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This document proposes the comparison of four statistical classification models for partial discharge (PD) classification as follows: k-nearest neighbors (KNN) model, probabilistic neural network (PNN) model, and other two statistical models using principal component analysis (PCA) for a data reduction approach combined with KNN and PNN models, so called, PCA-KNN model and PCA-PNN model. PD phenomena,...
Online group shopping is feasible if the online store comprises useful products. The product needs to attract customer interest. The customer will have varied choices to choose from the available products. For feasibility of Online group shopping by customers the products have to be properly classified. The site offers the customer to form a group based on his/her interest. The site gives the best...
A novel method for liveness detection of dorsal hand vein (DHV) based on AR model is proposed. Firstly, existing real DHV images are used to constitute a projection space based on modified principal component analysis (PCA). Unlike the previous works using the method of PCA, zero eigenvalues with their eigenvectors are used to constitute the projection space in this work. Secondly, test samples, including...
The aim of this paper is to describe the effect of training methods on the accuracy of PCA-KNN partial discharge (PD) classification model. This model used principal component analysis (PCA) combined with k-nearest neighbor (KNN) model, so called, PCA-KNN PD classification model for PD pattern classification. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air...
Bayesian filtering often relies on a reduced system state relating to robot internal variables only. The exogenous variables and their effects on the measurement process are then encompassed within a global observation noise model. Even if Bayes filters proved to be robust to such approximations, special care has to be taken to handle some of these exogenous effects, usually by introducing complex...
High-level synthesis ensures that a program code written in a programming language can be easily transferred into a hardware description language and thereby makes the design process faster and less demanding. However, professional synthesis tools are very complex and not well suited for deployment in embedded systems with limited resources. Evolutionary on-line synthesis is proposed in this paper...
Evaluating the accuracy of HMM-based and SVM-based spotters in detecting keywords and recognizing the true place of keyword occurrence shows that the HMM-based spotter detects the place of occurrence more precisely than the SVM-based spotter. On the other hand, the SVM-based spotter performs much better in detecting keywords and has higher detection rate. In this paper, we propose a rule based combination...
The problem of blind estimation of the room acoustic clarity index C50 from single-channel reverberant speech signals is presented in this paper. We analyze the performance of several machine learning methods for a regression task using 309 features derived from the speech signal and modeled with a Deep Belief Network (DBN), Classification And Regression Tree (CART) and Linear Regression (LR). These...
The aim of the paper is to study the possibility of advancing noised non-Gaussian processes recognition using the features based on higher-order statistics. Several new recognition features based on the higher-order statistics, the basis of advancing recognition results in the presence of noise, decision rules and recognition system frameworks we proposed in the paper. The efficiency test of the proposed...
Automatic recognition of handwritten texts in video lectures has important applications. In video lectures, the presenter usually writes on white / colored board. The video camera often captures the writing board along with certain other objects possibly including the presenter itself. Recognition of handwritten texts from such a video frame requires prior detection of the region of texts in the frame...
In masquerade attack, attacker impersonates legitimate user. Most of the masquerade detection techniques done so far are based on supervised learning techniques. But here in this paper masquerade detection based on unsupervised learning techniques are used. Various clustering algorithms used are K-Means, K-Medoid, Agglomerative clustering algorithm and DBSCAN. A comparative study is done based on...
PMD incurs nonlinear noise in Stokes vector direct detection systems. We propose a novel algorithm to mitigate the PMD-induced nonlinear noise. DGD tolerance is improved from 4 to 10 ps for a 93-Gb/s signal.
This paper addresses the problem of speech segregation by estimating the ideal binary mask (IBM) from noisy speech. Two methods will be compared, one supervised learning approach that incorporates a priori knowledge about the feature distribution observed during training. The second method solely relies on a frame-based speech presence probability (SPP) es-timation, and therefore, does not depend...
We consider physical layer security of massive MIMO systems in TDD mode. We show that with massive MIMO a passive eavesdropper is not very dangerous and must therefore be active and attack the training phase. An attack on the training phase is potentially very harmful to the physical layer security, and we therefore investigate three different schemes for detecting the presence of an active eavesdropper...
Research on motor learning has emphasized that errors drive motor adaptation. Thereby, several researchers have proposed robotic training strategies that amplify movement errors rather than decrease them. In this study, the effect of different robotic training strategies that amplify errors on learning a complex locomotor task was investigated. The experiment was conducted with a one degree-of freedom...
The use of word senses in place of surface word forms has been shown to improve performance on many computational tasks, including intelligent web search. In this paper we propose a novel approach to automatic discovery of word senses from raw text, a task referred to as Word Sense Induction (WSI). Almost all the WSI approaches proposed in the literature dealt with monolingual data and only very few...
Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining techniques have been adapted to reduce noisy information from extracted features but still contains some noises features. However, the noise features are extracted from the same training documents that good features extracted from. Therefore, the main problem is that some training documents...
Circular data is very relevant in many fields such as Geostatistics, Mobile Robotics and Pose Estimation. However, some existing angular regression methods do not cope with arbitrary nonlinear functions properly. Moreover, some other regression methods that do cope with nonlinear functions, like Gaussian Processes, are not designed to work well with angular responses. This paper presents two novel...
This paper addresses the challenging problem of recognition and classification of textured surfaces under illumination variation, geometric transformations and noisy sensor measurements. We propose a new texture operator, Adaptive Median Binary Patterns (AMBP) that extends our previous Median Binary Patterns (MBP) texture feature. The principal idea of AMBP is to hash small local image patches into...
This paper deals with a complex symbol recognition process considering a large number of classes and only one training image per class. Furthermore, the response times of recognition system should be short and the interpretation of results must be easy. In this particular case, both statistical and structural methods are not the most suitable. A new composite descriptor and a similarity measure are...
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