Results 21 to 30 of about 3,038,089 (290)
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction [PDF]
IEEE Transactions on Medical Imaging, 2019 Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, the traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the ...
Peng Bao +11 more
semanticscholar +1 more source
IEEE Transactions on Vehicular Technology, 2022 Integrated sensing and communication (ISAC) can provide efficient usage for both spectrum and hardware resources. A critical challenge, however, is to design the dual-functional waveform for simultaneous radar sensing and communication. In this paper, we
Ruoyu Zhang +5 more
semanticscholar +1 more source
Foundations and Trends® in Communications and Information Theory, 2019 Developing computationally-efficient codes that approach the Shannon-theoretic limits for communication and compression has long been one of the major goals of information and coding theory. There have been significant advances towards this goal in the last couple of decades, with the emergence of turbo codes, sparse-graph codes, and polar codes. These
Venkataramanan, Ramji +2 more
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Sparse Kronecker-Product Coding for Unsourced Multiple Access [PDF]
IEEE Wireless Communications Letters, 2021 In this letter, a sparse Kronecker-product (SKP) coding scheme is proposed for unsourced multiple access. Specifically, the data of each active user is encoded as the Kronecker product of two component codewords with one being sparse and the other being ...
Zeyu Han +4 more
semanticscholar +1 more source
A Probabilistic Analysis of Sparse Coded Feature Pooling and Its Application for Image Retrieval. [PDF]
PLoS ONE, 2015 Feature coding and pooling as a key component of image retrieval have been widely studied over the past several years. Recently sparse coding with max-pooling is regarded as the state-of-the-art for image classification. However there is no comprehensive
Yunchao Zhang +3 more
doaj +1 more source
Frontiers in Computational Neuroscience, 2019 Sparse coding models of natural images and sounds have been able to predict several response properties of neurons in the visual and auditory systems. While the success of these models suggests that the structure they capture is universal across domains ...
Eric McVoy Dodds +4 more
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Sparse Regression Codes for Multi-terminal Source and Channel Coding [PDF]
, 2012 We study a new class of codes for Gaussian multi-terminal source and channel coding. These codes are designed using the statistical framework of high-dimensional linear regression and are called Sparse Superposition or Sparse Regression codes.
Tatikonda, Sekhar, Venkataramanan, Ramji
core +1 more source
Scholarpedia, 2008 The(frequently updated) original version is avalable at http://www.scholarpedia.org/article/Sparse_coding Mammalian brains consist of billions of neurons, each capable of independent electrical activity. Information in the brain is represented by the pattern of activation of this large neural population, forming a neural code.
Foldiak, P, Endres, D M
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Sparse representation based intraframe and semi‐intraframe video coding schemes for low bitrates
IET Image Processing, 2021 This paper proposes some extensions of the successful sparse coding of still images to intraframe and semi‐intraframe video coding. The presented frameworks apply the efficient K‐singular value decomposition and recursive least squares dictionary ...
Maziar Irannejad +1 more
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Disaggregating Transform Learning for Non-Intrusive Load Monitoring
IEEE Access, 2018 This paper addresses the problem of energy disaggregation/non-intrusive load monitoring. It introduces a new method based on the transform learning formulation. Several recent techniques, such as discriminative sparse coding, powerlet disaggregation, and
Megha Gaur, Angshul Majumdar
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