Hand written documents are one of the most important sources of writing media in ancient Kerala. Due to improper storage of manuscripts, about 80% of them got degraded. Noises are one of the most common degrading fact...
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ISBN:
(纸本)9781538692790
Hand written documents are one of the most important sources of writing media in ancient Kerala. Due to improper storage of manuscripts, about 80% of them got degraded. Noises are one of the most common degrading factor which affects the visibility of the images and makes them unclear. The main noises found in manuscripts are pepper salt noise, speckle noise and holes on billings etc. There are many denoising methods available. In this paper we have made a literature analysis, both basic and advanced algorithms. Finally compared the output of each methods, gibbssampling technique gives more accurate output in handwritten manuscripts. But holes in documents were not removed. For this we have given an enhancement using Circle Hough Transform (CHT) Which detects the circles and can identify exact hole by comparing the boundary pixels of circle and background of the script. In future we can recognize the characters using Support Vector Machine(SVM) and Histogram of Orientation Gradients(HOG).
In this paper, a highly effective slice samplingalgorithm is proposed to estimate the graded response model that has been widely used in educational psychological assessments. The new algorithm not only avoids the Me...
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In this paper, a highly effective slice samplingalgorithm is proposed to estimate the graded response model that has been widely used in educational psychological assessments. The new algorithm not only avoids the Metroplis-Hastings algorithm boring adjustment the turning parameters to achieve an appropriate acceptance probability, but also overcomes the dependence of the traditional gibbs sampling algorithm on the conjugate prior distribution. Three simulation studies are conducted and a detailed analysis of sexual compulsivity scale (SCS) data is carried out to further illustrate the proposed methodology.
Orthogonal Time Frequency Space (OTFS) is a nextgeneration modulation scheme for high-mobility communication scenarios. Several existing OTFS signal detection methods focus on reducing the high-dimensional search comp...
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ISBN:
(纸本)9798350359220
Orthogonal Time Frequency Space (OTFS) is a nextgeneration modulation scheme for high-mobility communication scenarios. Several existing OTFS signal detection methods focus on reducing the high-dimensional search complexity induced by the OTFS frame structure while retaining the maximum likelihood (ML) optimality. One such detection method is the Markov Chain Monte Carlo (MCMC) based gibbs sampling algorithm which can converge to the optimal ML solution when initialized accurately. In this paper, we show that the gibbs sampler - due to an inaccurate initialization - fails to converge to the ML solution and instead exhibits a BER (bit error rate) floor in the high SNR (signal to noise ratio) regime, i.e., the probability of bit error does not reduce with increasing SNR or increasing iterations. To solve this problem, we propose a preprocessing step comprising either the zero-forcing (ZF), the minimum mean squared error (MMSE) or the decision feedback (DF) detection. The preprocessor output is used to initialize the gibbs sampler. Through extensive simulations, we demonstrate the efficacy of our proposal in solving the gibbs sampler's BER floor issue. Furthermore, we show that the proposed MMSEgibbs receiver outperforms the ZF-gibbs receiver at low SNRs, and the DF-gibbs exhibits superior performance compared to not only the ZF-gibbs or the MMSE-gibbs, but also an alternative Randomized-gibbs OTFS receiver in the literature.
This paper considers a step-stress accelerated dependent competing risks model under progressively Type-I censoring schemes. The dependence structure between competing risks is modeled by a general bivariate function,...
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This paper considers a step-stress accelerated dependent competing risks model under progressively Type-I censoring schemes. The dependence structure between competing risks is modeled by a general bivariate function, the cumulative exposure model is assumed and the accelerated model is described by the power rule model. The point and interval estimation of the model parameters and the reliability under normal usage level at mission time are obtained by using the maximum likelihood method and the asymptotic normal theory. We also consider the Bayesian estimators and the highest posterior density credible intervals based on conjugate priors, E-Bayesian, hierarchical Bayesian and empirical Bayesian methods. To illustrate the proposed methodology, the Marshall-Olkin bivariate exponential distribution is used to model the dependence structure between competing risks. A Monte Carlo simulation study and a real data analysis are presented to study the performance of different estimation methods.
Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the ...
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Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the probabilistic relationship between external latent classes and attribute mastery patterns. Furthermore, variational Bayesian (VB) inference and gibbssampling Markov chain Monte Carlo methods were developed for parameter estimation of the two-level DCMs. The results of a parameter recovery simulation study show that both techniques appropriately recovered the true parameters;gibbssampling in particular was slightly more accurate than VB, whereas VB performed estimation much faster than gibbssampling. The two-level DCMs with the proposed Bayesian estimation methods were further applied to fourth-grade data obtained from the Trends in International Mathematics and Science Study 2007 and indicated that mathematical activities in the classroom could be organized into four latent classes, with each latent class connected to different attribute mastery patterns. This information can be employed in educational intervention to focus on specific latent classes and elucidate attribute patterns.
Every Muslim is required to donate a specific portion of their assets as zakat when certain circumstances are met. Zakat is paid to be given to those who are eligible to receive it and is one of the pillars of Islam. ...
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This paper presents a new and flexible prognostics framework based on a higher-order hidden semi-Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOH...
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This paper presents a new and flexible prognostics framework based on a higher-order hidden semi-Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOHSMM extends the basic hidden Markov model (HMM) by allowing the hidden state to depend on its more distant history and assuming generally distributed state duration. An effective gibbs sampling algorithm is designed for statistical inference of the HOHSMM. We conduct a simulation study to evaluate the performance of the proposed HOHSMM sampler and examine the impacts of the distant-history dependency. We design a decoding algorithm to estimate the hidden health states using the learned model. Remaining useful life is predicted using a simulation approach given the decoded hidden states. The practical utility of the proposed prognostics framework is demonstrated by a case study on National Aeronautics and Space Administration (NASA) turbofan engines. We further compare the RUL prediction performance between the proposed HOHSMM and a benchmark mixture of Gaussians HMM prognostics method. The results show that the HOHSMM-based prognostics framework provides good hidden health-state assessment and RUL estimation for complex systems.
Heartbeat modeling allows to detect anomalies that reflect the functioning of the heart. Certain approaches face this problem by using Gaussian Mixture Models (GMMs) and other statistical classifiers by extracting the...
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ISBN:
(纸本)9783030210779;9783030210762
Heartbeat modeling allows to detect anomalies that reflect the functioning of the heart. Certain approaches face this problem by using Gaussian Mixture Models (GMMs) and other statistical classifiers by extracting the fiducial points provided by the MIT-BIH database. In this work, MIT-BIH database heartbeats are modeled into different heartbeat types from a single subject by using the gibbssampling (GS) algorithm. Firstly, a data pre-processing step is performed;this step involves several tasks such as filtering the raw signals from the MIT-BIH database and reducing the heartbeat types to five. Secondly, the GS is applied to the resulting signals of one subject. Thirdly, the Euclidean distance between each heartbeat type is calculated, and lastly, the Bhattacharyya distance is used to classify heartbeats. The results obtained by the GS algorithm were also compared to results obtained by applying the Expectation Maximization (EM) algorithm to the same data-set. Results allow to conclude that GS is a proper solution for separating each heartbeat type;by providing a significant difference between each heartbeat type which can be used for classification.
Traditional low-order regional Markov Random Fields (MRF) model is difficult to accurately describe the global connectivity of complex natural images and often leads to the over-smoothing of the segmentation results. ...
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ISBN:
(纸本)9781728113128
Traditional low-order regional Markov Random Fields (MRF) model is difficult to accurately describe the global connectivity of complex natural images and often leads to the over-smoothing of the segmentation results. To solve this problem, a high-order MRF image segmentation model with robust local spatial information is proposed. Firstly, the proposed model introduces the local spatial relationship of the image by using the Hamming distance between the neighborhood pixels in the local region, then establishes a weighted Gaussian mixture likelihood feature between the label space and the pixel intensity field, which provides the local spatial consistency constraint;Secondly, the spatial global constraint relationship of the far away distance is introduced based on the Robust P-n model, and the regional label consistency constraint of the MRF image segmentation model is established. Finally, based on Bayesian theory, a high-order MRF energy model with robust local spatial information for image segmentation is proposed, and the proposed model is optimized by gibbs sampling algorithm. Compared with Traditional low-order regional MRF model, experimental result shows that the proposed model can provide a better segmentation.
Document clustering for short texts has received considerable interest. Traditional document clustering approaches are designed for long documents and perform poorly for short texts due to the their sparseness represe...
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ISBN:
(纸本)9783319635798;9783319635781
Document clustering for short texts has received considerable interest. Traditional document clustering approaches are designed for long documents and perform poorly for short texts due to the their sparseness representation. To better understand short texts, we observe that words that appear in long documents can enrich short text context and improve the clustering performance for short texts. In this paper, we propose a novel model, namely DDMAfs, which (1) improves the clustering performance of short texts by sharing structural knowledge of long documents to short texts;(2) automatically identifies the number of clusters;(3) separates discriminative words from irrelevant words for long documents to obtain high quality structural knowledge. Our experiments indicate that the DDMAfs model performs well on the synthetic dataset and real datasets. Comparisons between the DDMAfs model and state-of-the-art short text clustering approaches show that the DDMAfs model is effective.
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