Computational Imaging Research Laboratory

• Every semester since fall 2022-present. 

The course prepares and engages undergraduate and graduate students in ambitious, long-term, large-scale, multidisciplinary project teams that are led by faculty, at the UofM. The VIP model, based on active learning, allows students to engage in research while earning credit and enables tiered mentoring from students at all academic years, thereby providing the opportunity of role modeling from upper-level undergraduate and graduate students as well as faculty. Special instructional units prepare undergraduate students on how to engage in effective and ethical research as part of a research team.

• Every fall 2014 - present (required graduate level course)

Electrical and computer engineering research methods, development issues and ethics in academia and engineering practice. Students learn how to conduct literature search, write progress reports, publications, and proposals as well as prepare and give oral and poster presentations.

• Fall 2008, Spring 2015 (special topic graduate-level courses)

A study of the principles of linear inverse problems, computational methods of their approximate solution, and practical application in imaging. Study of optimization methods and regularization principles for the solution of ill-posed inverse problems.

• Fall 2010 (special topic graduate-level course)

A study of special topics in computational optical imaging including compressive sensing, structured illumination, wavefront encoding to extend the depth of field, correct aberration and provide super resolution in imaging systems.

• Spring 2013 (special topic graduate-level course)

Mathematical tools to model and predict the action of imaging systems. Representation of images and systems in both continuous and discrete domains. Characterization of systems and their effect on the quality of their output images.

• Fall 2010-2013, spring 2011-2014 (required junior level course)

Elementary concepts of continuous-time signals and systems. Analysis of linear time-invariant systems: convolution, Fourier series, Fourier transforms and Laplace transforms. Principles of sampling and modulation.

• Spring 2003, fall 2003 (required freshman level course)

Fundamental aspects of engineering including physics and physical devices, mathematical modeling, analytical problem solving, engineering design, and laboratory experimentation. Course topics and skills are integrated in design projects on contemporary applications.

• Spring 2004 (freshman level course) 

Basic concepts on information representation (A/D conversion, binary representation), storage (magnetic, optical), transmission (wired, fiber-optic, radio and satellite), and security for various forms of information (audio, image, and video). Basic principles of operation for: high-tech devices (mobile phones, GPS devices etc.), telephone and computer networks, and the World Wide Web.

• Image Processing (graduate level course), fall 2012, spring 2014 & 2016. Theory and applications of digital image processing, sampling, quantization, enhancement, modeling and restoration of images; use of segmentation, descriptors, and pattern recognition.

• Fourier Optics (graduate level course), spring 2007-2010. Analysis of two-dimensional linear systems, scalar diffraction theory, Fresnel and Fraunhofer diffraction; Fourier transforming properties of lenses, spatial frequency analysis of optical systems, optical information processing and holography.

• Signals and Systems (Transform Methods) (junior level course), fall 2001, 2006-2009 and spring 2010. Elementary concepts of continuous-time and discrete-time signals and systems. Analysis of linear time-invariant systems: convolution, Fourier series, Fourier transforms, Laplace and Z transforms. Principles of sampling and modulation.

• Communication Theory (senior/graduate level course), spring 2001-2003, 2009, 2015, & 2025. Amplitude and angle modulation for the transmission of continuous-time signals. Analog-to-digital conversion and pulse code modulation. Transmission of digital data. Introduction to random signals and noise and their effects on communication. Overview of various communication technologies.

• Random Signals and Noise (graduate level course), fall 2012. Statistical methods for describing and analyzing random signals and noise; auto-correlation, cross-correlation, and spectral density functions; optimal linear filter theory.

• Electrical Engineering Senior Design Projects (senior level course), spring 1999. Working in teams, students address design tasks assigned by faculty (this semester, students were given specifications to design and build a motion detector). Projects are chosen to emphasize the design process, with the designers choosing one of several paths to a possible result. Collaboration with industry and all divisions of the University is encouraged.

• Introduction to Electrical Networks (sophomore/junior level course), fall 2004, 2005 and spring 2005, 2006. Elements, sources, and interconnects. Ohm's and Kirchhoff's laws, superposition and Thevenin's theorem; the resistive circuit, transient analysis, sinusoidal analysis, and frequency response.

• Electrical and Electronic Circuits Laboratory (sophomore level course), spring 2002, 2005 and fall 2002. Lectures and laboratory exercises on introductory networks and basic electronics.

• Guest Lectures on “Computational imaging for 3D microscopy” as part of Light Microscopy: Theory and Applications (graduate level Biology course) spring 2012.

• Guest Lectures on “Optics of microscopes” as part of Optical Imaging (grad. level course), spring 1991, 1994, and 1997.

• Guest Lecture on “Three-dimensional microscopy and deconvolution” as part of Optical Bioelectric Imaging (graduate level course in Biomedical Engineering), fall 2005.