His general research interests lie in the areas of statistical signal and array processing, time-frequency analysis, compressive sensing, and convex optimization for applications in wireless communications, radar, RFID, navigation, and assisted living. Visit the ASP Lab for more information about research activity.
The Computer Fusion Laboratory (CFL) is part of Temple University's Electrical and Computer Engineering Department under the leadership of department chair Dr. Li Bai. The CFL pursues research in various areas of engineering including distributed sensing and computing, multi-agent systems, wireless sensor networks, augmented reality and more. The think tank style laboratory explores what makes our world tick. For more information visit the Computer Fusion Laboratory website.
At the CSNAP laboratory, we investigate various topics in sensors and control theory with applications in biomedical engineering and aerospace engineering. Currently, we are performing research in tactile imaging sensors, navigation sensors, neural networks, and nonlinear statistical control theory. This laboratory is located in the Electrical and Computer Engineering Department at Temple University, Philadelphia, U.S.A. The laboratory is sponsored by the National Science Foundation, the United States Air Force, Pennsylvania Department of Health, BioStrategy Partners, and Temple University.
The lab pursues advanced research, analysis, testing, and development efforts in microwave and millimeter wave sensing modalities for a variety of defense and civilian applications. Currently, research investigations targets advances in passive synthetic aperture radar imaging, forward-looking ground penetrating radar, remote patient monitoring and eldercare, automotive radar, compressive sensing, and coexistence of radar and communications. Visit the MSIL lab website.
Developing technology for interfacing electronics to neural tissue. The lab is primarily interested in signal processing algorithms and implementations and mechanisms for recording and decoding electrical signals from the brain, with applications in Brain Machine Interfaces. Visit the Engineering the Interface lab.
The lab oversees the Neural Engineering Data Consortium in the development open source big data resources designed to accelerate progress in machine learning applications in bioengineering. The Institute also is part of a NIH grant with the primary goal to enable comparative research by automatically uncovering clinical knowledge from a vast BigData archive of clinical EEG signals and EEG reports and creation of open source databases that can be used for high performance deep learning models. Visit the website for more about the lab's publications and research.
