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Module Handbook Master Embedded Systems Engineering

MOD1-01 – Mathematics for Signals & Controls

Number	MOD1-01
Title	Mathematics for Signals & Controls
Language	English
Type of participation	Compulsory subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	1
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	none
Learning outcomes	Knowledge Knows basic theorems of complex analysis and linear algebra Knows relevant theoretical foundations of signal processing and control engineering Knows the most important concepts of probability theory Skills Can make use of analysis and linear algebra to describe physical phenomena Can make use of different domains for the description of signals Can apply probabilistic concepts Can make use of tools for numerical mathematics Competence – attitude Can discuss mathematical prerequisites of mechatronic systems with experts Understands experts for mathematics and translates between different domains
Course description and course structure	This course introduces the necessary mathematical concepts for signal processing and control engineering. It starts with a tailored review of real and complex analysis. A major focus is on different kinds of integral transforms that are of essential use in subsequent courses. A huge amount of physical phenomena can be described by sets of linear differential equations and thus the latter are dealt with in this course. Linear algebra plays a prominent role in case of systems with several states and/or multiple inputs and outputs. Usually, sensor signals are corrupted by noise or other sources of uncertainty. To be able to deal with those, probability theory is introduced. Matlab and Octave are used as examples for state of the art tools for numerical mathematics and as a preparation for following courses. Course Structure Real and complex analysis Fourier, Laplace and Z transform Differential equations Linear algebra Probability theory Introduction into Matlab/Octave Numerical mathematics Case Studies None – courses contain small labs Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures & Exercises Labs with Matlab/Octave E -learning modules on higher mathematics, tool tutorials
Assessment of course	Written Exam at the end of the course
Module mapping	Input for: MOD2-04 – Signals & Control Systems 1 MOD-E02 – Biomedical Systems MOD-E04 – Signals and Systems for Automated Driving MOD-E05 – Computer Vision MOD-E011 – Signals & Control Systems 2
References	James, Modern Engineering Mathematics, Pearson Education, 2015 Stroud, Engineering Mathematics, Macmillan Education, 2013 Oppenheim, Willsky, Nawab, Signals and Systems, Pearson Education, 2013

MOD1-02 – Distributed and Parallel Systems

Number	MOD1-02
Title	Distributed and Parallel Systems
Language	English
Type of participation	Compulsory subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	1
Duration	1 Semester
Frequency	Winter semester
Learning outcomes	Knowledge Knows theory of distributed and parallel systems Knows critical issues concerning reliable distributed systems Knows recent research about partitioning and scheduling for cyber physical systems Skills Can assess the feasibility of distributed CPS Can implement algorithms for distributed embedded systems Can model the behavior of distributed CPS Can apply state of the art tools and can develop new tools for distribution Competence - attitude Can setup tooling and design flows Can discuss distribution issues with computer scientists Understands the potential of concurrency in CPS
Course description and course structure	Distributed systems are groups of networked computers and/or embedded systems, which have a common goal for their work. The terms distributed computing and parallel computing have a lot of overlap and frequently the term concurrent computing is used in this field. There is no clear distinction between them. This course is a prerequisite for the deeper understanding of multicore and manycore systems. It builds the theoretical core knowledge about cyber physical systems (CPS) and about the current state of research in the field of embedded distributed systems. Course Structure 1. Architectures for distributes systems (in principle) 2. Communication a. Synchronous, Asynchronous b. Peer-to-Peer, Broadcast, Multicast c. Protocols 3. Time and States a. States and Timestamps b. Clocks 4. Coordination and Agreement a. Transactions and Concurrency Control b. Deadlocks c. Replication and Fault Tolerance 5. Scheduling/Partitioning/Distribution (Multicore/Manycore) 6. Cyber physical systems (CPS) 7. Dependable Systems 8. Programming Paradigms and Methods Skills trained in this course: theoretical and methodological skills
Teaching and training methods	Lectures & Exercises, AMALTHEA and TA tool labs e-learning modules on theoretical informatics, tool tutorials Presentation and discussion of an industry case by a partner company (e.g. Bosch, BHTC, TA)
Assessment of course	Written Exam (60 min) at the end of the course (50%) and individual homework (50%): paper/report about a recent topic from CPS research
Module mapping	Input for: MOD2-01- Mechatronic Systems Engineering MOD2-02 – Microelectronics & HW/SW Codesign MOD-E03 – SW Architectures for Embedded and Mechatronic Systems
References	G. Coulouris, J. Dollimore, T. Kindberg, G.Blair: Distributed Systems: Concepts and Design (5th ed.), Addison Wesley, May 2011 Hermann Kopetz, Real-Time Systems: Design Principles for Distributed Embedded Applications (Real-Time Systems Series), Springer, April 2011 P. Linington, Z. Milosevic, A. Tanaka, A. Vallecillo. Building Enterprise Systems with ODP: An Introduction to Open Distributed Processing, Chapman & Hall/CRC, September 2011 P. Koopmann. Better Embedded System Software, Drumnadrochit Education, 2010 Research Papers: Lamport, Chandy & Lamport Other recent research papers

MOD1-03 – Embedded Software Engineering

Number	MOD1-03
Title	Embedded Software Engineering
Language	English
Type of participation	Compulsory subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	1
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	computer science & programming
Learning outcomes	Knowledge Students know the characteristics of embedded (and real-time) systems Students know the most important SysML diagrams. Students know the syntax and semantic of the most important SysML diagrams. Students know modeling tools for embedded software systems. Students know processes and methods of embedded software engineering. Skills Students can choose SysML-Diagrams to model specific software aspects. Students can model structural aspects by means of block diagrams. Students can model constraints by means of parametric diagrams. Students can model control flow-based behavior by means of activity diagrams. Students can model message-based behavior by means of interaction diagrams. Students can model event-based behavior by means of state machines. Student can tailor processes and methods to specific project needs. Students can evaluate and use tools for embedded Software engineering. Competence - attitude Students develop an attitude to embedded software engineering according to modeling and processes. Students show a quality attitude according to embedded software engineering modeling. Students understand the main challenges of complex embedded software projects. Students understand the importance of modeling complex embedded software systems Students can improve their effectiveness and efficiency by using dedicated methods and tools to support engineering processes. Students understand the differences between software and embedded software systems projects and act accordingly
Course description and course structure	Embedded software engineering is a multidisciplinary approach for developing Solutions to complex engineering problems. The continuing increase in system complexity is demanding integrated engineering practices combining software engineering, control engineering, mechanical engineering, and electrical engineering. Therefore, modeling embedded systems often results in a mix of models from a multitude of disciplines. An integrated modelling approach is provided by SysML as an extension of the Unified Modeling Languague (UML ), version 2, which has become the de facto standard software modeling language. SysML is a robust language that addresses many of the embedded software engineering needs, while enabling the embedded software engineering community to leverage the broad base of experience and tool vendors that support UML. Embedded systems are often safety-critical applications where correct operation is vital to ensure the safety of the public and environment. Furthermore, these systems have to fulfill real-time requirements and they have to cope with restricted resources Finally, we focus on several development processes of embedded systems and their underlying tools. In addition to the lecture exercises are organized to give an insight how to use state of the art approaches and tools. Within small projects the students can contribute the gained knowledge by using these introduced tools and concepts. Course Structure Characteristics of Embedded (and real-time) Systems Motivation for Embedded Software Engineering Modeling of Embedded Systems Overview and Architecture of SysML SysML: Requirements and Use Cases SysML: Basic Concepts SysML: Modeling Structure with Blocks SysML: Modeling Constraints with Parametrics SysML: Modeling Control Flow-Based Behavior with Activities SysML: Modeling Message-Based Behavior with Interactions SysML: Modeling Event-Based Behavior with State Machines SysML Tools in General and Enterprise Architect Development Processes of Embedded Software Systems SW Quality Management, Software-Test Development Tools (e.g. Enterprise Architect, IBM Rational Rhapsody) Case Studies CS01: AMALTHEA tool chain – modeling tools CS05: M2M System – modeling with Enterprise Architect, IBM Rational Tools Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures introducing concepts, methods and tools Group work to train concepts and methods, to develop skills and to work on case studies Home work to add contributions on a case study as group work Presentations to communicate results Presentation and discussion of an industry case by a partner company (itemis or smart mechatronics)
Assessment of course	Written Exam at the end of the course (50%) and group work as homework (50%) with Enterprise Architect or IBM Rhapsody use case and demonstration/presentation
Module mapping	Input for: MOD2-01- Mechatronic Systems Engineering MOD2-02 – Microelectronics & HW/SW Codesign MOD-E04 – SW Architectures for Embedded and Mechatronic Systems MOD-E03 – Automotive Systems MOD-E07 – Model Based and Model Driven Design Connects to: MOD1-02- Distributed and Parallel Systems
References	Alt, O.: Modellbasierte Systementwicklung mit SysML: in der Praxis, Carl Hanser Verlag GmbH & Co. KG, März 2012, ISBN: 978-3446430662 Friedenthal, S.; Moore, A.; Steiner, R.: A Practical Guide to SysML: The Systems Modeling Language, Morgan Kaufmann, 2nd Edition, Oktober 2011, ISBN: 978-0123852069 Oshana, R.: Software Engineering for Embedded Systems: Methods, Practical Techniques, and Applications (Expert Guide), Newnes, Mai 2013, ISBN: 978-0124159174

MOD1-04 – Requirements Engineering

Number	MOD1-04
Title	Requirements Engineering
Language	English
Type of participation	Compulsory subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	1
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	none
Learning outcomes	Knowledge Knows frameworks and models for RE Knows relevant RE processes and interfaces to other processes Knows concepts and recent research on product line and variability management Skills Can model requirements with RE tools Can set up and integrate RE tools into tool chains and design flows Can derive requirements in a structured and comprehensive way Competence - attitude Understands the importance of RE in the early project phase Can set up and lead RE in a cross domain team
Course description and course structure	Requirements engineering (RE) is the very first activity in software, systems, and service development. This course builds on software engineering skills from 1st semester (UML, SysML). Deriving a comprehensive set of requirements is a mandatory and critical task in the early phase of the systems engineering design flow. Requirements are the starting point and main angle for design, verification & validation, and for the test and integration of systems. Configuration and change request management are connected with RE. Defining requirements and dealing with requirements in a structured way is still a major area for research on tools and methodologies – especially for large and complex mechatronic systems. In this module, students will get specific knowledge about the state of the art and the main future challenges in RE. Course Structure Introduction (What is a requirement?, problem vs. solution) Frameworks (e.g. Jackson's WRSPM Modell) Requirements Engineering Process (stakeholder, activities) System and system context Elicitation of requirements (techniques and supporting activities, Kano model) Textual requirements documents Requirements modeling (e.g. goal-oriented modeling, requirements patterns) Non-functional requirements Validation of requirements Requirements Management (attributes, prioritization, traceability, change management, RE tools, CMMI, ReqIF exchange format) Software product lines and variability management Case Studies CS01: AMALTHEA tool chain – application of product line management tool and ReqIF support CS02: HVAC Control System Demonstrator – setup in IBM Rational DOORS Skills trained in this course: practical, methodological, and personal skills
Teaching and training methods	Lectures introducing concepts, methods and tools Group work to train concepts and methods, to develop skills and to work on case studies Literature review and Essay writing Home work to add contributions on a case study as group work Presentations to communicate and demonstrate homework
Assessment of course	Paper/essay on literature review about recent research as individual homework (50%) and group work as homework (50%): DOORS demonstration and presentation of example
Requirements for award of credits	MOD1-03 - Embedded Software Engineering
Module mapping	MOD-E03 – Automotive Systems MOD-E07 – Model Based and Model Driven Design Connects to: MOD2-01 – Mechatronic Systems Engineering MOD2-03 – R&D Project Management
References	Pohl, K.; Requirements Engineering: Fundamentals, Principles, and Techniques, Springer 2010. Robertson, S. and Robertson, J.; Mastering the Requirements Process: Getting Requirements Right, Addison-Wesley, 2012. van Lamsweerde, A.; Requirements Engineering: From System Goals to UML Models to Software Specifications, John Wiley & Sons, 2009.

MOD1-05 – Scientific & Transversal Skills

Number	MOD1-05
Title	Scientific & Transversal Skills
Language	English
Type of participation	Compulsory subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 h (60 h + 120 h)
Semester	1
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	none
Learning outcomes	Knowledge Knows the foundations of each topic at least up to a bachelor level Skills Can apply the knowledge in the upcoming master courses Competence - attitude Can assess the gaps in own knowledge Can use a variety of tools, online-courses, tutorials to close the gaps through self-study
Course description and course structure	This module is tailored for new students with different levels of proficiency from their bachelor programmes. It is intended to close the gaps to the knowledge required for the master programme. Students select a minimum of 4 out of 7 compact courses on basic topics relevant for the further study programme. These compact courses will enable students with different backgrounds to get a smooth start into the master programme. Course Structure The programme offers a selection of about 7 compact courses. More compact courses might be added according to the needs of the individual student group: Compact Programming Course (Java) Modeling of Embedded Systems (UML) Embedded Systems Lab Project Mini Project Research Methods and Tools A (RMT-A) Engineering Communication 1 (German) Engineering Communication 1 (other language) Case Studies None – courses contain small labs Skills trained in this course: methodological, practical and scientific skills
Teaching and training methods	Lectures introducing concepts, methods and tools Labs to train practical skills Group work to train concepts and methods, to develop skills and to work on projects Literature review and essay writing Homework to contribute to projects as group work Presentations to communicate and demonstrate homework / project work
Assessment of course	tests (60 min) for each compact course, graded project work, compact course results are summarized for overall module grade
Requirements for award of credits	compulsory, students have to choose a minimum of 4 out of 7 courses, based on assessment of their prior knowledge
Module mapping	Input for: All other courses
References	Peter Marwedel, Embedded System Design, Springer (2nd Edition, 2011) Herbert Schildt, Java: A Beginner's Guide, McGraw-Hill Education (6th Edition, 2014) Joshua Bloch, Effective Java: A Programming Language Guide, Addison-Wesley (2nd Edition, 2008) Martina Seidl, Marion Scholz, Christian Huemer, Gerti Kappel: UML @ Classroom: An Introduction to Object-Oriented Modeling (Undergraduate Topics in Computer Science), Springer (2015)

MOD2-01 – Mechatronic Systems Engineering

Number	MOD2-01
Title	Mechatronic Systems Engineering
Language	English
Type of participation	Compulsory subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	MOD2-04 - Control Theory and Systems MOD1-03 - Embedded Software Engineering
Learning outcomes	Knowledge Knows CONSENS, INCOSE SE handbook, MechatronicUML Knows mechatronic systems engineering processes Knows Enterprise Architect and other relevant tools Skills Can model mechatronic systems Can apply methodology and state of the art tools on real use cases (e.g. printing machine) Can select tools and define tool chains and design flows Competence - attitude Can structure the early phase of mechatronic systems design Can lead cross domain design of mechatronic systems Understands issues from different domains and can integrate solutions into a holistic design
Course description and course structure	Mechatronics Systems Engineering is both a challenge and a chance. A holistic and well elaborated engineering process for complex mechatronic system/cyber physical systems is a mandatory requirement for developing future intelligent products. Teaching this new school of engineering is the major goal of the whole master programme and an attractive offer for a university of applied sciences. This module introduces the holistic engineering methodology and offers the big picture for the other modules. The focus is on the early phase of mechatronic systems design since this phase offers the biggest leverage for better technical systems. Topics like cross domain engineering and systems integration are addressed, too. The content of the course is largely inspired from finding of the BMBF Spitzencluster “it’s OWL” and the new Fraunhofer Institute “Entwurfstechnik Mechatronik”. A continuous transfer of new findings into this course is intended. Course Structure Motivation: Examples for Mechatronic Systems Characteristics of Mechatronic Systems Challenges Discipline-spanning development process Systems Engineering (according to INCOSE SE handbook) Conceptual Design of Mechatronic Systems CONSENS The Software Engineering Domain MechatronicUML Behavior synthesis Self-Optimization: Operator Controller Module (OCM) Application to Use Case (Printing Industry, Rail Cab) Case Studies CS07: Rail Cab – modeling with CONSENS (Enterprise Architect) CS07: Rail Cab – modeling with Mechatronic UML Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures, Labs (with Enterprise Architect and other tools), homework Access to tools and tool tutorials Access to recent research papers
Assessment of course	Written Exam at the end of the course (50%) and individual homework (50%): MechatronicUML model of an example
Requirements for award of credits	mechanics/physics, basics of embedded systems
Module mapping	MOD-E04 – SW Architectures for Embedded and Mechatronic Systems MOD-E06 – Formal Methods in Mechatronics MOD-E07 – Model Based and Model Driven Design Connects to: MOD1-04 – Requirements Engineering MOD2-03 - R&D Project Management
References	Jürgen Gausemeier, Franz Rammig, Wilhelm Schäfer (Editors): Self-optimizing Mechatronic Systems: Design the Future. HNI-Verlagsschriftenreihe, Band 223, 2008 P.L. Tarr, A.L. Wolf (eds.): Engineering of Software. Springer-Verlag Berlin Heidelberg 2011 K. Pohl, H. Hönninger, R. Achatz, M. Broy (Eds.): Model-Based Engineering of Embedded Systems: The SPES 2020 Methodology, Springer, 2012 INCOSE: Guide to the Systems Engineering Body of Knowledge - G2SEBoK: http://g2sebok.incose.org/app/mss/menu/index.cfm

MOD2-02 – Microelectronics & HW/SW Co-Design

Number	MOD2-02
Title	Microelectronics & HW/SW Co-Design
Language	English
Type of participation	Compulsory subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	MOD1-03 - Embedded Software Engineering electronics, basics of embedded systems
Learning outcomes	Knowledge Knows microelectronic components of embedded systems Knows digital systems design methodology and processes Knows tools and technologies for digital design Knows concept of virtual prototype and its application in HW/SW Codesign Skills Can compose an embedded system out of microelectronic components Can describe digital systems with SystemC or VHDL Can run a digital simulation Can assess synthesis and verification reports for simple designs Can run test and debug sessions with FPGAs Competence - attitude Can set up HW/SW Codesign projects for embedded systems Can choose and tailor the tool chain and methodology Can present and demonstrate the design flow for a digital design project
Course description and course structure	Digital Systems are the main hardware platform for embedded systems and the target of embedded SW development. A good knowledge and overview of available HW platforms is required. Furthermore, a concurrent engineering process (HW/SW Codesign) is used to develop state of the art embedded systems. The coordination of (more agile) SW development and (more V-model) HW development is a challenge. Digital system development is applying complex tools and tool chains. The goal of this module is to enable to students to select, to assess, and to develop digital target platforms for embedded systems. Course Structure Microelectronic Components for Embedded Systems DSP, Microcontroller FPGA ASIC, ASSP Memories Communication components (e.g. serial busses) PCB and standard circuits Digital systems design methodologies and processes ESL concepts SystemC VHDL/Verilog Simulation and validation HW/SW partitioning Verification and test Synthesis (on FPGA) Virtual Prototypes and HW/SW co-verification Tools and Tool Chains New Trends: Multicore/Manycore, SoC, 3D, MEMS Case Studies CS01: AMALTHEA tool chain – Use of Virtual Prototypes CS03: CoreVA – Implementation of IP blocks and testbenches in SystemC and VHDL CS04: Avionics Computer & Robots – Design and implementation on FPGA Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Teaching and training methods Lectures Labs with: SystemC and VHDL simulation (Mentor), FPGA synthesis (Mentor or Synopsis) and FPGA implementation (Xilinx or Lattice). Access to tools and tool tutorials (Europractice tool chain)
Assessment of course	Oral Exam at the end of the course (50%) and group work as homework (50%): SystemC or VHDL implementation, mapping on FPGA, demonstration and presentation
Module mapping	MOD-E08 – SoC Design Connects to: MOD2-03 - R&D Project Management
References	Documentation of Europractice – Mentor Graphics Tools and Cadence Tools Neil H.E. Weste, David Money Harris: “Integrated Circuit Design”, Pearson, 2011 Clive “Max” Maxfield (Editor): “FPGAs World Class Designs”, Newnes / Elsevier, 2009 Jack Ganssle (Editor): “Embedded Systems World Class Designs”, Newnes / Elsevier, 2008 Peter J. Ashenden: “Digital Design – An Embedded Systems Approach Using VHDL“, Morgan Kaufmann / Elsevier, 2008 Peter J. Ashenden: “The Designer’s Guide to VHDL 2nd Edition”, Morgan Kaufmann / Academic Press, 2002 Schaumont, Patrick: A Practical Introduction to Hardware/Software Codesign. Springer 2010 Bailey, Brian, Martin, Grant: ESL Models and their Application: Electronic System Level Design and Verification in Practice. Springer 2010

MOD2-03 – R&D Project Management

Number	MOD2-03
Title	R&D Project Management
Language	English
Type of participation	Compulsory subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	none
Learning outcomes	Knowledge Students know the basic body of knowledge for project management Students know processes, methods and tools for risk management for R&D projects (e.g. FMEA, @risk) Students know processes, methods and tools for configuration management (esp. from SW engineering) Students know processes, methods and tools for change and claim management Students know processes, methods and tools for quality management according to ISO9001 and TS16949 Students understand the importance of Reviews in R&D projects Students understand the main challenges of large R&D projects Skills Students can tailor processes and methods to the respective projects Students can apply the respective project management methodology Students can assess R&D projects and can extract relevant characteristics Students can develop new methods according to gaps in the existing methodology Students can do the complete planning and preparation of a real project case Students can develop relevant KPIs and scorecards for measuring effectiveness and efficiency Competence - attitude Students develop an attitude to project management according to engineering standards Students show a quality attitude according to engineering standards Students manage projects based on structured and well defined processes and in depth analysis Students can achieve high effectiveness and efficiency in running complex and innovative R&D projects Students understand the differences between small and large projects and act accordingly
Course description and course structure	course R&D project management is focusing on processes, methods and tools for the management of innovative research and development projects in engineering. R&D projects are characterized by creativity and a high degree of innovation and uncertainty. Advanced project management methodology has to deal with the uncertainty and has to foster creativity. Apart from this general problem, R&D project methodology has to be aligned with the engineering processes and with the different engineering domains. Topics like quality management, configuration management and specific tools for risk management are part of the methodology, too. The course enables students to understand and structure R&D projects and to choose appropriate tools and methods based on a proper analysis of the project characteristics. The students are able to tailor the methodology and they understand the remaining gaps in the methodology. They can develop new project management methods and tools to fill the gaps and they can do research to assess the effectiveness and efficiency of project management methodology in R&D. The course is based on one main project case study and several small cases for specific topics. Course Structure Characteristics of R&D projects Project management processes: planning, controlling (cost, time, quality) agile & lean V-model Milestones and Reviews Risk Management for R&D Projects Configuration & Release Management Change and Claim Management (incl. Patents) Quality Management (incl. CMMI) KPIs and Scorecards Large R&D projects and Cross Domain Projects Management of R&D organizations Engineering Communication 2 (German) Case Studies CS01: AMALTHEA tool chain – setup of the ITEA2 research project CS05: M2M System – management of a ZIM project Skills trained in this course: methodological and personal skills
Teaching and training methods	Lectures introducing concepts, methods and tools Group work to train concepts and methods, to develop skills and to work on case studies Home work to add contributions on a case study as group work Presentations to communicate results Presentation and discussion of an industry case by a partner company
Assessment of course	Oral Exam at the end of the course (50%) and group work as homework (50%): project kickoff/release report and presentation
Requirements for award of credits	MOD1-03 - Embedded Software Engineering
Module mapping	MOD-E10 – Automotive Systems Connects to: MOD1-04 – Requirements Engineering MOD2-01 – Mechatronic Systems Engineering MOD2-02 – Microelectronics & HW/SW Codesign
References	PMBOK® - 4th edition, PMI® 2008. Kerzner, Harold: Project Management: A Systems Approach to Planning, Scheduling, and Controlling, 10th edition, New York 2009 ICB - IPMA Competence Baseline, Version 3, PMA/GPM-Eigenverlag 1999 INCOSE – SE handbook

MOD2-04 – Signals and Control Systems 1

Number	MOD2-04
Title	Signals and Control Systems 1
Language	English
Type of participation	Compulsory subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	higher mathematics
Learning outcomes	Knowledge Knows relevant theoretical foundations of signal processing and control theory Knows mathematical background of linear feedback controllers Is aware of critical limitations of discrete time signals and the impact of sampling Knows basic analogue and digital filters Skills Can analyze systems and signals Can model linear feedback controllers for mechatronic systems Can apply and design digital filters Competence - attitude Can discuss control system design for mechatronic systems with experts Can lead cross domain design of control systems Understands control system experts and translates between different domains
Course description and course structure	Control theory is one major part of the description of the dynamic behavior of mechatronic systems. Control systems are the connection between the mechanical/physical world and the control task performed by the embedded system. The goal of this module is to enable students to interact with control system experts and to integrate their results into embedded and mechatronic systems. Cross Domain Engineering requires a deeper understanding of control tasks and the underlying principles of control theory, especially for digital control systems. A holistic view on control system topics is taught. The curriculum limited to linear systems and the course structure follows the book Modern Control Systems by Bishop/Dorf. An additional goal is to teach the use and the development of advanced tools for control system design. Course Structure State Variable Models State Feedback Control Systems Robust Control Systems Digital Control Systems Applications of the above Control Engineering with Matlab/Simulink Case Studies CS04: Avionics Computer & Robots – Control Algorithms CS04: Avionics Computer & Robots – MATLAB/Simulink implementation for Arm Type Robots Skills trained in this course: theoretical and methodological skills
Teaching and training methods	Lectures & Exercises, Matlab/Simulink labs e-learning modules on mathematics and control theory, tool tutorials
Assessment of course	Written Exam at the end of the course
Module mapping	MOD-E05 – Computer Vision MOD-E011 – Signals & Control Systems 2
References	P. Corke: Robotics, Vision and Control, Springer, 2013 R. Bishop, R. Dorf: Modern Control Systems, Pearson Education, 2010

MOD3-03 – Research Project (Thesis)

Number	MOD3-03
Title	Research Project (Thesis)
Language	English
Type of participation	Compulsory subject
Credits	18
Workload (self study and contact hours)	540 (40 (individual consulting and colloquium) + 500)
Semester	3
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	none
Learning outcomes	Knowledge Knows state of the art in a certain scientific field Knows open research questions in this field Knows relevant literature Knows methodology and tools to execute project Skills Can define and plan an own research project Can apply appropriate research methodology Can create own research findings Can describe project execution, methodology and findings in a scientific report Competence - attitude Can run an own more complex scientific research project Masters uncertainty and unknown topics in new area Can present and defend results (in colloquium or at a conference)
Course description and course structure	The research project is intended to introduce students into scientific research work in a bigger context. Students will participate in one of the ongoing research projects. They will contribute with an own sub project. The starting point is the definition of the research questions they want to answer and the selection of the appropriate methodology. The students will plan and execute their project independently with regular review and consulting. They will summarize their finding in a research project thesis (project report). The research project will be a preparation for further work on the master thesis. The intention of the research project is to familiarize with the research methodology in a certain scientific field and to formulate the scientific state of the art and the research questions. The student proves the ability to execute own and independent research on master level and with a certain complexity. Course Structure Students will select a topic from one of the ongoing projects in CPS and Embedded Systems. The will get individual consulting and feedback. During the semester the students will write a project thesis and present it in a colloquium at the end of the semester. Excellent results are intended to be published and presented (oral or poster) at a conference (can be done in connection with the master thesis, too). Case Studies None – topics will be selected from ongoing projects Skills trained in this course: theoretical, practical, methodological, and personal skills
Teaching and training methods	Project Work Writing of a scientific report Presentations to communicate and discuss the findings E-learning course on scientific work and scientific writing Individual review and feedback on papers and presentations
Assessment of course	project thesis about own research in an ongoing project as individual homework + presentation in colloquium (100%)
Module mapping	MOD4-01 – Master Thesis + Colloquium
References	According to topic

MOD-E01 – Applied Embedded Systems

Number	MOD-E01
Title	Applied Embedded Systems
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	none
Learning outcomes	Knowledge Knows standards and platforms for specific domain Knows target systems Has acquired overview of target domain Skills Can describe relevant characteristics and challenges of application domain Can model mechatronic systems for the domain Can apply methodology and state of the art tools on real use cases Can select tools and define tool chains and design flows Competence - attitude Can structure a real mechatronic systems design project Can communicate and find solutions with domain experts Understands issues from application domains and can integrate solutions into a holistic design
Course description and course structure	Applied embedded systems such as embedded controllers for industrial (i.e. robotics) applications are surrounded from sensors and actuators. Together with other embedded systems they can be groups of networked computers, which have a common goal for their work. This course gives an overview about the recent state of the art in embedded and cyber physical systems. Each semester, a selected CPS application will be analyzed in depth. This can be from robotic, energy, mobile communications or industrial scenarios (industry 4.0). The student will learn how to explore and structure a certain application domain and how to map the acquired skills and knowledge to that particular domain. CPS applications will be selected from recent research projects. Course Structure Introduction to the application domain Characteristics of CPS in the application domain Architectures for application specific CPS Standards Platforms and Frameworks Design methodology and processes Domain specific languages (DSL) and applications DSL engineering Tools and Tool Chain Integration Target Platforms and Code Generation Code generation Using real time operating systems (RTOS) Case Studies CS01: AMALTHEA tool chain – will be used for case study A recent use case from a research project will be discussed Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures, Labs (with AMALTHEA tools), homework Access to tools and tool tutorials Access to recent research papers
Assessment of course	Oral Exam at the end of the course (50%) and group work as homework (50%): modeling and target mapping of an example with AMALTHEA tools, demonstration and presentation
Module mapping	Requires: MOD1-02 – Distributed and Parallel Systems MOD1-03 - Embedded Software Engineering Connects to: MOD-E02 – Biomedical Systems MOD-E04 – SW Architectures for Embedded Systems MOD-E03 – Automotive Systems
References	AMALTHEA documentation Research papers of PIMES research group: http://www.fh-dortmund.de/en/fb/3/forschung/pimes/Eigene_Veroeffentlichungen.php

MOD-E02 – Smart Home & Smart Building & Smart City

Number	MOD-E02
Title	Smart Home & Smart Building & Smart City
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: every year - summer semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	Input from: MOD1-02 Software Architectures MOD1-03 Digital Systems 1 MOD2-02 Software-intensive Solutions MOD2-03 Digital Systems 2
Learning outcomes	Learning outcomes 7.1 Knowledge Knows relevant home automation systems and standards Know smart building concepts (e.g. BIM) Knows relevant trends and projects in Smart City Is aware of critical limitations, esp. safety and security issues 7.2 Skills Can design concepts for smart home/smart building/smart city systems Can implement IoT, Cloud and SW components into such systems Can apply state of the art tools and systems (e.g. KNX) Can select IoT and cloud platforms according to smart home/building/city requirements 7.3 Competence – attitude Can discuss smart home/building/city systems with experts Can lead cross domain design in this domain Can contribute within the Dortmund Smart City Alliance
Course description and course structure	Course Description The digital transformation is a major driver for the change in people’s living environment. It affects the technical design of infrastructure systems, starting from people’s home via larger buildings and reaching up to systems like cities or districts. It covers home automation, energy and mobility systems and assistance systems. The course introduces the trends, developments and standards from the smart home, smart building and smart city domains and put them into the context of software and IoT systems. The aim is to enable students to develop larger software systems within the given context and to integrate them with other IoT and cloud systems. Therefore, it is intended to form a domain specific view on the digital transformation. Course Structure 1. Smart home 1.1 Home automation 1.2 Standards and bus systems (e.g. KNX) 1.3 Energy and mobility in smart home systems 1.4 Ambient Assisted Living 2. Smart Building 2.1 Building Information Systems (BIM) 2.2 Safety and Security in Smart Buildings 2.3 Facility Management and Smart Building 3. Smart City 3.1 Smart City concepts and relevant trends 3.2 Integration of Logistics, Energy, Supplies and Mobility 3.3 Stakeholder and Citizen Involvement 3.4 Case Study: Smart City Alliance Dortmund Application Focus Project Smart Systems: students will set up and implement an example or a part of a Smart System (Home, Building, City). The respective case study will be taken from a recent R&D project or an industry case. The result will be a demonstrator system. Skills trained in this course: theoretical, practical and scientific skills and competences
Teaching and training methods	Teaching and training methods Theoretical knowledge: e-learning modules on Smart Systems, tool tutorials Practical Skills: Projects, Labs & Exercises, small project with Smart Systems Scientific Competences: own research on Smart Systems
Assessment of course	Assessment of the course: Written Exam at the end of the course (50%) and Individual programming task (50%): implementation of Smart System (or parts of it), demonstration of the results
Requirements for award of credits	Scientific Focus Students will do a scientific evaluation of the potential of Smart Systems usage in a specific domain (e.g. transportation) based on recent scientific literature. It is intended to take issues from the Smart City Alliance Dortmund or from ruhrvalley.
Module mapping	Input for: None
References	References to be defined

MOD-E03 – SW Architectures for Embedded and Mechatronic Systems

Number	MOD-E03
Title	SW Architectures for Embedded and Mechatronic Systems
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	programming, basics of embedded systems
Learning outcomes	Knowledge Knows concepts and structure of SW architectures for embedded systems Knows standards and frameworks Knows specific challenges (e.g. real time, functional safety) Skills Can define requirements and features for a specific problem Can develop a SW architecture for a specific problem Can model SW architectures with state of the art tools Can apply SW architecture standards to structure a project Competence - attitude Ensures quality and safety for embedded SW Can discuss and assess the advantages and disadvantages of different SW architectures Understands the main issues within research about SW architectures for embedded systems
Course description and course structure	The ongoing complexity increase in mechatronic solutions consequently leads to more complex embedded systems and embedded software. Therefore, advanced SW engineering methodology from large software development projects is consecutively applied in the embedded world, too. Software architectures help to structure, to manage and to maintain large embedded SW systems. They allow re-use, design patterns and component based development. In addition, specific topics like safety, SW quality, integration and testing are addressed by SW architectures and respective standards (e.g. AUTOSAR). In this module, students learn about the concepts and structure of SW architectures for embedded systems. Course Structure Characteristics of Embedded (and real-time) Systems Motivation for Architectures for Embedded and Mechatronic Systems Software Design Architecture for Embedded and Mechatronic Systems Patterns for Embedded and Mechatronic Systems Real-Time Building Blocks: Events and Triggers Dependable Systems Hardware's Interface to Embedded and Mechatronic Systems Layered Hierarchy for Embedded and Mechatronic Systems Development Software Performance Engineering for Embedded and Mechatronic Systems Optimizing Embedded and Mechatronic Systems for Memory and for Power Software Quality, Integration and Testing Techniques for Embedded and Mechatronic Systems Software Development Tools for Embedded and Mechatronic Systems Multicore Software Development for Embedded and Mechatronic Systems Safety-Critical Software Development for Embedded and Mechatronic Systems Case Studies CS01: AMALTHEA tool chain – front end will be used for modeling, Artop modeling tool for AUTOSAR will be used CS05: M2M System – architecture of the middleware will be used Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures, Labs (with AMALTHEA and Artop tools), homework Access to tools and tool tutorials Access to recent research papers Presentation of an industry case by partner BHTC GmbH
Assessment of course	Oral Exam at the end of the course (50%) and individual homework (50%): paper/essay on a recent research topic, presentation
Requirements for award of credits	MOD1-02 – Distributed and Parallel Systems MOD1-03 - Embedded Software Engineering MOD2-01 – Mechatronic Systems Engineering
Module mapping	Connects to: MOD-E01 – Applied Embedded Systems 1 & 2 MOD-E03 – Automotive Systems
References	Robert Oshana and Mark Kraeling, Software Engineering for Embedded Systems: Methods, Practical Techniques, and Applications, Expert Guide, 2013 Bruce Powel Douglass. Doing Hard Time: Developing Real-Time Systems with UML, Objects, Frameworks and Patterns. Addison-Wesley, May 1999 Bruce P. Douglass, Real-Time Design Patterns: Robust Scalable Architecture For Real-Time Systems, Addison-Wesley, 2009 F. Buschmann, R. Meunier, H. Rohnert, P. Sommerlad, and M. Stal. Pattern Oriented Software Architecture. John Wiley & Sons, Inc., 1996

MOD-E04 – Signals and Systems for Automated Driving

Number	MOD-E04
Title	Signals and Systems for Automated Driving
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	higher mathematics, programming, signal processing
Learning outcomes	Knowledge Knows common driver assistance components and architectures Knows basic signal processing algorithms for radars Knows state estimation algorithms Knows basics of related system engineering Skills Can develop tracking algorithms Can develop radar signal processing algorithms Can analyze requirements for subsystems of automated driving Competence – attitude Understands the challenges in the development of automated driving and can discuss with experts from different domains Can lead development of subsystems for automated driving Can lead system level tests for automated driving
Course description and course structure	Automated driving requires the use of a multitude of sensors, controllers and actuators installed on the vehicle. Additionally, vehicle to vehicle and vehicle to infrastructure communication will be necessary. This course gives an overview about technologies used for automated driving. It starts with an overview about current R&D trends and then covers several sensor technologies with a special focus upon radar. Students will learn basic principles of stochastic signal processing and its application to tracking and mapping. Motion models and vehicle control technologies will be discussed to gain further insight into requirements for sensors and algorithms. Additional focus of this course is on architectures and infrastructures for automated driving. This includes bus interfaces and SW architectures as well as the basic principles of systems engineering. ISO 26262 as well as legal frameworks and their application to automated driving will be discussed. In addition to the lecture, exercises and small projects give additional insight into the technologies and concepts introduced in this course. Course Structure Technology overview Sensors Radar Lidar Ultrasonic Camera Radar signal processing Detection Target estimation State estimation Vehicle motion models Random processes Tracking Target classification Mapping Actuators & Vehicle Control Bicycle model Longitudinal control Brake and steering systems Architectures Bus interfaces Car-to-X Safety domain controllers AUTOSAR System Engineering Quality Process standards Process models Requirement engineering SPICE ISO 26262 Basics Concept phase Product development Legal frameworks Vienna convention Relevant norms and legislation Case Studies CS08: Radar Systems for Automated Driving Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures, Labs (with Matlab/Simulink) Access to tools and tool tutorials Access to recent research papers Company visit
Assessment of course	Assessment of the course: Oral Exam at the end of the course (50%) and group work as homework (50%)
Requirements for award of credits	MOD1-01 - Mathematics for Controls & Signals
Module mapping	Connects to: MOD1-04 – Requirements Engineering MOD2-01 – Mechatronic Systems Engineering (MOD2-01) MOD-E03 – Automotive Systems MOD-E05 – Computer Vision
References	Winner et al., Handbook of Driver Assistance Systems, Springer reference, 2016 Pebbles, Radar Principles, John Wiley & Sons, 1998 Bar-Shalom et al., Estimation with Applications to Tracking and Navigation, John Wiley & Sons, 2001 Maurer et al., Autmotive Systems Engineering, Springer 2013

MOD-E05 – IoT & Edge Computing

Number	MOD-E05
Title	IoT & Edge Computing
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: every year - summer semester Maximal capacity: 25 students Course admittance prerequisites: (60 + 120)
Semester	2
Duration	1 Semester
Course admittance prerequisites	Input from: None
Learning outcomes	Learning outcomes 6.1 Knowledge Knows concepts and architectures of real-time embedded systems Knows key aspects of real-time networking Has acquired overview of cloud computing and selected cloud platforms 6.2 Skills Can implement, deploy and test simple IoT-systems Can set-up and utilize a cloud system Can analyze the E2E latency in distributed systems 6.3 Competence - attitude Can design an simple IoT system for a given set of requirements Can structure an IoT development project regarding function and time Can propose and implement measures to reduce latency in a distributed system
Course description and course structure	Course Description Internet of things (IoT) is a fundamental building block for digitization and the upcoming information society. This course provides insights into key IoT-technologies including embedded systems, networks and cloud computing. For the selection of use cases and technologies the course focuses on the area of Edge Computing. Within this area students learn about latency analysis and optimization in distributed systems. Last not least, the course offers hands on experiences with IoT and Edge Computing technologies through focused team projects and homework assignments. Course Structure Introduction Real-time Embedded Systems Real-Time Networking Cloud Computing Edge Computing Application Focus Students conduct a project about Edge Sensor Fusion Students work with Gabriel - Edge Computing Platform for Wearable Cognitive Assistance Skills trained in this course: theoretical, practical and scientific skills and competences
Teaching and training methods	Teaching and training methods E-learning modules and lectures on IoT and Edge Computing Small project with Eclipse IoT stack Access to the Open Edge Computing Initiative and the Living Edge Labs
Assessment of course	Assessment of the course: Oral Exam at the end of the course (50%) and individual programming task (50%): implementation of cloud based IoT system for a robot, demonstration of the result
Requirements for award of credits	Scientific Focus During the module recent topics from the Open Edge Computing Initiative will be discussed and papers from relevant conferences will be reviewed.
Module mapping	Input for: None
References	References Peter Marwedel: Embedded System Design, 2nd Edition, Springer, 2011 Andrew S. Tanenbaum, David J. Wetherall: Computer Networks, 5th Edition, Pearson Education, 2014 Thomas Erl, Zaigham Mahmood, Ricardo Puttini, Cloud Computing, Prentice Hall, 2013

MOD-E06 – Computer Vision

Number	MOD-E06
Title	Computer Vision
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	higher mathematics, basics of embedded systems
Learning outcomes	Knowledge Knows standards and platforms for computer vision Knows cameras, components, target systems Has acquired overview of algorithms and methods Skills Can model signal processing path for computer vision Can apply methodology and state of the art tools for computer vision Can adapt and modify/parameterize relevant algorithms Competence - attitude Can structure a real computer vision project Can integrate cameras and vision modules into mechatronic systems Can analyze mechatronic systems and derive requirements for computer vision
Course description and course structure	Computer Vision is both a basic technology and an application domain for mechatronic and embedded systems. It is used in automotive systems, robotics and biomedical systems. This module focus on the use in the biomedical application domain since Dortmund University of Applied Sciences and Arts has established a research centre on biomedical technology (BMT) in 2013. Research topics from this research centre and the research from pimes are defining the content of this module. The module introduces the basic algorithms and components for computer vision systems. In addition, students will learn about the application of that knowledge in the biomedical domain. The course will involve topics from a recent research project. Course Structure Introduction Position and Orientation Light and Color Image Creation Image Processing Feature Extraction Multiple Images Advanced Topics and Applications Case Studies CS10: Avionics Computer & Robots – Vision-Based Control CS06: Biomedical Systems - Medical Imaging Techniques: Algorithms and implementation Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures, Labs (with MATLAB/Simulink), homework Access to tools and tool tutorials Access to recent research papers
Assessment of course	Oral Exam at the end of the course (50%) and group work as homework (50%): modeling and target mapping of an example with Matlab/Simulink, demonstration and presentation
Requirements for award of credits	MOD1-01 – Mathematics for Controls & Signals MOD1-03 - Embedded Software Engineering MOD2-02 – Microelectronics & HW/SW-Codesgin MOD2-04 – Signals & Control Systems 1
Module mapping	Connects to: MOD-E01 – Applied Embedded Systems 1 & 2 MOD-E02 – Biomedical Systems MOD-E04 – SW Architectures for Embedded Systems MOD-E03 – Automotive Systems
References	P. Corke: Robotics, Vision and Control, Springer, 2013 R. Szeliski: Computer Vision: Algorithms and Applications, Springer, 2011 E. Gopi: Digital Signal Processing for Medical Imaging Using Matlab, Springer, 2013

MOD-E07 – Signals & Control Systems 2

Number	MOD-E07
Title	Signals & Control Systems 2
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	higher mathematics
Learning outcomes	Knowledge Knows relevant theoretical foundations of state variable compensators Knows concepts of optimal and robust control Knows approaches of adaptive signal processing and state estimation Knows concepts of predictive control Skills Can model complex control systems for mechatronic systems Can estimate states that are not measurable Can apply modern concepts like model predictive control Can select embedded system platforms according to controller requirements Competence - attitude Can discuss control system design and signal processing for mechatronic systems with experts Understands control system experts and translates between different domains Can lead cross domain design of control systems
Course description and course structure	Control theory is one major part of the description of the dynamic behavior of mechatronic systems. Control systems are the connection between the mechanical/physical world and the control task performed by the embedded system. This module extends the concepts from Signals & Control Systems 1 (MOD2-04) to systems with states that are not directly measurable and/or noise corrupted. For this purpose, observer structures, estimation and adaptive signal processing concepts are reviewed. Emphasis is put on digital control and signal processing to path the way to embedded processing. Based on those concepts, the linear quadratic controller is dealt with as one example to deal with noisy measurement and control signals. Furthermore, in order to incorporate control constraints, modern control strategies like model predictive control are studied. The goal of this module is to enable students to interact with control system experts and to integrate their results into embedded and mechatronic systems under consideration of real-world constraints. Course Structure State Variable Feedback Control Systems Optimal control Robust Control Systems Digital control Adaptive Signal Processing State estimation Linear Quadratic Gaussian Control Model Predictive Control Applications of the above Control Engineering with Matlab/Simulink Case Studies CS04: Avionics Computer & Robots – Control Algorithms CS04: Avionics Computer & Robots – MATLAB/Simulink implementation for Arm Type Robots Skills trained in this course: theoretical and methodological skills
Teaching and training methods	Lectures & Exercises Matlab/Simulink labs Tool tutorials
Assessment of course	Assessment of the course: Written Exam at the end of the course (50%) and group work as homework (50%) with Matlab/Simulink use case and demonstration/presentation
Requirements for award of credits	MOD2-04 – Signals & Control Systems 1
Module mapping	Connects to: MOD-E04 – Signals and Systems for Automated Driving MOD-E05 – Computer Vision
References	Stergiopoulos, Advanced Signal Processing, CRC Press, 2009 Kouvaritakis, Cannon, Model Predictive Control, Springer, 2015 P. Corke: Robotics, Vision and Control, Springer, 2013 R. Bishop, R. Dorf: Modern Control Systems, Pearson Education, 2010 Kay, S.; Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory, Prentice Hall,1993

MOD-E08 – Formal Methods

Number	MOD-E08
Title	Formal Methods
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Course admittance prerequisites	programming
Learning outcomes	Knowledge Knows methodology of formal verification Knows relevant theoretical background Knows specific requirements Skills Can methods on use case Can model verification artefacts (e.g. properties) Can use MechatronicUML approach and tools Competence - attitude Can research on state of the art and theoretical background Can demonstrate and discuss results in group Can structure scientific field and get overview
Course description and course structure	Software has become the driving force in the development of self-optimizing mechatronic systems. Such systems include hard-realtime coordination, which is realized by software, at the network level between distributed components as well as controllers which are more and more implemented by software. The communication goes beyond the use of system and environmental data from controllers. If necessary, complex status information about appropriate protocols and communication channels are exchanged, which themselves can massively influence the underlying behavior of the individual components. This development leads to extremely complex hybrid (discrete / continuous) software. In addition, self-optimizing mechatronic systems are often used in safety-critical environments. This enforces the use of formal verification techniques to ensure the correctness of specified properties. In the course concepts and methods for the modelling and verification of these mechatronic systems are introduced and formally described. In order to enable an efficient verification for such mechatronic systems, techniques like abstraction, decomposition as well as rule-based modelling are introduced. Here, these non orthogonal techniques are skillfully combined. One aim is to handle all models specified by all different domains. The presented approach for the model-based verification of mechatronic systems is massively characterized by the integration of efficient verification techniques for the different domains, based on their domain specific model-based knowledge. Course Structure Motivation: What are Formal Methods? Why should we use Formal Methods? When in the overall development process should we use Formal Methods? Model Checking Theorem Proving Testing Formal Verification in practice: The MechatronicUML Approach Recent Research: literature review AMALTHEA Methodology and Tool Chain Case Studies CS01: AMALTHEA tool chain – will be used to integrate formal verification tools CS02: HVAC control system demonstrator – will be used as example CS07: Rail Cab – will be used as an example Skills trained in this course: theoretical and methodological skills
Teaching and training methods	Lectures, Labs (with MechatronicUML), homework Access to recent research papers Literature review and discussion of results
Assessment of course	Assessment of the course: Written Exam at the end of the course (50%) and group work as homework (50%): verification of an example, demonstration and presentation
Requirements for award of credits	MOD1-02 – Distributed and Parallel Systems MOD1-03 - Embedded Software Engineering MOD2-01 – Mechatronic Systems Engineering
Module mapping	Connects to: MOD-E04 – SW Architectures for Embedded Systems
References	Spivey: The Z Reference Manual (http://spivey.oriel.ox.ac.uk/mike/zrm/zrm.pdf) E. Clarke et al.: Model Checking, MIT Press T. Fischer, J. Niere, L. Torunski, and A. Zündorf: Story Diagrams: A new Graph Rewrite Language based on the Unified Modeling Language. In Proc. of the 6th International Workshop on Theory and Application of Graph Transformation (TAGT), Paderborn, Germany, 1998 W. Reisig: Petrinetze: Modellierungstechnik, Analysemethoden, Fallstudien. Vieweg+Teubner, 2010 J. Bengtsson, W. Yi: Timed Automata: Semantics, Algorithms and Tools. In Lecture Notes on Concurrency and Petri Nets. W. Reisig and G. Rozenberg (eds.), LNCS 3098, Springer-Verlag, 2004 T. Eckart, C. Heinzemann, S. Henkler, M. Hirsch, C. Priesterjahn, W. Schäfer: Modeling and verifying dynamic communication structures based on graph transformations. Computer Science - Research and Development 28(1): S. 3-22, Feb. 2013 M. Hirsch: Modell-basierte Verifikation von vernetzten mechatronischen Systemen. Dissertation, Logos Verlag, 2008

MOD-E09 – System on Chip Design

Number	MOD-E09
Title	System on Chip Design
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	programming, electronics
Learning outcomes	Knowledge Knows basic components of SoCs Knows modern multicore/manycore architectures and ongoing research Knows SoC design tools and tool chains Skills Can develop an SoC from building blocks Can move a simple design through the whole tool chain Can select technology, constraints and layout Competence - attitude Understands ASIC design flow Can consult on SoC selection and decision about SoC design Masters set up and configuration of complex ASIC design tool chains
Course description and course structure	This course introduces Systems on Chip with a strong focus on Multi- and Many-core Systems on Chip (SoC) The course deals both with the technology and the building blocks of SoCs and with the design process and tool chain. Complex SoCs are the basic hardware platform for embedded systems. Their development is a major area for research about tools, methodologies and development processes. ASIC development projects and tool chains are complex in size, technology and project structure. Students learn about the architecture and capabilities of SoCs and about the design flow. Course Structure Main building blocks of SoCs IP-cores (processors, communication, memories, sources for IP-cores) on-chip communication (topologies, wishbone) system definition ESL: electronic specification language on-chip vs. off-chip memory debugging methodologies Multicore and Manycore architectures ASIP and Networks on Chip (NoC) ASIC design flow Design entry (VHDL) Pre-silicon verification Synthesis & technology libraries Layout and signal integrity Timing closure Power routing, clocks and resets Semiconductor test & production Case Studies CS03: CoreVA – ASIC implementation Europractice tools chain (Cadence and Mentor Graphics) and technology library Skills trained in this course: practical and methodological skills
Teaching and training methods	Lectures, Labs (with Europractice tools), homework Access to tool chains and tool tutorials Access to recent research papers Visit at Bielefeld university (CITEC) and Intel Mobile Communications GmbH
Assessment of course	Written Exam at the end of the course (50%) and group work as homework (50%): implementation of a CoreVA based design, demonstration and presentation
Requirements for award of credits	MOD1-02 – Distributed and Parallel Systems MOD1-03 - Embedded Software Engineering MOD2-02 – Microelectronics & HW/SW-Codesign
Module mapping	Connects to: MOD-E04 – SW Architectures for Embedded Systems MOD-E06 – Formal Methods in Mechatronics
References	Neil H.E. Weste, David Money Harris: “Integrated Circuit Design”, Pearson, 2011 Clive “Max” Maxfield (Editor): “FPGAs World Class Designs”, Newnes / Elsevier, 2009 Jack Ganssle (Editor): “Embedded Systems World Class Designs”, Newnes / Elsevier, 2008 Peter J. Ashenden: “Digital Design – An Embedded Systems Approach Using VHDL“, Morgan Kaufmann / Elsevier, 2008 Peter J. Ashenden: “The Designer’s Guide to VHDL 2nd Edition”, Morgan Kaufmann / Academic Press, 2002 Peter J. Ashenden: “The System Designer’s Guide to VHDL-AMS”, Morgan Kaufmann / Elsevier, 2003

MOD-E10 – Automotive Systems

Number	MOD-E10
Title	Automotive Systems
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	programming, basics of embedded systems
Learning outcomes	Knowledge Knows standards and platforms for automotive systems Knows target systems Knows specific requirements (e.g. safety) Has acquired overview of automotive application domain Skills Can develop automotive software with the AMALTHEA tool chain Can model an automotive system according to standards Can select tools and define tool chains and design flows Competence - attitude Can structure a real automotive system development project Can communicate and find solutions with automotive experts Ensures quality and safety of applications
Course description and course structure	Automotive systems are a major application domain for mechatronic and embedded systems. Due to the complexity and the specific requirements (e.g. safety) the domain specific engineering is well elaborated and leading edge in the embedded systems industry. The research centre pimes deals with various automotive partners and research projects. This course gives an overview about the recent state of the art in automotive systems and transfers recent findings into teaching. The student will learn how to explore and structure a certain automotive application and how to map the acquired skills and knowledge to that particular domain. Furthermore, the students will learn about domain specific standards, processes and frameworks. Course Structure Automotive Standards: e.g. AUTOSAR, Quality Standards, Automotive Spice Automotive development processes Tools in Automotive Engineering (ML/SL, Doors, Enterprise Architect) Automotive Supply Chain Automotive Software Development Functional Safety Testing and Verification Product Qualification Application Examples AMALTHEA Methodology and Tool Chain Case Studies CS01: AMALTHEA tool chain – will be used for the whole design flow CS02: HVAC control system demonstrator – will be used for modeling with Matlab/Simulink Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures, Labs (with AMALTHEA tools and Matlab/Simulink), homework Access to tools and tool tutorials Access to recent research papers Company visit at one of the partner companies (Bosch, BHTC)
Assessment of course	Oral Exam at the end of the course (50%) and group work as homework (50%): set up of an automotive system development project, modeling and target mapping of an example with AMALTHEA tools, demonstration and presentation
Requirements for award of credits	All semester 1 & 2 courses
Module mapping	Connects to: MOD-E01 – Applied Embedded Systems 1 & 2 MOD-E06 – Computer Vision MOD-E03 – SW Architectures for Embedded Systems
References	Klaus Hoermann, Markus Mueller, Lars Dittmann, Joerg Zimmer: Automotive SPICE in Practice. Rocky Nook Inc., US, 2008 Joerg Schaeuffele, Thomas Zurawka: Automotive Software Engineering, Bertrams, 2005 Markus Maurer, Hermann Winner (Eds.): Automotive Systems Engineering, Springer, 2013

MOD-E11 – Trends of Artificial Intelligence in Business Informatics

MOD-E12 – Model Based Systems Engineering

Number	MOD-E12
Title	Model Based Systems Engineering
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 h (60 h + 120 h)
Semester	2
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	programming skills (pref. Java), basics of embedded systems MOD1-02 – Distributed and Parallel Systems MOD1-03 - Embedded Software Engineering
Learning outcomes	Knowledge Knows typical challenges in developing future e.g. automotive embedded systems and how to address these using model based approaches Knows how to apply 3rd party tools in MBSE Has acquired an overview on the various views on automotive applications Skills Can model a system from e.g. the automotive context (software, hardware, global functionality) according to a real-world example Can assess an application using tools based on their model description Can select and develop own (rudimentary) tools as well as integrate these into design flows Competence - attitude Can structure a real model based systems engineering development project Can communicate and find solutions with domain experts Understands challenges in using heterogeneous hardware platforms
Course description and course structure	The demands on automotive computing platforms are continuously rising due to the increasing amount of software that is driven by new automotive functionalities. Deploying these applications to computing platforms will introduce several challenges, such as maintaining freedom from interference in safety-critical applications -as required by the ISO~26262 standard,- or meeting constraints such as timing requirements. As the complexity of those systems results in intricate and unforeseen impacts of product and project decisions on the system level, even in late development phases, an early assessment of design decisions will be a key factor for success. This course gives an overview about the recent state of the art in model based systems engineering with focus on the emerging trends in automotive systems and transfers recent findings into teaching. The student will learn how to explore and structure models of automotive systems – especially in the context of hardware/software co-design – and how to map the acquired skills and knowledge to that particular domain. Furthermore, the students will learn about developing and integrating own rudimentary tooling into the APP4MC platform. Course Strucure Trends and challenges for future automotive E/E architectures Automotive Standards: e.g. AUTOSAR, EastADL, Amalthea, … Eclipse APP4MC Modelling embedded systems Developing rudimentary tooling for analyzing resp. modifying existing models of applications Open Source and proprietary tools in Model based Automotive Engineering (e.g. Vector / TA Tool Suite, Inchron, Eclipse APP4MC Task Visualizer, …) Deploying software to embedded multi- and many-core hardware Code generation Testing and Verification Application Examples
Teaching and training methods	Lectures, Labs (with APP4MC and 3rd party tools), homework Access to tools and tool tutorials from industrial partners (e.g. Bosch, Inchron, Vector) Access to recent research papers Company visit from at least one of the partner companies
Assessment of course	Oral Exam at the end of the course (50%) and group work as homework (50%): set up of a MBSE development project in the context of an automotive application, modeling and deploying software to embedded multi-/many core hardware using APP4MC, demonstration and presentation
Module mapping	MOD-E01 – Applied Embedded Systems
References	APP4MC Documentation: https://www.eclipse.org/app4mc/documentation/ Amalthea Model: https://wiki.eclipse.org/images/5/5c/2013-06-04_AMALTHEA_Project.pdf Research papers in the context of model based systems engineering in the context of automotive development: https://scholar.google.de/citations?hl=en&user=iBKd0uAAAAAJ Peter Marwedel: Embedded System Design, 2nd Edition, Springer, 2011

MOD-E13 – Software for Robots

Number	MOD-E13
Title	Software for Robots
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 h (60 h + 120 h)
Semester	2
Frequency	Summer semester
Course admittance prerequisites	programming skills (C/C++ ) MOD1-02 - Distributed and Parallel Systems MOD1-03 - Embedded Software Engineering
Learning outcomes	Knowledge Knows typical challenges in developing software for mobile robots Knows how to use sensor and actuators on mobile robots Knows how to use computer vision, navigation and mapping tools/ methods/ algorithms Skills Can select and integrate typical tools used in robotics within software development projects Can implement software for mobile robots Can test and verify applications for mobile robots Competence - attitude Can structure robotic systems design project Can communicate and find solutions with domain experts Understands issues from the robots application domains and can integrate solutions into a holistic design
Course description and course structure	Robotic systems are usually very complex and utilize extensive functions as well as a high amount of actuators, sensors, and software-algorithms. The development and maintenance of software for such a robotic system is a challenge for developers and requires robotic specific domain knowledge. As the field of robotics ranges from enormous industry robots to small consumer robots, this course focuses on (small) low-cost mobile robots. Therefore a demonstration platform, the S4R rover is used to introduce students to typical challenges and applications for mobile robots. The course gives an overview of current trends and research fields for mobile robots and will focus on hand-on sessions to develop their software solutions. The student will learn to develop, implement, and test the software for the S4R rover in small student groups within the lecture and practice sessions. Individual homework assignments give students a more in-depth knowledge of relevant research topics. Course Structure 1. Introduction to mobile robotics 2. Introduction to the App4MC/ S4R rover - Hardware - Rover API - ROS (Robot Operating System) integration 3. Implementation of Computer Vision tools/ methods/ algorithms 4. Implementation of Navigation and Mappings tools/ methods/ algorithms 5. Application/ Use-Case definition and Implementation in small groups 6. Test and Verification 7. Presentation of Applications/ Use-Cases 8. Homework definition 9. Homework presentation Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures, Practice, homework Access to tools and tool tutorials Access to mobile robots demonstrators (7) Access to recent research papers
Assessment of course	Oral Exam at the end of the course (50%) and group work as homework (50%): Implementation of the software for a given mobile robot, testing software on hardware, development and implementation of a demonstration application, demonstration and presentation
Module mapping	MOD-E01 - Applied Embedded Systems MOD-E03 - SW Architectures for Embedded and Mechatronic Systems MOD-E06 - Computer Vision
References	Robotics, Vision and Control, Peter Corke (ISBN 978-3-319-54413-7) Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox (ISBN 978-0262201629) Embedded Robotics, Thomas Bräunl (ISBN 978-3-540-70534-5) Jahn, U.; Wolff, C.; Schulz, P. Concepts of a Modular System Architecture for Distributed Robotic Systems. Computers 2019, 8, 25. Höttger, Robert et al. “Combining Eclipse IoT Technologies for a RPI3-Rover along with Eclipse Kuksa.” Software Engineering (2018).

MOD-E14 – Embedded Systems Hardware Design and Rapid Prototyping

Number	MOD-E14
Title	Embedded Systems Hardware Design and Rapid Prototyping
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 h (60 h + 120 h)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Learning outcomes	Knowledge Knows principles of schematic and layout design for embedded systems Knows theoretical foundations of power- and signal integrity Knows theoretical foundations and norms required for EMI precompliance testing Skills Can create a schematic of an embedded system using modern design tools Can create a layout of an embedded systems while applying signal and power integrity principles Can assemble a PCB prototype with SMD components using different soldering techniques Can perform hardware debugging using modern measuring equipment Can perform compliance testing of high-speed interfaces Can perform EMI precompliance testing Competence - attitude Can break down a complex task into work packages and meet deadlines Can communicate and find solutions with domain experts Can present project status and results to an audience
Course description and course structure	This course covers all the steps from an idea to a working embedded system prototype. Rapid prototyping of embedded systems and electronic circuits in general is an essential tool in research and product development, because designing and prototyping is a cycle that is usually iterated a few times and should therefore be as fast as possible. Furthermore, the insights which result from rapid prototyping can directly go into the next design cycle. This course applies a project-based learning approach, where every student designs his own embedded system from schematic to layout. The complexity of the project can vary according to prior knowledge and experience of the individual student – it can be for example a simple 4-layer 32-bit microcontroller design using an ARM cortex M3 or a very complex 6 layer design using a Xilinx Zynq device, which is an integrated System on Chip and FPGA. After the layout is done the printed circuit boards (PCBs) will be manufactured externally. The students will then perform assembly and testing of their prototype. The practical lab work will be accompanied by lectures that present the theoretical foundations, which are necessary to create a good design and solve problems quickly. The presented topics include, principles of signal and power integrity of high-speed embedded systems, compliance measurements of modern interfaces like gigabit ethernet and EMI precompliance testing. Course Structure 1. Introduction to schematic design tools 2. Schematic design of an embedded system (homework + presentation) 3. Introduction to layout design tools 4. Principles of signal and power integritya. Target Impedance - Decoupling capacitors - Power planes - Impedance and length matching of traces for high speed signals 5. Microstrip antennas 6. Layout of an embedded system (homework + presentation) 7. Soldering techniques (classical, hot air, reflow) 8. Prototype assembly (lab work) 9. Hardware debugging techniques using modern measuring equipment 10. Testing and validation of embedded systems (lab work) - Code generation to activate peripherals for testing - Compliance testing of peripherals (i.e. Ethernet, DDR3, Bluetooth) 11. Theoretical fundamentals of EMI precompliance testing - Conducted emissions according to CISPR standards - Radiated emissions according to CISPR standards - Measurement methods (Antenna, LISN) 12. EMI precompliance testing (emissions) using a spectrum analyzer (lab work) Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lectures, lab work, homework Access to modern measuring equipment (oscilloscope, vector network analyzer) Access to recent research papers
Assessment of course	Oral presentation (10 min) at the end of the course (50%) and results from homework/lab work (50%)
Module mapping	MOD1-03 - Embedded Software Engineering MOD1-02 – Distributed and Parallel Systems MOD-E03 – SW Architectures for Embedded and Mechatronic Systems MOD-E10 – Automotive Systems
References	Principles of Power Integrity for PDN Design, Smith and Bogatin, Prentice Hall (2019) High-Speed Circuit Board Signal Integrity, Thierauf, Artech house (2017) Characterization of Power Distribution Networks, Novak and Miller, Artech House (2007) KiCad Like a Pro, Dalmaris, Tech Explorations (2018

MOD-E15 – Trends in Embedded and Mechatronic Systems

Number	MOD-E15
Title	Trends in Embedded and Mechatronic Systems
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 h (60 h + 120 h)
Semester	2
Duration	1 Semester
Course admittance prerequisites	Scientific & Transversal Skills (MOD1-05)
Learning outcomes	Knowledge Knows recent trends in Embedded and Mechatronic Systems Knows the relevant scientific literature Knows practical cases Skills Can do a structured literature review on a given topic Can design own research on the topic Can present research results Competence - attitude Can systematically explore a new scientific field Can organize research work in an unknown field Can synthesize and summarize findings in a meaningful way Shows curiosity in scientific research
Course description and course structure	The module will introduce and discuss recent topics from scientific research and industrial R&D. The goal is to make students familiar with the trends and to encourage own scientific and practical work in the respective field. The module will use presentations by scientists and practitioners to introduce topics. Literature work including structured literature reviews and discussion of relevant research papers will further enhance the practical knowledge. Industry presentations and visits can deliver practical insights. The module can introduce several different areas or topics, or it can dive deep into one topic. This can involve own research work of students, e.g. in order to develop a research paper for a conference (preferably the Dortmund International Research Conference). The module can also include practical labs or experiments. Individual project work or group work in small project teams can be used to develop new results. Presentations can be used to discuss the results. Course Structure Introduction of a new trend in Embedded and Mechatronics Systems Literature research and discussion of the state of the art (optional) company visit and /or discussion of practical cases Industry presentations Tool trainings and practical labs Own research, e.g. with experiments or projects Presentation of the results Preparation of a paper for a conference Skills trained in this course: theoretical, practical and methodological skills
Teaching and training methods	Lecturers and industry presentations Individual literature research Assignments, e.g. writing of a paper
Assessment of course	Oral Exam (30 min) at the end of the course (50%) and group work as homework (50%): research on a recent technology trend
Module mapping	Research Seminar Research Project (Thesis) (MOD3-03) Master Thesis and Colloquium
References	Specific for the recent research topic

MOD-E16 – Hardware Project

Number	MOD-E16
Title	Hardware Project
Language	English
Type of participation	Compulsory elective subject
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 h (60 h + 120 h)
Semester	2
Duration	1 Semester
Frequency	Winter semester
Course admittance prerequisites	MOD1-02 – Distributed and Parallel Systems MOD2-02 – Microelectronics & HW/SW-Codesign
Learning outcomes	Knowledge Students know development tools for hardware design Students know concepts and processes for hardware development Students know how to create test beds for hardware testing Students know hardware description languages (HDL), e.g. VHDL Skills Students can apply processes and methods to specific project needs Students can evaluate and use tools for developing hardware systems in a team Students can use tools to support the development process in a team Students can use tools to verify and test hardware Competence – attitude Can discuss and defend results in topics related to the lecture content Can work in a team on scientific topics Can understand lecture related content and translates between different domains
Course description and course structure	The aim of this course is to provide students with theoretical and practical experience in hardware engineering. Therefore, the students work in teams on real world tasks in cooperation with industry partners. The course focuses on the development of SoCs, FPGAs or Microcontroller based Embedded Systems. During the course, the students need to apply hardware engineering methodology and they need to use hardware engineering tool chains. In summary, the students implement the complete life cycle from requirements engineering to design over the development of a hardware system. Course Structure The course is training hardware engineering skills by applying the following competences (from previous modules) within a realistic project (e.g. industry case): 1. Circuit Design, especially for ASICs and PCBs 2. Hardware Architecture Design 3. Hardware Description Languages 4. Hardware Testing and Component Verification 5. Hardware Development Tool Chains (ASIC, FPGA or PCB) ( - Version control systems - Functional Modeling (e.g. VHDL, SystemC) - Verification and Simulation - Synthesis - Timing Analysis and Verfication - Layout and Design Rule Check - Documentation 6. Requirements Engineering 7. Project management, project planning, quality management
Teaching and training methods	Practical development projects (in the Chiplab) Tutorials for tools and processes Joint team reviews and team meetings Presentations to communicate and discuss the findings Individual review and feedback on the project
Requirements for award of credits	completion of the practical task with a demonstration + presentation in colloquium (30 min) (100%)
Module mapping	MOD-E09 – System on Chip Design
References	Europractice tools documentation (online)

S – Research Seminar

Number	S
Title	Research Seminar
Language	German
Type of participation	Compulsory elective subject
Credits	6
Workload (self study and contact hours)	180 (20 (individual consulting and colloquium) + 160)
Semester	2
Duration	1 Semester
Frequency	Summer semester
Course admittance prerequisites	none
Learning outcomes	Knowledge Knows state of the art in a certain scientific field Knows open research questions in this field Knows relevant literature Skills Can analyze scientific literature based on a comprehensive review Can write a paper/report according to scientific standards Can synthesize findings in own words Competence - attitude Can run an own small scientific research project Can present and defend results at a conference
Course description and course structure	The research seminar is intended to introduce students into scientific writing, literature review and into discussion of research questions in a scientific auditory. Students will write a scientific report or essay on a recent research topic from one of the ongoing projects. The seminar will be a preparation for further work on the research project thesis and the master thesis. The intention of the seminar is to explore a certain scientific field and to formulate the scientific state of the art and the open research questions. A motivation for students will be the possibility to publish and present excellent papers at a small conference. Instead of the seminar and the homework, the students can attend a third elective module. Course Structure Scientific Methodology is taught with a 3 days intensive course “Research Methods and Tools B (RMT-B)” which students attend together with students from other Master programmes. Students will select a topic from one of the ongoing projects in CPS and Embedded Systems. The will get individual consulting and feedback. During the semester the students will write a paper/report and present it in a colloquium at the end of the semester. Excellent papers will be published and presented (oral or poster) at the Dortmund International Research Conference at FH Dortmund. Case Studies None – topics will be selected from ongoing projects Skills trained in this course: theoretical, methodological, and personal skills
Teaching and training methods	Teaching and training methods Literature review and Essay writing Presentations to communicate and discuss the findings E-learning course on scientific work and scientific writing Individual review and feedback on papers and presentations
Assessment of course	exam for RMT-B (40%), Paper/essay on literature review about recent research as individual homework + presentation in colloquium (60%)
Module mapping	Input for: MOD3-02 – Research Project Thesis MOD4-01 – Master Thesis + Colloquium
References	German and European Research Agendas, recent research papers

Master Thesis and Colloquium

Number	103
Title	Master Thesis and Colloquium
Language	German
Type of participation	Compulsory subject
Credits	30
Hours per week	0
Workload (self study and contact hours)	900 h (60 h + 840 h)
Importance of the grade for the final grade	25
Semester	4
Duration	1 Semester
Frequency	Summer semester
Learning outcomes	Knowledge Knows state of the art in a certain scientific field Knows open research questions in this field Knows relevant literature Knows methodology and tools to execute project Knows how to document new findings according to scientific standards Skills Can define and plan an own research project Can apply appropriate research methodology Can create own research findings Can describe state of the art, methodology and findings in a scientific report Competence - attitude Can compare own findings with state of the art and do a critical discussion Can run an own scientific research project and create new findings Masters uncertainty and unknown topics in new area Can present and defend results (in colloquium or at a conference) Skills trained in this course: scientific, theoretical, practical, methodological, and personal skills
Course description and course structure	The master thesis is intended for the students to show their ability for scientific research work in a bigger context. Students will participate in one of the ongoing research projects. They will contribute with an own sub project and with own scientific results. The starting point is the definition of the research questions they want to answer and the selection of the appropriate methodology. The students will plan and execute their project independently with regular review and consulting. They will summarize their finding in a master thesis (scientific report). The intention of the master thesis is to apply the research methodology in a certain scientific field and to contribute own findings to that scientific field. The student proves the ability to execute own and independent research on master level and with a certain complexity. Furthermore, the master thesis proves the ability to summarize and publish the results according to scientific standards. Course Structure Students will select a topic from one of the ongoing projects in CPS and Embedded Systems. The will get individual consulting and feedback. During the semester the students will write a master thesis and present it in a colloquium at the end of the semester. Excellent results are intended to be published and presented (oral or poster) at a conference.
Teaching and training methods	Project Work Writing of a scientific report Presentations to communicate and discuss the findings E-learning course on scientific work and scientific writing Individual review and feedback on papers and presentations
Assessment of course	master thesis about own research in an ongoing project as individual homework + presentation in colloquium (30 min), (100%)
References	According to topic Aline Dresch, Daniel Pacheco Lacerda, José Antônio Valle Antunes Jr.: Design Science Research - A Method for Science and Technology Advancement, Springer, 2015

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