Results 281 to 290 of about 154,546 (314)

Signal processing with the sparseness constraint

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
An overview is given of the role of the sparseness constraint in signal processing problems. It is shown that this is a fundamental problem deserving of attention. This is illustrated by describing several applications where sparseness of solution is desired.
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Sparse Signal Processing

2014
Conventional sampling techniques are based on Shannon-Nyquist theory which states that the required sampling rate for perfect recovery of a band-limited signal is at least twice its bandwidth. The band-limitedness property of the signal plays a significant role in the design of conventional sampling and reconstruction systems.
Masoumeh Azghani, Farokh Marvasti
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Distributed processing in frames for sparse approximation

2008 42nd Annual Conference on Information Sciences and Systems, 2008
Beyond signal processing applications, frames are also powerful tools for modeling the sensing and information processing of many biological and man-made systems that exhibit inherent redundancy. In many cases, these systems are required to use distributed computational strategies to analyze and process the sensory information.
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Low Sidelobe Sparse Array Processing

Digital Signal Processing, 2002
Abstract Foster, S., Low Sidelobe Sparse Array Processing, Digital Signal Processing 12 (2002) 360–371 The coherent multiweight beamformer (CMWB) recently proposed by the author is analyzed from the point of view of its co-array structure. It is shown that for certain classes of sparse arrays it is possible to design CMWB beamformers to an ...
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Federated Sparse Gaussian Processes

2022
Xiangyang Guo, Daqing Wu, Jinwen Ma
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Sparse and irregular sampling in array processing

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2002
Rule-based thinning and element placement methods are first discussed. This includes random arrays, binned random arrays, periodic thinning, element shadowing properties, and fractal arrays. The algorithmic optimization of layouts of 1-D and 2-D sparse arrays is presented and it is empirically derived that the peak sidelobe level for an array with K ...
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Sparse Graph Processing with Soft-Processors

2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines, 2015
Modern FPGAs can be configured to exploit the large amount of on chip parallelism possible from the distributed SRAM memory blocks for algorithms operating on large sparse graphs. To simplify the programming and configuration of such memory-centric organizations, we can customize an array of soft processors for these graph algorithms. In particular, we
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Low-Rank and Sparse Representation for Hyperspectral Image Processing: A review

IEEE Geoscience and Remote Sensing Magazine, 2022
Jiangtao Peng, Weiwei Sun, Heng-Chao Li   +2 more
exaly  

A Fast Space-Time Adaptive Processing Algorithm Based on Sparse Bayesian Learning for Airborne Radar

Sensors, 2022
Shuguang Zhang
exaly  

Sparse Spectrum Gaussian Process Regression.

J. Mach. Learn. Res., 2010
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algorithm for regression tasks. We compare the achievable trade-offs between predictive accuracy and computational requirements, and show that these are typically superior to ...
Lázaro-Gredilla, M., Quiñonero-Candela, J., Rasmussen, C., Figueiras-Vidal, A.   +3 more
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