EENG479 – Prof. Mohab Mangoud Website

EENG479 Digital Signal Processing (DSP)

Course Description:

The course begins with the basics of discrete-time signals and systems, including Fourier analysis, sampling, and quantization. We then move on to digital filter design, covering FIR and IIR filters, filter structures, and design techniques. We also study the Z-transform, which is a powerful tool for analyzing and designing digital filters.

Test 1 15%

test 2 10%-15%

test 3 15%

HW+Lab 10%

Project (Matlab speech noise cancelation) = 10% =5%

Course Scope and outlines

Unit 1: Discrete-Time Signals and Systems

Definition of Discrete-Time Signals
Types of Discrete-Time Signals
Discrete-Time Systems
Linear Time Invariant Systems
Convolution of Discrete-Time Signals and Systems
Overview of Z-Transform and its applications

Unit 2: Design and Analysis of Digital Filters

Introduction to Digital Filters
Types of Digital Filters (FIR and IIR)
Design of Digital Filters using Different Methods (Windowing, Frequency Sampling, etc.)
Analysis of Digital Filters (Stability, Phase, Frequency Response)
Overview of Digital Filter Structures (Direct Form, Cascade Form, Parallel Form, etc.)

Unit 2: Matlab applications and Project

Final project/presentations:
- Students will work on a final project/presentation individually, applying the concepts learned in the course to a real-world application of their choice.

DSP6 DSP6Week # 1 : [M 18 Sept]

1.1 Course Objectives + DSP Introduction (DSP1) + (chapter1)

Week # 2 : [M 25 Sept ]

2.1 Discrete time signals (DSP2)

2.2 Typical sequences and sequence representation

2.3 The sampling Process

Week # 3 : [M 2 Oct]

2.4 Discrete Time systems (DSP3)

2.5 Time Domain characterization of LTI Discrete-Time systems (DSP4)

2.6 Simple interconnection schemes

Week # 4 : [M 9 Oct]

2.7 Finite-Dimensional LTI Discrete time systems (DSP5)

2.8 classification of LTI Discrete time systems

HW1 Submission Due date (W 11 Oct)

Week # 5 : [M 16 Oct]

Chapter (3)/ Discrete time Fourier Transform (DSP6)

Week # 6 : [M 23 Oct]

Z-Transform (DSP7)

inverse Z transform (DSP8)

HW2 Submission (W 24 Oct.)

W: 25 Oct: Test#1 (15%) {HW1 + HW2}

Week # 7 : [M 30 Oct]

Analog Filter Design Review

Digital Filter Design (DSP9-analog Filters)

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Mid-semester break for students: Sunday 5 Nov. – Thursday 9 Nov.

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Week # 8 : [M 13 Nov]

IIR (1) (DSP10_IIR1 )

Week # 9 : [M 20 Nov]

IIR (2) (DSP11_IIR2)

Week # 10 : [M 27 Nov]

IIR (3) .. (DSP12_IIR3)

Week # 11 : [M 4 Dec]

IIR (4)

HW3 Submission (W Dec 6)

Week # 12: [M 11 Dec ]

M: 11 Dec: Test#2 (15%)

FIR (Fixed / adjustable window) I : DSP13_FIR

Week # 13 : [M 18 Dec]

FIR (Fixed / adjustable window) II

Note (M 18 Dec – Official holiday)

HW4 Submission (W 20 Dec.)

Week # 14: [M 25 Dec]

FIR Design using Matlab (DSP14_FIR_Matlab)

W 25 Dec: Test#3 @ Lab (20%)

Week # 15: [M 1 Jan]

Course review and Project submission

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Last day of Classes Thursday 4 Jan 2024

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Academic Calendar : (S1&2-2023-2024-Calendar)

Final Exam : Monday: 8th Jan 2024 (11:30 – 1:30)

Projects Ideas / Examples (Matlab / Python)

Multirate signal processing (Downsampling and upsampling, Filter banks, Multirate filter design)
Adaptive filtering: (LMS algorithm, RLS algorithm, Kalman filter)
Audio Processing:
- Design and implement a noise reduction system for audio signals.
- Implement a system for separating individual voices from a mixed audio signal.
- Design and implement a system for enhancing the quality of a noisy audio signal.
Image Processing:
- Implement a system for enhancing the contrast of an image.
- Design and implement a system for denoising an image.
- Implement a system for edge detection in an image.
Speech Processing:
- Design and implement a speech recognition system.
- Implement a system for speaker identification.