1. SylabUZ
2. Faculty of Computer Science, Electrical Engineering and Automatics
3. winter term 2022/2023
4. Electrical Engineering - Second-cycle Erasmus programme
5. Signals parameters identification methods

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Signals parameters identification methods - course description

Aim of the course

• To provide the knowledge content of the issues related to signals represented in the time and the frequency domain.
• To familiarize students with modern methods of the signal parameters identification.
• To give skills in practical implementation the selected methods in a selected programming environment including the digital signal processing algorithms.
• To develop skills in analyze and evaluate the results of the signal parameters estimation.

Prerequisites

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• Basic knowledge of digital signal processing.
• Elementary programming skills in C language.

Scope

1. Notion of signal. Classifications of signals. Signal parameters.
2. Time domain signal representations. Analog-to-digital processing. Sampling. Sampling theorem. Signal recovery from samples. Quantization and quantization with the dither signal. The quantization error.
3. Frequency domain signal representation. Signal spectrum for continuous and discrete time. Discrete Fourier Transform (DFT). Determination of complex, amplitude and phase spectrum of signals using DFT. Spectral leakage. Resampling. Window functions.
4. Estimator definition. Estimation errors. The systematic and random errors. The mean squared error.
5. Discrete time and continuous time methods. Instantaneous methods. Threshold methods. Correlation methods. Digital filters. Averaging signals in the time domain. Monte Carlo method. Bayesian inference. Optimization methods.
6. Spectral methods. DFT interpolation methods (IpDFT). Autocorrelation in the frequency domain. Digital filters. Averaging signals in the frequency domain. Cepstral method. Maximum likelihood methods.

Teaching methods

• Lecture: conventional/traditional lecture with elements of discussion.
• Laboratory: laboratory exercises, work in groups with elements of discussion.

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

• Lecture: to receive a final passing grade student has to receive positive grade from written tests conducted at least once a semester.
• Laboratory: to receive a final passing grade student has to receive positive grades in all laboratory exercises provided for in the laboratory syllabus.

Calculation of the final grade = lecture 45% + laboratory 55%.

Recommended reading

1. Lyons R.G.: Understanding Digital Signal Processing, Prentice Hall, 2004.
2. Proakis J.G., Manolakis D.G.:Digital Signal Processing: Principles, Algorithms, and Applications, Prentice-Hall, 2007.
3. Powell R.: Introduction to Electric Circuits, Hodder Headline Group, 1995.

Further reading

1. Oppenheim A.V., Willsky A.S., Nawab H.: Signals & Systems, Prentice Hall, 1997.
2. Owen M.: Practical signal processing, Cambridge University Press, 2007.
3. Smith S.W.: Digital Signal Processing: A Practical Guide for Engineers and Scientists, Newnes, 2002.
4. Mitra S.: Digital Signal Processing: A Computer-Based Approach, McGraw-Hill, 2005.

Notes

Modified by dr hab. inż. Paweł Szcześniak, prof. UZ (last modification: 06-04-2022 22:33)