Research

Publications

See http://www2.ece.ohio-state.edu/~fasiha/sphinx/build/html/publications.html

Software

Background

I'm a graduate student at The Ohio State University (official page), working on a PhD in what could be broadly called statistical signal processing (in the electrical engineering department).

At this school, a PhD ordinarily consists of three major components, or three different research thrusts that all relate to some central goal.

My own research involves synthetic aperture radar (SAR) technology, where basically a radar is mounted on the side of an aircraft which flies around a scene of interest to generate 2d or 3d images of the scene it stares at. This is cool because you can put this aircraft eight miles away from the center of the scene (where maybe some unpleasant things are happening), and it can stare at a circular patch of ground three or four miles in diameter. Manmade objects like buildings and cars show up really well in a SAR image since they're made of metal or concrete, things that reflect electromagnetic energy nicely. (See a large SAR image that OSU folks will recognize.)

My research supports the effort to use SAR technology for tracking moving vehicles. Moving things in the scene cause huge problems in the SAR image: a car moving at 30 mph might, instead of showing up as a blob 3 feet wide in the final image, wind up being a long thin streak stretching across the entire image (thousands of feet). The artifacting is very unintuitive because (getting technical now), the motion blur happens in the frequency (Fourier) domain, and so the image artifacts are the Fourier transform of that motion blur. All kinds of blurring and warping and shifting happens to the image of a mover. (The example is from [1], page 1150.)

My work so far, the first component of my PhD, has been to establish best-case scenarios (Cramer-Rao bounds) for estimating the motion parameters of a moving vehicle from a SAR image. Specifically, my contribution has been to obtain these Cramer-Rao bounds for targets with random motion components (road jitter, engine noise, wind buffeting, etc.) because my advisors and I suspected that it was these tiny random motions that stymied many refocusing algorithms that should have worked really well. We have shown that even a target vibrating with standard deviation of 1 millimeter causes the minimum error in localizing it to double or triple, compared to if it wasn't jittering.

I'm also very interested in broadly nonparametric machine learning, meaning mammal-style cognition. I'm deeply moved by Jeff Hawkins and Dileep George's work at Numenta (Hawkins' great talk from a few years ago is online, as is George's dissertation). I hope to contribute to research along those lines and solve problems of interest to the SAR community. The potential for Numenta-style machine learning (completely different than every single algorithm in ordinary "machine learning") is of course vast and is truly the most exciting thing I can imagine working on.