Gaussian Component Decomposition and the Five-Component Profile of Pulsar 1451−68

Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is a category of important techniques for radar polarimetric applications. There are three key aspects concerned in the literature for the reconstruction methods, i.e., the scattering symmetric assumption, the reconstruction model, and the solving approach of the unknowns. Since CP measurements depend on the CP mode configurations, different reconstruction procedures were designed when the transmit wave varies, which means the reconstruction procedures were not unified. In this study, we propose a unified reconstruction framework for the general CP mode, which is applicable to the mode with an arbitrary transmitted ellipse wave. The unified reconstruction procedure is based on the formalized CP descriptors. The general CP symmetric scattering model-based three-component decomposition method is also employed to fit the reconstruction model parameter. Finally, a least squares (LS) estimation method, which was proposed for the linear π/4 CP data, is extended for the arbitrary CP mode to estimate the solution of the system of non-linear equations. Validation is carried out based on polarimetric data sets from both RADARSAT-2 (C-band) and ALOS-2/PALSAR (L-band), to compare the performances of reconstruction models, methods, and CP modes.

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Nonlinear Dispersive Component Decomposition: Algorithm and Applications

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3072105 ◽

2021 ◽

pp. 1-1

Author(s):

Mingxi Lv ◽

Hongguang Li

Keyword(s):

Decomposition Algorithm ◽

Dispersive Component ◽

Component Decomposition

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Influence of the Distribution of Tail States in a-Si:H on the Field Dependence of Carrier Drift Mobilities

MRS Proceedings ◽

10.1557/proc-808-a5.6 ◽

2004 ◽

Vol 808 ◽

Cited By ~ 1

Author(s):

Monica Brinza ◽

Evguenia V. Emelianova ◽

André Stesmans ◽

Guy J. Adriaenssens

Keyword(s):

Field Dependence ◽

Carrier Mobility ◽

Transport Model ◽

Chemical Vapor ◽

Gaussian Component ◽

Dispersive Transport ◽

Exponential Distributions ◽

Band Tail ◽

Multiple Trapping ◽

Gaussian Density

ABSTRACTExponential distributions of tail states have been able, within the framework of a multiple-trapping transport model, to account rather well for the time-of-flight photoconductivity transients that are measured with ‘standard’ a-Si:H, i.e. material prepared by plasma-enhanced chemical vapor deposition at ∼250°C. A field-dependent carrier mobility in the dispersive transport regime is part of the observations. However, samples prepared in an expanding thermal plasma, although still exhibiting the dispersive transients, fail to show this field dependence. The presence of a Gaussian component in the density of valence-band tail states can account for such behavior for the hole transients. Nanoscale ordered inclusions in the amorphous matrix are thought to be responsible for the Gaussian density of states contribution.

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Generating realistic Arabic handwriting dataset

International Journal of Engineering & Technology ◽

10.14419/ijet.v8i4.29786 ◽

2019 ◽

Vol 8 (4) ◽

pp. 460

Author(s):

Mahmoud I. Abdalla ◽

Mohsen A. Rashwan ◽

Mohamed A. Elserafy

Keyword(s):

Word Recognition ◽

Word List ◽

Holistic Approach ◽

Gaussian Mixture ◽

Normalization Method ◽

Gaussian Component ◽

Efficient System ◽

Mixture Regression ◽

Word Generation ◽

Input Text

During the previous year's holistic approach showing satisfactory results to solve ‎the ‎problem of Arabic handwriting word recognition instead of word letters ‎‎segmentation.‎ ‎In this paper, we present an efficient system for ‎ generation realistic Arabic handwriting dataset from ASCII input ‎text. We carefully selected simple word list that contains most Arabic ‎letters normal and ligature connection cases. To improve the ‎performance of new letters reproduction we developed our ‎normalization method that adapt its clustering action according to ‎created Arabic letters families. We enhanced Gaussian Mixture ‎Model process to learn letters template by detecting the ‎number and position of Gaussian component by implementing ‎Ramer-Douglas-Peucker‎ algorithm which improve the new letters ‎shapes reproduced by using and Gaussian Mixture Regression. ‎‎We learn the translation distance between word-part to achieve ‎real handwriting word generation shape.‎ Using combination of LSTM and CTC layer as a recognizer to validate the ‎efficiency of our approach in generating new realistic Arabic handwriting words inherit user handwriting style as shown by the experimental results.‎

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Strategies to Enhance the Performance of Gaussian Mixture Model Fitting for Uncertainty Quantification by Conditioning to Production Data

10.2118/204008-ms ◽

2021 ◽

Author(s):

Guohua Gao ◽

Jeroen Vink ◽

Fredrik Saaf ◽

Terence Wells

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

History Matching ◽

Gaussian Mixture ◽

Model Parameters ◽

Gaussian Component ◽

Large Set ◽

Cpu Time ◽

Fitting Method ◽

New Strategies

Abstract When formulating history matching within the Bayesian framework, we may quantify the uncertainty of model parameters and production forecasts using conditional realizations sampled from the posterior probability density function (PDF). It is quite challenging to sample such a posterior PDF. Some methods e.g., Markov chain Monte Carlo (MCMC), are very expensive (e.g., MCMC) while others are cheaper but may generate biased samples. In this paper, we propose an unconstrained Gaussian Mixture Model (GMM) fitting method to approximate the posterior PDF and investigate new strategies to further enhance its performance. To reduce the CPU time of handling bound constraints, we reformulate the GMM fitting formulation such that an unconstrained optimization algorithm can be applied to find the optimal solution of unknown GMM parameters. To obtain a sufficiently accurate GMM approximation with the lowest number of Gaussian components, we generate random initial guesses, remove components with very small or very large mixture weights after each GMM fitting iteration and prevent their reappearance using a dedicated filter. To prevent overfitting, we only add a new Gaussian component if the quality of the GMM approximation on a (large) set of blind-test data sufficiently improves. The unconstrained GMM fitting method with the new strategies proposed in this paper is validated using nonlinear toy problems and then applied to a synthetic history matching example. It can construct a GMM approximation of the posterior PDF that is comparable to the MCMC method, and it is significantly more efficient than the constrained GMM fitting formulation, e.g., reducing the CPU time by a factor of 800 to 7300 for problems we tested, which makes it quite attractive for large scale history matching problems.

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