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A unified approach to sparse signal processing

Farokh Marvasti, Arash Amini, Farzan Haddadi, Mahdi Soltanolkotabi, Babak Hossein Khalaj, Akram Aldroubi, Saeid Sanei, Janathon Chambers
2012 EURASIP Journal on Advances in Signal Processing  
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]

F. Marvasti, A. Amini, F. Haddadi, M. Soltanolkotabi, B. H. Khalaj, A. Aldroubi, S. Holm, S. Sanei, J. Chambers
2009 arXiv   pre-print
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

Farokh Marvasti, Arash Amini, Farzan Haddadi, Mehdi Soltanolkotabi, Babak Khalaj, Akram Aldroubi, Saeid Sanei, Jonathon Chambers
2012 EURASIP Journal on Advances in Signal Processing  
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

G. Vecchi, D. Labate, F. Canavero
1994 Radio Science  
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

Zhijin Qin, Xinwei Yue, Yuanwei Liu, Zhiguo Ding, Arumugam Nallanathan
2018 IEEE Communications Magazine  
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

Farokh Marvasti, Ali Mohammad-Djafari, Jonathon Chambers
2012 EURASIP Journal on Advances in Signal Processing  
[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

Shaoyang Li, Xiaoming Tao, Linhao Dong, Jianhua Lu
2015 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)  
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]

Vahid Khanagha, Khalid Daoudi
2013 Lecture Notes in Computer Science  
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

Amir Adler, Michael Elad, Yacov Hel-Or, Ehud Rivlin
2013 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)  
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

Amir Adler, Michael Elad, Yacov Hel-Or, Ehud Rivlin
2014 Journal of Signal Processing Systems  
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

Mostafa Salah, Osama A. Omer, Usama Sayed Mohammed
2016 JES. Journal of Engineering Sciences  
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

Qun Li, Honggang Zhang, Jun Guo, Bir Bhanu, Le An
2013 IEEE Signal Processing Letters  
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]

Daechul Park, Moon Ho
2011 Fourier Transforms - Approach to Scientific Principles  
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

Zhuolin Jiang, Zhe Lin, Larry S. Davis
2011 CVPR 2011  
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  
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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