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A simple phase error criterion (PEC-)-based adaptive algorithm for estimating the frequency of a complex sinusoidal signal in additive white Gaussian and impulsive noises is proposed. The proposed technique makes use of the instantaneous phase response of a first-order complex linear predictor (CLP) as a driving function to update the frequency parameter of the CLP. The proposed PEC is attractive due to its simplicity and high impulsive noise robustness. Theoretical analysis for the mean value of the estimated frequency and the steady-state mean square error (MSE) of the frequency estimate are derived in closed forms. Computer simulations are drawn to show the performance of the proposed frequency estimator. [ABSTRACT FROM AUTHOR]

We present a new classifier that uses a generalized Heronian mean (GHM) operator, and fuzzy robust principal component analysis (FRPCA) algorithms. The similarity classifier was earlier studied with other aggregation operators, including: the ordered weighted averaging (OWA), generalized mean, arithmetic mean among others. Parameters in the GHM operator makes the new classifier suitable for handling a variety of modeling problems involving parameter settings. Motivated by the nature of the GHM operator, we examine which FRPCA algorithm is suitable for use to achieve optimal performance of the new classifier. The effects of dimensionality reduction and fuzziness variable on classification accuracy are examined. The performance of the new classifier is tested on three real-world datasets: fertility, horse-colic, and Haberman's survival. Compared with previously studied similarity classifiers, the new method achieved improved classification accuracy for the tested datasets. In fertility dataset, the new classifier achieved improvements in accuracy of 1 4. 6 0 % , 1 9. 7 3 % , and 2 3. 0 0 % compared with the OWA, generalized mean, and arithmetic mean based classifiers respectively. The new classifier is simpler to implement since it does not require any weight generation criteria as the case is for the OWA based classifier. [ABSTRACT FROM AUTHOR]

Trace reconstruction considers the task of recovering an unknown string $\mathbf {x}\in \{0,1\}^{n}$ given a number of independent “traces”, i.e., subsequences of $\mathbf {x}$ obtained by randomly and independently deleting every symbol of $\mathbf {x}$ with some probability $p$. The information-theoretic limit of the number of traces needed to recover a string of length $n$ is still unknown. This limit is essentially the same as the number of traces needed to determine, given strings $\mathbf {x}$ and $\mathbf {y}$ and traces of one of them, which string is the source. The most-studied class of algorithms for the worst-case version of the problem are “mean-based” algorithms. These are a restricted class of distinguishers that only use the mean value of each coordinate on the given samples. In this work we study limitations of mean-based algorithms on strings at small Hamming or edit distance. We show that, on the one hand, distinguishing strings that are nearby in Hamming distance is “easy” for such distinguishers. On the other hand, we show that distinguishing strings that are nearby in edit distance is “hard” for mean-based algorithms. Along the way, we also describe a connection to the famous Prouhet-Tarry-Escott (PTE) problem, which shows a barrier to finding explicit hard-to-distinguish strings: namely such strings would imply explicit short solutions to the PTE problem, a well-known difficult problem in number theory. Furthermore, we show that the converse is also true, thus, finding explicit solutions to the PTE problem is equivalent to the problem of finding explicit strings that are hard-to-distinguish by mean-based algorithms. Our techniques rely on complex analysis arguments that involve careful trigonometric estimates, and algebraic techniques that include applications of Descartes’ rule of signs for polynomials over the reals. [ABSTRACT FROM AUTHOR]

In recent years nonlinear problems have several methods to be solved and utilize a well-known analytic tools such as homotopy analysis method. In general, homotopy analysis method had gain a wide focus and improvement especially in typical nonlinear problem. The aim of this paper is to use homotopy method of analysis to solve partial differential equation in addition to improve method's efficiency. The method in this paper is to apply approximation to Padé approach to obtain sufficient efficiency. As a result, the improvement has been verified by solving two cases beside a mean value comparison of the homotopy analysis method's squared error with the improved form. [ABSTRACT FROM AUTHOR]

The generalization of barycentric coordinates to arbitrary simple polygons with more than three vertices has been a subject of study for a long time. Among the different constructions proposed, mean value coordinates have emerged as a popular choice, particularly due to their suitability for the non-convex setting. Since their introduction, they have found applications in numerous fields, and several equivalent formulas for their evaluation have been presented in the literature. However, so far, there has been no study regarding their numerical stability. In this paper, we aim to investigate the numerical stability of the algorithms that compute mean value coordinates. We show that all the known methods exhibit instability in some regions of the domain. To address this problem, we introduce a new formula for computing mean value coordinates, explain how to implement it, and formally prove that our new algorithm provides a stable evaluation of mean value coordinates. We validate our results through numerical experiments. • A new algorithm for evaluating mean value coordinates. • A formal proof that this algorithm is numerically stable. • A comparison of the numerical stability of all known algorithms for mean value coordinates. [ABSTRACT FROM AUTHOR]

Stochastic algorithms for solving the Dirichlet boundary value problem for a second-order elliptic equation with coefficients having a discontinuity on a smooth surface are considered. It is assumed that the solution is continuous and its normal derivatives on the opposite sides of the discontinuity surface are consistent. A mean value formula in a ball (or an ellipsoid) is proposed and proved. This formula defines a random walk in the domain and provides statistical estimators (on its trajectories) for finding a Monte Carlo solution of the boundary value problem at the initial point of the walk. [ABSTRACT FROM AUTHOR]

Support vector machines (SVMs) are among the most robust and accurate methods in all well-known machine learning algorithms, especially for classification. The SVMs train a classification model by solving an optimization problem to decide which instances in the training datasets are the support vectors (SVs). However, SVs are intact instances taken from the training datasets and directly releasing the classification model of the SVMs will carry significant risk to the privacy of individuals, when the training datasets contain sensitive information. In this paper, we study the problem of how to release the classification model of kernel SVMs while preventing privacy leakage of the SVs and satisfying the requirement of privacy protection. We propose a new differentially private algorithm for the kernel SVMs based on the exponential and Laplace hybrid mechanism named DPKSVMEL. The DPKSVMEL algorithm has two major advantages compared with existing private SVM algorithms. One is that it protects the privacy of the SVs by postprocessing and the training process of the non-private kernel SVMs does not change. Another is that the scoring function values are directly derived from the symmetric kernel matrix generated during the training process and does not require additional storage space and complex sensitivity analysis. In the DPKSVMEL algorithm, we define a similarity parameter to denote the correlation or distance between the non-SVs and every SV. And then, every non-SV is divided into a group with one of the SVs according to the maximal value of the similarity. Under some certain similarity parameter value, we replace every SV with a mean value of the top-k randomly selected most similar non-SVs within the group by the exponential mechanism if the number of non-SVs is greater than k. Otherwise, we add random noise to the SVs by the Laplace mechanism. We theoretically prove that the DPKSVMEL algorithm satisfies differential privacy. The extensive experiments show the effectiveness of the DPKSVMEL algorithm for kernel SVMs on real datasets; meanwhile, it achieves higher classification accuracy than existing private SVM algorithms. [ABSTRACT FROM AUTHOR]

An online identification method that can simultaneously estimate the unknown system parameter and the unknown time-delay is proposed. Firstly, with the help of Lagrange mean value theorem, the system with time-delay can be transformed into two terms that can be identified by modified least-square algorithm and one term that represents an approximate error. Then, a modified least-square algorithm is introduced to estimate all the unknown parameters in case of external disturbances. Additionally, an restrain term are added in the covariance matrix to enhance the robustness to deal with the approximate error which is related to the estimated error of system parameter and time-delay. Also, the boundedness of the estimation error is guaranteed via Lyapunov stability theory. Finally, the effectivity of the proposed method is verified by simulations results. [ABSTRACT FROM AUTHOR]

As one of the major error sources, satellite signature effect should be reduced or even erased from the distribution of the post-fit residuals to improve the ranging precision. A simulation of satellite signature effect removal process for normal point algorithm is conducted based on a revised model of satellite response, which fully considers the structural and distribution characteristics of retroreflectors. In order to eliminate both long-term and short-term satellite signature effect, a clipping method for SLR data processing is proposed by defining the clipping location as 5.6 mm away from the mean value of the long-term fit residuals to select effective returns for normal points. The results indicate that, compared to normal points algorithm, the RMS per NP of LAGEOS-1 observation data processed by the clipping method is reduced from 62.90 ± 9.9 mm to 56.07 ± 4.69 mm, and the stability of RMS is improved 53%. This study improves the satellite signature effect model and simulates the fluctuation of normal points caused by satellite signature effect for the first time. The new method based on the simulation of satellite signature effect has stronger robustness and applicability, which can further minimize the influence of satellite signature effect on the SLR production and significantly improve the data property. [ABSTRACT FROM AUTHOR]

This paper introduces an online centered normalized least mean squares (OC-NLMS) algorithm for linear adaptive finite impulse response (FIR) filters and neural networks. As an extension of the normalized least mean squares (NLMS), the OC-NLMS algorithm features an approach of online input centering according to the introduced filter memory. This key feature can compensate the effect of concept drift in data streams, because such a centering makes the filter independent from the nonzero mean value of signal. This approach is beneficial for applications of adaptive filtering of data with offsets. Furthermore, it can be useful for real-time applications like data stream processing where it is impossible to normalize the measured data with respect to its unknown statistical attributes. The OC-NLMS approach holds superior performance in comparison to the NLMS for data with large offsets and dynamical ranges, due to its input centering feature that deals with the nonzero mean value of the input data. In this paper, the derivation of this algorithm is presented. Several simulation results with artificial and real data are also presented and analysed to demonstrate the capability of the proposed algorithm in comparison with NLMS. [ABSTRACT FROM AUTHOR]

The conventional matrix completion (MC) regularizes each singular value equally, and thus the rank cannot be well approximated, which greatly limits the flexibility and accuracy of MC usage. In this paper, a truncated MC algorithm using prior information to determine the threshold while generating the target rank is proposed for the wind turbine clutter suppression of weather radar. During the singular value shrinking process, an appropriate threshold is selected to obtain the optimal approximation of the sampling matrix. Specifically, the mean value of the diagonal element in the recovered weather matrix is calculated to improve the robustness of the recovery result effectively. Simulation results demonstrate that the proposed algorithm reduces the computational complexity as well as further improves the MC accuracy and realizes the effective suppression of the wind turbine clutter. [ABSTRACT FROM AUTHOR]

Recently, healthcare data analysis has become an attractive research topic. Data gathering is the first step in data analysis and processing. During the collection of the data, some errors may occur due to human mistakes, devices' errors, or the transmission process noise. The correct treatment of the missed data and outliers conserve the data size and improve the model's performance. This paper provides two enhanced algorithms to handle missing values and outliers in big datasets. The main idea is dividing the dataset into its different classes, or clustering it by using k-means++, then calculate the average value of each part, finally replace the missed data and outliers with its corresponding part mean value. The projected imputation and outliers' data handling algorithms are tested on a dataset called Pima Indian diabetic, which contains 2768 patients dividing into 952 diabetic and 1816 controls. Four classifiers (Random Forest, Decision Tree, Support Vector Machine, and Naïve Bayes) are used to evaluate the effect of the proposed algorithms. The results show that the proposed algorithms improve classification accuracy by 8% and decrease the RMSE by 17% over Deep Learning (DL). DL is the most powerful algorithms used in repairing the missed data. [ABSTRACT FROM AUTHOR]

Spherical harmonic analysis is widely used in all aspects of geoscience. Exact quadrature methods are available for the spherical harmonic analysis of band-limited point values at the grid points of equiangular and Gaussian grids. However, no similarly exact quadrature methods are available for the spherical harmonic analysis of area mean values over the blocks delineated by these grids. In this study, new algorithms appropriate for the exact spherical harmonic analysis of the band-limited area mean values over the blocks delineated by equiangular and Gaussian grids are proposed. For band-limited data, precision that is between that of the least-squares estimation method and of the approximate quadrature methods can be achieved by using the new algorithms. Regarding the computational complexity, fewer operations are needed by the new methods as compared to those needed by the least-squares estimation method and the approximate quadrature methods in the preparation stage when the maximum degree of the spherical harmonic analysis is very large. Simulation experiments are performed to compare the ability to recover the spherical harmonic coefficients by using the least-squares estimation method, the approximate quadrature methods and these new algorithms from aliased data with aliasing components of realistic magnitudes. The results suggest that these new algorithms, with time complexity one order less than that of the least-squares estimation method in the solving stage, perform roughly the same as the least-squares estimation method in recovering spherical harmonic coefficients from the aliased data. [ABSTRACT FROM AUTHOR]

In this article, a new n-step fuzzy adaptive output tracking prescribed performance control problem is investigated for a class of nontriangular structure nonlinear systems. In the control design process, the mean value theorem is used to separate the virtual state variables needed for the control design, and the implicit function theorem is exploited to assert the existence of the desired continuous control. The fuzzy logic systems are used to identify the unknown nonlinear functions and ideal controller, respectively. By constructing a novel iterative Lyapunov function, a new n-step adaptive backstepping control design algorithm is established. The prominent characteristics of the proposed adaptive fuzzy backstepping control design algorithm are as follows: one is that it can ensure the closed-loop control system is the semiglobally uniformly ultimately bounded and the tracking error can converge within the prescribed performance bounds. The other is that it solves the controller design problem for the nontriangular nonlinear systems that the previous adaptive backstepping design techniques cannot deal with. Two examples are provided to show the effectiveness of the presented control method. [ABSTRACT FROM AUTHOR]

This paper investigates the fully complex mini-batch gradient algorithm for training complex-valued neural networks. Mini-batch gradient method has been widely used in neural network training, however, its convergence analysis is usually restricted to real-valued neural networks and of probability nature. By introducing a new Taylor mean value theorem for analytic functions, in this paper we establish deterministic convergence results for the fully complex mini-batch gradient algorithm under mild conditions. The deterministic convergence here means that the algorithm will deterministically converge, and both the weak convergence and strong convergence will be proved. Benefited from the newly introduced mean value theorem, our results are of global nature in that they are valid for arbitrarily given initial values of the weights. The theoretical findings are validated with a simulation example. [ABSTRACT FROM AUTHOR]

Theoretical analyses of evolution strategies are indispensable for gaining a deep understanding of their inner workings. For constrained problems, rather simple problems are of interest in the current research. This work presents a theoretical analysis of a multi-recombinative evolution strategy with cumulative step size adaptation applied to a conically constrained linear optimization problem. The state of the strategy is modeled by random variables and a stochastic iterative mapping is introduced. For the analytical treatment, fluctuations are neglected and the mean value iterative system is considered. Nonlinear difference equations are derived based on one-generation progress rates. Based on that, expressions for the steady state of the mean value iterative system are derived. By comparison with real algorithm runs, it is shown that for the considered assumptions, the theoretical derivations are able to predict the dynamics and the steady state values of the real runs. [ABSTRACT FROM AUTHOR]

In order to better apply the cuckoo search (CS) algorithm in solving the problem of function extremum optimization, and further improve the phenomenon of low precision and slow convergence in the optimization process of algorithm, the two subpopulations CS algorithm based on mean value evaluation is proposed. On the one hand, the algorithm introduces dynamic inertia weight to adjust the lévy flight mechanism, thus dynamically constraining the moving step-size of each generation of population, so that the algorithm has certain self-adaptability. On the other hand, the algorithm changes the way of mutation in the preference random walk. First, the average fitness evaluation mechanism is used to divide the current population into two subpopulations: good and bad. Then, it adopts a directional mutation strategy for the better population, so that the individual can search purposefully. The worse population uses differential mutation mechanism of the disturbance items with the t -distribution characteristics, and makes the individual to search in the best orientation of current, so as to enhance the local search performance and accelerate the convergence rate of the algorithm. Theoretical analysis proves the convergence and time complexity of the algorithm in this paper. The simulation results show that the improved algorithm has good applicability in solving the function optimization problem, and the optimization results and convergence speed have been significantly improved in the algorithm. [ABSTRACT FROM AUTHOR]

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The algorithm of curve smoothing for diesel indicator diagram is introduced, and the concrete implementation process is expounded in detail. First, the multi cyclic mean value operation of indicator diagram data is performed. By probable error of calculating indicator diagram data, if probable error is greater than a specific value, and the predicted value is used instead of the singular point data. The average operation of the indicator diagram data is processed again. Finally, the five-point cubic smoothing method is used to smooth the indicator curve, and the smooth, continuous and gradual indicator diagram curve is obtained. The experiments show that the algorithm described in the paper can effectively filter the noise of cylinder pressure signal, and obtain a smooth indicator diagram curve, which verifies the effectiveness of the algorithm described in the paper. [ABSTRACT FROM AUTHOR]

The kill signals are alert about possible viruses that infect computer network to decrease the danger of virus propagation. In this work, we focus on a fractional-order SEIR-KS model in the sense of Caputo derivative to analyze the effects of kill signal nodes on the virus propagation. For this purpose, we first prove the existence and uniqueness of the model and give qualitative analysis. Then, we obtain the numerical solution of the model by using the Adams–Bashforth–Moulton algorithm. Finally, the effects of model parameters are demonstrated with graphics drawn by MATLAB program. [ABSTRACT FROM AUTHOR]

Abstract The path algorithm of the fused lasso signal approximator is known to fail in finding change points when monotonically increasing or decreasing blocks exist in the mean vector. In this paper, we first understand why the standard path algorithm by Hoefling (2010) fails in the primal optimization problem. We then propose a modified path algorithm for the consistent recovery of the change points and study its properties theoretically and numerically. Highlights • The path algorithm for fused lasso signal approximator fails in finding change points when monotonic blocks exist in the mean vector. • We understand why the standard path algorithm in the literature fails in the primal optimization problem. • We propose a modified path algorithm for the consistent recovery of the change points. [ABSTRACT FROM AUTHOR]

In order to solve the problem of data sparsity in the collaborative filtering algorithm, this paper proposed an improved collaborative filtering algorithm. The algorithm filled the unrated items in the scoring matrix using the prediction value calculated by the slope-one algorithm and then gave recommendations based on the user-based collaborative filtering method based on the filled user-item scoring matrix. Using the predictive value of slope-one algorithm as the backfill value could not only reduce the sparsity of the scoring matrix, but also ensured the diversity of backfill values, so as to reduce the recommended error caused by the single fill value such as mean value and median value. It performed half off cross-validation experiments on the MovieLens-lM dataset. The results show that the collaborative filtering algorithm based on the score prediction data effectively mitigates data sparsity and has better performance recommended effect. [ABSTRACT FROM AUTHOR]

Fixed point iterations play a central role in the design and the analysis of a large number of optimization algorithms. We study a new iterative scheme in which the update is obtained by applying a composition of quasi-nonexpansive operators to a point in the affine hull of the orbit generated up to the current iterate. This investigation unifies several algorithmic constructs, including Mann's mean value method, inertial methods, and multilayer memoryless methods. It also provides a framework for the development of new algorithms, such as those we propose for solving monotone inclusion and minimization problems. [ABSTRACT FROM AUTHOR]

We prove the mean-value theorem for functions analytic in starlike domains, propose an algorithm for finding the function of mean values, and study its analytic continuation. We present a differential equation for the function of mean values and the interpretation of the Lagrange formula for analytic functions in terms of the theory of differential equations. The set of points of the initial values of the function of mean values is analyzed and the upper bound for the radius of expansion of the function of mean values in Taylor's series is established. [ABSTRACT FROM AUTHOR]

We prove the generalized Hyers–Ulam stability of a mean value type functional equation f (x) - g(y) = (x - y)h(x + y) by applying a method originated from fixed point theory. [ABSTRACT FROM AUTHOR]

Ground surface temperatures (GST) are widely measured in mountain permafrost areas, but their time series data can be interrupted by gaps. Gaps complicate the calculation of aggregates and indices required for analysing temporal and spatial variability between loggers and sites. We present an algorithm to estimate daily mean GST and the resulting uncertainty. The algorithm is designed to automatically fill data gaps in a database of several tens to hundreds of time series, for example, the Swiss Permafrost Monitoring Network (PERMOS). Using numerous randomly generated artificial gaps, we validated the performance of the gap-filling routine in terms of (1) the bias resulting on annual means, (2) thawing and freezing degree-days, and (3) the accuracy of the uncertainty estimation. Although quantile mapping provided the most reliable gap-filling approach overall, linear interpolation between neighbouring values performed equally well for gap durations of up to 3-5 days. Finding the most similar regressors is crucial and also the main source of errors, particularly because of the large spatial and temporal variability of ground and snow properties in high-mountain terrains. Applying the gap-filling technique to the PERMOS GST data increased the total number of complete hydrological years available for analysis by 70 per cent (>450-filled gaps), likely without exceeding a maximal uncertainty of ± 0.25 °C in calculated annual mean values. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we study algorithms for Toeplitz matrix recovery. Inspired by the singular value thresholding (SVT) algorithm for matrix completion and the alternating directions iterative method, we first propose a new mean value algorithm for Toeplitz matrix recovery. Then we apply our idea to the augmented Lagrange multiplier (ALM) algorithm for matrix recovery and put forward four modified ALM algorithms for Toeplitz matrix recovery. Convergence analysis of the new algorithms is discussed. All the iterative matrices generated by the five algorithms keep a Toeplitz structure that ensures the fast singular value decomposition (SVD) of Toeplitz matrices. Compared with the original algorithms, our algorithms are far superior in the time of SVD, as well as the CPU time. [ABSTRACT FROM AUTHOR]

In this paper we present several representation theorems and averaging theorems for members of the difference class of secant updates introduced by Brodlie et al. (J Inst Math Appl 11:73-82, ). Major contributions are that the integral form of the mean-value theorem leads to a proof that the BFGS update is pointwise the infinite average of all the updates on the one-dimension manifold in the Dennis class that connects the DFP secant update to the Greenstadt update, and that it can be expressed as the pointwise average of these latter two updates. Analogous results hold for all secant updates that belong to the difference class. These results contribute new understanding of the structural properties of the highly popular BFGS secant update and other updates from the difference class. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new mean value algorithm for the Toeplitz matrix completion based on the singular value thresholding (SVT) algorithm. The completion matrices generated by the new algorithm keep a feasible Toeplitz structure. Meanwhile, we prove the convergence of the new algorithm under some reasonal conditions. Finally, we show the new algorithm is much more effective than the ALM (augmented Lagrange multiplier) algorithm through numerical experiments and image inpainting. [ABSTRACT FROM AUTHOR]

The consistent combination of uneven space-time orders in finite-difference time-domain (FDTD) algorithms is the subject of this paper. When low-order time integration is used in conjunction with high-order spatial expressions, the operation of the numerical scheme close to the stability limit causes degraded performance and slow convergence. By exploiting accuracy considerations, we derive an estimate of the optimum—much smaller—time-step size that ameliorates errors in a mean-value sense and leads to improved precision. To deal with the augmentation of the required iterations, the parallel implementation of the FDTD techniques on graphics processing units is pursued, ensuring faster code executions and more efficient models. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an algorithm based on the regularization and integral mean value methods, to handle the ill-posed multi-dimensional Fredholm equations, is introduced. The application of this algorithm is based on the transforming the first kind equation to a second kind equation by the regularization method. Then, by converting the first kind to a second kind, the integral mean value method is employed to handle the resulting Fredholm integral equations of the second kind. The efficiency of the approach will be shown by applying the procedure on some examples. [Copyright &y& Elsevier]

The article focuses on the approximation on the number π, and its decimal expansion using integer arithmetic algorithm without the process of subdivision. It says that the algorithm stratagem used in computing π is inefficient. It also mentions several methods that can be used to identify the decimal expansion of π including mean-value theorem, Riemann sums, and lattice points.

Abstract: In this paper, firstly a modified particle swarm optimization algorithm (MPSO) is developed, in which the mean value of past optimal positions for each particle and the mutation operation are considered for avoiding premature. In the optimization test, MPSO performs better than particle swarm optimization algorithm (PSO). Then MPSO is applied to solve four portfolio optimization models with the real data from the Hong Kong Stock Market, and optimal values are obtained when the number of swarm , respectively. Finally, actual return rates of these models are calculated in numerical experiments, and it is illustrated from these graphs of actual return rates that when considering higher return, Cai’s model performs better in short-term investment. [Copyright &y& Elsevier]

Abstract: To construct effective image sharpness measure with good discrimination ability, a multi-scale toggle operator based algorithm is proposed in this paper. Firstly, toggle operator is used to extract image details. And, the multi-scale theory is used in toggle operator to extract multi-scale image details. Then, the final image details are obtained through applying the pixel-wise maximum operation on the extracted multi-scale image details. Finally, the mean value of the obtained final image details is used as the constructed image sharpness measure. Experimental results on different images show that, the proposed image sharpness measure could correctly quantify the clarity of image and is suitable for discriminating the image clarity change. [Copyright &y& Elsevier]

This article gives a partial solution for the open question raised by Nguyen Thanh Hao [Tikhonov regularization algorithm for pseudomonotone variational inequalities, Acta Math. Vietnam., 31 (2006), 283–289] about uniqueness of the solution of the regularized problem VI(K, F ϵ) of a pseudomonotone variational inequality VI(K, F) for sufficiently small parameter ϵ > 0. It is proved that, under certain additional assumptions, the desired solution uniqueness holds for some classes of pseudoaffine variational inequalities and pseudomonotone variational inequalities. [ABSTRACT FROM AUTHOR]

Abstract: A new fast algorithm of calculating node voltage and branch power flow under N −1 network topology is proposed based on MVA power sensitivity in this paper. The voltage of system under base network topology is calculated based on Newton power flow approach. The branch outage is represented by the branch outage parameter. The first order derivative of voltage with respect to branch outage parameter is calculated by using the convergent power flow equation of the base network topology, further the voltage derivative with respect to branch MVA power can easily be obtained. The voltage under N −1 network topology can be obtained by correcting the voltage at base network topology using the sensitivity. The advantage of the proposed approach is its high speed and high accuracy, especially for the branch MW power flow. The proposed sensitivity is justified theoretically. The proposed method can be used very quickly to obtain the accurate node voltage under different N −1 network topologies, and then branch power flow violation and voltage violation can be obtained. Tests on IEEE 14, 30, 118, 300 bus systems and Hunan power system in China verify the correctness and practical utility of the approach. [Copyright &y& Elsevier]

ABSTRACT In this paper, we propose a new video composition method by 3D mean-value coordinate (MVC). 2D MVCs have been widely used in image composition; however, when 2D MVC is applied to a video sequence directly, because of over-blending and the lack of temporal consistency, some unnatural effects may appear in the final composite results. Although 3D Poisson editing can maintain spatial and temporal consistency, it also leads to high algorithm complexity. Instead of 3D Poisson editing, we use the 3D MVC to seamlessly blend a given source video patch into a target video sequence; this approach is able to achieve high-performance blending with less computation. We show that the combination of alpha matte-based approaches and our method can further refine the produced video when the boundaries of the source object and the target object are very different. Our algorithm can be paralleled and run on a graphics processing unit. The experimental results show that our method is effective and efficient. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel variant of quantum-behaved particle swarm optimization (QPSO) algorithm with the local attractor point subject to a Gaussian probability distribution (GAQPSO). The local attractor point in QPSO plays an important in that determining the convergence behavior of an individual particle. As such, the mean value and standard deviation of the proposed Gaussian probability distribution in GAQPSO are carefully selected. The distributions and diversities of the local attractor points in GAQPSO and QPSO are evaluated and compared. For the purpose of comparison, two variants of the GAQPSO algorithm are proposed by using a mutation probability and other types of probability distribution. The GAQPSO has been comprehensively evaluated on the suite of CEC2005 benchmark functions, and the experimental results are compared with those of the PSO and QPSO algorithms based on different probability distributions. It is shown by the results that the GAQPSO algorithm is an effective approach that can improve the QPSO performance considerably, that is, the GAQPSO algorithm is less likely to be stuck in local optima and hence it can achieve better solutions in most cases. [Copyright &y& Elsevier]

In this article, the problem of downlink subcarrier allocation in a cooperative wireless multiuser OFDM system is investigated. We consider a single-cell where all the users are paired in cooperative groups. The mobile users in each group amplify and forward their partner's data stream using a time division protocol. Based on the capacity contribution from the relaying terminal, a new parameter called cooperation coefficient is introduced as a function of the relaying subchannel. This parameter is used to modify the objective parameter of the subcarrier allocation procedure. Fairness-oriented and throughput-oriented algorithms are selected from the literature to test the proposed technique. Both algorithms are modified to employ the mean of cooperation coefficient in the objective parameter of the subcarrier allocation procedure and shown to have a better total throughput without any sacrifice. Using the mean of cooperation coefficient eliminates the need for the feedback of the relaying channel to the base station. It is shown that by using the mean value of the cooperation coefficient, instead of its instantaneous value, throughput of the system would still improve compared to the non-cooperative case. Using the mean of cooperation coefficient leads to an implementable cooperative subcarrier allocation algorithm. [ABSTRACT FROM AUTHOR]

Described herein are the advantages of using sub-sinograms for single photon emission computed tomography image reconstruction. A sub-sinogram is a sinogram acquired with an entire data acquisition protocol, but in a fraction of the total acquisition time. A total-sinogram is the summation of all sub-sinograms. Images can be reconstructed from the total-sinogram or from sub-sinograms and then be summed to produce the final image. For a linear reconstruction method such as the filtered backprojection algorithm, there is no advantage of using sub-sinograms. However, for nonlinear methods such as the maximum likelihood (ML) expectation maximization algorithm, the use of sub-sinograms can produce better results. The ML estimator is a random variable, and one ML reconstruction is one realization of the random variable. The ML solution is better obtained via the mean value of the random variable of the ML estimator. Sub-sinograms can provide many realizations of the ML estimator. We show that the use of sub-sinograms can produce better estimations for the ML solution than can the total-sinogram and can also reduce the statistical noise within iteratively reconstructed images. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 247-252, 2011; [ABSTRACT FROM AUTHOR]

The filtered backprojection algorithm is still one of the most important reconstruction methods in computed tomography. It is based on explicit formulas for recovering mollified versions of the original unknown function. In this paper, we derive such formulas for the spherical mean Radon transform with centers restricted to the boundary of a ball. In two spatial dimensions we present an efficient implementation strategy that leads to a stable reconstruction algorithm of filtered backprojection type. [ABSTRACT FROM AUTHOR]

Abstract: The zero-attracting LMS (ZA-LMS) algorithm is one of the recently published sparse LMS algorithms. It usesan l 1-norm penalty in the standard LMS cost function. In this paper, we perform convergence analysis of the ZA-LMS algorithm based on white input signals. The stability condition is examined and the steady-state mean square deviation (MSD) is derived in terms of the system sparsity, system response length, and filter parameters (step size and zero-attractor controller). In addition, we propose a criterion for parameter selection such that the ZA-LMS algorithm outperforms the standard LMS algorithm. The results are demonstrated through computer simulations. [Copyright &y& Elsevier]

The article focuses on the computations of Monte Carlo algorithms through mean-square optimization. It says that Monte Carlo algorithms comprise repeated statistical simulations of Markov chain on a computer with an estimate determined as arithmetic mean of sample values. It also mentions that solution within elementary subdomain through histogram method is replaced by mean integral value estimates, wherein piecewise smooth is assumed for the solution. Meanwhile, the convergence of the estimate can be solved through analogy that would correspond to Banach space by Chebyshev inequality and the complexity of the algorithm is defined for mean-square metric. Results show that variances are finite for the used statistical estimates.

The multi-Gaussian model is used in geostatistical applications to predict functions of a regionalized variable and to assess uncertainty by determining local (conditional to neighboring data) distributions. The model relies on the assumption that the regionalized variable can be represented by a transform of a Gaussian random field with a known mean value, which is often a strong requirement. This article presents two variations of the model to account for an uncertain mean value. In the first one, the mean of the Gaussian random field is regarded as an unknown non-random parameter. In the second model, the mean of the Gaussian field is regarded as a random variable with a very large prior variance. The properties of the proposed models are compared in the context of non-linear spatial prediction and uncertainty assessment problems. Algorithms for the conditional simulation of Gaussian random fields with an uncertain mean are also examined, and problems associated with the selection of data in a moving neighborhood are discussed. [ABSTRACT FROM AUTHOR]

Abstract  In the note, a simple proof is provided for the logarithmic convexity of extended mean values. [ABSTRACT FROM AUTHOR]

Abstract: This paper improves the algorithm for the construction of explicit bounds for the solutions of second order linear differential equations of the type , , described in a recent paper of the authors. The main improvements come from the introduction of new functionals devised by Trevisan and from the application of an enhanced version of the mean value theorem for integrals when some functions of the integrand are monotonic. [Copyright &y& Elsevier]

Abstract-Modern diesel engines are typically equipped with variable geometry turbo-compressor, exhaust gas recirculation (EGR) system, common rail injection system, and post-treatment devices in order to increase their power while respecting the emissions standards. Consequently, the control of diesel engines has become a difficult task involving five to ten control variables that interact with each other and that are highly nonlinear. Actually, the control schemes of the engines are all based on static lookup tables identified on test-benches; the values of the control variables are interpolated using these tables and then, they are corrected, online, by using the control techniques in order to obtain better engine's response under dynamic conditions. In this paper, we are interested in developing a mathematical optimization process that search for the optimal control schemes of the diesel engines under static and dynamic conditions. First, we suggest modeling a turbocharged diesel engine and its opacity using the mean value model which requires limited experiments; the model's simulations are in excellent agreement with the experimental data. Then the created model is integrated in a dynamic optimization process based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The optimization results show the reduction of the opacity while enhancing the engine's effective power. Finally, we proposed a practical control technique based on the neural networks in order to apply these control schemes online to the engine. The neural controller is integrated into the engine's simulations and is used to control the engine in real time on the European transient cycle (ETC). The results confirm the validity of the neural controller. [ABSTRACT FROM AUTHOR]

Groupwise registration of a set of shapes represented by unlabeled point sets is a challenging problem since, usually, this involves solving for point correspondence in a nonrigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape, represented by a probability density function, from multiple unlabeled point sets (represented by finite-mixture models), and registering them nonrigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets represented as finite mixtures of Gaussian densities. We motivate the use of the JS divergence by pointing out its close relationship to hypothesis testing. Essentially, minimizing the JS divergence is asymptotically equivalent to maximizing the likelihood ratio formed from a probability density of the pooled point sets and the product of the probability densities of the individual point sets. We derive the analytic gradient of the cost function, namely, the JS-divergence, in order to efficiently achieve the optimal solution. The cost function is fully symmetric, with no bias toward any of the given shapes to be registered and whose mean is being sought. A by-product of the registration process is a probabilistic atlas, which is defined as the convex combination of the probability densities of the input point sets being aligned. Our algorithm can be especially useful for creating atlases of various shapes present in images and for simultaneously (rigidly or nonrigidly) registering 3D range data sets (in vision and graphics applications), without having to establish any correspondence. We present experimental results on nonrigidly registering 2D and 3D real and synthetic data (point sets). [ABSTRACT FROM AUTHOR]

We investigate the average similarity of random strings as captured by the average number of 'cousins' in the underlying tree structures. Analytical techniques including poissonization and the Mellin transform are used for accurate calculation of the mean. The string alphabets we consider are m-ary, and the corresponding trees are m-ary trees. Certain analytic issues arise in the m-ary case that do not have an analog in the binary case. [ABSTRACT FROM AUTHOR]