T
T.C. Aysal
Researcher at Cornell University
Publications - 35
Citations - 1689
T.C. Aysal is an academic researcher from Cornell University. The author has contributed to research in topics: Weighted median & Higher-order statistics. The author has an hindex of 14, co-authored 35 publications receiving 1578 citations. Previous affiliations of T.C. Aysal include University of Delaware.
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Broadcast Gossip Algorithms for Consensus
TL;DR: It is proved that the random consensus value is, in expectation, the average of initial node measurements and that it can be made arbitrarily close to this value in mean squared error sense, under a balanced connectivity model and by trading off convergence speed with accuracy of the computation.
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Distributed Average Consensus With Dithered Quantization
TL;DR: An upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA) is derived and it is shown that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values.
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Robust Sampling and Reconstruction Methods for Sparse Signals in the Presence of Impulsive Noise
TL;DR: This paper proposes a robust nonlinear measurement operator based on the weighed myriad estimator employing a Lorentzian norm constraint on the residual error to recover sparse signals from noisy measurements and demonstrates that the proposed methods significantly outperform commonly employed compressed sensing sampling and reconstruction techniques in impulsive environments.
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Rayleigh-Maximum-Likelihood Filtering for Speckle Reduction of Ultrasound Images
T.C. Aysal,Kenneth E. Barner +1 more
TL;DR: This paper proposes a novel technique that is capable of reducing the speckle more effectively than previous methods and jointly enhancing the edge information, rather than just inhibiting smoothing.
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Constrained Decentralized Estimation Over Noisy Channels for Sensor Networks
T.C. Aysal,Kenneth E. Barner +1 more
TL;DR: The decentralized estimation model is extended to the case where imperfect transmission channels are considered, and the natural logarithm of the polynomial within the ROI showing that the function is log-concave is analyzed, thereby indicating that numerical methods, such as Newton's algorithm, can be utilized to obtain the optimal solution.