Coursera

Digital Signal Processing 1: Basic Concepts and Algorithms (Coursera)

Offered by École Polytechnique Fédérale de Lausanne,
Digital Signal Processing 1: Basic Concepts and Algorithms (Coursera)

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.

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In this series of four courses, you will 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.
Course 1 of 4 in the Digital Signal Processing Specialization.
What You Will Learn

  • The nature of discrete-time signals
  • Discrete-time signals are vectors in a vector space
  • Discrete-time signals can be analyzed in the frequency domain via the Fourier transform

Syllabus

WEEK 1
Module 1.1: Digital Signal Processing: the Basics
Introduction to the notation and basics of Digital Signal Processing

WEEK 2
Module 1.2: Signal Processing Meets Vector Space
Modeling signals as vectors in an appropriate vector space. Using linear algebra to express signal manipulations.

WEEK 3
Module 1.3: Fourier Analysis: the Basics
The fundamental concepts behind the Fourier transform and the frequency domain

WEEK 4
Module 1.4: Fourier Analysis: More Advanced Tools
Delving deeper in the world of Fourier analysis.

Go to Class
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