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The research is aimed at developing algorithms for the construction of automated systems to control active components of the electrical network. The construction of automated systems intended for the control of electric power systems requires high-speed mathematical tools. The method applied in the research to describe the object of control is based on the universal approach to the mathematical modelling...
Most missing data analysis techniques have focused on using model parameter estimation which depends on modern statistical data analysis methods such as maximum likelihood and multiple imputation. In fact, these modern methods are better than traditional methods (for example, complete data analysis and mean imputation approaches), and in many particular applications can give unbiased parametric estimation...
We study the satisfiability of ordering constraint satisfaction problems (CSPs) above average. We prove the conjecture of Gutin, van Iersel, Mnich, and Yeo that the satisfiability above average of ordering CSPs of arity k is fixed-parameter tractable for every k. Previously, this was only known for k=2 and k=3. We also generalize this result to more general classes of CSPs, including CSPs with predicates...
A collocation method is introduced for a class of Fred Holm integral equation of the second kind with weakly singular kernels. The key idea of this method is splitting the weakly singular kernel of the integral operator into finite parts so that the weak singularity is concentrated in one which can be analytically solved using integration by parts. Theoretical analysis and numerical examples show...
Thematic information detection is an important application of remote sensing image. Support vector machine (SVM) has been widely used in MODIS remote sensing detection. However, the difficulty of SVM application is how to select the suitable kernel function for remote sensing image. In this paper, the Sangeang Api volcanic ash cloud on May 30, 2014 is taken as an example, and the linear, polynomial,...
This paper presents a series of further modifications to the parallel algorithm used for finding digraphs realisations of the characteristic polynomial. What distinguishes the mentioned algorithm from other state-of-the-art solutions is the ability to find a complete set of existing solutions, not just a few of them. Moreover, solutions found tend to be minimal in terms of a rank of matrices created...
Polynomial systems occur in many areas of science and engineering. Unlike general nonlinear systems, the algebraic structure enablesto compute all solutions of a polynomial system. We describe our massively parallel predictor-corrector algorithmsto track many solution paths of a polynomial homotopy. The data parallelism that provides the speedups stems from theevaluation and differentiation of the...
This paper is dedicated to the stability analysis of a class of distributed delay systems with a non constant kernel. By the use of appropriate orthogonal polynomials, this kernel is expressed as the sum of a polynomial and an additive bounded function. The resulting system is then modeled by an interconnected system between a nominal finite dimensional linear system and a infinite dimensional system...
Kernel-based image classification methods rely on the considered kernel functions that can be chosen with respect to prior information on the adopted features. In remote sensing, histogram features have recently gained an increasing interest due to their capability to address several critical classification problems (e.g., the problem of curse of dimensionality) when appropriate kernels and classifiers...
Necessary and sufficient conditions are presented for the problem of model matching by non-regular static state feedback to have a solution. These conditions are expressed in terms of polynomial matrix equations and yield a construction of a matching feedback law. The problem is solved in full generality and the cases of special interest are subsequently discussed.
Unlike traditional methods that aim to approximate a function over a large compact set, a function approximation method is developed in this paper that aims to approximate a function in a small neighborhood of a state that travels within a compact set. The development is based on universal reproducing kernel Hilbert spaces over the n..dimensional Euclidean space. Three theorems are introduced that...
Accurate forecasting of upcoming trends in the capital markets is extremely important for algorithmic trading and investment management. Before making a trading decision, investors estimate the probability that a certain news item will influence the market based on the available information. Speculation among traders is often caused by the release of a breaking news article and results in price movements...
This paper presents novel means for estimating the polynomial static nonlinearity coefficients of a Wiener system in absence of a priori information about the linear block. To capture the system structure, the identification is performed with respect to a Volterra series model, whose kernels are parameterized in terms of Laguerre functions. A property of the resulting Volterra-Laguerre model is exploited...
Human body communication (HBC) is a short-range, wireless communication in the vicinity of, or inside a human body. In this paper, biometric authentication based on capacitive coupled HBC is presented for the wearable devices. In-situ experiments were conducted with 20 volunteers to investigate the feasibility. The S21 parameters of the HBC channel from one palm to the other within the frequency range...
Support vector machine (SVM) is a popular classifier dealing with small-scale datasets. It has outstanding performance compared to other classifiers. However the execution time is extremely long when training Big Data. The Graphics Processing Unit (GPU) is a massively parallel device which performs very well as a co-processor. NVIDIA proposed a programming platform, CUDA, in 2006, which makes it much...
This paper proposes a novel kernel-based mixture of experts model for linear regression. The method is novel in that it formulates the mixture of experts model for linear regression so that kernel functions can be used. This allows the method to work directly in terms of kernels and avoids the explicit introduction of the feature vector, allowing one to use feature spaces of high, even infinite dimensionality...
The production of Integrated Circuits (ICs) is subject to high quality standards, and many control steps are incorporated in manufacturing processes. In the same perspective, Statistical Process Control (SPC) methods are intensively used as decision tools for the sake of quality monitoring. However, these conventional SPC methods don't include spatial correlation in their analysis, which can limit...
The problem of resolving the fine details of a signal from its coarse scale measurements or, as it is commonly referred to in the literature, the super-resolution problem arises naturally in engineering and physics in a variety of settings. We suggest a unified convex optimization approach for super-resolution. The key is the construction of an interpolating polynomial in the measurements space based...
The Poisson summation formula (PSF), which relates the sampling of an analog signal with the periodization of its Fourier transform, plays a key role in the classical sampling theory. In its current forms, the formula is only applicable to a limited class of signals in L1. However, this assumption on the signals is too strict for many applications in signal processing that require sampling of non-decaying...
The fixed time-frequency resolution of the short-time Fourier transform has often been considered a major drawback. In this contribution we review recent results on a class of time-frequency transforms that adapt to a large class of frequency scales in the same sense that wavelet transforms are adapted to a logarithmic scale. In particular, we show that each transform in this class of warped time-frequency...
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