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Digital Signal Processing 3: Analog vs Digital
  • Level Professional
  • Duration 16 hours
  • Course by École Polytechnique Fédérale de Lausanne
  • Offered by

About

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.

Modules

Welcome to DSP Three!

    • 1 Readings
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1 Readings

  • Welcome to DSP Three!

Lesson 3.1.1: The Continuous-Time World

    • 3 Videos
    • 2 Readings
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3 Videos

  • 3.1.1.a The continuous-time paradigm
  • 3.1.1.b Continuous-time signal processing
  • 3.1.1.c Bandlimited functions

2 Readings

  • Introduction
  • What have we learned?

Lesson 3.1.2: Interpolation

    • 3 Videos
    • 2 Readings
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3 Videos

  • 3.1.2.a Polynomial interpolation
  • 3.1.2.b Local interpolation
  • 3.2.1.c Sinc interpolation

2 Readings

  • Introduction
  • What have we learned?

Lesson 3.1.3: Sampling of Bandlimited Functions

    • 3 Videos
    • 2 Readings
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3 Videos

  • 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

2 Readings

  • Introduction
  • What have we learned?

Assignments

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Homework for Module 3.1

1 Readings

  • Practice homework

Additional Resources

    • 1 Videos
    • 1 Readings
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1 Videos

  • Signal of the Day: Fukushima

1 Readings

  • Further reading

Python Notebooks

    • 1 Plugin
    • 1 Lab
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1 Labs

  • The Fukushima Disaster and Bandlimited Interpolation

Lesson 3.2.1: Sampling of Nonbandlimited Functions

    • 3 Videos
    • 2 Readings
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3 Videos

  • 3.2.1.a Raw sampling
  • 3.2.1.b Sinusoidal aliasing
  • 3.2.1.c Aliasing for arbitrary spectra

2 Readings

  • Introduction
  • What have we learned?

Lesson 3.2.2: Sampling strategies

    • 2 Videos
    • 1 Readings
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2 Videos

  • 3.2.2.a Sampling strategies
  • 3.2.2.b Bandpass sampling

1 Readings

  • Introduction

Assignments

    • 1 Readings
    • 1 Plugin
    • 1 Assignment
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1 Assignment

  • Homework for Module 3.2

1 Readings

  • Practice homework

Lesson 3.3.1: Multirate

    • 3 Videos
    • 2 Readings
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3 Videos

  • 3.3.1.a Upsampling
  • 3.3.1.b Downsampling
  • 3.3.2 FIR-based sampling rate conversion

2 Readings

  • Introduction
  • What have we learned?

Assignments

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Homework for Module 3.4

1 Readings

  • Practice Homework

Python Notebooks

    • 1 Plugin
    • 1 Lab
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1 Labs

  • Rational Sampling Rate Change

Lesson 3.4.1: Quantization

    • 2 Videos
    • 2 Readings
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2 Videos

  • 3.4.1.a Quantization
  • 3.4.1.b Clipping, saturation and companding

2 Readings

  • Introduction
  • What have we learned?

Lecture 3.4.2: Analog-to-Digital and Digital-to-Analog Converters

    • 1 Videos
    • 1 Readings
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1 Videos

  • 3.4.2 Analog-to-digital and digital-to-analog converters

1 Readings

  • Introduction

Lesson 3.4.3 Oversampling

    • 3 Videos
    • 2 Readings
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3 Videos

  • 3.4.3.a Practical sampling and interpolation
  • 3.4.3.b Oversampled D/A
  • 3.4.3.c Oversampled A/D

2 Readings

  • Introduction
  • What have we learned?

Assignments

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Homework for Module 3.4

1 Readings

  • Practice homework for Module 3.4

Additional Resources

    • 2 Videos
Show info about module content

2 Videos

  • MP3 Compression
  • Signal of the Day: Lehman Brothers

Python Notebooks

    • 1 Plugin
    • 1 Lab
Show info about module content

1 Labs

  • One-Bit Music

Auto Summary

Unlock the power of Digital Signal Processing with this comprehensive course, led by Coursera. Delve into the fundamentals of DSP, exploring discrete-time signals, Fourier analysis, filter design, and more. Ideal for professionals with a background in calculus and linear algebra, it features hands-on Python examples. With a duration of 960 minutes, choose between Starter and Professional subscriptions to suit your learning needs. Perfect for those eager to master DSP in the Science & Engineering domain.

Paolo Prandoni
Instructors

Paolo Prandoni

Martin Vetterli
Instructors

Martin Vetterli