After successfully completing this module, students will be able to:

Knowledge and understanding:

• Name essential properties of functional programming languages
.
• explain the concept of functional closures
.
• present the syntactic structure of lambda expressions.
explain the normalization process of lambda expressions.formulate basic program constructs as lambda expressions.explain different approaches to the typing of lambda expressions.explain the properties of dependent types.

Use, application and generation of knowledge:

• apply the basic rules of natural reasoning
• compare the rules of inference of constructive logic with the basic constructs of typed programs
• write elementary proofs as programs in a proof support system

Communication and cooperation:

• to mediate between different formal descriptions
• to formulate precisely
• to argue logically

Scientific self-understanding / professionalism:

• Formal specification of requirements
• ensure the reliability of subsystems via proofs of correctness
• to assess abstraction concepts

Technical and methodological competence:
After completing this course, students will be able to design and develop systems that are predictable in their temporal behavior. They know the technical parameters that are relevant for the selection of planning methods and can select and implement a suitable method based on the advantages and disadvantages. Students will be able to understand the special temporal aspects of synchronized processes and distributed systems in design and implementation.

• Hermann Kopetz. Real-Time Systems: Design Principles for Distributed Embedded Applications, Springer, 2011
• Dieter Zöbel. Echtzeitsysteme Grundlagen der Planung, Springer, 2008.
• Jane Liu. Real-Time Systems, Prentice Hall, 2000.
• Peter Marwedel. Eingebettete Systeme, Springer, 2007.
• Heinz Wörn und Dieter Brinkschulte. Echtzeitsysteme, Springer, 2005.
• Burns, A., Wellings, A.; Real-Time Systems and Programming Languages; Pearson Education Ltd., Third Ed. 2001.

Technical and methodological competence:
The student understands the principles, protocols and architecture of computer networks and the applications based on them. He/she applies network design procedures on layer 2 and layer 3, carries out the configuration of network components (router, switch) and plans the setup of virtual networks. He/she understands the design and implementation of communication protocols and is able to design and configure distributed systems with physical and virtual network components.

Social skills:
Based on practical demonstrations and experience gained through practical exercises, he/she is able to evaluate typical and recognized technologies and procedures in the areas of data network communication and the use of virtual network systems.

Technical and methodological competence:

After completing the course, students will be able to

• Classify the concept of the Internet of Things (IoT) and differentiate it from Machine 2 Machine Communication (m2m) and Industry 4.0

• Know the fields of application of IoT and specify their requirements for technology and architecture

• Understand IoT technologies, architectures and protocols and analyze existing IoT systems

• Classify wireless radio technologies such as UWB, LoRaWAN, Z-Wave, ZigBee, Bluetooth Smart in terms of range, data rate, interoperability and power consumption

• Understand routing protocols for ad hoc networking such as OLSR, AODV, DSR and implement them in your own systems

• Select architectures, technologies and protocols for given IoT applications and implement them in your own systems

• Design and implement new architectures and routing protocols for specific IoT applications

• Introduction

  • Motivation, definition, differentiation from m2m, Industry 4.0

  • Application areas and their requirements

  • Overview of layer models: ISO/OSI, TCP/IP, IPv6 and 6LoWPAN, Bluetooth Smart

  • Overview of radio transmission: ISM bands, licensed bands, UWB

  • Classification of technologies: IEEE 802.15.4, Bluetooth Smart, RFID, LoRaWAN

• Architectures and protocols of the IoT

  • Application layer protocols: CoAP, MQTT, GATT

  • Application layer protocol gateways: REST-HTTP/CoAP, REST-HTTP/GATT

  • Topologies: Star and tree topologies with central gateway, mesh networking, multi-gateway

  • Routing protocols: OLSR, AODV, DSR

  • IPv6, 6LoWPAN

• Basics of digital communication

  • Sampling of signals, Nyquist sampling theorem

  • Coding, modulation, Shannon Fano channel capacity

  • Multiple access methods: ALOHA, CSMA/CA, FDMA, TDMA, CDMA, OFDM

  • Radio transmission basics: Antennas, free space attenuation, Fresnel zone,

• Exemplary areas of application

  • Smart Home

    • Scenarios and their requirements

    • Technologies: Z-Wave, ZigBee, EnOcean

    • Exemplary implementation based on a current AAL research project

  • Logistics

    • Scenario Tracking & Tracing

    • Technologies: RFID, LoRaWAN, UWB

    • Exemplary implementation based on a current research project

Jan Höller: From machine-to-machine to the internet of things - introduction to a new age of intelligence, Elsevier, 2014

• Peter Waher: Learning Internet of Things - explore and learn about Internet of Things with the help of engaging and enlightening tutorials designed for Raspberry Pi, Packt Publishing, Birmingham, 2015

• Ralf Gessler, Thomas Krause: Wireless-Netzwerke für den Nahbereich, Eingebettete Funksysteme, ­ Vergleich von standardisierten und proprietären Verfahren, Vieweg+Teubner, 2009

• Martin Meyer: Kommunikationstechnik, Konzepte der modernen Nachrichtenübertragung, Vieweg+Teubner, 4. Auflage, 2011.

• Andrew S. Tanenbaum, David J. Wetherall: Computernetzwerke, 5. Auflage, Pearson Studium, 2012

The course deals with the development and analysis of machine learning methods in applications of computer science, medical informatics and general information systems. After successfully completing the course, students will have acquired the following skills:

Knowledge (knowledge):
- know the most important technical terms of machine learning and can use them to explain learning systems.
- They can implement and evaluate the use of machine learning methods for their own application tasks. To this end, students are familiar with typical applications of these methods.
- know project management methods for machine learning applications such as CRISP-DM
- know explanatory components for machine learning and can interpret the results
- know typical problems of machine learning such as overfitting and information leakage and can avoid them
- know the theoretical limits of machine learning systems and can describe these formally and use them to estimate the limits of their own applications
.

Skills
- can design, implement and analyze machine learning systems for specific application contexts in computer science.
- can question and discuss the ethical foundations and limitations of machine learning systems
- can select suitable machine learning methods for applications
- can implement simple deep learning solutions with JupyterHub
- can plan, implement, analyze and document industry-related mini-projects independently in a team

Competencies (personal and social skills)
- can formulate ideas and proposed solutions orally and in writing
- can solve tasks in exercises and practicals independently and present the results
- can independently develop and present theoretical content on the topic of machine learning from scientific literature
- can develop solutions cooperatively in the exercise and project phases
- can cooperatively plan, distribute and jointly carry out tasks for solutions in the project phases
- can argue in discussions in a goal-oriented manner and deal with criticism objectively
- can present the results of group work together
- can evaluate project results and formulate suggestions for improvement
- can recognize and reduce misunderstandings between discussion partners

Teaching the mathematical foundations of quantum computing, insofar as they are relevant to the successful study of computer science. Students should be familiar with the course content listed below and be able to master the central algorithms and assess their significance.

• The students understand the mathematical foundations of quantum mechanics, in particular linear algebra (vectors, matrices, tensor products) and the Bra-Ket notation.
• You will be able to explain the historical development and classification of quantum computing in quantum mechanics.


Use, application and generation of knowledge:

• Students analyze and implement basic quantum gates and their composition into quantum algorithms.
They apply quantum mechanics concepts such as the no-cloning theorem, quantum teleportation and dense coding in practice.
• You will use quantum algorithms (Deutsch, Grover, Shor) to solve specific problems and evaluate their efficiency.
They understand the quantum Fourier transform and implement a quantum adder based on QFT.You will evaluate methods of quantum cryptography with regard to their security and applicability.

Communication and cooperation:

• The students discuss central aspects of quantum computing and its mathematical foundations in working groups.
• They interpret and explain research results in the field of quantum computing.

Scientific self-image / professionalism:

• They reflect on ethical and security-relevant aspects of quantum cryptography and quantum computing.
You will recognize the potential and challenges of quantum computing for various areas of application.

• B. Lenze, Mathematik und Quantum Computing, Buch und E-Book, Logos Verlag, Berlin, 2020, zweite Auflage.

Ergänzend:

• M. Homeister. Quantum Computing verstehen, Springer Vieweg Verlag, Wiesbaden, 2018, fünfte Auflage.
• R.J. Lipton, K.W. Regan. Quantum Algorithms via Linear Algebra: A Primer, MIT Press, Cambridge MA, 2014.
• M.A. Nielson, I.L. Chuang. Quantum Computation and Quantum Information, Cambridge University Press, Cambridge, 2010.
• C.P. Williams. Explorations in Quantum Computing, Springer-Verlag, London, 2011, zweite Auflage.

After successful participation, students will be able to:

• name essential current cryptographic methods,
• understand the underlying mathematical concepts,
• be able to handle mathematical structures such as abelian groups, abstract rings, finite fields, polynomial rings, and elliptic curves with confidence,
to translate abstract mathematical concepts into concrete implementations in a programming language,assess the security of different cryptographic methods and their variants under given parameters,implement cryptographic procedures themselves,
• to attack cryptographic methods themselves,
• recognize and name potential weaknesses of an implementation (e.g. in terms of security, data volume, speed),
experiment with your own variants,
• critically analyze their own variants
.

• J. Buchmann, Einführung in die Kryptographie, Springer
• H. Delfs, H. Knebl, Introduction to Cryptography, Springer
• J. Hoffstein, J. Pipher, J.H. Silverman, An Introduction to Mathematical Cryptography, Springer
• A. Joux, Algorithmic Cryptanalysis, CRC Press
• A. Werner, Elliptische Kurven in der Kryptographie, Springer

• B. Lenze, Basiswissen Angewandte Mathematik -- Numerik, Grafik, Kryptik --, Buch und E-Book, Springer Vieweg Verlag, Wiesbaden, 2020, zweite Auflage.

Knowledge and understanding

The students:

• have in-depth, specialized knowledge in the field of multimodal human-machine interaction (MMI) and its embedding in networked intelligent environments.

• know current research issues, theoretical concepts and technological developments in the field of modern forms of interaction, including sensor-based, voice-based and tangible interaction.

• are able to analyze and critically reflect on interdisciplinary relationships between interaction technologies, cognitive processes and systemic requirements.

• have a scientifically sound understanding of the technical, methodological and ethical challenges in the design of multimodal user interfaces.

Use, application and generation of knowledge

Students are able to:

• systematically analyze novel forms of interaction, derive their potentials and limitations and transfer them to specific fields of application.

• to develop concepts, methods and models for multimodal user interfaces and to further develop them in a research-oriented manner.

• formulate their own scientific questions, develop hypotheses and apply empirical or experimental methods for validation.

• design, implement and critically evaluate innovative solutions for human-machine interaction systems.

• design complex interactive systems in the context of Ambient Assisted Living (AAL) and methodically integrate interdisciplinary requirements.

• transfer technological developments from research into concrete applications and develop new interaction paradigms.

Communication and cooperation

The students:

• are able to communicate their scientific findings and technical solutions clearly and persuasively to both peers and an interdisciplinary audience.

• are able to systematically work on challenging problems in interdisciplinary teams and develop research-led solutions.

• have strong discourse and argumentation skills to critically reflect on scientific work and technological developments.

• are able to professionally document, present and critically discuss research and development work.

• are proficient in methods of scientific collaboration, including peer review processes and collaborative research

Scientific self-image / professionalism

The students:

• have the ability to critically question scientific theories, methods and concepts and apply them to new problems in the field of human-machine interaction.

• are able to independently initiate research projects and develop innovative approaches for the further development of the MMI discipline.

• reflect on the social, ethical and application-related effects of interaction technologies and take these into account in their solutions.

• have a high degree of independence and initiative in acquiring knowledge and developing new scientific and technological concepts.

• understand themselves as part of the scientific community and can contribute their expertise to specialist conferences, publications and interdisciplinary research projects.

• Basics of interaction design from perception, work and cognitive psychology; theories of design: distributed cognition, activity theory, structuring theory; interaction modeling
• Description and use of contextual information for interaction analysis
• Intensification in the following technical areas:
  • Sensor-based interaction technologies,
  • Voice recognition and control,
  • Tangible interaction/camera projector systems;
• Ambient environments from the field of AAL, in the task areas:
  • Security & prevention (home emergency call, lighting systems, ),
  • Health and care (vital signs monitoring, fitness trackers, ),
  • Home and care (Google Nest, robotics, service portals, ),
  • Communication and social environment (voice control, communication solutions, );
• AAL platforms and Internet of Things infrastructures as the basis for multimodal interaction
.
• Approach (analysis, conception, methods, models) for the development of multimodal user interfaces.
• Problem solving using the example of a self-developed multimodal user interface from the field of AAL (student projects);

• • Rogers, I. (2012). HCI Theory: Classical, Modern, and Contemporary - Synthesis Lectures on Human-Centered Informatics. Morgen & Claypool.
  • Journal on Multimodal User Interfaces (2016), Volume 10, Springer International Publishing 2016
  • BMBF/VDE Innovationspartnerschaft AAL (Hrsg.) 2011: Ambient Assisted Living (AAL) Komponenten, Projekte, Services Eine Bestandsaufnahme, VDE Verlag.

• Balzert, H. (2008): Lehrbuch der Softwaretechnik - Softwaremanagement, Heidelberg: Spektrum Akademischer Verlag.
• Balzert, H. (2009): Lehrbuch der Softwaretechnik - Basiskonzepte und Requirements Engineering, 3. Auflage, Heidelberg: Spektrum Akademischer Verlag.
• Keller-Stoltenhoff, Leitzen, Ley (2017): Handbuch für die IT-Beschaffung (Band 1 und 2), Heidelberg: Rehm-Verlag.
• Mangold, P. (2009): IT-Projektmanagement kompakt, 3. erweiterte Auflage, Heidelberg: Spektrum Akademischer Verlag.
• Spitczok, N.; Vollmer, G., Weber-Schäfer, U. (2014): Pragmatisches IT-Projektmanagement, 2. überarbeitete Auflage, Heidelberg: dpunkt-Verlag.
• Vollmer, G. (2024): Vorlesungsunterlagen zur seminaristischen Lehrveranstaltung "Organisatorische und rechtliche Aspekte der IT-Beschaffung"
• Winkelhofer, G. (2005): Management- und Projekt-Methoden, 3. vollst. überarbeitete Auflage, Berlin, Heidelberg: Springer Verlag.

Technical and methodological competence:

• Students can explain the specific tasks of managers and differentiate them from specialist tasks.
Students know selected psychological principles of leadership and selected leadership theories.Students are familiar with selected leadership methods and can apply these in case studies and role plays.Students can analyze case descriptions of typical leadership situations and develop and argue solutions based on the theory they have learned.

Interdisciplinary methodological competence:

• The knowledge of psychological principles, the ability to analyze (conflict) situations and communication skills can be used by students in any professional situation.

Social skills:

 

• Group work promotes the ability to develop solutions with other (unfamiliar) students
.
• Role-playing games strengthen skills in dealing constructively with feedback and train the ability to observe communicative (conflict) situations.

Professional field orientation:

• Through guest contributions from HR managers and managers from the field, students learn what requirements are placed on managers in professional fields of computer science.