M.Eng. Poster Session 2016

Best in Electronic Devices & Materials (Analog, Digital, Optics, MEMS, Circuits); Best Overall Poster

Zero Power Wireless Sensors
Sahil Gupta

Best in Computer Systems (OS, Embedded, Networks, Architecture, Database)

Dongle, Database and GUI for Managing Unique Bird Tag IDs
Jinghan Du, Di Jiang, Rui Meng, Lei Zhang

Best in Internet Applications, Data Processing, Data Science, Cross-Platform Apps

Campus Events System
Yuxin Cao, Xiaoyu Guo

Best in Signal Processing

Separation of Singing Voice from Music
Tengli Fu

Best in Bio-Signals (Neural, Controls, Imaging, Bioinformatics)

Optical Coherence Elastography of Normal and Diseased Tissues
Asher Novick

The goal of this project was to acquire a library of spectroscopic optical coherence elastography (OCE) signatures for different biological tissues.  OCE is a relatively new biomedical imaging technique to improve upon the established practice of diagnosing diseased tissue by manual palpation. The OCE system uses the properties of coherent light to detect extremely small perturbations of the sample it scatters off of. OCE can provide higher resolutions and displacement sensitivity than its ultrasound or MRI counterparts by leveraging the high-resolution and ultra-precise phase-sensitivity of an optical coherence tomography (OCT) system. An experimental OCE setup was utilized to perform spectroscopic OCE imaging of various issues to determine if different tissue types can be distinguished by their spectroscopic OCE responses. Various samples were tested, including gelatin samples and tissue samples and it was demonstrated that the system can detect features through mechanical contrast with at least 40$\mu$m resolution. It was also shown that the complex mechanical response of different tissues can vary drastically as a function of frequency, leading to optimal excitation frequencies to image the mechanical properties of different features. 

Best in AI/Pattern Recognition (Computer Vision, Machine Learning, Robotics)

Automatic Assessment Generation via Machine Learning
Arjun Jauhari

Best in Communications (Information Theory, Network Coding, Digital Communication)

Resilient Voice Communication for the MITRE Corporation
Travis Cuvelier, Scott Zhao