# Teaching

My teaching schedule for academic year 2016-2017:

**2016 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.

**2017 Spring: ENG EC 700 A1 Special Topics: Computational Optical Imaging**

Recent years have seen the growth of **computational imaging** – imaging system that fully integrates computational processing into image-formation operation. The result is systems with capabilities that are not possible with traditional methods. Computational 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**.

In this class, we will discuss a variety of topics in computational optical imaging:

- Working principles of modern optical imaging systems, such as cameras, microscopes, and telescopes, from the signal-and-system point of view.
- This new framework allows us to better understand the principles behind a number of new imaging capabilities, such as
- Optical super-resolution
- Extended depth of field, Digital refocusing, 3D imaging
- Quantitative phase imaging

- The underlying principles enabling these capabilities will be discussed, including:
- Point-spread function / pupil engineering, Coded aperture, Light field imaging
- Adaptive optics / Phase conjugation
- Holography
- Synthetic aperture, Structured illumination
- Tomography
- Multi-spectral / hyperspectral imaging

- Signal processing and computational techniques that enable these new imaging modalities, topics include:
- Deconvolution
- Regularization
- Phase retrieval
- Compressed sensing

- This new framework allows us to better understand the principles behind a number of new imaging capabilities, such as

**Prerequisites:**

linear algebra, e.g. EK102 or MA142

linear systems, e.g. EC401

Fourier analysis, e.g. EC401

Multivariate Calculus, e.g., MA225

MATLAB Programming skills, e.g. EK127

**Readings:**

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:*

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

*Additional (optional) references:*

(For more in-depth reading on optics)

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

**IFO:** J. Goodman, Introduction to Fourier Optics, 3rd (Mac Higher/WH Freeman), ISBN 9780974707723

(For tomographic imaging)

**PCT: **C. Kak, M. Slaney, **Principles of Computerized Tomographic Imaging***,* Society of Industrial and Applied Mathematics, 2001.

**Syllabus** (Tentative)