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Unification of spatial brain dynamics in multiclass brain computer interface (BCI) paradigm reduces computational latencies by using lesser number of electrodes from the sensorimotor regions of the brain. We employ reduced number of channels without compromising performance notably. We apply three spatial filtering methods, i.e., Common Spatial Pattern (CSP), Regularized Common Spatial Pattern (RCSP)...
Fishers linear discriminant analysis (FLDA) is one of the well-known methods to extract the best features for multi-class discrimination. Recently Kernel discriminant analysis (KDA) has been successfully applied in many applications. KDA is one of the nonlinear extensions of FLDA and construct nonlinear discriminant mapping by using kernel functions. Otsu derived the optimum nonlinear discriminant...
This paper considers the conventional eigenvalue decomposition (EVD) based and widely linear (WL) channel estimations to alleviate inherent pilot contamination problems in multi-cell multi-user massive multiple-input multiple-output (MU-Massive-MIMO) systems. Both channel estimation schemes utilize the asymptotic orthogonality between the propagation vectors of different users which would be true...
Our paper presents a novel high dimensional probability density estimation technique using any dimensionality reduction method. Our method first performs subspace reduction using any matrix factorization algorithm and estimates the density in the low-dimensional space using sample-point variable bandwidth kernel density estimation. Subsequently, the high dimensional density is approximated from the...
We study the performance of distributed source coding in large wireless sensor networks obtained with enhanced correlation estimators. Distributed source coding is especially useful when data correlation exists since it tries to remove the redundancy in the information; and dense sensor networks are rich in correlations. Existing results from information theory show that this compression can be executed...
Verification decisions are often based on second order statistics estimated from a set of samples. Ongoing growth of computational resources allows for considering more and more features, increasing the dimensionality of the samples. If the dimensionality is of the same order as the number of samples used in the estimation or even higher, then the accuracy of the estimate decreases significantly....
The locally linear embedding (LLE) algorithm is considered as a powerful method for the problem of nonlinear dimensionality reduction. In this paper, a new method called globally-preserving based LLE (GPLLE) is proposed. It not only preserves the local neighborhood, but also keeps those distant samples still far away, which solves the problem that LLE may encounter, i.e. LLE only makes local neighborhood...
In this paper, an efficient signal-perturbation-free semi-blind approach is proposed for flat-fading channel estimation of multiple-input multiple-output (MIMO) systems with maximum ratio transmission (MRT). A novel transmit scheme is developed based on the eigenvalue decomposition of the correlation matrix of the transmitted signal. The new scheme is to send a small volume of data bearing the information...
We propose a new method to characterize a speaker within the Joint Factor Analysis (JFA) framework. Scoring within the JFA framework can be costly and a new method was proposed to produce an accurate score in a fast manner. However, this method is nonsymmetric and performs badly without any score normalization. We propose a new JFA scoring method that is both symmetrical and efficient. In the same...
In this paper, the problem of estimating the direction of arrival (DOA) in impulsive noise environments is considered. One possible way to model the impulsive noise is to introduce symmetric ??-stable (S??S) distribution. Robust co variation based MUSIC (ROC-MUSIC) and Fractional lower moment based MUSIC (FLOM-MUSIC) can be used to estimate the DOA under these conditions. But those methods require...
Eigendecomposition has been used to classify three-dimensional objects from two-dimensional images in a variety of computer vision and robotics applications. The biggest on-line computational expense associated with using eigendecomposition is the determination of the closest point on an image manifold embedded in a high-dimensional space. The dimensionality and complexity of the space is a result...
In this paper, an effective joint direction-of-arrival (DOA) and propagation delay estimation method is proposed for multipath signals impinging the uniform linear antenna array. In this presented method, the real and imaginary parts of the ith eigenvalue of a matrix are one-to-one related to the DOA and propagation delay of the ith path's signal. Thus, the paring of the estimated DOAs and delays...
We consider multiple-input multiple-output (MIMO) systems exploiting the full diversity order of a MIMO fading channel via optimal beamforming and combining. Specifically, an analytical characterization of the transient regime of a training-based MIMO system over arbitrarily correlated channels is presented. No channel state information is assumed to be available at either the transmitter or the receiver...
This paper addresses the issue of the optimization of the regularization constant in semi-blind channel estimation techniques, in which the training sequence-based criterion is combined linearly with the blind subspace criterion. In such semi-blind estimation techniques, the optimization of the regularizing constant with respect to the channel estimation error is mandatory, otherwise, the expected...
In this paper, we deal with the estimation of the ergodic capacity of large MIMO systems, using training sequences whose lengths are of the same order of magnitude than the number of antennas. In this context, the traditional estimator becomes inconsistent. Following the ideas developed by Girko in the context of the so-called theory of G-estimation, we propose a new estimator. We analyze its asymptotic...
Diverse pose estimation of three-dimensional (3D) object in the whole view-space remains a challenge in the field of pattern recognition. In this paper, a pose estimation algorithm of 3D object named isomap-eigenanalysis-regression (Isomap-E-R), which estimates arbitrary pose of 3D object in the whole view space, is proposed. For the training set, the low-dimensional embedding of input pattern set...
Current sign language recognition systems are still designed for signer-dependent operation only and thus suffer from the problem of interpersonal variability in production. Applied to signer-independent tasks, they show poor performance even when increasing the number of training signers. Better results can be achieved with dedicated adaptation methods. In this paper, we describe a vision-based recognition...
In this paper, a whitening-rotation (WR)-based semi-blind channel estimation method for a multiple input multiple output (MIMO) wireless communication system is investigated. The relationship between the estimation accuracy and the channel properties is analyzed. It is shown that the channel estimation minimum square error (MSE) of the investigated semi-blind estimation scheme is dependent on the...
Face super-resolution is to synthesize a high-resolution facial image from a low-resolution input, which can significantly improve the recognition for computer and human. In this paper, we propose a new method of super-resolution based on hybrid model including a linear model of eigenface super-resolution and a Bayesian formulation model. Principal Component Analysis (PCA) is used to approximately...
The aim of the present paper is to infer the slop of troposphere refractivity, from the measured propagation loss by means of a new artificial neural network structure. The proposed network consists of two cascade neural networks. At first by means of the Hebbian-based maximum eigenfilter, main features of the field at the observation points are extracted. Then the extracted features are fed to a...
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