socratic-software / stochastica

StochasticA is a textbook / website for an “Introduction to Stochastic Signal Processing”. Materials for this website can be found here. Be sure to read the README.md document if you want to know more about the implementation.

StochasticA 26 Laboratory Experiments

StochasticS (iOS) 59 Laboratory Experiments

StochasticS (Android) 59 Laboratory Experiments

StochasticA

Background

This repository contains the iBook / website for Introduction to Stochastic Signal Processing. The short name is StochasticA.

Speech, music, seismic vibrations, oil prices, and climate measurements are all examples of stochastic (random) signals. In this textbook, intended for individuals with prior training in introductory signal processing and introductory probability theory, we develop techniques to process such signals to extract useful information. We present case studies ranging from music to photographic images to oil prices to climate data to the motion of individual biomolecules.

This textbook, as an interactive textbook (iBook), makes use of your device's ability to display dynamic information through images, films, and animations and to hear the results of the techniques applied to music. At the end of every chapter there are homework problems ranging from easy to "olympic".

A new and exciting aspect is that we make use of your device's interactive capabilities to offer 26 laboratory experiments in signal processing. For the web-based implementation in this repository, the experiments use the speakers, display, and the graphical user interface (GUI). For a tablet and smartphone implementation that can be found here, this is extended to use the camera and the microphone providing a total of 59 laboratory experiments.

These experiments are not simulations; they are examples of real digital processing of signals in your device. In this time of at-home and online learning, this is the way to learn signal processing through study and experimentation.

IPR

Unless otherwise specified, html files are copyrighted and do not fall under the MIT License. Javascript (js) files do, in general, fall under the MIT License unless explicitly stated otherwise.

Privacy Policy

Our Privacy Policy can be found here

Chapter Contents

1. How to use this iBook
2. Prologue
3. Introduction
4. Characterization of Random Signals
5. Correlations and Spectra
6. Filtering of Stochastic Signals
7. The Langevin Equation — A Case Study
8. Characterizing Signal-to-Noise Ratios
9. The Matched Filter
10. The Wiener filter
11. Aspects of Estimation
12. Spectral Estimation
13. Appendices
14. Information

Last revision this document: 25 February 2022