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Instructor's Guide

Welcome to the instructor's guide for Signal Processing with AI: A College-Level Intelligent Textbook. This guide explains what is in the textbook, how to use each part of it in a course, and how to make your own customized copy. No prior technical knowledge is assumed. Every technical term is defined before it is used.

Before You Teach

Read this first: what this textbook is and is not

The author describes this site as a proof-of-concept. The About page states plainly that it is not a complete course on signal processing. It is a course that shows how generative AI can be used to create and maintain signal processing content.

Much of the content was generated with generative AI. The site's own Report Card gives "Mathematical and Technical Rigor" the lowest of its five summary grades (C+), and names Z-transforms and statistical signal processing as the areas most likely to contain subtle errors. Before you assign material from Chapter 8 (Advanced Transforms) or Chapter 12 (Stochastic Processes), check the derivations against a standard textbook. Review quiz questions before using them for graded work: the Report Card also notes that AI-written quizzes can have weak distractors (the wrong answer choices).

Things this textbook does not include, so you can plan for them:

  • Problem sets and answer keys. The chapters have no exercise sections. The FAQ describes weekly problem sets, a midterm project, and a capstone, but those assignments are for the instructor to write.
  • Per-chapter reading lists. There is one site-level References page, which lists the tools used to build the site, not signal processing readings.
  • Automatic grading or student tracking. Quizzes are self-check only (see Using the Quizzes).

About This Intelligent Textbook

What is an Intelligent Textbook?

An intelligent textbook is a digital textbook that goes beyond static text and images. It includes interactive simulations, self-checking quizzes, a searchable glossary, and a structured map of how concepts depend on each other. The goal is a richer learning experience than a printed book or a PDF can give.

The Five Levels of Intelligent Textbooks

Intelligent textbooks are grouped into five levels based on how interactive and adaptive they are:

Level Name Description Example Features
Level 1 Static Digital A PDF or basic web version of a print textbook Text and images only
Level 2 Interactive Adds interactive elements MicroSims, quizzes, searchable glossary
Level 3 Adaptive Adjusts content based on student performance Personalized learning paths
Level 4 AI-Assisted Includes an AI tutor Chatbot integration, automated feedback
Level 5 Fully Adaptive AI Continuously learns from student interactions Real-time content generation

This textbook is a Level 2 Interactive Intelligent Textbook. It has simulations, quizzes, a glossary, a FAQ, and a learning graph. It does not adapt to individual students and does not include an AI tutor.

What Makes This Textbook Different

  • Interactive MicroSims let students change a parameter in their browser and watch the signal respond. No software installation is required.
  • A learning graph of 200 concepts and 262 prerequisite links shows how every idea in the book connects to the others.
  • AI-assisted creation is part of the lesson. The course description includes learning objectives about using generative AI to explore, build, and test signal processing ideas (objectives 7, 9, 16, and 18).
  • Free and open source. The whole site is licensed under Creative Commons (see Understanding the License) and is designed to be forked and customized.

The Course at a Glance

Item Details
Audience College students with varied mathematics backgrounds
Credits 3 (the FAQ describes a 15-week semester)
Prerequisites Introductory electrical engineering or physics, basic calculus and linear algebra, programming basics in Python or MATLAB
Chapters 15, each with its own quiz
Concepts 200 (from the learning graph)
Interactive MicroSims 31 (22 on signal processing topics, 9 about course design)
Quiz questions 150 (10 per chapter)
Glossary terms 141
FAQ questions 87
Chapter text About 66,000 words, 4,000 to 5,000 words per chapter

The Course Description lists the 50 topics covered, the topics deliberately left out, and 18 learning objectives organized by Bloom's Taxonomy.

Using the Chapters

Chapter Map

The chapters build on each other, so students should work through them in order.

Chapters Topic Area Concepts
1 Mathematical foundations (complex numbers, linear algebra, calculus, probability, trigonometry) 25
2-3 Signals and systems (classification and operations of signals; linearity, time-invariance, causality, stability) 45
4 Convolution and correlation 10
5 Sampling and quantization (Nyquist criterion, aliasing) 15
6-7 Fourier analysis (series, transforms, DFT, FFT, windowing, spectral analysis) 20
8 Advanced transforms (Laplace, Z-transform, pole-zero analysis, wavelets, short-time Fourier transform) 15
9-10 Filter design (FIR and IIR filters, classical approximations, implementation) 25
11 Adaptive signal processing (LMS and RLS, noise cancellation, equalization) 10
12 Stochastic processes and random signals (noise, power spectral density) 10
13 Multirate signal processing and compression 10
14 Time-frequency analysis and advanced topics (spectrograms, non-stationary signals) 5
15 Applications and AI integration (DSP hardware, audio, image, video, AI-driven analysis) 10

The Chapters overview groups these into foundations (1-5), core topics (6-10), advanced topics (11-14), and applications (15).

What Each Chapter Contains

Chapters have a consistent skeleton:

  1. Summary. A short overview of what the chapter covers.
  2. Concepts Covered. A numbered list of the chapter's concepts. The numbers match the concept numbers in the learning graph.
  3. Prerequisites. Links to the earlier chapters the material builds on. Chapter 1 assumes only the prerequisites in the course description.
  4. Introduction. Motivation and context.
  5. Topic sections. One section per topic, with a subsection for each concept. Equations are written in LaTeX and rendered by a math library called MathJax.
  6. Interactive Demonstrations (six chapters). Collapsible MicroSim specifications. See MicroSim specifications in chapters.
  7. Comparison Tables (six chapters). Side-by-side summaries of related methods.
  8. Summary. A closing recap. Chapter 15 ends with "Summary and Looking Forward".

Each chapter has a separate Quiz page, listed directly under it in the left navigation.

Suggested 15-Week Pacing

The chapters vary in length (5 to 25 concepts each) but not in word count. One chapter per week fits a 15-week semester. This is a starting point, not a requirement.

Week Chapter Note
1 1 - Mathematical Foundations May be review for students who have completed the prerequisites
2 2 - Signals and Systems 25 concepts: introduce vocabulary quickly
3 3 - System Properties
4 4 - Convolution and Correlation Use the Convolution MicroSim
5 5 - Sampling and Quantization Use the Nyquist-Shannon Sampling MicroSim
6 6 - Fourier Analysis Pair with the FFT MicroSims
7 7 - DFT, FFT and Frequency Domain Analysis Natural place for a midterm
8 8 - Advanced Transforms Verify derivations first (see warning above)
9 9 - Filter Design Fundamentals
10 10 - Advanced Filter Design
11 11 - Adaptive Signal Processing
12 12 - Stochastic Processes Verify derivations first
13 13 - Multirate and Compression
14 14 - Time-Frequency Analysis Only 5 concepts: room to start the capstone
15 15 - Applications and AI Integration Capstone presentations

Suggested Classroom Use

  • Before class: Assign the chapter's Summary, Concepts Covered, and Introduction as reading, and have students look up unfamiliar terms in the glossary.
  • During class: Project a MicroSim. Ask students to predict what will happen when you move a slider, then test the prediction.
  • After class: Assign the chapter quiz as a self-check, then your own problem set.
  • Pacing: The FAQ estimates about 9 hours per week for students: 3 lecture, 2 lab, and 4 independent study.

Using the MicroSims

What is a MicroSim?

A MicroSim (short for "micro-simulation") is a small interactive simulation that runs in a web browser. Students change a value with a slider or button and immediately see the effect. MicroSims work on any device with a modern browser (Chrome, Firefox, Safari, or Edge) and require no installation.

Where to Find Them

All working MicroSims are listed under MicroSims in the left navigation and on the MicroSims overview page. They are built mostly with p5.js, a JavaScript library for drawing and animation, plus vis-network for graph diagrams.

Signal processing MicroSims (22):

Topic MicroSims
Math foundations Complex Plane, Euler's Formula Explorer, Mobius Transformation
Waves and tones Sine Wave, Tone Generator
Sampling Nyquist-Shannon Sampling
Convolution Convolution
Frequency analysis Basic FFT, FFT 2 Oscillators, FFT 3 Oscillators, FFT Waveform Types, FFT Size, FFT Butterfly, Frequency Detection
Live audio FFT From Microphone, FFT Live Audio with Parameters, FFT Sound File
Filters and effects RC Lowpass Filter, Base and Treble, Reverb Effect
Communications Analog Modulation
History Synth Timeline

Course-design MicroSims (9): AI Pace, Bloom's Taxonomy, Book Generation Workflow, Book Levels, Citation Graph, Learning Graph Viewer, Learning Graph V1, Timeline Viewer, and Word Cloud. These explain how the book was designed and are useful for a unit on AI in education.

Preview each MicroSim before class

Several MicroSim folders still contain a TODO.md file, which means work is in progress. Open each simulation you plan to use ahead of time. The FFT From Microphone MicroSim asks your browser for permission to use the microphone, and the audio MicroSims may need a click before they will play sound.

MicroSim Specifications in Chapters

Only Chapter 1 embeds running simulations (Complex Plane and Euler's Formula Explorer). In the other chapters, the Interactive Demonstrations sections contain collapsible blocks labelled "MicroSim: ..." (21 in total). Each block is a specification: a Purpose, a list of Features, Learning Objectives, and Implementation Requirements. They describe a simulation that could be built, and not all of them have been built.

The specifications have two uses:

  • Lesson planning. They state what each simulation should teach.
  • Student projects. Learning objectives 16 and 17 in the course description ask students to design algorithms and develop simulations, and objective 18 asks for projects that show how generative AI can help. A specification is a ready-made starting prompt for that kind of assignment.

Tips for Using MicroSims in Class

  1. Project them on a screen. Have students call out predictions before you move a slider.
  2. Give independent time. After a demonstration, let students experiment for 5-10 minutes on their own devices.
  3. Connect to the text. After exploring a simulation, have students re-read the matching chapter section.
  4. Try fullscreen. Some MicroSim pages have a "Run ... Fullscreen" button that opens the simulation at full size, which is easier to read on a projector.
  5. Check your connection. MicroSims load from the website, so they need an internet connection unless you build the site locally (see Customizing Your Own Textbook).

Embedding a MicroSim in Your Own Web Page

Embed MicroSims anywhere

You can add any MicroSim to any web page, such as a Google Site, a WordPress blog, a Canvas or Schoology page, or a plain HTML file. Paste one line of HTML:

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<iframe src="https://dmccreary.github.io/signal-processing/sims/YOUR-MICROSIM-NAME/main.html"
    width="100%" height="450px"
    scrolling="no">
</iframe>

Replace YOUR-MICROSIM-NAME with the folder name shown in your browser's address bar when you open the MicroSim page. An iframe is a web technology that displays one web page inside another.

A few MicroSims use a differently named HTML file, such as rc-lowpass-filter.html, instead of main.html. If the embed is blank, look in that MicroSim's folder under docs/sims/ in the GitHub repository to find the HTML file name and use it in place of main.html.

Using the Quizzes

What are the Quizzes?

Each chapter has a quiz page of 10 multiple-choice questions, 150 in total. Each question has four options. Students click Show Answer to reveal the correct answer, an explanation, the Concept Tested, and a link back to the chapter section that covers it.

How Quizzes Work

  • Open a quiz from the Quiz link under each chapter in the left navigation.
  • Quizzes are self-check tools. Nothing is scored, saved, or reported to you.
  • Questions are not labelled by cognitive level. The FAQ describes the quizzes as aligned with Bloom's Taxonomy, so read the questions to see which levels they actually reach.

Tips for Using Quizzes in Class

  • Exit ticket. Have students take the quiz at the end of a session.
  • Diagnostic. Assign the quiz before the chapter to see what students already know.
  • Peer discussion. Have pairs argue for an answer before revealing it.
  • Question bank. Copy questions into your own LMS to build graded tests. The license allows this (see below). Review each question first, as noted at the top of this guide.

Bloom's Taxonomy

Bloom's Taxonomy is a framework that classifies thinking skills from simple to complex. The course description's 18 learning objectives are organized with three at each level.

Level Name What it means Example verbs
L1 Remember Recall facts and definitions Define, list, name
L2 Understand Explain concepts in your own words Explain, describe, summarize
L3 Apply Use concepts to solve problems Use, implement, calculate
L4 Analyze Break down and examine relationships Differentiate, examine, interpret
L5 Evaluate Make judgments against criteria Assess, compare, critique
L6 Create Produce original work Design, develop, construct

The Bloom's Taxonomy MicroSim shows this visually. Because the chapters contain no exercises, you will need to write L5 and L6 assignments yourself.

Using the Glossary

The glossary is an alphabetical list of 141 terms. Each entry has a definition, and most include an example of how students meet the term in the course.

  • Find a term: Use your browser's find command (Ctrl+F or Cmd+F) on the glossary page, or use the site search bar at the top of any page.
  • Vocabulary preview: Before a chapter, have students look up its key terms.
  • Student-written definitions: Students write their own definition after reading, then compare it with the glossary.
  • Matching warm-up: Give students definitions and have them match the terms.

Using the FAQ

The FAQ has 87 questions in six groups:

Group Example questions
Getting Started What is this course about? What prerequisites do I need?
Core Concepts What is a signal? What is the FFT?
Technical Detail DFT versus FFT, window functions, the Laplace transform, poles and zeros
Common Challenges Why does my FFT output look wrong? Why is my filter unstable?
Best Practices How do I design a low-pass filter? How should I preprocess signals?
Advanced Topics Machine learning, convolutional neural networks, time-frequency analysis

Use FAQ questions as discussion starters, as homework support outside class hours, or as a study guide before an exam.

The Learning Graph

What is a Learning Graph?

A learning graph is a map of how concepts depend on each other. It is a directed acyclic graph (DAG): a set of concepts (nodes) joined by arrows (edges) that point from a prerequisite to the concept that needs it, with no loops. This book's graph has 200 concepts, 262 dependencies, and 12 categories:

Mathematical Foundations, Signal Fundamentals, System Properties, Convolution and Correlation, Sampling and Quantization, Fourier Analysis, Advanced Transforms, Filter Design, Adaptive Processing, Stochastic Processes, Advanced Topics, and Applications and AI.

How Instructors Can Use It

  • Prerequisite checking. Before teaching a concept, confirm students know the concepts that point to it.
  • Remediation. When a student struggles, trace the arrows backward to find the gap.
  • Curriculum mapping. Compare the graph to your syllabus to find coverage gaps.
  • Enrichment. Strong students can follow arrows forward past the current chapter.

Open the Learning Graph Viewer to explore it interactively. The Learning Graph section holds the full concept list, the taxonomy, and quality reports.

Stories and Circuits

These sections are supplementary. They are not required for the quizzes.

  • Stories are six illustrated narratives about the people and ideas behind the field: Claude Shannon, Hedy Lamarr, the FFT, radar in World War II, Robert Moog, and Ingrid Daubechies' wavelets. They make good short readings to open a class or to start a discussion about who gets credit for engineering ideas.
  • Circuits has circuit diagrams, such as inverting and non-inverting op-amps, plus a list of important analog circuits. Use them to connect filter mathematics to real hardware.

Feedback

Reporting Issues and Suggestions

This textbook is an open-source project hosted on GitHub, a website where software and content projects are developed collaboratively. You do not need to understand programming to report a problem.

A GitHub Issue is like a support ticket: a way to report an error, suggest an improvement, or ask a question.

  1. Go to the repository at github.com/dmccreary/signal-processing.
  2. Click the Issues tab, then the green New issue button.
  3. Give the issue a clear title, such as "Error in Chapter 8 Z-transform example".
  4. Describe which page has the problem, what you expected, and what you saw.
  5. Click Submit new issue.

You need a free GitHub account to submit issues. Otherwise, use the Contact page.

Useful feedback includes factual and mathematical errors (especially in the chapters flagged above), broken links, MicroSims that fail to load, and topics that should be covered.

Understanding the License

A license is a legal document that explains what others may do with a piece of work. A Creative Commons (CC) license is a standard, plain-language license for educational and creative content.

This textbook uses CC BY-NC-SA 4.0:

Code Name What it means
BY Attribution You must credit the original author
NC NonCommercial You may not use the material for commercial purposes
SA ShareAlike If you modify it, you must share your version under the same license
4.0 Version 4.0 The current version of the license

What You Can Do

  • Copy the textbook or individual chapters for your students.
  • Share the link with other teachers, students, or parents.
  • Print chapters for class use.
  • Modify the content: add examples, remove sections, or reorder chapters.
  • Translate it into other languages.
  • Build your own version based on this one.

What You Cannot Do

  • Sell the textbook or charge students for access to it.
  • Remove attribution. You must credit the original author, Dan McCreary.
  • Share a modified version under a different license.
  • Present the material as your own original work.

Note

If you are unsure whether your situation counts as "non-commercial", for example a paid training program, ask your institution or read the Creative Commons license page. This guide is not legal advice.

The full text is on the License page.

Customizing Your Own Textbook

You can create your own version of this textbook. The steps below explain how.

Key Technical Terms

  • Repository (repo): A folder on GitHub that holds all of a project's files.
  • Git: A tool that tracks changes to files over time.
  • Clone: Copying a repository to your own computer.
  • Fork: Copying a repository to your own GitHub account.
  • MkDocs: The software that turns the textbook's Markdown files into a website.
  • Markdown: A simple text format. **bold** makes bold, # Heading makes a heading, and - makes a bullet.
  • mkdocs.yml: The main configuration file. It controls the title, navigation, colors, and which features are enabled.

Step 1: Create a GitHub Account

If you do not have one, create a free account at github.com.

Step 2: Fork or Clone the Repository

Option A: Fork (easier). Open github.com/dmccreary/signal-processing, click Fork, and edit your copy on GitHub.

Option B: Clone (more control). Install Git, open a terminal, and run:

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git clone https://github.com/dmccreary/signal-processing.git

Step 3: Make Changes

All content is in the docs/ folder as Markdown (.md) files. Edit them with any text editor.

Title, description, and address. In mkdocs.yml, change these lines:

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site_name: Your Course Title
site_description: 'Your description'
site_author: 'Your Name'
site_url: 'https://YOUR-USERNAME.github.io/YOUR-REPO-NAME/'
repo_url: 'https://github.com/YOUR-USERNAME/YOUR-REPO-NAME'

Colors. The maroon color is set in docs/css/extra.css. Look for the --md-primary-fg-color lines near the top and change the hex color codes.

Logo and icon. Replace docs/img/logo-192.png (the header logo) and docs/img/favicon.ico (the browser tab icon) with your own images, keeping the same file names.

Chapters and navigation. The nav: section of mkdocs.yml lists every page in the left menu. Add, remove, or reorder entries to match your syllabus.

Step 4: Preview Your Changes Locally

  1. Install Python 3 from python.org.

  2. Install MkDocs, the Material theme, and the page-exclude plugin:

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    pip install mkdocs "mkdocs-material[imaging]" mkdocs-exclude

  3. From the project folder, start the preview server:

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    mkdocs serve

  4. Open http://127.0.0.1:8000/signal-processing/ in your browser. The page reloads when you save a file.

If you see an error mentioning Cairo

The site uses Material's social-card plugin, which needs a graphics library called Cairo. Either install Cairo (see the Material for MkDocs documentation on social cards) or delete the - social line under plugins: in mkdocs.yml. The site works without social cards.

Step 5: Publish Your Version

To publish your site for free with GitHub Pages, run:

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mkdocs gh-deploy

This builds the site and publishes it to https://YOUR-USERNAME.github.io/YOUR-REPO-NAME/ in about one to two minutes. If the page does not appear, open your repository's Settings, choose Pages, and select the gh-pages branch as the source.

Using the Prompts

The Prompts section records the AI prompts used to build this book, from the course description through the learning graph, glossary, and MicroSims. How We Built This Site explains the tool choices. If you adapt the book to a new subject or add chapters, reusing these prompts is the fastest route.

Analytics and Student Privacy

What is Web Analytics?

Web analytics measures how visitors use a website: which pages they open, how long they stay, and what device they use. For a textbook it can show which chapters students read and which they skip.

Google Analytics

Google Analytics is a free Google service that records website visits. To use your own account:

  1. Go to analytics.google.com and sign in.
  2. Create a property (Google's term for a tracked website). Google gives you a Measurement ID that looks like G-XXXXXXXXXX.

  3. In mkdocs.yml, make sure the setting sits inside the extra: section:

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    extra:
  analytics:
    provider: google
    property: G-YOUR-MEASUREMENT-ID

  4. Rebuild and deploy. Data typically appears within 24 to 48 hours.

Replace the original author's ID

mkdocs.yml contains the original author's Measurement ID. If you fork the book, replace it with your own. Otherwise your students' visits are counted in the author's property.

Analytics can show the most and least visited chapters, time on page, device types, and where visitors are located. It reports aggregate page views, not individual students.

xAPI and Individual Student Data

xAPI (Experience API) is a standard for recording detailed learning activity, such as "student answered question 3 correctly". The records are stored in a Learning Record Store (LRS).

This textbook does not currently send xAPI records. Quiz answers are never collected. If you add tracking, be aware of these rules first:

  • FERPA (Family Educational Rights and Privacy Act): U.S. law protecting student education records. Data that identifies individual students is covered.
  • GDPR (General Data Protection Regulation): European Union law that applies if any of your students are in the EU.
  • COPPA (Children's Online Privacy Protection Act): U.S. law for children under 13, relevant only if you use the book with younger learners.
  • State and institutional rules. Many U.S. states and institutions add their own requirements.

The anonymous aggregate analytics above are the safest option. Consult your institution's data privacy office before collecting individual student data.