Results 21 to 30 of about 48,878 (307)
A Sparsity-Assisted Fault Diagnosis Method Based on Nonconvex Sparse Regularization
IEEE Access, 2021 Sparse representation theory can be adopted for fault feature extraction and classification. Inspired by these two capabilities of sparse representation theory, this paper proposes a novel collaborative sparsity-assisted fault diagnosis (CSFD) method ...
Yijie Niu, Jiyou Fei
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Explicit Object Representation by Sparse Neural Codes [PDF]
, 2008 Neurons have been identified in the human medial temporal lobe (MTL) that display a strong selectivity for only a few stimuli (such as familiar individuals or landmark buildings) out of perhaps 100 presented to the test subject.
Waydo, Stephen J.
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IEEE Access, 2018 Decomposition of the signal on the orthogonal or nonorthogonal basis of the signal space is the traditional method for fault feature extraction in the field of inverter fault diagnosis.
Yunjun Yu, Shilei Pei
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Audio Source Separation Using Sparse Representations [PDF]
, 2010 This is the author's final version of the article, first published as A. Nesbit, M. G. Jafari, E. Vincent and M. D. Plumbley. Audio Source Separation Using Sparse Representations. In W.
Nesbit, Andrew +7 more
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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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On the Uniqueness and Stability of Dictionaries for Sparse Representation of Noisy Signals [PDF]
IEEE Transactions on Signal Processing, 2019 Learning optimal dictionaries for sparse coding has exposed characteristic sparse features of many natural signals. However, universal guarantees of the stability of such features in the presence of noise are lacking. Here, we provide very general conditions guaranteeing when dictionaries yielding the sparsest encodings are unique and stable with ...
Charles J. Garfinkle +1 more
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Fast Dictionary Learning for Sparse Representations of Speech Signals [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2011 For dictionary-based decompositions of certain types, it has been observed that there might be a link between sparsity in the dictionary and sparsity in the decomposition. Sparsity in the dictionary has also been associated with the derivation of fast and efficient dictionary learning algorithms.
Maria G. Jafari, Mark D. Plumbley
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Dictionary learning with large step gradient descent for sparse representations [PDF]
, 2012 This is the accepted version of an article published in Lecture Notes in Computer Science Volume 7191, 2012, pp 231-238.
Boris Mailhé +5 more
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Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks
IEEE Access, 2022 Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of ...
Irfan Ahmed +3 more
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Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
Applied Sciences, 2023 Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features ...
Qinglei Jiang +5 more
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