activity
20152020
most citedMeasurement Matrix Design for Compressive Detection with Secrecy Guarantees

2 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

eess.SP2020

Noisy One-bit Compressed Sensing with Side-Information

Swatantra Kafle, Thakshila Wimalajeewa, and Pramod K. Varshney

We consider the problem of sparse signal reconstruction from noisy one-bit compressed measurements when the receiver has access to side-information (SI). We assume that compressed…

cs.IT2017

Robust Detection of Random Events with Spatially Correlated Data in Wireless Sensor Networks via Distributed Compressive Sensing

Thakshila Wimalajeewa, Pramod K. Varshney

In this paper, we exploit the theory of compressive sensing to perform detection of a random source in a dense sensor network. When the sensors are densely deployed, observations a…

cs.IT2015

Joint Sparsity Pattern Recovery with 1-bit Compressive Sensing in Sensor Networks

Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa +1

We study the problem of jointly sparse support recovery with 1-bit compressive measurements in a sensor network. Sensors are assumed to observe sparse signals having the same but u…

cs.IT20152 cited

Measurement Matrix Design for Compressive Detection with Secrecy Guarantees

Bhavya Kailkhura, Sijia Liu, Thakshila Wimalajeewa +1

In this letter, we consider the problem of detecting a high dimensional signal based on compressed measurements with physical layer secrecy guarantees. We assume that the network o…

cs.IT2015

Wireless Compressive Sensing Over Fading Channels with Distributed Sparse Random Projections

Thakshila Wimalajeewa, Pramod K. Varshney

We address the problem of recovering a sparse signal observed by a resource constrained wireless sensor network under channel fading. Sparse random matrices are exploited to reduce…

stat.AP2015

Collaborative Compressive Detection with Physical Layer Secrecy Constraints

Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney

This paper considers the problem of detecting a high dimensional signal (not necessarily sparse) based on compressed measurements with physical layer secrecy guarantees. First, we…