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Signal Processing Curricula - Unlimited Possibilities

Signal-Processing-CurriculaThe modular style used at AllSignalProcessing.com enables a limitless number of ways to proceed through the material. You can use the material as a reference and study topics that you need to learn. Or you can proceed through the lessons in a systematic fashion. The lessons are ordered sequentially so you will naturally acquire the prerequisite knowledge if you follow them in the order provided. Several example signal processing curricula are provided here to illustrate several topical courses of study.

Typical University-Level Undergraduate Signal Processing Curriculum

Most undergraduate signal-processing classes will emphasize sampling and reconstruction of signals, the discrete Fourier transform, system analysis using the z-transform, and frequency selective filter design. This type of class often assumes knowledge of Fourier transforms and some systems theory and emphasizes the mathematical underpinnings of the methods.

Sample Curriculum Undergraduate Signal Processing

Broad Range of Signal Processing Topics with No Prior Background

One of the tenets of AllSignalProcessing.com is that a background in calculus is sufficient to learn signal processing. I regularly teach a course that covers a wide range of topics in signal processing for students with no prior background. We begin with the definition of a signal and end with topics that are typically covered in advanced graduate-level courses. The goal is to become a proficient user of signal processing methods.

Sample Curriculum Broad Range of Topics

Topical Signal Processing Curricula

There are a large number of ways to arrange the lessons to cover specific topics.  Two example topical signal processing curricula are provided here.

Spectral Analysis

If your main goal is to learn signal processing techniques for estimating spectra from data, then this curriculum is for you. It leads you through sampling, the discrete Fourier transform, the role of windows, and methods for estimating power spectra and coherence.

Sample Curriculum Spectral Analysis

Filtering and Filter Design

This sample curriculum is geared toward helping you learn signal processing methods for filtering data and designing filters.

Sample Curriculum Filtering and Filter Design

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Courses

  • Foundations

  • Time Domain LTI Systems

  • Fourier Series and Transforms

  • Sampling and Reconstruction

  • The DFT and Applications

  • The Z-Transform

  • Intro to Filter Design

  • IIR Filter Design

  • FIR Filter Design

  • Random Signal Characterization

  • Basis Representations of Signals

  • Estimation of Power Spectra and Coherence

  • Introduction to Signal Estimation and Detection Theory

  • MMSE Filtering and Least-Squares Problems

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