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

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

Written Exam (60 min) at the end of the course (50%) and individual homework (50%): paper/report about a recent topic from CPS research

Passed exam and passed semester assignments
 

Input for:

• MOD2-01- Mechatronic Systems Engineering
• MOD2-02 – Microelectronics & HW/SW Codesign
• MOD-E03 – SW Architectures for Embedded and Mechatronic Systems

5,00%

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

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

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
   1. SysML: Requirements and Use Cases
   2. SysML: Basic Concepts
   3. SysML: Modeling Structure with Blocks
   4. SysML: Modeling Constraints with Parametrics
   5. SysML: Modeling Control Flow-Based Behavior with Activities
   6. SysML: Modeling Message-Based Behavior with Interactions
   7. SysML: Modeling Event-Based Behavior with State Machines
   8. 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)

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

computer science & programming

Passed exam and passed semester assignments
 

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

5,00%

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

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

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

Case Studies

None – courses contain small labs

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

none

Written Exam at the end of the course

Passed exam and passed semester assignments
 

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-E07 – Signals & Control Systems 2

5,00%

 
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

 
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

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

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

none

• 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

5,00%

Pohl, K. (2010). Requirements Engineering: Fundamentals, Principles, and Techniques. Springer.

Robertson, S. and Robertson, J. (2012). Mastering the Requirements Process: Getting Requirements Right. Addison-Wesley.

van Lamsweerde, A. (2009). Requirements Engineering: From System Goals to UML Models to Software Specifications. John Wiley & Sons.

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

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:

1. Compact Programming Course (Java)
2. Modeling of Embedded Systems (UML)
3. Embedded Systems Lab Project
4. Mini Project
5. Research Methods and Tools A (RMT-A)
6. Engineering Communication 1 (German)
7. Engineering Communication 1 (other language)

Case Studies
None – courses contain small labs

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

none

tests (60 min) for each compact course, graded project work, compact course results are summarized for overall module grade

compulsory, students have to choose a minimum of 4 out of 7 courses, based on assessment of their prior knowledge

Input for: All other courses

5,00%

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

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

1. Motivation:
   1. Examples for Mechatronic Systems
   2. Characteristics of Mechatronic Systems
   3. Challenges
2. Discipline-spanning development process
3. Systems Engineering (according to INCOSE SE handbook)
4. Conceptual Design of Mechatronic Systems
   1. CONSENS
5. The Software Engineering Domain
   1. MechatronicUML
   2. Behavior synthesis
6. Self-Optimization: Operator Controller Module (OCM)
7. 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

• MOD2-04 - Control Theory and Systems
• MOD1-03 - Embedded Software Engineering

mechanics/physics, basics of embedded systems

passed exam and passed semester assignments
 

• 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

5,00%

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

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

1. Microelectronic Components for Embedded Systems
   1. DSP, Microcontroller
   2. FPGA
   3. ASIC, ASSP
   4. Memories
   5. Communication components (e.g. serial busses)
   6. PCB and standard circuits
2. Digital systems design methodologies and processes
   1. ESL concepts
   2. SystemC
   3. VHDL/Verilog
   4. Simulation and validation
   5. HW/SW partitioning
   6. Verification and test
   7. Synthesis (on FPGA)
3. Virtual Prototypes and HW/SW co-verification
4. Tools and Tool Chains
5. 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

• 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)

Passed exam and passed semester assignments
 

• MOD-E08 – SoC Design

Connects to:

• MOD2-03 - R&D Project Management

5,00%

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 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

1. Characteristics of R&D projects
2. Project management processes:
   1. planning, controlling (cost, time, quality)
   2. agile & lean
   3. 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)

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
 

Requires:

• MOD1-03 - Embedded Software Engineering

Passed exam and passed semester assignments
 

• MOD-E10 – Automotive Systems

Connects to:

• MOD1-04 – Requirements Engineering
• MOD2-01 – Mechatronic Systems Engineering
• MOD2-02 – Microelectronics & HW/SW Codesign

5,00%

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

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

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

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

higher mathematics

Written Exam at the end of the course

5,00%

Knowledge

• Knows standards and platforms for robotic vision
• Knows cameras, components, target systems
• Has acquired detailed knowledge of algorithms and methods

Skills

• Can model signal processing path for computer vision and robot kinematics
• Can apply methodology and  state of the art tools for robotic vision systems
• Can adapt and modify/parameterize relevant algorithms

Competence – attitude

• Can structure a real robotic vision project
• Can integrate cameras and vision modules into mechatronic systems
• Can analyze mechatronic systems and derive requirements for computer vision 

Course Description
The module deals advanced topics and methods for computer vision and robotic vision systems. Students have a deeper understanding of standards and components for robotic vision systems, such as cameras, processor hardware, robot kinematics, robotics software and their use in a variety of applications, such as mobile robotics or medical robotics. They know relevant computer vision and machine learning methods for the environmental perception of robotic systems.
Using tools such as MATLAB/Simulink or other toolboxes and high-level languages, students are able to implement more complex algorithms for robotic vision tasks (even on specialized hardware). The course will involve topics from recent research projects.

Course Structure
 

Teaching and training methods

• Lectures, Labs (with MATLAB/Simulink), homework
• Access to tools and tool tutorials
• Access to recent research papers

Requires:

• MOD1-01 – Mathematics for Controls & Signals
• MOD1-03 - Embedded Software Engineering
• MOD2-04 – Signals & Control Systems 1
• MOD-E06 – Robotic Vision

Passed exam and passed semester assignments
 

Connects to:

• MOD-E04 – Signals and Systems for Automated Driving
• MOD-E10 – Automotive Systems
• MOD-E17 -- Radar Systems

5,00%

References
P. Corke: Robotic Vision, https://doi.org/10.1007/978-3-030-79175-9, Springer, 2022
P. Corke: Robotics and Control, https://doi.org/10.1007/978-3-030-79179-7, Springer, 2022
Gong et al: Advanced Image and Video Processing Using MATLAB, Springer, 2019

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

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

1. Introduction to the application domain
2. Characteristics of CPS in the application domain
3. Architectures for application specific CPS
   1. Standards
   2. Platforms and Frameworks
   3. Design methodology and processes
4. Domain specific languages (DSL) and applications
   1. DSL engineering
   2. Tools and Tool Chain Integration
5. Target Platforms and Code Generation
   1. Code generation
   2. 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

none

Passed exam and passed semester assignments
 

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

5,00%

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

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

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

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

programming, basics of embedded systems

All semester 1 & 2 courses

Passed exam and passed semester assignments
 

Connects to:

• MOD-E01 – Applied Embedded Systems 1 & 2
• MOD-E06 – Computer Vision
• MOD-E03 – SW Architectures for Embedded Systems

5,00%

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

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

Oral presentation (10 min) at the end of the course (50%) and results from homework/lab work (50%)

Passed exam and passed semester assignments
 

5,00%

Knowledge

• Knows deep knowledge of formal verification methodologies
• Knows relevant theoretical background
• Knows, understands, and critically assesses specific system requirements

Skills

• Can apply advanced methods to novel and complex use cases
• Can designs and optimizes verification models and artefacts (e.g. properties)
• Can use and adapt UML approaches and tools (UPPAAL, TAPAAL) in innovative contexts

Competence - attitude

• Can research on state of the art and theoretical background
• Can present and critically discuss results in multidisciplinary teams
• Can structure and synthesize complex scientific fields to create new insights

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 soft- ware. 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 com- ponents. This development leads to extremely complex hybrid (discrete / continuous) software. In addi- tion, 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 speci- fied 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 diffe- rent domains, based on their domain specific model-based knowledge.

Course Structure

1. 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

Requires for ESE:

• MOD1-02 – Distributed and Parallel Systems
• MOD1-03 - Embedded Software Engineering
• MOD2-01 – Mechatronic Systems Engineering

- programming

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

Passed exam (accepted paper) and passed semester assignments

 

none

5,00%

Reisig, W. (2013). Understanding Petri Nets – Modeling Techniques, Analysis Methods, Case Studies. Springer.

Clarke, E.M., & Grumberg, O., & Peled. (1999). D.A.: Model Checking, MIT Press.

Baier, C., & Katoen, J.-P. (2008). Principles of Model Checking, MIT Press.

Spivey, J.M. (2001). The Z Reference Manual (https://github.com/Spivoxity/zrm/blob/master/zrm-pub.pdf).

Ruhela, V. (2012). Z Formal Specification Language – An Overview, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 01, Issue 06.

http://www.tapaal.net

http://www.uppaal.org

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

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

5,00%

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

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

Competence - attitude

• 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

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

1. Introduction
2. Real-time Embedded Systems
3. Real-Time Networking
4. Cloud Computing
5. Edge Computing

Application Focus

Students conduct a project about Edge Sensor Fusion
Students work with Gabriel - Edge Computing Platform for Wearable Cognitive Assistance


Scientific Focus

During the module recent topics from the Open Edge Computing Initiative will be discussed and papers from relevant conferences will be reviewed.


Skills trained in this course: theoretical, practical and scientific skills and competences

none

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

Passed exam and passed semester assignments

none

5,00%

Peter Marwedel. (2011). Embedded System Design, 2nd Edition. Springer.

Andrew S. Tanenbaum, David J. Wetherall. (2014). Computer Networks, 5th Edition. Pearson Education.

Thomas Erl, Zaigham Mahmood, Ricardo Puttini. (2013). Cloud Computing. Prentice Hall.

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

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

1. Trends and challenges for future automotive E/E architectures
2. Automotive Standards: e.g. AUTOSAR, EastADL, Amalthea, …
3. Eclipse APP4MC
4. Modelling embedded systems
5. Developing rudimentary tooling for analyzing resp. modifying existing models of applications
6. Open Source and proprietary tools in Model based Automotive Engineering (e.g. Vector / TA Tool Suite, Inchron, Eclipse APP4MC Task Visualizer, …)
7. Deploying software to embedded multi- and many-core hardware
8. Code generation
9. Testing and Verification
10. Application Examples

programming skills (pref. Java), basics of embedded systems

• MOD1-02 – Distributed and Parallel Systems
• MOD1-03 - Embedded Software Engineering

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

Passed exam and passed semester assignments
 

5,00%

Knowledge

• Knows relevant basics of wave propagation and antenna theory
• Knows basic elements of radar sensors including modulation
• Knows major blocks of radar signal processing including state estimation
• Knows current trends in radar signal processing

Skills

• Can implement basic algorithms like target detection, angle finding and sub-bin range estimation
• Can implement basics tracking algorithms

Competence – attitude

• Can discuss requirements and features in the area of automotive radar
• Understands limitations and translates between different domains
• Can lead cross domain usage of radar sensors 

Course Description
In conjunction with LiDAR and cameras, radars sensors are a key technology for automated driving. This module introduces students into radars sensors with an emphasis on signal processing. Several case studies are discussed based on Matlab-Code and usage of demonstration boards of vendors like Texas Instruments.

Course Structure

• Wave propagation and antennas
• Block diagram
• Modulation
• Spectral analysis
• State Estimation and Tracking
• Current trends in radar signal processing
• Applications

Teaching and training methods

• Lectures, Labs (with Matlab/Simulink)
• Access to tools and tool tutorials
• Access to recent research papers
• Access to demonstration boards
• Block week
• Guest talk by industry experts

Requires:

• MOD2-04 – Signals & Control Systems 1

Passed exam and passed semester assignments
 

Connects to:

• MOD-E04 – Signals and Systems for Automated Driving
• MOD-E06– Computer Vision

5,00%

References
Stergiopoulos, Advanced Signal Processing, CRC Press, 2009
 

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

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

• 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

none

exam for RMT-B (40%), Paper/essay on literature review about recent research as individual homework + presentation in colloquium (60%)

Passed exam and passed semester assignments
 

Input for:

• MOD3-02 – Research Project Thesis
• MOD4-01 – Master Thesis + Colloquium

Knowledge

• Knows standards and platforms for computer and robotic vision
• Knows cameras, components, target systems
• Has acquired overview of algorithms and methods

Skills

• Can model signal processing path for computer vision and robot kinematics
• Can apply methodology and  state of the art tools for robotic vision systems
• Can adapt and modify/parameterize relevant algorithms

Competence - attitude

• Can structure a real robotic vision project
• Can integrate cameras and vision modules into mechatronic systems
• Can analyze mechatronic systems and derive requirements for computer vision

Course Description
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 mobile robots (e.g. autonomous driving, unmanned air vehicles) industrial robots and biomedical applications (e.g. surgical robotics), since Dortmund University of Applied Sciences and Arts has established many research activities in these domains.   Research topics from research centres (biomedical technology, pimes) and other key areas of the university are defining the content of this module. The module introduces the basic algorithms and components for computer vision and robotic vision systems. In addition, students will learn about the application of that knowledge in the specific domain. The course will involve topics from a recent research project.

Course Structure

• Introduction to Robotic Vision
• 2D and 3D Geometry
• Camera Calibration
• Feature Extraction
• 3D Vision
• Paths and Trajectories
• Robot Kinematics and Motion
• Vision-based Robot Control
• Robotic Vision Project

Requires:

• MOD1-01 – Mathematics for Controls & Signals
• MOD1-03 - Embedded Software Engineering
• MOD2-04 – Signals & Control Systems 1

Passed exam and passed semester assignments
 

Connects to:

• MOD-E01 – Applied Embedded Systems
• MOD-E04 – Signals and Systems for Automated Driving
• MOD-E10 – Automotive Systems

5,00%

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

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

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

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

programming, basics of embedded systems

Connects to:

• MOD-E01 – Applied Embedded Systems 1 & 2
• MOD-E03 – Automotive Systems

5,00%

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

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

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

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

higher mathematics

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

Connects to:

• MOD-E04 – Signals and Systems for Automated Driving
• MOD-E05 – Computer Vision

5,00%

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

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

1. Technology overview
2. Sensors
   1. Radar
   2. Lidar
   3. Ultrasonic
   4. Camera
3. Radar signal processing
   1. Detection
   2. Target estimation
4. State estimation
   1. Vehicle motion models
   2. Random processes
   3. Tracking
   4. Target classification
   5. Mapping
5. Actuators & Vehicle Control
   1. Bicycle model
   2. Longitudinal control
   3. Brake and steering systems
6. Architectures
   1. Bus interfaces
   2. Car-to-X
   3. Safety domain controllers
   4. AUTOSAR
7. System Engineering
   1. Quality Process standards
   2. Process models
   3. Requirement engineering
   4. SPICE
8. ISO 26262
   1. Basics
   2. Concept phase
   3. Product development
9. Legal frameworks
   1. Vienna convention
   2. Relevant norms and legislation

Case Studies

CS08: Radar Systems for Automated Driving


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

higher mathematics, programming, signal processing

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

Connects to:

• MOD1-04 – Requirements Engineering
• MOD2-01 – Mechatronic Systems Engineering (MOD2-01)
• MOD-E03 – Automotive Systems
• MOD-E05 – Computer Vision

5,00%

Knowledge

• Knows relevant home automation systems and standards
• Know smart building concepts (e.g. VDC, BIM)
• Knows relevant trends and projects in Smart City
• Is aware of critical limitations, esp. safety and security issues

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
• Can apply and configure Smart City platforms (e.g. FIWARE)

Competence – attitude

• Can discuss smart home/building/city systems with experts
• Can lead cross domain design in this domain
• Can contribute to Smart City Alliances (e.g. Dortmund Smart City Alliance)

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 rea- ching 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 (e.g. VDC, 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 Smart City platforms, esp. FIWARE
3.4 Stakeholder and Citizen Involvement
3.5 Case Study: Smart City Alliance Dortmund

Students will be guided through a case study project. They form agile teams and collaborate in the project execution via IT tools. In addition, they write a scientific paper as group work.

• Lectures introducing concepts, methods and tools
• Project simulation (agile, virtual SW development projects with Scrum) on the case study of setting up a Smart Home Automation or Smart City platform. Several sprints are conducted over 2 months’ time. Review meetings with teachers and 2 reviews in the plenary
• Group work on writing a scientific paper, peer review by students and teachers. Excellent papers can be encouraged to submit at a conference, e.g. IEEE E-TEMS
• Presentations to communicate results

none

Assessment of the course: Theoretical knowledge: Oral exam at the end of the course (20%), Practical Skills: Group assessment on results of project simulation (50%) and Scientific Competences: paper presentation (30%)

Passed exam and passed semester assignments
 

Can be used as an elective in Master Embedded Systems Engineering

5,00%

Swathika, O. G., Karthikeyan, K., & Padmanaban, S. (Eds.). (2022). Smart Buildings Digitalization: IoT and Energy Efficient Smart Buildings Architecture and Applications. CRC Press.

Jadhav, N. Y. (2016). Green and smart buildings: advanced technology options. Springer.

Smeenk, H. G., & Petock, M. (2023). Internet of Things for Smart Buildings: Leverage IoT for smarter insights for buildings in the new and built environments. Packt Publishing Ltd.

Merz, H., Hansemann, T., & Hübner, C. (2009). Building automation. Heidelberg: Springer.

Halegoua, G. (2020). Smart cities. MIT press.

Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., ... & Portugali, Y. (2012).
Smart cities of the future. The European Physical Journal Special Topics, 214, 481-518.

Cirillo, F., Solmaz, G., Berz, E. L., Bauer, M., Cheng, B., & Kovacs, E. (2019). A standard-based open source IoT platform: FIWARE. IEEE Internet of Things Magazine, 2(3), 12-18.

Ahle, U., & Hierro, J. J. (2022). FIWARE for Data Spaces.

Wiecher, C., Tendyra, P., Wolff, C. (2022). Scenario-based Requirements Engineering for Complex Smart City Projects, 2022 IEEE European Technology and Engineering Management Summit (E-TEMS), Bilbao, Spain.

Wolff, C., Tendyra, P., Wiecher C. (2021). Agile Systems Engineering in Complex Scenarios, 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Cracow, Poland.

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

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

programming skills (C/C++ )

• MOD1-02 - Distributed and Parallel Systems
• MOD1-03 - Embedded Software Engineering

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

5,00%

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

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

1. Main building blocks of SoCs
   1. IP-cores (processors, communication, memories, sources for IP-cores)
   2. on-chip communication (topologies, wishbone)
   3. system definition
   4. ESL: electronic specification language
   5. on-chip vs. off-chip memory
   6. debugging methodologies
2. Multicore and Manycore architectures
   1. ASIP and Networks on Chip (NoC)
3. ASIC design flow
   1. Design entry (VHDL)
   2. Pre-silicon verification
   3. Synthesis & technology libraries
   4. Layout and signal integrity
   5. Timing closure
   6. Power routing, clocks and resets
   7. 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

programming, electronics

Connects to:

• MOD-E04 – SW Architectures for Embedded Systems
• MOD-E06 – Formal Methods in Mechatronics

5,00%

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

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

1. Introduction of a new trend in Embedded and Mechatronics Systems
2. Literature research and discussion of the state of the art
3. (optional) company visit and /or discussion of practical cases
4. Industry presentations
5. Tool trainings and practical labs
6. Own research, e.g. with experiments or projects
7. Presentation of the results
8. Preparation of a paper for a conference

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

Scientific & Transversal Skills (MOD1-05)

Oral Exam (30 min) at the end of the course (50%) and group work as homework (50%): research on a recent technology trend

Application Focus

Application of Machine Learning in Engineering, Medicine and Business Processes. Usage of Machine Learning models for structured and unstructured data. Miniprojects in collaboration with local companies.

Scientific Focus

Understanding of the function of classical and deep learning based machine learning algorithms. Knowledge about limitations and potential Explainability of methods. Rigorous evaluation of machine learning models, avoiding common pitfalls like overfitting, information leakage and others.
 

Course Description
This course gives an introduction into machine learning. From basic methods (nearest neighbour, decision trees, …) to modern deep learning approaches (Convolutional Neural Networks, Transformer architectures) everything will be introduced and applied in the lab practice. Structured and unstructured data (Video, Image, Audio, Text) will be considered with machine learning techniques. Machine Learning is not always the best solution (a hammer is not always the best tool), we discuss the limitations and ethical dimensions of potential solutions. A speciality of this course are mini-projects that are implemented by teams of participants in collaboration with local companies, who propose the topics. The mini-projects results will be presented in a workshop with company participants.

Course Structure

• • terminology of machine learning systems
• • Development of machine learning systems in KNime or other languages like python
• • design, implementation and evaluation of machine learning systems
• • linear models
• • supervised and unsupervised learning
• • neural networks
• • clustering, k-means
• • nearest-neighbour algorithms and lazy learning
• • decision trees
• • combination models, random forest, AdaBoost
• • Deep Learning (convolutional neural networks (CNN), long short-term memory (LSTM), Transformer (BERT))
• • Deep Learning Concepts - Transfer Learning, Data Augmentation, Generative Adversarial Networks (GAN)
• • Explainability of models
• • Applications for different modalities (text, image, sound), Word2Vec
• • theoretical concepts of machine learning (bias-variance dilemma, No Free Lunch Theorem)
• • methods to improve generalization abilities (regularisation, feature selection, dimension reduction,
• complexity adjustment)
• • solution of real world tasks in form of miniprojects in collaboration with local companies
• Workshop with industrial partners presenting the results of miniprojects

Teaching and training methods

• video lecture accompanying project work with final presentation,
• Flip teaching (inverted classroom) is used.
• completion of programming tasks on the computer, individually or in teams,
• lab practice with KNime

Passed exam and passed semester assignments
 

Learning outcomes
The students know modern machine learning methods and can design, implement, apply and analyze them in the context of general information systems as well as in the biomedical domain. They can evaluate existing methods and can judge, if machine learning algorithms are a potential solution for a given problem. They know several successful real-world applications of machine learning methods. They know and can apply formal and theoretical analysis methods in computational intelligence and machine learning. They are able to discuss the ethical problems of a given machine learning system.

5,00%

References

• Witten, E. Frank, M. Hall und C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4. Edition, Morgan Kaufmann (2017) – electronic version via intranet access possible
• C. M. Bishop, Pattern Recognition and Machine Learning, Springer (2006)
• E. Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), Third Edition, MIT Press (2014)
• I. Goodfellow, Y. Bengio und A. Courville: Deep Learning, MIT Press (2016) – free version available https://www.deeplearningbook.org

Application Focus

Application of Machine Learning in Engineering, Medicine and Business Processes. Usage of Machine Learning models for structured and unstructured data. Miniprojects in collaboration with local companies.

Scientific Focus

Understanding of the function of classical and deep learning based machine learning algorithms. Knowledge about limitations and potential Explainability of methods. Rigorous evaluation of machine learning models, avoiding common pitfalls like overfitting, information leakage and others.
 

Course Description
This course gives an introduction into machine learning. From basic methods (nearest neighbour, decision trees, …) to modern deep learning approaches (Convolutional Neural Networks, Transformer architectures) everything will be introduced and applied in the lab practice. Structured and unstructured data (Video, Image, Audio, Text) will be considered with machine learning techniques. Machine Learning is not always the best solution (a hammer is not always the best tool), we discuss the limitations and ethical dimensions of potential solutions. A speciality of this course are mini-projects that are implemented by teams of participants in collaboration with local companies, who propose the topics. The mini-projects results will be presented in a workshop with company participants.

Course Structure

• terminology of machine learning systems
• Development of machine learning systems in KNime or other languages like python
• design, implementation and evaluation of machine learning systems
• linear models
• supervised and unsupervised learning
• neural networks
• clustering, k-means
• nearest-neighbour algorithms and lazy learning
• decision trees
• combination models, random forest, AdaBoost
• Deep Learning (convolutional neural networks (CNN), long short-term memory (LSTM), Transformer (BERT))
• Deep Learning Concepts - Transfer Learning, Data Augmentation, Generative Adversarial Networks (GAN)
• Explainability of models
• Applications for different modalities (text, image, sound), Word2Vec
• theoretical concepts of machine learning (bias-variance dilemma, No Free Lunch Theorem)
• methods to improve generalization abilities (regularisation, feature selection, dimension reduction,
• complexity adjustment)
• solution of real world tasks in form of miniprojects in collaboration with local companies
• Workshop with industrial partners presenting the results of miniprojects

Teaching and training methods

• video lecture accompanying project work with final presentation,
• Flip teaching (inverted classroom) is used.
• completion of programming tasks on the computer, individually or in teams,
• lab practice with KNime

Passed exam and passed semester assignments
 

Learning outcomes
The students know modern machine learning methods and can design, implement, apply and analyze them in the context of general information systems as well as in the biomedical domain. They can evaluate existing methods and can judge, if machine learning algorithms are a potential solution for a given problem. They know several successful real-world applications of machine learning methods. They know and can apply formal and theoretical analysis methods in computational intelligence and machine learning. They are able to discuss the ethical problems of a given machine learning system.

5,00%

References

• Witten, E. Frank, M. Hall und C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4. Edition, Morgan Kaufmann (2017) – electronic version via intranet access possible
• C. M. Bishop, Pattern Recognition and Machine Learning, Springer (2006)
• E. Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), Third Edition, MIT Press (2014)
• I. Goodfellow, Y. Bengio und A. Courville: Deep Learning, MIT Press (2016) – free version available https://www.deeplearningbook.org

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

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

1. Introduction of a new trend in Embedded and Mechatronics Systems
2. Literature research and discussion of the state of the art
3. (optional) company visit and /or discussion of practical cases
4. Industry presentations
5. Tool trainings and practical labs
6. Own research, e.g. with experiments or projects
7. Presentation of the results
8. Preparation of a paper for a conference

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

Scientific & Transversal Skills (MOD1-05)

Oral Exam (30 min) at the end of the course (50%) and group work as homework (50%): research on a recent technology trend

5,00%

Specific for the recent research topic

Application Focus

Application of Machine Learning in Engineering, Medicine and Business Processes. Usage of Machine Learning models for structured and unstructured data. Miniprojects in collaboration with local companies.

Scientific Focus

Understanding of the function of classical and deep learning based machine learning algorithms. Knowledge about limitations and potential Explainability of methods. Rigorous evaluation of machine learning models, avoiding common pitfalls like overfitting, information leakage and others.
 

Course Description
This course gives an introduction into machine learning. From basic methods (nearest neighbour, decision trees, …) to modern deep learning approaches (Convolutional Neural Networks, Transformer architectures) everything will be introduced and applied in the lab practice. Structured and unstructured data (Video, Image, Audio, Text) will be considered with machine learning techniques. Machine Learning is not always the best solution (a hammer is not always the best tool), we discuss the limitations and ethical dimensions of potential solutions. A speciality of this course are mini-projects that are implemented by teams of participants in collaboration with local companies, who propose the topics. The mini-projects results will be presented in a workshop with company participants.

Course Structure

• • terminology of machine learning systems
• • Development of machine learning systems in KNime or other languages like python
• • design, implementation and evaluation of machine learning systems
• • linear models
• • supervised and unsupervised learning
• • neural networks
• • clustering, k-means
• • nearest-neighbour algorithms and lazy learning
• • decision trees
• • combination models, random forest, AdaBoost
• • Deep Learning (convolutional neural networks (CNN), long short-term memory (LSTM), Transformer (BERT))
• • Deep Learning Concepts - Transfer Learning, Data Augmentation, Generative Adversarial Networks (GAN)
• • Explainability of models
• • Applications for different modalities (text, image, sound), Word2Vec
• • theoretical concepts of machine learning (bias-variance dilemma, No Free Lunch Theorem)
• • methods to improve generalization abilities (regularisation, feature selection, dimension reduction,
• complexity adjustment)
• • solution of real world tasks in form of miniprojects in collaboration with local companies
• Workshop with industrial partners presenting the results of miniprojects

Teaching and training methods

• video lecture accompanying project work with final presentation,
• Flip teaching (inverted classroom) is used.
• completion of programming tasks on the computer, individually or in teams,
• lab practice with KNime

Learning outcomes
The students know modern machine learning methods and can design, implement, apply and analyze them in the context of general information systems as well as in the biomedical domain. They can evaluate existing methods and can judge, if machine learning algorithms are a potential solution for a given problem. They know several successful real-world applications of machine learning methods. They know and can apply formal and theoretical analysis methods in computational intelligence and machine learning. They are able to discuss the ethical problems of a given machine learning system.

5,00%

References

• Witten, E. Frank, M. Hall und C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4. Edition, Morgan Kaufmann (2017) – electronic version via intranet access possible
• C. M. Bishop, Pattern Recognition and Machine Learning, Springer (2006)
• E. Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), Third Edition, MIT Press (2014)
• I. Goodfellow, Y. Bengio und A. Courville: Deep Learning, MIT Press (2016) – free version available https://www.deeplearningbook.org

Learning outcomes

7.1 Knowledge 

• Graduates of the module master basic and advanced concepts of artificial intelligence and are able to apply current developments and methods of artificial intelligence to concrete practical issues in business informatics.
• The participants are able to confidently assess the benefits and limitations of the content and methods considered in relation to concrete practical applications of business informatics.
• The participants are confident in using current program libraries and are able to apply them to concrete problems in a project-oriented manner.

7.2 Skills 

• The participants are able to independently deal with current developments in the field of artificial intelligence and its specializations and current applications in the field of business informatics and to comprehend the core statements.

7.3 Competence – attitude 

• The participants are able to lead discussions on scientific issues (especially with regard to the applicability of the taught content for their field of study).
• The participants grasp the relevance of the taught contents for their field of study and are able to communicate this relevance adequately.
• The participants are able to discuss the challenges of the project tasks in project-oriented group work, identify possible alternative approaches and define, implement and evaluate justified approaches.

Course Description

As part of this course, current trends in artificial intelligence with a relevance in the field of business informatics (such as the development of chatbots, the analysis of the sentiment of texts using sentiment analysis, the optimization of classic problems in logistics or reinforcement learning) are introduced in their mathematical basics and methods and implemented in a project-oriented manner on various tasks.
Graduates of the module are able to understand the topics dealt with in the course and apply them practically to various questions.

Teaching and training methods

The course is taught in a project-oriented manner. In the first half of the semester, this involves teaching content in the form of interactive lectures and practicing the learned content in the form of small practical exercises. In the second half of the semester, the students work in groups to develop and implement specific practical applications, primarily in the field of business informatics.
 

Input from:

None

Scientific Focus

• Project work (50% of the final grade)
• Oral examination (50% of the final grade)

Input for:

None
 

5,00%

References

Stuart Russell und Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition, Pearson 2021

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)

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

none

project thesis about own research in an ongoing project as individual homework + presentation in colloquium (100%)

Passed exam and passed semester assignments
 

MOD4-01 – Master Thesis + Colloquium

15,00%

According to topic

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

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.

master thesis about own research in an ongoing project as individual homework + presentation in colloquium (30 min), (100%)