Results 41 to 50 of about 77,863 (306)
RIPless Based Radar Waveform Analysis in Sparse Microwave Imaging
Leida xuebao, 2013 The echo data can be modeled as the product of the Toeplitz matrix and reflectivity of the observed scene. The row of the Toeplitz matrix is the time-shift of the transmitted signal.
Zhao Yao +3 more
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Jixie chuandong, 2020 Sparse representation has a wide range of applications in the field of image processing and audio processing. Applying the sparse representation theory to the field of vibration signal processing can efficiently represent the periodic components of the ...
Xiaoyun Gong +3 more
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The application of sparse linear prediction dictionary to compressive sensing in speech signals
上海师范大学学报. 自然科学版, 2016 Appling compressive sensing (CS),which theoretically guarantees that signal sampling and signal compression can be achieved simultaneously,into audio and speech signal processing is one of the most popular research topics in recent years.In this paper,K ...
YOU Hanxu, LI Wei, LI Xin, ZHU Jie
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Deep Learning Meets Sparse Regularization: A signal processing perspective
IEEE Signal Processing Magazine, 2023 Deep learning has been wildly successful in practice and most state-of-the-art machine learning methods are based on neural networks. Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of deep neural networks.
Rahul Parhi, Robert D. Nowak
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Iterative thresholding for sparse approximations
, 2008 Sparse signal expansions represent or approximate a signal using a small number of elements from a large collection of elementary waveforms. Finding the optimal sparse expansion is known to be NP hard in general and non-optimal strategies such as ...
Blumensath, T. +3 more
core +1 more source
Remote Sensing, 2019 The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference.
Xiaokang Yao +4 more
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International Journal of Distributed Sensor Networks, 2020 Sparse decomposition has excellent adaptability and high flexibility in describing arbitrary complex signals based on redundant and over-complete dictionary, thus having the advantage of being free from the limitations of traditional signal processing ...
Hongchao Wang, Wenliao Du
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Concentration measures with an adaptive algorithm for processing sparse signals [PDF]
2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), 2013 In the L-estimation and compressive sensing some arbitrarily positioned samples of the signal are either so heavily corrupted by disturbances that it is better to omit them in the analysis or they are unavailable. If the considered signal with missing samples is sparse then we are still able to reconstruct these samples by using the well know ...
Ljubisa Stankovic +2 more
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Sub-Nyquist sampling of sparse and correlated signals in array processing
Digital Signal Processing, 2023 This paper considers efficient sampling of simultaneously sparse and correlated (S$\&$C) signals. Such signals arise in various applications in array processing. We propose an implementable sampling architecture for the acquisition of S$\&$C at a sub-Nyquist rate.
Ali Ahmed 0004 +2 more
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Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
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