V
Venugopal V. Veeravalli
Researcher at University of Illinois at Urbana–Champaign
Publications - 388
Citations - 14972
Venugopal V. Veeravalli is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Change detection & Wireless sensor network. The author has an hindex of 58, co-authored 377 publications receiving 13916 citations. Previous affiliations of Venugopal V. Veeravalli include Rice University & Carnegie Mellon University.
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Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization
TL;DR: This paper considers a distributed multi-agent network system where the goal is to minimize a sum of convex objective functions of the agents subject to a common convex constraint set, and investigates the effects of stochastic subgradient errors on the convergence of the algorithm.
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Decentralized detection in sensor networks
TL;DR: A binary decentralized detection problem in which a network of wireless sensors provides relevant information about the state of nature to a fusion center, and it is shown that having a set of identical binary sensors is asymptotically optimal, as the number of observations per sensor goes to infinity.
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Cooperative Sensing for Primary Detection in Cognitive Radio
TL;DR: This work designs a linear-quadratic (LQ) fusion strategy based on a deflection criterion for this problem, which takes into account the correlation between the nodes and shows that when the observations at the sensors are correlated, the LQ detector significantly outperforms the counting rule.
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Gaussian Interference Networks: Sum Capacity in the Low-Interference Regime and New Outer Bounds on the Capacity Region
TL;DR: New, improved outer bounds on the capacity region are developed and it is shown that treating interference as noise achieves the sum capacity of the two-user Gaussian interference channel in a low-interference regime, where the interference parameters are below certain thresholds.
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Multihypothesis sequential probability ratio tests .I. Asymptotic optimality
TL;DR: These results provide a complete generalization of the results given by Veeravalli and Baum, where it was shown that the quasi-Bayesian MSPRT is asymptotically efficient with respect to the expected sample size for i.i.d. observations.