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Distributed Compressed Estimation Based on Compressive Sensing [PDF]
IEEE Signal Processing Letters, 2015 This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation.
Songcen Xu +2 more
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Distributed compressive video sensing
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009Low-complexity video encoding has been applicable to several emerging applications. Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity to the order of that for still image encoding. In addition, compressive sensing (CS) has been applicable to directly capture compressed image data efficiently.
Li-Wei Kang, Chun-Shien Lu
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Compressed Sensing for Distributed Systems
2015This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new.
COLUCCIA, GIULIO +2 more
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Measurement compression in distributed compressive video sensing
2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), 2010In some application scenarios a video codec with simple encoder and complex decoder is desired. Distributed video coding (DVC) and compressive sensing (CS) theory proposed recently are two techniques suitable to such scenarios, and several video coding schemes that combine CS with DVC have appeared.
Bojin Zhuang, Xiaoran Hao, Anni Cai
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Mobile distributed compressive sensing for spectrum sensing
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014This paper studies the effect of mobility on the sensing performance of a cognitive radio network with mobile nodes. The secondary nodes sense the spectrum using a distributed compressive sensing approach to detect the available channels. Distributed compressive sensing is suggested to reduce the number of samples by exploiting correlation between the ...
Shahram Shahbazpanahi +2 more
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Sensing matrix optimization in Distributed Compressed Sensing
2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 2009Distributed Compressed Sensing (DCS) seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated. In this paper we consider the scenario in which the (overcomplete) bases for common component and innovations are different.
Antonio Artés-Rodríguez +1 more
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Distributed Compressed Sensing
2015This chapter first introduces CS in the conventional setting where one device acquires one signal and sends it to a receiver, and then extends it to the distributed framework in which multiple devices acquire multiple signals. In particular, we focus on two key problems related to the distributed setting. The former is the definition of sparsity models
Enrico Magli +2 more
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Distributed Compressed Sensing for biomedical signals
2011 3rd International Conference on Awareness Science and Technology (iCAST), 2011This paper presents a novel iterative greedy algorithm for Distributed Compressed Sensing (DCS) scenario based on backtracking technique, which is denoted by DCS-SAMP. The algorithm can reconstruct several input signals simultaneously, even when the measurements are contaminated with noise and without any prior information of their sparseness.
Zhiwen Liu, Qun Wang
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Distributed Compressive Hyperspectral Image Sensing
2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2010A novel compression framework called distributed compressed hyper spectral image sensing (DCHIS) is proposed in this paper. In our framework, the random measurements of each spectral band are obtained using compressed sensing (CS) encoding independently at the encoder.
Haiying Liu +3 more
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Distributed sparse signal sensing based on compressive sensing OFDR
Optics Letters, 2020The maximum detectable vibration frequency of an optical frequency domain reflectometry (OFDR) system is limited by the tunable rate of the laser source. Unlike uniform sampling with the time-resolved method, the sampling frequency is randomly modulated so that the vibration signal applied on the interrogation fiber is sampled by a multi-frequency sub ...
Zengguang Qin +6 more
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