In this paper, we propose a low complexity realization method for compensating for nonlinear distortion. Generally, nonlinear distortion is compensated for by a linearization system using a Volterra kernel. However, this method has a problem of requiring a huge computational complexity for the convolution needed between an input signal and the 2nd-order Volterra kernel. The Simplified Volterra Filter (SVF), which removes the lines along the main diagonal of the 2nd-order Volterra kernel, has been previously proposed as a way to reduce the computational complexity while maintaining the compensation performance for the nonlinear distortion. However, this method cannot greatly reduce the computational complexity. Hence, we propose a subband linearization system which consists of a subband parallel cascade realization method for the 2nd-order Volterra kernel and subband linear inverse filter. Experimental results show that this proposed linearization system can produce the same compensation ability as the conventional method while reducing the computational complexity.

This paper presents a view independent video-based face recognition method using posterior probability in Kernel Fisher Discriminant (KFD) space. In practical environment, the view of faces changes dynamically. Robustness to view changes is required for video-based face recognition in practical environment. Since the view changes induce large non-linear variation, kernel-based methods are appropriate. We use KFD analysis to cope with non-linear variation. To classify image sequence, the posterior probability in KFD space is used. KFD analysis assumes that the distribution of each class in high dimensional space is Gaussian. This makes the computation of posterior probability in KFD space easy. The combination of KFD space and posterior probability of image sequence is the main contribution of the proposed method. The performance is evaluated by using two face databases. Effectiveness of the proposed method is shown by the comparison with the other feature spaces and classification methods.

Kernel-based learning algorithms have been successfully applied in various problem domains, given appropriate kernel functions. In this paper, we discuss the problem of designing kernel functions for binary regression and show that using a bell-shaped cosine function as a kernel function is optimal in some sense. The rationale of this result is based on the Karhunen-Loeve expansion, i.e., the optimal approximation to a set of functions is given by the principal component of the correlation operator of the functions.

This paper presents a robust object tracking method under pose variation and partial occlusion. In practical environment, the appearance of objects is changed dynamically by pose variation or partial occlusion. Therefore, the robustness to them is required for practical applications. However, it is difficult to be robust to various changes by only one tracking model. Therefore, slight robustness to variations and the easiness of model update are required. For this purpose, Kernel Principal Component Analysis (KPCA) of local parts is used. KPCA of local parts is proposed originally for the purpose of pose independent object recognition. Training of this method is performed by using local parts cropped from only one or two object images. This is good property for tracking because only one target image is given in practical applications. In addition, the model (subspace) of this method can be updated easily by solving a eigen value problem. Performance of the proposed method is evaluated by using the test face sequence captured under pose, partial occlusion, scaling and illumination variations. Effectiveness and robustness of the proposed method are demonstrated by the comparison with template matching based tracker. In addition, adaptive update rule using similarity with current subspace is also proposed. Effectiveness of adaptive update rule is shown by experiment.

A comparison of performances is made of three text-independent speaker identification methods based on dual Penalized Logistic Regression Machine (dPLRM), Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) with experiments by 10 male speakers. The methods are compared for the speech data which were collected over the period of 13 months in 6 utterance-sessions of which the earlier 3 sessions were for obtaining training data of 12 seconds' utterances. Comparisons are made with the Mel-frequency cepstrum (MFC) data versus the log-power spectrum data and also with training data in a single session versus in plural ones. It is shown that dPLRM with the log-power spectrum data is competitive with SVM and GMM methods with MFC data, when trained for the combined data collected in the earlier three sessions. dPLRM outperforms GMM method especially as the amount of training data becomes smaller. Some of these findings have been already reported in [1]-[3].

The partial projection filter (PTPF) for a given observation operator provides an optimal signal restoration in the presence of both the signal space and observation space noises. However, restoration error by the filter still depends on the observation operator which consists of measurement and sampling processes. In this paper, we determine a sampling operator which minimizes the restoration error by the PTPF. We see that under some assumptions about noise statistics, the restoration error by the PTPF is divided into two terms corresponding to the error arising from the signal space noise and that from the observation space noise. It has been found that although the restoration error due to the signal space noise is independent of the sampling operator, the restoration error arising from the observation space noise can arbitrarily be decreased by increasing the number of sample points in the proposed sampling operator. An illustrative example of optimal sampling in the trigonometric polynomial space is also given.

The current scheme of access control judges the legality of each access based on immediate information without considering associate information hidden in a series of accesses. Due to the deficiency, access control systems do not efficiently limit attacks consist of ordinary operations. For trusted operating system developments, we extended RBAC and added negative procedural constraints to refuse those attacks. With the procedural constraints, the access control of trusted operating systems can discriminate attack trials from normal behaviors. This paper shows the specification of the extended concept and model, and presents simple analysis results.

This paper presents a modification of kernel-based Fisher discriminant analysis (FDA) to design one-class classifier for face detection. In face detection, it is reasonable to assume "face" images to cluster in certain way, but "non face" images usually do not cluster since different kinds of images are included. It is difficult to model "non face" images as a single distribution in the discriminant space constructed by the usual two-class FDA. Also the dimension of the discriminant space constructed by the usual two-class FDA is bounded by 1. This means that we can not obtain higher dimensional discriminant space. To overcome these drawbacks of the usual two-class FDA, the discriminant criterion of FDA is modified such that the trace of covariance matrix of "face" class is minimized and the sum of squared errors between the average vector of "face" class and feature vectors of "non face" images are maximized. By this modification a higher dimensional discriminant space can be obtained. Experiments are conducted on "face" and "non face" classification using face images gathered from the available face databases and many face images on the Web. The results show that the proposed method can outperform the support vector machine (SVM). A close relationship between the proposed kernel-based FDA and kernel-based Principal Component Analysis (PCA) is also discussed.

For a kernel-based topographic map formation, kMER (kernel-based maximum entropy learning rule) was proposed by Van Hulle, and some effective learning rules related to kMER have been proposed so far with many applications. However, no discusions have been made concerning the determination of the number of units in kMER. This letter describes a unit-pruning rule, which permits automatic contruction of an appropriate-sized map to acquire the global topographic features underlying the input data. The effectiveness and the validity of the present rule have been confirmed by some preliminary computer simulations.

This paper presents an analysis of the applicability of Sparse Kernel Principal Component Analysis (SKPCA) for feature extraction in speech recognition, as well as, a proposed approach to make the SKPCA technique realizable for a large amount of training data, which is an usual context in speech recognition systems. Although the KPCA (Kernel Principal Component Analysis) has proved to be an efficient technique for being applied to speech recognition, it has the disadvantage of requiring training data reduction, when its amount is excessively large. This data reduction is important to avoid computational unfeasibility and/or an extremely high computational burden related to the feature representation step of the training and the test data evaluations. The standard approach to perform this data reduction is to randomly choose frames from the original data set, which does not necessarily provide a good statistical representation of the original data set. In order to solve this problem a likelihood related re-estimation procedure was applied to the KPCA framework, thus creating the SKPCA, which nevertheless is not realizable for large training databases. The proposed approach consists in clustering the training data and applying to these clusters a SKPCA like data reduction technique generating the reduced data clusters. These reduced data clusters are merged and reduced in a recursive procedure until just one cluster is obtained, making the SKPCA approach realizable for a large amount of training data. The experimental results show the efficiency of SKPCA technique with the proposed approach over the KPCA with the standard sparse solution using randomly chosen frames and the standard feature extraction techniques.

This paper proposes a Real-Time Compression Architecture (RTCA), which maximizes the efficiency of web services, while reducing the response time at the same time. The developed architecture not only guarantees the freshness of compressed contents but also minimizes the time needed to compress the message, especially when the traffic is heavy.

This paper describes an approach to feature extraction in speech recognition systems using kernel principal component analysis (KPCA). This approach represents speech features as the projection of the mel-cepstral coefficients mapped into a feature space via a non-linear mapping onto the principal components. The non-linear mapping is implicitly performed using the kernel-trick, which is a useful way of not mapping the input space into a feature space explicitly, making this mapping computationally feasible. It is shown that the application of dynamic (Δ) and acceleration (ΔΔ) coefficients, before and/or after the KPCA feature extraction procedure, is essential in order to obtain higher classification performance. Better results were obtained by using this approach when compared to the standard technique.

The choice of kernel is an important issue in the support vector machine algorithm, and the performance of it largely depends on the kernel. Up to now, no general rule is available as to which kernel should be used. In this paper we investigate two kernels: Gaussian RBF kernel and polynomial kernel. So far Gaussian RBF kernel is the best choice for practical applications. This paper shows that the polynomial kernel in the normalized feature space behaves better or as good as Gaussian RBF kernel. The polynomial kernel in the normalized feature space is the best alternative to Gaussian RBF kernel.

An iterative method is proposed to solve integral equations (IEs) of the second kind with Picard-kernel in linear complexity, i.e.O(N). The particular IE considered describes the process of scattering of a plane wave incident on an inhomogeneous slab. The collocation method with triangle basis functions is used to derive a linear system of equations, which is solved for a test problem with the BiCGSTAB method. To reduce the number of iterations, an efficient preconditioning operator is introduced.

In this paper a new adaptive multi-input multi-output (MIMO) channel estimation and multiuser detection algorithm based kernel space iterative inversion is proposed. The functions of output signals are mapped from a low dimensional space to a high dimensional reproducing kernel Hilbert space. The function of the output signals is represented as a linear combination of a set of basis functions, and a Mercer kernel function is constructed by the distribution function. In order to avoid finding the function f(.) and g(.), the correlation among the output signals is calculated in the low dimension space by the kernel. Moreover, considering the practical application, the algorithm is extended to online iteration of mixture system. The computer simulation results illustrated that the new algorithm increase the performance of channel estimation, the global convergence, and the system stability.

We implement an efficient Secure Socket Layer (SSL) accelerator, which is embedded in the kernel level and utilizes kernel threads as the same number of CPUs. In comparison with the conventional Apache with/without our SSL accelerator, the SSL accelerator significantly improves the web-server performance by up to 200%.

This paper proposes a new approach to fuzzy inference system for modeling nonlinear systems based on measured input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the extended kernel method. The extended kernel method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. Especially, the process of linear transformation is needed in order to solve difficulty determining the type of kernel function which presents the nonlinear mapping in according to nonlinear system. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated results of the proposed technique are illustrated by examples involving benchmark nonlinear systems.

For suppressing inter symbol interference, the support vector machine mutliuser detector (SVM-MUD) was adopted as a nonlinear method in direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. To solve the problems of the complexity of SVM-MUD model and the number of support vectors, based on recursive least squares support vector machine (RLS-SVM) and Riemannian geometry, a new algorithm for nonlinear multiuser detector is proposed. The algorithm introduces the forgetting factor to get the support vectors at the first training samples, then, uses Riemannian geometry to train the support vectors again and gets less improved support vectors. Simulation results illustrated that the algorithm simplifies SVM-MUD model at the cost of only a little more bit error rate and decreases the computational complexity. At the same time, the algorithm has an excellent effect on suppressing multipath interference.

In this paper, we propose a novel higher order time-frequency distribution (GDH) for a discrete time signal. This distribution is defined over the original discrete time-frequency grids through a delicate discretization of an equivalent expression of a higher order distribution, for a continuous time signal, in [4]. We also present a constructive design method, for the kernel of the GDH, by which the distribution satisfies (i) the alias free condition as well as (ii) the marginal conditions. Numerical examples show that the proposed distributions reasonably suppress the artifacts which are observed severely in the Wigner distribution and its simple higher order generalization.

Support vector machines (SVMs), newly introduced in the 1990s, are promising approach to pattern recognition. They are able to handle linearly nonseparable problems without difficulty, by combining the maximal margin strategy with the kernel method. This paper addresses a novel SVM-based classification scheme of land cover from polarimetric synthetic aperture radar (SAR) data. Polarimetric observations can reveal existing different scattering mechanisms. As the input into SVMs, the polarimetric feature vectors, composed of intensity of each channel, sometimes complex correlation coefficients and textural information, are prepared. Classification experiments with real polarimetric SAR images are satisfactory. Some important properties of SVMs, for example the relation between the number of support vectors and classification accuracy, are also investigated.