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About the Signal Processing with AI Website

This website is designed as a proof-of-concept site to help both instructors and students who are interested in learning about how AI can be used to teach signal processing.

Warning

This is not a complete course on signal processing. It is a course that helps instructors and students learn how to use generative AI tools to create content in a signal processing course.

This website uses generative AI to create content around a learning graph and encourages both instructors and students to use generative AI to create and modify content such as simulations and animations of signal processing concepts.

We have used the following workflow to generate much of this website.

You can hover various items to see what tasks they execute.

Version 1: A Proof of Concept

Version 1 of this book was built to demonstrate a proof of concept for the paper Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities by Jarvis Haupt, Qin Lu, Yanning Shen, Jia Chen, Yue Dong, Dan McCreary, Mehmet Akçakaya, and Georgios B. Giannakis (arXiv:2509.08950, September 2025).

The paper explores how generative AI tools such as large language models and coding assistants can support signal processing education, and it illustrates those ideas through the concept of a "smart textbook." This website is a working example of that concept: a learning graph, AI-generated chapters, and interactive MicroSims, all created with the workflow shown above.

Key References

The following two versions of the paper are the key references for how this book was created:

  1. Haupt, J., Lu, Q., Shen, Y., Chen, J., Dong, Y., McCreary, D., Akçakaya, M., & Giannakis, G. B. (2025). Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities. arXiv:2509.08950. https://arxiv.org/abs/2509.08950
  2. Haupt, J., Lu, Q., Shen, Y., Chen, J., Dong, Y., McCreary, D., Akçakaya, M., & Giannakis, G. B. (2026). Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities. IEEE Signal Processing Magazine, 43(1), 32–46. Special Issue on Artificial Intelligence for Education: A Signal Processing Perspective. Published 18 February 2026. DOI: 10.1109/MSP.2025.3600846. IEEE Xplore

What is Signal Processing?

Signal Processing is the analysis, manipulation, and interpretation of signals to extract meaningful information, enhance signal quality, or transform signals into more useful forms.

Core Definition

Signal processing involves mathematical and computational techniques applied to time-varying data (signals) such as audio, video, sensor measurements, or communication data. The field encompasses both analog and digital methods for filtering, amplifying, modulating, compressing, and analyzing signals to achieve specific objectives like noise reduction, feature extraction, or data compression.

Key Characteristics

Input: Raw signals from various sources (microphones, cameras, sensors, antennas)

Processing: Mathematical operations including filtering, transformation, correlation, and statistical analysis

Output: Enhanced, modified, or analyzed signals that serve specific applications

Fundamental Applications

Signal processing is essential in telecommunications, audio/video systems, medical imaging, radar, control systems, and data analysis. Common operations include removing unwanted noise from audio recordings, compressing images for storage, extracting features from sensor data, and converting analog signals to digital format for computer processing.

Mathematical Foundation

The discipline relies heavily on concepts from linear algebra, calculus, probability theory, and Fourier analysis to represent signals in different domains (time, frequency, spatial) and apply appropriate transformations to achieve desired results.

About the Author

Dan McCreary

Dan McCreary is a semi-retired AI researcher, solution architect, and educator who has spent more than three decades helping Fortune 100 organizations reason over massive datasets. At Optum he founded the Generative AI Center of Excellence and led the team that built one of the world's largest healthcare knowledge graphs — spanning over 25 billion vertices — to unify member, provider, and patient insights. Dan's deep background in knowledge representation and systems thinking underpins the precise learning graphs and intelligent textbook workflows used throughout this course.

He is the co-author of Making Sense of NoSQL (Manning Publications), the founding chair of the NoSQL Now! conference, and a frequent keynote speaker on semantic search, ontology strategy, and AI hardware. Beyond industry, Dan has mentored students as a STEM volunteer since 2014 and now applies the same rigor to building open educational resources. You can visit the Intelligent Textbooks Case Studies to see over 87 textbooks that Dan has created or co-created with other authors.

Selected Credentials

  • B.A. in Physics and Computer Science from Carleton College
  • M.S.E.E. from the University of Minnesota
  • MBA coursework at the University of St. Thomas
  • Patent holder in semantic search and ontology management techniques
  • Advocate for large-scale Enterprise Knowledge Graph adoption across healthcare and education
  • Long-time promoter of accessible, low-cost AI-powered learning experiences

Co-Authors of the Paper

The paper that Version 1 of this book was built to support was written by a team of eight authors. The short bios below are summarized from the author biographies on IEEE Xplore, and titles and affiliations are those listed at the time of publication.

  • Jarvis Haupt is an associate professor and associate department head in the Department of Electrical and Computer Engineering at the University of Minnesota. His research interests include statistical signal processing, machine learning, and optimization.
  • Qin Lu is an assistant professor at the University of Georgia, which she joined in 2023 after a postdoctoral position at the University of Minnesota. She received her Ph.D. from the University of Connecticut in 2018, and her research interests include signal processing, machine learning, and communications.
  • Yanning Shen is an associate professor in the Department of Electrical Engineering and Computer Science at the University of California, Irvine. Her research focuses on trustworthy machine learning and learning over graphs. She was selected as a Stanford Rising Star in EECS in 2017 and named one of MIT Technology Review's 35 Innovators Under 35 Asia Pacific in 2022.
  • Jia Chen is an associate professor of teaching in the Department of Electrical and Computer Engineering at the University of California, Riverside. She received her Ph.D. in electrical engineering from the University of Texas at Arlington in 2016 and was a postdoctoral fellow at the University of Minnesota from 2017 to 2019.
  • Yue Dong is an assistant professor of computer science at the University of California, Riverside. Her research covers controllable, trustworthy, and efficient large language models, with more than 40 papers at top natural language processing and machine learning venues.
  • Dan McCreary is an AI education researcher who specializes in knowledge representation and in using learning graphs and large language models to create intelligent textbooks. He is an intelligent textbook consultant at Kelly-McCreary & Associates and holds an M.S.E.E. from the University of Minnesota. See About the Author above for his full bio.
  • Mehmet Akçakaya is the Jim and Sara Anderson Chair Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, where he has been on the faculty since 2015. He received his Ph.D. from Harvard University, and his research interests include image processing, artificial intelligence, medical physics, inverse problems, and signal processing.
  • Georgios B. Giannakis holds a Presidential Endowed Chair in the Department of Electrical and Computer Engineering at the University of Minnesota, where he has been since 1999 after time at the University of Virginia. He received his Ph.D. from the University of Southern California. His research interests include data science, signal processing, communications, and networking, and he has published more than 500 journal articles.

How to Cite This Book

If you reference this textbook in academic work, curriculum proposals, lesson plans, or other publications, please use one of the following citation formats.

APA (7th edition)

McCreary, D. (2026). Signal Processing with AI. https://dmccreary.github.io/signal-processing/

Chicago (17th edition)

McCreary, Dan. 2026. Signal Processing with AI. https://dmccreary.github.io/signal-processing/.

MLA (9th edition)

McCreary, Dan. Signal Processing with AI. 2026, dmccreary.github.io/signal-processing/.

BibTeX

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@book{mccreary2026signalprocessing,
  title     = {Signal Processing with AI},
  author    = {McCreary, Dan},
  year      = {2026},
  url       = {https://dmccreary.github.io/signal-processing/},
  note      = {Interactive intelligent textbook}
}

To cite a specific chapter, append the chapter number and title — for example:

McCreary, D. (2026). Chapter 1: Mathematical Foundations. In Signal Processing with AI. https://dmccreary.github.io/signal-processing/chapters/01-mathematical-foundations/

To cite the paper that Version 1 of this book was built to support, see the Key References above: arXiv:2509.08950 or the IEEE Signal Processing Magazine article.

License

This work is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). You are free to share and adapt the material for non-commercial purposes as long as you give appropriate credit and share your adaptations under the same license.