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Compressive sensing in distributed radar sensor networks using pulse compression waveforms

Lei Xu, Qilian Liang, Xiuzhen Cheng, Dechang Chen
2013 EURASIP Journal on Wireless Communications and Networking  
Inspired by recent advances in compressive sensing (CS), we introduce CS to the radar sensor network (RSN) using pulse compression technique.  ...  Our idea is to employ a set of stepped-frequency (SF) waveforms as pulse compression codes for transmit sensors, and to use the same SF waveforms as the sparse matrix to compress the signal in the receiving  ...  Acknowledgements This study was supported in part by National Science Foundation under Grants CNS-0964713, CNS-1017662, CNS-0963957, CNS-0964060, and Office of Naval Research under Grant N00014-11-1-0071  ... 
doi:10.1186/1687-1499-2013-36 fatcat:zego5r4sangebebonkwywmxv7u

Kinetic Compressive Sensing [article]

Michele Scipioni, Luigi Landini (1 and 2), Ciprian Catana, Julie C. Price DII, University of Pisa, Harvard Medical School, Boston, MGH Center for Clinical Data Science, Boston)
2018 arXiv   pre-print
Real FDG PET human brain data (Siemens mMR, 40min) images were also processed.  ...  We propose a method, which we name kinetic compressive sensing (KCS), based on a hierarchical Bayesian model and on a novel reconstruction algorithm, that encodes sparsity of kinetic parameters.  ...  The method, which we name kinetic compressive sensing (KCS), is based on a hierarchical Bayesian model, and it can be described as an Ordered-Subsets Maximum-A-Posteriori One-Step-Late reconstruction algorithm  ... 
arXiv:1803.10045v1 fatcat:e5p6u4wfbzhmnbinkr5s3d7eqq

Compressive Sensing with Optical Chaos

D. Rontani, D. Choi, C.-Y. Chang, A. Locquet, D. S. Citrin
2016 Scientific Reports  
It has been shown that randomness can lead to effective sensing mechanisms 7 .  ...  As such, CS permits an extremely parsimonious way to store and transmit large and important classes of signals and images that would be far more data intensive should they be sampled following the prescription  ...  acknowledges the financial support of the Fondation Supélec and the Fonds Européen de Développement Régional (FEDER) through the projects PHOTON and APOLLO and the IAP P7/35 (BELSPO) with the Photonics@be  ... 
doi:10.1038/srep35206 pmid:27910863 pmcid:PMC5133581 fatcat:lwn2mkpkpnbmtgz5pjl7lbffsq

Signal compression in wireless sensor networks

M. F. Duarte, G. Shen, A. Ortega, R. G. Baraniuk
2011 Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences  
A key intuition in this paper is that exploiting spatial correlation requires non-processed or raw data to be transmitted from certain raw nodes so that correlations can be exploited at other aggregating  ...  In this paper, we focus on the challenges posed by spatial compression, i.e. exploiting redundancy in data across neighbouring nodes.  ...  R.G.B. was supported in part by grants NSF CCF-0431150 and CCF-0728867. M.F.D. was supported in part by NSF Supplemental Funding DMS-0439872 to UCLA-IPAM, P.I.R. Caflisch.  ... 
doi:10.1098/rsta.2011.0247 pmid:22124085 fatcat:cnu4lg6mafeydpe7wlqcanqp3a

Data Compression [chapter]

Shashi Shekhar, Hui Xiong
2008 Encyclopedia of GIS  
The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density.  ...  Data compression has important application in the areas of file storage and distributed systems.  ...  The savings achieved by data compression can be dramatic; reduction as high as 80% is not uncommon [Reghbati 19811 .  ... 
doi:10.1007/978-0-387-35973-1_239 fatcat:n2zi47ejyfecdfvmk3b2d6cxgi

Compressed sensing for digital holographic microscopy

Marcio M. Marim, Michael Atlan, Elsa D. Angelini, J.-C. Olivo-Marin
2010 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
Our approach combines a sparsity minimization algorithm to reconstruct the image and digital holography to perform quadratureresolved random measurements of an optical field in a diffraction plane.  ...  This paper describes an original microscopy imaging framework successfully employing Compressed Sensing for digital holography.  ...  Instead of sampling the entire data and then compress it to eliminate redundancy, CS performs a compressed data acquisition.  ... 
doi:10.1109/isbi.2010.5490084 dblp:conf/isbi/MarimAAO10 fatcat:dtzo6zgc5jhjnmcbvrxbxyphta

Compressive Network Coding for Approximate Sensor Data Gathering

Chong Luo, Jun Sun, Feng Wu
2011 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011  
This paper presents a joint source and network coding scheme, called compressive network coding (CNC), for approximate data gathering in wireless sensor networks.  ...  Injecting the concept of compressive sensing into network coding avoids the all-or-nothing effect of network decoding, allowing CNC to achieve graceful degradation in data precisions when the energy budget  ...  In small-scale sensor networks without stringent delay constraint, CS can be used to exploit the temporal correlations to improve the data gathering precision. B.  ... 
doi:10.1109/glocom.2011.6134085 dblp:conf/globecom/LuoSW11 fatcat:t4ax2srzzfgbzegtem7ltuttrq

Population Genetics in Compressible Flows

Simone Pigolotti, Roberto Benzi, Mogens H. Jensen, David R. Nelson
2012 Physical Review Letters  
In the absence of a velocity field and fitness advantage, number fluctuations lead to a coarsening dynamics typical of the stochastic Fisher equation.  ...  We study competition between two biological species advected by a compressible velocity field.  ...  In this case, such velocity can be offset by advection.  ... 
doi:10.1103/physrevlett.108.128102 pmid:22540626 fatcat:3smmnse5a5e4tpwdonjkriuuay

Compressed sensing in fluorescence microscopy

Gianmaria Calisesi, Alberto Ghezzi, Daniele Ancora, Cosimo D'Andrea, Gianluca Valentini, Andrea Farina, Andrea Bassi
2021 Progress in Biophysics and Molecular Biology  
Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium.  ...  CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains.  ...  Additionally, this system can be used combined with a machine learning approach in data analysis [131] .  ... 
doi:10.1016/j.pbiomolbio.2021.06.004 pmid:34153330 fatcat:25tdxqc5vvgazebdflrwmscpc4

Compressed Sensing and Electron Microscopy [chapter]

Peter Binev, Wolfgang Dahmen, Ronald DeVore, Philipp Lamby, Daniel Savu, Robert Sharpley
2012 Modeling Nanoscale Imaging in Electron Microscopy  
In this paper, we shall describe the foundations of Compressed Sensing and then examine which parts of this new theory may be useful in EM.  ...  Compressed Sensing (CS) is a relatively new approach to signal acquisition which has as its goal to minimize the number of measurements needed of the signal in order to guarantee that it is captured to  ...  We are also very grateful to Nigel Browning for providing tomography data. We would also like to thank Andreas Platen for his assistance in preparing the numerical experiments.  ... 
doi:10.1007/978-1-4614-2191-7_4 fatcat:6jexqnvg2ff5dhykr45ogg6qre

Lane Compression

Yousun Ko, Alex Chadwick, Daniel Bates, Robert Mullins
2021 ACM Transactions on Embedded Computing Systems  
This article presents Lane Compression, a lightweight lossless compression technique for machine learning that is based on a detailed study of the statistical properties of machine learning data.  ...  The proposed technique profiles machine learning data gathered ahead of run-time and partitions values bit-wise into different lanes with more distinctive statistical characteristics.  ...  Dynamic data can be more sensitive to buffer size than static data, resulting in a slightly lower compression rate than that of static data.  ... 
doi:10.1145/3431815 fatcat:zoimkqqn2be6xiasj3kt3eiage

Compressed sensing with off-axis frequency-shifting holography

Marcio M. Marim, Michael Atlan, Elsa Angelini, Jean-Christophe Olivo-Marin
2010 Optics Letters  
This work reveals an experimental microscopy acquisition scheme successfully combining Compressed Sensing (CS) and digital holography in off-axis and frequency-shifting conditions.  ...  CS is a recent data acquisition theory involving signal reconstruction from randomly undersampled measurements, exploiting the fact that most images present some compact structure and redundancy.  ...  Instead of sampling the entire data and then compressing it to eliminate redundancy, CS performs a compressed data acquisition.  ... 
doi:10.1364/ol.35.000871 pmid:20237627 fatcat:xextqdl3zfcaretbhetkr4t5nu

Compressive sensing of streams of pulses

Chinmay Hegde, Richard G. Baraniuk
2009 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton)  
For a length-N signal with sparsity K, merely M = O (K log N ) ≪ N random linear projections (measurements) can be used for robust reconstruction in polynomial time.  ...  Compressive Sensing (CS) has developed as an enticing alternative to the traditional process of signal acquisition.  ...  Thus, CS can be viewed as a new information acquisition paradigm which exploits a simple signal model (sparsity) to motivate a low-complexity representation (non-adaptive random measurements) for discrete-time  ... 
doi:10.1109/allerton.2009.5394833 fatcat:5vol4hiuybebpar3zvs3ravbey

Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering

Chong Luo, Feng Wu, Jun Sun, Chang Wen Chen
2010 IEEE Transactions on Wireless Communications  
Second, although the sparsity of sensor readings is pervasive, it might be rather complicated to fully exploit it.  ...  We carry out simulation experiments over both synthesized and real sensor data. The results confirm that CDG can preserve sensor data fidelity at a reduced communication cost.  ...  In addition, the CDG framework offers great flexibility in exploiting cross-domain sparsity patterns, some of which are hard to be utilized by conventional in-network compression schemes such as random  ... 
doi:10.1109/twc.2010.092810.100063 fatcat:zk36ysed4zfp3ag4da4l557giy

Compressive Sensing and Reconstruction of Crop Growth Environmental Information

LIHONG ZHANG, HUARUI WU
2017 DEStech Transactions on Computer Science and Engineering  
By exploiting orthogonal matching pursuit method, it is found that the reconstructed environmental information data are consistent with the measured data.  ...  Based on compression sensing, we construct the approach to crop growth environmental information monitoring.  ...  CS adopts nontraditional linear measurements in the form of randomized projections, which can be denoted as y = ΦD.  ... 
doi:10.12783/dtcse/aiea2017/14958 fatcat:6cf2qoh5fbgntccp4wmpboaniq
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