Course detail

# Data Acquisition, Analysis and Processing

The course is dedicated to the analysis of digital signals in time and frequency domain. Emphasis is placed on the orthogonal transformation in particular DFT, fast algorithms FFT, and wavelet transformations. Part of the course is devoted to mathematical perations with time series and digital filtering.

Learning outcomes of the course unit

Student is able to:
- describe the types of physical signals,
- interpret the basic principles of data analysis methods,
- explain the importance of orthogonal transformations and give examples,
- explain the principles of FFT algorithms and methods for time - frequency analysis,
- describe the principles of wavelet transformations and discuss the results,
- explain the results of spectral and cepstral analysis,
- explain the principles of digital signal filtering,
- design a filter with the required properities.

Prerequisites

The student who writes the subject should be discuss the basic terms of signal theory. Generally, the required knowledge of the subjects BMA1, BMA2, knowledge about programming LabVIEW.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Otnes,R.K.-Enochson,L. Applied Time Series Analysis,Wiley (EN)
Rabiner,R.L.-Gold,B. Theory and Application of Digital Signal Processing.,Prentice Hall (EN)
Smith, S.W. Digital Signal Processing. California Technical Publishing, San Diego, California 1999 (EN)

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.
Techning methods include lectures and computer laboratorie.
Students have to write a single project/assignment during the course.

Assesment methods and criteria linked to learning outcomes

up to 30 points for the evaluation computer.
up to 70 points for the final written examination.

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

1. Signal and its properties
2. Time series and its model
3. Linear time-invariant systems, discrete convolultion
4. Discrete correlation, evaluation of dependency phenomena
5. Orthogonal function, discrete Fourier transform
6. Properties of DFT
7. Principles of fast DFT algorithms (FFT)
8. Introduction to digital filters (FIR and IIR)
9. Digital filter design
10. Numerical derivation and integration, data interpolation
11. Spectral analysis, Cepstrum
12. Other orthogonal transformations (Hilbert, Wavelets)
13. Time-frequency analysis (STFT and other)

Aims

The aim of the course is to provide students with an overview and information in digital signal processing. The emphasis is placed to frequency and spectral analysis and digital filtering of signals.

Specification of controlled education, way of implementation and compensation for absences

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Classification of course in study plans

• Programme EEKR-M1 Master's

branch M1-KAM , 1. year of study, winter semester, 6 credits, compulsory

• Programme EEKR-CZV lifelong learning

branch ET-CZV , 1. year of study, winter semester, 6 credits, compulsory

#### Type of course unit

Lecture

26 hours, optionally

Teacher / Lecturer

Laboratory exercise

39 hours, compulsory

Teacher / Lecturer