Title:  Signals and Systems 

Code:  ISS 

Ac.Year:  2018/2019 

Sem:  Winter 

Curriculums:  

Language of Instruction:  Czech 

Public info:  http://www.fit.vutbr.cz/study/courses/ISS/public/ 

Credits:  6 

Completion:  examination (written) 

Type of instruction:  Hour/sem  Lectures  Seminar Exercises  Laboratory Exercises  Computer Exercises  Other 

Hours:  39  12  0  0  14 

 Exams  Tests  Exercises  Laboratories  Other 

Points:  51  25  0  12  12 



Guarantor:  Černocký Jan, doc. Dr. Ing. (DCGM) 

Deputy guarantor:  Burget Lukáš, doc. Ing., Ph.D. (DCGM) 

Lecturer:  Černocký Jan, doc. Dr. Ing. (DCGM) 
Instructor:  Beneš Karel, Ing. (DCGM) Grézl František, Ing., Ph.D. (DCGM) Kodym Oldřich, Ing. (DCGM) Landini Federico Nicolás (DCGM) Mošner Ladislav, Ing. (DCGM) Silnova Anna (DCGM) Skácel Miroslav, Ing. (DCGM) Žmolíková Kateřina, Ing. (DCGM) 

Faculty:  Faculty of Information Technology BUT 

Department:  Department of Computer Graphics and Multimedia FIT BUT 

Prerequisites:  

Schedule: 



Learning objectives: 

  To learn and understand basic theory of signals and linear systems with
continuous and discrete time. To introduce to random signals. The
emphasis of the course is on spectral analysis and linear filtering  two basic building blocks of modern communication and machine learning systems. 
Description: 

  Continuous and discrete time signals and systems. Spectral analysis in
continuous time  Fourier series and Fourier transform. Systems with
continuous time. Sampling and reconstruction. Discretetime signals and
their frequency analysis: Discrete Fourier series and Discretetime
Fourier transform. Discrete systems. Twodimensional signals and
systems. Random signals. 
Knowledge and skills required for the course: 

  basic maths and statistics 
Subject specific learning outcomes and competencies: 

  Students will learn and understand basis of description and
analysis of discrete and continuoustime signals and systems. They will
also obtain practical skills in analysis and filtering in MATLAB/Octave. 
Generic learning outcomes and competencies: 

  Students will deepen their knowledge in mathematics and statistics and
apply it on real problems of signal processing. 
Why is the course taught: 

  Probably anyone has already placed a call from a cellphone. Probably everyone took a picture and stored it in JPG format. The algorithms of digital signal processing can be found behind both applications  filtering (in case of a mobile codec its for example a filter that changes its characteristics every 20 milliseconds depending on your voice) and spectral analysis (in JPG image encoding, little squares of 8x8 pixels are compared with cosine signals with different speeds). Both examples are however only a miniscule part of a vast number of signal and data processing applications, all around us  from commanding the ABS system in your car to satellite communications. Moreover, signal processing is an important component of machine learning (also called "artificial intelligence") that is nowadays influencing almost all sectors of economy and normal life. ISS won't teach you everything, but it will give you solid mathematical bases and intuition to build upon. 
Syllabus of lectures: 

  Digital filters  fundamentals and
practical usage
 Frequency analysis using DFT  fundamentals
and practical usage
 Image processing (2D signals)  fundamentals
and practical usage
 Random signals  fundamentals and
practical usage
 Applications of signal processing and
introduction to theory
 Frequency analysis of continuous time
signals
 Continuous time systems
 From continuous to discrete  sampling,
quantization
 Discrete signal sin more detail
 Spectral analysis of discrete signals in more detail.
 Digital filtering in more detail
 Random signals in more detail
 Applications and advanced topics of signal
processing

Syllabus of numerical exercises: 

  Complex numbers, cosines and complex exponentials and operations therewith
 Basics, filtering, frequency analysis
 Continuous time signals: energy, power, Fourier series, Fourier transform
 Continuous time systems and sampling
 Operations with discrete signals, convolutions, DTFT, DFT
 Digital filtering and random signals

Syllabus  others, projects and individual work of students: 

 The project aims at practical experience with signals and systems in Matlab/Octave. Its study etap contains solved exercises on the following topics:
 Introduction to MATLAB
 Projection onto basis, Fourier series
 Processing of sounds
 Image processing
 Random signals
 Sampling, quantization and aliasing
The project itself follows with an individual signal for each student, see http://www.fit.vutbr.cz/study/courses/ISS/public/#proj 
Fundamental literature: 


 Oppenheim A.V., Wilski A.S.: Signals and systems, Prentice Hall, 1997

Study literature: 

  http://www.fit.vutbr.cz/study/courses/ISS/public/
 Jan, J., Kozumplík, J.: Systémy, procesy a signály. Skriptum VUT v Brně, VUTIUM, 2000.
 Šebesta V.: Systémy, procesy a signály I., Skriptum VUT v Brně, VUTIUM, 1997.
 Jan J., Číslicová filtrace, analýza a restaurace signálů, VUT v Brně, VUTIUM, 2002, ISBN 8021415584.

Controlled instruction: 

   participation in numerical exercises is not checked, but tests are conducted in them, each worth 2 points.
 Groups in numerical exercises are organized according to
inscription into schedule frames.
 Replacing missed exercises (and obtaining the points) is possible by (1) attending the exercise and the test with another group, (2) solving all tasks in given assignment and presenting them to the tutor, (3) examination by the tutor or course responsible after an appointment. Options (2) and (3) are valid max. 14 days after the missed exercises, not retroactively at the end of the course.

Progress assessment: 

   6 tests in numerical exercises, each 2 pts, total 12 pts.
 halfsemester exam, written materials, computers and calculators prohibited, 25 pts.
 submission of project report  12 pts.
 final exam  51 pts., written materials, computers and calculators prohibited, list of basic equations will be at your disposal. The minimal number of points which can be obtained from the final
exam is 17. Otherwise, no points will be assigned to the student.

