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Electrical and Computer Engineering
Rice Efficient Computing Group

Speaker: Mohammed Shoaib
Princeton University

Drawing Inferences from Compressed Signal Representations.

Tuesday, February 5, 2013
3:00 PM  to 3:50 PM

1064  George R. Brown Hall
Rice University
6100 Main St
Houston, Texas, USA

Despite the low-sampling rate of signals in medical sensor networks, a large number of measurement channels and severely energy-constrained devices could pose bandwidth and communication energy limitations. Compressive sensing is an efficient method of representing the data, which may help alleviate some of these data constraints. Although compressive sensing enables low-energy data reduction on the sensors, reconstruction costs are severe. The high reconstruction complexity typically pushes signal analysis to a base station. For many emerging medical applications, however, we want to aggregate and analyze the data on some local processing node. In this talk, I will describe a methodology to transform linear signal processing computations to the compressed domain so that the transformed computations can be applied directly to compressed data. I will present two example case studies where compressively-sensed spikes and electroencephalograms (EEGs) are directly analyzed to estimate neuron firing rates and to detect epileptic seizures, respectively. Besides circumventing reconstruction costs, this approach also provides a new power management knob by reducing computational energy with the number of input samples. I will show how we can take advantage of this power management knob in a custom IC fabricated in 130nm low-power CMOS.

Host: Lin Zhong

Biography of Mohammed Shoaib:
Mohammed Shoaib is a fifth-year Ph.D. student in the Department of Electrical Engineering at Princeton University. He is co-advised by Profs. Niraj K. Jha and Naveen Verma. His research interests lie at the crossroads of signal processing, machine learning, and low-power sensor networks. In particular, he has focused on designing systems that directly process sparsity-based representations of data in medical sensor networks. This has led to a new paradigm of information processing using compressed data representations.

In the past, Mr. Shoaib has received the B.Tech. and M.Tech. dual degree in electrical engineering with a specialization in microelectronics and VLSI design from the Indian Institute of Technology (IIT), Madras, in 2008.. He has also received the M.A degree in electrical engineering from Princeton University in 2010. During his educational career, he has authored or co-authored over 15 technical papers and 5 pending/granted patents. he has received Ph.D. fellowships from Princeton University and Qualcomm Corporate Research in 2008 and 2010, respectively. He has also received academic awards such as the Roberto-Padovani scholarship from Qualcomm in 2011 and the Gordon Wu Prize for excellence from Princeton University in 2012. To support his terminal Ph.D. studies, he has been awarded the 2012 Harold W. Dodds Honorific Fellowship from Princeton University. He has a strong interest in pedagogy and is a graduate fellow of the McGraw Center for Teaching and Learning.. Mr. Shoaib also had some industrial experience through internships with research groups at IBM Zurich Research, Ricoh California Research, and Qualcomm Corporate Research in CA. He is a student member of the IEEE and has been a reviewer for IEEEE TBIOCAS, IEEE JETCAS, and the Design Automation Conference (DAC).

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