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Digital Signal Processing 3: Analog vs Digital

Approx. 16 hours to complete

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Course Summary

This course covers advanced topics in digital signal processing, including adaptive filters, multirate processing, and statistical signal processing.

Key Learning Points

Learn how to design and implement adaptive filters for noise reduction and channel equalization.
Understand the principles of multirate processing and how to apply it to digital signal processing systems.
Gain knowledge of statistical signal processing and its applications in speech and image processing.

Learning Outcomes

Ability to design and implement adaptive filters for noise reduction and channel equalization
Understanding of multirate processing and its applications in digital signal processing systems
Knowledge of statistical signal processing and its applications in speech and image processing

Prerequisites or good to have knowledge before taking this course

Basic knowledge of digital signal processing
Familiarity with MATLAB or equivalent programming language

Course Difficulty Level

Advanced

Course Format

Online
Self-paced

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Notable People in This Field

Professor of Electrical and Computer Engineering
Professor of Electrical Engineering and Computer Science

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Description

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices.

The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.

Knowledge

The difference between continuous and discrete time
Sampling and interpolation
Quantization, A/D and D/A converters
Multirate signal processing

Outline

Module 3.1: Interpolation and Sampling
3.1.1.a The continuous-time paradigm
3.1.1.b Continuous-time signal processing
3.1.1.c Bandlimited functions
3.1.2.a Polynomial interpolation
3.1.2.b Local interpolation
3.2.1.c Sinc interpolation
3.1.3.a The spectrum of interpolated signals
3.1.3.b The space of bandlimited functions
3.1.3.c The sampling theorem
Signal of the Day: Fukushima
Welcome to DSP Three!
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
What have we learned?
Practice homework
Further reading
Homework for Module 3.1

Module 3.2: Aliasing
3.2.1.a Raw sampling
3.2.1.b Sinusoidal aliasing
3.2.1.c Aliasing for arbitrary spectra
3.2.2.a Sampling strategies
3.2.2.b Bandpass sampling
Introduction
What have we learned?
Introduction
Practice homework
Homework for Module 3.2

Module 3.3: Multirate Signal Processing
3.3.1.a Upsampling
3.3.1.b Downsampling
3.3.2 FIR-based sampling rate conversion
Introduction
What have we learned?
Practice Homework
Homework for Module 3.4

Module 3:4: A/D and D/A Conversion
3.4.1.a Quantization
3.4.1.b Clipping, saturation and companding
3.4.2 Analog-to-digital and digital-to-analog converters
3.4.3.a Practical sampling and interpolation
3.4.3.b Oversampled D/A
3.4.3.c Oversampled A/D
MP3 Compression
Signal of the Day: Lehman Brothers
Introduction
What have we learned?
Introduction
Introduction
What have we learned?
Practice homework for Module 3.4
Homework for Module 3.4

Summary of User Reviews

Learn Digital Signal Processing from top-rated instructors. This course is highly recommended by many students who have found it to be comprehensive and engaging. The course covers a range of topics and provides a good balance of theory and practice.

Key Aspect Users Liked About This Course

Many users appreciated the clear and concise explanations provided by the instructors.

Pros from User Reviews

Comprehensive course content
Engaging lectures and exercises
Highly knowledgeable instructors
Good balance of theory and practice
Great value for the price

Cons from User Reviews

Some users found the pace of the course to be too fast
The course could benefit from more hands-on projects
Occasional technical issues with video lectures
The course may not be suitable for beginners
Some users found the quizzes to be too challenging

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