Regular courses

Advanced Control Theory - 4 credits

This course describes basic concepts and techniques for the analysis and design of a number of advanced model based controllers: amongst others robust controllers (H-infinity), internal model controllers, and feedforward controllers. To limit the complexity of the maths, the course is limited to the simplest class of systems: linear single input single output systems. Moreover, the course does not put the emphasis on maths but on the hands on experience: design, implementation, and critical evaluation of the advanced controllers. Therefore the course also provides practical insight that is very useful to design and implement the controllers.

Advanced Measurement and Identification - 4 credits

Engineers and scientists build models to understand, describe, predict and control the behaviour of the environment. In order to create these models it is necessary to combine the mathematical models with (noisy) measurements.  

In this advanced course, we will explore the consequences of having systems which do not satisfy the basic assumptions made when making frequency response function measurements. That is, we will learn how to deal with nonlinearities and time variations. 

Also, an introduction is given to the measurement of very high frequency signals and systems, and to the use of recursive identification techniques. In addition to a sound theoretical basis, we will also provide the students with hand on experiences in the labs.

CAE-tools for the Design of Analog Electronic Circuits - 4 credits

Analysis and design of analog electronic circuits is done using computer aided engineering (CAE) tools. The methods used comprise dc analysis, ac analysis, transient analysis, harmonic balance analysis, shooting method, large-signal / small signal analysis,… It is evident that every tool is optimized to analyze a specific type of circuit or analysis. Take for example the transient simulation which is available in SPICE. This transient simulator is not suited for analyzing nonlinear microwave circuits or for the noise analysis in mixers. To solve this problem, simulation techniques such as harmonic balance were developed. Harmonic balance assumes that all signal are either periodic of quasi-periodic. This makes harmonic balance unsuited for non-quasi-periodic signals, but makes the technique superior for the analysis of nonlinear microwave circuit and the noise analysis of mixer.
The aim of the course is to teach the future engineer the pros and contras of the different available simulation tools. This way, he/she should be able to judge which CAE-tool is the most appropriate for solving his/her design or analysis problem. Besides the choice of the analysis tool, there is also the problem of setting the simulation parameters correctly. This requires some background in the actual implementation of the simulations techniques. Hence, a theoretical background of the different simulation techniques must be given in the course. In addition to the theoretical aspect, it is important to have practical experience with the simulation tools. This will lead to a future engineer which does not lose time by simulating with a sub-optimal simulation tool.

Capita selecta Telecom - 3 credits

The content changes year by year since the selected subjects in telecommunication are chosen in function of the industrial expectations and realisations. Subjects treated in the recent years are e.g.: xDSL and ADSL and VDSL2 in particular, GSM (GPRS) networks, Wi-Fi and meshed networks, RFID, UMTS, WiMAX, SACD coding versus Dolby or DTS, GNSS, Tetra, IP-TV, UWB...

Identification of Dynamical Systems - 4 credits

This course describes the various steps one has to go through for obtaining a linear dynamic model of a process. It starts with the choice (design) of the measurement setup, the choice (design) of the excitation signal, the choice of the parametric model (discrete time, continuous time, parametric versus non-parametric noise model...), the estimation of the parametric model (identification toolboxes in Matlab), till finally the model selection and the model validation. Hereby the influence of each error source (stochastic measurement errors, systematic measurement errors, non-linear distortions, time-variant effects, model errors...) on the final result is studied in detail.

Each step is illustrated thoroughly by means of real life examples.

The course starts with the basic techniques necessary to predict the stochastic behaviour of an estimator (consistency, bias, efficiency, probability density function, robustness). Furthermore, the continuous thread throughout the entire course is the formulation of the maximum likelihood solution starting from the measured data. This approach has the advantage that provides the estimated model with an estimate of its uncertainty.

Industrial Measurement Environments - 4 credits

This course discusses some topics in industrial measurement setups and systems. The course concentrates on a general perspective over the field. A selection of the topics can be found below

  • The role of metrology in innovation and in society.
  • Definitions of measurements. Metrology and the meter convention. Role of electrical measurements and overview of their building blocks.
  • Computer controlled measurement systems: evolution and advantages of automated systems in industrial measurements and control
  • Interfaces: electrical lines and interface busses and their most important properties.
  • Interface standards for computer controlled systems: EIA232, IEEE488.
  • Instrumentation controllers and controller software, Software frameworks for instrument control.
  • Industrial control via PLC.
  • Industrial systems based on Ethernet, Hart, DeviceNet, CAN, Fieldbus and PROFIBUS.

Measurement and Identification - 4 credits

Engineers and scientists built models to understand, describe, predict and control the behaviour of the environment. In order to create these models it is necessary to combine the mathematical models with (noisy) measurements. In this course general methods are given in order to obtain good measurements, and to use these data to build a model.

  • Measurement techniques
  • Analog specification of measurement hardware
  • Stochastic characterisation of measurement problems.
  • Computer controlled measurement systems
  • Data based modeling:

What are the challenges in data based modeling?
- Tools for analyzing Estimators
- Linear Least Squares
- Nonlinear Least squares
- Maximum Likelihood Method
- Bayesian Approach
- Neural Networks
- Tuning the Model Complexity

Measuring and Modelling of Nonlinear Systems - 3 credits

Goal: to give an intuitive insight in the behaviour of nonlinear systems. For that purpose we first provide the attendees with a theoretical framework that will be used next to develop a number of tools that can be easily used in practice to characterize nonlinear systems...

Physical Communication - 6 credits

ntroduction to the modulation and coding schemes / detection and estimation of signals.

Multiplexing and Carrier techniques.

  1. Time Division Multiplexing (TDMA)
  2. Multi-carrier Communications: OFDM and MIMO systems (FDMA)
  3. Spread Spectrum Communications.(CDMA)

Physical layer / hardware realizations for

  1. (broadband) wireless communications, including the channel (=propagation) modelling for wireless communication systems.
  2. wired communications systems: elements of Digital Subscriber Loops (xDSL)
  3. optical communication systems

All properties will be demonstrated using wireless standards (WiFi, DAB, Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, LoRa Sensor and Ad Hoc Networks) and wired standard (ADSL, VDSL, ...)

RF/Microwave Design Techniques: from Datasheet to Product - 4 credits

  • System-level architectural analysis of RF/microwave receiver/transmitters.
  • Design of the different RF/microwave sub-systems (LNA, mixer, filters, antenna’s) using commercial software.
  • Analysis of critical building blocks
  1. Simulation using electromagnetic field solvers
  2. Measurement of prototype realization
  • ESD protection of circuit and systems
  • Tolerance and yield analysis / optimization
  1. Monte-Carlo techniques
  2. Worst-case distance methods
  • Realization and measurement of the transmitter/receiver circuit.
  • Reporting on the design

Signal Theory - 4 credits

The general content is as follows:

Introduction to signal and information theory: terminology, elements in a communication system, signal classification: deterministic, stochastic, and periodic signals.

The properties of stochastic signals:.

Estimation of signals in the sense of the least squares.

Study of modulations: modulation-demodulation techniques + power spectra

Information theory.

Development of codes with unequal length and optimized coding.

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