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Courses from Halmstad (1st Semester)

ALGORITHMS AND DATA STRUCTURES FOR PROBLEM SOLVING

Goals

The course aims at providing knowledge on algorithm complexity, algorithm design and classical data structures. The aim of the course is also to improve programming abilities in a modern programming language, currently Java.

The course builds on basic programming knowledge and practice and prepares the student to participate in larger programming projects. It introduces currently-accepted techniques to solve frequently occurring problems and prepares the student to make informed choices from several alternative solutions. The course also illustrates how some more advanced programming language constructs can be used.

On completion of the course the student shall be able to

Contents

Abstract data types, basic data structures, recursion, divide and conquer, asymptotic analysis of execution time, algorithms for sorting and searching, data structures for searching, graph algorithms. Examples of advanced data structures: Binary Decision Diagrams. Examples of advanced algorithm design techniques: dynamic programming.

Prerequisites

Basic course in programming techniques.

Instruction and Examination

Examination consists of two elements: written examination of the theory, and project results presented both in writing and oral.

 

EMBEDDED SYSTEMS PROGRAMMING

Goals

The course introduces programming techniques suitable for embedded systems. The course addresses mainly techniques for concurrency, real-time and reactivity. The course also addresses programming language support for concurrency, real-time and reactivity.

The course is based on knowledge and experience in sequential programming. On completion of the course students will have acquired experience in programming embedded systems that execute on one or several processors, that comply with time constraints and that can interact with the physical environment.

On completion of the course students will be able to

Contents

Programming close to hardware. Programs that react to events. Concurrent programming: programs organized as concurrent threads, a kernel supporting threads, programming language support; problems with threads that share memory, protecting data with encapsulation, object orientation. Reactive programming: a programming discipline for organizing concurrent programs using reactive objects that cooperate via synchronous and asynchronous method calls; programming language support. Real-time programming: specification of and support for real time; deadlines, baselines, periodic processes and event controlled processes.

Prerequisites

The course Algorithms and data structures 7.5 credits.

Instruction and Examination

The course is examined by means of laborations for which written or spoken reports should be passed and a graded written exam. To pass the laborative part of the course all laborations should be passed.

 

IMAGE ANALYSIS

Goals

The student shall acquire knowledge and practical experience of digital image analysis, of primarily 2D images.

After completing the course, the student shall be able to

Contents

The following elements are included in the course: image representation, sampling and quantization, geometric transformation and interpolation, linear and non-linear filtering, local orientation and scale, shape, texture.

Prerequisites

Knowledge in mathematics, programming and computer systems from a bachelor of science programme.

Instruction and Examination

For the grade "Passed" presence at computer exercise sessions and approvals of 2/3'rd of these are required. Higher grades require further exercises to be completed.

 

MULTIVARIABLE CALCULUS

Goals

The course aims to provide insight and skills in real multi-variable analysis - in particular within areas of relevance to applications in technology and the physical sciences.

The course is set to provide the necessary background knowledge for advanced level Mathematics courses as well as application-oriented courses, e.g., Image analysis and Learning and self-organizing systems.

Upon completion of the course the student shall be able to:

Contents

Real-valued functions of several variables. Limits and continuity. Partial derivatives, differentiability, chain-rule, gradient, and directional derivative. Taylor's formula and local extrema. Optimization on compact and non-compact sets with and without constraints. Double- and triple integrals. Basic vector calculus. Curve- and surface integrals. Greens formula. Potential fields.

Prerequisites

Basic knowledge of linear algebra and single-variable analysis at bachelor level.

Instruction and Examination

Examination is in the form of a written exam.

 

OPTICS, VISION AND CAMERAS

Goals

The course aims at providing an overview of how images are formed and recorded. The course is particularly directed towards practical use of optical components and cameras with the purpose of giving a basis for work with development of image recording and other measurements techniques based on using light and cameras.

Upon completion of the course the student shall be able to:

Contents

Classical optics. Optical instruments and image forming systems such as the eye, camera, telescope and microscope. The wave properties of light. Interference and diffraction. CCD-cameras and other forms for electronic image capturing.

Prerequisites

-

Instruction and Examination

Examination is by written exam and approved laboratory works.

 

RANDOM PROCESSES

Goals

The course aims at giving basic knowledge about random processes, mainly stationary.

Upon completion of the course the student shall be able to:

Contents

Random processes, Poisson process and stationary processes. Something about generalised functions and Stiltjes integrals. Covariance function, spectral density function and sampling. Gaussian processes, Filtration, AR and MA processes. Inference for stationary processes. Signal adjusted filter, Wiender filter.

Prerequisites

Mathematical analysis, linear algebra and a basic course in probability theory and statistical theory.

Instruction and Examination

Examination is by written exam and approved laboratory experiments.

 

SIGNAL ANALYSIS AND REPRESENTATION

Goals

The course gives basic knowledge of how signals and systems are characterized and analysed. Only systems that are linear and time invariant (LTI-systems) are considered in this course.

Upon completion of the course the student should be able to

Contents

Sampling and reconstruction. Mathematic models in the time domain: difference equation and convolution sum. Transforms in discrete time: z-transform, poles and zeros, fourier transform and discrete fourier transform. Window functions. Digital filters: FIR and IIR filters. Design of digital filters.

Prerequisites

The course requires a course in Electrical Circuits and Transform Theory or equivalent knowledge.

Instruction and Examination

Examination is through approved laboratory exercises and a written exam.

Courses from Wroclaw (1st Semester)

Advanced Algorithms and Data Structures

Goals

Knowledge of advanced algorithms and skills and its implementation

Contents

- Asymptotic order notation.
- Data structures: lists, priority queues, hashing tables, trees (BST, Black-Red-Tree, B-Tree, heap, set), graphs.
- Design strategies: divide and conquer, dynamic programming, greedy algorithms, backtracking.
- Problems: sorting, searching, adding and deleting, Huffman codes, knapsack problem, problems for computational geometry, algorithms for number theory, minimum spanning tree, shortest paths, matching in graph, network flow, string matching.
- Approximation algorithms, probabilistic and randomized algorithms.
- NP-complexity.

Prerequisites

Algorithms and Data Structures

Instruction and Examination

One test at the end of lectures and classes

 

Advanced Databases

Goals

This course will provide practical guidelines for applying the design rules. Students will proceed through each of the phases of the database design process to achieve a good understanding of its key aspects. Course participants are expected to create a database design for a selected fragment of reality and implement a corresponding relational database in a selected database management system

Contents

The following main topics are in the scope of the subject:
- The role and importance of databases, basic characteristics, an overview of the database design methodology, project documentation,
- Analyzing a selected part of reality. Creating business use case model,
- Modeling use cases. Scenarios, threads, actors,
- Tracing requirements, assessing the consistency of models,
- Analysis models, class diagrams,
- Logical model. Rules for mapping class diagrams onto relational models,
- Rules for specification of the relational database model. The SQL 2003 standard,
- Physical model, verification criteria,
- Transactions, locks and isolation levels, and
- Rules for improving database performance.

Prerequisites

The basics (fundamentals) of database

Instruction and Examination

Lecture: passing final test grade.

Project: completing the project according to the schedule, including the implementation of the database design in a selected DBMS

 

Advanced Topics in Artificial Intelligence

Goals

To acquaint students with machine learning concept and methods. Knowledge Discovery from data – methods, usefulness.

Contents

Introduction to machine learning, deduction versus induction. Presentation of selected induction methods – decision tree generation, rules induction.
Nature based methods – evolutionary algorithms, neural networks, artificial immune systems, ant colony systems: idea, properties and usefulness.
Complex methods – ensembles of classifiers.
Knowledge Discovery from data – phases, useful methods.

Prerequisites

Programming skills, course Advances Algorithms and Data Structures

Instruction and Examination

Test / exam (50%) and completion of an assignment project (50%)

 

Digital Image Processing

Goals

Presentation of structures and formats of digital images, techniques of image digitalisation in scanners and digital photo cameras, methods and algorithms of image processing and compression as well as  techniques of non-linear digital video editing.

Contents

Digital image classification. Raster of printed images. Format conversion. Image digitalization. Software for digital image processing. Digital image transformation. Digital image compression. Special effects and filters. Scanners Construction. Scanning Techniques. 3D Scanners. Image deformations during digitalisation process. Image correction techniques. Mora effects. Digital photo cameras. Digital movie cameras. Mpeg and other video formats. Codecs. Computer animations. Technology DVD. Rules of non-linear digital video editing. Virtual reality. Cyberspace.

Prerequisites

-

Instruction and Examination

Exam writing or test

 

Expert Systems

Goals

To provide students with the review of basic problems and methods related to application and design of expert systems, especially, of problems and methods developed in our research group.

Contents

History, application areas and perspectives of automated reasoning and expert systems.
Typical components and structure of expert systems.
Main tasks corresponding to the roles of: a user, a designer, an expert, a knowledge engineer, a programmer.
Expert systems based on relational knowledge representation.
Expert systems based on logical knowledge representation (propositional logic).
Expert systems based on predicate calculus.
Application of other logics (fuzzy, modal) and hybrid approaches.

Prerequisites

Algebra, logics, set theory

Instruction and Examination

Written. Answers to 3 out of 15 questions within the scope of the lecture.

 

Multimedia Information Systems

Goals

The main aim of the course is delivering the knowledge in the field of Multimedia Information Systems, its development problems and basic theories. The matter is discussed from different points of views:  psychological, anthropological and technological. Also problems of managing a multimedia production, a storyboard development, a prototype development, a final multimedia title authoring and its evaluation methods are covered by this course.

Contents

Lecture contents:
Introduction to the multimedia systems. Psychological aspects of multimedia systems. Multimedia systems applications. Multimedia platforms and its technologies. Multimedia systems design. Multimedia title storyboarding.
Multimedia authoring tools. Multimedia Web Applications.  Multimedia Web Applications tools and technologies. Macromedia Director.  Macromedia Flash basics. Multimedia usage in Macromedia Flash. Prototyping and evaluation of multimedia information systems. Hypermedia, definitions, applications and methodologies. Cyberspace, virtual worlds on-line, VRML and CULT 3D.

Laboratory:
Multimedia on-line applications evaluation, multimedia off-line applications evaluation, multimedia application storyboard writing, evaluation of multimedia applications that resembles chosen subject, the application authoring in Macromedia Director or Flash based on the storyboard

Prerequisites

It is assumed that the course participants are familiar with computer graphics basics, discrete mathematics, computer architectures, and have elementary programming experiences in C++ or Java. Necessary courses: Computer Architectures,  Algorithms and Data Structures, Object Oriented Programming and Design

Instruction and Examination

Examination is in the form of a single-choice test. To pass the course, all laboratory works, and the written exam, must be completed and approved.

 

Operations Research in Computer Science

Goals

A student should receive necessary knowledge and abilities to apply operations research methods for solving selected basic problems from the area of computer systems and networks, e.g. allocation, scheduling, transportation and flow problems.

Contents

Description of basic decision making problems for complex operation systems. Allocation of recourses and tasks for independent  and dependent operations with time models. Selected decision making problems for complex operations with uncertainty. Introduction to scheduling problems (basic methods and algorithms). Computational complexity of decision and optimisation problems. NP-problems. Maximum flow problem and transportation problem. New trends in scheduling theory and its applications to computer systems and networks.

Prerequisites

-

Instruction and Examination

Final test

 

Parallel Computer Architecture

Goals

The aim of the course is to present to students different parallel computer architectures with respect to different parallelism models.

Contents

The material presented during lectures  is supported by laboratory work and a  seminar part. The course contents: Taxonomy of parallel computer architectures (Flynn and others) - shared memory, distributed memory and distributed shared memory computers. Static and dynamic interconnection networks, typical topologies, different routing strategy. Pipeline, vector and array processors, multiprocessor systems (bus based and switching systems). Methods for increasing speed: higher clock frequency, architectural improvements, more functional blocks, and system scalability. Memory models, utilisation of cache memory. Superscalar architectures - identification of conflicts and it's avoiding, branch prediction algorithms, automatic reordering of program execution. Non-conventional way of processing - dataflow systems, reduction computers, systolic and neuronal architectures.

Prerequisites

It is assumed that the course participants are familiar with basic computer organisation, computer architecture and computer programming.

Instruction and Examination

Examination is in the form of a written exam.

 

System Modelling and Analysis

Goals

The course aims to provide insight and skills in system modelling and analysis.

Contents

The material presented during lectures is supported by class work and a seminar part. The course contents:
- Models in systems research (model classification, typical problem of analysis, design, optimization, and control)
- Description and some characteristics of physical signals (random description, Fourier, Z and Laplace’ transformations)
- Typical plant descriptions (static and dynamic models, state equations, differential and difference equations, transfer function, time and frequency analysis)
- Network models, elements of queuing problems
- Fundamental identification problems (identification of static plant – deterministic and probabilistic case, identification of dynamic models)
- Selected problems of modeling of complex systems.
- Systems described by the relation 

Prerequisites

Knowledge of mathematics from a bachelor of science program.

Instruction and Examination

Examination is in the form of a written exam.

 

Theory of Information and Signals

Goals

The aim of this course is to introduce some concepts of signal transmission in various transmission media using different methods of modulation, coding, multiplexing, protection as well as different organization of transmission and transmission systems.

Contents

  1. Information theories and statistical theory of information; their application in signals transmission and receiving systems.
  2. Information model of transmission channel models and theorem of optimal coding
  3. Information systems models (general- and specific-purposes systems)
  4. Transmission channels and their organization for information transmission purposes
  5. Fourier transformation and their features in transmission systems analysis and design
  6. Amplitude, frequency and phase modulation
  7. Amplitude, frequency and phase shift keying
  8. Linear and non-linear pulse-code modulation and delta modulation (sampling, quantization and coding)
  9. Discrete signal transmission
  10. Time- and frequency-domain multiplexing
  11. Signal compression
  12. Quality of communication – detection and correction coding and applications (example)
  13. Quality of communication – automatic request of retransmission and applications (example)
  14. Organization of teletraffic in various topological structures (bus, ring, star)
  15. Standards in digital signal transmission systems (digital hierarchies)

Prerequisites

No

Instruction and Examination

Exam at the end of semester 

Courses from Lemgo (2nd Semester)

Communication for Distributed Systems

Goals

The course is intended to provide knowledge about distributed real-time systems, network simulation, domain-specific implementations of communication networks, and test equipment

Contents

Lecture:

1) Introduction to distributed systems: What are distributed Systems, Requirements for distributed Real-time systems , Communication approaches,
2) System theory and technologies: Basic communication concept, Layered Communications System, OSI Model, Protocols and Frames, OSI Layer, Technologies used at different layers, Synchronisation and clocks
3) Performance Evaluation of Communication Systems: Network Simulation: Basic Simulation Modeling, OPNET Modeler A Tool for Discrete Event Simulation, Recap: Probabilities and Statistics, Create Models and Validation, Review of Basic Probabilities and Statistics, Analysis of Simulation Results
4) Communication Protocol Engineering with UML

Lab:

1) Exercises related to lectures
2) Protocol Engineering with Telelogics TAU
3) Measurements at physical layers and layer 2 investigations.
4) Socket programming and evaluation
5) Simulation of a Communication Network with OPNET Simulator

Prerequisites

-

Instruction and Examination

Written examination (3 hours). Bonus points of project work will be included. The course grade equals the grade of the written examination.

 

Information Fusion

Goals

Information Fusion identifies the concept of combining data from different information sources, such as sensors or human experts. The conceptual strategy is based on obtaining new or more certain information by data combination. In numerous applications it is not possible to capture all necessary information or features by a single sensor source. In such cases more sensors and additive expert’s know-how can generate more precise data regarding different real world systems, e.g. robots, machines and equipment, data experts systems, cognitive systems and so on.

Contents

The following topics are highlighted:

Prerequisites

Mathematics for undergraduates, Signals and Systems or System Modeling and Analysis, Image Analysis or Digital Image Processing

Instruction and Examination

programming project with presentation (30 min), graded

 

Innovation and Development Strategies

Goals

The student obtains knowledge about fundamental principles and methods for innovation and development processes based on intercultural R&D strategies, knowledge management, portfolio analysis, risk management, and patent strategies for international companies.

Contents

Prerequisites

Elementary management skills

Instruction and Examination

Oral examination and written report

 

Intelligent Sensors

IMPORTANT: This course is currently not running!

Goals

-

Contents

-

Prerequisites

-

Instruction and Examination

-

 

Network Security

Goals

The students acquire a solid knowledge about threads to security and privacy in networked and distributed systems. Different security mechanisms specified in current network protocols are known and can be rated with respect to their applicability.

Necessary background from the field of applied cryptography is provided in the lecture.

The students carry out a detailed study of some selected security related protocol or recently published attack (project work).

Contents

Networking applications and protocols and their vulnerabilities, IT Security (Aims, Threads, Secure Programming), Applied cryptography (basic mechanisms, selected algorithms and their applications), Public key infrastructures (PKI), Security and privacy in networked and distributed systems: Data link layer security (IEEE 802.11, Bluetooth), network layer security (IPsec), and transport and application layer security mechanisms (TLS). Selected protocols and recent attacks are studied in depth (project work).

Prerequisites

Basic knowledge of networking and IP related protocols

Instruction and Examination

Successful completion of lab exercises and project work. Written examination. The course grade equals the grade of the written examination.

 

Signal Processing Algorithms

Goals

The course shall provide knowledge in the field of linear and nonlinear digital signal processing algorithms and their hardware implementations. Especially nonlinear concepts in digital signal processing are of actual interest in a wide area of signal, bio-systems, image and multimedia processing applications. After the course the student is able to analyze and map algorithms onto different hardware platforms, such as DSPs and ASICs (FPGAs).

Contents

One keypoint is the implementation of algorithms in DSPs and FPGAs or ASICs with the help of linear systolic arrays (LSAs). The theory and practical aspects of systolic designs, optimal array scheduling, Cut-Set-Retiming procedures and the design of processing elements (PEs) as well as hardware-software co-design will be highlighted.
Different LSA-implementations for hardware accelerators will be discussed:
Correlation and Convolution
FIR filters
Wavelets
Spectral transforms
1D- and 2D- position invariant transforms (PIT) and Fuzzy-Pattern-Classification (FPC)

Prerequisites

Mathematics for undergraduates, Signals and Systems, Digital Design

Instruction and Examination

Project with presentation (30 min), graded

 

System Modeling and Simulation

Goals

Enable students to model and simulate embedded and real time systems. Those models can then be used i) to improve the design and implementation process, ii) to improve the system's documentation and maintainability, iii) to support the system diagnosis, and iv) to serve as a basis for the testing of the system both using PC-based simulations and Hardware-in-the-Loop Tests.

Contents

Block I: Advantages of System Models
Block II: Overall System Models: System components, Classification of system components, Examples: SysML, Non-functional system features, Domain Specific Languages
Block III: SW Structure Models: SW Components, Example: AUTOSAR, Service-oriented models
Block IV: SW Behavior Models: Automatas, Continuous controller models (e.g. SL), Code generation from behavior models, Simulation of automatas and continuous controller models
Block V: Plant Models: Discreet models, ODE-based models, physical, DAE-based and hybrid models (e.g. Modelica), Simulation of these models, Probabilistic models, Realtime topics

Prerequisites

Basic knowledge of computer languages, software development and control engineering.

Instruction and Examination

Successful completion of lab exercises and project work. Written examination. The course grade equals the grade of the written examination.

 

Software Engineering for Web Services

Goals

This course exposes students to state of the art WWW technologies for building business to business applications. Extensive lab time is provided for the development of simple client/server applications using Web Services.

Contents

Services are services offered via the Web. These services are requested by clients using the http protocol. Typical Web services are employed by E-commerce applications like online-shops or business-to-business applications. This course teaches and practises the development of Web Services based on Java using several software technologies. After an introduction to these specific technologies the major part of the course consists of a Web Service development project.

Prerequisites

Instruction and Examination

Exercise problems, written examination. The course grade equals the grade of the written examination.

 

Wireless Communications

Goals

Students acquire system theoretical knowledge of the physical and MAC layer of modern radio systems.

They are able to determine and to model real propagation channel characteristics. They can assess the performance limits of wireless systems including modulation and channel coding.

They learn how to use appropriate simulation and network planning tools in order to predict the quality and the limitations of wireless radio systems.

Contents

Mobile radio channels (multipath propagation, Doppler effects, Bello functions, channel measurements and characterization, channel modelling)

Advanced modulation methods (theoretical limitations, spread spectrum systems, multicarrier systems, ultra wide band radio)

Channel coding including space-time codes, MIMO (multiple input multiple output) systems

Further topics: software defined radio (SDR), cognitive radio systems

Prerequisites

Signals and linear systems, basics of modulation, basics of random processes

Instruction and Examination

Project work (2 students per group) with presentation, lab reports, written examination.

Bonus points from project work and lab reports will be considered in written examination. The course grade equals the grade of the written examination.

Courses from Halmstad (2nd Semester)

AUTONOMOUS MECHATRONICAL SYSTEMS

Goals

The goal of the course is for students to gain knowledge on how to integrate sensors and actuators in an autonomous mechatronical system.

Upon completion of the course the student shall be able to:

Contents

The lectures present methods for designing autonomous mechatronical systems with focus on signal processing of sensor values, basic image processing, and some principals of different controls of actuators.

In the project part the students will build and program a mobile autonomous robot. The programmed robot shall solve a predefined task. The project contains different parts that have to be solved with for example image processing algorithms or navigation. The robots are constructed with Lego parts, sensors, actuators, colour camera and DSP-processor. The students work in group of 2 or 3 students per project.

Prerequisites

Knowledge equivalent tot he courses Signal analysis and representation 7.5 credits, Digital control theory 7.5 credits, Embedded systems programming 7.5 credits, cooperating Intelligent Systems 7.5 hp, and Intelligent vehicles 7.5 credits. Programming in C is also required.

Instruction and Examination

Examination is to show that the robot solves the predefined task and some written report.

 

CHANNEL CODING AND DIGITAL COMMUNICATIONS

Goals

The course aims at giving a general understanding of digital communication and how to efficiently transmit information from a source to a destination.

Upon completion of the course the student shall be able to

Contents

An introduction on how to evaluate the quality of the received information is provided, as well as what factors limit and determine the performance of a communication system. Review of signals and random processes. Sampling and quantisation. Description of some modulation and demodulation methods. Definition of some noise and channel models. Matched filter and optimum detection. Overview of channel capacity and channel coding. Linear block and convolutional codes, code properties and error performance. Modulation and coding trade-offs.

Prerequisites

The course Signal analysis and representation 7.5 credits. Note that courses in mathematical statistics, digital signal processing and programming will facilitate the task of assimilating the course content.

Instruction and Examination

Examination is made in the form of requirements on completed and approved written reports from the home assignments as well as a written exam.

 

CYBER-PHYSICAL SYSTEMS

Goals

The course serves as an introduction to how computer languages are described and processed, with a certain focus on programming languages. The course introduces techniques used to describe the syntax and implementation of computer languages. After completing the course, the student should be able to design and implement simple computer languages.

The course makes use of the students' experience with computer languages and their understanding of computer organization and provides insights that will allow them to learn new computer languages and to participate in the implementation of small computer languages.

Students will be able to take advanced courses in compiler techniques and on concepts of programming languages.

On completion of the course the student shall be able to

Contents

Regular expressions, finite state automata, lexer generators, lexicographic analysis of computer languages. Context free grammars, push-down automata, parse trees, abstract syntax, parser generators, syntactical analysis of computer languages. Context analysis, bindings, environments, scope, types. Compilation of programming languages, intermediate representation, code generation. The course features discussion of scientific literature with focus on, for example, domainspecific languages, functional languages or languages for distributed applications.

Prerequisites

Knowledge equivalent to the courses Applied mathematics for computer science and engineering 7.5 credits and Embedded systems programming 7.5 credits.

Instruction and Examination

An oral exam based on the project is used to evaluate the students.

 

DIGITAL CONTROL

Goals

The student should get an understanding for discrete-time control systems, how to analyze, design and implement digital controllers.

Upon completion of the course the student shall be able to:

Contents

The course is focusing on control systems described by difference equations and how such models can be estimated and used in model-based control design. Special emphasis is put on practical design criteria where model uncertainties are taken into account. Moreover, an implementation structure for constrained controller actuation is considered. Also, optimal reference tracking and disturbance rejection is studied.

Prerequisites

Basic course in automatic control and the course Signal Analysis and Representation 7.5 credits or the course Signals and Systems 7.5 Credits, or equivalent.

Instruction and Examination

The examination is oral after passed computer exercises.

 

DISTRIBUTED REAL-TIME SYSTEMS

Goals

Upon completion of the course the student shall be able to:

Contents

The course treats basic functions in real time operating system kernels; prioritization, load rejection, and scheduling, partitioning, allocation and distribution of tasks over single and multiple processors; synchronization and communication between distributed tasks; architecture and design principles for real-time embedded and distributed systems.

Prerequisites

Courses in Discrete Mathematics and Computer Systems including basic knowledge in programming and operating systems.

Instruction and Examination

Examination is based on a weighted average of seminar participation and presentations, project results and their documentation and presentation, and a final written examination.

 

EMBEDDED PARALLEL COMPUTING

Goals

The course is intended to provide knowledge of how parallel computing can be used as a way to meet application demands in embedded systems, such as performance and power efficiency. Further, it is intended to give a general insight into current research and development in regard to parallel architectures and computation models. Parallelism of various types exists in all modern computer architectures, and knowledge about how to apply parallelism is necessary, in particular, when designing embedded computer systems.

Upon completion of the course, the student shall be able to:

Contents

The course is divided into a lecture part, a laboratory part including a small project, and a seminar part.

The lecture part initially gives a motivation for parallelism, based on demands on embedded computing (such as performance and power efficiency) and applications that require parallelism. Then it presents the fundamentals of parallel architectures (forms of parallelism, SIMD, MIMD, dataflow, reconfigurable architectures, interconnection networks, etc.) and parallel programming models (shared memory, message passing, stream programming, communicating sequential processes, process networks, etc.). Example architectures and programming techniques are presented and discussed.

The laboratory part provides hands-on experience of embedded parallel computing, primarily based on manycore processors on a chip and their available programming tools.

In the seminar part of the course, course participants make detailed studies of various sub-areas or specific architectures and lead seminars in these. The university's research projects are included in these special studies.

Prerequisites

The courses Cooperating Intelligent Systems 7.5 credits, Embedded Systems Programming 7.5 credits, and Applied Mathematics for Computer Science and Engineering 7.5 credits, or equivalent. Basic and continuation courses in computer organization, digital logic design, and computer programming from Bachelor programme.

Instruction and Examination

Examination of the lecture part of the course is by written exam at the end of the course. Bonus points for the written exam may be earned through participation in the seminars and providing correct answers on the written quizzes. The quality of the specific seminar or seminars that the student is responsible for is also weighed into the final grade.

 

INTELLIGENT VEHICLES

Goals

The goal of the course is to provide advanced knowledge for being able to develop intelligent vehicles and mobile robots with the emphasis on sensor systems, signal processing and control and regulation. The course focuses on sensor fusion, i.e. how information from several sensors should best be combined.

Upon completion of the course the student shall be able to:

Contents

The course addresses: Dead-reckoning and kinematics models, indoor navigation systems, outdoor navigation systems (e.g. GPS-based systems), sensor fusion (with a focus on the Kalman filter and the Extended Kalman filter), path-planning, vehicle control and obstacle avoidance, human-machine interaction.

Prerequisites

The courses Applied mathematics for computer science and engineering 7.5 credits, Signal analysis and representation 7.5 credits, Cooperating Intelligent Systems, and Control theory 7.5 credits or equivalent are prerequisites for the course. It is also recommended that the student has basic knowledge of mathematical statistics.

Instruction and Examination

Examination is in the form of a written or oral exam and exercises.

 

LEARNING SYSTEMS

Goals

The course aims at providing an overview of the field machine learning; learning and self-organizing systems for classification and prediction.

Upon completion of the course, the student shall be able to

Contents

Overview of learning systems. Overview of classification and regression. Overview of products on the market and common application areas for learning systems. Important aspects and standard methods in learning systems. The most common techniques and models for learning systems will be introduced e.g., artificial neural networks and self-organizing maps..

Prerequisites

The courses Cooperating Intelligent Systems 7.5 credits, Signal analysis and representation 7.5 credits and Applied mathematics for computer science and engineering 7.5 credits, or equivalent.

Instruction and Examination

Examination is by approved projects, seminar presentations, and an oral or written exam.

 

MODERN COMMUNICATION SYSTEMS AND NETWORKS

Goals

The course shall give understanding of important methods, architectures, and implementations of modern communication systems and networks. The aim of the course is to give experiences in obtaining information from advanced-level literature and scientific papers, and of critical examination of scientific results from fields that involves communication in the Internet, LAN, and other networks.

Upon completion of the course the student shall be able to

Contents

Selected subjects will be treated in form of lectures and seminars. The focus is put on currently important fields, which means that the seminar subjects will be adapted for the actual course start. Although, possible subjects to be penetrated can be mentioned: routing in large internet networks (e.g., BGP4), multimedia communications, traffic models, VLAN, switch and router architectures, active networks, TCP details, application protocols, multicasting, protocols for optical networks, networks in parallel and distributed systems, system area networks (e.g. Infiniband), admission control, Internet QoS (RSVP, DiffServ, RTP etc), and IP telephony. Each student shall, in group, do a larger project exercise or write a paper (investigation, simulation, experiment or similar) to get deeper understanding of a specific subfield.

Prerequisites

The course Data communication I 7.5 credits or equivalent.

Instruction and Examination

Examination is done in form of quizzes in connection with some of the lectures, written exam, project reports and presentations.

 

REAL-TIME NETWORKING

Goals

The goal of the course is to give understanding and knowledge of communication and networks in embedded systems and essential concepts and methods used in such systems. Especially, the course shall give understanding of performance and real-time analysis of the communication systems and networks used in networked embedded systems. Furthermore, the course shall give examples from state-of-the-art research within the field.

Upon completion of the course, the student shall be able to

Contents

Introduction to embedded networking and temporal control of communication. Processor and network scheduling. Static and dynamic scheduling. Preemptive and non-preemptive scheduling. Time-driven scheduling. Resource constraints. Precedence constraints. Priority inversion. Jitter handling. Holistic scheduling. Complexity analysis. Real-time analysis for distributed systems. Real-time networks and protocols (Industrial Ethernet, field buses etc). Networks for safety-critical applications (e.g., FlexRay, TTP, AFDX). Real-time analysis of field bus communication. Real-time analysis of switched networks. Wireless real-time communication including sensor networks. Performance evaluation and simulation.

Each student shall, in group, do a larger project exercise where a specific protocol, communication scheduling method or similar is simulated and evaluated.

Prerequisites

The courses Cooperating Intelligent Systems 7.5 credits, Embedded Systems Programming 7.5 credits, and Applied Mathematics for Computer Science and Engineering 7.5 credits, the course Data Communication I, or equivalent.

Instruction and Examination

Examination is done in form of written assignments, written exam and project report.

 

WIRELESS COMMUNICATION SYSTEMS

Goals

The objective of the course is to give a basic understanding and knowledge of wireless communication systems, to be able to analytically rate different technologies for wireless applications.

Upon completion of the course the student shall be able to

Contents

Part I Basic radio technology

Antennae, wave propagation, fading, channel coding, modulation multiplexing, spread spectrum.

Part II Application areas

Satellite communication, cellular systems, short range wireless systems.

Part III Student project

Each student conducts an individual project (investigation, implementation, simulation) to gain a deeper knowledge in a specific area within the subject of wireless communication.

Prerequisites

A course in data communication.

Instruction and Examination

The examination of theory part is conducted by written exam; the grades of fail together with the pass grades of 3, 4 and 5 will be awarded for this part of the course. The examination of the individual assignment is conducted by means of a written report; grades of fail or pass will be awarded for this part of the course.

Courses from Esbjerg (3rd Semester)

Computer Vision

Goals

That the student obtains knowledge about fundamental theories, methods, and techniques for computer based manipulation and analysis of video pictures, visualization with computer graphics, and virtual reality.

Contents

The duality between computer manipulation and analysis of video pictures and computer generation of synthetic pictures (computer graphics and virtual reality) is the main theme of the course.  Methods and techniques for visualization and picture manipulation and interpretation as well as the use of computer vision systems for robot navigation and virtual reality are covered.

Prerequisites

Programming, algorithms and data structures, image analysis

Instruction and Examination

oral examination, pass/fail grade, or examined as part of the project exam (with marks assigned) for those students choosing Computer Vision as semester theme

 

Control Theory

Goals

That the student obtains knowledge about fundamental principles and methods of conventional and modern control theories and their inducstrial applications.

Contents

Dynamic system models and response, feedback control, root-locus method, frequency-response analysis and design, PID control, state-space analysis and design, digital control systems, multivariable control, kalman filters, basic nonlinear control, Matlab/Simulink, case studies.

Prerequisites

general mathematics and physics, elementary matrix algebra, Laplace- and Z-transforms

Instruction and Examination

oral examination, pass/fail grade, or examined as part of the project exam (with marks assigned) for those students choosing Control Theory as semester theme

 

Database Systems

Goals

That the student obtains knowledge about fundamental and selected advanced, current topics and issues in the development and use of database systems.

Contents

Database fundamentals:  introduction, database models (including entity-relational, relational, and object-oriented), SQL, integrity constraints, indexing and hashing, query processing and optimization, transactions, concurrency control.  Example advanced, current topics:  data mining, spatial databases, temporal databases, database tuning, data warehousing, etc.

Prerequisites

Programming, algorithms and data structures.

Instruction and Examination

oral examination, pass/fail grade

 

Fuzzy Logic

Goals

That the student obtains knowledge about fundamental principles and models in fuzzy logic and fuzzy logic based techniques and their applications in information systems.

Contents

Fuzzy set theory and fuzzy logic, fuzzy aggregation operators, fuzzy relations, fuzzy knowledge representation, fuzzy logic algorithms, possibility theory, fuzzy classification, and object recognition, applications in information systems.

Prerequisites

programming, algorithms, and data structures

Instruction and Examination

oral examination, pass/fail grade, or examined as part of the project exam (with marks assigned) for those students choosing Fuzzy Logic as semester theme

 

Semester Project

Goals

The goals are to enhance the students' learning experience through having them work with, apply, synthesize, and reflect upon the information and materials they receive through lectures as they put it to use to carry out the group project.

Contents

The contents of the project is dependent upon the semester theme chosen.  The possible themes are computer vision systems, fuzzy logic information technology, and control systems.

Prerequisites

The prerequisites are dependent upon the semester theme chosen, as this determines which project unit courses that are taken.  See corresponding course template descriptions for details.

Instruction and Examination

A project report is produced and handed in to the project supervisor.  An exam is carried out in which the students present their project to the supervisor and a sensor who are then allowed to ask questions in order to evaluate and assess the students.

 

Software Technology

Goals

That the student obtains theoretical and practical knowledge about advanced topics in sofware technology, including the software development process, software evolution, software architecture, and software tools.

Contents

Advanced analysis, design and implementation, software development methods, advanced topics in programming languages, architectural abstractions (frameworks, patterns, components), conceptual modeling.

Prerequisites

Programming, algorithms and data structures.

Instruction and Examination

examined as part of the project exam (with marks assigned)

Courses from Lemgo (3rd Semester)

Management Skills and Business Administration

Goals

The students
… are familiar with financing and accounting models of  medium-sized enterprises and know the meaning of outside financing.
… know methods and instruments of business management and controlling.
… are familiar with means and methods of marketing.
…understand strategies and models of internationalisation and globalisation.
…know the basics of project management and have already done projects themselves.
… are able to handle modern media and have gained experience in presentations.
… are familiar with aspects of teamwork / teamroles.
… have developed strategies to deal with stress and conflicts.
… know the conventions for writing a letter of application and a CV .
… are familiar with typical questions in job interviews and typical tasks in assessment centers.

Contents

Prerequisites

Being open minded

Instruction and Examination

Presentation with grade

 

Project Work

Goals

The goals are to enhance the students' learning experience through having them work with, apply, synthesize, and reflect upon the information and materials they received through lectures as they put it to use in the group project.

As the project work is a team work of two or three students, they need to meet respective requirements and rules.

Contents

Possible topics are offered from the area of industrial information technology. The students need to organize the project work, check state-of-the-art solutions for the given problem, suggest a proposal, investigate the proposal and provide the results.

Project work proposals can be found here.

Prerequisites

The prerequisites are dependent upon the project theme chosen.

Instruction and Examination

Each student has to produce a project report of approx. 30 pages and hand it in to the project supervisor. The project team has to present the results, where each member has to present her/his contribution. Presentation time for one student approx. 15 min.

The supervisor is allowed to ask questions in order to evaluate and assess the students.

 

Scientific Methods and Writing

Goals

Students acquire basic knowledge about scientific writing and presenting. They understand typical structures of scientific papers and typical presentation styles. Good and bad examples for written, scientific English are discussed.

Contents

  1. Motivation
  2. The Principles behind good and bad presentation styles
    1. Content
    2. Structure
    3. Design and Layout
    4. The state of the mind
  3. Hints for good presentations and slides
  4. Hints for writing scientific papers
  5. Hands-on training: A paper and a presentation on a computer science topic

Prerequisites

none

Instruction and Examination

Project work including a written scientific paper and a presentation (grade is not based on the content but on the writing and presentation skills)

 

Seminar

Goals

Students learn to approach a given topic in a scientific way. This includes (i) a literature research, (ii) a state-of-the-art overview, and (iii) the compilation into a paper and a presentation.

Contents

  1. Introduction
    1. What is science and what not?
    2. Engineering and computer science as special fields of science
  2. Each student chooses a topic and and generates a state-of-the-art overview. This is done in form of a scientific paper. This work overlaps with the course “Scientifc Methods and Writing”.
  3. The work is presented in a seminar.

Prerequisites

none

Instruction and Examination

Project work including a written scientific paper and a presentation (grade is only based on the content, not on the writing and presentation skills)

Courses from Wroclaw (3rd Semester)

Information System Modelling and Analysis

Goals

The aim of the subject is to prepare students:
- to the lecture on software development methodologies,
- to participate in a group project on software system development.

Contents

The following main topics are in the scope of the subject:
- notions used in modern object-oriented approach to model-driven software system development,
- representation of the notions in the UML, recently used standard modeling language, and
- outline of the UML application in software systems development, especially in domain analysis and software requirement.

Prerequisites

Practise in object-oriented programming

Instruction and Examination

Final test

 

Project

Goals

The aim of the course is to provide practical guidelines for project management and the Master Thesis preparation.

 

Contents

During the course students will select the subject of the project related to different courses chosen in current or previous semesters as well as a topic of their Master Thesis. Students will be familiarized with the routines employed for the thesis preparation.

Prerequisites

No

Instruction and Examination

One test at the end of lectures and classes 

 

Software System Development

Goals

Build a part of software system according to presented methodology
Give experiences in using UML for software system design and documentation
Give experiences in using different CASE tools during software project

Give an overview of the most popular attempts to software development.

Contents

The lecture deals with different aspects of software engineering, especially presents:

  1. Basic notions from classical (heavy) methodologies, e.g. Rational Unified Process (RUP), and Unified Software Development Process (USDP);
  2. MDA approach to software development;
  3. Basic notions from agile methodologies, e.g. eXtreme Programming;
  4. Chosen aspects of user interface (UI) designing on the technical level, in particular techniques and guidelines supporting GUI window  design.
  5. Code and documents quality attributes, and chosen software quality
    metrics.

The aim of the project is to build a software system prototype. The students work in teams. They follow USDP/RUP methodology to build the prototype. They experience with building basic intermediate artifacts (e.g. vision of the system, software requirement specification, design model, deployment model), as a final product itself.

Prerequisites

Basic knowledge of Unified Modelling Language (UML)
Basic knowledge of database design and database management systems
Good knowledge of object-oriented paradigm
Good experiences in programming

Instruction and Examination

Test with 5-10 closed questions and 5-10 opened.