module handbook for the master's program · 2019-05-09 · discrete event simulation and 3d...

1 English translation is only for information. The legal documents are the German regulations. See http://www.cs.uni-magdeburg.de/ordnungenma.html.

Module Handbook

for the Master's program

Digital Engineering

at

Otto-von-Guericke University Magdeburg

Faculty of computer science

from 30.09.2012


The Master's program Digital Engineering (DigiEng)

Graduates of the Master's program in digital engineering are engineers with a strong knowledge of IT methods for the development, construction and operation of complex engineering products and systems, used in production engineering or in the automotive industry. The training qualifies the graduates for demanding activities and leadership roles in the planning and implementation of projects, on the use of modern IT solutions, such as virtual and augmented reality in application of engineering science, as well as in the field of industrial, semi-industrial and academic research. Through its multidisciplinary knowledge the graduates are capable of taking an interface function within interdisciplinary development. The course provides essential skills to perform academic research and advance industrial development. This is achieved through a combination of methods of computer science / engineering and domains. Special project work with objectives, contents and scopes over comparable offers prepare students optimally for the unique challenges of interdisciplinary research. In addition to the technical content of current technologies for the development and operation of engineering solutions, a strong focus lies on methodical knowledge, which is a necessary prerequisite for their successful application. The key skills taught in the course have a focus on interdisciplinary communication and project work. Selected content of the course will be offered in coordination and cooperation with partners in industry-related research.


Table of Contents

1.Computer science fundamentals for engineers ....................................................................................................... 6

Computer Graphics I .................................................................................................................................. 7

Databases .................................................................................................................................................. 9

Introduction to Simulation ...................................................................................................................... 10

Principles of Computer Hardware ........................................................................................................... 11

Software and systems engineering ......................................................................................................... 13

2.Engineering for Computer Science ........................................................................................................................ 14

General Electrical Engineering ................................................................................................................ 15

Digital Information Processing ................................................................................................................ 16

Finite element method (FEM) * .............................................................................................................. 18

Concepts, methods and tools for product lifecycle management .......................................................... 20

Product data modeling ............................................................................................................................ 22

3.Human Factors ........................................................................................................................................................ 24

Human Factors ........................................................................................................................................ 25

Organizational and staff development, team work, problem solving in groups (Basic) ......................... 27

4.Methods of Digital Engineering ............................................................................................................................. 29

CAx applications (CAA) ............................................................................................................................ 30

CAx Management (CAM) ......................................................................................................................... 31

COMPUTER AIDED GEOMETRIC DESIGN ................................................................................................. 32

Computer tomography - theory and application .................................................................................... 34

Digital Planning in automation technology ............................................................................................. 36

Discrete event simulation and 3D visualization ...................................................................................... 38

Communication technology for digital engineering ................................................................................ 39

Machine learning for medical systems .................................................................................................... 41

Product modeling and visualization (PMV) ............................................................................................. 42

Virtual Commissioning ............................................................................................................................ 43

5.Methods of computer science ................................................................................................................................. 44

Data Mining ............................................................................................................................................. 45

Interactive Systems ................................................................................................................................. 47

Mobile Communication ........................................................................................................................... 49


Secure Systems ........................................................................................................................................ 51

Visualization ............................................................................................................................................ 52

6.Interdisciplinary team project ............................................................................................................................... 54

Interdisciplinary team project * .............................................................................................................. 55

7.Specialization ........................................................................................................................................................... 56

Adaptronics * .......................................................................................................................................... 57

Advanced Database Models .................................................................................................................... 58

Advanced Machine Learning ................................................................................................................... 59

Advanced Topics in Databases ................................................................................................................ 60

Applied Discrete Modeling ...................................................................................................................... 62

Bayesian Networks .................................................................................................................................. 63

Image coding * ........................................................................................................................................ 65

Computational Fluid Dynamics ............................................................................................................... 66

Introduction to Data Warehousing ......................................................................................................... 68

Digital Filter * .......................................................................................................................................... 70

Distributed Data Management ............................................................................................................... 71

Introduction to Medical Imaging ............................................................................................................. 72

Embedded Networks ............................................................................................................................... 73

Advanced Programming Concepts for Tailor-made Data Management ................................................. 75

Flow Visualization .................................................................................................................................... 77

Fuzzy Systems .......................................................................................................................................... 78

Information and Coding Theory .............................................................................................................. 80

Data Mining for Changing Environments ................................................................................................ 81

Cognitive Systems * ................................................................................................................................. 83

Mesh Processing ...................................................................................................................................... 84

Modeling with population balances ........................................................................................................ 85

Multimedia Retrieval ............................................................................................................................... 87

Numerical Methods in Biomechanics * ................................................................................................... 89

Security of embedded systems * ............................................................................................................ 90

Speech processing * ................................................................................................................................ 91

Electromagnetic Theory .......................................................................................................................... 92


Theory of electrical cables ....................................................................................................................... 93

THREE-DIMENSIONAL & ADVANCED INTERACTION ................................................................................ 95

Introduction to concurrency control ....................................................................................................... 97

Transport phenomena in granular, particulate and porous media ........................................................ 98

Uncertain knowledge .............................................................................................................................. 99

Distributed Real-Time Systems ............................................................................................................. 100

8.Digital Engineering Project .................................................................................................................................. 102

Digital Engineering Project * ................................................................................................................. 103


1.Computer science fundamentals for engineers


Module name: Computer Graphics I

Module level, if applicable:

Abbrevation, if applicable:

Subheading, if applicable:

Classes, if applicable:

Semester:

Module coordinator: Professor of Visual Computing

Lecturer: Prof. Dr. Holger Theisel

Language: German

Classification within the curriculum:

CV-B compulsory section 2 Semester IngINF-B: Major: computer science techniques INF-B: Major: Computer Graphics / Image Processing

WIF-B: elective computer science / computer science economy

Teaching format / class hours per week during the semester:

Lecture, tutorial

Workload: Attendance time: 2 SWS lectures 2 SWS excercisess

Independent work: 94 h completion of exercises

Credit points: 5 Credit Points = 150 h = 4 h = 56h Attendance time + 94h independent work, grading scale according to examination regulations

Requirements under the examination regulations:

none

Recommended prerequisites:

Module: introduction to computer science

Targeted learning outcomes:

Learning outcomes and competences acquired:

Acquisition of basic knowledge about the most important algorithms in computer graphics

Recognize fundamental principles of computer graphics allows rapid incorporation into new graphics packages and graphics libraries

Ability to use graphical approaches for different applications of computer science

Content: Introduction, history, application areas of computer graphics

Modeling and acquisition of graphical data

Graphical application programming

Transformations

Clipping

Rasterization and anti-aliasing

Lighting

Radiosity


Texturing

Visibility

Raytracing

Overview of modern concepts of computer graphics

Study / exam achievements: Services: - Successful processing of the exercises - Meet the OpenGL programming task

Examination: written, 2 hours

Note

Inputs according to the specification at the beginning of the semester

Forms of media:

Literature: J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes: Computer Graphics – Principles and Practice (second Edition). Addison-Wesley Publishing Company, Inc., 1996

J. Encarnacao, W. Straßer, R. Klein: Gerätetechnik, Programmierung und Anwendung graphischer Systeme, Teil I und II. Oldenbourg, München, Wien, 1966, 1997

D. Salomon: Computer Graphics Geometric Modeling, Springer, 1999

A. Watt: 3D Computer Graphics. Addison-Wesley Publishing Company, Inc., 2000


Module name: Databases


Abbrevation, if applicable: 100391

Subheading, if applicable: DB I


Semester: 3 IF IngIF, WIF

5 CV

Module coordinator: Professor of Practical computer science / information systems and databases

Lecturer: Prof. Gunter Saake

Language: German


IF IngIF, CV: computer science 1

WIF: computer science


Lecture, tutorial

Workload: Attendance time: 2 SWS lectures 2 SWS tutorial Independent work: Exercises and exam preparation

Credit points: 5 credit points = 150h = 4h = 56h Attendance time + 94h independent work

Grading scale according to examination regulations


None


None


Learning objectives and competences acquired: Basic understanding of database systems (terms, basic concepts) Ability to design a relational database

Knowledge of relational database languages Ability to develop database applications

Content: Properties of database systems Architectures Conceptual design of a relational database

Relational database model Mapping of ER schema to relations Database languages (relational algebra, SQL) Formal design criteria and normalization theory

Application programming

Other database concepts such as views, triggers, assignment of rights

Study / exam achievements: Check or bill: written

Forms of media:

Literature: See http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/db1/index.html

http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/db1/index.html


Module name: Introduction to Simulation


Abbrevation, if applicable: ItS



Semester: 5

Module coordinator: Professor of simulation

Lecturer: Graham Horton

Language: lecture:English / exercises: German and English


B-CV: CV WPF FIN INF area

B-INF: WPF computer science consolidation (Applied computer science or computer engineering computer science systems) B IngINF: Compulsory

WIF-B: elective


Lectures, exercises

Workload: Attendance time = 56 hours 2 SWS lecture

2 SWS excercises Independent work = 94 h

- Processing of homework & exam preparation

Credit points: 5 credit points


-


Mathematics I-III


Ability to carry out a semester-long project, using basics of simulation, event-oriented modeling and programming, abstract modeling and applications of computer science in other fields

Content: Event simulation, random variables, random number generation, statistical data analysis, ordinary differential equations, numerical integration, AnyLogic simulation system, stochastic Petri nets, queuing

Study / exam achievements: Rated: Written test, 120 min

Ungraded: Homework + 20 min discussion note

Forms of media:

Literature: Banks, Carson, Nelson, Nicol: Discrete-Event Simulation See www.sim.ovgu.de

Other Title during summer semester: "Modeling and Simulation"


Module name: Principles of Computer Hardware


Abbrevation, if applicable: TI-I



Semester: 1

Module coordinator: Chair of Technical computer science

Lecturer:

Language: German


PF IF, B 1

PF IngINF, B 1

WPF CV, B 1-5

WIF WPF, B 1-5


Lecture, exercises

Workload: Attendance time: 2 SWS lecture

2 SWS exercises Independent work: Processing of exercise and programming assignments & exam preparation

Credit points: 5 Credit Points = 150 h = 4 h = 56h Attendance time+ 94h independent work. Grading scale according Prufungsordnung


none


none


Learning objectives and competences acquired:

Ability to understand the basic structure of computers as a layered model of different levels of abstraction and describe

Competency, components of the digital logic level to design independently,

In-depth knowledge available via the machine level of a digital computer.

Understanding of the principles for performance improvement through pipelined and parallel processing

Content: - Combinational Switching Networks - Sequential derailleurs - Computer arithmetic

- Building a computer - Instruction set and addressing

- Pipelined and parallel processing

Study / exam achievements: Services: Processing of exercise and programming tasks


Examination: written

Forms of media:

Literature: Will be announced in the lecture


Module name: Software and systems engineering

Module level, if applicable

Abbrevation, if applicable: S & SE



Semester: 1

Module coordinator: Professor of Computer Systems in engineering and other

Lecturer:

Language: English (German optional)



Lecture, exercises

Workload: Attendance time: 2 SWS lecture 2 SWS excercisess Independent work: Analyzing, modeling, presenting

Credit points: 6 Credit Points = 180 h = 4 h = 56h Attendance time + 124h independent work Grading scale according to examination regulations


none


none


Learning objectives and competences acquired: Understanding and application of process models and modeling languages for analysis and design Understanding and applying different approaches to requirements engineering Understanding and applying different methods for detection of fulfilling requirements of

Content: Process models, UML, SysML, programming languages/ restriction / technical boundary technical Systems, requierements modelling / acceptance / traceability

Study / exam achievements:

Forms of media: Lecture, student presentation, exercise

Literature:


2.Engineering for Computer Science


Name of the module General Electrical Engineering

Contents and objectives of the module


Convey basic knowledge of electrical engineering, electronics, electronic components, and the structure and performance of electrical machines and drives

Development of skills for independent solving electrical engineering tasks and

Aptitude for practical tests on electrical systems and components

Contents:

Fundamentals of Electrical Engineering

DC circuits

AC Technology

Electric field

Magnetic field

Electronics

Electrical Machines and Drives

Measurement of electrical quantities

Protection measures

Methods of Teaching Lecture, exercise, internship

Prerequisites for participation

Mathematics, Physics

Applicability of the module Creditability in consecutive courses: Compulsory and elective in different bachelor's degree programs for non-electrical engineers

Requirements for awarding credit points

Examination for admission to the internship in the summer semester, course certificate, exam without any aids at the end of the module

Credits and grades 8 credit points = 240h (96h Attendance time + 144h independent work) Grading scale according to examination regulations

Workload Attendance time: over 2 semesters 2 SWS lecture over 2 semesters 1 SWS excercises and internship

independent work: Reworking lecture, solve exercises, prepare laboratory experiments, exam preparation

Frequency of occurrence each year - first part in WS, second part in SS

Duration of module two semesters

Responsible Palis / Lindemann


Modules Digital Information Processing

Objectives and contents Objectives:

The participant has to overview of basic problems and methods of digital signal processing.

The participant understands the functionality of a digital signal processing system and can Mathematically explain the mode of operation.

The participant can assess assurance applications in terms of stability and other markers. He / She can calculate the frequency response and reconstruction of analogue signal.

The participant can perform synthesis calculations and assessments as well on stochastically excited digital systems.

The participant can apply this knowledge in a field of specialization, egMedical Signal Analysis

Contents: 1. Digital signal and digital LTI system 2. Z-Transform and Difference Equations 3. Sampling and Reconstruction 4. Synthesis and analysis of search system 5. Discrete and Fast Fourier Transform 6. Processing of Stochastic signal by LTI system:

Correlation Techniques and model-based system (ARMA) 7. Selected Specialization topics, eg Medical Signal

Analysis

Teaching Lecture and exercises

Books used: Wendemuth, A (2004): “Grundlagen der Digitalen Signalverarbeitung”, 268 pages, Springer Verlag, Heidelberg. ISBN: 3-540-21885-8 Oppenheim, A; Schafer R (1975): “Digital Signal Processing” 784 pages, Prentice Hall, ISBN: 0132146355

Prerequisits Bachelor's degree in Electrical Engineering or related studies

Knowledge of signals and systems, analog Fourier transformations

Usability of the module Master Courses in the Faculty of Electrical Engineering and Information Technology, and other Master courses

Exam Mandatory participation in exercise classes, successful results in exercises / written exam at the and of the course

Credit points 4 credit points = 120 h (42 h time of attendance and 78 h autonomous work)

Work load Time of attendance 2 hours / week - lecture 1 hours / week - exercises

Autonomous work


postprocessing of lectures preparation of exercises and exam

Availability Winter semester, every year

Duration One semester

Responsibility Prof. Dr. A. Wendemuth, FEIT-IESK


Name of the module Finite element method (FEM) *

Aims and Content of the

module

Aims and content

In this course, students will learnt to use the finite element method as

an approximation method for solving of practical tasks of engineering

(mechanical engineering, automotive, machine tool, aerospace).

The course focuses on problems of mechanics of solids by use of three-

dimensional models (solid and surface models). In the lectures the most important theoretical basis for understanding

the modeling and evaluation of the results (fault analysis, power

adaptation) are taught.

In the exercises, the material based on practical tasks is deepened, and

in the internship, the students independently solve a complex task

whose successful processing is a prerequisite for admission to the

examination.

Lecture series

1. Introduction to the course (including an overview of commercial software

tools) 2. Adapted problem modeling with volume and shell elements (shell models

versus 3D continuum models) 3. Finite volume elements (shape functions, isoparametric element concept,

numerical integration, and hourglass-locking phenomena, super

convergence) 4. Shell finite elements (Ahmad elements, Kirchhoff and Mindlin elements,

discrete Kirchhoff elements, patch test, item selection) 5. Coupling of shell elements with 3D solid elements (constraints, weak form of

coupling,) 6. Dynamic structure calculations (eigenvalues, model reduction by Gyan and

Craig-Bampton modal method, time integration, frequency domain methods,

model updating). 7. Overview of the FEM for solving general (coupled) field problems (heat

conduction, heat stress). 8. Summary and Outlook (Non-linear FEM, optimization)

Exercises (2h every 14 days)

Calculation of tasks on the computer using commercial FEM software

Internship (14 weeks 2 hours)

Independent preparation of an individual project (group project)

Methods of Teaching Lectures (2 SWS), exercises (1 SWS) internship (1 credit hour)

Prerequisites for participation TM, Computational Mechanics and FEM

Applicability of the module There are no interactions with other modules

Grade and credit points Oral examination


Credits 5 ECTS

Workload Attendance time: 4 SWS (lectures, exercises, internship) Independent

Processing a Project

Frequency of occurrence annually

Duration of module one semester

Responsible Prof. U. Gabbert


Module name: Concepts, methods and tools for product lifecycle management


Abbrevation, if applicable: PLM



Semester:

Module coordinator: Chair for Applied computer science / computer-aided engineering systems

Lecturer:

Language: German / if necessary English



Lecture, exercises, tutorials

Workload: Attendance time

2 SWS lecture

2 SWS excercises / tutorial

Ability to work independently

Solution of exercises including tutorial tasks

Exam Preparation

Credit points: 6 Credit Points = 180 h

(56 h Attendance time + 124 hours independent work) Grading scale according to examination regulations


none

Recommended prerequisites: Knowledge of software development based on UML

Knowledge of document management

Knowledge in the design of data structures

Fundamentals in Mechanical Engineering

Fundamentals in CAD / CAE / CAM

Knowledge from the field of computer-assisted engineering systems

Targeted learning outcomes: Objectives & Competences to be Acquired

Acquisition of knowledge of concepts, methods, procedures and tools for PLM

Acquire an understanding of product data and their significance for the business processes of manufacturing companies

Acquisition of basic skills for the uniform production, processing and management of technical product data and documents


Ability to solve individual problems for product data management in the context of specific PLM strategies

Ability to develop, design and implementation company-specific PLM strategies

Content: Topics of the lecture

Methodological fundamentals for product data management

Methodological fundamentals for PLM

Concepts and tools for analysis and modeling of integrated product models

Tools for PDM / PLM integration (CAD, CAE)

Organizational requirements of PDM / PLM introduction

Economic aspects of PDM / PLM introduction

PDM / PLM implementation strategies

System architectures for PDM / PLM

Concrete PDM - systems and their features and capabilities

Corporate realized as concrete solutions

Content exercise / tutorial

Exercises related to selected content of the lectures

Solution of a specific PLM project (example) for all phases in the context of a concrete example

Study / exam achievements: Services

Completion of exercises and the project with successful presentation in the exercises

Examination

Spoken

Forms of media:

Literature: R. Anderl, H. Grabowski, A. Polly: Integriertes Produktmodell. Entwicklungen zur Normung von CIM, Beuth-Verlag

M. Eigner, R. Stelzer: Produktdatenmanagement-Systeme: Ein Leitfaden für Product Development und Life Cycle Management, Springer-Verlag

V. Arnold, H. Dettmering, T. Engel, A. Karcher: Produkt Lifecycle Management, Springer-Verlag

A.-W. Scheer, M. Boczanski, M. Muth, W.-G. Schmitz, U. Segelbacher: Prozessorientiertes Product Lifecycle Management , Springer-Verlag

Own script


Name of the module Product data modeling


In this course the following content will be taught:

Classification of components of technical systems in terms of

their model characteristics

Mediation of the methodological basis for product data

description, including: feature systems, semantic networks

and forms of notation suchAs XML and class diagrams

Essential standards of performance in the field suchAs IEC

61360, ecl @ ss, ETIM, BMEcat PROLIST

Notion of a feature-based information model

mechanical, electrical and automation engineering application

examples

Contents:

In many areas of machine and plant automation technologies

and the efficient flow of information between different life cycle

phases, tools and operating engineers are becoming increasingly

important.There is a trend to gradually replace routine jobs of

engineering through automated or semi-automated technical

processes. To this unique and digitally available description of

the components of technical systems are required. The

descriptions will be referred to as the product data, which will be

merged as mechatronic models. This course provides the basics

for digital modeling of product data technology systems.

Methods of Teaching Lecture (2) + Exercise (1)


Basic knowledge in computer science and software development

Applicability of the module There is no interaction with other modules. Eligibility: elective master's degree in "Digital Engineering"


Participation in the courses

Examination at the end of the module, grade scale according to

examination regulations, award of points accourding to written or

oral examination

Credits and grades 5 Credit Points = 120 h (42 h Attendance time + 78 hours independent work) Grading scale according to examination regulations

Workload Attendance time: weekly lectures, 2 SWS weekly exercises 1 SWS

Independent work: Reworking the lecture Solution of exercises and exam preparation

Frequency of occurrence Every xxx - Semester



Responsible Prof. Dr. Christian Diedrich, FEIT-IFAT


3.Human Factors


Module name: Human Factors

Module level, if applicable ---

Abbrevation, if applicable: ---

Subheading, if applicable: ---

Classes, if applicable: Ergonomics

Semester: 1

Module coordinator: Deml

Lecturer: Brennecke, Deml

Language: German, if necessary English



Lecture and lecture accompanying exercises

Workload: Attendance time:

Lecture: 2 SWS

Exercise: 1 SWS

Independent work:

Follow the lectures

Preparation of written examination

Credit points: 3 Credit Points = 75 h (42 h Attendance time + 33 hours independent work)


Attending lectures

The written examination


---


The aim of this lecture is to convey the relevant relationship between humans, technology and organization needed for engineering behavior. The participants should acquire methods and standards to make work humane. It will give the need to plan the relational structure-man-technology-organization and designed so that the human performance potencies can be optimally utilized and further refined and that no damaging or impairing effects on health and well-being of the people arise. In this way, the cost can be realized in unity with the humanity of the work. The courses offer work science basics and instructions or pulses for engineers, who are not working as specialists in the design work,

Content: Object, definition, objectives and elements of Industrial Science

Physiological and psychological foundations of the work

Workplace design

Design of computer work

Work environment design (noise, lighting)

Work organization


Human information processing

Human-Machine Interaction

Human error and reliability

Time Management

Health and Safety

Study / exam achievements:

Written examination

Forms of media: PowerPoint

Literature: Is provided in the lecture


Module name: Organizational and staff development, team work, problem solving in groups (Basic)

Module level, if applicable ---

Abbrevation, if applicable: OPE

Subheading, if applicable: ---

Classes, if applicable: ---

Semester: 1

Module coordinator: Dr.-Ing. Sonja Schmicker

Lecturer: Dr.-Ing. Schmicker, Dipl.-Kff. Silke Schröder

Language: German



Lecture with integrated exercise


Lecture and integrated exercise: 4 SWS

Independent work:

Preparation and review of the lectures or Exercises

Preparation for the oral examination

Credit points: 4 Credit Points = 100h (56h Attendance time + 44h independent work)


Attending lectures or Exercises

Passing the oral examination


---


The aim of the event is to learn methods for facilitation of group work. For this purpose, theoretical knowledge and practical training in the areas of organizational and personal development, intra-and inter-personal communication, intra-and inter-group behavior, creativity and structured problem solving are taught and realised.

Content: Overview of tasks and functions of human resources and organizational development

current trends in personal and organizational development

Derivation of requirements for the competence development

Design, approaches to group and team work and employee participation in the economy

social and communicative skills in group work

Control of dynamic processes on the theme-centered interaction (TCI)

Application of creativity techniques in group work

systematic and methodical action in problem solving

Presentation of group work.

Study / exam Oral examination


achievements:

Forms of media: Multimedia (overhead projector, PowerPoint, flipchart, pin board, TV / video, etc.)

Literature: Is provided in the event


4.Methods of Digital Engineering


Course: M.Sc.Integrated Design Engineering (IDE)

Module:

CAx applications (CAA)

Objectives of the module: Learning objectives and competences acquired:

Learn about different CAx applications and their correlations

Mastering the essential elements of product lifecycle management

Dominate Simple PDM applications

Learn and master simple simulation method

Contents:

Product Lifecycle Management

Process Modeling

Networks

CAPP and NC systems, CAM systems, Flexible manufacturing systems, handling systems

Simulation method

PDM applications and databases

Methods of Teaching: Lectures and exercises with corresponding scripts and excercises. Media: overhead projector, blackboard

Prerequisite for participation: Attend the lecture: introduction IDE

Workload: Attendance time: 42h Lectures: 2 SWS lectures, 2 SWS excercisess.

Working independently 108h: Wrapping up the lectures, preparation of exercises and the written examination

Assessment / Credits: Examination requirement: Participation in lectures and exercises (at least 75%). Written examination (120 min duration). Grading scale according to examination regulations.

5 credit points

Responsible for Module: Prof. Dr.-Ing. Dr, h.c. Sándor Vajna, FMB - LMI

Suggested Reading: Lecture notes and excercises and Vajna, We-ber, Bley, Zeman: CAx for engineers, Springer 2008



Module:

CAx Management (CAM)

Objectives of the module:

Awaken the understanding of the needs of the CAx management

Getting to know and applying relevant procedure-wise introduction to and migration of a CAx system

Getting to know and applying of methods for determining the efficiency of CAx systems and applications

Master the basic elements of the management of CAx systems

Exploring methods to predict costs of product costs in the various phases of the product life cycle

Contents: Methods and procedures to

Introduction and migration of CAx technology

Cost of CAx systems (including Costs, benefit, investment procedures of the business economics)

Evaluation of the benefits of new technologies in the product development process with the BAPM procedure

Product Lifecycle Costing

Efficient system management

Methods of Teaching: Lectures and exercises with corresponding scripts and excercises. media: beamer, overhead projector, blackboard

Prerequisite for participation: Attend the lecture: introduction IDE




5 credit points

Responsible for Module: Prof. Dr.-Ing. Dr, hcSándor Vajna, FMB-IMK/LMI

Suggested Reading: Lecture notes and excercises and Vajna, Weber, Bley, Zeman: CAx für Ingenieure, Springer 2008


Module name: COMPUTER AIDED GEOMETRIC DESIGN


Abbrevation, if applicable: CAGD



Semester:

Module coordinator: Chair for Applied computer science / Visual Computing

Lecturer: Prof. Dr. Holger Theisel

Language: German / English on demand


WPF Bachelor CV: CV electives WPF Bachelor IF: consolidation AI / consolidation CG / BV

WPF Bachelor IngIF: elective computer science techniques

Bachelor WIF WPF: computer science electives


Lecture and exercise / 4 SWS

Workload: Attendance time: 3 SWS lecture / 1 SWS exercise

Independent work: Reworking the lecture

Solve the exercises

Credit points: 5 Credit Points = 150 h (56h Attendance time + 94h independent work), grade scale according to examination regulations


none

Recommended prerequisites: Fundamentals of computer graphics Mathematics I to III

Targeted learning outcomes: Learning objectives and competences acquired:

Learn the most important techniques for curve and surface modeling

Understanding of the underlying theoretical principles

Applying the approaches to other problems in computer science (data interpolation Datenapproximation, data extrapolation, numerical methods)

Content: Differential geometry of curves and surfaces Bezier curves Bezier spline curves B-spline curves Rational curves Polar forms Bezier and tensor product B-spline surfaces Bezier surfaces over triangles Surface interrogation and fairing Subdivision curves and surfaces

Study / exam achievements: Exam prerequisite: Successful Exercises processing


Oral examination

Note

Inputs according to the specification at the beginning of the semester

Forms of media: PowerPoint, video, black board

Literature: G. Farin. Curves and Surfaces for Computer Aided Geometric Design. Morgan Kaufmann, 2002. Fourth edition.

G. Farin and D. Hansford. The Essentials of CAGD. AK Peters, 2000.

J. Hoschek and D. Lasser. Grundlagen der Geometrischen Datenverarbeitung. B.G. Teubner, Stuttgart, 1989. (English translation: Fundamentals of Computer Aided Geometric Design, AK Peters.)

G. Farin. NURB Curves and Surfaces. AK Peters, Wellesley, 1995.


Name of the module Computer tomography - theory and application


Objectives of the Module (Competencies):

Understanding of the systems theory of imaging systems Overview of the physics and operation of computer tomography Understanding of the mathematical method for tomographical reconstructing Overview of current research areas in tomographic imaging

Contents:

Starting with the system theory of imaging systems follows the treatment of the physical properties of the X-ray radiation and its interaction with matter.In the second part the X-ray-based imaging projection will be discussed. In the third part follows the study of the exact mathematical method of tomographic imaging and the treatment of various image reconstruction methods.The individual issues are:

System theory of imaging systems Physical Basics X-ray tubes and X-ray detectors Projection imaging Reconstruction techniques: Fourier-based methods, filtered back-projection, algebraic method, statistical method Geometries: parallel, fan-and cone-beam Implementation Issues Image artifacts and their corrections

Methods of Teaching Lecture, excercises

Prerequisites for participation Digital signal processing, principles of physics

Applicability of the module Creditable for all master programs of other departments whose academic regulations allow.


Oral examination

Credits and grades 5 credit points = 150h (42h + 108h independent work) Grading scale according to examination regulations

Workload Attendance time: 2 SWS lecture, 1 SWS exercise

independent work

Frequency of occurrence Every year in SS


Responsible Prof. Dr. rer. nat. George Rose (FEIT-IESK)


Data Management for Engineering Applications *

(Prof. Saake) Lecture (English) in preparation Module description will be given later


Name of the module Digital Planning in automation technology


Learning objectives and competencies acquired are divided into the following parts.

Planning process with the phases of project management

Support of engineering using modern CAE systems

Provision of specific requirements of the process and production technology

Information Technical description of the technical and organizational processes

Contents:

The configuration of process control systems (PLT) systems based on distributed process control systems is a complex knowledge and teaching area, which was placed in recent years on a sound economic knowledge base.Training objective of this lecture, is to provide these conceptual and methodological principles systematically. This is done from the point that the planning process and the resulting planning information and documents are increasingly created digitally, stored and reused.The individual phases and content of the continuous project management are described and the basics of computerized aided engineering are taught.In this way, students are capable of working with engineers from other disciplines, such as process engineers, plant engineers and other investment partners, cooperatively. The students should be able to critically deal with the concept of automation objects apart to formulate the automation goals and tasks and to influence the automation-based design of technological systems in order to enhance the effectivenes



The course is suitable for students of engineering science students from the 4th Semester.



Mandatory participation in the exercises, successful execution of

the exercises





Frequency of occurrence Every year in xx-semester




Module name: Discrete event simulation and 3D visualization


Abbrevation, if applicable: DiSi3D



Semester:

Module coordinator: Chair for Applied computer science

Lecturer: Prof. Dr. Thomas Schulze

Language:

Curriculum


Lectures, frontal exercises and independent work

Workload: Attendance time: Weekly Lecture 2 SWS Weekly exercise 2 SWS Independent work: Exercises and exam preparation

Credit points: 6 Credit Points = 180 h (42 h Attendance time + 138 hours independent work) Grading scale according to examination regulations


none

Recommended prerequisites: none

Targeted learning outcomes: Learning objectives and competences to be acquired:

Basic understanding of discrete simulation

Ability to implement discrete simulation systems

Methods and techniques in applications of discrete simulation

Content: Worldviews of the simulation and its implementation

Methods and techniques for validation and verification

Experiment design and management

Simulation and Optimization

Distributed Simulation

Study / exam achievements: Regular attendance at lectures and excercises; solve the exercises and successful presentation in the exercises Written or oral exam at the end of the module

Forms of media:

Literature: A. Law and D. Kelton (2003) Simulation Modeling and Analysis. New York , McGraw-Hill J. Banks, John S. Carson and Barry Nelson.(2003).Discrete-Event System Simulation Prentice Hall J. Banks (eds) (1998).Handbook of Simulation.John Wiley & Sons


Name of the module Communication technology for digital engineering


Learning outcomes and competences to be acquired: 1. Introduction to Communication Technology

Teaching the concepts information, information-bearing signals, modulation, noise, transmission channels, channel capacity, as well as source and channel coding

Development of mathematical models for the treatment of the above concepts Description and quantitative treatment of information transmission systems

Teaching of engineering scientific decision base for the design of information transmission systems

2. Information and Coding Theory

Teaching the concepts of information theory information content, entropy, redundancy, source coding, channel capacity, channel coding, Hamming space and Hamming distance.

Mathematical modeling for the above concepts.

A method for the treatment of selected source and channel coding.

Treatment of selected error-correcting decoding method. Contents: 1. Introduction to Communication Technology

Mathematical representation of the signals as an information carrier in the time and frequency domain (Fourier series and Fourier Transform)

The sampling theory and the digitization of the signals

Source coding and data compression

Mathematical description of the noise

Noise performance of the transmission channels; calculate the bit error rate

Treatment of selected digital transmission systems in the baseband (PCM, DPCM, ....)

Treatment of selected digital transmission systems in the passband (ASK, PSK, FSK, QAM, ....)

2. Information and Coding Theory

Information content and entropy of discrete information.

Redundancy, memory and source coding (Shannon-Fano and Huffman method).

Continuous sources.

Discrete and continuous channels, and channel capacity Kanalentropien

Channel coding and Hamming space

Linear block codes

Cyclic codes

Syndrome decoding

Methods of Teaching 2 lectures per 2 SWS + 2 exercises per 1 SWS

Prerequisites for participation Mathematics, Physics, Fundamentals of Electrical Engineering Literature: see script

Applicability of the module Digital Engineering


Examination

Credits and grades 8 credit points = 240 h (84 h Attendance time + 156 hours independent work) Grading scale according to examination regulations

Workload Attendance time: 6SWS Weekly lectures and excercises Independent work

Frequency of occurrence Once per year


Duration of module 2 semesters

Responsible Prof. Omar, FEIT-IESK


Module name: Machine learning for medical systems





Semester:

Module coordinator: Chair for Information Retrieval

Lecturer:

Language: German

Curriculum


Lecture, exercises

Workload: Attendance time: 2 SWS lecture 2 SWS excercises Independent work: Processing of exercise and programming tasks, follow the lecture

Credit points: 6 Credit Points = 180 h = 4 h = 56h Attendance time + 124h independent work Grading scale according to examination regulations


none


Algorithms and Data Structures

Targeted learning outcomes: Learning objectives and competences acquired: Foundations of learning theory and in-depth understanding of issues and concepts of machine learning methods Knowledge of fundamental data structures and algorithms of machine learning that enable students to apply these approaches to real data analysis problems.

Content: Term learning and version spaces; learning of decision trees, neural networks, Bayesian learning, instance-based learning and cluster analysis, association rules, amplifying learning; hypotheses evaluation

Study / exam achievements: Services: Processing of exercise and programming tasks and successful presentation of the results in the exercises Examination: oral

Forms of media:

Literature:



Module:

Product modeling and visualization (PMV)

Objectives of the module:

Understanding the need and role of a consistent product model for the product lifecycle

Get to know different strategies and options for product modeling and visualization of systems of different modeling philosophy

Relevant features of the product modeling

Meet the relevant functions of the optimization of components

Proficient use of the design data in a visualization system (VR)

Contents:

Integrated model with different partial models for product modeling and visualization

Basics of parametric and feature technology (standard and advanced features)

Basics of macro programming in CAx systems

Modelling strategies and techniques

Visualization strategies and techniques

Strength Analysis in CAx systems

Component optimization

Methods of Teaching: Lectures and exercises with the corresponding scripts and excercise manuals. media: beamer, overhead projector, blackboard

Prerequisite for participation: Attend the lecture: introduction IDE demonstrable knowledge in Siemens PLM NX CAx system




5 credit points

Responsible for Module: Prof. Dr.-Ing. Dr, hcSándor Vajna, FMB-IMK/LMI

Suggested Reading: Lecture notes and excercises and Vajna, Weber, Bley, Zeman: CAx for engineers, Springer 2008


Name of the module Virtual Commissioning


Objectives of the course are:

Classification of machine and plant simulation with

an emphasis on virtual and hybrid operation in the

digital planning and operational life cycle phases

Mediation of the automation aspects of the virtual

commissioning

Teaching the basics of the model components

used in the virtual commissioning

Mediation of integration technologies in the PLM

Contents:

In the early planning and production phase simulation tools are

used in the engineering of technical systems for validation and

validation of the design, to test the control software as well as

training purposes for the user.The real non-existing system

components are treated by simulation and are therefore referred

to as virtual. Thus, a stepwise approach from fully virtual to real

and full functioning technical system is possible (hybrid

operation). The simulation is performed in an interdisciplinary

environment between mechanical, electrical and automation

engineering..



Basic knowledge in computer science and software development



Participation in the courses

Examination at the end of the module, grade scale according to

examination regulations, award for points according after written

or oral exam




Frequency of occurrence Every xxx - Semester




5.Methods of computer science


Module name: Data Mining

Module level, if applicable: Bachelor, also: Master DKE

Abbrevation, if applicable: DM



Semester: Bachelor: from 3 (course-related), Master: from 1

Module coordinator: Professor of Applied Computer science / business computer science II - KMD

Lecturer: Dr. Myra Spiliopoulou

Language: German


Bachelor CV: WPF INF from 4 Semester

Bachelor INF: WPF INF from 4 Semester

Bachelor INGINF: INF from WPF 4 Semester

Bachelor WIF: WPF WIF 5th Semester, WPF INF WPF from 5 Semester

Master DKE: WPF "Methods I" from 1 Semester


Lectures (2 SWS), exercise (2 hours)

Workload: Attendence time: 2 hrs Lecture + 2 hrs exercise Independent work:

Pre-and post-preparation of the lecture

Development of solutions to the exercises

Preparation for the final exam

Credit points: 5 Credit Points = 150 h = 4 h =

Attendance time 56h + 94h independent work



None

Recommended prerequisites: None


Acquisition of basic knowledge about data mining

Application of data mining skills to solve real, simplified problems

Familiarity with data mining tools

Superior way of dealing with German-and English-language literature on the Subject

Content: Data and data preparation for data mining

Data mining methods: classification, clustering, association rules discovery of

Data mining tools and software suites

Case studies

Study / exam achievements: Examination: oral

Note

Preliminary work according to the specification at the beginning of the semester


Forms of media:

Literature: Main Source: Pan-Ning Tan, Steinbach, Vipin Kumar."Introduction to Data Mining", Wiley, 2004: excerpts, from chapter 1-4, 6-8

Specific topics and examples from H. H. Hippner, U. Küsters, M. Meyer, K. Wilde (ed.) „Handbuch Data Mining im Marketing (Knowledge Discovery in Marketing Databases)“, Vieweg, 2001.


Module name: Interactive Systems





Semester: 5, 6

Module coordinator: Chair for Applied computer science / visualization

Lecturer: Prof. Dr. Bernhard Preim

Language: German



Lecture, tutorial


2 SWS excercises Independent work:

Wrapping up the lecture

Solving exercises Project Development


Requirements for Examination regulations:

none

Recommended prerequisites: Algorithms and Data Structures


Basic understanding of human-computer interaction

Application of knowledge about human perception in the design and evaluation of user interfaces

Tasks and user-dependent selection of interaction techniques

Ability to independently design, implementation and interpretation of user studies

Mastery of the usability engineering in compliance with conditions and resource constraints (systematic generating good usable systems)

Content: Technical foundations of human-computer interaction (window, menu and dialog systems)

Interaction techniques and interactive tasks

Cognitive foundations of human-computer interaction

Analysis of tasks and users

Prototype development and evaluation

Specification of user interfaces

Study / exam achievements: Examination requirements, see Course Examination: written, 2 hours

Forms of media:


Literature: Preim / Dachselt: Interactive Systems. Springer 2010


Module name: Mobile Communication


Abbrevation, if applicable: MOBKOM



Semester:

Module coordinator: Chair of Technical computer science / systems and real-time communication

Lecturer: Edgar Nett

Language:


Master CSE / IF / WIF: Applied computer science

Master CSE / CV: Technical computer science (TI) Master IF / WIF: Network Computing


Lecture, practical and theoretical exercises, independent work

Workload: Presence time = 56 h

2 SWS lecture

2 SWS excercises Independent Work = 124 h

Processing of exercise and programming assignments & exam preparation



none

Recommended prerequisites: Participation in introductory courses on distributed and embedded systems is recommended


Comprehensive overview of requirements and principles of mobile communication

Ability to analyze the basic design alternatives and their inherent trade-offs and classify

Competence in the practical application of a WLAN

Content: Content

Technical Basics - Media access method

- Media access protocols (wired / wireless) - Wireless LANs (technologies, standards, applications) - Security issues - Network protocols (IP mobile, ad hoc networks, route selection) Transport Protocols / Mobile TCP

Study / exam achievements: Successful completion of the exercises and programming assignments


Examination: Oral or written

Forms of media:

Literature: Literature data on the current web page for the module (http://euk.cs.ovgu.de/de/lehrveranstaltungen)


Module name: Secure Systems


Abbrevation, if applicable: SISY



Semester: from 4

Module coordinator: Jana Dittmann, FIN-ITI

Lecturer: Jana Dittmann, FIN-ITI

Language: German


Mandatory: IngINF, B, INF, B and WIF; B

Elective: CV, B (as INF subject) DigiEng, M (as methods of computer science


Lectures, excercises / 4 SWS

Workload: Presence time = 56h

2 SWS lecture

2 SWS excercises

Ability to work independently = 94h

Solution of exercises and exam preparation

Credit points: 5 Credit Points = 150 h = 4 h = 56h Attendance time + 94h independent work



"Algorithms and Data Structures", "Theoretical foundations of computer science"

Recommended prerequisites: Technical fundamentals of computer science


Assess the reliability capabilities of IT security

Ability to generate threat analysis

Skills for selection and evaluation of security mechanisms and creation of IT security concepts

Content: IT security issues and IT security threats

Design principles of secure IT systems

Security Policies

Selected security mechanisms

Study / exam achievements: Regular attendance at lectures and excercises:

Rating: examination (written, 2h, no inputs)

Note: the announcement of the necessary inputs in the lecture

Forms of media:

Literature: Literature see http://wwwiti.cs.uni-magdeburg.de/iti_amsl/lehre/


Module name: Visualization





Semester: 5

Module coordinator: Chair for Applied computer science / visualization

Lecturer:

Language: German


CV-B: Mandatory 5 Sem

IngINF-B: Major: computer science techniques INF-B: Major: Applied computer science

INF-B: Major: Computer Graphics / Image Processing



Lecture. Exercise



Edit the exercises and follow the lectures, exam preparation

Credit points: 5 Credit Points = 150 h = 4 h = 56h + 94h independent work time presence


none

Recommended prerequisites: Computer Graphics I, Mathematics I to III

Targeted learning outcomes: Learning Objectives: This course provides basic knowledge about how large amounts of structured data, represented, visualized, and can be explored interactively. The focus is on meth diodes of 3D visualization. Competences to be acquired:

Assessment of visualization objectives, selection and Evaluation of visualization techniques

Application of fundamental principles in computer-based visualization

Use and adjustment of the fundamental algorithms Visualization to solve application problems

Evaluation of algorithms in terms of their cost and the quality of the results

Content: Visualization objectives and quality criteria

Foundations of visual perception

Data structures in the visualization


Fundamental Algorithms (isolines, color illustrations, Interpolation approximation of gradients and Curvatures)

Direct and indirect visualization of volume data

Visualization of multi-parameter data

Flow visualization (static and dynamic visualization of vector fields, vector field topology)

Study / exam achievements: Examination requirements: see Course

Examination: written 2 hours

Forms of media:

Literature: P und M Keller (1994) Visual Cues, IEEE Computer Society Press

H. Schumann, W. Müller (2000) Visualisierung: Grundlagen und allgemeine Methoden, Springer Verlag, Heidelberg

W. Schroeder, K. Martin, B. Lorensen (2001) The Visualization Toolkit: An object-oriented approach to 3d graphics, 3. Aufl. Springer Verlag, Heidelberg

R S Wolff und L Yaeger (1993) Visualization of Natural Phenomena, Springer

A. Telea (2007) Data Visualization, AK Peters


6.Interdisciplinary team project


Interdisciplinary team project *

Module description will be given later The aim of this "small" project is in addition to the depression reached in bases in the complementary field of science, especially the development of key competencies of interdisciplinary work on the basis of a defined task that will be processed by students in a team. The the organization and content will be supervised by two teachers from the areas of engineering and computer science


7.Specialization


Name of the module Adaptronics *

Aims and Content of the

module

Aims and content Adaptronics creates a new class of technical, elastomechanical systems by using

new materials and activatable faster digital controller that can be automatically

adapted to different environmental conditions. Adaptronics has four target areas

of technical applications • Contour adjustment by elastic deformation • Vibration reduction by structure-borne noise interference • Noise reduction through active measures • Life increase by structurally integrated component monitoring

The students should learn and train, as is typical for the engineering profession

interdisciplinary thinking in engineering to hand the interdisciplinary research

field Adaptronics. Adaptronics combines materials science, mechanical, electrical

and control engineering knowledge and skills. The exercises are performed as

laboratory exercises. In the lab, the students independently solve complex tasks

whose successful processing is a prerequisite for admission to the examination.

Lecture series 9. Overview of Adaptroncis, applications from research 10. Integrable structure of sensors and actuators 11. Conformal structure integration of actuators and sensors 12. Target field contour adaptation: Methods of morphing. 13. Target field vibration suppression: structure-borne noise

interference, eradication, compensation 14. Target field sound reduction: Concepts of Active Noise

Reduction 15. Autonomous Systems - Concepts of Energy Harvesting 16. Concepts integrated component monitoring 17. Regulation 18. Reliability / robustness

Accompanying laboratory course

Carrying out experiments to Adaptronics measurements, analysis and

presentation of results

Methods of Teaching Lectures (2 SWS) internship (2 SWS)

Prerequisites for participation No special conditions are required, desirable: Principles of Adaptive

Systems (BA degree)

Applicability of the module There are no interactions with other modules

Grade and credit points Participation in the lab, oral examination

Credits 5 ECTS

Workload Attendance time: 2 SWS (lecture) and practical, Independent edit the

experiments, construction of test protocols, presentation of results

Frequency of occurrence Annually


Responsible Professor Michael Sinapius, IFME


Modulename: Advanced Database Models



Subheading, if applicable: ADBM


Semester:

Professor of Practical computer science / information systems and databases

Lecturer: Dr. Eike Schallehn

Language: English

Classification within the Classification within the curriculum:

Teaching format / class SWS during the semester:

Lectures (2 SWS) and exercises (2 hours)

Workload: Workload: 180h (56h attendences + 124 hours self-study)

Credit points: 6 credit points Degree Accor ding to the "Examination Regulations"

Examination requirements under the regulations:

none

Recommended prerequisites: Database introduction course

Targeted learning outcomes: Comprehension of different non-relational database models, their basic concepts, and their historical development

Comprehension of implications of non-relational data models for query processing and application development

Competence to use non-relational DBMS and based on their specific capabilities

Competence to develop databases and according applications using non-relational databases

Content: Overview and history of database models

NF2-, object-oriented, object-relational, and semi-structured database models

Application of the database models and design methodologies (extended ERM, UML, ODMG, XML Schema, etc.)

Foundations of query languages (OQL, SQL:2003, XPath/XQuery, etc.) and query processing for non-relational data models

Study / exam achievements: Participation and active involvement in the course and the exercises, successful realization of the exercises and final examination, oral exam (30 minutes)

Forms of Forms of media:

Literature:


Module name: Advanced Machine Learning


Abbrevation, if applicable: AML



Semester:

Module coordinator: Professor of Data and Knowledge Engineering

Lecturer:

Language:

Curriculum


Presentation of theory in the classroom, exercises and student projects

Workload: theory (2 hours per week) exercise in the lab and project work (2 hours per week) Homework (124 h): Study of the theoretical material Realization of the exercises and the student projects Preparation for the final examination

Credit points: 6 Credit Points = 180h (56 h Attendance time + 124 hours independent work) Grading scale according to examination regulations


none

Recommended prerequisites: Basic knowledge in machine learning, data mining, or related fields.


In recent years, machine learning has become one of the core disciplines in artificial intelligence research and related areas. This lecture is devoted to advanced methods and techniques of machine learning that go beyond the topics that are typically covered by introductory courses in the field.

A successful attendance of the course will enable the student to solve practical machine learning and data mining problems by state-of-the-art methods, to analyze and evaluate the results from a theoretical point of view, and to develop new, specialized approaches for particular problems whenever needed.

Content: Content - Introduction and overview of machine learning - Model assessment and selection - Ensemble Methods and Boosting - Variable and Feature Selection - ROC-Analysis - Kernel-based learning

Study / exam achievements: final examination

Forms of media:

Literature:


Modulename: Advanced Topics in Databases


Abbrevation, if applicable: AdvDB



Semester:

Professor of Practical computer science / databases and

Information Systems

Lecturer:

Language: English




Workload: Classes (2 hours per week) Exercises in the lab and project work (2 hours per week) Homework (124 h):

Further Studies

Realization of the exercises and the student projects

Preparation for the final examination

Credit points: 6 Credit Points = 180h (56h Attendance time + 124h self-study) Grades according to the ”Prüfungsordnung“


None

Recommended prerequisites: Knowledge about database foundations and about principles of internal database operations

Targeted learning outcomes: In the lecture students will be made familiar with most recent technological developments in data management. The first goal is to enable the attendees to use these new technologies in their professional careers in industry. Furthermore, the lecture focuses on aspects currently addressed in scientific research being on the verge to wide usage in current applications, and this way, enabling students to participate in academic and industrial research.

Content: Topics of the lecture will frequently change in accordance with current research directions in the database community and represent cutting-edge aspects as for instance

Indexing and storage techniques for new applications and data types,

Data management for embedded devices and sensor networks,

Self-management capabilities of database management systems,etc.

Study / exam achievements: Participation and active involvement in the course and the exercises Successful realization of the exercises, student projects and final examination


Oral Exam (30 Minutes)


Literature: Cf. http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/advdb/

http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/advdb/


Module name: Applied Discrete Modeling


Abbrevation, if applicable: ADM

Subheading, if applicable: Applications of stochastic models, especially in CV, DKE and Digital Engineering


Semester:

Module coordinator: Professor of simulation

Lecturer:

Language: German, English when needed

Curriculum


Lectures, exercises, project work

Workload: Lecture: 2 SWS

Exercise & Internship: 2 SWS

Homework and project work, self-study

Credit points: 6 Credit Points = 180h (56h attendance time + 124h self-study) Grading scale according to examination regulations


None

Recommended prerequisites: Mathematics for Engineers Programming skills

Targeted learning outcomes: Participants learn about Markov chains and selected applications and solution methods

Participants learn about non-Markovian stochastic processes and can model them in different ways and simulate

The participants know hidden Markov and non-Markov processes

Participants learn about selected research topics of the Chair

The participants can implement the learned models and procedures and apply them to problems in the research areas of the university, particularly in the medical and engineering

Content: Discrete-time and continuous-time Markov chains

Applications and programming of calculation method for Markov chains

Method of additional variables

Proxel simulation and phase distributions

Modeling with latent models

Programming solution methods for various model classes

Modeling and solution of problems in medicine and engineering

Study / exam achievements: Project work and oral examination

Forms of media:

Literature: Selected recent scientific articles


Module name: Bayesian Networks

Module level, if applicable: Master

Abbrevation, if applicable: BN



Semester: 1

Module coordinator: Professor of Practical computer science / computational intelligence

Lecturer: Prof. Dr. Rudolf Kruse

Language: English


WPF CMA, M 1-2

WPF CV, M 1-3

WPF DKE, M 1-3

WPF IF, M 1-2

WPF IngINF, M 1-2

PF IT, D IE 5

PF IT, D-TIF 5

WPF MS, M 1-3

WPF SPTE, D from 5

WPF Stat, M 1-3

WIF WPF, M 1-2

WPF WLO, D from 5



Workload: Presence time = 56 hours

2 SWS lecture

2 SWS excercises Self-employed = 124 hours

Pre-and post-processing of lecture and exercise

Editing exercises and programming assignments

Credit points: 6 credit points of 180 hours of work


None

Recommended prerequisites: Fundamentals of Probability and Statistics

Targeted learning outcomes: Provision of basic concepts and methods of Bayesian networks and related methods for decision support

The participant can apply techniques for the design of Bayesian networks

The participant can apply data analysis methods for problem solving

The participant knows and understands exemplary applications of Bayesian networks whose basic functioning

Content: Methods for representing uncertain knowledge

Dependency analysis

Learning process

Tools for designing Bayesian networks


Propagation, updating, revision

Decision Support with Bayesian networks

Non-standard method for decision support such as Fuzzy Models

Case studies of industrial and medical applications

Study / exam achievements: Examination in oral form, length: 30 minutes required inputs: o Processing of two-thirds of the exercises o Successful presentation in the exercises

Note

o Processing of two-thirds of the exercises o Successful presentation in the exercises o Successful completion of the oral colloquium

Forms of media:

Literature: Christian Borgelt, Matthias Steinbrecher and Rudolf Kruse. Graphical Models: Representations for Learning, Reasoning and Data Mining

(2nd edition).John Wiley & Sons, Chiche-art, United Kingdom, 2009. Christian Borgelt, Heiko Timm und Rudolf Kruse. Unsicheres und vages Wissens. Kapitel 9 in Günther Görz, Claus-Rainer Rollinger, und Josef Schneeberger (ed.). Handbuch der künstlichen Intelligenz. Oldenbourg, München, 2000. Enrique del Castillo, Jose M. Gutierrez, Ali S. Hadi. Expert Systems and Probabilistic Network Models.Springer, New York, NY, USA, 1997. F inn V. Jensen.An Introduction to Bayesian Networks. UCL Press, London, United Kingdom, 1996. Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (2nd edition). Morgan Kau fmann, San Mateo, CA, USA, 1992.


Image coding *

(Dr. Gerald Krell)

Course Module description will be given later


Module name: Computational Fluid Dynamics


Abbrevation, if applicable: CFD



Semester:

Module coordinator: Professor for Fluid Dynamics

Lecturer: Dr.-Ing. G. Janiga

Language: English

Curriculum


Lectures, Exercises with computer hands-on

Workload: Presence: Weekly lecture 1 SWS Weekly exercises with computer hands-on 2 SWS Autonomous work: Complementary reading, final project work

Credit points: 3 Credit Points = 90h (42 h presence + 48 h autonomous work) Grades following official instructions


Fluid Dynamics

Recommended prerequisites: Advanced Fluid Dynamics

Targeted learning outcomes: Aims and competences:

Students participating in this course will get both a solid theoretical knowledge of Computational Fluid Dynamics (CFD) as well as a practical experience of problem-solving on the computer.

Best-practice guidelines for CFD are discussed extensively.

CFD-code properties and structure are described and the students first realize their own, simple CFD-code, before considering different existing codes with advantages and drawbacks.

At the end of the module, the students are able to use CFD in an autonomous manner for solving a realistic test-case, including a critical check of the obtained solutions.

Content: Content

Introduction and organization, main discretization methods

Vector- and parallel computing, supercomputers, optimal computing loop.

Validation procedure, Best Practice Guidelines.

Linear systems of equations and iterative solution methods.


Practical solution of unsteady problems, explicit and implicit methods, stability.

Gridding and grid independency.

Practical CFD, importance and choice of physical models.

Properties and computation of turbulent flows.

Properties and computation of Non-newtonian flows.

Properties and computation of multi-phase flows.

Preparation of final CFD project as teamwork

Study / exam achievements: Success: Oral defense of final CFD project Exam: oral

Forms of media:

Literature: Ferziger and Peric, “Computational Methods for Fluid Dynamics”, Springer (2002) Further literature given during first lecture


Module name: Introduction to Data Warehousing



Subheading, if applicable: DWT


Semester:


Lecturer: Veit Köppen

Language:



Lectures, excercises and practical exercises in the laboratory (including presentation to the exercise group) and independent work (solving exercises, literature study)

Workload: Attendance time: weekly lectures, 2 SWS

weekly exercises 2 SWS

Independent work:

Exercises and exam preparation

Credit points: 6 Credit Points = 180h (56h Attendance time in lectures and exercises + 124h independent work) Grading scale according to examination regulations


None

Recommended prerequisites: Event "Databases I" and "Databases II"


Understanding of the data warehousing approach

Understanding of database technologies in the field of Data Warehouses

Ability to use DW-specific DBMS functionality

Ability to design and development of a data warehouse application

Content: The data warehouse approach, distinction

Architecture

Extract-Transform-Load

OLAP and Multidimensional Data Model

Implementation in databases

Query processing and optimization

Index and storage structures

Business Intelligence

Study / exam achievements: Regular attendance of lectures and exercises Examination Admission requirements: To be determined by instructor


Oral or written exam (depending on number of participants) at the end of the module

Forms of media:

Literature: http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/dw/index.html

http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/dw/index.html


Digital Filter *

(2C +1 R)

Prof. Dr. Abbas Omar



Modulename: Distributed Data Management


Abbrevation, if applicable: DDM



Semester:

Professor of Practical computer science / information systems and databases

Lecturer: Dr. Eike Schallehn

Language: English




Workload: 180h (56 h contact hours + 124 h self-study)

Credit points: 6 Credit Points Grades according to the "Prüfungsordnung"


none

Recommended prerequisites: Database introduction course

Targeted learning outcomes: Comprehension of basic principles and advantages of distributed data management

Competence to develop distributed databases

Comprehension of query and transaction processing in distributed and parallel databases

Competence to optimize the run-time performance and satisfy requirements regarding reliability and availability of distributed systems

Content: Overview and classification of distributed data management (distributed DBMS, parallel DBMS, fedrated DBMS, P2P)

Distributed DBMS: architecture, distribution design, distributed query processing and optimization, distributed transactions, and transactional replication

Parallel DBMS: fundamentals of parallel processing, types of parallelization in DBMS, parallel query processing

Study / exam achievements: Participation and active involvement in the course and the exercises, successful realization of the exercises and final examination, oral exam (30 minutes)


Literature:


Name of the module Introduction to Medical Imaging


Learning outcomes and competences acquired: Imaging is the most important form of medical diagnostics today. In this course an overview of the modalities of modern medical imaging is given. Therefore the principle, the operation as well as the most important medical applications will be presented and the advantages and disadvantages in terms of image quality and patient risks will be discussed. Knowledge of the required data processing step, and further optional image processing are mediated to the participants . That knowledge is reinforced in the exercises and especially within an internship

Contents: Fluoroscopy Computed tomography Nuclear medical imaging (PET, SPECT) Ultrasound Imaging MRI


Prerequisites for participation Mathematics, physics, fundamentals of electrical engineering, basic medical terms

Applicability of the module There is no interaction with other modules. Eligibility: Elective in the Bachelor study programs of the Faculty


Regular attendance of lectures, processing exercises, participation in the internship. Exam or oral exam or participation form

Credits and grades 5 Credit Points = 150 h (42 h Attendance time + 108 hours independent work) plus optional internship:

Workload Attendance time: weekly lectures: 1 semester * 2 SWS weekly exercises: 1 semester 1 credit hour *

Independent work: Nachbreitung of lectures, editing exercises, Preparation and review of laboratory experiments, PREPARATIONS for the exam

Frequency of occurrence each year in WS

Duration of module One or two semesters

Responsible Prof. G. Rose, FEIT, IESK


Module name: Embedded Networks


Abbrevation, if applicable: EN



Semester:

Module coordinator: Professor EOS

Lecturer: Prof. Dr. Jörg Kaiser

Language: German or English on request


Master's programs



Workload: 2 SWS lecture


Solving exercises and exam preparations

Credit points: 6 Credit Points = 180h (56h Attendance time + 124 hours self-study) Grading scale according to examination regulations


Bachelor etc.

Recommended prerequisites: Participation in "Communication and Networks" and "principles and components of embedded systems" is recommended.


Understanding of the special characteristics and problems in networks of industrial automation, automotive networks and wireless sensor networks.

Ability to grasp the far-reaching implications of quality properties in safety-critical and resource-constrained embedded networks to classify and evaluate them.

Skills for practical implementation of system properties and applications of an embedded network.

Content: Basics:

Reliability and fault tolerance

Time and clock synchronization

The physical layer transmission

Bandwidth and transmission capacity

Coding and synchronization

Embedded systems for safety-critical applications

Master-slave networks


Time-Triggered Networks Token-based networks CSMA networks

Wireless Sensor Networks:

Protocols for wireless networks Energy saving concepts

Study / exam achievements: Services

Regular attendance and the lectures and exercises

Completion of exercises

Examination: oral (30 min)

Forms of media:

Literature: will be announced on the web page of the lecture


Module name: Advanced Programming Concepts for Tailor-made Data Management


Abbrevation, if applicable: EPMD



Semester: See below


Lecturer: Norbert Siegmund

Language: German


WPF CV, B from 5 - computer science WPF IF, B from 5 - computer science WPF IngINF, B from 5 - mathematics and computer science WIF WPF, B from 5 - computer science / computer science economy WPF CV, M 1-2 - Software Engineering and Algorithm WPF DigiEng, M 1-3 - Methods of computer science WPF DKE, M 1-3 - Fundamentals of theory and Pr computer science WPF IF, M 1-2 - Algorithms and Complexity WPF IngINF, M 1-2 - Software Engineering and Algorithm WIF WPF, M 1-2 - Algorithms and Complexity WPF CV, i - (Practical / Applied) computer science WPF IF, I - II computer science / theoretical computer science WPF INGIF; i - computer science I or II after election WIF WPF, i - computer science III


2 hrs Lecture + 2 hrs exercise / Internship

Workload: 5 CP: 150h = 56h attendence time + 94h independent work 6 CP: 180h = 150h + 30h additional tasks

Credit points: 5 or 6 CP CP to choice


Regular attendance of lectures and exercises. Oral exam at the end of the module and project work.

Recommended prerequisites: Fundamentals of Software Engineering are required ; Basic knowledge of compiler construction and concepts of Programming languages are recommended

Targeted learning outcomes: Understanding of the limitations of traditional programming paradigms regarding the development of information systems

Knowledge of modern, advanced programming paradigms with focus on the development of customized systems

Ability to evaluate, select and apply advanced programming techniques

Content: Introduction to the problem of customized systems using the example of embedded DBMS

Modeling and implementation of software


product lines

Introduction to basic concepts (including Separation of concerns, information hiding, modularization, structured programming and design)

Overview of advanced programming concepts, etc. Components, design patterns, meta-object protocols and aspect-oriented programming, feature-oriented programming and collaborations

Study / exam achievements: Lecture and lecture accompanying exercises with questionnaires including a programming internship at a selected topic of the lecture; Independent processing of the exercises and the selected topic as a prerequisite for the exam Exam / bill: oral

Forms of media:

Literature: See http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/epmd/

http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/epmd/


Module name: Flow Visualization


Abbrevation, if applicable: FlowVis



Semester:


Lecturer:

Language:



Lectures, exercises

Workload: Lecture: 2 SWS

Exercise: 2 SWS

Homework, programming models example, self-study

Credit points: 6 Credit Points = 180h (56h attendance time + 124h self-study) Grading scale according to examination regulations


none

Recommended prerequisites: degree computer graphics 1 neccessary


Participants will acquire knowledge of the main methods for flow visualization

Some methods are independently implemented and evaluated in the exercises

Participants are able to independently analyze a simple flow data visually using the existing or self-designed tools.

Content: Mathematical foundations of vector and tensor fields

Obtaining stream data

Direct methods for flow visualization

Texture based methods for flow visualization

Geometry-based methods for flow visualization

Feature-based methods for flow visualization

Topological methods for flow visualization

Visualization of tensor fields

Study / exam achievements: Visual analysis of a given data flow rate

oral exam at the end of the semester

Forms of media:

Literature:


Module name: Fuzzy Systems


Abbrevation, if applicable: FS



Semester: 1

Module coordinator: Professor of Practical computer science / computational intelligence

Lecturer: Prof. Dr. Rudolf Kruse

Language: English


WPF CMA, M 1-3

WPF CV, M 1-2

WPF DKE, M 1-3

WPF IF, M 1-2

WPF IngINF, M 1-2

PF IT, D IE 5

PF IT, D-5 from TIF

WPF MA, D-AFIF 5-8

WPF MS, M 2-3

WPF PH, D from 5

WPF SPTE, D from 5

WPF Stat, M 1-3

WIF WPF, M 1-2



Workload: Presence time = 56 hours

2 SWS lecture

2 SWS excercises

Self-employed = 124 hours

Pre-and post-processing of lecture and exercise

Editing exercises and programming assignments

Credit points: 6 credit points for 180 hours of work


None

Recommended prerequisites: Knowledge of a programming language

Algorithms and Data Structures

Machine Learning, Data Mining

Algebra, optimization

Targeted learning outcomes: Application of adequate modeling techniques for the design of fuzzy systems

Using the methods of fuzzy data analysis and fuzzy rule learning

Ability to develop fuzzy systems

Content: Introduction to fuzzy set theory, the fuzzy logic and fuzzy


arithmetic

Applications of control engineering, the approximate closing and the data analysis

Study / exam achievements: Examination in oral form, length: 30 minutes required inputs: o Processing of at least two-thirds of all exercises during the

semester o Successful presentation of two exercises

Note: o Processing of at least two-thirds of all exercises during the

semester o Successful presentation of two exercises o Timely submission of two programming tasks o Successful completion of the oral colloquium

Regardless of the type of the examination a regular and active participation in lectures and exercises are required.

Forms of media:

Literature: Michael R. Berthold and David J. Hand. Intelligent Data Analysis: An Introduction (2nd edition).Springer-Verlag, Berlin, 2002. Christian Borgelt, Frank Klawonn, Rudolf Kruse, and Detlef Nauck. Neuro-Fuzzy Systems (3rd edition).Vieweg, brewing nschweig / Wiesbaden, 2003. George J. Klir and Bo Yuan. Fuzzy Sets and Fuzzy Logic - Theory and Applications.Prentice Hall, Upper Saddle River, NJ, 1995. Rudolf Kruse, Jörg Gebhardt, und Frank Klawonn. Fuzzy-Systeme (2nd edition). Teubner, Stuttgart, 1994. Rudolf Kruse, Jörg Gebhardt and Frank Klawonn. Foundations of Fuzzy Systems.Wiley, Chichester, United Kingdom, 1994. Kai Michels, Frank Klawonn, Rudolf Kruse, und Andreas Nürnberger. Fuzzy-Regelung. Springer-Verlag, Heidelberg, 2002.


Module name: Information and Coding Theory

Engl. Module name:





Semester: 3rd-6th

Module coordinator: Professor of High Frequency and Communication Technology

Lecturer:

Language: German


CV-B, application-image Information Technology (Elective)


Lecture and optional exercise

Workload: Attendance time

2SWS (lecture) + 1 SWS (optional exercise) Independent work

Lecture follow up

Credit points: 3 Credit Points = 90h (28h Attendance time +62 h independent Work) Grading scale according to examination regulations


nobe

Recommended prerequisites: University basic knowledge in mathematics

Targeted learning outcomes: Learning outcomes and competences to be acquired:

Exchange of information theoretical concepts information content, entropy, redundancy, source coding, channel capacity, channel coding, Hamming space and Hamming distance

Creation of mathematical model for the above Concepts

A method for the treatment of selected resources and channel coding

Treatment of selected error-correcting decoding method

Content: Information content and entropy of discrete information

Redundancy, memory and source coding (Shannon-Fano and Huffman method)

Continuous sources

Discrete and continuous channels, channel entropies and channel capacity

Channel coding and Hamming space

Linear block codes

Cyclic codes

Syndrome decoding

Study / exam achievements: Oral examination or participation form

Forms of media:

Literature:


Module name: Data Mining for Changing Environments


Abbrevation, if applicable: DMCE



Semester: 1-2 (for 4-semester courses: 1-3)

Module coordinator: Professor of Applied Computer science / business computer science II - KMD

Lecturer: Dr. Myra Spiliopoulou

Language: English, German by arrangement


Elective: Master CV, DKE, INF, INGINF, WIF

Master CV: WPF Focus:

Methods of Data and Knowledge Engineering (MDKE)

Master DKE: WPF in focus

Methods I

Methods II

Master INF: WPF in the priority areas:

Applied computer science

Computational Intelligence

Data-intensive scenarios

Economic computer science

Master INGINF as WPF INF in the priority areas


Data-intensive scenarios

Methods of Data and Knowledge Engineering (MDKE)

Master WIF:

WIF WPF WPF INF or in the priority areas Business Intelligence

Very Large Business Applications

Information Systems in Management Exchange focus INF under


Computational Intelligence Data-intensive scenarios


Lectures (2 SWS), exercise (2 hours)

Workload: Workload: attendences: 2 hrs Lecture + 2 hrs exercise Independent work:

Pre-and post-preparation of the lecture

Development of solutions to the exercises

Preparation for the final exam

Credit points: 6 Credit Points = 180 h = 4 h =

Attendance 56h + 124h independent work


Requirements under the None


examination regulations:

Recommended prerequisites: Basics: Data Mining


Understanding of the side effects of obsolete models and profiling for prediction and decision making in business

Acquisition of knowledge about learning methods for adapting and comparison of models

Acquisition of knowledge about learning methods for data streams

Feeling comfortable with English literature related to the topic

Content: Incremental learning methods

Learning methods for data streams

Applications, including: analytical CRM, analysis of social networks, , analysis of blogs

Study / exam achievements: Examination: oral

Forms of media:

Literature: Mainly scientific articles, see

http:omen.cs.uni-magdeburg.de/itikmd


Cognitive Systems *

(Prof. Wendemuth)



Module name: Mesh Processing





Semester: 5, 6


Lecturer: Dr. Christian Horse Inn

Language: German / English on demand


CV-B: Elective area Computational IngINF-B: Major: computer science techniques INF-B: Major: Computer Graphics / Image Processing



Seminar, internship

Workload: Attendance time: 3 hours lecture / 1 hour exercise

Independent work: Exercises



none

Recommended prerequisites: Mathematics I, Mathematics II, Computer Graphics 1


Knowledge and skills in handling Triangle meshes

Implementation and evaluation of some basic Algorithms

Content: Foundations, discrete differential geometry

Data structures for triangle meshes

Quality measures for networks

Smoothing networks

Parameterization of networks

Decimation and remeshing

Editing and deformation of networks

Numerical Aspects

Study / exam achievements: Preliminary examinations will be announced in the lecture

Oral examination 30 min.

Forms of media:

Literature: s Course


Module name: Modeling with population balances


Abbrevation, if applicable: PBM



Semester:

Module coordinator: Professor for Thermal Process Engineering

Lecturer: Jun.-Prof. Dr.-Ing. M. Peglow

Language: English

Curriculum


Lectures and Exercises

Workload: Presence: Weekly Lecture 2 SWS Weekly exercises with hands-on 1 SWS Autonomous work: Complementary reading and self-learning

Credit points: 3 Credit Points = 90 h (42 h attendance time+ 48 h autonomous work) Grades following official instructions



Targeted learning outcomes: Aims and competences: The participants will learn to

characterize systems with coupled properties involving density functions

model processes like nucleation, growth and agglomeration

solve population balances (analytical solutions, momentum approaches, sectional models)

apply population balances to real problems, in particular for process engineering

Content: Content

Concept of population balances, properties of disperse systems

Interaction between particles and continuous phase

Relevant properties (internal coordinates)

Temporal solution

Heat, mass and momentum transfer between the disperse and the continuous phases

Interactions between individual particles of the disperse phase

Detailed consideration of key processes: nucleation, growth, breakage, agglomeration

Study / exam achievements: Exam: oral

Forms of media:

Literature: Ramkrishna, “Population balances: theory and applications to


particulate systems in engineering”, Academic Press (2000) Further literature given during first lecture


Module name: Multimedia Retrieval


Abbrevation, if applicable: ME



Semester: 1 (Master)

Module coordinator: Professor of Data and Knowledge Engineering

Lecturer: Prof. Dr.-Ing. Andreas Nürnberger

Language: German


CV, DKE, INGIF, WIF


Lectures, frontal exercises, independent work (solving exercises, literature studies, ...)

Workload: Attendance time: weekly lectures, 2 SWS


Independent work: Exercises & Test Preparation



none

Recommended prerequisites: Basic knowledge of databases

Targeted learning outcomes: Learning objectives and competences acquired: Basic understanding of search in collections of multimedia data

Knowledge of concepts of information retrieval

Knowledge for similarity calculation between media objects Knowledge of algorithms and data structures for efficient

similarity computation

Knowledge of the production and use of descriptive characteristics (features) from multimedia objects (text, image, sound, video)

Ability to select and assess alternative approaches to similarity search for specific scenarios of the (interactive) search

Content: Introduction and concepts Principles of information retrieval Feature extraction and transformation process Distance functions Algorithms and data structures for efficient search

Query languages User interface for multimedia retrieval systems

Study / exam achievements: Regular attendance of lectures Solving the exercises and successful presentation in the exercises Written or oral exam at the end of the module


Forms of media: PowerPoint, Blackboard

Literature: Ähnlichkeitssuche in Multimedia-Datenbanken (Ingo Schmitt), Oldenbourg Wissenschaftsverlag GmbH, München, 2005.

Modern Information Retrieval (Ricardo Baeza-Yates and Berthier Ribiero-Neto), Addison Wesley, 1999.

Foundations of Statistical Natural Language Processing

(Chris Manning and Hinrich Schütze), MIT Press, Cambridge, MA, 1999.

Information Retrieval: Data Structures and Algorithms (William B. Frakes and Ricardo Baeza-Yates), Prentice-Hall, 1992.

Soft Computing in Information Retrieval (Fabio Crestani and Gabriella Pasi), Physica Verlag, 2000.


Name of the module Numerical Methods in Biomechanics *


Learning outcomes and competences acquired: In this course, students will acquire knowledge in the application of numerical methods for computer-oriented mechanics with a particular focus on biomechanical and medical applications.The course provides an introduction to the mathematical model creation the basics of approximate calculation of technical problembs. The students will be introduced to today's popular sof tware tools used for solving technical problems known and acquire skills to solve problems of biomechanics independently.

Contents: Overview of modern numerical methods Introduction to the modeling of problems in biomechanics Fundamentals of discretization and learning about important discretization: o Finite Difference Method o Finite-volume method o Finite Element Method Introduction to multi-body dynamics Numerical solution of selected problems of biomechanics: o Strength of bone, problems of stability o Notch stress problems o Biological optimization principle o Forces in motion processes (running, jumping)

Methods of Teaching Lecture, exercise, small project work


Engineering mechanics in the amount of 6-8 SWS; biomechanics internship (1 SWS)

Applicability of the module There are no interactions with other modules Creditable for all master programs of other faculties, whose study regulations allow it.


Oral examination

Credits and grades 4 hours / 8 credit points = 150 h (42 h Attendance time + 108 hours + 90 hours of project work independently) Grading scale according to examination regulations

Workload Attendance time: 2 hours lecture, 1 SWS exercise Independent work: reworking the lecture, independent editing a project, exam preparation

Frequency of occurrence Every year in WS

Duration of module A semester

Module Coordinator Prof. U. Gabbert, Prof. Strackeljan FMB, IFME


Security of embedded systems *

(Prof. Dittmann) Lecture (German) Module description will be given later


Speech processing *

(Prof. Wendemuth)



Name of the module Electromagnetic Theory


Learning outcomes and competences acquired: Mediation of the system of Maxwell's equations as a basis for the physical understanding and the mathematical description of electric, magnetic and electromagnetic phenomena

Systematic treatment of the electromagnetic fields and adequate computational methods as well as making reference to the real problems in the fields of electrical engineering, electronics, communications technology

Development of skills for solving specific tasks

Contents:

Maxwell's equations in differential and integral form and the derivation of general conclusions as well as a classification of electromagnetic fields.

On this basis, the treatment of the different field types follows.

An electrostatic field, flow stationary electric field, a stationary magnetic field flows, quasi-stationary electromagnetic field wavefields

Methods of Teaching Lecture, excercies

Prerequisites for participation GET 1 and 2 and Get 3

Applicability of the module Bachelor ETIT


Exam 180 min

Credits and grades 6 hours / 8 credit points = 240 h (84 h Attendance time + 156 hours independent work) Grading scale according to examination regulations

Workload attendance timein SS: 2 hours lecture, 1 SWS exercise attendance timein WS: 2 hours lecture, 1 SWS exercise Independent work: solving exercises and exam preparation

Frequency of occurrence Every year starting in SS

Duration of module Two semesters

Responsible Prof. Dr.-Ing. Marco Leone (FEIT-IGET)


Name of the module Theory of electrical cables

Content and Aims module


Deeper physical insight into compensation and propagation processes on line connections for fast temporal changes or high frequencies, when their expansion with respect to the delay time and wave length can not be neglected.

Knowledge of the basic solutions and approximate models in special cases in the fields of energy, electronics / circuit technology and communication technology

Mathematical description and analysis of the dynamic processes on lines in the time and frequency domain at any line suppressor circuit: Transmission line equations in complex form, reflection coefficient, SWR, resistance transformation, Smith chart, quadrupole compensation ciruits, chain ladder

Multiple lines: Line differential equation systems, parameter matrices, modal transformation.

Contents:

Introduction:Conducted electromagnetic waves and wave types.

TEM waves on lines: Derivation of the differential equations and differential equivalent circuit of the double line solution in the time and frequency domain, lossless and lossy case Phase and Group velocity.

Non-stationary time-domain analysis: Simple transients, reflection and refraction, wave equivalent circuits, multiple reflection (wave schedule, Bergeronverfahren, Network (SPICE) model of the double line, impulse behavior in dispersive lines

Stationary analysis in the frequency domain: Electricity and voltage along the lossy line, quadrupole presentation, impedance transformation.

Multiple lines: Definition and differential equivalent circuit transmission line equations and Wave equation, Modal (eigenmodes) solution, line crosstalk


Requirements for participation

Fundamentals of Electrical Engineering I-III, Theoretical Electrical Engineering

Availability of Module

Compulsory subject in the Electrical Engineering option Elective in all other options

Prerequisite for the award of Credit points

Oral examination


Power points Notes

SWS 3/4 Credit Points = 120 h (42 h attendance time + 78 h independent work)

Workload Attendance time: 2 hours lecture, 1 SWS exercise Independent work: exercises, exam preparation

Frequency of occurrence Every year in the summer term


Responsible Prof. Dr.-Ing. M. Leone, FEIT-IGET


Module name: THREE-DIMENSIONAL & ADVANCED INTERACTION


Abbrevation, if applicable: TAI



Semester: Winter semester

Module coordinator: ISG: User Interface & Software Engineering AG, AG visualization

Lecturer: Jun.-Prof. Dr.-Ing. Raimund Dachselt, Prof. Dr.-Ing. hat. Bernhard Preim

Language: English


Master CV: Applications of Computational Master CSE / IF / WIF: Applied computer science Master CSE / CV: software and algorithm engineering

Master DKE: Applications FIN-diploma courses, advanced study




2 SWS lecture

2 SWS exercise

Independent work:

Reworking the lecture

Edit the seminar-exercises

Exam Preparation

Credit points: 6 Credit Points = 180 h (2 * 28h Attendance time + 124h independent work)


nobe

Recommended prerequisites: Lecture Interactive Systems, Lecture User Interface Engineering, further conditions will be announced in the lecture


Understanding the nature and importance of future user interfaces as well as related challenges and problems

Acquaintance, analysis and evaluation of technologies, interaction techniques and methods for the development of advanced user interfaces

Ability to select appropriate technologies and interaction techniques in the field of three-dimensional modern and post-WIMP user interfaces

Ability to critically analyze scientific literature and knowledge of scientific publishing

Ability to work on own research postgraduate level in the area of advanced user interfaces


Content: Introduction to Post-WIMP and Reality-based User Interfaces

3D-Interaction: Tasks, Devices, 3D-Widgets, 3D UIs

Augmented Reality Interaction

Pen-based Interaction Techniques and Sketching

Multitouch: Technologies, Gestures, Applications

Gestural Interaction: Tracking, Freehand Gestures

Tangible Interaction

Advanced Topics: Gaze-based Interaction, Organic Interfaces, Everywhere Interfaces

Study / exam achievements: Oral examination


Forms of media: PowerPoint, blackboard, video, software demonstrations

Literature: Bowman, Kruijff, Laviola, Jr., Poupyrev: "3D User Interfaces: Theory and Practice", Addison-Wesley, 2004

Müller-Tomfelde (Ed.): "Tabletops - Horizontal Interactive Displays", Springer, 2010

Saffer: "Designing Gestural Interfaces", O'Reilly Media, 2008

Shaer, Hornecker: "Tangible User Interfaces: Past, Present and Future Directions". In Foundations and Trends in Human-Computer Interaction, 3 (1), 2010

Further references during the lecture and on the current web page for the module ( http://isgwww.cs.uni-magdeburg.de/uise/Studium/WS2010/VorlesungTAI/ )

http://isgwww.cs.uni-magdeburg.de/uise/Studium/WS2010/VorlesungTAI/

http://isgwww.cs.uni-magdeburg.de/uise/Studium/WS2010/VorlesungTAI/


Module name: Introduction to concurrency control



Subheading, if applicable: TV


Semester:


Lecturer: Dr. Thomas Leich

Language:



Lectures, frontal exercises, independent work (solving exercises, literature studies, ...)


weekly lectures, 2 SWS


Independent work:

Exercises & Test Preparation



none

Recommended prerequisites: Event "Databases"


Basic understanding of the problem of transaction management Knowledge of theoretical foundations Knowledge of algorithms and methods for synchronizing

Knowledge of algorithms and procedures to maintain the ACID properties

Content: Transaction concept

Serialisierbarkeitstheorie

Synchronization method

Recovery and data backup

Transaction management in distributed database systems (Distributed synchronization, Distributed Commit, etc.)

Advanced transaction models

Study / exam achievements: Regular attendance of lectures Solving the exercises and successful presentation in the exercises Written or oral exam at the end of the module

Forms of media:

Literature: See http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/tv/index.html

http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/tv/index.html


Module:

Transport phenomena in granular, particulate and porous media

Objectives of the Module (Competencies):

Dispersed solids find broad industrial application as raw materials (e.g. coal), products (e.g. plastic granulates) or auxiliaries (e.g. catalyst pellets). Solids are in this way involved in numerous important processes, e.g. regenerative heat transfer, adsorption, chromatography, drying, heterogeneous catalysis. To the most frequent forms of the dispersed solids belong fixed, agitated and fluidized beds. In the lecture the transport phenomena, i.e. momentum, heat and mass transfer, in such systems are discussed. It is shown, how physical fundamentals in combination with mathematical models and with intelligent laboratory experiments can be used for the design of processes and products, and for the dimensioning of the appropriate apparatuses. ● Master transport phenomena in granular, particulate and porous media ● Learn to design respective processes and products ● Learn to combine mathematical modelling with lab experiments

-

Content

● Transport phenomena between single particles and a fluid ● Fixed beds: Porosity, distribution of velocity, fluid-solid transport phenomena Influence of flow maldistribution and axial dispersion on heat and mass transfer Fluidized beds: Structure, expansion, fluid-solid transport phenomena ● Mechanisms of heat transfer through gas-filled gaps ● Thermal conductivity of fixed beds without flow Axial and lateral heat and mass transfer in fixed beds with fluid flow ● Heat transfer from heating surfaces to static or agitated bulk materials ● Contact drying in vacuum and in presence of inert gas ● Heat transfer between fluidized beds and immersed heating elements

Teaching methods: Lectures / Exercises

Prerequisite for participation:

Workload: 3 SWS

Attendance: 42 hours

Self-study: 48 hours

Assessment / examination / Credits:

- M 3 CP

Responsible for Module: Prof. Tsotsas


Name of the module Uncertain knowledge



Understanding of the concepts for dealing with uncertain knowledge in modeling, estimation, classification and decision making

Ability of development and parameterization of a Bayesian network

Understanding the concepts of estimation theory and their use

Ability to use stochastic filtering

Contents:

Foundations of uncertain knowledge processing

Bayesian networks, topology, parameterization, inference

Stochastic estimation

Wiener filter

Kalman filter

Methods of Teaching Lecture and exercises

Prerequisites for participation Fundamentals of Probability and Statistics

Applicability of the module There is no interaction with other modules. Eligibility:

Elective in Master Electrical and Computer Engineering Faculty

Elective in Master in other faculties


Exam or oral exam


Workload Attendance time: Weekly Lectures: 2 SWS

Independent work: Preparation of the lectures, preparation for the exam

Frequency of occurrence each year in WS


Responsible Prof. G. Rose, FEIT, IESK


Module name: Distributed Real-Time Systems


Abbrevation, if applicable: VES



Semester:

Module coordinator: Chair of Technical computer science / systems and real-time communication

Lecturer: Edgar Nett

Language:


Master IngINF / IF / WIF: Applied computer science

Master IngINF / CV: Technical computer science (TI) Master IF / WIF: Network Computing



Workload: Presence time = 56 h

2 SWS lecture

2 SWS excercises Independent Work = 124 h

Processing of exercise and programming assignments & exam preparation



none

Recommended prerequisites: Participation in introductory courses on distributed and embedded systems is recommended


Comprehensive overview of the equirements of Real-time systems and their applications

Ability to control and analyze the basic design principles and their inherent trade-offs

Competence in the practical application of a real-time operating system and its rogramming

Content: Algorithms for CPU Schedulung

Design of real-time communication protocols (wired / wireless)

Routing - protocols

Memory access protocols (Priority Version)

Clock synchronization

Models of real-time and embedded systems

Study / exam achievements: Services:

Regular attendance at lectures and excercises,

Successful completion of exercises Examination: written or oral

Forms of media:

Literature: Literature data on the current web page for the module


(http://euk.cs.ovgu.de/de/lehrveranstaltungen)


8.Digital Engineering Project


Digital Engineering Project *

Module description will be given later In parallel with the professional specialization in the 3rd Semester, students are involved in a project Digital engineering. Here students will be directly integrated into ongoing research projects, which will be offered by cooperating faculties and in cooperation with and under utilization of resources by partners of industry-related research, such as the Virtual Development and Training Centre (VDTC). In addition to their specialization an introduction to scientific work follows, for example through participation in scientific publications or participation in scientific events.

module handbook for the master's program · 2019-05-09 · discrete event simulation and 3d...

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