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Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey

Mohammed Asaduzzaman Abo-Zahhad, Sabah M. Ahmed, Mohammed Asaduzzaman Farrag, Khaled Ali BaAli
2018 Journal of Signal and Information Processing  
This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole.  ...  Example of these algorithms are Basis Pursuit (BP), Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP), Gradient Pursuit, Stage-wise Orthogonal Matching Pursuit (StOMP), Regularized Orthogonal Matching  ...  If the measurements contain complex value, DFT was used as sparsifying basis, Tree based Orthogonal Matching Pursuit (TOMP) would be expedient algorithm [38] .  ... 
doi:10.4236/jsip.2018.92008 fatcat:uxre4m3lfzcqpkvlbeokm33rou

Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary Sparse Representation [article]

Fei Li, Pingping Zhang, Huchuan Lu
2018 arXiv   pre-print
band selection methods.  ...  In this paper, we present a novel learning framework for band selection based on the idea of sparse representation.  ...  matching pursuit (OMP) [36] , [37] or subspace pursuit (SP) [38] .  ... 
arXiv:1802.06983v1 fatcat:7hts2aa76vflfangqkk3jqwviq

Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

Keng-Hao Liu, Shih-Yu Chen, Hung-Chang Chien, Meng-Han Lu
2018 Remote Sensing  
Such a revolutionary method is called progressive sample processing of band selection (PSP-BS).  ...  Band selection (BS) is one of the important topics in hyperspectral image (HSI) processing. Many types of BS algorithms were proposed in the last decade.  ...  Orthogonal Matching Pursuit-Based BS (OMPBS) The OMPBS [12] is a sequential BS method based on a self-sparse regression (SSR) model [36] .  ... 
doi:10.3390/rs10030367 fatcat:sg2yjtasmba5fih5e7tybr6sku

A Review on Techniques for the Extraction of Transients in Musical Signals [chapter]

Laurent Daudet
2006 Lecture Notes in Computer Science  
instruments -the choice of method is critical.  ...  We have classified some amongst the most common methods according to the nature of their outputs.  ...  Matching Pursuit and extensions The Matching Pursuit [16] is an iterative method that selects one atom at a time.  ... 
doi:10.1007/11751069_20 fatcat:jek6yyvduzf77psswbdsqyfguy

Compressed-Sensing Reconstruction Based on Block Sparse Bayesian Learning in Bearing-Condition Monitoring

Jiedi Sun, Yang Yu, Jiangtao Wen
2017 Sensors  
The features extraction was based on orthogonal sparse basis representation, and the sparse solution problem was solved by l1-minimization.  ...  Only with the over-complete dictionary that represents the same fault type with the original signal, the reconstruction signal with matching pursuit (MP) algorithm has the smallest error.  ...  pursuit; (4) Group least absolute shrinkage and selection operator; (5) Groupbasis pursuit; and (6) Struct orthogonal matching pursuit.  ... 
doi:10.3390/s17061454 pmid:28635623 pmcid:PMC5492445 fatcat:3te7keml7bfidhjhy4mlyuimeq

Compressive Spectrum Sensing for Cognitive Radio Networks [article]

Fatima Salahdine
2018 arXiv   pre-print
In the deciding process, sensing results are analyzed and decisions are made based on these results.  ...  One of the main challenges of cognitive radio is the wideband spectrum sensing. Existing spectrum sensing techniques are based on a set of observations sampled by an ADC at the Nyquist rate.  ...  Examples of these techniques are matching pursuit (MP) [91] , orthogonal matching pursuit (OMP) [81] , stagewise orthogonal matching pursuit (StOMP) [82] , compressive sampling matching pursuit (CoSaMP  ... 
arXiv:1802.03674v1 fatcat:6ddexxzymjfqzpcw6cfoia42xy

Compressive imaging and deep learning based image reconstruction methods in the "SURPRISE" EU project

Enrico Magli, Tiziano Bianchi, Donatella Guzzi, Cinzia Lastri, Vanni Nardino, Lorenzo Palombi, Valentina Raimondi, Davide Taricco, Diego Valsesia
2021 Zenodo  
The method is based on the ISTA-NET+ neural network, which has suitably generalized in order to match the optical design of the SURPRISE instrument and the related functional requirements.  ...  This paper reports on the performance and the features of a deep learning method selected for image reconstruction.  ...  The main representatives are Matching Pursuit [7] , Orthogonal Matching Pursuit [8] , Compressed Sampling Matching Pursuit [9] , Regularized Orthogonal Matching Pursuit [10] and Subspace Pursuit  ... 
doi:10.5281/zenodo.5575136 fatcat:dz5eqcisdjgz7gbkda7b7qc2gq

Convolutional Sparse Coding based Channel Estimation for OTFS-SCMA in Uplink [article]

Anna Thomas, Kuntal Deka, P. Raviteja, Sanjeev Sharma
2021 arXiv   pre-print
The proposed method maintains a minimal overhead equivalent to a single user without compromising on the estimation error.  ...  This paper presents a channel estimation technique based on the convolutional sparse coding (CSC) approach for OTFS-SCMA in the uplink.  ...  Obtaining the initial estimates sequentially: Instead of selecting the initial J elements from the overall set of JN elements, we can strategically choose the initial values based on a groupby-group greedy  ... 
arXiv:2107.09893v1 fatcat:y57be3ryurhznfvmpscx57nr6u

Hyperspectral Band Selection by Multitask Sparsity Pursuit

Yuan Yuan, Guokang Zhu, Qi Wang
2015 IEEE Transactions on Geoscience and Remote Sensing  
strategy to handle the high computational burden associated with groupwise-selection-based methods; and 3) a novel MTSP-based criterion to evaluate the performance of each candidate band combination.  ...  This paper focuses on groupwise band selection and proposes a new framework, including the following contributions: 1) a smart yet intrinsic descriptor for efficient band representation; 2) an evolutionary  ...  In their work, a sequential forward selection strategy is also employed based on the same initialization procedure as [30] , and the expected band set is the one yielding the high classification accuracy  ... 
doi:10.1109/tgrs.2014.2326655 fatcat:vitxys6yh5fyxjjmvfera4dgk4

GLUP: Yet another algorithm for blind unmixing of hyperspectral data

Rita Ammanouil, Andre Ferrari, Cedric Richard, David Mary
2014 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)  
Comparisons with state-of-the-art methods on synthetic and real data sets show the efficiency of our approach.  ...  Group Lasso regularization is used to extract the endmembers. The estimation problem is convex, and solved with the Alternating Direction Method of Multipliers.  ...  Matching Pursuit methods [11] such as the recently published Self Dictionary Simultaneous Orthogonal Matching Pursuit (SDSOMP) [12] sequentially identify the indices of the pixels that have the largest  ... 
doi:10.1109/whispers.2014.8077546 dblp:conf/whispers/AmmanouilFRM14 fatcat:khxyeixf45frvg3mfxcvyar37a

Topics in Compressed Sensing [article]

Deanna Needell
2009 arXiv   pre-print
This gap between the two approaches was bridged when we developed and analyzed the greedy algorithm Regularized Orthogonal Matching Pursuit (ROMP).  ...  This method provides strong guarantees and stability, but relies on Linear Programming, whose methods do not yet have strong polynomially bounded runtimes.  ...  Orthogonal Matching Pursuit One such greedy algorithm is Orthogonal Matching Pursuit (OMP), put forth by Mallat and his collaborators (see e.g. [47] ) and analyzed by Gilbert and Tropp [62] .  ... 
arXiv:0905.4482v1 fatcat:kdczbzko3rhllp3hjqu3i6eovy

Cone-based joint sparse modelling for hyperspectral image classification

Ziyu Wang, Rui Zhu, Kazuhiro Fukui, Jing-Hao Xue
2018 Signal Processing  
The new algorithm is called non-negative simultaneous orthogonal matching pursuit (NN-SOMP).  ...  To solve the C-JSM problem, we also propose a new algorithm through introducing the non-negativity constraint into the simultaneous orthogonal matching pursuit (SOMP) algorithm.  ...  The sparse model can be approximately solved by greedy algorithms such as orthogonal matching pursuit (OMP) [15] (l 0norm based methods) or by convex optimisation problems such as the Lasso (l 1 -norm  ... 
doi:10.1016/j.sigpro.2017.11.001 fatcat:tbehjlhuszarjbipkygzmkxzri

Classification of underwater mammals using feature extraction based on time-frequency analysis and BCM theory

Q.Q. Huynh, L.N. Cooper, N. Intrator, H. Shouval
1998 IEEE Transactions on Signal Processing  
Different feature extraction methods and different wavelet representations are studied.  ...  to one group of signals (without knowing if they belong to the same class or not) while retaining selectivity to another set of signals.  ...  The selective response of BCM neurons to a specific frequency band was mainly seen for the whale signals due to the feature vectors becoming orthogonal to the class of porpoise sounds.  ... 
doi:10.1109/78.668783 fatcat:c5j4v3qvtbhznol46txbdlkolu

Environmental sound recognition: A survey

Sachin Chachada, C.-C. Jay Kuo
2013 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference  
Furthermore, sequential learning methods have been used to capture the long-term variation of environmental sounds.  ...  Research on ESR has significantly increased in the past decade.  ...  Second, the Orthogonal Matching Pursuit (OMP), which is a variant of BMP, was used.  ... 
doi:10.1109/apsipa.2013.6694338 dblp:conf/apsipa/ChachadaK13 fatcat:7qgpnmhnvzhpvizv4hoovahobm

Classification of satellite-based radio frequency transient recordings using sparse approximations over learned dictionaries

Daniela I. Moody, David A. Smith
2014 Journal of Applied Remote Sensing  
Ongoing research at Los Alamos National Laboratory studies the Earth's radio frequency (RF) background utilizing satellite-based RF observations of terrestrial lightning.  ...  The Fast On-orbit Recording of Transient Events (FORTE) satellite provided a rich RF lightning database.  ...  sparse representations, such as orthogonal matching pursuit 22 or an l 1 basis pursuit, 23 can also be used, but they lead to higher computational demands.  ... 
doi:10.1117/1.jrs.8.084794 fatcat:lw4fbqx4p5hr5n6mv5hifkzjd4
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