Results 211 to 220 of about 2,999 (264)
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Robust compression using Compressive Sensing (CS)
2010 IEEE ANDESCON, 2010This work introduces a new technique of robust compression on signals and images, known as Compressive Sensing (CS). It is a new and advanced technique which can reconstruct sparse signals from a few random acquired samples, achieved to avoid the Nyquist's criteria.
L. M. Merino, L. E. Mendoza
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Neural Computing and Applications, 2020
At present, information entropies of cipher images gotten by some CS-based image cryptosystems are lower than 7, which make them vulnerable to entropy attack. To cope with this problem, we propose a novel image compression–encryption method based on compressive sensing (CS) and game of life (GOL). Encryption architecture of permutation, compression and
Zhihua Gan +4 more
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At present, information entropies of cipher images gotten by some CS-based image cryptosystems are lower than 7, which make them vulnerable to entropy attack. To cope with this problem, we propose a novel image compression–encryption method based on compressive sensing (CS) and game of life (GOL). Encryption architecture of permutation, compression and
Zhihua Gan +4 more
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An Investigation of 2D Spine Magnetic Resonance Imaging (MRI) with Compressed Sensing (CS)
Skeletal Radiology, 2021To investigate the feasibility of compressed sensing MRI (CS-MRI) in the application of 2D spinal imaging and compare its performance with conventional MR imaging (non-CS-MRI).The CS imaging protocol was optimized on 5 volunteers. Non-CS-MRI and CS-MRI of 2D sagittal T1 weighted imaging (WI), Sag T2WI, and axial T2WI were performed for 71 patients (22 ...
Jianxing, Qiu +6 more
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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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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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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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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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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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