Light Stimulation and Humidity Measurement in the Optogenetics Laboratory

Senior Design Poster Presentation

Senior Design Demo 2 Poster

My group and I worked with Dr. Matthew Thimgan in the Missouri S&T biology department to create a light stimulation apparatus to support sleep research on drosophila flies. The apparatus stimulates electrical channels in a genetically modified fly to induce sleep, a type of research called optogenetics. In addition, we also created a humidity measurement apparatus to ensure the flies are breeding in an environment that is not too dry.

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Active Compression-Decompression CPR in Microgravity

One of my most exciting research experiences at Missouri S&T was getting to fly aboard NASA’s Weightless Wonder (also known as the Vomit Comet) to test Active Compression-Decompression CPR for long-term space travel. For three years I was on a team called Miners in Space where we would design and propose an experiment to the NASA Reduced Gravity Education Flight Program. We were accepted into the program three times, and I flew aboard the Weightless Wonder for two of those years. We successfully demonstrated that this form of CPR could in fact be used on a spacecraft if the need arose. I created an outreach program that reached over one thousand students in five states each of the three years that I was on the team. I also performed electrical engineering design and fabrication, post-experiment data analysis, and helped write the proposals, safety reports, and final reports every year.

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Convolution and Wavelet Neural Networks Applied to EEG Brain-Control Interface

Undergraduate Research Conference Poster

OURE Final Report

I performed EEG research as part of the Missouri S&T program Opportunities for Undergraduate Research Experiences (OURE). I worked with Dr. Donald C. Wunsch and the Missouri S&T Applied Computational Intelligence Laboratory for this project exploring the possibility of using wavelet or convolution neural networks for motor-imagery classification. These types of neural networks are becoming very popular in image analysis for object tagging or facial recognition, so I though to apply them to the challenging problem of the EEG brain-control interface. In the end, these algorithms did not perform better than the best work out there, but I provided a few ideas I had moving forward with this approach.

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Image Compression Performance Using Total Variation Minimization and Noiselet Transform

Final Report Presentation

Final Report PDF

I worked on this project for a class at Missouri S&T called Machine Vision. For the project I recreated some of the work done in [1] comparing image compression techniques to illustrate the benefits of compressive sampling. The technique demonstrated in [1] showed that random noiselets could be used for compression using l1 minimization recovery, and that this technique could actually yield better visual results than the typical discrete cosine transform alone. Oddly enough I showed that the benefits of using this technique did not hold when tested on images other than the demonstration used in the article. I was certainly surprised by this result!

[1] Romberg, J. (2008). Imaging via Compressive Sampling. IEEE Signal Processing Magazine, 25(2), 14{20. doi:10.1109/MSP.2007.914729

Performance Comparison of K-Means and Expectation Maximization with Gaussian Mixture Models for Clustering

Final Report Presentation

Final Report PDF

I created this as a final project for a course at Missouri S&T called Statistical Decision Theory. I implemented the EM-GMM algorithm [1] in Matlab and compared the results with the built-in k-means function. EM-GMM is cool because it builds a generative model of the data, so you can use it for clustering if you make certain assumptions or you can use it to understand something about the data without actually looking at the data itself.

It was a challenging course because I don’t have a background in communications besides the introductory courses every EE takes, but we got some freedom for the final project that I really enjoyed!

[1] A. P. Dempster, N. M. Laird, and D. B. Rubin, \Maximum likelihood from incomplete data via the em algorithm,”Journal of the royal statistical society. Series B (methodological), pp. 1-38,1977.