Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

VK (lecture with integrated course, project, exercises or mini-seminar)

2 SWS, 4 ECTS

Description

A general presentation of core systems components of intelligent transportation systems: a) traffic theory; b) traffic control, c) traffic sensors; d) selected systems and services

Topics

1. Course Plan, Organization and Evaluation Policy

2. Definitions, Overall Context and Motivation

3. Introduction to Transportation Systems

4. Overview of ITS (Intelligent Transportation Systems)

5. Positioning, Navigation Principles, and Location-based

Services

6. Basics of Road Traffic Theory

7. Principles of Traffic Information Sensing + Sensor
technologies

8. Basics of Road Traffic Control

9. Toll Collection: Principles and Systems

10. Road Safety: issues, technologies, modelling, assessment

11. Students Short Seminar on Selected Topics

Keywords

Road traffic theory, traffic management, traffic sensing, road safety, driver assistance, mobility systems

Prior knowledge

none

Learning objective

Acquire advanced and deep knowledge on core components of intelligent transportation systems

Recommended Literature

Language

English

Related Lectures

• Traffic Simulations Lab (KU)
• Methods of Transportations Informatics (KU)
• GIS and Expert Systems for Transportation

Recommended Lectures

• Traffic Telematics II
• Logistics Systems Engineering
• Wireless Communication

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

VK (lecture with integrated course, project, exercises or mini-seminar)

2 SWS, 4 ECTS

Description

A general presentation of core systems components of intelligent transportation systems: a) traffic theory; b) traffic control, c) traffic sensors; d) selected systems and services

Topics

1. Introduction to Transportation

2. Basics of "Road Traffic Theory

3. Traffic Control Principles

4. Principles of Traffic Information Sensing + Sensor technologies

5. Advanced Traveller Information Systems

6. Data Fusion at the Traffic Management Center

7. Toll Collection: Principles and Systems

8. Transportation Risk and Safety Assessment + Road safety basics

9. Urban Mobility Transportation System

10. Students Seminar on Selected Topics

Keywords

Road traffic theory, traffic management, traffic sensing, road safety, driver assistance, mobility systems

Prior knowledge

Learning objective

Acquire advanced and deep knowledge on core components of intelligent transportation systems

Recommended Literature

• Traffic Engineering (4th Edition), Roger P. Roess , Elena S. Prassas , William R. McShane
• Traffic Flow Fundamentals , May
• Intelligent Transport Systems: Cases and Policies, Roger Stough , Edward Elgar
• Fundamentals of Intelligent Transportation Systems Planning, Mashrur A. Chowdhury, Adel W. Sadek ,
• Wireless Communications for Intelligent Transportation Systems, Scott D Elliott , Daniel J Dailey
• Introduction to Transportation Systems, Joseph Sussman

Language

English

Related Lectures

• Traffic Simulations Lab (KU)
• Methods of Transportations Informatics (KU)
• GIS and Expert Systems for Transportation

Recommended Lectures

• Traffic Telematics I
• Logistics Systems Engineering
• Wireless Communication

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

VK (lecture with integrated course, project, exercises or mini-seminar)

2 SWS, 4 ECTS

Description

• THEORETICAL BASICS
• EXAMPLES of selected Telecommunication Systems [Students' seminars]

Topics

1. Introduction to Telecommunications

2. Mathematical Basics -1: GRAPH THEORY

3. Mathematical Basics -2: TRAFFIC THEORY

4. Network Reliability & Availability basics

5. The OSI Model

6. Standards and regulation issues

7. Convergence of Telecommunication Systems

8. Billing issues in Telecommunication Systems

9. Value added Services in Telecommunications

10. Telecommunication Systems for Intelligent Transportation Systems

11. Students' short seminar on selected Telecom. Systems

Keywords

Prior knowledge

Learning objective

Recommended Literature

Language

English

Related Lectures

Recommended Lectures

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

VK (lecture with integrated course, project, exercises or mini-seminar)

2 SWS, 4 ECTS

Description

Topics

1. Introduction to Logistics Systems

2. Shared Solutions to Common Challenges

3. Freight Transportation: An Overview

4. Understanding Supply Chain Networks

5. Supply Chain Network Processes and performance measurements

6. Modelling and Managing Supply Chain Networks

7. Information Technology in Supply Chain Management & Logistics

8. Forecasting Logistics Requirements

9. Designing the Logistics Networks

10. Solving Inventory Management Problems

11. Designing and Operating a Warehouse

Keywords

Prior knowledge

Learning objective

Recommended Literature

Language

English

Related Lectures

Recommended Lectures

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

VK

2 SWS, 4 ECTS

Description

The goal of this course is to give the students the algorithmic methods at the heart of successful data mining-including tried and true techniques. Finally, some concrete applications of practical interest will be provided.

Topics

1. Introduction to MATLAB

2. Mathematical basics

3. Data selection and preparation

4. Bayesian classifier

5. Linear models

6. Non linear models

7. Prediction

8. Time Series Analysis

9. Collaborative filtering

10. Optimization metrics

11. Evaluation Metrics

Keywords

Optimization, Classification, Prediction, Optimization

Prior knowledge

Basics of linear algebra and basics in programming

Learning objective

• Traffic data analysis (cameras and microphones for vehicle and pedestrian detection).
• Traffic Flow prediction, Traffic speed prediction, Traffic sign recognition.
• Complex traffic patterns analysis (shock waves and adaptive traffic control).
• Driver behaviour analysis based on different types of sensors.

Recommended Literature

• Data Mining: Practical Machine Learning Tools and Techniques
  # Publisher: Morgan Kaufmann; 2 edition (June 22, 2005)
  # Ian H. Witten, Eibe Frank
• Principles of Neuro Computing for Science & Engineering
  # Fredric M. Ham, Ivica Kostanic
• George Papadourakis, "Introduction to Neural Networks", Lecture Notes

Language

English

Related Lectures

Recommended Lectures

Fundamentals of Image Processing

Seminar on Pattern Recognition and Data Mining in Intelligent Vehicle Technologies

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Bachelor Curriculum, Informationstechnik

• Informationstechnische Vertiefung

Recommended Semester

1. Semester Information Technology (Master)

4. Semester Information Technology (Bakk.)

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

VK

2 SWS, 3 ECTS

Description

It is an introductory course to the fundamentals of digital image processing. It emphasizes general principles of image processing, rather than specific applications.

Topics

1. Introduction (color systems & image formats)

2. Thresholding Techniques

3. Edge Detection

4. Binary Shape Analysis

5. Boundary Pattern Analysis

6. Mathematical Morphology

7. Hough Transformation

8. Fourier transformation

9. Polygon and Corner Detection

10. Image Filtering

11. Image interpolation

12. Epipolar Geometry

13. Camera Calibration

Keywords

Computer Vision, Filtering, Image transformation

Prior knowledge

none

Learning objective

• Digital image processing
• Machine Vision
• Applied maths

Recommended Literature

• Machine Vision, E.R. Davies Elsevier, Third Edition
• Digital Image Processing (3rd Edition) Rafael C. Gonzalez (Author), Richard E. Woods (Author)

Language

English

Related Lectures

Recommended Lectures

• Data Mining in Intelligent Transportation and Logistics
• Fundamentals of Image Processing
• Seminar on Pattern Recognition and Data Mining in Intelligent Vehicle Technologies

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II
• Research Track

Recommended Semester

1. Semester Information Technology (Master)

4. Semester Information Technology (Bakk.)

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

VK

2 SWS, 4 ECTS

Description

Topics

1. Dynamic Vision based Intelligence

2. Autonomous landing of airplanes by dynamic machine vision

3. Visual servoing based on dynamic vision

4. Adaptive Deformable Models for Graphics and Vision

5. Introduction to Predictive Filters with some application example in computer vision

6. Robust pedestrian detectio

7. Cellular neural networks for motion estimation and obstacle detection

8. An Adaptive Architecture for Hierarchical Vision Systems

9. Vision Guided Landing of an Autonomous Helicopter in Hazardous Terrain

10. Feature tracking using vision on an autonomous airplane

11. Implementation of a CNN-based perceptual framework on a roving robot

12. Traffic sign detection for driver support systems

13. Stereo vision: principles + CNN (cellular neural networks) based approach

14. Principles of scene analysis and image understanding

Keywords

Prior knowledge

Learning objective

• Basics of Machine Vision + deep research related to selected applications example in transportation

Recommended Literature

Language

English

Related Lectures

Recommended Lectures

Semester

Summer semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Research Track "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Bachelor Curriculum, Informationstechnik

• Informationstechnische Vertiefung
• Bachelorarbeit und Seminar

Recommended Semester

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

SE

2 SWS, 4 ECTS

Description

In the lecture we deal with various aspects of pattern recognition and their applications in Image processing for INTELLIGENT VEHICLES TECHNOLOGIES and ROBOTICS. Different classification methods for both statistical and stochastical approaches will be presented.
Comprehensive introductions to Advanced Driver Assistance Systems(ADAS) and to ROBOTICS will be provided.

Topics

1. Introduction/Overview to Pattern Recognition

2. Introduction, k-nearest-neighbor

3. k-means, Gaussian distributions, Gaussian classifier

4. Principal components analysis

5. linear regression, pseudo-inverse

6. Fisher linear discriminants

7. Perceptron, Perceptron learning algorithm

8. Multi-layered neural networks, backpropagation

9. Support Vector Machine

10. Applications in Image processing / machine Vision

11. Introduction to ROBOTICS

12. Introduction to Advanced Driver assistance Systems (ADAS)

Keywords

Prior knowledge

Learning objective

• Object detection and recognition
• Advanced applications of artificial intelligence (audio/video patterns recognition)
• Advanced analysis skills and complex problem solving

Recommended Literature

• Data Mining: Practical Machine Learning Tools and Techniques, 2 edition (June 22, 2005), Ian H. Witten, Eibe Frank
• Principles of Neuro Computing for Science & Engineering, Fredric M. Ham, Ivica Kostanic

Language

English

Related Lectures

Recommended Lectures

Semester

Summer semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Ass.Prof. Jean Chamberlain Chedjou , Prof. Kyandoghere Kyamakya

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at, Jean.Chedjou@aau.at

Type of lecture and number of credits

KU

2 SWS, 3 ECTS

Description

The first part of this lecture provides in-depth information concerning the modelling process with specific applications in transportation.

The second part considers several mathematical models (e.g. ODEs and/or PDEs) and, some classical analysis methods are used to derive approximate analytical solutions. The theory of stability is considered with application to some concrete mathematical models.

The third part is concerned with the investigation of several mathematical models using both numerical and experimental methods. A comparison is performed between the analytical, numerical and experimental results.

The fourth part of the lecture is focused on Neuro-computing. Some basics of Neuro-computing are provided and some applications are considered in transportation (e.g. solving ODEs, PDEs, etc.). The optimization concept is considered together with some specific applications of practical interest in transportation.

Topics

1. Fundamentals of dynamical systems

2. Analysis methods for nonlinear dynamical systems

3. Applications of the analysis methods to concrete mathematical models

4. Numerical solution of Nonlinear Ordinary differential Equations

5. Numerical solution of Partial differential Equations

6. Background of the Oscillatory theory

7. Bifurcation analysis and applications to concrete mathematical models

8. Application of the oscillator theory in image processing

9. Fundamentals of Neuro-computing

10. Background of Optimization and related applications in transportation

11. Application of the CNN paradigm

Keywords

Modelling, ODEs, PDES, Stability, Analytical methods, Numerical methods, Oscillatory theory, Bifurcation, Image processing based on nonlinear oscillators, Experimental methods, Neuro-computing, ANN, CNN, Optimization in Transportation.

Prior knowledge

Good basics in System-theory, Mathematics and Programming

Learning objective

• Mastering of the Modelling concept in engineering.
• Mastering of the Analytical Methods for solving continuous models (ODEs and PDEs) and discrete models.
• Mastering of the Numerical methods for solving continuous models and discrete
• Mastering of the Neuro-computing concept and applications for solving ODEs and PDEs
• Mastering of the Optimization concept together with some specific applications in transportation

Recommended Literature

• T Steven H. Strogatz,2000, "Nonlinear Dynamics and Chaos, With applications to Physics, Biology, Chemistry, and Engineering"
• Armin Fuchs, 2013, "Nonlinear Dynamics in Complex System: Theory and Applications for the Life Neuro- and Natural Sciences,"
• 3-G. Radons and R. Neugebauer, 2003, "Nonlinear Dynamics of Production Systems"

Language

English

Related Lectures

Recommended Lectures

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Prof. Kyandoghere Kyamakya

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

KU

3 SWS, 3 ECTS

Description

The goal of this lecture is to help the students to develop fundamental GIS skills in a variety of areas such as data conversion, map symbology, and projections.
The students will be able to use different GIS tools (ArcGIS) and to write their own GIS applications based on C#.

Topics

1. Fundamentals of GIS

2. Terrestrial data structures, representing the real world

3. GIS Data Structures representing the world in a computer

4. Data Quality An essential ingredient

5. Data Input: preparation, integration, and editing (Image processing basics, Satellites, GPS, Remote sensing)

6. Data analysis and modelling

7. Fundamentals of expert systems

8. First order logic

9. Resource Description Framework

10. Ontology Web Language

11. Semantic Web Rule Language

12. GIS application examples

Keywords

Expert Systems, Modelling, Ontology, Logic,

Prior knowledge

Basic programming skills

Learning objective

• Design of GIS systems and its implementations
• How to use GIS tools (ArcGIS)
• Design of expert systems and its implementations

Recommended Literature

• The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management [Paperback], Michael C. Daconta
• Getting to Know ArcGIS Desktop [Paperback], Tim Ormsby
• Artificial Intelligence: A Systems Approach, M. Tim Jones

Language

English

Related Lectures

• Data Mining in Intelligent Transportation and Logistics
• Fundamentals of Image Processing
• Seminar on Pattern Recognition and Data Mining in Intelligent Vehicle Technologies

Recommended Lectures

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

n.n

Transportation Informatics Group, Institute of Smart Systems Technologies

Email:

Type of lecture and number of credits

KU

2 SWS, 3 ECTS

Description

In this Lab. we are focusing on the fundamental concepts of image processing and machine vision. Different topics such as Image enhancement, blob analysis, transformations, grouping and fitting, geometric vision, Hough transform, recognition and basic of Artificial Neural Network have been considered. Student can practice how to implement image processing algorithms in Matlab and OpenCV with C++.

Topics

1. Machine Vision Introduction and Image formation

2. Grouping and fitting

3. Geometric vision

4. Recognition

5. Artificial Neural Network

Keywords

Image processing, machine vision, image enhancement, MATLAB, recognition, matching, ANN, blob analysis, Hough transform.

Prior knowledge

Basics in Mathematic, Familiar with Matlab programming.

Learning objective

They can solve simple image processing and machine vision problems with MATLAB.

Recommended Literature

• Digital Image Processing (2nd Edition) Publisher: Prentice Hall; 2nd edition (January 15, 2002)
• Image Processing: The Fundamentals Publisher: Wiley; 2 edition (May 17, 2010)

Language

English

Related Lectures

Recommended Lectures

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Ass.Prof. Jean Chamberlain Chedjou

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: Jean.Chedjou@aau.at

Type of lecture and number of credits

KU

2 SWS, 3 ECTS

Description

Topics

1. General Introduction; Systems Theory; Principles of modeling Dynamic Systems.

2. Basics of MATLAB + SIMULINK

3. Analog Computing - 1 (Basics)

4. Analog Computing - 2 (CNN)

5. Basics of Artificial Neural Networks (ANN)

6. Basics of Graph Theory

7. Queuing-1

8. Queuing-2 + Examples in MATLAB

9. Basics of the theory of traffic flow: Mathematical models for microscopic and macroscopic flows- simulation concepts.

10. Traffic- control based on the concept of "Nonlinear Dynamics"

11. Modeling of "Optimization problems" and solutions based on the CNN- paradigm

12. Statistical Analysis of Stochastic Scenarios/Phenomena

13. Scheduling - Basics + Examples

14. Operations Research - Basics

15. Small project on one of the themes addressed

Keywords

Prior knowledge

Learning objective

Recommended Literature

Language

English

Related Lectures

Recommended Lectures

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Ass.Prof. Jean Chamberlain Chedjou

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: Jean.Chedjou@aau.at

Type of lecture and number of credits

KU

2 SWS, 3 ECTS

Description

Topics

1. Introduction to Traffic Simulation, Management and control.

2. Overview of the traffic Simulation models

3. Overview of the traffic Management, Control and Optimization Systems/tools (Synchro7, Cube5, Vissim, Visum, Spot, Utopia, Transyt, Sidra, SCOOT, SCAT, etc.)

4. Simulation of Traffic scenarios using "SYNCHRO-7"

5. Simulation of Traffic scenarios using "CUBE-5"

6. Traffic Control using the "CNN- paradigm"

7. Analysis of "Optimization problems" in Transportation using the "CNN- paradigm"

8. Simulation of "Graphs & Queuing" in Transportation Using the CNN- paradigm.

9. Modeling and Simulation of Logistic scenarios using "VENSIM".

10. Modeling and Simulation of Logistic scenarios using "STELLA".

11. Small project on one of the themes addressed

Keywords

Prior knowledge

Learning objective

Recommended Literature

Language

English

Related Lectures

Recommended Lectures

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"
• Technical supplementary subject I and II

Recommended Semester

Teacher

Ahmad Haj Mosa

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: Ahmad.HajMosa@aau.at

Type of lecture and number of credits

KU

2 SWS, 3 ECTS

Description

This Lab delivers an overview of machine vision and image processing. Our focus will be in some basic and advanced methods such us Image enhancement, Houch transform , Edge detection, face detection. Handwriting recognition, Active apprience modeling, 3D reconstruction. and image enhancement.
Also we deliver some pattern recognition and machine learning methods to build a smart vision system. Student can practice how to implement image processing algorithms in Matlab and OpenCV with C#

Topics

1. Introduction to Machine Vision

2. Object Representation and Description

3. Spatial Processing

4. Frequency Domain Processing

5. Image Restoration

6. Advanced Filtering Techniques (Diffusion filtering).

7. Segmentation

8. Advanced Segmentation Methods (Mean shift, Active contours, Watersheds)

9. Camera vision (2D&3D)

10. Image Stitching (Panorama)

11. Motion Estimation and Object tracking(Kalman Tracking, Optical Flow)

12. Visual Pattern Recognition (PCA, LDA, SVM)

13. Statistical Shape Modeling (AAM,ASM)

14. Some Medical Imaging Examples

Keywords

Prior knowledge

Basics in Mathematic, Familiar with Matlab programming or .NET

Learning objective

Build smart vision systems using Matlab or C#

Recommended Literature

• Digital Image Processing (2nd Edition), Publisher: Prentice Hall; 2nd edition (January 15, 2002)
• Image Processing: The Fundamentals, Publisher: Wiley; 2 edition (May 17, 2010)
• Design your own PC Visual Processing and Recognition System in C#

Language

English

Related Lectures

Recommended Lectures

Semester

Summer semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization "Intelligent Transportation Systems"

Recommended Semester

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

SE

2 SWS, 6 ECTS

Description

In this research seminar topical subjects in the area of Intelligent Transportation Systems will be regarded. Based on topical scientific publications on a particular subject participants develop a powerpoint presentation and a paper on the selected topic, and finally give a talk for their fellow students and their supervisor. NOTE: The seminar will be held in blocks. Topics will be presented and assigned in the first meeting. Also the semester's schedule will be presented in the first meeting.

Topics

Keywords

Prior knowledge

Learning objective

• improve their knowledge in a particular field.
• learn to read and interpret scientific publications.
• prepare a presentation by themselves.
• practice to give a presentation and to discuss with an audience.

Recommended Literature

Scientific Publications

Language

English

Related Lectures

Recommended Lectures

Semester

Summer semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Research Track

Recommended Semester

Teacher

Prof. Kyandoghere Kyamakya,

Transportation Informatics Group, Institute of Smart Systems Technologies

Email: kyandoghere.kyamakya@aau.at

Type of lecture and number of credits

KU

8 SWS, 12 ECTS

Description

Students work independently on a research oriented project.

Topics

• Adaptive traffic control concepts: local control (Overview, models, most relevant literature)
• Adaptive traffic control concepts: area control, network control
• Road safety: basics, modeling, infrastructure related and in-vehicle consideration
• Microscopic traffic simulation at a junction by using the system dynamics modeling paradigm; tool: STELLA or VENSIM or POWERSIM
• How can social networks be integrated in the ATIS (Advanced Traveler Information Systems)
• Use of Cellular Neural Network to solve selected graph theoretical problems: shortest path search and minimum spanning tree on a stochastic graph
• Driver Emotion detection concepts: overview and comprehensive tutorial
• Energy logistics: how supply chain management concepts are applicable for smart electrical energy networks

Keywords

Prior knowledge

Learning objective

Recommended Literature

Language

English

Semester

Winter semester

Allocation in the Curriculum

Master Curriculum, Information Technology

• Technical specialization Intelligent Transportation Systems"

Recommended Semester

Teacher

Prof. Dr.-Ing. habil. Wolfram Büttner,

Institute of Applied Informatics

Email: Wolfram.Buettner@aau.at

Type of lecture and number of credits

VK

2 SWS, 3 ECTS

Description

The control of such diverse processes as railroading or manufacturing, chemical processes in a steel mill or information processing on a chip follows a common principle: The control algorithm (controller) owns an image or abstraction of the dynamics of the process consisting of sequences of states. If the process is in a certain state and the controller receives some input, i.e. a command or some feedback from the process, then the controller will compute a suitable control action (output). This action will influence the dynamics of the process in such a way that the "new" image of the process equals a "next" state which the controller also computes. In short, the process is controlled by a sequence of suitable control actions which ensure that the process achieves its objective. A discrete controller has only finitely many states, inputs and outputs. The lecture will teach fundamental techniques for specifying, synthesizing and verifying discrete controllers. Representative tasks as arising in the design of discrete controllers will demonstrate how to apply those techniques.

Topics

• Demonstration of the variety of control tasks required by operating a train system
• Classification of control tasks and their mathematical tooling according to characteristics of inputs, outputs and states (finite, continuous, deterministic, random, etc.)
• Running examples for discrete controllers (control of the switches and signals of a train system by an interlocking system, management of memory accesses of a processor)

• Finite Boolean Algebra ( phenomena, "most general" Boolean algebra, B2 n = Boolean algebra of 2-valued functions in n binary variables (Boolean functions)
• Details of B2 n (representation, solving equations, searching for zero′s of Boolean functions)
• Finite automata (phenomena, Mealy automata, representation, functional equivalence, refinement)
• Properties of finite automata (propositional/temporal logic, automata built by properties)
• Principles of formal design

• Automated synthesis of a controller for a manufacturing cell
• Specification, programming, simulation-based and formal verification of a SDRAM-controller
• Simulation, generation of test data and formal verification of an adder by means of a suitable extension of PROLOG
• Functional Safety analysis of an airbag controller

Keywords

Prior knowledge

Learning objective

Recommended Literature

Language

German

Related Lectures

Recommended Lectures