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Distributed Compressive Sensing Based Spectrum Sensing Method
2018For multi-antenna system, the difficulties of preforming spectrum sensing are high sampling rate and hardware cost. To alleviate these problems, we propose a novel utilization of distributed compressive sensing for the multi-antenna case. The multi-antenna signals first are sampled in terms of distributed compressive sensing, and then the time-domain ...
Yanping Chen 0007 +2 more
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Perceptually-aware distributed compressive video sensing
2015 Visual Communications and Image Processing (VCIP), 2015By combining the advantages of distributed video coding (DVC) and compressive sensing (CS), distributed compressive video sensing (DCVS) poses itself as a very promising low-complexity video coding framework for distributed applications. In order to improve the rate-distortion performance of DCVS, much research efforts have been focused on exploring ...
Jin Xu 0005 +3 more
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A greedy pursuit algorithm for distributed compressed sensing
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012We develop a greedy pursuit algorithm for solving the distributed compressed sensing problem in a connected network. This algorithm is based on subspace pursuit and uses the mixed support-set signal model. Through experimental evaluation, we show that the distributed algorithm performs significantly better than the standalone (disconnected) solution ...
Dennis Sundman +2 more
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A Decentralized Reconstruction Algorithm for Distributed Compressed Sensing
Wireless Personal Communications, 2017This paper considers the distributed compressed sensing (DCS), where each node has a common component and an innovation component. Most existing reconstruction methods for this DCS model are actually centralized, where the measurements of each signal are utilized together at a certain node.
Wenbo Xu 0003 +3 more
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A joint recovery algorithm for distributed compressed sensing
Transactions on Emerging Telecommunications Technologies, 2012ABSTRACTDistributed compressed sensing exploits the correlation among multiple signals to reduce the number of measurements required for recovery. In this paper, we propose a recovery algorithm for a type of joint sparsity model, where all signals share a common sparse component and each individual signal contains a sparse innovation component.
Wenbo Xu 0003 +3 more
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Distributed Outlier Detection using Compressive Sensing
Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 2015Computing outliers and related statistical aggregation functions from large-scale big data sources is a critical operation in many cloud computing scenarios, e.g. service quality assurance, fraud detection, or novelty discovery. Such problems commonly have to be solved in a distributed environment where each node only has a local slice of the entirety ...
Ying Yan 0006 +6 more
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A survey on distributed compressed sensing: theory and applications
Frontiers of Computer Science, 2014The compressed sensing (CS) theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account.
Hongpeng Yin +3 more
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A robust and efficient algorithm for distributed compressed sensing
Computers & Electrical Engineering, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qun Wang, Zhiwen Liu
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Distributed Video Coding Based on Compressed Sensing
2012 IEEE International Conference on Multimedia and Expo Workshops, 2012Compressed Sensing (CS) is a new approach to signal acquisition that can potentially allow us to design very simple video encoders that can be implemented on mobile devices with limited resources. However, previously proposed CS based video codec either require a conventional video codec or a feedback channel for effective operation, thus increasing ...
Yousuf Baig +2 more
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Distributed Compressive Sensing for Correlated Information Sources
2017The abstract should summarize the contents of the paper and should Distributed Compressive Sensing (DCS) improves the signal recovery performance of multi signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. In this paper, we propose a novel algorithm, which improves detection performance even without a priori-
Jeong-Hun Park +5 more
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