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Sparse Deconvolution Using Support Vector Machines [PDF]
EURASIP Journal on Advances in Signal Processing, 2008 Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them ...
Aníbal R. Figueiras-Vidal +5 more
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Frame Coherence and Sparse Signal Processing [PDF]
2011 IEEE International Symposium on Information Theory Proceedings, 2011 The sparse signal processing literature often uses random sensing matrices to obtain performance guarantees. Unfortunately, in the real world, sensing matrices do not always come from random processes.
Bajwa, Waheed U. +2 more
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Sparse Signal Processing Concepts for Efficient 5G System Design
IEEE Access, 2015 As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges, and what are the key drivers is part of ...
Gerhard Wunder +3 more
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Method of Range Ambiguity Suppression Combining Sparse Reconstruction and Matched Filter
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Synthetic aperture radar (SAR) images are often affected by range ambiguity due to antenna sidelobe characteristics and pulse operating mechanism. The work of range ambiguity suppression focuses on both SAR system design and signal processing. On the one
Meng Qi +4 more
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A Multi-Source Separation Approach Based on DOA Cue and DNN
Applied Sciences, 2022 Multiple sound source separation in a reverberant environment has become popular in recent years. To improve the quality of the separated signal in a reverberant environment, a separation method based on a DOA cue and a deep neural network (DNN) is ...
Yu Zhang +3 more
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Method of Range Ambiguity Suppression Combining Sparse Reconstruction and Matched Filtering
Leida xuebao, 2022 To a certain extent, SAR images are affected by range ambiguity due to antenna sidelobe characteristics and pulse operating system. The work of range ambiguity suppression focuses on SAR system design and signal processing.
Meng QI +5 more
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Applied Artificial Intelligence, 2022 In recent years, with the advancement of classical signal processing approaches, numerous works have been performed on the automatic ECG detection schemes for the enhancement of the effectiveness of the identification of the type of ECG heartbeats.
Sampath A, Sumithira T. R
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Current Developments of Sparse Microwave Imaging
Leida xuebao, 2014 The sparse microwave imaging combines the sparse signal processing theory with radar imaging to obtain new theory, new system, and new methodology of microwave imaging.
Wu Yi-rong +5 more
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A novel sparse representation algorithm for AIS real-time signals
EURASIP Journal on Wireless Communications and Networking, 2018 Sparse representation of signals based on a redundant dictionary is a new signal representation theory. Recent research activities in this field have concentrated mainly on the study of dictionary design and sparse decomposition algorithms.
Shuaiheng Huai, Shufang Zhang
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Sparse model identification using a forward orthogonal regression algorithm aided by mutual information [PDF]
, 2006 A sparse representation, with satisfactory approximation accuracy, is usually desirable in any nonlinear system identification and signal processing problem.
Billings, S.A., Wei, H.L.
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