The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring *** method is de...
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The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring *** method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array *** to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse *** sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse ***,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried *** research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.
Shear wave is suitable for long weld defect detection in offshore platforms with its advantages of long-distance propagation, small attenuation, and high accuracy. Owing to dispersion, multi-mode, and strong backgroun...
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Shear wave is suitable for long weld defect detection in offshore platforms with its advantages of long-distance propagation, small attenuation, and high accuracy. Owing to dispersion, multi-mode, and strong background noises, the defect signal can generally be overwhelmed. To solve this problem, a new sparse-based defect detection method is proposed for weld feature guided waves with a fusion of shear wave characteristics. First, an over-complete dictionary is established combining wavenumber and scattering characteristics of shear wave. Then, the split augmented Lagrangian shrinkage algorithm is introduced in the basis pursuit algorithm for the sparse solution. The defect features of the signal can be sparsely extracted. The effectiveness is evaluated via simulation studies and further verified through the practical experiment data. Compared with the wavelet atom method, the location error of the proposed method reduces by about 1.0% to only 0.564% in simulation and about 2.0% to only 0.671% in practical experiment.
The advancements in today's multimedia applications demand high quality speech signal transmission as well as storage. The limited availability of bandwidth and storage capacity necessitates the development of bet...
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ISBN:
(纸本)9781538665756
The advancements in today's multimedia applications demand high quality speech signal transmission as well as storage. The limited availability of bandwidth and storage capacity necessitates the development of better compression techniques for speech signals. Compressive sensing is an emerging technique in signal processing and it provides a novel framework for speech signal compression. In compressive sensing, a signal can be exactly reconstructed if it is naturally sparse or in some sparsifying basis. In this paper, the sparsification of speech signal is done by applying Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Linear Predictive Coding (LPC). The sparsified speech signal is compressive sensed to reduce the number of samples. The original signal is reconstructed using different algorithms like basispursuit (BP), l1 regularized least squares (l1 ls) and Orthogonal Matching pursuit (OMP). The quality of reconstructed speech signal is quantitatively expressed using different metrics like Mean Square Error (MSE), Segmental Signal to Noise Ratio (SSNR) and Perceptual Evaluation of Speech Quality (PESQ). For a 60 percentage of samples, the value of MSE obtained by using a combination of sparsifying basis DCT and reconstruction algorithm BP is 0.00018122. Using the same conditions the value of SSNR and PESQ is found to be11.3 dB and 2.574 respectively.
An analysis of air pollution by suspended particulate matter (PM10) in Brno, the second largest urban agglomeration of the Czech Republic, based on generalized linear model (GLM) is presented. Average daily concentrat...
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An analysis of air pollution by suspended particulate matter (PM10) in Brno, the second largest urban agglomeration of the Czech Republic, based on generalized linear model (GLM) is presented. Average daily concentrations coming from PM10 monitoring for the period 1998-2005 have been processed. The measured meteorological factors: air temperature and humidity, direction and wind speed were considered as covariates along with some additional seasonal factors. Three standard and six GLMs with strongly rank-deficient design matrix have been applied. The rank deficiency is due to overparameterization which allows one more precise modeling involving, among others, identification of significant air pollution sources (PSs). From each of them the parameter estimates were obtained using both standard estimation procedure and a new sparse parameter estimation technique based on a four-step modification of the basis pursuit algorithm originally suggested for time-scale analysis of digital signals. As the standard estimation algorithms often fail due to numerical instability caused by strong overparameterization, we have applied this new computationally intensive approach allowing us to reliably identify nearly zero parameters in the model and thus to find numerically stable sparse solutions. The goal of the analysis was to identify the model and algorithm yielding most precise 1-day forecasts of the level of pollution by PM10 with regard to the meteorological and seasonal covariates. Copyright (C) 2009 John Wiley & Sons, Ltd.
Finding a sparse approximation of a signal from an arbitrary dictionary is a very useful tool to solve many problems in signal processing. Several algorithms, such as basispursuit (BP) and Matching pursuits (MP, also...
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Finding a sparse approximation of a signal from an arbitrary dictionary is a very useful tool to solve many problems in signal processing. Several algorithms, such as basispursuit (BP) and Matching pursuits (MP, also known as greedy algorithms), have been introduced to compute sparse approximations of signals, but such algorithms a priori only provide suboptimal solutions. In general, it is difficult to estimate how close a computed solution is from the optimal one. In a series of recent results, several authors have shown that both BP and MP can successfully recover a sparse representation of a signal provided that it is sparse enough, that is to say if its support (which indicates where are located the nonzero coefficients) is of sufficiently small size. In this paper we define identifiable structures that support signals that can be recovered exactly by l(1) minimization (basispursuit) and greedy algorithms. In other words, if the support of a representation belongs to an identifiable structure, then the representation will be recovered by BP and MP. In addition, we obtain that if the output of an arbitrary decomposition algorithm is supported on an identifiable structure, then one can be sure that the representation is optimal within the class of signals supported by the structure. As an application of the theoretical results, we give a detailed study of a family of multichannel dictionaries with a special structure (corresponding to the representation problem X = AS Phi(T)) often used in, e. g., under-determined source separation problems or in multichannel signal processing. An identifiable structure for such dictionaries is defined using a generalization of Tropp's Babel function which combines the coherence of the mixing matrix A with that of the time-domain dictionary Phi, and we obtain explicit structure conditions which ensure that both l(1) minimization and a multichannel variant of Matching pursuit can recover structured multichannel representations.
Chen, Donoho a Saunders (1998) studují problematiku hledání řídké reprezentace vektorů (signálů) s použitím speciálních přeurčených systémů vektorů vyplňují...
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Chen, Donoho a Saunders (1998) studují problematiku hledání řídké reprezentace vektorů (signálů) s použitím speciálních přeurčených systémů vektorů vyplňujících prostor signálu. Takovéto systémy (někdy jsou také nazývány frejmy) jsou typicky vytvořeny buď rozšířením existující báze, nebo sloučením různých bazí. Narozdíl od vektorů, které tvoří konečně rozměrné prostory, může být problém formulován i obecněji v rámci nekonečně rozměrných separabilních Hilbertových prostorů (Veselý, 2002b; Christensen, 2003). Tento funkcionální přístup nám umožňuje nacházet v těchto prostorech přesnější reprezentace objektů, které, na rozdíl od vektorů, nejsou diskrétní. V této disertační práci se zabývám hledáním řídkých representací v přeurčených modelech časových řad náhodných veličin s konečnými druhými momenty. Numerická studie zachycuje výhody a omezení tohoto přístupu aplikovaného na zobecněné lineární modely a na vícerozměrné ARMA modely. Analýzou mnoha numerických simulací i modelů reálných procesů můžeme říci, že tyto metody spolehlivě identifikují parametry blízké nule, a tak nám umožňují redukovat původně špatně podmíněný přeparametrizovaný model. Tímto významně redukují počet odhadovaných parametrů. V konečném důsledku se tak nemusíme starat o řády modelů, jejichž zjišťování je většinou předběžným krokem standardních technik. Pro kratší časové řady (100 a méně vzorků) řídké odhady dávají lepší predikce v porovnání s těmi, které jsou založené na standardních metodách (např. maximální věrohodnosti v MATLABu - MATLAB System Identification Toolbox (IDENT)). Pro delší časové řady (500 a více) obě techniky dávají v podstatě stejně přesné predikce. Na druhou stranu řešení těchto problémů je náročnější, a to i časově, nicméně výpočetní doba je stále přijatelná.
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