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The purpose of this paper is to improve the lifetime ratio of wireless sensor networks for maintaining the battery level at a desired point for better improvement of network health.

Sensor point network (SPN) is used for variety of applications like weather check, tracking of undesirable vehicles and delivery of data to end points. The proposed special high health sensing point (SHHSP) scheme will overcome several limitations of existing game theory approaches with respect to delay, health and overall throughput.

The simulation results of the proposed SHHSP scheme confirms the excellence over the existing works examined with respect to delay, hops, energy consumed, nutrition SP, harmful SP, throughput and overhead.

It is proposed for a smart communication system in IoT, where in the communication between the sensing point network to its neighbouring sensing network is carried out by selection of SHHSP, this is implemented by using the remaining energy and distance vector with respect to control station. The system is applicable to weather check and can also be used in tracking of vehicles in a vehicle ad hoc networks.

It is subsidized to the IoT system and vehicle-to-vehicle communication system where in the safety is of utmost concern. The system is concentrated on the battery concern of SPN in a pool of SPNs.

Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the modelik’s parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.

The aim of the study is to show that merging two areas of mathematics – topology and discrete optimization – could result in a viable option to solve classical or specialized integer problems.

In the paper, discrete topology concepts are applied to propose a metaheuristic algorithm that is capable to solve binary programming problems. Particularly, some of the homotopy for paths principles are used to explore the solution space associated with four well-known NP-hard problems herein considered as follows: knapsack, set covering, bi-level single plant location with order and one-max.

Computational experimentation confirms that the proposed algorithm performs in an effective manner, and it is able to efficiently solve the sets of instances used for the benchmark. Moreover, the performance of the proposed algorithm is compared with a standard genetic algorithm (GA), a scatter search (SS) method and a memetic algorithm (MA). Acceptable results are obtained for all four implemented metaheuristics, but the path homotopy algorithm stands out.

A novel metaheuristic is proposed for the first time. It uses topology concepts to design an algorithmic framework to solve binary programming problems in an effective and efficient manner.

This paper is the description of a new two‐grid algorithm to solve frictional contact problems. A regularized formulation is introduced and the discretized problem is solved using an internal non linear two‐grid technique coupled with a diagonal fixed point algorithm. Mathematical background is given, and superconvergence is obtained.

The purpose of the paper is to present a method for efficiently obtaining the steady‐state solution of the quasi‐static Maxwell's equations in case of nonlinear material properties and periodic excitations.

The fixed‐point method is used to take account of the nonlinearity of the material properties. The harmonic balance principle and a time periodic technique give the periodic solution in all nonlinear iterations. Owing to the application of the fixed‐point technique the harmonics are decoupled. The optimal parameter of the fixed‐point method is determined to accelerate its convergence speed. It is shown how this algorithm works with iterative linear equation solvers.

The optimal parameter of the fixed‐point method is determined and it is also shown how this method works if the equation systems are solved iteratively.

The convergence criterion of the iterative linear equation solver is determined. The method is used to solve three‐dimensional problems.

We present an approach for blind separation of acoustic sources produced from multiple speakers mixed in realistic room environments. We first transform recorded signals into the time‐frequency domain to make mixing become instantaneous. We then separate the sources in each frequency bin based on an independent component analysis (ICA) algorithm. For the present paper, we choose the complex version of fixedpoint iteration (CFPI), i.e. the complex version of FastICA, as the algorithm. From the separated signals in the time‐frequency domain, we reconstruct output‐separated signals in the time domain. To solve the so‐called permutation problem due to the indeterminacy of permutation in the standard ICA, we propose a method that applies a special property of the CFPI cost function. Generally, the cost function has several optimal points that correspond to the different permutations of the outputs. These optimal points are isolated by some non‐optimal regions of the cost function. In different but neighboring bins, optimal points with the same permutation are at almost the same position in the space of separation parameters. Based on this property, if an initial separation matrix for a learning process in a frequency bin is chosen equal to the final separation matrix of the learning process in the neighboring frequency bin, the learning process automatically leads us to separated signals with the same permutation as that of the neighbor frequency bin. In each bin, but except the starting one, by chosen the initial separation matrix in such a way, the permutation problem in the time domain reconstruction can be avoided. We present the results of some simulations and experiments on both artificially synthesized speech data and real‐world speech data, which show the effectiveness of our approach.

This article considers Inverse Gaussian distribution as the basic lifetime model for the test units. The unknown model parameters are estimated using the method of moments, the method of maximum likelihood and Bayesian methods. As part of maximum likelihood analysis, this article employs an expectation-maximization algorithm to simplify numerical computation. Subsequently, Bayesian estimates are obtained using the Metropolis–Hastings algorithm. This article then presents the design of optimal censoring schemes using a design criterion that deals with the precision of a particular system lifetime quantile. The optimal censoring schemes are obtained after taking into account budget constraints.

This article first presents classical and Bayesian statistical inference for Progressive Type-I Interval censored data. Subsequently, this article considers the design of optimal Progressive Type-I Interval censoring schemes after incorporating budget constraints.

A real dataset is analyzed to demonstrate the methods developed in this article. The adequacy of the lifetime model is ensured using a simulation-based goodness-of-fit test. Furthermore, the performance of various estimators is studied using a detailed simulation experiment. It is observed that the maximum likelihood estimator relatively outperforms the method of moment estimator. Furthermore, the posterior median fares better among Bayesian estimators even in the absence of any subjective information. Furthermore, it is observed that the budget constraints have real implications on the optimal design of censoring schemes.

The proposed methodology may be used for analyzing any Progressive Type-I Interval Censored data for any lifetime model. The methodology adopted to obtain the optimal censoring schemes may be particularly useful for reliability engineers in real-life applications.

The purpose of the paper is to show the application of the fixed‐point method with the T, Φ‐Φ formulation to get the steady‐state solution of the quasi‐static Maxwell's equations with non‐linear material properties and periodic excitations.

The fixed‐point method is used to solve the problem arising from the non‐linear material properties. The harmonic balance principle and a time periodic technique give the periodic solution in all non‐linear iterations. The optimal parameter of the fixed‐point method is investigated to accelerate its convergence speed.

The Galerkin equations of the DC part are found to be different from those of the higher harmonics. The optimal parameter of the fixed‐point method is determined.

The establishment of the Galerkin equations of the DC part is a new result. The method is first used to solve three‐dimensional problems with the T, Φ‐Φ formulation.

A hybrid method is described that seeks to combine the efficiency of a quasi‐Newton method capable of locating stable and unstable equilibrium configurations with a robust homotopy method that is capable of tracking equilibrium paths with turning points while exploiting symmetry and sparsity of the Jacobian matrices. Numerical results are presented for a shallow arch problem.