My teaching schedule for academic year 2018-2019:

  • 2019 Fall: ENG EC 401  Signals and Systems

Continuous-time and discrete-time signals and systems. Convolution sum, convolution integral. Linearity, time-invariance, causality, and stability of systems. Frequency domain analysis of signals and systems. Filtering, sampling, and modulation. Laplace transform, z-transform, pole-zero plots. Linear feedback systems. Includes lab. 4 cr.

  • 2019 Spring: ENG EC 522 Computational Optical Imaging


Recent years have seen the growth of computational optical imaging – optical imaging system that tightly integrates optical hardware and computational algorithms. The result is imaging systems with capabilities that are well beyond with traditional methods. Computational optical imaging systems have a wide range of applications in consumer photography, scientific and biomedical imaging, microscopy, defense, security and remote sensing. This course looks at this new design approach as it is applied to modern optical imaging, with the focus on the tools and techniques at the convergence of physical optics, and signal processing.


linear algebra, e.g. EK102 or MA142
linear systems, e.g. EC401
Fourier analysis, e.g. EC401
Multivariate Calculus, e.g., MA225
MATLAB / Python Programming skills, e.g. EK127


There is no single textbook that sufficiently covers all the materials in this course. Below is a list of books that can prove useful for various parts of this course. You are also expected to rely on lecture notes and supplementary material that will be uploaded regularly to the course website, such as journal papers.

Main Text for the math in the course:

M. Bertero, P. Boccacci, Introduction to Inverse Problems in Imaging, (Taylor), ISBN 9780750304351

Additional (optional) references:

(For more in-depth reading on optics)

D. Brady, Optical Imaging and Spectroscopy, (Wiley), ISBN 9780470048238

J. Goodman, Introduction to Fourier Optics, 4th Edition. ISBN: 978-1-319-11916-4

Syllabus (Tentative)