Text for all my DSP courses: (DSPCSP)
Digital Signal Processing - A Computer Science Perspective
(Published by John Wiley, Sept 2000)
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There are ten copies in the library.
DSP Algorithms and Applications 0368.3464
First Semester 2020-21, Sundays 17:00-20:00, on-line lecture (ZOOM)
LECTURE ATTENDANCE IS MANDATORY! Course syllabusCourse policy
[Oct 18] Motivation, What is a signal ? (sections 2.1-2.4)
[Oct 25] Signal arithmetic (2.4), Signal space (2.5), Fourier series (3.4), Fourier demo,
Time and frequency domains (sections 2.6, 3.2, 4.1),
Complex exponentials and negative frequencies (3.6),
negative frequency demo, DFT (4.7)
[Nov 08] Systems (6.1-6.4), Filters (6.5), Example filters : LP/HP/BP/BS/notch (7.1), MA filters (6.6), Convolution (6.8),
MA filters in the frequency domain (6.7)
[Nov 15] AR filters (6.9), FIR and IIR filters (6.12), System identification - Easy case (6.12)
First Semester 2019-20, Sundays 17:00-20:00, Dan David 003
LECTURE ATTENDANCE IS MANDATORY! Course syllabusCourse policy
Course over!
Lectures (readings )
[Oct 27] Motivation, What is a signal ? (section 2.1-2.4)
[Nov 3] Signal arithmetic (2.4), Signal space (2.5), Fourier series (3.4), Fourier demo,
Time and frequency domains (sections 2.6, 3.2, 4.1),
Complex exponentials and negative frequencies (3.6),
negative frequency demo, DFT (4.7)
[Nov 17] z Transform (4.10), Systems (6.1-6.4), Filters (6.5), Example filters : LP/HP/BP/BS/notch (7.1)
[Nov 24] Exercise - zT, MA filters (6.6), Convolution (6.8), IR and FR,
MA filters in the frequency domain (6.7), AR filters (6.9),
Filter design (7.7), FIR and IIR filters (6.12),
System identification - Easy case (6.12)
System identification - Hard case (6.13)
[Dec 1] Exercises - finding the IR and FR of MA and AR filters,
Filters in the z domain (6.14, 7.5), Pole-zero plots (7.6)
First Semester 2018-19, Sundays 17:00-20:00, Lev Auditorium
LECTURE ATTENDANCE IS MANDATORY! Course syllabusCourse policy
course over!
Lectures (readings )
[Oct 14] Motivation, What is a signal ? (section 2.1-2.4), signal space (2.5)
[Oct 21] Fourier series (3.4), Fourier demo,
Time and frequency domains (sections 2.6, 3.2, 4.1),
Complex exponentials and negative frequencies (3.6),
negative frequency demo,
DFT (4.7)
[Oct 28] The sampling theorem (2.8), wagon-wheel demo,
Hilbert Transform (4.12),
Uncertainty theorem (4.4), z Transform (4.10), Systems (6.1)
[Nov 04] Systems (6.1-6.4), Filters (6.5), Example filters : LP/HP/BP/BS/notch (7.1),
Convolution (6.8), IR and FR, MA filters (6.6),
MA filters in the frequency domain (6.7), AR filters (6.9),
System identification - Easy case (6.12)
First Semester 2017-18, Sundays 17:00-20:00, Lev Auditorium
LECTURE ATTENDANCE IS MANDATORY! Course syllabusCourse policy
Course over!
Lectures (readings )
[Oct 22] Motivation, What is a signal ? (section 2.1-2.4)
[Oct 29] Signal space (2.5),
Fourier demo,
Fourier series (3.4),
Time and frequency domains (sections 2.6, 3.2, 4.1),
Complex exponentials and negative frequencies (3.6),
negative frequency demo,
DFT (4.7)
[Nov 19] Systems (6.1-6.4), Filters (6.5), Example filters : LP/HP/BP/BS/notch (7.1),
Convolution (6.8), Moving averages (6.6), AR filters (6.9),
started system identification
[Nov 26] Filters - practice session,
system identification (6.12,6.13),
Filters in the frequency domain (6.7,6.9), filter design (7.7),
FIR and IIR filters (6.12),
Filters in the z domain (6.14, 7.5), Pole-zero plots (7.6),
filter parameter conversions
[Oct 30] Motivation, What is a signal ? (section 2.1-2.3)
[Nov 06] Signals (sections 2.2-4), Signal space (2.5),
Fourier demo,
Spectrum (4.1)
[Nov 13] Fourier series (3.4),
Complex exponentials and negative frequencies (3.6),
negative frequency demo,
The sampling theorem (2.8), wagon-wheel demo,
Time and frequency domains (sections 2.6, 3.2),
DFT (4.7)
[Nov 20] Hilbert Transform (4.12), The uncertainty theorem (4.4), z Transform (4.10)
[Nov 27] Systems (6.1-6.4), Filters (6.5), Example filters : LP/HP/BP/BS/notch (7.1),
Moving averages (6.6), AR filters (6.9)
[Dec 04] Convolution (6.8), Filters in the frequency domain (6.7,6.9), filter design (7.7),
system identification (6.12,6.13)
First Semester 2015-16, Sundays 17:00-19:30 (only 1 break), Dan David 001
LECTURE ATTENDANCE IS MANDATORY! Course syllabusCourse policy
Course over !
Lectures (readings )
[Oct 18] Motivation, What is a signal ? (section 2.1-2.3)
[Oct 25] Signals (sections 2.2-4), Signal space (2.5), Fourier demo, Fourier series (3.4)
[Nov 01] Complex exponentials and negative frequencies (3.6),
negative frequency demo,
The sampling theorem (2.8), wagon-wheel demo,
Spectrum (4.1), Time and frequency domains (sections 2.6, 3.2), DFT (4.7)
[Nov 08] Hilbert Transform (4.12), The uncertainty theorem (4.4), z Transform (4.10), Systems (6.1-6.4)
[Nov 15] Filters (6.5), Convolution (6.8), Filters - LP/HP/BP/notch (7.1), Moving averages (6.6), AR filters (6.9), Filters in the frequency domain (6.7,6.9), filter design (7.7)
Seminar in DSP Algorithms and Applications 0368-3328
Second Semester 2015, Sundays 17:00-19:00, Dan David 204
Each participant in the seminar will present a DSP algorithm or application.
Each presentation will include a software demonstration.
Topics and dates will be available on a first-come/first-served basis.
Subject matter must be coordinated with the instructor.
Course grade will depend both on depth of understanding, clarity of presentation, and creativity.