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Course info
KIV / AZS
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Course description
Department/Unit / Abbreviation
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KIV
/
AZS
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Digital Signal Processing and Analysis
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
6
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
3
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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12 / -
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0 / -
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2 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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10
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
Yes
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Fundamental theoretical course |
No
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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KIV/AVD, KIV/SDSZ
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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Aquire basic knowledge about digital signal, its representation and about the methods of signal processing and analysis in time, frequency and time-frequency domains.
Learn basic methods of digital filter design.
Be able to apply present methods of signal processing and analysis on real data.
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Requirements on student
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Credits: written test, students project development
Exam: discussion on students project, examine questions
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Content
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1. Introduction to digital signal processing, signals, systems, application areas of DSP
2. Basic continous time signals and its properties, sampling, quantization,
3. Time domain signal processing, LTI systems, description, impuls response, convolution,
correlation, autocorrelation function
4. Random signals, basic properties and description
5. Z-transform,
6. Frequency domain signal processing, DFT, properties, FFT algorithms
7. Digital filters I: introduction, filter classification, finite-impulse response filter (FIR)
8. Digital filters II: nfinite impulse response filters (IIR), filter design
9. Spectral anlalysis of signals
10. Wavelet transform, properties, application, matching pursuit algorithm
11. Application of digital signal processing methods
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Basic:
Ambardar, Ashok. Digital signal processing : a modern introduction. Toronto : Thomson, 2007. ISBN 0-534-40509-6.
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Recommended:
Jan, Jiří. Číslicová filtrace, analýza a restaurace signálů. 2., upr. a rozš. vyd. V Brně : VUTIUM, 2002. ISBN 80-214-1558-4.
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Recommended:
Davídek, Vratislav; Sovka, Pavel. Číslicové zpracování signálů a implementace. 1. vyd. Praha : ČVUT, 1996. ISBN 80-01-01530-0.
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Recommended:
Sanjit K. Mitra. Digital Signal Processing. A Computer-Based Approach. McGraw-Hill, 2002. ISBN 978-0071226073.
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Recommended:
Orfanidis, Sophocles J. Introduction to signal processing. Upper Saddle River : Prentice Hall, 1996. ISBN 0-13-209172-0.
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Recommended:
S.W. Smith. The Scientist and Engineer's Guide to Digital Signal Processing. California Technical Publishing, 1999.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Contact hours
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65
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Graduate study programme term essay (40-50)
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45
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Preparation for comprehensive test (10-40)
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20
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Preparation for an examination (30-60)
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30
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Total
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160
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
aplikovat na zadaný problém základní znalosti z matematické analýzy,numerické matematiky,pravděpodobnosti a statistiky |
navrhnout řešení daného problému |
algoritmizovat zadaný problém a implementovat tento problém v některém z programovacích jazyků |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
algoritmizovat zadaný problém |
programovat v některém ze základních programovacích jazyků Java, C/C++, Matlab) |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
vysvětlit základní pojmy z oblasti ananlýzy a zpracování signálů |
reprezentovat číslicový signál a provádět základní operace s číslicovými signály |
popsat a klasifikovat jednoduchý lineární a časově invariantní číslicové systémy |
transformovat signály do frekvenční a časově frekvenční oblasti |
stanovit impulsní a frekvenční odezvu lineárních časově invariantních systémů |
navrhovat jednoduché číslicové filtry |
aplikovat uvedené metody při zpracování a analýze reálných signálů |
Skills - skills resulting from the course: |
analyzovat problém z oblasti číslicového zpracování signálů |
navrhnout řešení problému včetně volby vhodných metod pro zpracování signálů |
implementovat problém v některém z programovacích jazyků (Java, C/C++, Matlab) |
analyzovat dosažené výsledky a zhodnotit je |
používat některý z nástrojů vhodných pro zpracování signálů (Matlab, Labview, Octave apod.) |
Competences - competences resulting from the course: |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Test |
Written exam |
Skills - skills achieved by taking this course are verified by the following means: |
Seminar work |
Skills demonstration during practicum |
Competences - competence achieved by taking this course are verified by the following means: |
Skills demonstration during practicum |
Seminar work |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Interactive lecture |
One-to-One tutorial |
Seminar classes |
Skills - the following training methods are used to achieve the required skills: |
Laboratory work |
Practicum |
Competences - the following training methods are used to achieve the required competences: |
Practicum |
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