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Feature selection for clustering is a challenging problem due to the absence of class labels. Existing approaches can select a feature subset to maintain clustering performance while reducing dimensionality. However, we are faced with two problems: (1) there could be many sets of features that seem equally good, and (2) these features are sensitive to small data perturbation, or the selection instability...
The improvement of known, classic way of reconstruction of measurand has been presented. The measuring system output signal is followed by classic feedback system containing “associated model” of measuring system which is represented with structure defined by state variable scheme. If error of follow-up action is small, then output of associated model and its accessible derivatives can be used to...
Baseline in signals is a relatively complicated problem in analysis of signals obtained in various analytical techniques such as chromatography and spectroscopy. In this article there are presented results of tests on four algorithms for a baseline estimation in chromatographic signals. Two of them are based on a polynomial fitting in a region of detected peaks. Another two algorithms, i.e. assymetric...
In many practical situations, we monitor a system by continuously measuring the corresponding quantities, to make sure that any abnormal deviation is detected as early as possible. Often, we do not have readily available algorithms to detect abnormality, so we need to use machine learning techniques. For these techniques to be efficient, we first need to compress the data. One of the most successful...
3D visualization of numerical marine forecast data still pose a challenge for oceanographer due to accuracy requirement and the highly complex behaviour of dynamic fluid. Existing ocean surface model simulate dynamics either with parametric/spectral methods or physically-based methods, which still suffer from lacking real world information. In this paper, we propose a novel coarse-to-fine ocean surface...
We propose to address the handwritten digits recognition (HWDR) problem by using a two-dimensional (2-D) discrete cosine transform (DCT) based sparse principal component analysis (PCA) algorithm for fast classification. The gain of processing speed is achieved by utilizing the ability of 2-D DCT for energy compaction and signal decorrelation. The proposed algorithm was applied to the mixed national...
The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms. Among others, evolutionary algorithms, reinforcement agents, and neural networks have been reportedly extended into their “opposition-based” version to become faster and/or more accurate. However,...
Parallelizability of an algorithm is nowadays a highly desirable property as computer hardware is becoming increasingly parallel. In this paper, a formulation of the particle filtering algorithm, suitable for parallel or distributed computing, is proposed. From the particle set, a series expansion is fitted to the posterior probability density function. The global information provided by the particles...
Software evaluation of elementary functions usually requires three steps: a range reduction, a polynomial evaluation, and a reconstruction step. These evaluation schemes are designed to give the best performance for a given accuracy, which requires a fine control of errors. One of the main issues is to minimize the number of sources of error and/or their influence on the final result. The work presented...
Approximate spectral clustering (ASC), a recently popular approach for unsupervised land cover identification, applies spectral clustering on a reduced set of data representatives (found by sampling or quantization). ASC enables extraction of clusters with different characteristics by utilizing various information types (such as distance, local density distribution and data topology) for accurate...
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...
A typical floating-point environment includes support for a small set of about 30 mathematical functions such as exponential, logarithm, trigonometric and hyperbolic functions. These functions are provided by mathematical software libraries (libm), typically in IEEE754 single, double and quad precision. This article suggests to replace this libm paradigm by a more general approach: the on-demand generation...
The atan2 function computes the polar angle arctan(y/x) of a point given by its cartesian coordinates. It is widely used in digital signal processing to recover the phase of a signal. This article studies for this context the implementation of atan2 with fixed-point inputs and outputs. It compares the prevalent CORDIC shift-and-add algorithm to two multiplier-based techniques. The first one computes...
Apache Hadoop system is a software framework with the capability to process large-scale datasets across a cluster of distributed machines using MapReduce programming model. However, there are two main challenges for system administrators to manage the Hadoop system, (1) system administrators are difficult to tune the parameters appropriately since the behaviors and characteristics of large-scale distributed...
The article describes the numerical modeling of the problem of determining the spatial coordinates of the fire seat by multipoint electro-optical system. The algorithm for determining the spatial coordinates of the seat of fire based on Newton's method is proposed. The results of testing the algorithm on the basis of theoretically obtained initial data for three-dimensional protected object are represented...
Service functionality can be provided by more than one service consumer. In order to choose the service which creates the most benefit before its consumption, a selection based on previous measurable experiences by other consumers is beneficial. In this paper, we present the results of our analysis of two machine learning approaches to predict the best service within this selection problem. The first...
This paper deals with the study of dependence of subpixel accuracy of edge detection in 1-D images on number of used samples. Our study includes four methods for edge detection with sub-pixel accuracy in 1-D images: method based on approximation of real image function with erf function, moment-based edge operator, technique using spatial moments of the image function and the method based on wavelet...
If a biometric template is compromised, the invasion of user privacy is inevitable. Since human biometric is irreplaceable and irrevocable, such an invasion implies a permanent loss of user identity. As a result, many transformation methods are proposed to convert the biometric template into non-invertible version of itself. However, if the transformation functions are not carefully designed, the...
Wireless localization using signal strength has been very popular in commercial applications due to the wide availability of 802.11 WiFi networks. However, signal strength information alone provides very rough location estimates. In this paper we consider supplementing the receiver of each user with a ranging unit required for accurate positioning. By allowing range-based cooperation between the users,...
This paper presents an approach based on the curve fitting method for the design of non-iterative divider circuits with accuracy and area-delay product (ADP) trade-offs. The curved surfaces representing the quotient are partitioned into several regions, each of which is then approximated by a square/triangular plane. The planes are obtained by using the curve fitting method for accuracy optimization...
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