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Conditional Random Field</title> <meta name="description" content="Search results for: Gaussian Conditional Random Field"> <meta name="keywords" content="Gaussian Conditional Random Field"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Gaussian Conditional Random Field"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 10677</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Gaussian Conditional Random Field</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10677</span> Facial Expression Recognition Using Sparse Gaussian Conditional Random Field</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadamin%20Abbasnejad">Mohammadamin Abbasnejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The analysis of expression and facial Action Units (AUs) detection are very important tasks in fields of computer vision and Human Computer Interaction (HCI) due to the wide range of applications in human life. Many works have been done during the past few years which has their own advantages and disadvantages. In this work, we present a new model based on Gaussian Conditional Random Field. We solve our objective problem using ADMM and we show how well the proposed model works. We train and test our work on two facial expression datasets, CK+, and RU-FACS. Experimental evaluation shows that our proposed approach outperform state of the art expression recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20Conditional%20Random%20Field" title="Gaussian Conditional Random Field">Gaussian Conditional Random Field</a>, <a href="https://publications.waset.org/abstracts/search?q=ADMM" title=" ADMM"> ADMM</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence" title=" convergence"> convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20descent" title=" gradient descent"> gradient descent</a> </p> <a href="https://publications.waset.org/abstracts/26245/facial-expression-recognition-using-sparse-gaussian-conditional-random-field" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26245.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">356</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10676</span> Generative AI: A Comparison of Conditional Tabular Generative Adversarial Networks and Conditional Tabular Generative Adversarial Networks with Gaussian Copula in Generating Synthetic Data with Synthetic Data Vault</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lakshmi%20Prayaga">Lakshmi Prayaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandra%20Prayaga.%20Aaron%20Wade"> Chandra Prayaga. Aaron Wade</a>, <a href="https://publications.waset.org/abstracts/search?q=Gopi%20Shankar%20Mallu"> Gopi Shankar Mallu</a>, <a href="https://publications.waset.org/abstracts/search?q=Harsha%20Satya%20Pola"> Harsha Satya Pola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data%20generation" title="synthetic data generation">synthetic data generation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20tabular%20GAN" title=" conditional tabular GAN"> conditional tabular GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20copula" title=" Gaussian copula"> Gaussian copula</a> </p> <a href="https://publications.waset.org/abstracts/183000/generative-ai-a-comparison-of-conditional-tabular-generative-adversarial-networks-and-conditional-tabular-generative-adversarial-networks-with-gaussian-copula-in-generating-synthetic-data-with-synthetic-data-vault" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183000.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10675</span> Non-Universality in Barkhausen Noise Signatures of Thin Iron Films</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arnab%20Roy">Arnab Roy</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Anil%20Kumar"> P. S. Anil Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We discuss angle dependent changes to the Barkhausen noise signatures of thin epitaxial Fe films upon altering the angle of the applied field. We observe a sub-critical to critical phase transition in the hysteresis loop of the sample upon increasing the out-of-plane component of the applied field. The observations are discussed in the light of simulations of a 2D Gaussian Random Field Ising Model with references to a reducible form of the Random Anisotropy Ising Model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Barkhausen%20noise" title="Barkhausen noise">Barkhausen noise</a>, <a href="https://publications.waset.org/abstracts/search?q=Planar%20Hall%20effect" title=" Planar Hall effect"> Planar Hall effect</a>, <a href="https://publications.waset.org/abstracts/search?q=Random%20Field%20Ising%20Model" title=" Random Field Ising Model"> Random Field Ising Model</a>, <a href="https://publications.waset.org/abstracts/search?q=Random%20Anisotropy%20Ising%20Model" title=" Random Anisotropy Ising Model"> Random Anisotropy Ising Model</a> </p> <a href="https://publications.waset.org/abstracts/17529/non-universality-in-barkhausen-noise-signatures-of-thin-iron-films" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17529.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">388</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10674</span> Fast Bayesian Inference of Multivariate Block-Nearest Neighbor Gaussian Process (NNGP) Models for Large Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Gonzales">Carlos Gonzales</a>, <a href="https://publications.waset.org/abstracts/search?q=Zaida%20Quiroz"> Zaida Quiroz</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcos%20Prates"> Marcos Prates</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Several spatial variables collected at the same location that share a common spatial distribution can be modeled simultaneously through a multivariate geostatistical model that takes into account the correlation between these variables and the spatial autocorrelation. The main goal of this model is to perform spatial prediction of these variables in the region of study. Here we focus on a geostatistical multivariate formulation that relies on sharing common spatial random effect terms. In particular, the first response variable can be modeled by a mean that incorporates a shared random spatial effect, while the other response variables depend on this shared spatial term, in addition to specific random spatial effects. Each spatial random effect is defined through a Gaussian process with a valid covariance function, but in order to improve the computational efficiency when the data are large, each Gaussian process is approximated to a Gaussian random Markov field (GRMF), specifically to the block nearest neighbor Gaussian process (Block-NNGP). This approach involves dividing the spatial domain into several dependent blocks under certain constraints, where the cross blocks allow capturing the spatial dependence on a large scale, while each individual block captures the spatial dependence on a smaller scale. The multivariate geostatistical model belongs to the class of Latent Gaussian Models; thus, to achieve fast Bayesian inference, it is used the integrated nested Laplace approximation (INLA) method. The good performance of the proposed model is shown through simulations and applications for massive data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Block-NNGP" title="Block-NNGP">Block-NNGP</a>, <a href="https://publications.waset.org/abstracts/search?q=geostatistics" title=" geostatistics"> geostatistics</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20process" title=" gaussian process"> gaussian process</a>, <a href="https://publications.waset.org/abstracts/search?q=GRMF" title=" GRMF"> GRMF</a>, <a href="https://publications.waset.org/abstracts/search?q=INLA" title=" INLA"> INLA</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20models." title=" multivariate models."> multivariate models.</a> </p> <a href="https://publications.waset.org/abstracts/170871/fast-bayesian-inference-of-multivariate-block-nearest-neighbor-gaussian-process-nngp-models-for-large-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170871.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10673</span> Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Zeng">Cheng Zeng</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20Michailidis"> George Michailidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Hitoshi%20Iyatomi"> Hitoshi Iyatomi</a>, <a href="https://publications.waset.org/abstracts/search?q=Leo%20L.%20Duan"> Leo L. Duan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20density%20estimation" title="conditional density estimation">conditional density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20generation" title=" image generation"> image generation</a>, <a href="https://publications.waset.org/abstracts/search?q=normalizing%20flow" title=" normalizing flow"> normalizing flow</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20dimension%20reduction" title=" supervised dimension reduction"> supervised dimension reduction</a> </p> <a href="https://publications.waset.org/abstracts/171067/normalizing-flow-to-augmented-posterior-conditional-density-estimation-with-interpretable-dimension-reduction-for-high-dimensional-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171067.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10672</span> Adaptive Target Detection of High-Range-Resolution Radar in Non-Gaussian Clutter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lina%20Pan">Lina Pan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In non-Gaussian clutter of a spherically invariant random vector, in the cases that a certain estimated covariance matrix could become singular, the adaptive target detection of high-range-resolution radar is addressed. Firstly, the restricted maximum likelihood (RML) estimates of unknown covariance matrix and scatterer amplitudes are derived for non-Gaussian clutter. And then the RML estimate of texture is obtained. Finally, a novel detector is devised. It is showed that, without secondary data, the proposed detector outperforms the existing Kelly binary integrator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%20clutter" title="non-Gaussian clutter">non-Gaussian clutter</a>, <a href="https://publications.waset.org/abstracts/search?q=covariance%20matrix%20estimation" title=" covariance matrix estimation"> covariance matrix estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20detection" title=" target detection"> target detection</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a> </p> <a href="https://publications.waset.org/abstracts/24879/adaptive-target-detection-of-high-range-resolution-radar-in-non-gaussian-clutter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10671</span> Asymptotic Spectral Theory for Nonlinear Random Fields</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karima%20Kimouche">Karima Kimouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider the asymptotic problems in spectral analysis of stationary causal random fields. We impose conditions only involving (conditional) moments, which are easily verifiable for a variety of nonlinear random fields. Limiting distributions of periodograms and smoothed periodogram spectral density estimates are obtained and applications to the spectral domain bootstrap are given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20nonlinear%20processes" title="spatial nonlinear processes">spatial nonlinear processes</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20estimators" title=" spectral estimators"> spectral estimators</a>, <a href="https://publications.waset.org/abstracts/search?q=GMC%20condition" title=" GMC condition"> GMC condition</a>, <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20method" title=" bootstrap method"> bootstrap method</a> </p> <a href="https://publications.waset.org/abstracts/12479/asymptotic-spectral-theory-for-nonlinear-random-fields" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12479.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">451</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10670</span> Finding the Elastic Field in an Arbitrary Anisotropic Media by Implementing Accurate Generalized Gaussian Quadrature Solution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Kabir">Hossein Kabir</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Hossein%20Hassanpour%20Mati-Kolaie"> Amir Hossein Hassanpour Mati-Kolaie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the current study, the elastic field in an anisotropic elastic media is determined by implementing a general semi-analytical method. In this specific methodology, the displacement field is computed as a sum of finite functions with unknown coefficients. These aforementioned functions satisfy exactly both the homogeneous and inhomogeneous boundary conditions in the proposed media. It is worth mentioning that the unknown coefficients are determined by implementing the principle of minimum potential energy. The numerical integration is implemented by employing the Generalized Gaussian Quadrature solution. Furthermore, with the aid of the calculated unknown coefficients, the displacement field, as well as the other parameters of the elastic field, are obtainable as well. Finally, the comparison of the previous analytical method with the current semi-analytical method proposes the efficacy of the present methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20elastic%20media" title="anisotropic elastic media">anisotropic elastic media</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-analytical%20method" title=" semi-analytical method"> semi-analytical method</a>, <a href="https://publications.waset.org/abstracts/search?q=elastic%20field" title=" elastic field"> elastic field</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20gaussian%20quadrature%20solution" title=" generalized gaussian quadrature solution"> generalized gaussian quadrature solution</a> </p> <a href="https://publications.waset.org/abstracts/74780/finding-the-elastic-field-in-an-arbitrary-anisotropic-media-by-implementing-accurate-generalized-gaussian-quadrature-solution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74780.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">321</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10669</span> System of Linear Equations, Gaussian Elimination</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Khan">Rabia Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nargis%20Munir"> Nargis Munir</a>, <a href="https://publications.waset.org/abstracts/search?q=Suriya%20Gharib"> Suriya Gharib</a>, <a href="https://publications.waset.org/abstracts/search?q=Syeda%20Roshana%20Ali"> Syeda Roshana Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper linear equations are discussed in detail along with elimination method. Gaussian elimination and Gauss Jordan schemes are carried out to solve the linear system of equation. This paper comprises of matrix introduction, and the direct methods for linear equations. The goal of this research was to analyze different elimination techniques of linear equations and measure the performance of Gaussian elimination and Gauss Jordan method, in order to find their relative importance and advantage in the field of symbolic and numeric computation. The purpose of this research is to revise an introductory concept of linear equations, matrix theory and forms of Gaussian elimination through which the performance of Gauss Jordan and Gaussian elimination can be measured. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=direct" title="direct">direct</a>, <a href="https://publications.waset.org/abstracts/search?q=indirect" title=" indirect"> indirect</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20stage" title=" backward stage"> backward stage</a>, <a href="https://publications.waset.org/abstracts/search?q=forward%20stage" title=" forward stage"> forward stage</a> </p> <a href="https://publications.waset.org/abstracts/33569/system-of-linear-equations-gaussian-elimination" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33569.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">595</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10668</span> Gaussian Probability Density for Forest Fire Detection Using Satellite Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benkraouda">S. Benkraouda</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Djelloul-Khedda"> Z. Djelloul-Khedda</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Yagoubi"> B. Yagoubi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forest%20fire" title="forest fire">forest fire</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20fire%20detection" title=" forest fire detection"> forest fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title=" satellite image"> satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20distribution" title=" normal distribution"> normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20gaussian%20model" title=" theoretical gaussian model"> theoretical gaussian model</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20infrared%20matrix%20image" title=" thermal infrared matrix image"> thermal infrared matrix image</a> </p> <a href="https://publications.waset.org/abstracts/118320/gaussian-probability-density-for-forest-fire-detection-using-satellite-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118320.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10667</span> Using Gaussian Process in Wind Power Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hacene%20Benkhoula">Hacene Benkhoula</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Badreddine%20Benabdella"> Mohamed Badreddine Benabdella</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Bouzeboudja"> Hamid Bouzeboudja</a>, <a href="https://publications.waset.org/abstracts/search?q=Abderrahmane%20Asraoui"> Abderrahmane Asraoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20power" title="wind power">wind power</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussien%20process" title=" Gaussien process"> Gaussien process</a>, <a href="https://publications.waset.org/abstracts/search?q=modelling" title=" modelling"> modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/41876/using-gaussian-process-in-wind-power-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41876.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">417</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10666</span> Bayesian Flexibility Modelling of the Conditional Autoregressive Prior in a Disease Mapping Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Davies%20Obaromi">Davies Obaromi</a>, <a href="https://publications.waset.org/abstracts/search?q=Qin%20Yongsong"> Qin Yongsong</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Ndege"> James Ndege</a>, <a href="https://publications.waset.org/abstracts/search?q=Azeez%20Adeboye"> Azeez Adeboye</a>, <a href="https://publications.waset.org/abstracts/search?q=Akinwumi%20Odeyemi"> Akinwumi Odeyemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The basic model usually used in disease mapping, is the Besag, York and Mollie (BYM) model and which combines the spatially structured and spatially unstructured priors as random effects. Bayesian Conditional Autoregressive (CAR) model is a disease mapping method that is commonly used for smoothening the relative risk of any disease as used in the Besag, York and Mollie (BYM) model. This model (CAR), which is also usually assigned as a prior to one of the spatial random effects in the BYM model, successfully uses information from adjacent sites to improve estimates for individual sites. To our knowledge, there are some unrealistic or counter-intuitive consequences on the posterior covariance matrix of the CAR prior for the spatial random effects. In the conventional BYM (Besag, York and Mollie) model, the spatially structured and the unstructured random components cannot be seen independently, and which challenges the prior definitions for the hyperparameters of the two random effects. Therefore, the main objective of this study is to construct and utilize an extended Bayesian spatial CAR model for studying tuberculosis patterns in the Eastern Cape Province of South Africa, and then compare for flexibility with some existing CAR models. The results of the study revealed the flexibility and robustness of this alternative extended CAR to the commonly used CAR models by comparison, using the deviance information criteria. The extended Bayesian spatial CAR model is proved to be a useful and robust tool for disease modeling and as a prior for the structured spatial random effects because of the inclusion of an extra hyperparameter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Besag2" title="Besag2">Besag2</a>, <a href="https://publications.waset.org/abstracts/search?q=CAR%20models" title=" CAR models"> CAR models</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20mapping" title=" disease mapping"> disease mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=INLA" title=" INLA"> INLA</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20models" title=" spatial models"> spatial models</a> </p> <a href="https://publications.waset.org/abstracts/77683/bayesian-flexibility-modelling-of-the-conditional-autoregressive-prior-in-a-disease-mapping-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10665</span> Interaction of Tungsten Tips with Laguerre-Gaussian Beams</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abhisek%20Sinha">Abhisek Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Debobrata%20Rajak"> Debobrata Rajak</a>, <a href="https://publications.waset.org/abstracts/search?q=Shilpa%20Rani"> Shilpa Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ram%20Gopal"> Ram Gopal</a>, <a href="https://publications.waset.org/abstracts/search?q=Vandana%20Sharma"> Vandana Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interaction of femtosecond laser pulses with metallic tips has been studied extensively, and they have proved to be a very good source of ultrashort electron pulses. A study of the interaction of femtosecond Laguerre-Gaussian (LG) laser modes with Tungsten tips is presented here. Laser pulses of 35 fs pulse durations were incident on Tungsten tips, and their electron emission rates were studied for LG (l=1, p=0) and Gaussian modes. A change in the order of the interaction for LG beams is reported, and the difference in the order of interaction is attributed to ponderomotive shifts in the energy levels corresponding to the enhanced near-field intensity supported by numerical simulations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=femtosecond" title="femtosecond">femtosecond</a>, <a href="https://publications.waset.org/abstracts/search?q=Laguerre-Gaussian" title=" Laguerre-Gaussian"> Laguerre-Gaussian</a>, <a href="https://publications.waset.org/abstracts/search?q=OAM" title=" OAM"> OAM</a>, <a href="https://publications.waset.org/abstracts/search?q=tip" title=" tip"> tip</a> </p> <a href="https://publications.waset.org/abstracts/139164/interaction-of-tungsten-tips-with-laguerre-gaussian-beams" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139164.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">265</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10664</span> Evidence of Conditional and Unconditional Cooperation in a Public Goods Game: Experimental Evidence from Mali</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Laura%20Alzua">Maria Laura Alzua</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Adelaida%20Lopera"> Maria Adelaida Lopera</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper measures the relative importance of conditional cooperation and unconditional cooperation in a large public goods experiment conducted in Mali. We use expectations about total public goods provision to estimate a structural choice model with heterogeneous preferences. While unconditional cooperation can be captured by common preferences shared by all participants, conditional cooperation is much more heterogeneous and depends on unobserved individual factors. This structural model, in combination with two experimental treatments, suggests that leadership and group communication incentivize public goods provision through different channels. First, We find that participation of local leaders effectively changes individual choices through unconditional cooperation. A simulation exercise predicts that even in the most pessimistic scenario in which all participants expect zero public good provision, 60% would still choose to cooperate. Second, allowing participants to communicate fosters conditional cooperation. The simulations suggest that expectations are responsible for around 24% of the observed public good provision and that group communication does not necessarily ameliorate public good provision. In fact, communication may even worsen the outcome when expectations are low. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20cooperation" title="conditional cooperation">conditional cooperation</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20choice%20model" title=" discrete choice model"> discrete choice model</a>, <a href="https://publications.waset.org/abstracts/search?q=expectations" title=" expectations"> expectations</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20goods%20game" title=" public goods game"> public goods game</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20coefficients%20model" title=" random coefficients model"> random coefficients model</a> </p> <a href="https://publications.waset.org/abstracts/43314/evidence-of-conditional-and-unconditional-cooperation-in-a-public-goods-game-experimental-evidence-from-mali" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43314.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">306</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10663</span> Characterization of Probability Distributions through Conditional Expectation of Pair of Generalized Order Statistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zubdahe%20Noor">Zubdahe Noor</a>, <a href="https://publications.waset.org/abstracts/search?q=Haseeb%20Athar"> Haseeb Athar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, first a relation for conditional expectation is developed and then is used to characterize a general class of distributions F(x) = 1-e^(-ah(x)) through conditional expectation of difference of pair of generalized order statistics. Some results are reduced for particular cases. In the end, a list of distributions is presented in the form of table that are compatible with the given general class. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20order%20statistics" title="generalized order statistics">generalized order statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=order%20statistics" title=" order statistics"> order statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=record%20values" title=" record values"> record values</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20expectation" title=" conditional expectation"> conditional expectation</a>, <a href="https://publications.waset.org/abstracts/search?q=characterization" title=" characterization"> characterization</a> </p> <a href="https://publications.waset.org/abstracts/22898/characterization-of-probability-distributions-through-conditional-expectation-of-pair-of-generalized-order-statistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22898.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">460</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10662</span> Base Change for Fisher Metrics: Case of the q-Gaussian Inverse Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20I.%20Loaiza%20Ossa">Gabriel I. Loaiza Ossa</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20A.%20Cadavid%20Moreno"> Carlos A. Cadavid Moreno</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20C.%20%20Arango%20Parra"> Juan C. Arango Parra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is known that the Riemannian manifold determined by the family of inverse Gaussian distributions endowed with the Fisher metric has negative constant curvature κ= -1/2, as does the family of usual Gaussian distributions. In the present paper, firstly, we arrive at this result by following a different path, much simpler than the previous ones. We first put the family in exponential form, thus endowing the family with a new set of parameters, or coordinates, θ₁, θ₂; then we determine the matrix of the Fisher metric in terms of these parameters; and finally we compute this matrix in the original parameters. Secondly, we define the inverse q-Gaussian distribution family (q < 3) as the family obtained by replacing the usual exponential function with the Tsallis q-exponential function in the expression for the inverse Gaussian distribution and observe that it supports two possible geometries, the Fisher and the q-Fisher geometry. And finally, we apply our strategy to obtain results about the Fisher and q-Fisher geometry of the inverse q-Gaussian distribution family, similar to the ones obtained in the case of the inverse Gaussian distribution family. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=base%20of%20changes" title="base of changes">base of changes</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20geometry" title=" information geometry"> information geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20Gaussian%20distribution" title=" inverse Gaussian distribution"> inverse Gaussian distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20q-Gaussian%20distribution" title=" inverse q-Gaussian distribution"> inverse q-Gaussian distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20manifolds" title=" statistical manifolds"> statistical manifolds</a> </p> <a href="https://publications.waset.org/abstracts/138122/base-change-for-fisher-metrics-case-of-the-q-gaussian-inverse-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138122.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">244</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10661</span> Propagation of Cos-Gaussian Beam in Photorefractive Crystal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Keshavarz">A. Keshavarz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A physical model for guiding the wave in photorefractive media is studied. Propagation of cos-Gaussian beam as the special cases of sinusoidal-Gaussian beams in photorefractive crystal is simulated numerically by the Crank-Nicolson method in one dimension. Results show that the beam profile deforms as the energy transfers from the center to the tails under propagation. This simulation approach is of significant interest for application in optical telecommunication. The results are presented graphically and discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beam%20propagation" title="beam propagation">beam propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=cos-Gaussian%20beam" title=" cos-Gaussian beam"> cos-Gaussian beam</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title=" numerical simulation"> numerical simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=photorefractive%20crystal" title=" photorefractive crystal"> photorefractive crystal</a> </p> <a href="https://publications.waset.org/abstracts/33883/propagation-of-cos-gaussian-beam-in-photorefractive-crystal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33883.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">499</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10660</span> Gaussian Particle Flow Bernoulli Filter for Single Target Tracking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyeongbok%20Kim">Hyeongbok Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Lingling%20Zhao"> Lingling Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaohong%20Su"> Xiaohong Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Junjie%20Wang"> Junjie Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Bernoulli filter is a precise Bayesian filter for single target tracking based on the random finite set theory. The standard Bernoulli filter often underestimates the number of targets. This study proposes a Gaussian particle flow (GPF) Bernoulli filter employing particle flow to migrate particles from prior to posterior positions to improve the performance of the standard Bernoulli filter. By employing the particle flow filter, the computational speed of the Bernoulli filters is significantly improved. In addition, the GPF Bernoulli filter provides a more accurate estimation compared with that of the standard Bernoulli filter. Simulation results confirm the improved tracking performance and computational speed in two- and three-dimensional scenarios compared with other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bernoulli%20filter" title="Bernoulli filter">Bernoulli filter</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title=" particle filter"> particle filter</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20flow%20filter" title=" particle flow filter"> particle flow filter</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20finite%20sets" title=" random finite sets"> random finite sets</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20tracking" title=" target tracking"> target tracking</a> </p> <a href="https://publications.waset.org/abstracts/162210/gaussian-particle-flow-bernoulli-filter-for-single-target-tracking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162210.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">92</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10659</span> Combining the Dynamic Conditional Correlation and Range-GARCH Models to Improve Covariance Forecasts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piotr%20Fiszeder">Piotr Fiszeder</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcin%20Fa%C5%82dzi%C5%84ski"> Marcin Fałdziński</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Moln%C3%A1r"> Peter Molnár</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dynamic conditional correlation model of Engle (2002) is one of the most popular multivariate volatility models. However, this model is based solely on closing prices. It has been documented in the literature that the high and low price of the day can be used in an efficient volatility estimation. We, therefore, suggest a model which incorporates high and low prices into the dynamic conditional correlation framework. Empirical evaluation of this model is conducted on three datasets: currencies, stocks, and commodity exchange-traded funds. The utilisation of realized variances and covariances as proxies for true variances and covariances allows us to reach a strong conclusion that our model outperforms not only the standard dynamic conditional correlation model but also a competing range-based dynamic conditional correlation model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=volatility" title="volatility">volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=DCC%20model" title=" DCC model"> DCC model</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20and%20low%20prices" title=" high and low prices"> high and low prices</a>, <a href="https://publications.waset.org/abstracts/search?q=range-based%20models" title=" range-based models"> range-based models</a>, <a href="https://publications.waset.org/abstracts/search?q=covariance%20forecasting" title=" covariance forecasting"> covariance forecasting</a> </p> <a href="https://publications.waset.org/abstracts/107388/combining-the-dynamic-conditional-correlation-and-range-garch-models-to-improve-covariance-forecasts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107388.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">183</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10658</span> Statistical Characteristics of Distribution of Radiation-Induced Defects under Random Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Selyshchev">P. Selyshchev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We consider fluctuations of defects density taking into account their interaction. Stochastic field of displacement generation rate gives random defect distribution. We determinate statistical characteristics (mean and dispersion) of random field of point defect distribution as function of defect generation parameters, temperature and properties of irradiated crystal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=irradiation" title="irradiation">irradiation</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20defects" title=" primary defects"> primary defects</a>, <a href="https://publications.waset.org/abstracts/search?q=interaction" title=" interaction"> interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=fluctuations" title=" fluctuations"> fluctuations</a> </p> <a href="https://publications.waset.org/abstracts/10105/statistical-characteristics-of-distribution-of-radiation-induced-defects-under-random-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10105.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">343</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10657</span> CPPI Method with Conditional Floor: The Discrete Time Case</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hachmi%20Ben%20Ameur">Hachmi Ben Ameur</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean%20Luc%20Prigent"> Jean Luc Prigent</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an extension of the CPPI method, which is based on conditional floors. In this framework, we examine in particular the TIPP and margin based strategies. These methods allow keeping part of the past gains and protecting the portfolio value against future high drawdowns of the financial market. However, as for the standard CPPI method, the investor can benefit from potential market rises. To control the risk of such strategies, we introduce both Value-at-Risk (VaR) and Expected Shortfall (ES) risk measures. For each of these criteria, we show that the conditional floor must be higher than a lower bound. We illustrate these results, for a quite general ARCH type model, including the EGARCH (1,1) as a special case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CPPI" title="CPPI">CPPI</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20floor" title=" conditional floor"> conditional floor</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH" title=" ARCH"> ARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=VaR" title=" VaR"> VaR</a>, <a href="https://publications.waset.org/abstracts/search?q=expected%20ehortfall" title=" expected ehortfall"> expected ehortfall</a> </p> <a href="https://publications.waset.org/abstracts/43188/cppi-method-with-conditional-floor-the-discrete-time-case" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43188.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10656</span> Frequency Offset Estimation Schemes Based on ML for OFDM Systems in Non-Gaussian Noise Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, frequency offset (FO) estimation schemes robust to the non-Gaussian noise environments are proposed for orthogonal frequency division multiplexing (OFDM) systems. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise environments is proposed, and then, the complexity of the ML estimation scheme is reduced by employing a reduced set of candidate values. In numerical results, it is demonstrated that the proposed schemes provide a significant performance improvement over the conventional estimation scheme in non-Gaussian noise environments while maintaining the performance similar to the estimation performance in Gaussian noise environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20offset%20estimation" title="frequency offset estimation">frequency offset estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood" title=" maximum-likelihood"> maximum-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%20noise%0D%0Aenvironment" title=" non-Gaussian noise environment"> non-Gaussian noise environment</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=training%20symbol" title=" training symbol"> training symbol</a> </p> <a href="https://publications.waset.org/abstracts/9430/frequency-offset-estimation-schemes-based-on-ml-for-ofdm-systems-in-non-gaussian-noise-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9430.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">353</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10655</span> Upgraded Cuckoo Search Algorithm to Solve Optimisation Problems Using Gaussian Selection Operator and Neighbour Strategy Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mukesh%20Kumar%20Shah">Mukesh Kumar Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Tushar%20Gupta"> Tushar Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An Upgraded Cuckoo Search Algorithm is proposed here to solve optimization problems based on the improvements made in the earlier versions of Cuckoo Search Algorithm. Short comings of the earlier versions like slow convergence, trap in local optima improved in the proposed version by random initialization of solution by suggesting an Improved Lambda Iteration Relaxation method, Random Gaussian Distribution Walk to improve local search and further proposing Greedy Selection to accelerate to optimized solution quickly and by “Study Nearby Strategy” to improve global search performance by avoiding trapping to local optima. It is further proposed to generate better solution by Crossover Operation. The proposed strategy used in algorithm shows superiority in terms of high convergence speed over several classical algorithms. Three standard algorithms were tested on a 6-generator standard test system and the results are presented which clearly demonstrate its superiority over other established algorithms. The algorithm is also capable of handling higher unit systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economic%20dispatch" title="economic dispatch">economic dispatch</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20selection%20operator" title=" gaussian selection operator"> gaussian selection operator</a>, <a href="https://publications.waset.org/abstracts/search?q=prohibited%20operating%20zones" title=" prohibited operating zones"> prohibited operating zones</a>, <a href="https://publications.waset.org/abstracts/search?q=ramp%20rate%20limits" title=" ramp rate limits"> ramp rate limits</a> </p> <a href="https://publications.waset.org/abstracts/116798/upgraded-cuckoo-search-algorithm-to-solve-optimisation-problems-using-gaussian-selection-operator-and-neighbour-strategy-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116798.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10654</span> A Hazard Rate Function for the Time of Ruin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sule%20Sahin">Sule Sahin</a>, <a href="https://publications.waset.org/abstracts/search?q=Basak%20Bulut%20Karageyik"> Basak Bulut Karageyik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a hazard rate function for the time of ruin to calculate the conditional probability of ruin for very small intervals. We call this function the force of ruin (FoR). We obtain the expected time of ruin and conditional expected time of ruin from the exact finite time ruin probability with exponential claim amounts. Then we introduce the FoR which gives the conditional probability of ruin and the condition is that ruin has not occurred at time t. We analyse the behavior of the FoR function for different initial surpluses over a specific time interval. We also obtain FoR under the excess of loss reinsurance arrangement and examine the effect of reinsurance on the FoR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20time%20of%20ruin" title="conditional time of ruin">conditional time of ruin</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20time%20ruin%20probability" title=" finite time ruin probability"> finite time ruin probability</a>, <a href="https://publications.waset.org/abstracts/search?q=force%20of%20ruin" title=" force of ruin"> force of ruin</a>, <a href="https://publications.waset.org/abstracts/search?q=reinsurance" title=" reinsurance"> reinsurance</a> </p> <a href="https://publications.waset.org/abstracts/55648/a-hazard-rate-function-for-the-time-of-ruin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55648.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">405</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10653</span> Bayesian Analysis of Change Point Problems Using Conditionally Specified Priors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Golnaz%20Shahtahmassebi">Golnaz Shahtahmassebi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Maria%20Sarabia"> Jose Maria Sarabia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this talk, we introduce a new class of conjugate prior distributions obtained from conditional specification methodology. We illustrate the application of such distribution in Bayesian change point detection in Poisson processes. We obtain the posterior distribution of model parameters using a general bivariate distribution with gamma conditionals. Simulation from the posterior is readily implemented using a Gibbs sampling algorithm. The Gibbs sampling is implemented even when using conditional densities that are incompatible or only compatible with an improper joint density. The application of such methods will be demonstrated using examples of simulated and real data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=change%20point" title="change point">change point</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20inference" title=" bayesian inference"> bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=Gibbs%20sampler" title=" Gibbs sampler"> Gibbs sampler</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20specification" title=" conditional specification"> conditional specification</a>, <a href="https://publications.waset.org/abstracts/search?q=gamma%20conditional%20distributions" title=" gamma conditional distributions"> gamma conditional distributions</a> </p> <a href="https://publications.waset.org/abstracts/141782/bayesian-analysis-of-change-point-problems-using-conditionally-specified-priors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141782.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">189</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10652</span> Nonparametric Quantile Regression for Multivariate Spatial Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Arnaud%20Kanga">S. H. Arnaud Kanga</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Hili"> O. Hili</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dabo-Niang"> S. Dabo-Niang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20quantile" title="conditional quantile">conditional quantile</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel" title=" kernel"> kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric" title=" nonparametric"> nonparametric</a>, <a href="https://publications.waset.org/abstracts/search?q=stationary" title=" stationary"> stationary</a> </p> <a href="https://publications.waset.org/abstracts/109937/nonparametric-quantile-regression-for-multivariate-spatial-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109937.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10651</span> Non Classical Photonic Nanojets in near Field of Metallic and Negative-Index Scatterers, Purely Electric and Magnetic Nanojets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dmytro%20O.%20Plutenko">Dmytro O. Plutenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexei%20D.%20Kiselev"> Alexei D. Kiselev</a>, <a href="https://publications.waset.org/abstracts/search?q=Mikhail%20V.%20Vasnetsov"> Mikhail V. Vasnetsov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present the results of our analytical and computational study of Laguerre-Gaussian (LG) beams scattering by spherical homogeneous isotropic particles located on the axis of the beam. We consider different types of scatterers (dielectric, metallic and double negative metamaterials) and different polarizations of the LG beams. A possibility to generate photonic nanojets using metallic and double negative metamaterial Mie scatterers is shown. We have studied the properties of such nonclassical nanojets and discovered new types of the nanojets characterized by zero on-axes magnetic (or electric) field with the electric (or magnetic) field polarized along the z-axis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=double%20negative%20metamaterial" title="double negative metamaterial">double negative metamaterial</a>, <a href="https://publications.waset.org/abstracts/search?q=Laguerre-Gaussian%20beam" title=" Laguerre-Gaussian beam"> Laguerre-Gaussian beam</a>, <a href="https://publications.waset.org/abstracts/search?q=Mie%20scattering" title=" Mie scattering"> Mie scattering</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20vortices" title=" optical vortices"> optical vortices</a>, <a href="https://publications.waset.org/abstracts/search?q=photonic%20nanojets" title=" photonic nanojets"> photonic nanojets</a> </p> <a href="https://publications.waset.org/abstracts/80428/non-classical-photonic-nanojets-in-near-field-of-metallic-and-negative-index-scatterers-purely-electric-and-magnetic-nanojets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80428.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">221</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10650</span> Gaussian Operations with a Single Trapped Ion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bruna%20G.%20M.%20Ara%C3%BAjo">Bruna G. M. Araújo</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20M.%20M.%20Q.%20Cruz"> Pedro M. M. Q. Cruz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this letter, we review the literature of the major concepts that govern Gaussian quantum information. As we work with quantum information and computation with continuous variables, Gaussian states are needed to better describe these systems. Analyzing a single ion locked in a Paul trap we use the interaction picture to obtain a toolbox of Gaussian operations with the ion-laser interaction Hamiltionian. This is achieved exciting the ion through the combination of two lasers of distinct frequencies corresponding to different sidebands of the external degrees of freedom. First we study the case of a trap with 1 mode and then the case with 2 modes. In this way, we achieve different continuous variables gates just by changing the external degrees of freedom of the trap and combining the Hamiltonians of blue and red sidebands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paul%20trap" title="Paul trap">Paul trap</a>, <a href="https://publications.waset.org/abstracts/search?q=ion-laser%20interaction" title=" ion-laser interaction"> ion-laser interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20operations" title=" Gaussian operations"> Gaussian operations</a> </p> <a href="https://publications.waset.org/abstracts/18445/gaussian-operations-with-a-single-trapped-ion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18445.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">685</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10649</span> On Generalized Cumulative Past Inaccuracy Measure for Marginal and Conditional Lifetimes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20Ghosh">Amit Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Chanchal%20Kundu"> Chanchal Kundu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, the notion of past cumulative inaccuracy (CPI) measure has been proposed in the literature as a generalization of cumulative past entropy (CPE) in univariate as well as bivariate setup. In this paper, we introduce the notion of CPI of order α (alpha) and study the proposed measure for conditionally specified models of two components failed at different time instants called generalized conditional CPI (GCCPI). We provide some bounds using usual stochastic order and investigate several properties of GCCPI. The effect of monotone transformation on this proposed measure has also been examined. Furthermore, we characterize some bivariate distributions under the assumption of conditional proportional reversed hazard rate model. Moreover, the role of GCCPI in reliability modeling has also been investigated for a real-life problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cumulative%20past%20inaccuracy" title="cumulative past inaccuracy">cumulative past inaccuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=marginal%20and%20conditional%20past%20lifetimes" title=" marginal and conditional past lifetimes"> marginal and conditional past lifetimes</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20proportional%20reversed%20hazard%20rate%20model" title=" conditional proportional reversed hazard rate model"> conditional proportional reversed hazard rate model</a>, <a href="https://publications.waset.org/abstracts/search?q=usual%20stochastic%20order" title=" usual stochastic order"> usual stochastic order</a> </p> <a href="https://publications.waset.org/abstracts/79608/on-generalized-cumulative-past-inaccuracy-measure-for-marginal-and-conditional-lifetimes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79608.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">253</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10648</span> Simulation of Propagation of Cos-Gaussian Beam in Strongly Nonlocal Nonlinear Media Using Paraxial Group Transformation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Keshavarz">A. Keshavarz</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Roosta"> Z. Roosta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, propagation of cos-Gaussian beam in strongly nonlocal nonlinear media has been stimulated by using paraxial group transformation. At first, cos-Gaussian beam, nonlocal nonlinear media, critical power, transfer matrix, and paraxial group transformation are introduced. Then, the propagation of the cos-Gaussian beam in strongly nonlocal nonlinear media is simulated. Results show that beam propagation has periodic structure during self-focusing effect in this case. However, this simple method can be used for investigation of propagation of kinds of beams in ABCD optical media. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=paraxial%20group%20transformation" title="paraxial group transformation">paraxial group transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlocal%20nonlinear%20media" title=" nonlocal nonlinear media"> nonlocal nonlinear media</a>, <a href="https://publications.waset.org/abstracts/search?q=cos-Gaussian%20beam" title=" cos-Gaussian beam"> cos-Gaussian beam</a>, <a href="https://publications.waset.org/abstracts/search?q=ABCD%20law" title=" ABCD law"> ABCD law</a> </p> <a href="https://publications.waset.org/abstracts/52660/simulation-of-propagation-of-cos-gaussian-beam-in-strongly-nonlocal-nonlinear-media-using-paraxial-group-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52660.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">342</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Gaussian%20Conditional%20Random%20Field&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Gaussian%20Conditional%20Random%20Field&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Gaussian%20Conditional%20Random%20Field&page=4">4</a></li> <li class="page-item"><a class="page-link" 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