Foundations of Signal Processing for Engineers and Data Scientists

Special Invite from Instructor Barry Van Veen

Signal Processing is a core technology behind many of the consumer products we use in our everyday lives and is a critical component of industrial products and manufacturing processes. This course introduces the foundations of signal processing methods used to acquire and manipulate digitized signals such as voice, images, radar, position, environmental and medical measurements.

Through a combination of theory, examples, and hands-on experience, attendees will learn the following:

• What is signal processing?
• Principles for digitizing data
• How to interpret signals in terms of frequency
• Common methods for manipulating signals, including filters and the fast Fourier transform
• Applications of advanced methods such as modeling and adaptive interference cancellation

No prior knowledge of signal processing is assumed.  Prior exposure to STEM fields will be helpful.

Learning Outcomes

After this course, we expect that attendees should be able to:

1. Understand the language and foundational concepts of the field of signal processing
2. Know how to sample signals for subsequent analysis
3. Recognize the tradeoffs involved in estimating spectra from sampled data
4. Design basic frequency selective filters
5. Appreciate advanced applications such as predictive modeling, equalization, and interference cancellation

• Anyone in engineering, manufacturing or technology roles, including:
  • R&D and Design engineers
  • Data scientists
  • Quality engineers

• Company leadership looking for a better understanding of how signal processing works and why it is important for current and future products and processes

Module 1 – Introduction to Signals and Signal Processing

• Introduction & example signal processing applications
• Systems for acquiring and manipulating signals
• The role of sinusoids in signal representation
• Properties of continuous- and discrete-time frequency

Module 2 – Sampling signals and reconstructing signals from samples

• Aliasing and its consequences
• Choosing sampling parameters
• Reconstructing signals from samples

Module 3 – Spectral Analysis and the Fast Fourier Transform

• Spectral analysis overview
• The fast Fourier transform (FFT)
• Challenges of computing spectra from a finite number of samples
• Resolution and dynamic range tradeoffs when windowing data

Module 4 – Frequency Selective Filter Design

• Properties of FIR and IIR filters
• Filter specification diagram
• Role of filter phase response
• Using Python to design filters

Module 5 – Introduction to Minimum Mean-Squared Error Filtering

• Separating signals using properties other than frequency
• Modeling systems
• Equalization – correcting distortion
• Interference cancellation
• Linear prediction

Barry Van Veen

Barry D. Van Veen (S’81-M’86-SM’97-F’02) was born in Green Bay, WI. He received the B.S. degree from Michigan Technological University in 1983 and the Ph.D. degree from the University of Colorado in 1986, both in electrical engineering. He was an ONR Fellow while working on the Ph.D. degree.

He has been with the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison since 1987 and is currently Lynn H. Matthias Professor of Electrical and Computer Engineering. His research interests include signal processing for sensor arrays, biomedical applications of signal processing and machine learning, and instructional methods for improving STEM education.

Dr. Van Veen was a recipient of a 1989 Presidential Young Investigator Award from the National Science Foundation and a 1990 IEEE Signal Processing Society Paper Award. He served as an associate editor for the IEEE Transactions on Signal Processing and on the IEEE Signal Processing Society’s Statistical Signal and Array Processing Technical Committee and the Sensor Array and Multichannel Technical Committee. He received the Byron Bird Award for Excellence in a Research Publication from the College of Engineering in 2020.

In recognition of outstanding teaching, Dr. Van Veen received the 1997 Holdridge Teaching Excellence Award from the ECE Department, the 2014 Spangler Award for Technology Enhanced Instruction from the College of Engineering, the 2015 Chancellor’s Distinguished Teaching Award, and the 2017 Benjamin Smith Reynolds Award for Teaching Engineers at the University of Wisconsin.  He coauthored “Signals and Systems,” (1st Ed. 1999, 2nd Ed., 2003 Wiley) with Simon Haykin and is the Chief Education Officer at AllSignalProcessing.com, a website devoted to signal processing instruction. Dr. Van Veen is a Fellow of the IEEE.