Results 61 to 70 of about 63,884 (167)
, 2015 In this paper, we analyze the impact of compressed sensing with complex random matrices on Fisher information and the Cram\'{e}r-Rao Bound (CRB) for estimating unknown parameters in the mean value function of a complex multivariate normal distribution ...
Cochran, Douglas +4 more
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HDIHT: A High-Accuracy Distributed Iterative Hard Thresholding Algorithm for Compressed Sensing
IEEE Access, 2020 Iterative hard thresholding (IHT) is a beneficial tool for the recovery of sparse vectors in compressed sensing. In this study, we propose a high-accuracy distributed iterative hard thresholding algorithm (HDIHT) with explicit consideration given to the ...
Xiaming Chen, Zhuang Qi, Jianlong Xu
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Shock and Vibration, 2021 Planetary gear transmission system is an important transmission part of large machinery and is prone to failure. Aiming at the problem of how to extract fault information from vibration signals of nonlinear and nonstationary planetary gearboxes, a ...
Zhe Wu +4 more
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Replica Analysis and Approximate Message Passing Decoder for Superposition Codes
, 2014 Superposition codes are efficient for the Additive White Gaussian Noise channel. We provide here a replica analysis of the performances of these codes for large signals.
Barbier, Jean, Krzakala, Florent
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Distributed Compressed Sensing of Sensor Data
, 2010 Intelligent Information processing in distributed wireless sensor networks has many different optimizations by which redundancies in data can be eliminated, and at the same time the original source signal can be retrieved without loss. The data-centric nature of sensor network is modeled, which allows environmental applications to measure correlated ...
Vasanth Iyer, Dhananjay Singh
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A Novel Decentralized Scheme for Cooperative Compressed Spectrum Sensing in Distributed Networks
International Journal of Distributed Sensor Networks, 2015 Compressed sensing (CS) recently turns out to be an effective approach to alleviate the sampling bottleneck in wideband spectrum sensing. However, the computation overhead incurred by compressed reconstruction is nontrivial, especially in a power ...
Huang Jijun, Zha Song
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Distributed Sparse Signal Recovery For Sensor Networks
, 2013 We propose a distributed algorithm for sparse signal recovery in sensor networks based on Iterative Hard Thresholding (IHT). Every agent has a set of measurements of a signal x, and the objective is for the agents to recover x from their collective ...
Eldar, Yonina C. +2 more
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Distributed Compressed Sensing of Jointly Sparse Signals [PDF]
Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005., 2006 Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for recon- struction. In this paper we expand our theory for distributed compressed sensing (DCS) that enables new distributed cod- ing algorithms for multi-signal ensembles that exploit both intra-
Marco F. Duarte +4 more
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A Distributed Compressed Sensing-based Algorithm for the Joint Recovery of Signal Ensemble [PDF]
Radioengineering, 2018 This paper considers sparsity-aware adaptive compressed sensing acquisition and the joint reconstruction of intra- and inter-correlated signals in the wireless sensor networks via distributed compressed sensing.
J. A. Jahanshahi +2 more
doaj
Efficient and Robust Distributed Digital Codec Framework for Jointly Sparse Correlated Signals
IEEE Access, 2019 In this paper, we propose a novel distributed digital transmission framework for two jointly sparse correlated signals. First, the non-zero coefficients of each signal are quantized by a standard quantizer or a novel distributed quantizer, as appropriate.
Xuechen Chen, Fan Li, Xingcheng Liu
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