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Recovery ... reporting overview; Connecticut recovery; CT recovery ... progress
, 2011 Updated quarterly; Began in 2009.; Title from home page last modified 10/29/2009 (viewed Oct. 29, 2009).; At head of title: State of Connecticut.; "On October 1, state agencies that have received Recovery Act funds began reporting to the federal ...
core +3 more sources
Signal recovery under cumulative coherence [PDF]
Journal of Computational and Applied Mathematics, 2019 This paper considers signal recovery in the framework of cumulative coherence. First, we show that the Lasso estimator and the Dantzig selector exhibit similar behavior under the cumulative coherence. Then we estimate the approximation equivalence between the Lasso and the Dantzig selector by calculating prediction loss difference under the condition ...
Peng Li 0025, Wengu Chen
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Uncertainty Relations and Sparse Signal Recovery [PDF]
, 2021 Chapter in Information-theoretic Methods in Data Science, M. Rodrigues and Y.
Erwin Riegler, Helmut Bölcskei
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Robust Beamforming-Aided Signal Recovery for MIMO VLC System With Incomplete Channel
IEEE Access, 2019 Visible light communication (VLC) based on light emitting diodes (LEDs) has attracted much attention because of its high data rate and energy efficiency.
Huiqin Du, Chong Zhang, Zujian Wu
doaj +1 more source
Leida xuebao, 2023 Facing the increasingly complex electromagnetic interference environment, Synthetic Aperture Radar (SAR) interference suppression has become an urgent problem to be solved.
Zhaoyun HAN +4 more
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On one approximate method for recovering a function from its autocorrelation function
Известия высших учебных заведений. Поволжский регион: Физико-математические науки, 2022 Background. When solving many physical and technical problems, a situation arises when only operators (functionals) from the objects under study (signals, images, etc.) are available for observations (measurements). It is required to restore the object
I.V. Boykov, A.A. Pivkina
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Spatio‐temporal signal recovery under diffusion‐induced smoothness and temporal correlation priors
IET Signal Processing, 2022 In this work, the signal recovery problem regarding incomplete and noisy spatio‐temporal signals is studied. A spatio‐temporal signal is considered as a time‐varying graph signal and a diffusion‐induced first‐order Markov signal model is developed to ...
Shiyu Zhai +3 more
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Adaptive algorithm for sparse signal recovery [PDF]
Digital Signal Processing, 2019 Spike and slab priors play a key role in inducing sparsity for sparse signal recovery. The use of such priors results in hard non-convex and mixed integer programming problems. Most of the existing algorithms to solve the optimization problems involve either simplifying assumptions, relaxations or high computational expenses.
Fekadu L. Bayisa +3 more
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An efficient hybrid three-term conjugate gradient method with practical applications [PDF]
Iranian Journal of Numerical Analysis and OptimizationThe conjugate gradient method is widely used for large-scale unconstrained optimization due to its efficiency in iteration number and computing time. In this paper, we present a new parameter $\beta_{k}$ by hybridizing the Liu-Storey parameter $\beta_{k}^
Youcef Elhamam Hemici +2 more
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ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals
Sensors, 2021 This paper presents an innovative method for multiple lead electrocardiogram (ECG) monitoring based on Compressed Sensing (CS). The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on ...
Pasquale Daponte +3 more
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