LAUSR

Li–Yau gradient bounds on compact manifolds under nearly optimal curvature conditions

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0 Published in 2018 at "Journal of Functional Analysis"

DOI: 10.1016/j.jfa.2018.02.001

Abstract: Abstract We prove Li–Yau type gradient bounds for the heat equation either on manifolds with fixed metric or under the Ricci flow. In the former case the curvature condition is | R i c −… read more here.

Keywords: yau gradient; compact manifolds; nearly optimal; bounds compact ... See more keywords

Nearly optimal covariate designs—Part I

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1 Published in 2017 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2016.1217020

Abstract: ABSTRACT We propose to discuss at length several examples from standard text books. All of these examples deal with analysis of covariance (ANCOVA) models and related analyses of data. We intend to capitalize on our… read more here.

Keywords: designs part; optimal covariate; covariate designs; nearly optimal ... See more keywords

Nearly Optimal Robust Positioning Patterns

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1 Published in 2022 at "IEEE Transactions on Information Theory"

DOI: 10.1109/tit.2021.3118905

Abstract: A robust positioning pattern is a large array in which the contents of any subarray of given dimension can determine the subarray’s position, even if they are corrupted by errors. In this paper, we propose… read more here.

Keywords: nearly optimal; positioning; positioning patterns; optimal robust ... See more keywords

Nearly Optimal Adaptive Sequential Tests for Object Detection

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1 Published in 2020 at "IEEE Transactions on Signal Processing"

DOI: 10.1109/tsp.2020.2986542

Abstract: Object detection in a cluttered environment, involving noisy measurements of signal over time, is a central problem in radar, sonar, optical, and communications applications. We consider the problem of detecting an object assuming that the… read more here.

Keywords: object detection; nearly optimal; sprt; adaptive sprt ... See more keywords

Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions

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0 Published in 2020 at "IEEE Transactions on Signal Processing"

DOI: 10.1109/tsp.2020.2990154

Abstract: In this paper, we study the problem of compressed sensing using binary measurement matrices and $\ell _1$-norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of… read more here.

Keywords: binary matrices; sensing using; basis pursuit; using binary ... See more keywords