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The paper discusses the fractional Rössler system and the dependence of its dynamics on some entry parameters. An explicit algorithm for a priori determination of fractional Hopf bifurcations is derived and scenarios documenting a route of the system from stability to chaos are performed with respect to a varying system’s fractional order as well as to a varying system’s coefficient. Contrary to the existing results, the searched values of the fractional Hopf bifurcations follow directly from a revealed analytical dependence between these two systems’ entries. Their various critical values are established and confirmed by numerical experiments demonstrating not only the loss of stability of an equilibrium point, but also other phenomena of transition to chaotic behavior. In addition, we suggest an active control method for synchronization of two chaotic fractional-order Rössler systems. Our theoretical analysis enables to synchronize them for any value of a free parameter under which the master system displays a chaotic behavior. [ABSTRACT FROM AUTHOR]

Rational approximations for fractional order systems are discussed in this paper. A fix-pole method is derived whereby a fractional order system is efficiently approximated by a low order integer system via simple algebraic algorithms. An adjacent congruent triangle (ACT) criterion is presented to analyze our method and compare it with existing results in a unified framework. The proposed fix-pole method has a trade-off that sacrifices partly the fitting error in exchange for simple approximations of complicated fractional order systems. The quality of the fix-pole approximations can be improved to be acceptable in practical or numerical implementations by narrowing the fitting frequency band. Finally, numerical examples illustrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy. [ABSTRACT FROM AUTHOR]

This paper is an attempt to improve the quality of Gouraud shading with minimum increase in the computation requirements. It presents algorithms for intermediate quality shading i.e. the quality of shading is better than Gouraud shading and nearly comparable to the quality of bi-quadratic Phong shading, at the same time, algorithms are very fast as compared to bi-quadratic Phong shading. Three algorithms are discussed. The first algorithm uses hybrid shading in which bi-quadratic interpolation of intensity is performed on the edges of a triangle and linear interpolation of intensity is performed on the scan line. Hybrid shading, in which bi-quadratic interpolation of normals is performed on the edges and linear interpolation of intensity is performed on the scan line, has been proposed in the second algorithm. In the third algorithm, bi-cubic interpolation of intensity is performed on the edges and linear interpolation of intensity is performed on the scan line. The paper also presents an incremental method for fast calculation of area coordinates and shape functions enhancing the speed of the algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we carefully reexamine the chaotic RF model, first studied by Rabinovich and Fabrikant [1979], and have found many new and rich complex dynamics of the model that were mostly not reported before. The chaotic RF model has proved to be a great challenge to classical numerical methods, in the sense that most classical numerical methods have not been very successful in the study of complex dynamics of this special RF model. Therefore, in this paper, we present and apply a special numerical method, the Local Iterative Linearization (LIL) method, along with a special Turbo Pascal code based on this accurate LIL algorithm, for a careful numerical study of this complex RF model. Many interesting new findings are summarized and reported in this paper. [ABSTRACT FROM AUTHOR]

Differing from the conventional back-propagation (BP) neural networks, a novel multi-input Euler polynomial neural network, in short, MIEPNN (specifically, 4-input Euler polynomial neural network, 4IEPNN) is established and investigated in this paper. In order to achieve satisfactory performance of the established MIEPNN, a weights and structure determination (WASD) algorithm with pruning-while-growing (PWG) and twice-pruning (TP) techniques is built up for the established MIEPNN. By employing the weights direct determination (WDD) method, the WASD algorithm not only determines the optimal connecting weights between hidden layer and output layer directly, but also obtains the optimal number of hidden-layer neurons. Specifically, a sub-optimal structure is obtained via the PWG technique, then the redundant hidden-layer neurons are further pruned via the TP technique. Consequently, the optimal structure of the MIEPNN is obtained. To provide a reasonable choice in practice, several different MATLAB computing routines related to the WDD method are studied. Comparative numerical-experiment results of the 4IEPNN using these different MATLAB computing routines and the standard multi-layer perceptron (MLP) neural network further verify the superior performance and efficacy of the proposed MIEPNN equipped with the WASD algorithm including PWG and TP techniques in terms of training, testing and predicting. [ABSTRACT FROM AUTHOR]

This paper introduces the capacitated vehicle routing problem with recycling in reverse logistics, and designs a food chain algorithm for it. Some illustrative examples are selected to conduct simulation and comparison. Numerical results show that the performance of the food chain algorithm is better than the genetic algorithm, particle swarm optimization as well as quantum evolutionary algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a general autonomous van der Pol-Duffing oscillator is studied. Several issues, such as periodic bifurcations and the dynamical structures of the system are investigated either analytically or numerically. Especially, a phenomenon of hidden attractors is noticed and an algorithm for the location of hidden attractors is given. The obtained results show that hidden attractors exist around chaotic attractors. [ABSTRACT FROM AUTHOR]

The interaction between a bridge and a train moving on the bridge is a coupled dynamic problem. The equations of motion of the bridge and the vehicle are coupled by the time dependent contact forces. At each time step, the motion of the bridge influences the forces transferred to the vehicle and this, in turn, changes the forces acting on the bridge. In this paper, a comparison of three different time domain solution algorithms for the coupled equation of motion of the train-bridge system is presented. Guidelines are given for a good choice of the time step. [ABSTRACT FROM AUTHOR]

As Dempster-Shafer theory spreads in different application fields, and as mass functions are involved in more and more complex systems, the need for algorithms randomly generating mass functions arises. Such algorithms can be used, for instance, to evaluate some statistical properties or to simulate the uncertainty in some systems (e.g., data base content, training sets). As such random generation is often perceived as secondary, most of the proposed algorithms use straightforward procedures whose sample statistical properties can be difficult to characterize. Thus, although such algorithms produce randomly generated mass functions, they do not always produce what could be expected from them (for example, uniform sampling in the set of all possible mass functions). In this paper, we briefly review some well-known algorithms, explaining why their statistical properties are hard to characterize. We then provide relatively simple algorithms and procedures to perform efficient random generation of mass functions whose sampling properties are controlled. [ABSTRACT FROM AUTHOR]

Frequency-dependent image interference is an inevitable impairment in wideband quadrature receivers. To suppress this interference, this paper presents a non-data-aided adaptive compensation algorithm, optimal block adaptive filtering algorithm based on circularity (OBA-C). This technique exploits the concept that the image interference in wireless systems causes the received complex signal to lose its nature of circularity. Then the OBA-C algorithm restores the circularity of the signal to compensate for the image interference. To avoid manually selecting a step size, the presented algorithm employs the complex Taylor series expansion to optimally update the adaptive filter coefficients. This technique fully exploits the degrees of freedom of the system, and generates an individual update for each filter coefficient at each iteration. Computer simulations are carried out to test the performance of the OBA-C for practical levels of image interference. The simulation results illustrate that the OBA-C achieves fast convergence and excellent image rejection performance. Other advantages of OBA-C are also analyzed, including the robustness against radio frequency impairments and different levels of image interference. [ABSTRACT FROM AUTHOR]

Escape time algorithm is a classical algorithm to calculate the Julia sets, but it has the disadvantage of dull color and cannot record the iterative process of the points. In this paper, we present the equipotential point algorithm to calculate the Julia sets by recording the strike frequency of the points in the iterative process. We calculate and analyze the Julia sets in the complex plane by using this algorithm. Finally, we discuss the iteration trajectory of a single point. [ABSTRACT FROM AUTHOR]

In this paper, we present a simple and accessible way to enhance the stable behaviors of a chaotic dynamical system which models a cancer growth, as presented in [Itik & Banks, 2010]. The algorithm presented in [Danca et al., 2012], approximates numerically any attractor of a system belonging to a defined class of dynamical systems, by alternating the control parameter in relatively short periods of time. When switching the control parameter within a set of values corresponding to some chaotic behaviors, the result may be a stable evolution or, reversely a chaotic behavior may be obtained by switching the parameter within a set of values corresponding to stable evolutions. This apparently surprising phenomenon is, in fact, a generalization of the known Parrondo's paradox. [ABSTRACT FROM AUTHOR]

The handoff rate is identified in a hybrid channel allocation algorithm of a wireless and mobile network. In this network model, both new calls and handoff calls to a cell have possibilities to borrow channels from the control center if all allocated channels to the cell are used up. In addition, there is a buffer with finite size reserved exclusively for handoff calls in a cell when they arrived but are not be able to borrow channels from the control center. In this paper, we derived a novel and theoretical expression of the handoff rate in the network first. This analytical expression for handoff rate is a general form which covers several special cases discussed in the literature. Then, an efficient iterative algorithm to calculate this handoff rate is proposed also. By using this algorithm, the handoff rate and the blocking probability of both new calls and handoff calls will be successfully derived at the same time. Furthermore, our numerical analysis validates the algorithm and the corresponding theoretic expression. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of learning to classify texts by exploiting information derived from clustering both training and testing sets. The incorporation of knowledge resulting from clustering into the feature space representation of the texts is expected to boost the performance of a classifier. We present an empirical study of the proposed algorithm on a variety of datasets. We attempt to answer various questions such as the effect of independence or relevance amongst features, the effect of the size of the training set and the effect of noise. The results are encouraging, revealing that information resulting from clustering can create text classifiers of high-accuracy. [ABSTRACT FROM AUTHOR]

In this paper the classical and generalized numerical Rogers-Ramanujan continued fractions are extended to a polynomial continued fraction in one and two dimensions. Using the new continued fractions, the fundamental recurrence formulas and a fast algorithm, based on matrix formulations, are given for the computation of their transfer functions. The presented matrix formulations can provide a new perspective to the analysis and design of Ladder-continued fraction filters in one and two dimensions signal processing. The simplicity and efficiency of the presented algorithms are illustrated by step-by-step examples. [ABSTRACT FROM AUTHOR]

In this paper, we derive an algorithm to solve the linear quadratic (LQ) optimal regulator problems. The new approach is based on efficient Legendre and Chebyshev formulae at the Chebyshev-Gauss-Lobatto points. The technique enjoys advantages of both the Legendre and Chebyshev approximations near the end points. To show the numerical behavior of the proposed method, the simulation results of an example are presented. [ABSTRACT FROM AUTHOR]

Currently, two-dimensional dyadic wavelet transform (2D-DWT) is habitually considered as the one presented by Mallat, which is defined by an approximation component, two detail components in horizontal and vertical directions. This paper is to introduce a new type of two-dimensional dyadic wavelet transform and its application so that dyadic wavelet can be studied and used widely furthermore. (1) Two-dimensional stationary dyadic wavelet transform (2D-SDWT) is proposed, it is defined by approximation coefficients, detail coefficients in horizontal, vertical and diagonal directions, which is essentially the extension of two-dimensional stationary wavelet transform for orthogonal/biorthogonal wavelet filters. (2) ε-decimated dyadic discrete wavelet transform (DDWT) is introduced and its relation with 2D-SDWT is given, where ε is a sequence of 0's and 1's. (3) Mallat decomposition algorithm based on dyadic wavelet is introduced as a special case of ε-decimated DDWT, and so a face recognition algorithm based on dyadic wavelet is proposed, and experimental results are given to show its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we describe an algorithm for computing biorthogonal compactly supported dyadic wavelets related to the Walsh functions on the positive half-line ℝ+. It is noted that a similar technique can be applied in very general situations, e.g., in the case of Cantor and Vilenkin groups. Using the feedback-based approach, some numerical experiments comparing orthogonal and biorthogonal dyadic wavelets with the Haar, Daubechies, and biorthogonal 9/7 wavelets are prepared. [ABSTRACT FROM AUTHOR]

This paper presents a topology optimization method for dynamic problems with an improved bi-directional evolutionary structural optimization (BESO) technique. The sensitivity derivation for the frequency optimization problem in the case of multiple eigenvalues and for the stiffness–frequency optimization problem is proposed. Algorithms for a filter scheme, sensitivity history-averaging, and sensitivity global-ranking are used in the present method. Techniques for adaptively removing alternative elements and eliminating singular and single-hinged elements are proposed. Solution-convergence and localized modes are discussed through numerical examples. Results show that the improved BESO method is capable of solving the frequency optimization problem and the multi-objective optimization problem for stiffness and frequency effectively. [ABSTRACT FROM AUTHOR]

In this paper, we examine the wavelet characterization of Hardy space H1, and show that the H1-norm is a good choice for modelizing the oscillating patterns. Furthermore, we give the discrete representation of H1-norm by using wavelet coefficients, and apply it to the variational image decomposition models. Finally, we give the iterative algorithm, and present various numerical results on images to demonstrate the potential of our methods. [ABSTRACT FROM AUTHOR]

Weighted and controlled frames have been introduced recently to improve the numerical efficiency of iterative algorithms for inverting the frame operator. In this paper, we develop systematically these notions, including their mutual relationship. We will show that controlled frames are equivalent to standard frames and so this concept gives a generalized way to check the frame condition, while offering a numerical advantage in the sense of preconditioning. Next, we investigate weighted frames, in particular their relation to controlled frames. We consider the special case of semi-normalized weights, where the concepts of weighted frames and standard frames are interchangeable. We also make the connection with frame multipliers. Finally, we analyze weighted frames numerically. First, we investigate three possibilities for finding weights in order to tighten a given frame, i.e. decrease the frame bound ratio. Then, we examine Gabor frames and how well the canonical dual of a weighted frame is approximated by the inversely weighted dual frame. [ABSTRACT FROM AUTHOR]

Neural network was introduced to sneak circuit analysis (SCA) in previous works. However, it may generate suspect results which were hard to explain. To overcome the shortcomings, this paper proposed a novel neural network model based on circuit architecture, named CArNN, which is used as an individual of an ensemble. In CArNN, neurons represented system components, and weights represented the joints between components. Models of neurons are sigmoid functions. Clone selection algorithm was used to train CArNNs population. The trained antibodies were used as individuals of an ensemble. The inputs of CArNN are states of switches, and the outputs are states of functional components. Ensemble predicted all possible functions of circuit. The sneak circuits can be discovered by comparing the predicted and designed functions. The results revealed that CArNNs can exactly discover sneak circuits. [ABSTRACT FROM AUTHOR]

In recent years, image coding based on wavelet transform has made rapid progress. In this paper, quincunx lifting scheme in wavelet transform is introduced and all phase interpolation filter banks which can be used in the lifting scheme for prediction and update are designed. Based on the basic idea of set partitioning in hierarchical trees (SPIHT) algorithm, the binary tree image coding algorithm is proposed. Just like SPIHT, the encoding algorithms can be stopped at any compressed file size or let run until the compressed file is a representation of a nearly lossless image. The experimental results on test images show that compared with SPIHT algorithm, the PSNRs of the proposed algorithm are superior by about 0.5 dB at the same bit rates and the subjective quality of reconstructed images is also better. [ABSTRACT FROM AUTHOR]

Beam search algorithm, as an adaptation of branch and bound method, is regarded as one of the effective approaches in solving combinational optimization problems. In this paper, a new beam search algorithm for the large-scale permutation flow shop scheduling problem (FSP) is proposed. A new branching scheme is addressed and compared with the traditional branching scheme. With the new branching scheme, the number of partial schedules in the search tree can be greatly reduced. Based on a simple simulated annealing algorithm, partial schedules are globally evaluated. Numerical experiments show that good solutions of large-scale FSPs could be found with the proposed algorithm in a short time. [ABSTRACT FROM AUTHOR]

In this paper, we examine the localization problem of compact invariant sets of systems with the differentiable right-side. The localization procedure consists in applying the iterative algorithm based on the first order extremum condition originally proposed by one of authors for periodic orbits. Analysis of a location of compact invariant sets of the Lanford system is realized for all values of its bifurcational parameter. [ABSTRACT FROM AUTHOR]

Reconstruction of freeform surfaces from scattered coordinate data is a difficult problem encountered in many surface fitting and geometric modeling applications. Conventional tessellation and parametric surface fitting techniques are limited because they require prior knowledge about the connectivity between the sampled points. The method of surface reconstruction described in this paper exploits the learning capability of a self-organizing feature map (SOFM) to adaptively fit a deformable sphere to the unorganized 3D coordinate data. The learning algorithm automatically establishes the connectivity between the measured points by iteratively changing the topological relationships within the map. By incorporating additional constraints during the learning process it is possible to have the deformable map follow the shape of objects with surface holes and cavities. Several examples of freeform surfaces with varying levels of complexity are discussed in order to demonstrate the performance of the algorithm. [ABSTRACT FROM AUTHOR]

Iterated function system (IFS) models have been explored to represent discrete sequences where the attractor of an IFS is self-affine either in R2 or R3 (R is the set of real numbers). In this paper, the self-affine IFS model is extended from R3 to Rn (n is an integer and greater than 3), which is called the multi-dimensional self-affine fractal interpolation model. This new model is presented by introducing the defined parameter "mapping partial derivative." A constrained inverse algorithm is given for the identification of the model parameters. The values of new model depend continuously on all of the variables. That is, the function is determined by the coefficients of the possibly multi-dimensional affine maps. So the new model is presented as much more general and significant. [ABSTRACT FROM AUTHOR]

One of the major challenges facing real time world applications that employ Bayesian networks, is the design and development of efficient inference algorithms. In this paper we present an approximate real time inference algorithm for Bayesian Networks. The algorithm is an anytime reasoning method based on probabilistic inequalities, capable of handling fully and partially quantified Bayesian networks. In our method the accuracy of the results improve gradually as computation time increases, providing a trade-off between resource consumption and output quality. The method is tractable in providing the initial answers, as well as complete in the limiting case. [ABSTRACT FROM AUTHOR]

This paper presents a fast algorithm for solving the n-queens problem. The basic idea of this algorithm is to use pre-computed solutions in 75% of the cases, while the remaining cases are solved by calling the Sosic's algorithm. The novelty of this algorithm is in the observation that these pre-computable cases exhibit a modular nature. In addition, the pre-computed solutions run 100 times faster than Sosic's algorithm in most cases. [ABSTRACT FROM AUTHOR]

Deconvolution of a noisy signal in a periodic band-limited wavelet basis exhibits visual artifacts in the neighbourhood of discontinuities. This phenomenon is similar to that appearing in denoising with compactly-supported wavelet transforms and can be reduced by "cycle spinning" as in Coifman and Donoho [3]. In this paper we present an algorithm which "cycle-spins" a periodic band-limited wavelet estimator over all circulant shifts in O(n(log(n))2) steps. Our approach is based on a mathematical idea and takes full advantage of the Fast Fourier Transform. A particular feature of our algorithm is to bounce from the Fourier domain (where deconvolution is performed) to the wavelet domain (where denoising is performed). For both smooth and boxcar convolutions observed in white noise, we illustrate the visual and numerical performances of our algorithm in an extensive simulation study of the \tt{WaveD} estimator recently proposed by Johnstone, Kerkyacharian, Picard, and Raimondo [8]. All figures presented here are reproducible using the \tt{WaveD} software package. [ABSTRACT FROM AUTHOR]

In this paper we propose a technique that reconstructs high-resolution images with improved super-resolution algorithms, based on Irani and Peleg iterative method, and employs our suggested initial interpolation, robust image registration, automatic image selection and image enhancement post-processing. When the target of reconstruction is a moving object with respect to a stationary camera, high-resolution images can still be reconstructed, whereas previous systems only work well when we move the camera and the displacement of the whole scene is the same. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]