4 ECTS credits
110 h study time

Offer 1 with catalog number 4019809ENR for all students in the 1st semester at a (E) Master - advanced level.

1st semester
Enrollment based on exam contract
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Taught in
Partnership Agreement
Under interuniversity agreement for degree program
Faculteit Ingenieurswetenschappen
Educational team
John Lataire (course titular)
Activities and contact hours
24 contact hours Lecture
24 contact hours Seminar, Exercises or Practicals
Course Content

General goal of the course 

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.

A selection of advanced data-driven modelling and estimation techniques is discussed. At the end of the course, the students should be able to select the appropriate tool for each problem, understand the influence of the tuneable parameters, and implement them on simple practical problems.

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

Short outline

  • High-frequency Measurements When working with systems that operate at very high-frequencies (~ GHz, e.g. telecom applications), conventional sampling approaches are inadequate to process the data. Mixed analog-digital techniques, in combination with frequency conversions and sub-sampling will be discussed to circumvent this problem.
  • Recursive identification with exponential forgetting factor. This technique allows to perform parametric identification in real-time, as measurement data becomes available gradually.
  • Kalman filtering is a recursive technique, which allows to track the states of systems, based on observations. This is very often used in e.g. navigation. A link between physics based modelling and state-space models will be developed.
  • Gaussian process regression is a Bayesian approach to fit a model to the data. It is often used in machine learning applications, and has the advantage of conveniently mitigating the problem of model complexity selection. This approach will also be applied to obtain improved estimates of FRFs of LTI systems.
  • Measurement and data-driven modelling of LTV systems Although the Linear Time Invariant (LTI) framework is very convenient, it only allows for approximate descriptions of the reality. In this part, we will discuss the tools to detect, quantify and describe the time-varying behaviour of systems. These include the non-parametric estimation Time-varying transfer functions and the parametric estimation of differential equations with time-varying coefficients.

Study Material  

Lecture notes are available that cover the complete material, including the labs. 


  • P. Eykhoff, System Identification, London, John Wiley and Sons, 1974. 
  • G.C. Goodwin and R.L. Payne, Dynamic System Identification. New York, Academic Press, 1977. 
  • L. Ljung, System Identification : theory for the user. Englewood Cliffs, Prentice-Hall, 1987.  
  • J. Schoukens and R. Pintelon, Identification of Linear Systems : A practical guideline for accurate modeling. London, Pergamon Press 1991.  
  • T. Soderstrom and P. Stoica, System Identification, Englewood Cliffs, Prentice-Hall, 1998.  
  • Oran E Brigham , The Fast Fourier Transform, Addison Wesley 
  • Pintelon and Schoukens, System Identification. A frequency domain approach. IEEE press, John Wiley, 2012.  
  • Schoukens, Pintelon, and Rolain.  Mastering system identification in 100 Exercises. IEEE press, John Wiley, 2012. 
  • Morrisson , Solving interference problems in electronics, Wiley
Additional info

Prior knowledge 

  • A basic knowledge of statistics is needed 
  • A basic knowledge of signals and systems theory is needed. Mainly, the Discrete and Continuous Fourier Transforms, the impulse response functions, and transfer functions are to be understood 
  • Measurements of Frequency response functions are understood and can be performed in practice 
  • A basic knowledge in system identification or in data-driven modelling is expected
Learning Outcomes

Algemene competenties

To successfully complete the course, you are expected to master theoretic concepts. 

  • Understand the derivation of the discussed recursive algorithms, and the influence of the associated tuneable parameters
  • Understand the concepts of Bayesian estimators, including the formulation of the prior knowledge and the a posteriori estimate in the case of a Gaussian prior.
  • Understand the formulation of prior knowledge, specific for the gaussian process regression of transfer functions of linear time invariant systems
  • Understand and explain the appearance of time-varying behaviour in engineering applications. 
  • Understand, explain and interpret analogue (high-frequency) spectral measurements

Since for an engineer practical application of the material is crucial, you are also expected to 

  • Construct a recursive estimator for a linear regression model
  • Construct a state-space model for a given simple physical system
  • Construct a Kalman filter for a given state-space model to predict and estimate the states of a system, based on measurements.
  • Independently design and realize a spectral measurement on the hardware platform provided in the labs 
  • Apply the detection and quantification tools for time variations 
  • Implement the non-parametric estimator of the time-varying transfer function
  • Relate practical hardware settings and choices to the theoretic developed concepts 

Factors that determine the judgement 

  • You are critical with respect to your explanations and results 
  • You solve simple, practical problems that are in direct relation to the course 
  • You fluently understand the hypotheses used in the theory and can indicate their importance. 
  • You show some practical measurement experience 
  • You express yourself in a clear, structured way, both in oral and written communication 

This course contributes to the following programme outcomes of the Master in Electronics and Information Technology Engineering:

The Master in Engineering Sciences has in-depth knowledge and understanding of

1. exact sciences with the specificity of their application to engineering
3. the advanced methods and theories to schematize and model complex problems or processes

The Master in Engineering Sciences can

4. reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)
6. correctly report on research or design results in the form of a technical report or in the form of a scientific paper
8. collaborate in a (multidisciplinary) team
9. work in an industrial environment with attention to safety, quality assurance, communication and reporting
10. develop, plan, execute and manage engineering projects at the level of a starting professional
11. think critically about and evaluate projects, systems and processes, particularly when based on incomplete, contradictory and/or redundant information

The Master in Engineering Sciences has

12. a creative, problem-solving, result-driven and evidence-based attitude, aiming at innovation and applicability in industry and society
13. a critical attitude towards one's own results and those of others
16. an attitude of life-long learning as needed for the future development of his/her career

The Master in Electronics and Information Technology Engineering:

17. Has an active knowledge of the theory and applications of electronics, information and communication technology, from component up to system level.
18. Has a profound knowledge of either (i) nano- and opto-electronics and embedded systems, (ii) information and communication technology systems or (iii) measuring, modelling and control.

19. Has a broad overview of the role of electronics, informatics and telecommunications in industry, business and society.
20. Is able to analyze, specify, design, implement, test and evaluate individual electronic devices, components and algorithms, for signal-processing, communication and complex systems.
21. Is able to model, simulate, measure and control electronic components and physical phenomena.


The final grade is composed based on the following categories:
Oral Exam determines 50% of the final mark.
Practical Exam determines 50% of the final mark.

Within the Oral Exam category, the following assignments need to be completed:

  • Exam on the theory with a relative weight of 1 which comprises 50% of the final mark.

Within the Practical Exam category, the following assignments need to be completed:

  • Exam on the labs with a relative weight of 1 which comprises 50% of the final mark.

Additional info regarding evaluation

Oral examination on the complete material. Emphasis is on the understanding of the material, just reproducing without understanding is not sufficient to succeed. 

For some of the labs, a report will be requested. The grading of those labs will be determined from the quality of the report and the oral discussion demonstrating the mastering of the concepts and tools handled in the lab.

The grade in first session of the exam parts (theory or labs) may be transferred to the second session if all following conditions are met:

  • the grade of the part is at least 12/20
  • the student explicitly requests this transfer before the start of the second session exam to the titular of this course.
Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Master of Electronics and Information Technology Engineering: Standaard traject (only offered in Dutch)
Master of Photonics Engineering: On campus traject
Master of Photonics Engineering: Online/Digital traject
Master of Electrical Engineering: Standaard traject BRUFACE J