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Analysis and utility of atmospheric compensation of simulated compressive sensing (CS) measurements
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013Compressive sensing (CS) takes advantage of the spatial and spectral redundancy in hyperspectral imagery to take fewer measurements than traditional sensors. We simulate compressively sensed hyperspectral airborne images of a HyMap image of Cooke City, Montana using the Coded Aperture Snapshot Spectral Imager Dual Disperser (CASSI-DD) sensor model ...
Maria Busuioceanu +3 more
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Computational ghost imaging: advanced compressive sensing (CS) technique
SPIE Proceedings, 2012A novel efficient variational technique for speckle imaging is discussed. It is developed with the main motivation to filter noise, to wipe out the typical diffraction artifacts and to achieve crisp imaging. A sparse modeling is used for the wave field at the object plane in order to overcome the loss of information due to the ill-posedness of forward ...
Astola Jaakko, Katkovnik Vladimir
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Application of Compressive Sensing (CS) to Wide-Band Cognitive Radio signals
International Uni-Scientific Research Journal, 2023Compressive Sensing (CS) is a digital signal processing developed theory that encloses the signal sampling and compression, based on the sparsity characteristics of signal. This can decrease sampling rate, so reduce computational complexity of the system without degrading the performance of the system.
Mohammed Nagah +3 more
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Compressed Sensing (CS) for musical signal processing based on structured class of sensing matrices
2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016Compressed Sensing (CS) is a novel signal compression technique in which signal is compressed while sensing. The compressed signal is recovered with only few number of observations compared to conventional Shannon-Nyquist sampling and thus reducing the storage requirements.
Yuvraj V. Parkale, Sanjay L. Nalbalwar
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Application of Compressed Sensing (CS) for ECG Signal Compression: A Review
2016Compressed Sensing (CS) is a fast growing signal processing technique that compresses the signal while sensing and enables exact reconstruction of the signal if the signal is sparse with a few numbers of measurements only. This scheme results in reduction of storage requirement and low power consumption of system compared to Nyquist sampling theorem ...
Yuvraj V. Parkale, Sanjay L. Nalbalwar
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AxC-CS: Approximate Computing for Hardware Efficient Compressed Sensing Encoder Design
2019 32nd IEEE International System-on-Chip Conference (SOCC), 2019In this paper, we present an approximate computing framework for hardware-efficeint compressed sensing encoder design exploiting application-level error-resiliency, termed as AxC-CS (\underline {A}ppro\underline {x}imate \underline {C}omputing for \underline {C}ompressed \underline {S}ensing).
Wenfeng Zhao +3 more
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ECO CS: Energy consumption optimized compressive sensing in group sensor networks
Computer Networks, 2018Abstract Compressive sensing (CS) is a widely employed technique in sensor networks for energy-efficient data transmission. In recent years, the group-based network structures, e.g., regionalized and clustered networks, have been proposed to work with compressive sensing to reduce the energy cost of boundary sensors.
Hao Yang 0002, Xiwei Wang
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On the use of compressive sensing (CS) exploiting block sparsity for neural spike recording
2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2016This paper presents a novel compressive sensing (CS) algorithm for neural spike recording that exploits the concept of block sparsity in both dictionary training and signal reconstruction. Initially, the block K-SVD (BK-SVD) algorithm is employed to train a block-sparsifying dictionary for neural spikes, followed by the block sparse Bayesian learning ...
Hossein Zamani +2 more
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2016 International Conference on Machine Learning and Cybernetics (ICMLC), 2016
Compressed sensing MRI (CS-MRI) and compressed sensing sensitivity encoding (CS-SENSE) only include two regularization items, total variation (TV) and Wavelet, which leads to artifacts remaindering in 1-D random sampling. In order to improve the performance of them, a new regularization item-Contourlet is introduced to constrain the solution with the ...
Jie Song, Zhi-Wu Liao
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Compressed sensing MRI (CS-MRI) and compressed sensing sensitivity encoding (CS-SENSE) only include two regularization items, total variation (TV) and Wavelet, which leads to artifacts remaindering in 1-D random sampling. In order to improve the performance of them, a new regularization item-Contourlet is introduced to constrain the solution with the ...
Jie Song, Zhi-Wu Liao
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Demonstration of a DMD-based Compressive Sensing (CS) Spectral Imaging System
CLEO:2011 - Laser Applications to Photonic Applications, 2011We present a DMD-based spectral imaging system, which uses a DMD to impose CS measurements on the spatial/spectral information of the imaging scene. The original spatial/spectral information can be reconstructed from the CS measurements numerically.
Yuehao Wu +3 more
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