Results 231 to 240 of about 22,937 (263)
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2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
In this paper we present a novel compressed sensing (CS) algorithm for the recovery of compressible, possibly time-varying, signal from a sequence of noisy observations. The newly derived scheme is based on the acclaimed unscented Kalman filter (UKF), and is essentially self reliant in the sense that no peripheral optimization or CS algorithm is ...
Avishy Carmi +2 more
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In this paper we present a novel compressed sensing (CS) algorithm for the recovery of compressible, possibly time-varying, signal from a sequence of noisy observations. The newly derived scheme is based on the acclaimed unscented Kalman filter (UKF), and is essentially self reliant in the sense that no peripheral optimization or CS algorithm is ...
Avishy Carmi +2 more
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2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
The effect of off-grid atoms has become the prominent problem in application of the Compressed Sensing (CS) techniques to the cases where there is an underlying continuous parametrization. In this work, we develop a generalizing CS framework which shows that sampling to a finite grid is not necessary toward compressive estimation.
Ashkan Panahi, Mats Viberg
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The effect of off-grid atoms has become the prominent problem in application of the Compressed Sensing (CS) techniques to the cases where there is an underlying continuous parametrization. In this work, we develop a generalizing CS framework which shows that sampling to a finite grid is not necessary toward compressive estimation.
Ashkan Panahi, Mats Viberg
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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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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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2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
A traditional assumption underlying most data converters is that the signal should be sampled at a rate exceeding twice the highest frequency. Practical signals often posses a sparse structure so that a large part of the bandwidth is not exploited. In this paper, we consider a framework for utilizing this sparsity in order to sample such analog signals
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A traditional assumption underlying most data converters is that the signal should be sampled at a rate exceeding twice the highest frequency. Practical signals often posses a sparse structure so that a large part of the bandwidth is not exploited. In this paper, we consider a framework for utilizing this sparsity in order to sample such analog signals
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Rethinking Compressive Sensing
2018 26th European Signal Processing Conference (EUSIPCO), 2018In this paper we show that Compressive Sensing (CS) can be casted as an impulse response estimation problem. Using this interpretation we re-obtain some theoretical results of CS in a simple manner. Moreover, we prove that in the case of a randomly generated sensing matrix, reconstruction probability depends on the kurtosis of the distribution used for
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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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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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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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Using compression codes in compressed sensing
2016 IEEE Information Theory Workshop (ITW), 2016Data compression and compressed sensing algorithms exploit the structure present in a signal for its efficient representation and measurement, respectively. While most state-of-the-art data compression codes take advantage of complex patterns present in signals of interest, this is not the case in compressed sensing.
Farideh Ebrahim Rezagah +3 more
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