I love producing educational materials.
A central challenge
is to choose explanations that are as simple as possible
without being wrong. While this is also true in
communicating research results, the goal of simplifying
comes to the fore when writing for broader audiences
Along with a few other expository works,
I have written four articles for IEEE Signal
By far the greatest effort, though, is in coauthoring a pair of textbooks with Martin Vetterli and Jelena Kovačević:
The first book introduces foundations for signal processing,
with geometric insights and unifications of discrete- and continuous-time coming from the Hilbert space formalism.
It thoroughly develops sampling and interpolation results in a manner that is extendable to general linear acquisition systems.
It also discusses approximation, estimation, compression, and basics of time-frequency localization.
The second book develops various signal expansion tools aside from global Fourier methods.
This includes the wavelet and local Fourier representations that are common in signal processing.
- Theoretical Foundations of Transform Coding
V. K. Goyal,
IEEE Signal Processing Mag., vol. 18, no. 5, pp. 9-21, Sept. 2001.
- Multiple Description Coding: Compression Meets the Network
V. K. Goyal,
IEEE Signal Processing Mag., vol. 18, no. 5, pp. 74-93, Sept. 2001.
- Compressive Sampling and Lossy Compression
V. K. Goyal, A. K. Fletcher, and S. Rangan,
IEEE Signal Processing Mag., vol. 25, no. 2, pp. 48-56, March 2008.
- Advances in Single-Photon Lidar for Autonomous Vehicles
J. Rapp, J. Tachella, Y. Altmann, S. McLaughlin, and V. K. Goyal,
IEEE Signal Processing Mag., vol. 37, no. 4, pp. 62-71, July 2020.
BU ENG EC 381 Probability Theory in Electrical and Computer Engineering: Fall 2016
BU ENG EC 416 Introduction to Digital Signal Processing: Spring 2018
BU ENG EC 516 Digital Signal Processing: Spring 2014, Fall 2017, Spring 2018
BU ENG EC 717 Image Reconstruction and Restoration: Spring 2020, Fall 2022
BU ENG EK 100 Freshman Seminar: Fall 2019
BU ENG EK 381 Probability, Statistics, and Data Science for Engineers: Spring 2019, Fall 2019
MIT 6.041 Probabilistic Systems Analysis: Spring 2004, Fall 2004, Spring 2006, Spring 2008, Fall 2008, Spring 2010, Fall 2010
MIT 6.341 Discrete-Time Signal Processing: Spring 2005, Fall 2006, Fall 2009
MIT 6.342 Wavelets, Approximation, and Compression: Fall 2005, Spring 2007, Spring 2009, Spring 2011