Results 281 to 290 of about 1,002,666 (308)
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IEEE Transactions on Signal Processing, 2008
The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B.
Shihao Ji 0001, Ya Xue, Lawrence Carin
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The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B.
Shihao Ji 0001, Ya Xue, Lawrence Carin
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IEEE Transactions on Information Forensics and Security, 2015
Identifying a signal’s origin and how it was acquired is an important forensic problem. While forensic techniques currently exist to determine a signal’s acquisition history, these techniques do not account for the possibility that a signal could be compressively sensed.
Xiaoyu Chu +2 more
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Identifying a signal’s origin and how it was acquired is an important forensic problem. While forensic techniques currently exist to determine a signal’s acquisition history, these techniques do not account for the possibility that a signal could be compressively sensed.
Xiaoyu Chu +2 more
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2008 42nd Annual Conference on Information Sciences and Systems, 2008
Reliable wireless communications often requires accurate knowledge of the underlying multipath channel. This typically involves probing of the channel with a known training waveform and linear processing of the input probe and channel output to estimate the impulse response.
Waheed Uz Zaman Bajwa +3 more
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Reliable wireless communications often requires accurate knowledge of the underlying multipath channel. This typically involves probing of the channel with a known training waveform and linear processing of the input probe and channel output to estimate the impulse response.
Waheed Uz Zaman Bajwa +3 more
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On Compressive orthonormal Sensing
2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016The Compressive Sensing (CS) approach for recovering sparse signal with orthonormal measurements has been studied under various notions of coherence. However, existing notions of coherence either do not exploit the structure of the underlying signal, or are too complicated to provide an explicit sampling scheme for all orthonormal basis sets ...
Yi Zhou 0017 +2 more
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Spatiotemporal compressed sensing for video compression
2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), 2017We present a hardware-friendly spatiotemporal compressed sensing framework for video compression. The spatiotemporal compressed sensing incorporates random sampling in both spatial and temporal domain to encode the video scene into a single coded image. During decoding, the video is reconstructed using dictionary learning and sparse recovery.
Tao Xiong +6 more
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Near-Optimal Compression for Compressed Sensing
2015 Data Compression Conference, 2015In this note we study the under-addressed quantization stage implicit in any compressed sensing signal acquisition paradigm. We also study the problem of compressing the bit-stream resulting from the quantization. We propose using Sigma-Delta (a#x03A3;a#x0394;) quantization followed by a compression stage comprised of a discrete Johnson-Linden Strauss ...
Rayan Saab +2 more
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Comparison of Common Algorithms for Single-Pixel Imaging via Compressed Sensing
Sensors, 2023Dong Wang, Gao Lei
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Terahertz compressed sensing imaging based on line array detection
Optics and Lasers in Engineering, 2023Siliang Liu, Xi Sixing, Xiaoxue Hu
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Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
SIAM Journal on Imaging Sciences, 2008Wotao Yin +2 more
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