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The Common Vector (CV) method is a linear method, which allows to discriminate between classes of data sets, such as those arising in image and word recognition. In this paper a variation of this method is introduced for finding the projection vectors of each class as elements of the intersection of the null space of that class' covariance matrix and the range space of the covariance matrix of the...
The purpose of this study is to investigate the effects of different forms of between-class scatter matrices on multi-class problems. Two different between-class scatter matrices are defined in Fisher's linear discriminant analysis (FLDA) and the classification rates better than that of classical FLDA are obtained for TI-digit database. In this study, the criteria that give separate subspaces for...
In this work we propose a novel unsupervised algorithm for designing multispectral filters that are tuned for local anomaly detection algorithms. This problem is formulated as a problem of channel reduction in hyperspectral images, which is performed by replacing subsets of adjacent spectral bands by their means. An optimal partition of hyperspectral bands is obtained by minimizing the Maximum of...
An efficient update algorithm is presented which tracks the left and right singular vectors and singular values of a transfer matrix, using input and output vectors and without explicitly computing the matrix itself. Such an algorithm has many potential applications in multiple-input multiple-output wireless communication systems in which the channel parameters change slowly with time. Examples of...
The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
This paper presents the work done to improve the recognition rate in an isolated word recognition problem with single utterance training. The negative effect of errors (due to insufficient training data) in estimated model parameters is compensated by fusing the information obtained from HMM evaluation and those generated for the word length and voicing at the beginning and end of the word. A Bayesian...
Computationally scalable speaker recognition systems are highly desirable in practice. To achieve this objective, we use a two-stage architecture for text-prompted speaker recognition. In this system, the input speech is first segmented on subword boundaries using a Viterbi alignment. The second stage applies a polynomial classifier to each subword for verification. Through a simple approximation,...
This paper presents a way of accelerating the evaluation and simulation of nonlinear POD models by using feedforward neural networks. Traditionally, Proper Orthogonal Decomposition (POD) and Galerkin projection have been employed to reduce the high-dimensionality of the discretized systems used to approximate Partial Differential Equations (PDEs). Although a large model-order reduction can be obtained...
Per tone equalization has been proposed in [6] [7] as an alternative to time domain equalization for DMT-based systems. In this paper, an iterative initialization scheme based on so-called RLS with inverse updating is presented for these equalizers. Simulation results show convergence with an acceptably small number of training symbols. Complexity calculations are made for per tone equalization and...
This paper is concerned with adaptive prediction for lossless image coding. A new predictor which is an adaptive combination of a set of fixed predictors with a transform domain LMS based predictor is proposed. When a context-based arithmetic encoder is used to encode the prediction error, the compression performance of the proposed algorithm is better than that of the state-of-the-art algorithms...
Experts should analyse systems in order to define would-be faults in systems. As a result of this analysis, there will be a set of priori known faults supporting off-line teaching of neural networks. Unfortunately, it is impossible to define all faults in the design phase. As a result, a priori unknown faults may appear in systems. A priori unknown faults modify the distribution of the input patterns...
The control of an acoustic echo canceller (AEC) is an essential part of hands-free telephone sets. Due to the fact that no single estimator is yet known to reliably control the AEC, various estimators should be implemented. Nevertheless, the combination of several estimators is quite difficult and usually determined heuristically. In this paper, an approach for automatic combination of estimators,...
Frequently, the increasing level of automation requires a systematic consideration of numerous interacting components influenced by internal feedback mechanisms as well as interactions with human operators under varying environmental conditions. This places demands on modeling, which in general cannot be satisfied by traditional modeling concepts. In this paper, a model approach for complex technical...
The use of feature vectors obtained by concatenation of different features for text independent speaker identification from clean and telephone speech is studied. The composite feature vectors are examined with GMM and VQ models used to classify speakers. Linear discriminant analysis (LDA), a statistical tool designed to select a reduced set of features for best classification, is applied to enhance...
This paper deals with the problem of finding optimal training sequences for adaptive equalizers in TDMA systems. Such sequences give the equalizer a good initial value, are required for symbol-timing recovery and can provide a small but robust amount of information by utilizing a small set of codes with strong discrimination power. While every single task has been dealt with in literature, the combination...
The concept of optimal hyperplane has been recently proposed in the context of statistical learning theory. The important property of an optimal hyperplane is that it provides maximum margins to each class to be separated. Obviously, such a decision boundary is expected to yield good generalization. Currently, the support vector machines (SVM) are probably one of the very few models (if not the only...
The problem of the path generation for the autonomous robot vehicle in environment with stationary and moving obstacles is considered. An algorithm, named MKBC, based on modified Kohonen rule and behavioral cloning is developed. The MKBC algorithm, as improvement of RBF neural network, uses the training values as weighting values, rather then values from the previous time. This enables an intelligent...
Automatic stroke recognition of badminton video footages plays an important role in the process of analyzing players and building up statistics. Yet recognizing activities from broadcast videos is a challenging task due to person dependant body postures and blurring of the fast moving body parts. We propose a robust and an accurate approach for badminton stroke recognition using dense trajectories...
In this paper, a new variant of bacterial foraging optimization (BFO) algorithm, called bacterial foraging optimization algorithm with varying population (named BFVPA) is proposed for Fuzzy Vector Quantization based image compression. The work shows how BFVPA can be effectively utilized for reduction in average distortion measure between training and reconstructed image and how it can improve upon...
Grid systems have emerged as a means of sharing computational resources and information. Providing services for accessing, sharing and modifying large databases is a crucial task for grid management systems. This paper proposes an artificial neural network (ANN) prediction mechanism that provides an enhancement to data replication solutions within grid systems. Current replication services often exhibit...
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