4 ECTS credits
110 h study time

Offer 1 with catalog number 4019801ENR for all students in the 2nd semester at a (E) Master - advanced level.

Semester
2nd semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Taught in
English
Partnership Agreement
Under interuniversity agreement for degree program
Faculty
Faculteit Ingenieurswetenschappen
Department
Electricity
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 complex dynamical processes. In order to construct these models it is necessary to extract the unknown parameters of these mathematical models from (noisy) measurements. In this course we first learn how to measure the characteristics of a dynamic system (Spectral analysis, frequency response function measurements). Next, we explain how experimental data can be turned into good mathematical models. Each concept is illustrated by a simple example. Besides a sound theoretical basis, we will also provide the students with hands-on experience in the labs.

Outline:

Transfer function measurements:

  • The Fourier based spectrum analyser
    • The front-end of an acquisition channel: Signal conditioning, the anti-alias filter, discretisation in time/amplitude
    • Using the DFT for spectral analysis (Perfect reconstruction, leakage, definition of the bin), and the use of windows to reduce leakage
  • DFT-based network analysis
    • Properties of Linear Time-Invariant systems (Transfer function, Response to periodic excitations)
    • Transfer function estimation using periodic and non-periodic excitation signals, pros's and cons of different excitation signals
    • Design of periodic excitation signals (multisines) for a specified frequency band and frequency resolution
    • Working with non-steady-state measurements: the appearance of the transient term, and transient suppression via non-rectangular windows
    • Noise influence on the estimated transfer function
      • Bias, variance, and asymptotic error of the estimates
      • Reducing the variability of a measured transfer function via different averaging approaches

Data-driven modelling:

  • Basic tools for analysing estimators
    • Stochastic convergence, law of large numbers, central limit theorem, Cramér-Rao lower bound
    • Stochastic properties: consistency, (asymptotic) bias, (asymptotic) covariance, (asymptotic) efficiency, (asymptotic) normality, robustness
  • Linear least squares (LLS)
    • Differences between the cases of noiseless and noisy regression matrix, and related stochastic properties,
    • Noisy regression matrix: how to make the LLS consistent (e.g. compensated least squares, instrumental variables method)
  • Nonlinear least squares and maximum likelihood methods
  • Above estimators in the context of LTI parametric transfer function estimation
  • Neural networks
    • Neural network models: feed forward, recurrent, and convolutional
    • Universal approximation theorem
    • Training via back propagation -- stochastic gradient descent
    • Early stopping
  • Tuning the model complexity: cross-validation techniques and use of penalty terms

Hands-on-experience

  • Five labs will illustrate the different parts of the course with hands-on experiments and Matlab exercises.
Additional info

Prior knowledge

Basic skills are expected in:

  • statistics (sample statistics, distributions, covariance matrix)
  • system theory (impulse response, frequency response, stability)
  • digital signal processing (FFT)
  • measurement techniques (voltages, currents and impedances)

Course Material

  • The study material and lab notes are available on the online study platform (Canvas)
  • Printed lecture notes are available at the student shop of the university
Learning Outcomes

General Competences

General expectation

An independent problem solving and critical attitude are main skills for an engineer and are therefore mandatory for any item treated in this course.

Detailed outcomes Measurement Techniques

To successfully complete the course, the student is expected to

  1. know and understand the Fourier transform, the discrete Fourier transform (DFT), the normalised frequency (bin), the relationship between the measurement length and the frequency resolution of spectral measurements, and the concept of a Frequency Response Function (FRF) of a Linear Time Invariant System
  2. know and be able to explain the importance of the hardware conditions and setup, specifically the generation and acquisition channels, and the influence of noise, for spectral and FRF measurements
  3. understand and apply leakage suppressing techniques in spectral measurements,
  4. estimate the FRF of an LTI system from measured (or simulated) data, in different experimental conditions
  5. be able to design an experimental setup and excitation signal based on the (well-defined) constraints of a given case study, and implement this design to an LTI system on the hardware platform provided in the labs

Detailed outcomes Data-driven modelling

  1. be able to analyse an estimator and determine its stochastic properties
  2. understand and apply the concepts of linear least squares, nonlinear least squares, maximum likelihood, neural network modeling
  3. implement simple estimators of models of linear time invariant dynamic systems
  4. understand and apply the different techniques for tuning the model complexity: validation data, penalty terms, regularization, early stopping

Program learning outcomes

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

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
9. work in an industrial environment with attention to safety, quality assurance, communication and reporting
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.
19. Has a broad overview of the role of electronics, informatics and telecommunications in industry, business and society.
20. Can 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.

Grading

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:

  • Theory with a relative weight of 10 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 10 which comprises 50% of the final mark.

Additional info regarding evaluation

The grade in first session of one or more of the exam parts (Theory measurements, Theory Data-driven modelling, Exam on the labs) may be transferred to 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 Electrical Engineering: Standaard traject BRUFACE J