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UID:www.tcs.tifr.res.in/event/791
DTSTAMP:20230914T125938Z
SUMMARY:A Sparse Recovery Framework for Fast and Efficient Network Applicat
ions
DESCRIPTION:Speaker: Mayank Bakshi (The Chinese University of Hong Kong\nDe
partment of IE\nSha Tin\, New Territories\nHong Kong\n )\n\nAbstract: \nM
odern communication networks present both significant challenges as well a
s opportunities that are distinct from traditional networks. For example\,
while the huge number of connected devices in applications such as IoT (I
nternet of Things) suggests stringent bandwidth and complexity requirement
s for communication tasks\, often\, the underlying sparsity in the problem
greatly reduces the resources needed. With the above motivation in mind\,
in this talk\, we present a class for sparse recovery algorithms that are
optimal or near-optimal in terms of the speed of decoding and the number
of measurements needed.\n\nWe discuss a novel conceptual framework -- "pic
king and peeling" -- for fast and efficient algorithms for four important
sparse recovery problems -- compressive sensing\, compressive phase retrie
val\, group testing\, and network tomography. Using this primitive\, we be
gin by describing our compressive sensing algorithm SHO-FA (for SHOrt and
FAst) which achieves a decoding complexity of O(k) while using only O(k) m
easurements (this is the fastest possible performance in the order sense).
Next\, we will briefly describe our algorithms for the other three proble
ms. For each of these problems\, our algorithms are either order-optimal o
r near-optimal both in terms of two metrics - the number of measurements a
nd the time complexity of decoding. Finally\, we examine another metric fo
r performance - the energy required by the VLSI circuit that implements th
ese algorithms. Surprisingly\, even algorithms that are efficient in terms
of time complexity are no longer efficient in terms of decoding energy. W
e show a novel information theoretic lower bound on the decoding energy re
quired in compressive sensing and briefly describe some ideas that may all
ow us to approach this bound.\n\nBio: Mayank Bakshi is a Research Assistan
t Professor at the Institute of Network Coding\, Chinese University of Hon
g Kong. Prior to this\, he obtained his B.Tech and M.Tech from IIT Kanpur
in 2003 and 2005 respectively\, and PhD from Caltech in 2011\, all in Elec
trical Engineering. From 2012-2014\, he was a post-doctoral fellow at Inst
itute of Network Coding\, CUHK. His research interests include sparse sign
al recovery\, information theoretic security\, and network coding.\n
URL:https://www.tcs.tifr.res.in/web/events/791
DTSTART;TZID=Asia/Kolkata:20170719T110000
DTEND;TZID=Asia/Kolkata:20170719T120000
LOCATION:A-201 (STCS Seminar Room)
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