- Signal and System Modeling, Representation and Estimation
- Multirate Signal Processing
- Sampling and Reconstruction
- Nonlinear Systems and Signal Processing
- Filter Design
- Adaptive Signal Processing
- Statistical Signal Processing
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Views- Read more about Accelerated Spectral Clustering Using Graph Filtering of Random Signals
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10 Views- Read more about ProSparse Denoise: Prony's based Sparse Pattern Recovery in the Presence of Noise
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7 Views- Read more about ANALYSIS OF DISTRIBUTED ADMM ALGORITHM FOR CONSENSUS OPTIMIZATION IN PRESENCE OF ERROR
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7 Views- Read more about Oligopoly Dynamic Pricing: A Repeated Game with Incomplete Information
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We consider an oligopoly dynamic pricing problem where the demand model is unknown and the sellers have different marginal costs. We formulate the problem as a repeated game with incomplete information. We develop a dynamic pricing strategy that leads to a Pareto-efficient and subgame-perfect equilibrium and offers a bounded regret over an infinite horizon, where regret is defined as the expected cumulative profit loss as compared to the ideal scenario with a known demand model.
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4 Views- Read more about BAYESIAN TUNING FOR SUPPORT DETECTION AND SPARSE SIGNAL ESTIMATION VIA ITERATIVE SHRINKAGE-THRESHOLDING
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3 Views- Read more about Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices
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- Read more about The Graph FRI Framework–Spline Wavelet Theory and Sampling on Circulant Graphs
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6 Views- Read more about Distributed Generalized Likelihood Ratio Tests: Fundamental Limits and Tradeoffs
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This paper focuses on the problem of distributed composite
hypothesis testing in a network of sparsely interconnected
agents, in which only a small section of the field modeling
parametric alternatives is observable at each agent. A recursive
generalized likelihood ratio test (GLRT) type algorithm
in a distributed setup of the consensus-plus-innovations form
is proposed, in which the agents update their parameter estimates
and decision statistics by simultaneously processing
the latest sensed information (innovations) and information
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15 Views- Read more about Cramer-Rao bound for sparse signals fitting the low-rank model with small number of parameters
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ICASSP 2016 presentation, Session: SPTM-P14 - Compressed Sampling and Sparsity, Friday, March 25, 8:30-10:30
ICASSP2016.pdf
ICASSP2016.pdf (360) - Categories:
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