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检索条件"主题词=Matching pursuit algorithms"

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A Reduced-Complexity Doubly Orthogonal matching pursuit Algorithm for Power Amplifier Sparse Behavioral Modeling

A Reduced-Complexity Doubly Orthogonal Matching Pursuit Algo...

引用

IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR)

作者： Becerra, Juan A. Madero-Ayora, Maria J. Reina-Tosina, Javier Crespo-Cadenas, Carlos Garcia-Frias, Javier Arce, Gonzalo Univ Seville Dept Teoria Senal & Comunicac Seville Spain Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

ISBN: (纸本)9781538659472

This work presents a simplified version of the doubly orthogonal matching pursuit (DOMP) algorithm to estimate the coefficients of a power amplifier (PA) Volterra behavioral model. The kernels are sequentially selected from the complete model, which requires the pseudoinversion of the measurement matrix after adding a new column. The proposed enhancement avoids the matrix inversions, reducing the computation complexity.

关键词： matching pursuit algorithms Mathematical model Predistortion Correlation Runtime Computational modeling Wireless communication

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Path Orthogonal matching pursuit for Sparse Reconstruction and Denoising of SWIR Maritime Imagery 31

Path Orthogonal Matching Pursuit for Sparse Reconstruction a...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Doster, Timothy Emerson, Tegan Olson, Colin US Naval Res Lab Opt Sci Div 4555 Overlook Ave SW Washington DC 20375 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We introduce an extension that may be used to augment algorithms used for the sparse decomposition of signals into a linear combination of atoms drawn from a dictionary such as those used in support of, for example, compressive sensing, k-sparse representation, and denoising. Our augmentation may be applied to any reconstruction algorithm that relies on the selection and sorting of high-correlation atoms during an analysis or identification phase by generating a "path" between the two highest-correlation atoms. Here we investigate two types of path: a linear combination (Eu-clidean geodesic) and a construction relying on an optimal transport map (2-Wasserstein geodesic). We test our extension by performing image denoising and k-sparse representation using atoms from a learned overcomplete kSVD dictionary. We study the application of our techniques on SWIR imagery of maritime vessels and show that our methods outperform orthogonal matching pursuit. We conclude that these methods, having shown success in our two tested problem domains, will also be useful for reducing "basis mismatch" error that arises in the recovery of compressively sampled images.

关键词： matching pursuit algorithms Dictionaries Image reconstruction Noise reduction Imaging Correlation Standards

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Orthogonal matching pursuit for Sparse Quantile Regression 14

Orthogonal Matching Pursuit for Sparse Quantile Regression

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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： Aravkin, Aleksandr Lozano, Aurelie Luss, Ronny Kambadur, Prabhanjan IBM Corp TJ Watson Res Ctr Yorktown Hts NY 10598 USA Bloomberg LP New York NY 10022 USA

ISBN: (纸本)9781479943036

We consider new formulations and methods for sparse quantile regression in the high-dimensional setting. Quantile regression plays an important role in many data mining applications, including outlier-robust exploratory analysis in gene selection. In addition, the sparsity consideration in quantile regression enables the exploration of the entire conditional distribution of the response variable given the predictors and therefore yields a more comprehensive view of the important predictors. We propose a generalized Orthogonal matching pursuit algorithm for variable selection, taking the misfit loss to be either the traditional quantile loss or a smooth version we call quantile Huber, and compare the resulting greedy approaches with convex sparsity-regularized formulations. We apply a recently proposed interior point methodology to efficiently solve all formulations, provide theoretical guarantees of consistent estimation, and demonstrate the performance of our approach using empirical studies of simulated and genomic datasets.

关键词： greedy algorithms pattern matching regression analysis conditional distribution data mining gene selection generalized orthogonal matching pursuit algorithm genomic datasets greedy approaches interior point methodology outlier-robust exploratory analysis quantile Huber simulated datasets sparse quantile regression variable selection Convergence IP networks MATLAB matching pursuit algorithms Servers Sparse matrices Vectors matching pursuit algorithms Input variables Sparse matrix greedy algorithm Quantile Internet Protocol networks Pattern matching Servers response variable data mining MATLAB matching pursuits Converge regression analysis

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Feature extraction based on nonparametric waveform estimation and matching pursuit

Feature extraction based on nonparametric waveform estimatio...

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IEEE International Conference on Information Acquisition

作者： Fan, Hong Feng, Wuwei Meng, Qingfeng Zhang, Youyun Fan, Hong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shannxi Prov Peoples R China Shannxi Normal Univ Sch Comp Sci Xian Peoples R China

ISBN: (纸本)1424405289

This paper presents a novel method for extracting feature waveforms of the compound signal, which combined the nonparametric waveform estimation based on filter bank with the conventional matching pursuit (MP). At each iteration of the MP, the proposed method expressed the extracted feature waveform that has the best matched with the signal local structure with a series expansion of a set of nonparametric basis functions which generated by filtering the template signal through a bandpass filter bank. A prior signal information is no more require for a template signal owe to apply the adaptive template signal in this method. So, the construction of general described by some parameters is unnecessary and it felicitates the method for a wide variety of applications. Simulated and experimental results verify the practicability and effectiveness of this algorithm, even if the noise frequency band coincides with that of the feature waveform of the signal.

关键词： Feature extraction matching pursuit algorithms Data mining Band pass filters Filter bank Signal generators Signal resolution Matched filters Filtering Frequency

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SPARSE BAYESIAN LEARNING VIA VARIATIONAL BAYES FUSED WITH ORTHOGONAL matching pursuit 2

SPARSE BAYESIAN LEARNING VIA VARIATIONAL BAYES FUSED WITH OR...

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2nd Annual Intermountain Conference on Engineering, Technology, and Computing (IETC)

作者： Shekaramiz, Mohammad Moon, Todd K. Utah Valley Univ Engn Dept Orem UT 84058 USA Utah State Univ Elect & Comp Engn Logan UT USA

ISBN: (纸本)9781665486538

We address here the non-sparse signal reconstruction behavior of the Gaussian-inverse-Gamma model, in the context of compressive sensing using sparse Bayesian learning with variational Bayes (VB) inference. We estimate the numerical sparsity level of the signal of interest using sparse Bayesian learning and VB inference. Then, we feed the estimated sparsity level along with the estimated variance on the components of the sparse signal to the orthogonal matching pursuit algorithm to refine the reconstruction results. The results show the performance improvement of sparse signal recovery, with a reasonable computation cost.

关键词： Fuses matching pursuit algorithms Radar detection Signal reconstruction Inference algorithms Bayes methods Numerical models

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Comparing gaussian and chirplet dictionaries for time-frequency analysis using matching pursuit decomposition 3

Comparing gaussian and chirplet dictionaries for time-freque...

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3rd IEEE International Symposium on Signal Processing and Information Technology

作者： Ghofrani, S McLernon, DC Ayatollahi, A Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

ISBN: (纸本)0780382927

As convergence of the matching pursuit (MP) decomposition is not dependent upon the type of atom used, we are free to assume different dictionaries. In this paper we compare the chirplet and Gaussian atoms, because both always give positive values for the Wigner-Ville distribution, and therefore the MP distribution is also always positive (as mathematically required). We show that when the MP decomposition is applied to analyze a time-varying signal, the chirplet atom is better than the Gaussian atom in tracking the instantaneous frequency. Although computational more demanding, we see that it also has a faster convergence rate. Finally, the resolution of the extracted MP distribution with the chirplet atom can also be clearly observed to be superior.

关键词： Chirp Convergence Dictionaries Frequency domain analysis Iterative algorithms matching pursuit algorithms Signal analysis Signal resolution Time frequency analysis

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SENSITIVITY SIMULATION OF COMPRESSED SENSING BASED EW RECEIVER USING ORTHOGONAL matching pursuit ALGORITHM

SENSITIVITY SIMULATION OF COMPRESSED SENSING BASED EW RECEIV...

引用

IEEE National Aerospace and Electronics Conference (NAECON)

作者： Liou, Lee L. Lin, David M. Lin, Ethan Chen, Chien-In H. US Air Force Res Lab Dayton OH 45431 USA Wright State Univ Dept Elect Engn Dayton OH 45435 USA

ISBN: (纸本)9781479946891

The wideband coverage of the traditional FFT-based electronic warfare (EW) receiver is limited by the sampling frequency of the analog-to-digital converter (ADC). Compressed sensing (CS) based wideband EW digital receiver has been proposed and under development to process wideband sparse signals with reduced sampling rate. In this study, we calculate the sensitivity of a CS based EW receiver. Two modulation schemes of CS are used. One is pseudorandom chip (PRC) and the other is non-uniform sampling (NUS) modulation. The orthogonal matching pursuit (OMP) algorithm is used to process CS data. The insufficient nature of the sparcity of the signal adversely affects the signal detection probability for both PRC and NUS modulation schemes. Methods to remedy this problem in both modulation schemes are proposed. The improvement in the signal detection and thus the sensitivity performance of the CS based receiver are presented.

关键词： analogue-digital conversion compressed sensing electronic warfare fast Fourier transforms signal detection ADC FFT-based electronic warfare receiver analog-to-digital converter nonuniform sampling modulation orthogonal matching pursuit algorithm pseudorandom chip sampling frequency sensitivity simulation signal detection probability wideband EW digital receiver wideband sparse signals Accuracy Frequency measurement matching pursuit algorithms Modulation Probability density function Receivers Sensitivity Compressed sensing electronic warfare matching pursuit algorithms analog digital converter signal acquisition Receivers Analog-digital conversion frequency measurement Fast Fourier transform Probability density function SAMPLING FREQUENCY AC to DC converters

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Improved Channel Estimation for ISDB-T using Modified Orthogonal matching pursuit over fractional delay TU6 channel

Improved Channel Estimation for ISDB-T using Modified Orthog...

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Annual Summit and Conference of Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA)

作者： Paderna, Ryan Higashino, Takeshi Okada, Minoru Nara Inst Sci & Technol Informat Sci 8916-5 Takayama Nara 6300192 Japan

ISBN: (纸本)9786163618238

This paper proposed an oversampling Modified Orthogonal matching pursuit based channel estimation for ISDB-T in fractional delay TU6 channel. The simulation shows that the proposed method has better bit-error-rate performance compared to the conventional method. In addition, the oversampling MOMP requires only less computational cost to improved the performance in fractional delay.

关键词： OFDM modulation channel estimation compressed sensing error statistics ISDB-T bit-error-rate performance compressed sensing problem fractional delay TU6 channel improved channel estimation integrated services digital broadcasting-terrestrial orthogonal frequency division multiplexing oversampling MOMP oversampling modified orthogonal matching pursuit based channel estimation Channel estimation Communication standards Data communication Delays Digital multimedia broadcasting matching pursuit algorithms Multiplexing

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Efficient Orthogonal matching pursuit using sparse random projections for scene and video classification

Efficient Orthogonal Matching Pursuit using sparse random pr...

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IEEE International Conference on Computer Vision (ICCV)

作者： Vitaladevuni, Shiv N. Natarajan, Pradeep Prasad, Rohit Natarajan, Prem Raytheon BBN Technol Cambridge MA 02138 USA

ISBN: (纸本)9781457711022

Sparse projection has been shown to be highly effective in several domains, including image denoising and scene/object classification. However, practical application to large scale problems such as video analysis requires efficient versions of sparse projection algorithms such as Orthogonal matching pursuit (OMP). In particular, random projection based locality sensitive hashing(LSH) has been proposed for OMP. In this paper, we propose a novel technique called Comparison Hadamard random project ion (CHRP) for further improving the efficiency of LSH within OMP. CHRP combines two techniques: (1) The Fast Johnson-Lindenstrauss Transform (FJLT) which uses a randomized Hadamard transform and sparse projection matrix for LSH, and (2) A chlioptas' random projection that uses only addition and comparison operations. Our approach provides the robustness of FJLT while completely avoiding multiplications. We empirically validate CHRP's efficacy by performing a suite of experiments for image denoising, scene classification, and video categorization. Our experiments indicate that CHRP significantly speeds-up OMP with negligible loss in classification accuracy.

关键词： Approximation methods Artificial neural networks Complexity theory Dictionaries matching pursuit algorithms Sparse matrices Vectors

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Sparse Representation Classification via Fast matching pursuit for Face Recognition

Sparse Representation Classification via Fast Matching Pursu...

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Japan-Africa Conference on Electronics, Communications, and Computers (JAC-ECC)

作者： Abdel-Sayed, Michael M. Khattab, Ahmed Abu-Elyazeed, Mohamed F. Cairo Univ Dept EECE Giza Egypt

ISBN: (纸本)9781538613597

Face recognition is a widely studied pattern recognition problem. One of the most crucial components of face recognition problems is classification. Sparse representation-based classification (SRC) has been recently proposed to considerably improve the classification performance by using the compressed sensing theory. However, SRC utilizes l(1) minimization for recovery. Despite being optimal, l(1) minimization is computationally expensive, and hence, not applicable in real-time applications. In this paper, we present the Fast matching pursuit (FMP) which is a compressed sensing recovery algorithm that results in a recognition time that is only 4% to 10% of that of l(1) minimization and approximately half the time of existing related matching pursuit approaches. This significant speedup does not come at the expense of any degradation in the recognition rate.

关键词： matching pursuit algorithms Minimization Databases Training Compressed sensing Face Correlation

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