MODULE HANDBOOK Version 12 - Dortmund University of ...€¦ · Study Programme overview 1st...

EMBEDDED SYSTEMS FOR

MECHATRONICS

MASTER PROGRAMME

2017

MODULE HANDBOOK

Version 12

Table of Contents

Mathematics for Controls & Signals (MOD1-01) ....................................................... 7

Distributed and Parallel Systems (MOD1-02) ............................................................ 9

Embedded Software Engineering (MOD1-03) ......................................................... 11

Requirements Engineering (MOD1-04) .................................................................... 14

Introduction to Embedded Systems Design (MOD1-05) ....................................... 16

Mechatronic Systems Engineering (MOD2-01) ....................................................... 18

Microelectronics & HW/SW-Co-Design (MOD2-02) ................................................ 20

R&D Project Management (MOD2-03) ...................................................................... 22

Signals & Control Systems 1 (MOD2-04) ................................................................. 25

Research Project (Thesis) (MOD3-03) ...................................................................... 27

Master Thesis + Colloquium (MOD4-04) .................................................................. 29

Applied Embedded Systems (MOD-E01) ................................................................. 32

Biomedical Systems (MOD-E02) ............................................................................... 34

SW Architectures for Embedded and Mechatronic Systems (MOD-E03) ........... 36

Signals and Systems for Automated Driving (MOD-E04) ...................................... 38

Internet of Things (MOD-E05) ................................................................................... 41

Computer Vision (MOD-E06) ..................................................................................... 43

Signals & Control Systems 2 (MOD-E07) ................................................................ 45

Formal Methods in Mechatronics (MOD-E08) ......................................................... 47

System on Chip Design (MOD-E09) ......................................................................... 49

Automotive Systems (MOD-E10) .............................................................................. 51

Research Seminar (S) ................................................................................................ 53

Study Programme overview

1st semester (winter semester)

module

examin-

ation

mod-nr-/

exam-nr

student workload

ECTS

points contact hours

self-study

(hrs) SWS1 hours

Mathematics for Signals & Controls MOD1-01 10110/11 4 60 120 6

Distributed and Parallel Systems MOD1-02 10120/21 4 60 120 6

Embedded Software Engineering MOD1-03 10130/31 4 60 120 6

Requirements Engineering MOD1-04 10140/41 4 60 120 6

Introduction to Embedded Systems

Design MOD1-05 10150/51 4 60 120 6

Total 5 20 300 600 30

2nd semester (summer semester)

module

examin-

ation

mod-nr/

exam-nr

student workload ECTS


self-study

(hrs) SWS1 hours

Mechatronic Systems Engineering MOD2-01 10210/11 4 60 120 6

Microelectronics & HW/SW Co-

Design MOD2-02 10220/21 4 60 120 6

R&D Project Management MOD2-03 10230/31 4 60 120 6

Signals and Control Systems 1 MOD2-04 10240/41 4 60 120 6

Elective 1 * MOD2-05 10250 4 60 120 6

Total 5 20 300 600 30

3rd semester (winter semester)

module examin-

ation

mod-nr/

exam-nr

student workload ECTS


self-study

(hrs) SWS1 hours

Elective 2 * MOD3-01 10310 4 60 120 6

Elective 3 * MOD3-02 10320 4 60 120 6

Research Project (Thesis) MOD3-03 10330/31 0 0 540 18

Total 3 8 120 780 30

4th semester (summer semester)

module

examin-

ation

mod-nr/

exam-nr

student workload

ECTS


self-study

(hrs) SWS1 hours

Master Thesis and Colloquium P 103 0 0 900 30

Total 5 20 300 600 30

1 SWS= weekly hours per semester

*cf. Attachment 2

Attachment 2: Catalogue of Compulsory Elective Modules

Catalogue of Compulsory Module (Electives 1, 2 and 3)*

Module module examin-ation

mod-nr/

exam-nr

student workload

ECTS-Punkte

contact hours self-study (hrs)

SWS1 hours

Applied Embedded Systems MOD-E01

10401 4 60 120 6

Biomedical Systems MOD-E02

10402 4 60 120 6

SW Architectures for Embedded and Mechatronic Systems

MOD-E03 10403 4 60 120 6

Signals and Systems for Automated Driving *** MOD-E04

10404 4 60 120 6

Internet of Things MOD-E05

10405 4 60 120 6

Computer Vision *** MOD-E06

10406 4 60 120 6

Signals & Control Systems 2 *** MOD-E07

10407 4 60 120 6

Formal Methods in Mechatronics MOD-E08

10408 4 60 120 6

System on Chip Design MOD-E09

10409 4 60 120 6

Automotive Systems MOD-E10

10410 4 60 120 6

Research Seminar S 10411 180 6

Module(s) from cooperating institutions 10421

Module(s) from study courses of the home institution**

10431

* From the Catalogue of Compulsory Electives a minimum of 3 modules must be completed with an examination

(MOD2-05, MOD3-01 and MOD3-02). More than 18 credit points may be obtained which will be marked in the

certificate.

** If compulsory elective modules of the Ruhr Master School (RMS) are part of the course programmes of Dortmund

University of Applied Sciences and Arts (Fachhochschule Dortmund), students must complete the examinations

within their own course programme.

Upon application, modules of the course programmes participating in the RMS may be elected.

*** At least 1 of the following Modules must be taken as an Elective: MOD-E04, MOD-E06, or MOD-E07.

M O D U L E S

Mathematics for Controls & Signals (MOD1-01) .................................................... 7

Distributed and Parallel Systems (MOD1-02) ......................................................... 9

Embedded Software Engineering (MOD1-03) ....................................................... 11

Requirements Engineering (MOD1-04) ................................................................. 14

Introduction to Embedded Systems Design (MOD1-05)……………………………16

Mechatronic Systems Engineering (MOD2-01) .................................................... 18

Microelectronics & HW/SW-Co-Design (MOD2-02) .............................................. 20

R&D Project Management (MOD2-03) ................................................................... 22

Signals & Control Systems 1 (MOD2-04) .............................................................. 25

Research Project (Thesis) (MOD3-03) ................................................................... 27

Master Thesis + Colloquium (MOD4-04) ............................................................... 29

Mathematics for Controls & Signals (MOD1-01) Code Number

10110/11

Workload

180 h

Credits

6

Semester

Sem. 1

Frequency

annually

Duration

1 Semester

1 Course Title

Mathematics for Controls &

Signals

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group Size

25 students

2 Course Description

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 kind 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 input and output. 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.

3 Course Structure

1. Real and complex analysis 2. Fourier, Laplace and Z transform 3. Differential equations 4. Linear algebra 5. Probability theory 6. Introduction into Matlab/Octave 7. Numerical mathematics

4 Case Studies

None – courses contain small labs

5 Parameters

Course characteristics: compulsory

Course frequency: every year - winter semester

Maximal capacity: 25 students

Course admittance prerequisites: none

Skills trained in this course: theoretical, practical and methodological skills

Assessment of the course: Written Exam at the end of the course

Teaching staff: Prof. Dr. Andreas Becker, (Prof. Dr. Thomas Felderhoff)

6 Learning outcomes

6.1 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

6.2 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

6.3 Competence – attitude

Can discuss mathematical prerequisites of mechatronic systems with experts

Understands experts for mathematics and translates between different domains

7 Teaching and training methods

Lectures & Exercises

Labs with Matlab/Octave

E -learning modules on higher mathematics, tool tutorials

8 Course 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

9 References James, Modern Engineering Mathematics, Pearson Education, 2015

Stroud, Engineering Mathematics, Macmillan Education, 2013

Oppenheim, Willsky, Nawab, Signals and Systems, Pearson Education, 2013

Distributed and Parallel Systems (MOD1-02) Code Number

10120/21

Workload

180 h

Credits

6

Semester

Sem. 1

Frequency

annually

Duration

1 Semester

1 Course Title

Distributed and Parallel

Systems

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group Size

25 students


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.

3 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

4 Case Studies

CS01: AMALTHEA tool chain – Scheduling & Partitioning tools (e.g. TA tools)

5 Parameters

ECTS: 6

Hours of study in total: 180

Weekly hours per semester: 4

- Contact hours: 60

- Self-Study hours: 120




Course admittance prerequisites: computer science & programming

Skills trained in this course: theoretical and methodological skills

Assessment of the course: Written Exam at the end of the course (50%) and individual

homework (50%): paper/report about a recent topic from CPS research

Teaching staff: Prof. Dr. Burkhard Igel, (Prof. Dr. Erik Kamsties)

6 Learning outcomes

6.1 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

6.2 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

6.3 Competence - attitude

Can setup tooling and design flows

Can discuss distribution issues with computer scientists

Understands the potential of concurrency in CPS


Lectures & Exercises, AMALTHEA and TA tool labs

e-learning modules on theoretical informatics, tool tutorials

8 Course mapping

Input for:

MOD2-01- Mechatronic Systems Engineering

MOD2-02 – Microelectronics & HW/SW Codesign

MOD-E04 – SW Architectures for Embedded and Mechatronic Systems

MOD-E07 – Model Based and Model Driven Design

9 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

Embedded Software Engineering (MOD1-03) Code Number

10130/31

Workload

180 h

Credits

6

Semester

Sem. 1

Frequency

annually

Duration

1 Semester

1 Course Title

Embedded Software

Engineering

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Characteristics of Embedded (and real-time) Systems

2. Motivation for Embedded Software Engineering

3. Modeling of Embedded Systems

4. Overview and Architecture of SysML

a. SysML: Requirements and Use Cases

b. SysML: Basic Concepts

c. SysML: Modeling Structure with Blocks

d. SysML: Modeling Constraints with Parametrics

e. SysML: Modeling Control Flow-Based Behavior with Activities

f. SysML: Modeling Message-Based Behavior with Interactions

g. SysML: Modeling Event-Based Behavior with State Machines

h. SysML Tools in General and Enterprise Architect

5. Development Processes of Embedded Software Systems

6. SW Quality Management, Software-Test

7. Development Tools (e.g. Enterprise Architect, IBM Rational Rhapsody)

4 Case Studies

CS01: AMALTHEA tool chain – modeling tools

CS05: M2M System – modeling with Enterprise Architect, IBM Rational Tools

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: computer science & programming


Assessment of the 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

Teaching staff: Prof. Dr. Stefan Henkler, (Prof. Dr. Martin Hirsch)

6 Learning outcomes

6.1 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.

6.2 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.


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


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)

8 Course mapping

Input for:

MOD2-01- Mechatronic Systems Engineering



MOD-E03 – Automotive Systems


Connects to:

MOD1-02- Distributed and Parallel Systems

9 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

Requirements Engineering (MOD1-04) Code Number

10140/41

Workload

180 h

Credits

6

Semester

Sem. 1

Frequency

annually

Duration

1 Semester

1 Course Title

Requirements Engineering

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Introduction (What is a requirement?, problem vs. solution)

2. Frameworks (e.g. Jackson's WRSPM Modell)

3. Requirements Engineering Process (stakeholder, activities)

4. System and system context

5. Elicitation of requirements (techniques and supporting activities, Kano model)

6. Textual requirements documents

7. Requirements modeling (e.g. goal-oriented modeling, requirements patterns)

8. Non-functional requirements

9. Validation of requirements

10. Requirements Management (attributes, prioritization, traceability, change management,

RE tools, CMMI, ReqIF exchange format)

11. Software product lines and variability management

4 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

5 Parameters

ECTS: 6



- Contact hours: 60



Course frequency: every year - summer semester



Skills trained in this course: practical, methodological, and personal skills

Assessment of the 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

Teaching staff: Prof. Dr. Erik Kamsties, (n.n.)

6 Learning outcomes

6.1 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

6.2 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


Understands the importance of RE in the early project phase

Can set up and lead RE in a cross domain team



Group work to train concepts and methods, to develop skills and to work on case

studies

Literature review and Essay writing


Presentations to communicate and demonstrate homework

8 Course mapping

Input for:



Requires:

MOD1-03 - Embedded Software Engineering

Connects to:

MOD2-01 – Mechatronic Systems Engineering

MOD2-03 – R&D Project Managemen

9 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.

Introduction to Embedded Systems Design (MOD1-05) Code Number

10150/51

Workload

180 h

Credits

6

Semester

Sem. 1

Frequency

annually

Duration

1 Semester

1 Course Title

Introduction to Embedded

Systems Design

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 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:

1. Compact Electronics Course 2. Compact Programming Course 3. Modeling of Embedded Systems (UML) 4. Applications of Embedded Systems 5. Tools and Techniques for Embedded Systems Design 6. Embedded Systems Lab Project 7. Engineering Communication 1 (German)

4 Case Studies

None – courses contain small labs

5 Parameters

ECTS: 6


Weekly hours per semester: block courses

- Contact hours: 60 (4 x 15 h)

- Self-Study hours: 120 (including tutorials)

Course characteristics: compulsory, students have to choose a minimum of 4 out of 7

courses, based on assessment of their prior knowledge




Skills trained in this course: theoretical, methodological and practical skills

Assessment of the course: tests for each compact course, graded project work, compact

course results are summarized for overall module grade

Teaching staff: Prof. Dr. Rolf Schuster, professors + tutors for each compact course

6 Learning outcomes

6.1 Knowledge

Knows the foundations of each topic at least up to a bachelor level

6.2 Skills

Can apply the knowledge in the upcoming master courses


Can assess the gaps in own knowledge

Can use a variety of tools, online-courses, tutorials to close the gaps through self-study



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

8 Course mapping

Input for: All other courses

9 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)

P. Wilson, The Circuit Designer's Companion, Newnes (3rd Edition, 2012)

Mechatronic Systems Engineering (MOD2-01) Code Number

10210/11

Workload

180 h

Credits

6

Semester

Sem. 2

Frequency

annually

Duration

1 Semester

1 Course Title

Mechatronic Systems

Engineering

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Motivation: a. Examples for Mechatronic Systems b. Characteristics of Mechatronic Systems c. Challenges

2. Discipline-spanning development process 3. Systems Engineering (according to INCOSE SE handbook) 4. Conceptual Design of Mechatronic Systems

a. CONSENS 5. The Software Engineering Domain

a. MechatronicUML b. Behavior synthesis

6. Self-Optimization: Operator Controller Module (OCM) 7. Application to Use Case (Printing Industry, Rail Cab)

4 Case Studies

CS07: Rail Cab – modeling with CONSENS (Enterprise Architect)

CS07: Rail Cab – modeling with Mechatronic UML

5 Parameters

ECTS: 6


Weekly hours per semester: 4 - Contact hours: 60 - Self-Study hours: 120




Course admittance prerequisites: mechanics/physics, basics of embedded systems


Assessment of the course: Written Exam at the end of the course (50%) and individual homework (50%): MechatronicUML model of an example


6 Learning outcomes

6.1 Knowledge

Knows CONSENS, INCOSE SE handbook, MechatronicUML

Knows mechatronic systems engineering processes

Knows Enterprise Architect and other relevant tools 6.2 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 6.3 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


Lectures, Labs (with Enterprise Architect and other tools), homework

Access to tools and tool tutorials

Access to recent research papers

8 Course mapping

Input for:


MOD-E06 – Formal Methods in Mechatronics

MOD-E07 – Model Based and Model Driven Design Requires:

MOD2-04 - Control Theory and Systems

MOD1-03 - Embedded Software Engineering Connects to:

MOD1-04 – Requirements Engineering

MOD2-03 - R&D Project Management

9 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

Microelectronics & HW/SW-Co-Design (MOD2-02) Code Number

10220/21

Workload

180 h

Credits

6

Semester

Sem. 2

Frequency

annually

Duration

1 Semester

1 Course Title

Microelectronics & HW/SW-

Co-Design

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Microelectronic Components for Embedded Systems

a. DSP, Microcontroller b. FPGA c. ASIC, ASSP d. Memories e. Communication components (e.g. serial busses) f. PCB and standard circuits

2. Digital systems design methodologies and processes a. ESL concepts b. SystemC c. VHDL/Verilog d. Simulation and validation e. HW/SW partitioning f. Verification and test g. Synthesis (on FPGA)

3. Virtual Prototypes and HW/SW co-verification 4. Tools and Tool Chains 5. New Trends: Multicore/Manycore, SoC, 3D, MEMS

4 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

5 Parameters

ECTS: 6






Course admittance prerequisites: electronics, basics of embedded systems


Assessment of the 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

Teaching staff: Prof. Dr. Peter Schulz, (Prof. Dr. Carsten Wolff)

6 Learning outcomes

6.1 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 6.2 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 6.3 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


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)

8

Course mapping Input for:

MOD-E08 – SoC Design Requires:


MOD2-03 - R&D Project Management

9 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

R&D Project Management (MOD2-03) Code Number

10230/31

Workload

180 h

Credits

6

Semester

Sem. 2

Frequency

annually

Duration

1 Semester

1 Course Title

R&D Project Management

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


The 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.

3 Course Structure

1. Characteristics of R&D projects 2. Project management processes:

a. planning, controlling (cost, time, quality) b. agile & lean c. V-model

3. Milestones and Reviews 4. Risk Management for R&D Projects 5. Configuration & Release Management 6. Change and Claim Management (incl. Patents) 7. Quality Management (incl. CMMI) 8. KPIs and Scorecards 9. Large R&D projects and Cross Domain Projects 10. Management of R&D organizations 11. Engineering Communication 2 (German)

4 Case Studies

CS01: AMALTHEA tool chain – setup of the ITEA2 research project

CS05: M2M System – management of a ZIM project

5 Parameters

ECTS: 6







Skills trained in this course: methodological and personal skills

Assessment of the course: Oral Exam at the end of the course (50%) and group work as homework (50%): project kickoff/release report and presentation

Teaching staff: Prof. Dr. Carsten Wolff, (Dr. Oliver Hempel)

6 Learning outcomes

6.1 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 6.2 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


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



Group work to train concepts and methods, to develop skills and to work on case studies


Presentations to communicate results

Presentation and discussion of an industry case by a partner company

8 Course mapping

Input for:

MOD-E10 – Automotive Systems Requires:





9 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

Signals & Control Systems 1 (MOD2-04) Code Number

10240/41

Workload

180 h

Credits

6

Semester

Sem. 2

Frequency

annually

Duration

1 Semester

1 Course Title

Signals & Control Systems

1

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. State Variable Models

2. State Feedback Control Systems

3. Robust Control Systems

4. Digital Control Systems

5. Applications of the above

6. Control Engineering with Matlab/Simulink

4 Case Studies

CS04: Avionics Computer & Robots – Control Algorithms

CS04: Avionics Computer & Robots – MATLAB/Simulink implementation for Arm Type

Robots

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: higher mathematics



as homework (50%) with Matlab/Simulink use case and demonstration/presentation

Teaching staff: Prof. Dr. Andreas Becker, (Prof. Dr. Jörg Thiem)

6 Learning outcomes

6.1 Knowledge

Knows relevant theoretical foundations of control theory

Know mathematical background of controllers

Is aware of critical limitations of control systems

6.2 Skills

Can model control systems for mechatronic systems

Can implement digital control systems into embedded systems

Can apply state of the art tools and can develop tools for control system design

Can select embedded system platforms according to controller requirements


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


Lectures & Exercises, Matlab/Simulink labs

e-learning modules on mathematics and control theory, tool tutorials

8 Course mapping

Input for:


MOD-E011 – Signals & Control Systems 2

9 References

P. Corke: Robotics, Vision and Control, Springer, 2013

R. Bishop, R. Dorf: Modern Control Systems, Pearson Education, 2010

Research Project (Thesis) (MOD3-03) Code Number

10330/31

Workload

540 h

Credits

18

Semester

Sem. 3

Frequency

annually

Duration

1 Semester

1 Course Title

Research Project (Thesis)

Contact hours

0 SWS / 0 h

Self-Study

540 h

Planned Group

Size

25 students


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.

3 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).

4 Case Studies

None – topics will be selected from ongoing projects

5 Parameters

ECTS: 18


Weekly hours per semester: only colloquium

- Contact hours: 40 (individual consulting and colloquium)






Skills trained in this course: theoretical, practical, methodological, and personal skills

Assessment of the course: project thesis about own research in an ongoing project as

individual homework + presentation in colloquium (100%)

Teaching staff: all professors

6 Learning outcomes

6.1 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

6.2 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


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)


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

8 Course mapping

Input for:

MOD4-01 – Master Thesis + Colloquium

9 References

According to topic

Master Thesis + Colloquium (MOD4-04) Code Number

103

Workload

900 h

Credits

30

Semester

Sem. 4

Frequency

annually

Duration

1 Semester

1 Course Title

Master Thesis + Colloquium

Contact hours

0 SWS / 0 h

Self-Study

900 h

Planned Group

Size

25 students


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.

3 Course Structure



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.

4 Case Studies


5 Parameters

ECTS: 30


Weekly hours per semester: only colloquium






Course admittance prerequisites: max. 1 module from semester 1 - 3 not finished.

Skills trained in this course: theoretical, practical, methodological, and personal skills

Assessment of the course: master thesis about own research in an ongoing project as

individual homework + presentation in colloquium


6 Learning outcomes

6.1 Knowledge




Knows methodology and tools to execute project

Knows how to document new findings according to scientific standards

6.2 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


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)


Project Work

Writing of a scientific report




8 Course mapping

None – can be based on research project thesis

9 References

According to topic

E L E C T I V E S

Applied Embedded Systems (MOD-E01) .............................................................. 32

Biomedical Systems (MOD-E02)............................................................................ 34

SW Architectures for Embedded and Mechatronic Systems (MOD-E03) .......... 36

Signals and Systems for Automated Driving (MOD-E04) .................................... 38

Internet of Things (MOD-E05) ................................................................................ 41

Computer Vision (MOD-E06) .................................................................................. 43

Signals & Control Systems 2 (MOD-E07) .............................................................. 45

Formal Methods in Mechatronics (MOD-E08)....................................................... 47

System on Chip Design (MOD-E09) ...................................................................... 49

Automotive Systems (MOD-E10) ........................................................................... 51

Research Seminar (S) ............................................................................................. 53

Applied Embedded Systems (MOD-E01) Code Number

10401

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

Applied Embedded

Systems 1

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Introduction to the application domain

2. Characteristics of CPS in the application domain

3. Architectures for application specific CPS

a. Standards

b. Platforms and Frameworks

c. Design methodology and processes

4. Domain specific languages (DSL) and applications

a. DSL engineering

b. Tools and Tool Chain Integration

5. Target Platforms and Code Generation

a. Code generation

b. Using real time operating systems (RTOS)

4 Case Studies

CS01: AMALTHEA tool chain – will be used for case study

A recent use case from a research project will be discussed

5 Parameters

ECTS: 6



- Contact hours: 60


Course characteristics: elective





Assessment of the 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

Teaching staff: Prof. Dr. Burkhard Igel, (Prof. Dr. Carsten Wolff)

6 Learning outcomes

6.1 Knowledge

Knows standards and platforms for specific domain

Knows target systems

Has acquired overview of target domain

6.2 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


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


Lectures, Labs (with AMALTHEA tools), homework



8 Course mapping

Requires:

MOD1-02 – Distributed and Parallel Systems


Connects to:


MOD-E04 – SW Architectures for Embedded Systems


9 References

AMALTHEA documentation

Research papers of PIMES research group:

http://www.fh-dortmund.de/en/fb/3/forschung/pimes/Eigene_Veroeffentlichungen.php

Biomedical Systems (MOD-E02) Code Number

10402

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

Biomedical Systems

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


Biomedical Systems are a major application domain for mechatronic and embedded systems. 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. Students will learn about the biomedical application domains and about embedded systems and signal processing in that domain. The course will be based on a recent research project.

3 Course Structure

1. Introduction on Biomedical Systems 2. Description of real biomedical data and modelling 3. Stochastic signals and statistical parameters 4. Linear time-variant signals 5. Methods for time-frequency-analysis (wavelets) 6. Applications on ECG-data 7. Applications on other selected data (EEG, motion analysis, foot pressure measuring) 8. Modeling and Implementation with Matlab/Simulink

4 Case Studies

CS06: Biomedical Systems - Artificial Hand: will be used for modeling with

Matlab/Simulink

CS06: Biomedical Systems - Long Term Analysis of Medical Signals: will be used for

modeling with Matlab/Simulink

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: higher mathematics, basics of embedded systems



as homework (50%): modeling and target mapping of an example with Matlab/Simulink,

demonstration and presentation

Teaching staff: Prof. Dr. Thomas Felderhoff, (n.n.)

6 Learning outcomes

6.1 Knowledge

Knows standards and platforms for biomedical systems


Has acquired overview of medical application domain

6.2 Skills

Can describe relevant characteristics and challenges of biomedical systems

Can model signal processing systems for biomedical use

Can apply methodology and state of the art tools on real use cases



Can structure a real signal processing project in medicine

Can communicate and find solutions with domain experts

Understands issues from biomedical application domain and can integrate solutions into

a holistic design


Lectures, Labs (with Matlab/Simulink), homework



8 Course mapping

Requires:

MOD1-01 – Mathematics for Controls & Signals


Connects to:

MOD2-04 - Signals & Control Systems 1

MOD-E01 – Applied Embedded Systems 1 & 2



9 References

Bruce, E.N.; Biomedical Signal Processing and Signal Modeling, Wiley-Interscience, 2000

Devasahayam, S.R.; Signals and Systems in Biomedical Engineering: Signal Processing and

Physiological Systems Modeling, Springer, 2012

Gacek, A. and Pedrycz, W.; ECG Signal Processing, Classification and Interpretation, Springer,

2012

Northrop, R.B.; Signals and Systems Analysis in Biomedical Engineering, CRC press, 2010

Rangayyan, R.M.; Biomedical Signal Analysis: A Case-Study Approach, Wiley-IEEE Press, 2001

Semmlow, J.; Signals and Systems for Bioengineers: A MATLAB-Based Introduction,

Academic Press, 2011

SW Architectures for Embedded and Mechatronic Systems (MOD-E03) Code Number

10403

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

SW Architectures for

Embedded and Mechatronic

Systems

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Characteristics of Embedded (and real-time) Systems 2. Motivation for Architectures for Embedded and Mechatronic Systems 3. Software Design Architecture for Embedded and Mechatronic Systems 4. Patterns for Embedded and Mechatronic Systems 5. Real-Time Building Blocks: Events and Triggers 6. Dependable Systems 7. Hardware's Interface to Embedded and Mechatronic Systems 8. Layered Hierarchy for Embedded and Mechatronic Systems Development 9. Software Performance Engineering for Embedded and Mechatronic Systems 10. Optimizing Embedded and Mechatronic Systems for Memory and for Power 11. Software Quality, Integration and Testing Techniques for Embedded and Mechatronic

Systems 12. Software Development Tools for Embedded and Mechatronic Systems 13. Multicore Software Development for Embedded and Mechatronic Systems 14. Safety-Critical Software Development for Embedded and Mechatronic Systems

4 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

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: programming, basics of embedded systems


Assessment of the course: Oral Exam at the end of the course (50%) and individual

homework (50%): paper/essay on a recent research topic, presentation


6 Learning outcomes

6.1 Knowledge

Knows concepts and structure of SW architectures for embedded systems

Knows standards and frameworks

Knows specific challenges (e.g. real time, functional safety)

6.2 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


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


Lectures, Labs (with AMALTHEA and Artop tools), homework



Presentation of an industry case by partner BHTC GmbH

8 Course mapping

Requires:




Connects to:



9 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

Signals and Systems for Automated Driving (MOD-E04) Code Number

10404

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

Signals and Systems for

Automated Driving

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Technology overview 2. Sensors

a. Radar b. Lidar c. Ultrasonic d. Camera

3. Radar signal processing a. Detection b. Target estimation

4. State estimation a. Vehicle motion models b. Random processes c. Tracking d. Target classification e. Mapping

5. Actuators & Vehicle Control a. Bicycle model b. Longitudinal control c. Brake and steering systems

6. Architectures a. Bus interfaces b. Car-to-X c. Safety domain controllers d. AUTOSAR

7. System Engineering a. Quality Process standards b. Process models c. Requirement engineering d. SPICE

8. ISO 26262 a. Basics b. Concept phase c. Product development

9. Legal frameworks a. Vienna convention b. Relevant norms and legislation

4 Case Studies CS08: Radar Systems for Automated Driving

5 Parameters

ECTS: 6






Course admittance prerequisites: higher mathematics, programming, signal processing


Assessment of the course: Oral Exam at the end of the course (50%) and group work as homework (50%)

Teaching staff: Prof. Dr. Andreas Becker

6 Learning outcomes

6.1 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 6.2 Skills

Can develop tracking algorithms

Can develop radar signal processing algorithms

Can analyze requirements for subsystems of automated driving 6.3 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


Lectures, Labs (with Matlab/Simulink)



Company visit

8 Course mapping

Requires:

MOD1-01 - Mathematics for Controls & Signals Connects to:


MOD2-01 – Mechatronic Systems Engineering (MOD2-01)



9 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

Internet of Things (MOD-E05) Code Number

10405

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

Internet of Things

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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 student will 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.

3 Course Structure

1. Introduction

2. Real-time Embedded Systems

3. Real-Time Networking

4. Cloud Computing

5. Edge Computing

4 Case Studies

CS11: Edge Sensor Fusion

CS12: Gabriel - Edge Computing Platform for Wearable Cognitive Assistance

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: Basics in embedded systems, networks and

programming



as homework (50%)

Teaching staff: Prof. Dr. Rolf Schuster

6 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


Can design a 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


Lectures, group project, homework



8 Course mapping

Requires:


MOD1-03 – Embedded Software Engineering

MOD1-05 – Introduction to Embedded System Design

Connects to:




MOD-E01 – Applied Embedded Systems 1


9 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

Computer Vision (MOD-E06) Code Number

10406

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

Computer Vision

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Introduction 2. Position and Orientation 3. Light and Color 4. Image Creation 5. Image Processing 6. Feature Extraction 7. Multiple Images 8. Advanced Topics and Applications

4 Case Studies

CS10: Avionics Computer & Robots – Vision-Based Control

CS06: Biomedical Systems - Medical Imaging Techniques: Algorithms and

implementation

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: higher mathematics, basics of embedded systems



as homework (50%): modeling and target mapping of an example with Matlab/Simulink,

demonstration and presentation

Teaching staff: Prof. Dr. Jörg Thiem, (Dr. Roland Brockers)

6 Learning outcomes

6.1 Knowledge

Knows standards and platforms for computer vision

Knows cameras, components, target systems

Has acquired overview of algorithms and methods

6.2 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 algortihms


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


Lectures, Labs (with Matlab/Simulink), homework



8 Course mapping

Requires:

MOD1-01 – Mathematics for Controls & Signals


MOD2-02 – Microelectronics & HW/SW-Codesgin


Connects to:





9 References


R. Szeliski: Computer Vision: Algorithms and Applications, Springer, 2011

E. Gopi: Digital Signal Processing for Medical Imaging Using Matlab, Springer, 2013

Signals & Control Systems 2 (MOD-E07) Code Number

10407

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

Signals & Control Systems

2

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. State Variable Feedback Control Systems

2. Optimal control

3. Robust Control Systems

4. Digital control

5. Adaptive Signal Processing

6. State estimation

7. Linear Quadratic Gaussian Control

8. Model Predictive Control

9. Applications of the above

10. Control Engineering with Matlab/Simulink

4 Case Studies

CS04: Avionics Computer & Robots – Control Algorithms

CS04: Avionics Computer & Robots – MATLAB/Simulink implementation for Arm Type

Robots

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: higher mathematics



as homework (50%) with Matlab/Simulink use case and demonstration/presentation

Teaching staff: Prof. Dr. Andreas Becker, (Prof. Dr. Jörg Thiem)

6 Learning outcomes

6.1 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

6.2 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


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


Lectures & Exercises

Matlab/Simulink labs

Tool tutorials

8 Course mapping

Requires:


Connects to:



9 References

Stergiopoulos, Advanced Signal Processing, CRC Press, 2009

Kouvaritakis, Cannon, Model Predictive Control, Springer, 2015


R. Bishop, R. Dorf: Modern Control Systems, Pearson Education, 2010

Kay, S.; Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory, Prentice

Hall,1993

Formal Methods in Mechatronics (MOD-E08) Code Number

10408

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

Formal Methods in

Mechatronics

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Motivation: a. What are Formal Methods? b. Why should we use Formal Methods? c. When in the overall development process should we use Formal Methods?

2. Model Checking 3. Theorem Proving 4. Testing 5. Formal Verification in practice: The MechatronicUML Approach 6. Recent Research: literature review 7. AMALTHEA Methodology and Tool Chain

4 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

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: programming



as homework (50%): verification of an example, demonstration and presentation

Teaching staff: Prof. Dr. Martin Hirsch, (Prof. Dr. Stefan Henkler)

6 Learning outcomes

6.1 Knowledge

Knows methodology of formal verification

Knows relevant theoretical background

Knows specific requirements

6.2 Skills

Can methods on use case

Can model verification artefacts (e.g. properties)

Can use MechatronicUML approach and tools


Can research on state of the art and theoretical background

Can demonstrate and discuss results in group

Can structure scientific field and get overview


Lectures, Labs (with MechatronicUML), homework


Literature review and discussion of results

8 Course mapping

Requires:



MOD2-01 – Mechatronic Systems Engineering Connects to:


9 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

System on Chip Design (MOD-E09) Code Number

10409

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

System on Chip Design

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Main building blocks of SoCs a. IP-cores (processors, communication, memories, sources for IP-cores) b. on-chip communication (topologies, wishbone) c. system definition d. ESL: electronic specification language e. on-chip vs. off-chip memory f. debugging methodologies

2. Multicore and Manycore architectures a. ASIP and Networks on Chip (NoC)

3. ASIC design flow a. Design entry (VHDL) b. Pre-silicon verification c. Synthesis & technology libraries d. Layout and signal integrity e. Timing closure f. Power routing, clocks and resets g. Semiconductor test & production

4 Case Studies

CS03: CoreVA – ASIC implementation

Europractice tools chain (Cadence and Mentor Graphics) and technology library

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: programming, electronics

Skills trained in this course: practical and methodological skills


as homework (50%): implementation of a CoreVA based design, demonstration and

presentation

Teaching staff: Prof. Dr. Peter Schulz, (Prof. Dr. Carsten Wolff)

6 Learning outcomes

6.1 Knowledge

Knows basic components of SoCs

Knows modern multicore/manycore architectures and ongoing research

Knows SoC design tools and tool chains

6.2 Skills

Can develop an SoC from building blocks

Can move a simple design through the whole tool chain

Can select technology, constraints and layout


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


Lectures, Labs (with Europractice tools), homework

Access to tool chains and tool tutorials


Visit at Bielefeld university (CITEC) and Intel Mobile Communications GmbH

8 Course mapping

Requires:



MOD2-02 – Microelectronics & HW/SW-Codesign

Connects to:


MOD-E06 – Formal Methods in Mechatronics

9 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

Automotive Systems (MOD-E10) Code Number

10410

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

Automotive Systems

Contact hours

4 SWS / 60 h

Self-Study

120 h

Planned Group

Size

25 students


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.

3 Course Structure

1. Automotive Standards: e.g. AUTOSAR, Quality Standards, Automotive Spice

2. Automotive development processes

3. Tools in Automotive Engineering (ML/SL, Doors, Enterprise Architect)

4. Automotive Supply Chain

5. Automotive Software Development

6. Functional Safety

7. Testing and Verification

8. Product Qualification

9. Application Examples

10. AMALTHEA Methodology and Tool Chain

4 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

5 Parameters

ECTS: 6



- Contact hours: 60





Course admittance prerequisites: programming, basics of embedded systems



as homework (50%): set up of an automotive system development project, modeling and

target mapping of an example with AMALTHEA tools, demonstration and presentation

Teaching staff: Prof. Dr. Carsten Wolff, (Prof. Dr. Erik Kamsties)

6 Learning outcomes

6.1 Knowledge

Knows standards and platforms for automotive systems


Knows specific requirements (e.g. safety)

Has acquired overview of automotive application domain

6.2 Skills

Can develop automotive software with the AMALTHEA tool chain

Can model an automotive system according to standards



Can structure a real automotive system development project

Can communicate and find solutions with automotive experts

Ensures quality and safety of applications


Lectures, Labs (with AMALTHEA tools and Matlab/Simulink), homework



Company visit at one of the partner companies (Bosch, BHTC)

8 Course mapping

Requires:

All semester 1 & 2 courses

Connects to:




9 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

Research Seminar (S) Code Number

10411

Workload

180 h

Credits

6

Semester

Frequency

annually

Duration

1 Semester

1 Course Title

Research Seminar

Contact hours

0 SWS / 0 h

Self-Study

180 h

Planned Group

Size

25 students


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.

3 Course Structure



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.

4 Case Studies


5 Parameters

ECTS: 6


Weekly hours per semester: only introduction course and colloquium







Skills trained in this course: theoretical, methodological, and personal skills

Assessment of the course: Paper/essay on literature review about recent research as

individual homework + presentation in colloquium (100%)


6 Learning outcomes

6.1 Knowledge




6.2 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


Can run an own small scientific research project

Can present and defend results at a conference


Literature review and Essay writing




8 Course mapping

Input for:

MOD3-02 – Research Project Thesis

MOD4-01 – Master Thesis + Colloquium

9 References

German and European Research Agendas, recent research papers

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