Faculty 07 of Information, Media and Electrical Engineering

Master Media Technology PO3

Module Manual

Master of Science | Version: 3.7.2026-04-21-15-02-23.87d540b9

The most recent version of this handbook can be found here:
https://f07-studieninfo.web.th-koeln.de/mhb/current/en/MaMT2020.html

Program Descriptionđź”—

The Master's degree course in Media Technology deepens your theoretical and practical knowledge of the development of complex media technologies under interdisciplinary conditions and should enable you to work scientifically and to apply and expand scientific knowledge. In addition to specialist skills, a key qualification is the ability to work successfully and efficiently in a team on complex technical tasks under interdisciplinary conditions.

Occupational fields and sectors

Media technology graduates can work in the field of research and development in many sectors. These include the broadcasting and telecommunications industry, audio and video technology, entertainment industry, internet companies, automotive industry, medical technology, industrial automation, surveillance technology, manufacturers of (special) cameras, multimedia technology, CAD and 3D application development, as well as research institutes. For example, you will work as a development and planning engineer or in research with the prospect of taking on management and project responsibility.

Course of study

The course extends over 3 semesters, whereby it is possible to start in both the winter and summer semesters. In the first two semesters, students take 6 different elective modules and 3 compulsory modules; the 3rd semester is reserved for the Master's thesis and the colloquium.

The degree program offers a broad spectrum of knowledge in the field of media technology, which is based on 3 specializations (see list on next page). This allows you to set individual priorities in your studies without restricting the necessary breadth. The modules from these 3 specializations are available for the elective modules, of which at least 3 must be selected. Further modules from the range offered by the technical faculties of TH Köln can also be selected. If desired, one of the 6 elective modules can come from a non-technical faculty. This allows you to round off your profile by incorporating interdisciplinary skills. Students can specialize and choose up to 2 major fields of study.

Students who have passed at least 3 elective modules in a specialization have successfully completed the specialization. However, students can also choose not to take a specialization and, for example, take 2 modules from different specializations.

A special feature of the course is the Master's project. Here you develop a sophisticated technical system in a group, from the project idea and conception to the realization, testing and acceptance of the system. Here you can combine your knowledge and skills from the various fields of media technology in a team and acquire skills in the area of project implementation and responsibility. The compulsory module “Applied Mathematics” must be taken by all students, as it teaches skills in the field of mathematics that go beyond those taught in the Bachelor's degree and which are necessary to achieve the study objectives in the field of scientific education.

The two compulsory modules “Master's main seminar” and “Master's project” are offered every semester. It has been shown in the past that this flexible arrangement is particularly advantageous for students who have obtained their Bachelor's degree at other universities in order to give them a good start to their studies. You will find the study plan on the next page.

Expectations of applicants

The Master's degree course in Media Technology is consecutive to the Bachelor's degree course of the same name. However, under certain conditions it is also open to graduates from other fields of study, e.g. computer science, physics or electrical engineering.

Even more so than on the Bachelor's degree course, you should have a high level of motivation and commitment to independently explore challenging topics in media technologies. This also requires you to work independently and enjoy getting to the bottom of complex issues.

Graduate Profileđź”—

Graduates of the M. Sc. Media Technology degree program are able to develop, research and manage media technology systems with scientific depth and an interdisciplinary approach. They take responsibility for innovations in research and industry, actively shape complex developments in the fields of audio, video, VR/AR, AI and embedded systems and qualify for management positions or an academic career. In contrast to the Bachelor's degree, the focus is on research skills, in-depth system development and management responsibility.

The Master's degree course in Media Technology builds consistently on the foundations acquired in the Bachelor's degree course, but systematically expands these with research-oriented, analytical and management skills. While the Bachelor's course provides a broad technical qualification with a strong application orientation, the Master's course focuses on scientific in-depth study, systemic complexity and the ability to shape innovations in a dynamic technological and social environment.

The focus is on:

  • The independent handling of challenging technical issues,
  • The ability to conduct scientifically sound research and development,
  • The assumption of responsibility in interdisciplinary teams, projects and organizations.

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

  • They design, analyze and evaluate complex media technology systems incorporating the latest scientific findings and technologies (e.g. VR/AR, deep learning, image and signal processing, embedded systems, audiovisual coding).
  • They are able to plan and implement technical systems taking into account ethical, social, ecological and economic aspects.
  • They take on leadership and management tasks in interdisciplinary project teams and lead research and development projects.
  • Through the Master's project, they acquire distinctive skills in the conception, realization and presentation of complex systems in a realistic project environment.
  • They master scientific work at a high level and thus qualify for doctoral studies.
  • Strong communication skills, intercultural sensitivity and the ability to organize themselves make them competent specialists and managers in an international environment.
  • The Master's qualifies students for careers in research and development, technical management, software development, product management, media production, system integration and other areas of innovation - or for further academic qualifications.

Fields of Actionđź”—

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.

Competenciesđź”—

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 Plansđź”—

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.

Modulesđź”—

The modules of the degree program are described below in alphabetical order. If the course recognized for the module has a different ID, this different ID is indicated in brackets after the module ID.

Module Overview

Module ID Module Name Rotation ECTS Lecturer
AMA Angewandte Mathematik S 5 Grünvogel
AMS Special Aspects of Mobile Autonomous Systems W 5 Yuan
ARP Alternative Rechnerarchitekturen und Programmiersprachen W 5 Hartung
ATM (HI) Ausgewählte Themen der Medientechnologie W 5 Civelek
AVT Audio- und Videotechnologien W 5 Ruelberg TSA
AVV Algorithmen der Videosignalverarbeitung W 5 Ruelberg BIL
CI Computational Intelligence W 5 Bartz
CSO Computersimulation in der Optik W 5 Weigand
DBT Digitale Bildtechnik W 5 Fischer BIL
DLO Deep Learning und Objekterkennung S 5 Salmen BIL
DSP Digital Signal Processing W 5 Elders-Boll
ERMK (GER) Entrepreneurship, Gewerblicher Rechtsschutz, Market Knowledge S+W 5 Ladrière
ESD Embedded Systems Design S 5 Cremer
ESY Eingebettete Systeme in der Medientechnologie W 5 Poggemann BIL
FTV Forschungsprojekt virtuelle und erweiterte Realität S+W 5 Grünvogel IMA
ITF IT-Forensik W 5 Bornemann
KOLL (MAKOLL) Kolloquium zur Abschlussarbeit S+W 3 alle
LCSS Large and Cloud-based Software-Systems S 5 Wörzberger
MAA Masterarbeit S+W 27 alle
MCI Mensch-Computer-Interaktion S 5 Schild IMA
MLWR Maschinelles Lernen und wissenschaftliches Rechnen S 5 Rhein BIL
MP Masterprojekt S+W 15 alle
PAP Parallele Programmierung S 5 Fuhrmann IMA
PM Project Management S 5 Erkan
QEKS (SEKM) Qualitätsgesteuerter Entwurf komplexer Softwaresysteme W 5 Kreiser
QM Quantenmechanik W 5 Oberheide
RFSD RF System Design W 5 Kronberger
SEM Masterhauptseminar Medientechnologie W 10 alle
THI Theoretische Informatik S 5 Randerath
TSVP Technologien und Systeme der Videoproduktion S 5 Reiter TSA
VAE Virtual Acoustic Environments W 5 Pörschmann IMA
VAO Forschungsprojekt virtuelle Akustik und objektbasiertes Audio S+W 5 Reiter TSA
VER Virtuelle und erweiterte Realität W 5 Fuhrmann et al. IMA

đź”—

Module ID AMA
Module Name Angewandte Mathematik
Type of Module Mandatory Module
Recognized Course AMA - Applied Matheamtics
ECTS credits 5
Language deutsch, englisch bei Bedarf
Duration of Module 1 Semester
Recommended Semester 1
Frequency of Course every summer term
Module Coordinator Prof. Dr. Stefan Grünvogel
Lecturer(s)
Prof. Dr. Stefan Grünvogel (Professor Fakultät IME)

Learning Outcome(s)

WAS:
Eine mathematische Beschreibung einer medientechnologischen Aufgabenstellung ableiten bzw. ein mathematisches Modell eines medientechnologischen Systems entwerfen.

WOMIT:
Durch Definition von Systemgrenzen sowie der Beschreibung mit Hilfe mathematischer Notation und formaler Sprache.

WOZU:
Um die Aufgabenstellung mit Hilfe mathematischer Algorithmen lösen zu können bzw. einer Simulation zu erstellen.
WAS:
Geeignete numerische Lösungs- bzw. Simulationsverfahren für ein gegebenes Problem (Simulation eines System, Lösen einer Aufgabenstellung) auswählen.

WOMIT:
Analyse und Kenntnisse der grundlegenden theoretischen Eigenschaften (Kondition, Stabilität, Rechenaufwand) mathematischer Algorithmen.
Die zugehörige Theorien und ihre Grenzen kennen und verstehen.
Selbsständiges

WOZU:
Um nach Wahl des Verfahrens das passendes Softwaresystem auswählen zu können bzw. eigene numerische Verfahren zu implementieren.
WAS:
Numerische Verfahren zu Lösung für ein gegebenes Problem anwenden

WOMIT:
Verwendung von vorhandener Softwaresystemen und / oder Implementierung eigener numerischer Verfahren zu Lösung einer Aufgabenstellung.

WOZU:
Um letztendlich eine die Aufgabenstellung zu lösen um damit zu wissenschaftliche Erkenntnisse zu gelangen oder komplexe Medientechnologien zu entwickeln.
WAS:
Bewertung und Dokumentation der Ergebnisse der numerischer Verfahren.

WOMIT:
Eine Bewertung der Ergebnisse basiert auf den Kenntnissen der Eigenschaften der verwendeten Algorithmen (Kondition, Stabilität, Rechenaufwand). Zur Dokumentation wird die mathematische korrekte Notation und formale Sprache verwendet.

WOZU:
Um die erlangten wissenschaftlichen Erkenntnisse bzw. Lösungen richtig einzuschätzen und in interdisziplinärem Kontext zu kommunizieren.

Module Contents

Seminar

Knowledge of numerical mathematics is taught according to the Flipped Classroom concept.

Topcis:
Numerics and error analysis
Solving linear equations (direct, iterative)
eigen vectors
singular value decomposition
solving nonlinear equations
nonlinear least-squares
optimization methods
interpolation
intergration and differentiation
numerical software
Project

Mathematical description of a complex media technology problem which requires at least the knowledge conveyed in the seminar part of the course in order to be solved.
Analysis of the problem and selection of a solution methode based on this.
Selection of a software system or implementation of a corresponding algorithmic solution method.
Written documentation and critical evaluation of the results.
Explanation of the individual work steps
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 The classical topics in engineering mathematics:
- analysis of one and several variables (differentiation, intergration, Taylor)
- linear algebra (general vector spaces, linear mappings, matrices, vectors, norm, scalar product)
Mandatory Prerequisites Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
Recommended Literature
  • Solomin: Numerical Algorithms, CRC Press
  • Chapra,Canale: Numerical Methods for Engineers, McGraw-Hill
  • Quarteroni, Saleri, Gervasio: Scientific Computing with MATHLAB and Octave, Springer
  • Dahmen, Reusken: Numerik für Ingenieure und Naturwissenschaftler, Springer
  • Deuflhard, Hohmann: Numerische Mathematik 1, de Gruyter
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs		 AMA in Master Medientechnologie PO4
Specifics and Notes
Last Update 19.7.2025, 14:32:16

đź”—

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
Lecturer(s)
Prof. Dr. Chunrong Yuan (Professorin 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
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
Recommended Literature
  • Siegwart et.al.: Introduction to autonomous mobile robots, MIT Press, 2010
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

đź”—

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
Last-time Course winter term 2025
Module Coordinator Prof. Dr. René Wörzberger
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
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
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
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 10.12.2025, 08:41:04

đź”—

Module ID ATM
Module Name Ausgewählte Themen der Medientechnologie
Type of Module Elective Modules
Recognized Course HI - Haptic interfaces
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
Lecturer(s)
Dr. Civelek Turhan (wissenschaftlicher Mitarbeiter Fakultät IME)

Learning Outcome(s)

- Die Studierenden verstehen und erklären die haptische Wahrnehmung durch theoretische Konzepte und praktische Anwendungen, identifizieren Entwicklungsplattformen für haptische Anwendungen durch die Kombination von Theorie und Praxis, erkennen haptische Schnittstellenpositionen durch theoretisches Wissen und praktische Erfahrung und entwickeln immersive Virtual-Reality-Anwendungen mit haptischem Feedback.

- Die Studierenden sind in der Lage, Steuerungen für die Teleoperation zu identifizieren und zu implementieren, um eine zuverlässige Fernsteuerung von Geräten zu ermöglichen, Stabilitätsprobleme in VR- und Teleoperationssystemen zu beheben, Tests für die Benutzerwahrnehmung und das Feedback in haptischen Systemen und virtuellen Umgebungen zu entwickeln, Anwendungsbereiche haptischer Geräte in verschiedenen Bereichen wie Medizin, Spiele, Simulation und Rehabilitation zu erläutern, aktuelle VR- und Haptik-Technologien und ihre ethischen und sozialen Auswirkungen durch die Kombination von Theorie und kritischem Denken zu diskutieren, Haptik- und VR-Forschung zu bewerten, um Stärken, Schwächen und die Qualität der Forschung zu ermitteln, und Forschungspräsentationen zu haptischen Schnittstellen und virtueller Realität zu entwerfen.

WOMIT:
Die Kompetenzen werden zunächst über die Vorlesung durch die Dozenten vermittelt und danach im Praktikum anhand konkreter Aufgabenstellung von den Studierenden vertieft. Im Präsentationsteil der Lehrveranstaltung recherchieren die Studierenden anhand von Fachartikeln und anderen Informationsquellen neue Konzepte der virtuellen und erweiterten Realität mit Haptik zu vorgegebenen Themen und stellen diese in einer Präsentation vor.

WOZU:
Die sichere Anwendung der Grundlagen von Virtual Reality mit Haptik ist eine Voraussetzung für die Entwicklung komplexer interaktiver haptischer Anwendungen und Systeme. Darüber hinaus ermöglicht das Grundlagenwissen die Bewertung bestehender Systeme und wissenschaftlicher Arbeiten im Bereich der Haptik.

Module Contents

Lecture

Description of input and output devices as well as specific hardware for haptic and virtual reality
Describe the areas of application of haptic devices, data structures and algorithms in VR applications.
Describing haptic user interfaces: Presentation, interaction and navigation in virtual 3D scenarios with force feedback.
Explain algorithmic and mathematical principles for tracking, rendering and collision detection.
Describe stability issues in VR and teleoperation systems due to latency, haptics, control and image quality.
Describe VR and haptic technologies and their ethical and social implications.
Describe critical thinking to identify strengths, weaknesses and quality of research
Research Project

- Recognize basic characteristics of haptic devices, perception and interfaces.
- Developing VR applications with haptic devices and analyzing haptic systems.
- Testing haptic systems and procedures and evaluating scientific information and contexts.
- Presenting own scientific results, applying methods of haptic systems.
- Implementing basic teleoperation controls, taking into account stability and ethical aspects.
- Designing tests and reflecting on ethical aspects of haptic VR technologies.
- Self-organization and application of linguistic and intercultural skills.
Teaching and Learning Methods
  • Lecture
  • Research Project
Examination Types with Weights cf. exam regulations
Workload 150 Hours
Contact Hours 34 Hours ≙ 3 SWS
Self-Study 116 Hours
Recommended Prerequisites Bachelor level knowledge of VR, AR and XR and software language skills such as C#, C++ and Python
Mandatory Prerequisites Research Project requires attendance in the amount of: 6 Termine
Capacity-limited admission no
Recommended Literature
  • Thorsten A. Kern, et al., Engineering Haptic Devices, Springer International Publishing, 2023.
  • MatjaĹľ Mihelj, Janez Podobnik, Haptics for Virtual Reality and Teleoperation, Springer Dordrecht, 2012.
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

đź”—

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
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
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
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
Included in Specialization TSA - Technologien und Systeme audiovisueller Medien
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

đź”—

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
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)
Capacity-limited admission no
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
Included in Specialization BIL - Bildtechnologie
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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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
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
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
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 14.11.2025, 08:28:37

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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
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 accompanying exam (ULP)
Capacity-limited admission no
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
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 5.9.2025, 17:36:59

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Module ID DBT
Module Name Digitale Bildtechnik
Type of Module Elective Modules
Recognized Course DBT - Digital Imaging
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 Fischer
Lecturer(s)
Prof. Dr. Gregor Fischer (Professor Fakultät IME)

Learning Outcome(s)

Was:
Digitale Bildtechniken kommen in vielfältiger Weise in der Medienindustrie zum Einsatz. Die Bildkette digitaler Kameras, die im Rahmen der LV als exemplarische Anwendung herangezogen und analysiert wird, umfasst verschiedene Technologien wie Farbbildtechnik, HDR-Bildtechnik oder bildtechnische Verfahren.

Womit:
Durch die Vorlesung werden theoretische Kenntnisse der Bildtechnik exemplarisch vermittelt und in Zusammenhang mit den aktuellen Entwicklungen gebracht.
In einem in die LV integrierten begleitenden Praktikum entwickeln die Studierenden eigenständig algorithmische Lösungskonzepte und setzen diese in Matlab-Programme um.

Wozu:
Die Studierenden können akuelle Verfahren zur digitalen Bildtechnik entwickeln und in Hard- und Software implementieren. Sie können bildtechnische Verfahren analysieren, beurteilen und umsetzen sowie fachliche Führungs- und Projektverantwortung übernehmen.

Module Contents

Lecture / Exercises

Color Imaging
Color capturing with electronic sensors
Color detectors
Demosaicking
Optical antialiasing filters
Color management for DSCs
ICC profiles computing with least squares fit
Testing color accuracy
Color appearance models
Multispectral Imaging
Spectral sensitivities estimation by means of a general method to stabilize an instable set of linear equations
Statistics of natural spectra (Principal Components Analysis)
Spectral stimulus estimation
HDR Imaging
HDR capturing technology
Contrast management
photo receptor model
unsharp masking
retinex algorithm
Automatic control
Imaging Methods
Automatic white balancing
Grey world approach
Color-by-Correlation
Dichromatic reflection model
MTF management
MTF measurement
filter design for MTF optimization and sharpening
Adaptive sharpening
Denoising
Modelling of sensor noise
Locally adaptive smoothing filter
Wiener filtering
Bilateral filtering
Non-Local-Means filtering
Defect pixel / cluster correction
Describe the function and effects of different imaging methods
Lab

analyse optical and electronic imaging characteristics
recognize and assess imaging defects
realize imaging methods by software programmin according to a given specification or scientific paper
measure optical and electronic imaging characteristics or defects
implement new imaging methods according to a given specification or scientific paper
optimize imaging methods by basic mathematical optimization methods
compare image quality of different imaging methods
document results
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 none
Mandatory Prerequisites
  • Lab requires attendance in the amount of: 10 Termine
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
Recommended Literature

  • R.W.G. Hunt, The Reproduction of Color

  • M. Fairchild, Color Appearance Models, Wiley, 2nd ed.

  • G. C. Holst, T. S. Lomheim, CMOS/CCD Sensors and Camera Systems, SPIE

  • J. Nakamura, Image Sensors and Signal Processing for Digital Still Cameras, Taylor & Francis

  • Reinhard/Ward/Pattanaik/Debevec, High Dynamic Range Imaging, Elsevier 2010

  • R. Gonzales/R. Woods/Eddins, Digital Image Processing Using Matlab, Prentice Hall, 2004

  • W. Pratt, Digital Image Processing, Wiley, 4th ed., 2007

  • A. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1988
Included in Elective Catalog
Included in Specialization BIL - Bildtechnologie
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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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, englisch bei Bedarf
Duration of Module 1 Semester
Recommended Semester 1-2
Frequency of Course every summer term
Module Coordinator Prof. Dr. Jan Salmen
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
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
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
Included in Specialization BIL - Bildtechnologie
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 24.12.2025, 09:23:09

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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
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 accompanying exam (ULP)
Capacity-limited admission no
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
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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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
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
Capacity-limited admission no
Recommended Literature
Included in Elective Catalog WBA - Wahlbereich Allgemein
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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Module ID ESD
Module Name Embedded Systems Design
Type of Module Elective Modules
Recognized Course ESD - Embedded Systems Design
ECTS credits 5
Language englisch
Duration of Module 1 Semester
Recommended Semester 1-2
Frequency of Course every summer term
Module Coordinator Prof. Dr. Markus Cremer
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
Capacity-limited admission yes, according to approved request
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
Included in Specialization
Use of the Module in
Other Study Programs
Permanent Links to Organization ILU course
Specifics and Notes
Last Update 16.12.2025, 17:05:01

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Module ID ESY
Module Name Eingebettete Systeme in der Medientechnologie
Type of Module Elective Modules
Recognized Course ESY - Embedded Systems in Media Technology
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. Dirk Poggemann
Lecturer(s)
Prof. Dr.-Ing. Dirk Poggemann (Professor Fakultät IME)

Learning Outcome(s)

WAS: Studierende lernen aktuell verwendete Eingebettete Systeme in Kamerasystemen kennen, am Beispiel von FPGAs implementieren die Studierenden die Ansteuerung von Bildsensoren und Bilderarbeitungsalgorithmen für Kamerasysteme; Sie analysieren die Vor- und Nachteile unterschiedlicher Eingebetteter Systeme und aktuelle Trends in der Verwendung Eingebetteter Systeme in Kamerasystemen.

WOMIT: Der Dozent vermittelt die Grundlagen zu Eingebetteten Systemen und verwendeten Hardwarebeschreibungssprachen, im Praktikum werden in praktischen Versuchen Ansteuerung und Verarbeitung in FPGAs implementiert. In der Vorlesung werden aktuelle wissenschaftliche Veröffentlichungen zur Verwendung Eingebetteter Systeme in der Medientechnologie, z.B. für die Bildverarbeitung, besprochen.

WOZU: Ermöglicht das Erstellen technischer Systeme im Bereich Kameratechnik und die wissenschaftliche Auseinandersetzung mit Eingebetteten Systemen in der Medientechnologie.

Module Contents

Lecture

- Microprocessors
- FPGAs
- Hardware Description Languages
- Designprocess
- Test and Debug
- Control of CCD- and CMOS-Image-Sensors
- Image Processing Algorithms
Lab

exercises with FPGA-Board and CMOS-Image-Sensor
Project
Teaching and Learning Methods
  • Lecture
  • Lab
  • Project
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
Capacity-limited admission no
Recommended Literature
  • H.Flügel, FPGA-Design mit Verilog, Oldenbourg
  • D.G.Bailey, Design for Embedded Image Processing on FPGAs, Wiley
  • F.Kesel, Entwurf von digitalen Schaltungen und Systemen mit HDLs und FPGAs, Oldenbourg
Included in Elective Catalog
Included in Specialization BIL - Bildtechnologie
Use of the Module in
Other Study Programs		 ESY in Master Medientechnologie PO4
Specifics and Notes
Last Update 4.9.2025, 13:17:16

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Module ID FTV
Module Name Forschungsprojekt virtuelle und erweiterte Realität
Type of Module Elective Modules
Recognized Course FTV - Research Project 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 term
Module Coordinator Prof. Dr. Stefan Grünvogel
Lecturer(s)
Prof. Dr. Stefan Grünvogel (Professor Fakultät IME)

Learning Outcome(s)

WAS:
Selbstständig relevante wissenschaftliche Fragestellungen oder Hypothesen im Bereich VR / AR bewerten und entwickeln.

WOMIT:
Selbstständig wissenschaftliche Literatur im Bereich der virtuellen und erweiterten Realität durchdringen, zusammenfassen und präsentieren.
Fortgeschrittene Datenstrukturen und Algorithmen für VR/AR-Anwendungen erklären und vergleichen.

WOZU:
Um zukünftig wissenschaftlich zur arbeiten und wissenschaftlich Erkenntnisse anzuwenden und zu erweitern. (H2)
WAS:
Mit Hilfe verschiedener Methoden nach Antworten wissenschaftlicher Fragestellungen im Bereich VR / AR suchen.

WOMIT:
Es werden Werkzeuge und Methoden zur Entwicklung von VR/AR-Anwendungen verwendet und fortgeschrittene Technologien in VR und AR weiterentwickeln.
Dabei werden rechtliche und ethische Rahmenbedingungen und Nutzungsrechte berücksichtigt.

WOZU:
Es werden alle zukünftigen Handlungsfelder des Masterstudiengangs adressiert.
WAS:
Den eigenen Forschungsprozess selbst gestalten und reflektieren.

WOMIT:
Phasenübergreifende Qualitätssicherung und Anwendung wissenschaftlich fundierter und nachvollziehbarer Methoden sowie fachspezifischer Standards.

WOZU:
Dieses Learning-Outcome ist für das später wissenschaftliche Arbeiten notwendig.
WAS:
Forschungsergebnisse aufbereiten, kommunizieren und präsentieren.

WOMIT:
Das Zustandekommen der Forschungsergebnisse wird nachvollziehbar dokumentiert. In einer Abhandlung, die wissenschaftlichen Standards genügt, werden die Ergebnisse dargestellt und eine Fachpublikum präsentiert.

WOZU:
Um zukünftig wissenschaftliche Erkenntnisse zu erweitern und um in Führungs- bzw. Projektverantwortung in Fachteams kommunizieren zu können.

Module Contents

Project

Explain and compare data structures and algorithms for VR/AR applications.
Describe multimodal user interfaces.
Describe input and output devices as well as specific hardware of virtual and augmented reality.
Explain algorithmic and mathematical basics.
Summarize and present scientific literature in the field of virtual and augmented reality.
Explain and compare advanced data structures and algorithms for VR/AR applications.
Use tools and methods for the development of VR/AR applications and further develop advanced technologies in VR and AR.
Legal and ethical framework conditions and rights of use will be considered.
Cross-phase quality assurance and application of scientifically sound and comprehensible methods as well as subject-specific standards.
The results of the research will be documented in a comprehensible manner. The results will be presented to a specialist audience in a treatise that meets scientific standards.
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
  • Modul VER: Kentnisse der Begriffe aus dem Bereich VR und AR sowie die Fertigkeit, selbstständig VR / AR - Anwendungen zu erstellen.
  • Modul MCI: Grundlagen des Experiment Designs sowie der statistischen Auwertung.
  • Knowledge of VR and AR terms and the competence to create VR / AR applications. Basics of experiment design and statistical evaluation.
Mandatory Prerequisites Project requires attendance in the amount of: 3 Termine
Capacity-limited admission no
Recommended Literature
  • Relevante Foschungsliteratur. z.B IEEE VR, EuroVR, Siggraph, Sigchi usw.
Included in Elective Catalog
Included in Specialization IMA - Interaktive Medienanwendungen
Use of the Module in
Other Study Programs		 FTV in Master Medientechnologie PO4
Specifics and Notes
Last Update 12.12.2025, 13:49:47

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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
Capacity-limited admission no
Recommended Literature
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 10.12.2025, 14:29:12

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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
Capacity-limited admission no
Recommended Literature
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes See also examination regulations §29.
Last Update 14.11.2025, 14:33:56

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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
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
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
Recommended Literature
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs
Intro Video
Permanent Links to Organization Ilu course
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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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
Capacity-limited admission no
Recommended Literature
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs
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 14.11.2025, 08:32:05

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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
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
Capacity-limited admission no
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
Included in Specialization IMA - Interaktive Medienanwendungen
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 26.2.2026, 10:11:31

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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, englisch bei Bedarf
Duration of Module 1 Semester
Recommended Semester 1-2
Frequency of Course every summer term
Module Coordinator Prof. Dr. Beate Rhein
Lecturer(s)
Prof. Dr. Beate Rhein (Professorin 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 accompanying exam (ULP)
Capacity-limited admission no
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
Included in Specialization BIL - Bildtechnologie
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 5.1.2026, 08:46:30

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Module ID MP
Module Name Masterprojekt
Type of Module Mandatory Module
Recognized Course MP - Master's project
ECTS credits 15
Language deutsch und englisch
Duration of Module 1 Semester
Recommended Semester 2
Frequency of Course every term
Module Coordinator Studiengangsleiter(in) Master Medientechnologie (undefined)
Lecturer(s)
verschiedene Dozenten*innen (diverse lecturers)

Learning Outcome(s)

Ablauf eines Projektes strukturieren
Technische Informationen zu Projektgegenstand beschaffen
Technische Aufgabe in sinnvolle Teilaufgaben zerlegen
Spezifikation des Projektgegenstandes erstellen
Software strukturiert erstellen (spezifizieren, erstellen, testen, dokumentieren)
Gesamtsystem erstellen
Benötigte technische Informationen identifizieren
Technische Entscheidungen nach dem Stand der Technik und Wissenschaft treffen
Komplexe Aufgaben arbeitsteilig bearbeiten
Projektfortschritt kontrollieren, notwendige Korrekturmaßnahmen identifizieren und umsetzen
Projektergebniss einem größeren Publikum präsentieren

Module Contents

Research Project
Teaching and Learning Methods Research Project
Examination Types with Weights cf. exam regulations
Workload 450 Hours
Contact Hours 12 Hours ≙ 1 SWS
Self-Study 438 Hours
Recommended Prerequisites keine
Mandatory Prerequisites Research Project requires attendance in the amount of: 3 Termine
Capacity-limited admission no
Recommended Literature
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs		 MP in Master Medientechnologie PO4
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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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
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
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
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
Included in Specialization IMA - Interaktive Medienanwendungen
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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Module ID PM
Module Name Project Management
Type of Module Elective Modules
Recognized Course PM - Project Management
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. Uwe Dettmar
Lecturer(s)
Said Erkan (Lehrbeauftragter)

Learning Outcome(s)

What? Learn how to create a project plan as part of their study

How? by applying project management concepts and processes to their "real-life" projects allowing them to generate immediate results that are usable in their future business situation. It acquires hands-on experience in applying new concepts and techniques in a project team environment and gain the confidence to take this forward to their environment.

Why? To be prepared managing projects during their work life of proficiency.

Module Contents

Seminar

Students learn basic PM methods, PM organizations, PM Tools
Project Management Fundamentals, and Project Initiation based on the PMI curriculum by presentations and team work, and source browsing.
Project

Practice project management concepts and processes to their "real-life" projects in classroom and team work and One by One sessions. Learn about planning, execution, controlling, and closing a project
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 Some basic knowledge on project management
Mandatory Prerequisites
Capacity-limited admission no
Recommended Literature
  • PMP Handbook
  • www.scrumalliance.org
Included in Elective Catalog WBA - Wahlbereich Allgemein
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 21.9.2025, 19:12:28

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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
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 accompanying exam (ULP)
Capacity-limited admission yes, according to approved request
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
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 22.10.2025, 11:22:18

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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
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 Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
Recommended Literature
  • Harris – Moderne Physik, Pearson Verlag
  • Feynman - Vorlesungen über Physik Band III:Quantenmechanik, Oldenbourg Verlag
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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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
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
  • Lab requires attendance in the amount of: 3 Labortermine und 1 Präsentationstermin
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
Recommended Literature
  • Kraus & Carver Eletromagnetics, McGraw Hilll, 2006.
  • Michale Steer, Microwave and RF Design
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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Module ID SEM
Module Name Masterhauptseminar Medientechnologie
Type of Module Mandatory Module
Recognized Course SEM - Advanced Seminar on Media Technology
ECTS credits 10
Language deutsch und englisch
Duration of Module 1 Semester
Recommended Semester 1
Frequency of Course every winter term
Module Coordinator Studiengangsleiter(in) Master Medientechnologie (undefined)
Lecturer(s)
verschiedene Dozenten*innen (diverse lecturers)

Learning Outcome(s)

- In ein anspruchvolles wissenschaftliches Thema aus dem Bereich der Medientechnologie einarbeiten
- Grundlegende Techniken der Arbeitsorganisation und -dokumentation beherrschen
- Angemessene Präsentationstechnik auswählen und beherrschen
- Fähigkeit zur freien Rede und anschaulicher Darstellung demonstrieren
- Fachliche Fragen sicher und angemessen formulieren (auch als Zuhörer)
- Auf Zuhörerfragen eingehen
- Angemessenes Feedback als Zuhörer geben
- Anspruchsvolle Themen kurz, prägnant und eingprägsam schriftlich darstellen
- Zielgruppengerechte Aufbereitung und Präsentation der eigenen Arbeitsergebnisse

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 none
Mandatory Prerequisites Research Project requires attendance in the amount of: 2 Termine
Capacity-limited admission no
Recommended Literature
Included in Elective Catalog
Included in Specialization
Use of the Module in
Other Study Programs		 SEM in Master Medientechnologie PO4
Specifics and Notes
Last Update 14.11.2025, 09:22:55

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Module ID THI
Module Name Theoretische Informatik
Type of Module Elective Modules
Recognized Course THI - Theoretical Computer Science
ECTS credits 5
Language deutsch
Duration of Module 1 Semester
Recommended Semester 1-2
Frequency of Course every summer term
Module Coordinator Prof. Dr. Hubert Randerath
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
Capacity-limited admission no
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
Included in Specialization
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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Module ID TSVP
Module Name Technologien und Systeme der Videoproduktion
Type of Module Elective Modules
Recognized Course TSVP - Technologies and Systems of Video Production
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. Ulrich Reiter
Lecturer(s)
Prof. Dr.-Ing. Ulrich Reiter (Professor Fakultät IME)

Learning Outcome(s)

WAS: Studierende analysieren aktuelle und zukünftige Produktionstechnologien und Systeme audiovisueller Medien hinsichtlich unterschiedlicher Faktoren wie Anwendbarkeit, Potential, Kosten/Nutzen, etc. in verschiedenen exemplarischen Anwendungsszenarien. Sie lernen, Technologien aus teilweise anderen Anwendungsgebieten mit Hilfe wissenschaftlicher Methoden auf ihre Einsatzmöglichkeit in der Medienproduktion hin zu untersuchen. Die kritische Auseinandersetzung mit der technischen Literatur und die Anwendung der Regeln guten wissenschaftlichen Arbeitens befähigt sie, wissenschaftliche begründete Aussagen zu treffen.

WOMIT: Dazu führen sie in kleinen Teams eine Literaturrecherche sowie evtl. Befragungen und Interviews mit Experten durch, mit Hilfe derer sie die betreffenden Technologien verstehen und eine Einordnung vornehmen können. Zum Abschluss des Projektes fertigen sie einen Bericht an und halten einen Fachvortrag.

WOZU: Studierenden wird ein kritischer Umgang mit neuen Technologien ermöglicht, da sie wissenschaftlich arbeiten können. Sie können komplexe Technologien analysieren, daraus technologische Empfehlungen ableiten und somit fachliche Führungs- und Projektverantwortung übernehmen.

Module Contents

Project

- mastering of methods of good scientific practice, especially with respect to information retrieval as well as to documentation and presentation of expert knowledge
- expert knowledge in specific topics from the field of Technologies and Systems for Audiovisual Media Production, as well as from neighboring disciplines that are already or will become potentially relevant for the field of media production technologies
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 - basic knowledge in media production technologies and systems
Mandatory Prerequisites Project requires attendance in the amount of: 2 Termine
Capacity-limited admission no
Recommended Literature
  • diverse aktuelle Papers zum jeweiligen Thema
Included in Elective Catalog
Included in Specialization TSA - Technologien und Systeme audiovisueller Medien
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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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 winter term
Module Coordinator Prof. Dr.-Ing. Christoph Pörschmann
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
Capacity-limited admission no
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
Included in Specialization IMA - Interaktive Medienanwendungen
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 23.3.2026, 15:47:14

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Module ID VAO
Module Name Forschungsprojekt virtuelle Akustik und objektbasiertes Audio
Type of Module Elective Modules
Recognized Course VAO - Research Project in Virtual Acoustics and Object Based Audio
ECTS credits 5
Language deutsch, englisch bei Bedarf
Duration of Module 1 Semester
Recommended Semester 1-2
Frequency of Course every term
Module Coordinator Prof. Dr.-Ing. Ulrich Reiter
Lecturer(s)
Prof. Dr.-Ing. Ulrich Reiter (Professor Fakultät IME)

Learning Outcome(s)

WAS: Studierende lernen Technologien aus den Themengebieten Virtuelle Akustik und Objektbasierte Audioproduktion zu analysieren, zu implementieren und anzuwenden. Sie lernen, fachspezifische Aufgabenstellungen mit Hilfe wissenschaftlicher Methoden und in einem begrenzten Zeitraum zu lösen. Die kritische Auseinandersetzung mit den gefundenen Lösungen und die Anwendung der Regeln guten wissenschaftlichen Arbeitens befähigt sie, wissenschaftliche Aussagen zu treffen.

WOMIT: Dazu führen sie in kleinen Teams Projekte durch, in denen sie die kennengelernten Technologien exemplarisch implementieren und/oder anwenden. Zum Abschluss des Projektes fertigen sie eine Dokumentation an und halten einen Fachvortrag.

WOZU: Studierenden wird ein kritischer Umgang mit neuen Technologien ermöglicht, da sie wissenschaftlich arbeiten können. Sie können komplexe Technologien entwickeln und fachliche Führungs- und Projektverantwortung übernehmen.

Module Contents

Project

- development of a deep understanding for the properties of object based audio technologies
- knowledge of simulation methods of virtual acoustics
- confident handling of object based audio technologies and methods of virtual acoustics
- mastering of methods of good scientific practice, especially with respect to information retrieval as well as to documentation and presentation of project results
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 - knowledge of acoustics / room acoustics as well as audio engineering / digital audio technology
- basic knowledge of audio signal processing and algorithms
Mandatory Prerequisites Project requires attendance in the amount of: 80 % der Termine und Präsentation
Capacity-limited admission no
Recommended Literature
  • diverse aktuelle Papers zum Thema
Included in Elective Catalog
Included in Specialization TSA - Technologien und Systeme audiovisueller Medien
Use of the Module in
Other Study Programs		 VAO in Master Medientechnologie PO4
Specifics and Notes
Last Update 19.7.2025, 14:32:16

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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
Lecturer(s)

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
  • Participation in final examination only after successful participation in accompanying exam (ULP)
Capacity-limited admission no
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
Included in Specialization IMA - Interaktive Medienanwendungen
Use of the Module in
Other Study Programs
Specifics and Notes
Last Update 19.7.2025, 14:32:16

Electives Catalogsđź”—

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.
  • Information on calculating interim grades and study progress in connection with elective catalogs can be found in the Examination Office's Merkblatt Leistungspunkte-Berechnung im Wahlbereich der Studiengänge.

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.

đź”—

In diesem Wahlbereich können neben den Modulen des Wahlbereichs WMM auch Module zur Vermittlung fachübergreifender Kompetenzen aus dem Master-Angebot der TH Köln gewählt werden. (s. Prüfungsordnung Anlage 1, Abschitt f, Absatz 3)

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

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

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

Modules of the faculty

Module ID Module Name Rotation ECTS Lecturer
AMS Special Aspects of Mobile Autonomous Systems W 5 Yuan
ARP Alternative Rechnerarchitekturen und Programmiersprachen W 5 Hartung
AVT Audio- und Videotechnologien W 5 Ruelberg TSA
AVV Algorithmen der Videosignalverarbeitung W 5 Ruelberg BIL
CI Computational Intelligence W 5 Bartz
CSO Computersimulation in der Optik W 5 Weigand
DBT Digitale Bildtechnik W 5 Fischer BIL
DLO Deep Learning und Objekterkennung S 5 Salmen BIL
DSP Digital Signal Processing W 5 Elders-Boll
ERMK (GER) Entrepreneurship, Gewerblicher Rechtsschutz, Market Knowledge S+W 5 Ladrière
ESD Embedded Systems Design S 5 Cremer
ESY Eingebettete Systeme in der Medientechnologie W 5 Poggemann BIL
FTV Forschungsprojekt virtuelle und erweiterte Realität S+W 5 Grünvogel IMA
ITF IT-Forensik W 5 Bornemann
LCSS Large and Cloud-based Software-Systems S 5 Wörzberger
MCI Mensch-Computer-Interaktion S 5 Schild IMA
MLWR Maschinelles Lernen und wissenschaftliches Rechnen S 5 Rhein BIL
PAP Parallele Programmierung S 5 Fuhrmann IMA
PM Project Management S 5 Erkan
QEKS (SEKM) Qualitätsgesteuerter Entwurf komplexer Softwaresysteme W 5 Kreiser
QM Quantenmechanik W 5 Oberheide
RFSD RF System Design W 5 Kronberger
THI Theoretische Informatik S 5 Randerath
TSVP Technologien und Systeme der Videoproduktion S 5 Reiter TSA
VAE Virtual Acoustic Environments W 5 Pörschmann IMA
VAO Forschungsprojekt virtuelle Akustik und objektbasiertes Audio S+W 5 Reiter TSA
VER Virtuelle und erweiterte Realität W 5 Fuhrmann et al. IMA

đź”—

Hier können die angebotenen Wahlmodule sowie, nach vorheriger Genehmigung, sonstige Module aus dem Angebot der Fakultät gewählt werden. Drei der im Rahmen der Wahlmodule WMM1-6 gewählten Module müssen aus der Liste der Module eines Studienschwerpunktes stammen Aus dem Institut für Medien- und Phototechnik ist z.Z. folgendes Angebot für das Sommersemester vorgesehen:

You must select modules of 25 ECTS credit points in total out of this catalog.

Modules of the faculty

Module ID Module Name Rotation ECTS Lecturer
AMS Special Aspects of Mobile Autonomous Systems W 5 Yuan
ARP Alternative Rechnerarchitekturen und Programmiersprachen W 5 Hartung
AVT Audio- und Videotechnologien W 5 Ruelberg TSA
AVV Algorithmen der Videosignalverarbeitung W 5 Ruelberg BIL
CI Computational Intelligence W 5 Bartz
CSO Computersimulation in der Optik W 5 Weigand
DBT Digitale Bildtechnik W 5 Fischer BIL
DLO Deep Learning und Objekterkennung S 5 Salmen BIL
DSP Digital Signal Processing W 5 Elders-Boll
ESD Embedded Systems Design S 5 Cremer
ESY Eingebettete Systeme in der Medientechnologie W 5 Poggemann BIL
FTV Forschungsprojekt virtuelle und erweiterte Realität S+W 5 Grünvogel IMA
ITF IT-Forensik W 5 Bornemann
LCSS Large and Cloud-based Software-Systems S 5 Wörzberger
MCI Mensch-Computer-Interaktion S 5 Schild IMA
MLWR Maschinelles Lernen und wissenschaftliches Rechnen S 5 Rhein BIL
PAP Parallele Programmierung S 5 Fuhrmann IMA
QEKS (SEKM) Qualitätsgesteuerter Entwurf komplexer Softwaresysteme W 5 Kreiser
QM Quantenmechanik W 5 Oberheide
RFSD RF System Design W 5 Kronberger
THI Theoretische Informatik S 5 Randerath
TSVP Technologien und Systeme der Videoproduktion S 5 Reiter TSA
VAE Virtual Acoustic Environments W 5 Pörschmann IMA
VAO Forschungsprojekt virtuelle Akustik und objektbasiertes Audio S+W 5 Reiter TSA
VER Virtuelle und erweiterte Realität W 5 Fuhrmann et al. IMA

Specializationsđź”—

The following section outlines the major fields of study defined in this degree program (see also §24 of the examination regulations). The following information and regulations apply to all major fields of study:

  • A major field of study is considered successfully completed if at least 3 of the included modules have been successfully accomplished.
  • The completed areas of specialization are listed in a separate appendix to the degree certificate; if there is more than one, only parts of it may be listed upon request to the Examination Office.
  • Upon request, a major field of study can be supplemented with additional suitable modules. Such a request must be submitted informally to the program director at least six months before planned participation in a module to be supplemented. The examination board decides on the acceptance of the request in consultation with the program director and appropriately qualified teaching staff.

đź”—

Anhand industrienaher Fallbeispiele werden in einem projektbezogenen Lehrkonzept Methoden und Techniken entwickelt, die intelligente Bild- und Videoanwendungen inkl. Hard- und Software von der Bildsensorik bis hin zu Objekterkennung und -verfolgung umsetzen. Insbesondere werden optische und elektronische Kameraeigenschaften modelliert, und diese Modelle zur Erzeugung von Trainingsdaten zu Deep Learning von neuronalen Faltungsnetzen genutzt. Zu den Highlights des Schwerpunkts Bildtechnik gehören: - Systemdesign kameratechnischer Systeme mit Controller- oder FPGA-basierter Steuerung der Bildsensorik und schneller Verarbeitung der Bildsignale - Verfahren zur Bildverbesserung (Farboptimierung, Image Enhancement) und Computational Photography (Mehrfachbildaufnahmetechniken wie HDR-Imaging oder Image Stacking) - Verfahren zur Bild- und Videokompression inkl. Bewegungsprediktion - Lokal adaptive Filterfunktionen (Rauschunterdrückung, Verschärfung) und Objekterkennung (Gesichter, Himmel, Vegetation …) mit neuronalen Faltungsnetzen (CNN)

Modules of the faculty

Abbr. Module Name Rotation ECTS Lecturer
AVV Algorithmen der Videosignalverarbeitung W 5 Ruelberg
DBT Digitale Bildtechnik W 5 Fischer
DLO Deep Learning und Objekterkennung S 5 Salmen
ESY Eingebettete Systeme in der Medientechnologie W 5 Poggemann
MLWR Maschinelles Lernen und wissenschaftliches Rechnen S 5 Rhein

Modules of other faculties or universities

Affiliation Module Name ECTS
TH Köln - Fak. 10 Bildbasierte Computergrafik 5

đź”—

In diesem Profil beschäftigen wir uns mit der Entwicklung von Algorithmen und Datenstrukturen zur Erzeugung von interaktiven Medienanwendungen, insbesondere im Bereich Virtual und Augmented Reality. Wir untersuchen aktuelle Themen zum Thema Mensch-Computer-Interaktion und führen eigenständige Forschungsprojekte durch.

Modules of the faculty

Abbr. Module Name Rotation ECTS Lecturer
FTV Forschungsprojekt virtuelle und erweiterte Realität S+W 5 Grünvogel
MCI Mensch-Computer-Interaktion S 5 Schild
PAP Parallele Programmierung S 5 Fuhrmann
VAE Virtual Acoustic Environments W 5 Pörschmann
VER Virtuelle und erweiterte Realität W 5 Fuhrmann et al.

đź”—

In diesem Profil werden aktuelle Technologien und Systeme audiovisueller Medien im Rahmen eines projektbezogenen Lehrkonzeptes exemplarisch untersucht, angewandt und weiterentwickelt. Im Fokus stehen dabei insbesondere: - Verfahren der Virtuellen Akustik, die interaktiv einen realitätsgetreuen räumlichen Klangeindruck vermitteln können, sowie zugehörige objektbasierte Audiokonzepte und Simulationsmethoden - komplexe Technologien und Systeme der Video-/Medien-Produktion, ihr Zusammenspiel sowie die daraus resultierenden Anforderungen und Workflows - Verfahren und Technologien zur Distribution von Mediendaten (Video- und Audiokompression, Übertragung, Multiplexing ...)

Modules of the faculty

Abbr. Module Name Rotation ECTS Lecturer
AVT Audio- und Videotechnologien W 5 Ruelberg
TSVP Technologien und Systeme der Videoproduktion S 5 Reiter
VAO Forschungsprojekt virtuelle Akustik und objektbasiertes Audio S+W 5 Reiter

Examination Typesđź”—

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 Matrixđź”—

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 - Entwicklung und Design HF2 - Forschung und Innovation HF3 - Leitung und Management HF4 - Qualitätssicherung und Te... K.1 - Entwicklung und Konzeptio... K.2 - Prüfung und Bewertung kom... K.3 - Wissenschaftliches Arbeit... K.4 - Projektmanagement und Tea... K.5 - Selbstorganisation und au... K.6 - Kommunikation und interku... K.7 - Technische und naturwisse... K.8 - Nachhaltigkeit und gesell... K.9 - Analyse, Simulation und A... K.10 - Führungs- und Entscheidun... K.11 - Anwendung ethischer Werte... K.12 - Integratives Denken und H... K.13 - Innovation und Kreativitä... SK.1 - Global Citizenship SK.2 - Internationalisierung SK.3 - Interdisziplinarität SK.4 - Transfer
AMA Angewandte Mathematik ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
AMS Special Aspects of Mobile Autonomous Systems ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
ARP Alternative Rechnerarchitekturen und Programmiersprachen ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
ATM Ausgewählte Themen der Medientechnologie ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
AVT Audio- und Videotechnologien ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
AVV Algorithmen der Videosignalverarbeitung ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
CI Computational Intelligence ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
CSO Computersimulation in der Optik ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
DBT Digitale Bildtechnik ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
DLO Deep Learning und Objekterkennung ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
DSP Digital Signal Processing ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
ERMK Entrepreneurship, Gewerblicher Rechtsschutz, Market Knowledge
ESD Embedded Systems Design ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
ESY Eingebettete Systeme in der Medientechnologie ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
FTV Forschungsprojekt virtuelle und erweiterte Realität ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
ITF IT-Forensik ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
KOLL Kolloquium zur Masterarbeit ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
LCSS Large and Cloud-based Software-Systems ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
MAA Masterarbeit ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
MCI Mensch-Computer-Interaktion ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
MLWR Maschinelles Lernen und wissenschaftliches Rechnen ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
MP Masterprojekt ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
PAP Parallele Programmierung ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
PM Project Management ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
QEKS Qualitätsgesteuerter Entwurf komplexer Softwaresysteme ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
QM Quantenmechanik ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
RFSD RF System Design ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
SEM Masterhauptseminar Medientechnologie ⬤ ⬤ ⬤ ⬤ ⬤
THI Theoretische Informatik ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
TSVP Technologien und Systeme der Videoproduktion ⬤ ⬤ ⬤ ⬤
VAE Virtual Acoustic Environments ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
VAO Forschungsprojekt virtuelle Akustik und objektbasiertes Audio ⬤ ⬤ ⬤ ⬤ ⬤ ⬤
VER Virtuelle und erweiterte Realität ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤ ⬤

Version Historyđź”—

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
3.7 2026-04-21-15-02-23.87d540b9 (SNAPSHOT)
  1. Update zu Sprachangaben
  2. Darstellung evtl. Kapazitätsbeschränkungen je Modul
  3. Verweis auf Merkblatt Leistungspunkte-Berechnung im Wahlbereich der Studiengänge im Eingangstext zu Wahlbereichen
  4. Modul 'Bildbasierte Computergrafik' in Schwerpunkt 'BIL - Bildtechnologien' aufgenommen
  5. Turnus von Virtual Acoustic Environments (VAE) auf Wintersemester geändert
  6. Wahbereich WMM erweitert (AMS, ARP, CI, CSO, DSP, ESD, LCSS, MLWR, QEKS, QM, RFSD, THI)
  7. Wahlbereich WBA zur Abbildung der PO Anhang 1, Abschnitt f), Absatz 3 eingeführt.
 Link
3.6 2025-10-07-08-46-00
  1. Abweichende Lehrveranstaltungkürzel in Klammern neben Modulkürzeln dargestellt, bspw. QEKS (SEKM) oder ERMK (GER)
  2. Turnusse in Tabellen (Wahlbereiche, Studienschwerpunkte/Vertiefungspakete) dargestellt
  3. Sortierbare Tabellen in Wahlbereiche, Studienschwerpunkte/Vertiefungspakete
 Link
3.5 2025-09-08-09-32-00
  1. Diverse hängende Referenzen von Wahlbereichs-, Schwerpunkts- bzw. Vertiefungspaket-Tabellen in den Modul-Abschnitt korrigiert. Fehlende Module sind jetzt vorhanden.
  2. Eine Modulbeschreibung beinhaltet nun auch Angaben, in welchen Wahlbereichen und Studienschwerpunkten bzw. Vertiefungspakten das jeweilige Modul enthalten ist.
  3. Anwesenheiten in ESY reduziert
  4. CSO mit Prüfungsform für begleitende Prüfung
  5. Prüfungsordnungsversionen statt Jahreszahlen
  6. Modulkürzel ohne Studiengang
 Link
3.4 2024-12-06-08-45-55
  1. Begutachtete Version für Reakkreditierung 2024
  2. Neues Layout für sämtliche Modulhandbücher
 Link
3.3 2024-07-06-12-00-00
  1. Neues Modul "IT-Forensik" für Masterstudiengänge Technische Informatik, Medientechnologie und Elektrotechnik
  2. Neues Modul "Ausgewählte Themen der Medientechnologie" und Lehrveranstaltung "Haptic Interfaces"
 Link
3.2 2024-06-11-14-00-00
  1. Neue(s) Modul und Lehrveranstaltung Themen der Medientechnologie
 Link
3.1 2024-02-23-15-00-00
  1. Generelle Überarbeitung des Layouts
  2. Eingangstexte bei Wahlmodulkatalogen und Schwerpunkten überarbeitet und POs angeglichen
 Link
3.0 2023-02-24-20-00-00
  1. Allgemeine Bereinigung von kaputten Links (http 404)
 Link
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