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SUMMARY:A Sparse Recovery Framework for Fast and Efficient Network Applica
tions
DTSTART;VALUE=DATE-TIME:20170719T053000Z
DTEND;VALUE=DATE-TIME:20170719T063000Z
DTSTAMP;VALUE=DATE-TIME:20180722T085941Z
UID:indico-event-5756@cern.ch
DESCRIPTION:Modern communication networks present both significant challen
ges as well as opportunities that are distinct from traditional networks.
For example\, while the huge number of connected devices in applications s
uch as IoT (Internet of Things) suggests stringent bandwidth and complexit
y requirements for communication tasks\, often\, the underlying sparsity i
n the problem greatly reduces the resources needed. With the above motivat
ion in mind\, in this talk\, we present a class for sparse recovery algori
thms that are optimal or near-optimal in terms of the speed of decoding an
d the number of measurements needed.\n\nWe discuss a novel conceptual fram
ework -- "picking and peeling" -- for fast and efficient algorithms for fo
ur important sparse recovery problems -- compressive sensing\, compressive
phase retrieval\, group testing\, and network tomography. Using this prim
itive\, we begin by describing our compressive sensing algorithm SHO-FA (f
or SHOrt and FAst) which achieves a decoding complexity of O(k) while usin
g only O(k) measurements (this is the fastest possible performance in the
order sense). Next\, we will briefly describe our algorithms for the other
three problems. For each of these problems\, our algorithms are either or
der-optimal or near-optimal both in terms of two metrics - the number of m
easurements and the time complexity of decoding. Finally\, we examine anot
her metric for performance - the energy required by the VLSI circuit that
implements these algorithms. Surprisingly\, even algorithms that are effic
ient in terms of time complexity are no longer efficient in terms of decod
ing energy. We show a novel information theoretic lower bound on the decod
ing energy required in compressive sensing and briefly describe some ideas
that may allow us to approach this bound.\n\nBio: Mayank Bakshi is a Rese
arch Assistant Professor at the Institute of Network Coding\, Chinese Univ
ersity of Hong Kong. Prior to this\, he obtained his B.Tech and M.Tech fro
m IIT Kanpur in 2003 and 2005 respectively\, and PhD from Caltech in 2011\
, all in Electrical Engineering. From 2012-2014\, he was a post-doctoral f
ellow at Institute of Network Coding\, CUHK. His research interests includ
e sparse signal recovery\, information theoretic security\, and network co
ding.\n\nhttps://indico.tifr.res.in/indico/conferenceDisplay.py?confId=575
6
LOCATION: A-201 (STCS Seminar Room)
URL:https://indico.tifr.res.in/indico/conferenceDisplay.py?confId=5756
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