
Last Update	19.7.2025, 14:32:16

Additional module variant with same learning outcomes

Module ID	XIM
Module Name	Fachübergreifende Kompetenzen und Soft Skills
Type of Module	Elective Modules
Recognized Course	PLSYP - Project Management for System Design Lab
ECTS credits	5
Language	deutsch, englisch bei Bedarf
Duration of Module	1 Semester
Recommended Semester	1-2
Frequency of Course	every winter term
Module Coordinator	Prof. Dr. Rainer Bartz/Professor Fakultät IME
Lecturer(s)	Prof. Dr. René Wörzberger/Professor Fakultät IME
Learning Outcome(s) Die Studierenden lernen, über die fachbezogenen Grenzen ihres Studiums hinweg zu schauen. Sie sind in der Lage, internationale, inter/transdisziplinäre und/oder interkulturelle Aspekte ihres zukünftigen Berufs zu erkennen, einzuordnen, ihr Verhalten darauf einzustellen und auch in fremdem Kontext sicher zu agieren. Das konkrete Lehrangebot wird in der Regel erst kurzfristig zu Beginn des jeweiligen Semesters festgelegt. Es kann unterschiedlichste Themen behandeln; ene Zusammenarbeit mit anderen Fakultäten oder Instutionen ist vorgesehen. Je nach konkret gewähltem Lehrangebot werden die u.a. Kompetenzen unterschiedlich intensiv vermittelt. Das Modul kann auch durch Teilnahme an mehreren verschiedenen kleineren Lehrveranstaltungen erfüllt werden, sofern diese zu den den Modulzielen beitragen und die erforderlichen ECTS-Punkte in Summe erreicht sind.
Module Contents Project PLSYP participants (Master students) outline a project assignment as part of a project catalog suitable to be worked on by Bachelor students in the course "System Design Practicum". The assignment is to be formulated as a Power Point one-pager according to a provided template. PLSYP participants demonstrate that they can present the requirements and objectives of a complex project in an appealing way in a very limited space. PLSYP participants offer at least one remote information session between mid-July and mid-September so that interested SYP participants can learn about the exact task at hand. PLSYP participants thus demonstrate that they can explain and sell their project ideas in detail. PLSYP participants arrange regular meetings with assigned SYP participants to discuss requirements and progress. PLSYP participants are able to respond comprehensively to detailed questions about requirements, but without prejudging the SYP participants' own technical decisions. PLSYP participants discuss advantages and disadvantages of different technical designs and implementations with SYP participants. They are able to reach consensus with SYP participants on the desired implementation variant. PLSYP participants keep an eye on the schedule created by SYP participants, proactively address SYP participants in the event of major deviations, and attempt to identify and eliminate causes (unrealistic planning, unclear requirements, problems with the technical basis, etc.). In this way, they demonstrate that they can reliably perform even repetitive activities in project management. PLSYP participants attend SYP milestone meetings, especially those in which SYP participants demonstrate their progress. They follow up on the presentations and ask specific questions. PLSYP participants thoroughly review SYP participants' deliverables (documents and source code). They provide written feedback to the SYP participants, discuss it and check its implementation. In this way, they demonstrate how they can influence the quality of the product without working directly on it. PLSYP participants provide the SYP module supervisor with an assessment of their SYP team's performance and can justify and defend it. (The actual assessment of the SYP team is ultimately done by the SYP module supervisor for reasons of examination law).
Teaching and Learning Methods	Project
Examination Types with Weights	cf. exam regulations
Workload	150 Hours
Contact Hours	12 Hours ≙ 1 SWS
Self-Study	138 Hours
Recommended Prerequisites	Previous participation in the systems design practicum or equivalent.
Mandatory Prerequisites	Project requires attendance in the amount of: 3 Fachgespräche
Recommended Literature
Included in Elective Catalog
Use of the Module in Other Study Programs	PLSYP in Master Informatik und Systems-Engineering PO1
Permanent Links to Organization	Ilu
Specifics and Notes	The course is offered throughout the winter semester. Participation in PLSYP depends in particular on whether sufficient SYP participants are interested and can therefore not be guaranteed.
Last Update	19.7.2025, 14:32:16

Electives Catalogs🔗

The following shows which modules can be selected in a particular elective area. The following notes and regulations apply to all elective areas:

When choosing modules from elective catalogs, the conditions formulated in Specializations also apply.
The semester in which elective modules of an elective catalog can typically be taken can be found in the study plans.
As a rule, modules are only offered in either the summer or winter semester. This means that any required accompanying examination can only be taken in this semester. The summative examinations for modules in Faculty 07 are usually offered in the examination period after each semester.
A completed module is recognized for a maximum of one elective area, even if it is listed in several elective areas.
There is an admission restriction for some modules. More information on this can be found in the announcements on admission restrictions.
Registration and admission to non-faculty modules are subject to deadlines and other conditions set by the faculty or university offering the module. Their admission cannot be guaranteed. Students must contact the relevant external lecturer in good time to find out whether they are allowed to take part in an external module and what they need to do to register and participate.
Upon application, a suitable modules can be added to the elective area. Such an application must be submitted informally to the head of degree program at least four months before the planned participation in that module. The examination board decides on the acceptance of the application in consultation with the head of degree program and suitable teaching staff. A study achievement to be recognized
- must fit in with the intended graduate profile of the degree program and contribute to its achievement,
- must be oriented towards learning outcomes and must not serve solely to impart knowledge,
- must correspond to the qualification level of a Master's degree program,
- must represent a meaningful increase in competence against the background of the intended course of study,
- must have been completed by an examination and
- must not be identical in terms of content and learning outcomes to coursework that has already been completed.
Modules are not listed below,
- which in the past were only recognized for an elective catalog as part of individual recognition procedures or
- which in the past were only recognized for an elective catalog as part of a stay abroad and the associated individual learning agreement.

Stays abroad

Students who have integrated a stay abroad into their studies and have completed coursework at a foreign university can have this recognized upon application and with the approval of the examination board.
A Learning Agreement must be concluded with the Faculty's Recognition Officer before the start of the stay abroad. In particular, it is agreed for which mandatory modules or elective catalogs the coursework completed abroad will be recognized.

AUS - Auslandssemester 🔗

Hier werden an einer ausländischen Hochschule erbrachte Leistungen nach vorheriger Absprache anerkannt, wenn ihr Umfang dem eines Semesters entspricht Das konkrete Lehrangebot richtet sich nach der ausländischen Hochschule.

VMT - Vertiefung Mathematik 🔗

Für dieses Wahlmodul stehen folgende Module zur Verfügung.

You must select modules of 5 ECTS credit points in total out of this catalog.

Modules of the faculty:

Module ID	Module Name	ECTS
HIM	Advanced Mathematics	5
KOGA	Kombinatorische Optimierung und Graphenalgorithmen	5
KRY	Cryptography	5
MLWR	Maschinelles Lernen und wissenschaftliches Rechnen	5

Modules of other faculties or universities:

Affiliation	Module Name	ECTS
TH Köln (Fakultät 10)	Multivariante Statistik	6

VTI - Vertiefung Technische Informatik 🔗

You must select modules of 15 ECTS credit points in total out of this catalog.

Modules of the faculty:

Module ID	Module Name	ECTS
AMC	Advanced Multimedia Communications	5
AMS	Special Aspects of Mobile Autonomous Systems	5
ARP	Alternative Rechnerarchitekturen und Programmiersprachen	5
CI	Computational Intelligence	5
ESD	Embedded Systems Design	5
IIS	Intelligent Information Systems	5
LCSS	Large and Cloud-based Software-Systems	5
NGN	Next Generation Networks	5
QC	Quantum Computing	5
QEKS	Qualitätsgesteuerter Entwurf komplexer Softwaresysteme	5

Modules of other faculties or universities:

Affiliation	Module Name	ECTS
TH Köln (Fakultät 10)	Advanced Machine Learning	6
Universität Köln	Quantum Information Theory	6

W - Allgemeiner Wahlbereich 🔗

You must select modules of 20 ECTS credit points in total out of this catalog.

This elective catalog particularly includes all modules from the following areas:

Electives Catalog VTI - Vertiefung Technische Informatik
Electives Catalog VMT - Vertiefung Mathematik

Modules from these other areas are printed normally in the following, original modules from this elective area are printed in bold.

Modules of the faculty:

Module ID	Module Name	ECTS
ACC	Advanced Channel Coding	5
AMC	Advanced Multimedia Communications	5
AMS	Special Aspects of Mobile Autonomous Systems	5
ARP	Alternative Rechnerarchitekturen und Programmiersprachen	5
AVT	Audio- und Videotechnologien	5
AVV	Algorithmen der Videosignalverarbeitung	5
CI	Computational Intelligence	5
CSO	Computersimulation in der Optik	5
DLO	Deep Learning und Objekterkennung	5
DMC	Digital Motion Control	5
DSP	Digital Signal Processing	5
ESD	Embedded Systems Design	5
HIM	Advanced Mathematics	5
IBD	InnoBioDiv	5
IIS	Intelligent Information Systems	5
ITF	IT-Forensik	5
KOGA	Kombinatorische Optimierung und Graphenalgorithmen	5
KRY	Cryptography	5
LCSS	Large and Cloud-based Software-Systems	5
MCI	Mensch-Computer-Interaktion	5
MLWR	Maschinelles Lernen und wissenschaftliches Rechnen	5
NGN	Next Generation Networks	5
PAP	Parallele Programmierung	5
QC	Quantum Computing	5
QEKS	Qualitätsgesteuerter Entwurf komplexer Softwaresysteme	5
QM	Quantenmechanik	5
RFSD	RF System Design	5
SIM	Simulation in der Ingenieurswissenschaft	5
VAE	Virtual Acoustic Environments	5
VER	Virtuelle und erweiterte Realität	5

Modules of other faculties or universities:

Affiliation	Module Name	ECTS
TH Köln (Fakultät 10)	Advanced Machine Learning	6
TH Köln (Fakultät 10)	Data Driven Modeling	6
TH Köln (Fakultät 10)	Data Science and Ethics	6
TH Köln (Fakultät 10)	Domain-Driven Design of Large Software Systems	6
TH Köln (Fakultät 09)	Modellierung von Energiesystemen	5
TH Köln (Fakultät 10)	Multivariante Statistik	6
Universität Köln	Quantum Information Theory	6
Hochschule Bonn-Rhein-Sieg	Virtuelle Private Netze	6

X - Fachübergreifende Kompetenzen und Soft Skills 🔗

In diesem Wahlbereich können Module zu außerfachlichen, nicht-technischen Themen belegt werden. Im Folgenden werden nur Module dargestellt, die regelmäßig angeboten werden. Es sind aber auch einmalig oder unregelmäßig angebotene Module in diesem Wahlbereich wählbar, beispielsweise Module, die von der Kompetenzwerkstatt angeboten werden. Die Anerkennung eines solchen, unten nicht aufgeführten Moduls für diesen Wahlbereich muss per E-Mail an die Studiengangleitung vor der Teilnahme geklärt werden. Ist die Prüfung eines in diesem Wahlbereich gewählten Moduls benotet, so wird die Note nicht im Abschlusszeugnis dargestellt und fließt auch nicht in die Gesamtnote ein.

You must select modules of 5 ECTS credit points in total out of this catalog.

Modules of the faculty:

Module ID	Module Name	ECTS
ERMK	Entrepreneurship, Gewerblicher Rechtsschutz, Market Knowledge	5
ETH	Ethik	5
PHTM	Philosophische Handlungstheorie Master	5
PLET	Projektleitung	5
PLSYP	Projektleitung Systementwicklungs-Projekt	5
XGA	Gremienarbeit	0

Modules of other faculties or universities:

Affiliation	Module Name	ECTS
TH Köln (Kompetenzwerkstatt)	Als Führungskraft begeistern - Die Basics im Leadership	2
TH Köln (Fakultät 10)	Data Science and Ethics	6
TH Köln (Kompetenzwerkstatt)	Design Thinking	3
TH Köln (Kompetenzwerkstatt)	Digitale Arbeitswelt	5
TH Köln (Kompetenzwerkstatt)	Entrepreneurship - Grundlagenveranstaltung	6
TH Köln (Kompetenzwerkstatt)	Entwicklung von Geschäftsszenarios bei Existenzgründung	6
TH Köln (Kompetenzwerkstatt)	Intellectual Activism - feministische Verbindungen zwischen Forschung und Praxis	3
TH Köln (Kompetenzwerkstatt)	Interkulturelle Teamarbeit - Vielfalt als Erfolgsfaktor	3
TH Köln (Kompetenzwerkstatt)	Kommunikative Kompetenz in Führungssituationen	2
TH Köln (Kompetenzwerkstatt)	Konflikte verstehen und effektiv lösen	2
TH Köln (Kompetenzwerkstatt)	Konfliktlösungs- und Verhandlungstechniken	6
TH Köln (Fakultät 10)	Leadership Principles and Strategic Management	6
TH Köln (Kompetenzwerkstatt)	Persönliche Karriereentscheidungen mit dem Entscheidungsnavi	1
TH Köln (Kompetenzwerkstatt)	Praxisprojekt: Komplexe Herausforderungen der digitalen Arbeitswelt	6
TH Köln (Kompetenzwerkstatt)	Produktentwicklungsmethoden	6
TH Köln (Fakultät 10)	Projektmanagement (deutsch)	6
TH Köln (Fakultät 10)	Projektmanagement (englisch)	6
TH Köln (Kompetenzwerkstatt)	Rhetorik in der Gesprächsführung	3
TH Köln (Kompetenzwerkstatt)	Rhetorik in der Verhandlungstechnik	3
TH Köln (Fakultät 04)	Risk Management 1	5
TH Köln (Kompetenzwerkstatt)	Selbstlernmodul Moderation	2
TH Köln (Kompetenzwerkstatt)	Stärken stärken und Schwächen nutzen	1
TH Köln (Fakultät 10)	StartUp Bootcamp	5
TH Köln (Fakultät 04)	Steuern	6
TH Köln (Kompetenzwerkstatt)	Unternehmensführung im öffentlichen Sektor	6

Specializations🔗

There are no specializations in the study program.

Im Masterstudiengang Technische Informatik gibt es keine Studienrichtungen oder -schwerpunkte

Examination Types🔗

The forms of examination referenced in the module descriptions are explained in more detail below. The explanations are taken from the examination regulations, §19ff. In case of deviations, the text of the examination regulations applies.

(Digital) Written exam

Written, paper-based or digitally supported examination. Details are regulated in §19 of the examination regulations.

Oral examination

Examination to be taken orally. Details are regulated in §21 of the examination regulations.

Oral contribution

See §22, para. 5 of the examination regulations: An oral contribution (e.g. paper, presentation, negotiation, moderation) serves to determine whether students are capable of independently working on a practice-oriented task within a specified period of time using scientific and practical methods and presenting it in a technically appropriate manner by means of verbal communication. This also includes answering questions from the auditorium regarding the oral presentation. The duration of the oral presentation is determined by the examiner at the beginning of the semester. The facts relevant to the grading of the oral presentation are to be recorded in a protocol; students should also submit the written documents relating to the oral presentation for documentation purposes. Students must be notified of the grade no later than one week after the oral presentation.

Technical discussion

See §22, Para. 8 of the examination regulations: A technical discussion serves to determine professional competence, understanding of complex technical contexts and the ability to solve problems analytically. Students and examiners have roughly equal speaking time in the technical discussion in order to enable a discursive technical exchange. One or more discussions are held with an examiner during the semester or in summary form. Students should present and explain practice-related technical tasks, problems or project plans from the degree program and explain the relevant technical background, theoretical concepts and methodological approaches for processing the tasks. Possible solutions, procedures and considerations for solving the problem must be discussed and justified. The facts relevant to the grading of the technical discussion must be recorded in a protocol.

Project work

See §22, Para. 6 of the examination regulations: The project work is an examination that consists of independently working on a specific problem under supervision using scientific methodology and documenting the results. In addition to the quality of the answer to the question, the organizational and communicative quality of the implementation, such as slides, presentations, milestones, project plans, meeting minutes, etc., are also relevant for assessment.

Lab report

See §22, para. 10 of the examination regulations: An internship report (e.g. experimental protocol) serves to determine whether students are capable of independently carrying out a practical laboratory task within a specified period of time, as well as documenting, evaluating and reflecting on the process and results in writing. Preparatory homework may be required before the actual experiment is carried out. Technical discussions may take place during or after the experiment. Internship reports can also be admitted to the examination in the form of group work. Students must be notified of the assessment of the practical placement report no later than six weeks after submission of the report.

Exercise lab

See §22, para. 11 of the examination regulations: The examination form “practical training” tests the technical skills in the application of the theories and concepts learned in the lecture as well as practical skills, for example the use of development tools and technologies. For this purpose, several tasks are set during the semester, which are to be solved either alone or in group work, on site or as homework by a given deadline. The solutions to the tasks must be submitted by the students in (digital) written form. The exact criteria for passing the examination will be announced at the beginning of the corresponding course.

Exercise lab under examination conditions

See §22, para. 11, sentence 5 of the examination regulations: A “practical training course under examination conditions” is a practical training course in which the tasks are to be completed within the time frame and under the independent conditions of an examination.

Term paper

See §22, para. 3 of the examination regulations: A term paper (e.g. case study, research) serves to determine whether students are capable of independently completing a specialist task in written or electronic form using scientific and practical methods within a specified period of time. The topic and scope (e.g. number of pages of the text section) of the term paper are determined by the examiner at the beginning of the semester. A declaration of independence must be signed and submitted by the candidate. In addition, technical discussions may be held.

Learning portfolio

A learning portfolio documents the student competence development process by means of presentations, essays, excerpts from internship reports, tables of contents of term papers, notes, to-do lists, research reports and other performance presentations and learning productions, summarized as so-called “artefacts”. The learning portfolio only becomes an examination item in conjunction with the student's reflection (in writing, orally or in a video) on the use of these artifacts to achieve the learning objective previously made transparent by the examiner. During the creation of the learning portfolio, feedback on development steps and/or artifacts is given over the course of the semester. A revised form of the learning portfolio - in handwritten or electronic form - is submitted as the examination result following the feedback.

Single / Multiple choice

See §20 of the examination regulations.

Access colloquium

See §22, para. 12 of the examination regulations: An entrance colloquium serves to determine whether the students fulfill the specific requirements to be able to work independently and safely on a defined practical laboratory task using scientific and practical methods.

(Intermediate) Certificate

See §22, para. 7 of the examination regulations: A test/intermediate test certifies that the student has completed a piece of coursework (e.g. draft) to the required standard. The scope of work to be completed and the required content and requirements can be found in the respective module description in the module handbook and in the assignment.

Open book preparation

The open book assignment (OBA) is a short term paper and therefore an unsupervised written or electronic examination. It is characterized by the fact that, according to the examiner's declaration of aids, all aids are generally permitted. Special attention is drawn to the safeguarding of good scientific practice through proper citation etc. and the requirement of independence in the performance of each examination.

Thesis

Bachelor's or Master's thesis as defined in the examination regulations §25ff: The Master's thesis is a written assignment. It should show that the student is capable of independently working on a topic from their subject area within a specified period of time, both in its technical details and in its interdisciplinary contexts, using scientific and practical methods. Interdisciplinary cooperation can also be taken into account in the final thesis.

Colloquium

Colloquium for the Bachelor's or Master's thesis as defined in the examination regulations §29: The colloquium serves to determine whether the student is able to present the results of the Master's thesis, its technical and methodological foundations, interdisciplinary contexts and extracurricular references orally, to justify them independently and to assess their significance for practice.

Profile Module Matrix🔗

The following section describes the extent to which the modules of the degree program support and develop the competencies and fields of action of the study program as well as certain study program criteria as defined by the University of Applied Science TH Köln.

Abbr.	Module Name	HF1 - Komplexe Rechner-, Kommun...	HF2 - Wissenschaftlich arbeiten...	HF3 - Fachliche Führungs- und P...	K.1 - Komplexe Systeme und Proz...	K.2 - Gesellschaftliche Vertret...	K.3 - Komplexe Aufgaben selbstä...	K.4 - Fachwissen erweitern und ...	K.5 - Aufkommende Technologien ...	K.6 - Probleme wissenschaftlich...	K.7 - Wissenschaftliche Ergebni...	K.8 - Situations- und sachgerec...	K.9 - Sich selbst organisieren	K.10 - Anerkannte Methoden für w...	K.11 - Sprachliche und interkult...	K.12 - Projekte organisieren und...	SK.1 - Global Citizenship	SK.2 - Internationalisierung	SK.3 - Interdisziplinarität	SK.4 - Transfer
ACC	Advanced Channel Coding	⬤	⬤		⬤	⬤	⬤	⬤	⬤				⬤	⬤		⬤	⬤	⬤
AMC	Advanced Multimedia Communications	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤		⬤	⬤		⬤
AMS	Special Aspects of Mobile Autonomous Systems	⬤	⬤		⬤		⬤		⬤		⬤		⬤						⬤	⬤
ARP	Alternative Rechnerarchitekturen und Programmiersprachen	⬤	⬤		⬤	⬤	⬤	⬤	⬤	⬤	⬤		⬤	⬤		⬤
AVT	Audio- und Videotechnologien	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤		⬤	⬤		⬤
AVV	Algorithmen der Videosignalverarbeitung	⬤	⬤	⬤	⬤	⬤	⬤	⬤
CI	Computational Intelligence	⬤	⬤	⬤	⬤	⬤	⬤		⬤	⬤	⬤		⬤			⬤
CSO	Computersimulation in der Optik	⬤	⬤	⬤	⬤	⬤	⬤		⬤	⬤	⬤		⬤	⬤
DLO	Deep Learning und Objekterkennung	⬤	⬤		⬤	⬤	⬤	⬤		⬤			⬤		⬤
DMC	Digital Motion Control	⬤	⬤		⬤	⬤			⬤	⬤	⬤	⬤	⬤		⬤	⬤
DSP	Digital Signal Processing	⬤	⬤		⬤	⬤	⬤		⬤				⬤			⬤		⬤
ERMK	Entrepreneurship, Gewerblicher Rechtsschutz, Market Knowledge
ESD	Embedded Systems Design	⬤			⬤	⬤		⬤	⬤	⬤	⬤		⬤					⬤	⬤	⬤
ETH	Ethik				⬤	⬤	⬤		⬤	⬤		⬤			⬤	⬤	⬤		⬤
FP	Forschungsprojekt	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤		⬤	⬤	⬤
HIM	Advanced Mathematics					⬤	⬤						⬤					⬤
IBD	InnoBioDiv				⬤	⬤	⬤		⬤	⬤	⬤						⬤	⬤	⬤	⬤
IIS	Intelligent Information Systems	⬤	⬤	⬤	⬤	⬤	⬤		⬤	⬤	⬤		⬤			⬤
ITF	IT-Forensik	⬤			⬤	⬤	⬤	⬤	⬤				⬤	⬤		⬤
KOGA	Kombinatorische Optimierung und Graphenalgorithmen	⬤	⬤		⬤	⬤	⬤		⬤	⬤	⬤					⬤
KOLL	Kolloquium zur Masterarbeit	⬤	⬤	⬤					⬤	⬤
KRY	Cryptography	⬤	⬤		⬤	⬤			⬤				⬤					⬤
LCSS	Large and Cloud-based Software-Systems	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤		⬤			⬤		⬤		⬤
MAA	Masterarbeit	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤			⬤	⬤
MCI	Mensch-Computer-Interaktion	⬤	⬤		⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤		⬤
MLWR	Maschinelles Lernen und wissenschaftliches Rechnen						⬤		⬤			⬤	⬤		⬤	⬤			⬤	⬤
NGN	Next Generation Networks	⬤	⬤		⬤	⬤	⬤	⬤	⬤	⬤			⬤					⬤
PAP	Parallele Programmierung	⬤	⬤		⬤	⬤	⬤		⬤	⬤	⬤		⬤
PHTM	Philosophische Handlungstheorie Master																⬤		⬤
PLET	Projektleitung	⬤	⬤	⬤			⬤	⬤		⬤		⬤		⬤	⬤	⬤	⬤		⬤
PLSYP	Projektleitung Systementwicklungs-Projekt			⬤		⬤		⬤						⬤		⬤	⬤
QC	Quantum Computing	⬤	⬤															⬤
QEKS	Qualitätsgesteuerter Entwurf komplexer Softwaresysteme	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤
QM	Quantenmechanik		⬤		⬤	⬤			⬤	⬤	⬤		⬤
RFSD	RF System Design		⬤		⬤	⬤	⬤			⬤			⬤					⬤	⬤
SIM	Simulation in der Ingenieurswissenschaft	⬤	⬤	⬤	⬤		⬤	⬤	⬤				⬤	⬤		⬤
THI	Theoretische Informatik	⬤	⬤		⬤	⬤			⬤	⬤	⬤					⬤
VAE	Virtual Acoustic Environments	⬤	⬤		⬤	⬤	⬤	⬤						⬤		⬤			⬤	⬤
VER	Virtuelle und erweiterte Realität	⬤	⬤		⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤	⬤		⬤	⬤
XGA	Gremienarbeit
XIM	Fachübergreifende Kompetenzen und Soft Skills			⬤		⬤	⬤	⬤				⬤	⬤		⬤

Version History🔗

The table below lists the different versions of the course offer. The versions are sorted in reverse chronological order with the currently valid version in the first row. The individual versions can be accessed via the link in the right-hand column on the right.

Version	Date	Changes	Link
3.12	2025-09-08-09-32-00	Modul PLSYP hatte teils Angaben von SYP. Korrigiert. Diverse hängende Referenzen von Wahlbereichs-, Schwerpunkts- bzw. Vertiefungspaket-Tabellen in den Modul-Abschnitt korrigiert. Fehlende Module sind jetzt vorhanden. Eine Modulbeschreibung beinhaltet nun auch Angaben, in welchen Wahlbereichen und Studienschwerpunkten bzw. Vertiefungspakten das jeweilige Modul enthalten ist. CSO mit Prüfungsform für begleitende Prüfung Prüfungsordnungsversionen statt Jahreszahlen Modulkürzel ohne Studiengang Anwesenheitspflicht in XGA - Gremienarbeit	Link
3.11	2024-12-06-08-45-55	Begutachtete Version für Reakkreditierung 2024 Neues Layout für sämtliche Modulhandbücher	Link
3.10	2024-07-06-12-00-00	Neues Modul "IT-Forensik" für Masterstudiengänge Technische Informatik, Medientechnologie und Elektrotechnik	Link
3.9	2024-06-11-14-00-00	Mitarbeit in Gremien im Bachelor/Master Technische Informatik und Bachelor Elektrotechnik	Link
3.8	2024-02-23-15-00-00	Generelle Überarbeitung des Layouts Eingangstexte bei Wahlmodulkatalogen und Schwerpunkten überarbeitet und POs angeglichen Modellierung von Energiesystemen der Fakultät 09 als wählbares Modul im allgemeinen Wahlkatalog im Master Technische Informatik	Link
3.7	2023-09-01-14-30-00	Neue(s) Modul und Lehrveranstaltung "InnoBioDiv" im Master Communication Systems and Engineering, Technische Informatik	Link
3.6	2023-07-20-15-00-00	Neue Lehrveranstaltung "Projektleitung Systementwurfs-Praktikum" im Master Technische Informatik (anerkennbar für XIM)	Link
3.5	2023-03-14-18-00-00	LCSS aktualisiert	Link
3.4	2023-03-09-18-00-00	Wahlmodule in Bachelor Elektrotechnik angeglichen; Links zu Ilias in Wörzberger-Lehrveranstaltungen	Link
3.3	2023-03-06-14-00-00	Neue Lehrveranstaltung "Software Engineering für die Automatisierungstechnik", Modulbeschreibungen für Kolloquium und Masterarbeit im Master Communications Systems and Networks, externes Modul "Steuern" für X1 in Master Technische Informatik	Link
3.2	2023-02-24-20-00-00	Allgemeine Bereinigung von kaputten Links (http 404)	Link
3.1	2023-02-16-15-13-00	Weitere Wahlmodule in Master Technische Informatik eingefügt (aus MaTIN2020_Aushang_FAQ.pdf)	Link
3.0	2023-02-12-17-30-00	Modul "Entrepreneurship, Gewerblicher Rechtsschutz, Market Knowledge" und Lehrveranstaltung "Gewerblicher Rechtsschutz" eingerichtet	Link

Impressum

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Haftungshinweis

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