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A unified approach to sparse signal processing
2012
EURASIP Journal on Advances in Signal Processing
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A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. ...
The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. ...
Holm from University of Oslo who contributed a section in sparse array design, M. Nouri Moghadam, the director of the Newton Foundation, H. Saeedi from University of Massachusetts, and K. ...
doi:10.1186/1687-6180-2012-44
fatcat:sbby5qh65bh5jd57i567br7el4
A Unified Approach to Sparse Signal Processing
[article]
2009
arXiv
pre-print
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A unified view of sparse signal processing is presented in tutorial form by bringing together various fields. ...
The key applications of sparse signal processing are sampling, coding, spectral estimation, array processing, component analysis, and multipath channel estimation. ...
CONCLUSION A unified view of sparse signal processing has been presented in tutorial form. ...
arXiv:0902.1853v1
fatcat:lxd3crtgd5fl5ofshryzm5pkfi
A unified approach to sparse signal processing
2012
EURASIP Journal on Advances in Signal Processing
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A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. ...
The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. ...
Holm from University of Oslo who contributed a section in sparse array design, M. Nouri Moghadam, the director of the Newton Foundation, H. Saeedi from University of Massachusetts, and K. ...
doi:10.1186/preaccept-1686979482577015
fatcat:7zjru2zjpne3rceluc5empoanq
Fractal approach to lightning radiation on a tortuous channel
1994
Radio Science
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Primary: Harmonic analysis, especially wavelets and time-frequency analysis and their applications to multidimensional signal and image processing. ...
Krim, A Shearlet approach to Edge Analysis and Detection, IEEE Trans. Image Process 18(5) (2009), 929-941. 33. D. Labate, and G. ...
Referee for the engineering journals: IEEE Transactions on Image Processing; IEEE Transactions on Signal Processing; IEEE Transactions on Information Theory. ...
doi:10.1029/93rs03030
fatcat:ebom6565bvgk3pzfrakyxsharu
User Association and Resource Allocation in Unified NOMA Enabled Heterogeneous Ultra Dense Networks
2018
IEEE Communications Magazine
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Particularly, a unified NOMA framework is proposed, including power-domain NOMA and code-domain NOMA, which can be configured flexibly to serve different applications scenarios. ...
As a further advance, the unified NOMA framework enabled HUDNs is further investigated, with particular focuses on the user association and resource allocation. ...
As shown inFig. 2(a), when using the sparse matrix for PD-NOMA, multiple users' signals are mapped into RBs in the first row of sparse matrix, while the rest rows of the sparse matrix are set to zeros. ...
doi:10.1109/mcom.2018.1700497
fatcat:ytmmj2rawrc2rjybuf4nvj4s6q
Editorial
2012
EURASIP Journal on Advances in Signal Processing
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[1] , entitled A unified approach to sparse signal processing which provides a tutorial review of sparse signal recovery using various techniques with minimal sampling measurements, in effect, compressed ...
Many of the articles in this special issue are related to applications of sparse signal processing. ...
doi:10.1186/1687-6180-2012-90
fatcat:odv3jhtw35aopips5muxgblkja
A nonparametric Bayesian approach to joint multiple dictionary learning with separate image sources
2015
2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
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approaches still represent image samples with a unified dictionary A set of dictionaries for image samples should be optimally learned appropriate dictionary number from the image data and characterize ...
learning provides a framework of sparse representations for high-dimensional signals (e.g., images) By seeking for the closest matching dictionary, the images of interest can be represented as the superposition ...
doi:10.1109/globalsip.2015.7418379
dblp:conf/globalsip/LiTDL15
fatcat:32ids3dkgzef3ccr7el4q4lc5q
Efficient GCI Detection for Efficient Sparse Linear Prediction
[chapter]
2013
Lecture Notes in Computer Science
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We propose a unified non-linear approach that offers an efficient closed-form solution for the problem of sparse linear prediction analysis. ...
hence, the resulting cost function approaches the ideal l0-norm cost function for sparse residual recovery. ...
on a novel multi-scale non-linear signal processing approach called the MMF. ...
doi:10.1007/978-3-642-38847-7_3
fatcat:6yjokytegjb5vfshbrooyg4u7u
Sparse coding with anomaly detection
2013
2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
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This approach provides a unified solution both for jointly sparse and independently sparse data vectors. ...
The proposed approach utilizes the Alternating Direction Method of Multipliers (ADMM) to recover simultaneously the sparse representations and the outliers components for the entire collection. ...
This paper presented a unified approach for simultaneous sparse coding and anomaly detection for both jointly-sparse and independentlysparse signal models. ...
doi:10.1109/mlsp.2013.6661898
dblp:conf/mlsp/AdlerEHR13
fatcat:nojlsbmyorhwlbzzmpbwctjhdi
Sparse Coding with Anomaly Detection
2014
Journal of Signal Processing Systems
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This approach provides a unified solution both for jointly sparse and independently sparse data vectors. ...
The proposed approach utilizes the Alternating Direction Method of Multipliers (ADMM) to recover simultaneously the sparse representations and the outliers components for the entire collection. ...
This paper presented a unified approach for simultaneous sparse coding and anomaly detection for both jointly-sparse and independentlysparse signal models. ...
doi:10.1007/s11265-014-0913-0
fatcat:fmw7fa26erg2ndm3chjgpbtvd4
JOINT COMPRESSIVE SENSING FRAMEWORK FOR SPARSE DATA/CHANNEL ESTIMATION IN NON-ORTHOGONAL MULTICARRIER SCHEME
2016
JES. Journal of Engineering Sciences
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The same CS approach for both data recovery and adaptive channel estimation in a unified sparsely manner is used. ...
In this paper, a new Sparse Frequency Division Multiplexing (SFDM) approach is suggested to generate sparse multicarrier mapping in frequency domain based on the huge combinatorial domain. ...
The Proposed unified sparse data/channel estimation framework It is a new MC scheme operating entirely in CS domain through sparsely mapping subcarriers for allowing the sparse CS approaches to be applied ...
doi:10.21608/jesaun.2016.117615
fatcat:dzwju26chngpjcg2ghlbqnm4wi
Reference-Based Scheme Combined With K-SVD for Scene Image Categorization
2013
IEEE Signal Processing Letters
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In addition to using a reference-set for images representation, we also associate the reference-set with training data in sparse codes during the dictionary learning process. ...
The reference-set is combined with the reconstruction error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. ...
ACKNOWLEDGMENT The authors would like to thank Mehran Kafai and Zhen Qin for helpful discussions. ...
doi:10.1109/lsp.2012.2228852
fatcat:meloit4r4jhuhed7ihapajr23i
Orthogonal Discrete Fourier and Cosine Matrices for Signal Processing
[chapter]
2011
Fourier Transforms - Approach to Scientific Principles
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Introduction The A DFT (Discrete Fourier Transform) has seen studied and applied to signal processing and communication theory. ...
to Scientific Principles
, (A-9) www.intechopen.com Orthogonal Discrete Fourier and Cosine Matrices for Signal Processing ...
doi:10.5772/15501
fatcat:js5vv57s5vdhtevwn4aicv6is4
Learning a discriminative dictionary for sparse coding via label consistent K-SVD
2011
CVPR 2011
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More specifically, we introduce a new label consistent constraint called 'discriminative sparse-code error' and combine it with the reconstruction error and the classification error to form a unified objective ...
A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. ...
More sophisticated approaches [30, 24, 20, 33] unify these two processes into a mixed reconstructive and discriminative formulation. Our approach also falls into this category. Ref. ...
doi:10.1109/cvpr.2011.5995354
dblp:conf/cvpr/JiangLD11
fatcat:zf36qwbpbnbcbgv6oexjrgtehq
Table of Contents
2019
IEEE Signal Processing Letters
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Li 898 A Unified Convolutional Beamformer for Simultaneous Denoising and Dereverberation . . . . T. Nakatani and K. ...
Narayanan 893 An ADMM-Based Approach to Robust Array Pattern Synthesis . . . . . . . . . . . . . . . . . . . . . J. Yang, J. Lin, Q. Shi, and Q. ...
doi:10.1109/lsp.2019.2912090
fatcat:npx3yfrzwbf7ldnesd4yfq6n6m
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