Computational Methods for Differential Equations
Volume:7 Issue: 4, Summer 2019

Network-on-chip (NoC) paradigm, which is based on a modular packet-switched mechanism, effectively addresses many of the on-chip communication challenges such as wiring complexity, communication latency, and bandwidth of many-core systems. In designing an efficient NoC, topology and routing algorithm are the most important challenging issues that have significant impact on area, latency and power consumption. The goal of this paper is designing a fuzzy-basedrouting algorithm for a NoC architecture with honeycomb topology. The proposed algorithm is a livelock and deadlock free routing algorithm based on fuzzy logic for hexagonal zones with flat triple coordinate system. The analysis of simulation results demonstrates that the proposed algorithm, provides higher performance in terms of latency, power consumption, throughput and area than a traditional fuzzy-based routing algorithm for mesh-based NoC architectures. Comparing to a non-fuzzy routing algorithm for honeycomb NoCs, the proposed scheme performs faster with higher throughput with anegligible area and power consumption overhead.

Recent advances in the context of Internet of Things (IoT) have led to the emergence of many useful IoT applications with different Quality of Service (QoS) requirements. The fog-cloud computing systems offer a promising environment to provision resources for IoT application services. However, providing an efficient solution to service placement problem in such systems is a critical challenge. To address this challenge, in this paper, we propose a QoS-aware service placement policy for fog-cloud computing systems that places the most delay-sensitive application services as closer to the clients as possible. We validate our proposed algorithm in the iFogSim simulator. Results demonstrate that our algorithm achieves significant improvement in terms of service latency and execution cost compared to simulators built-in policies.

The Analysis of radio wave propagation is a crucial part in designing an efficient wireless communication system. The Geographic information system (GIS) can be incorporated into this procedure because most of the factors in radio wave propagation are geographic features. For reliable cellular mobile communication received signal level must be above the receiver sensitivity. Determining of network zone in which this requirement is satisfied is done in the phase of planning of the mobile network by using various methods of field strength prediction. In this Paper, a system for automatic measurement of signal level in Global System for Mobile Communication (GSM) 900/1800 MHz channel with Global Position System (GPS) localization is tested in a field driving test carried out in Suaimania City, Kurdistan Region, Iraq. The result indicates that through the use of GSM, GPS and GIS (G3) technologies a map could be developed and used to study the signal strength of a particular location. In addition, the multipath fading phenomena effect of the mobile radio coverage could be also analyzed based on the wireless communication principles.

Nowadays; the use of mobile technology becomes part of the daily activity in our life. It’s impossible the advantage of any mobile instruments without disadvantage. The disadvantage of unprotected mobile is creating various problems. The most popular problem is that on mobile. Once the mobile owner has stored any personal data into own mobile phone device. It might be the personal data needed at a time such as (Forgetting, lost) anywhere. Furthermore, this makes the phone owner worry about all personal data and data face real danger from someone else because of stored personal data. The mobile device might be forgotten at home, work or anywhere, and the owner of the device unable to control the phone and very hard to reach stored data such as (Photos, Videos, Document files, Contact numbers, etc.). Our proposed application solves the subject matter that mentioned the remote control of Mobile using short messaging service (SMS) instead of the use of the Internet just in case the internet facility is not always available but the SMS availability of SMS is higher than the internet facility to change some of the mobile features. The suggested mobile application was modeling to designed and implemented to control mobile through using SMS only. The application can change ringing mode or send SMS containing mobile last location or also can search for a specific name in the contact names and send it to another mobile by analysing the incoming messages if the message contains specific message format (pin code, Action, reply). The suggested application was build and tested in the android operating system and in the real mobile device.

Voice is one of the vital biometrics in human identification and/or verification area. In this paper, two different models are proposed for speaker identification which are a 1D convolutional neural network (CNN) and feature based model. In the feature based model, three global spectral based features including Mel Frequency Cepstral Coefficient (MFCC), Linear Prediction Code (LPC) and Local Binary pattern (LBP) are fed to an SVM and k-NN classifiers. Results show that MFCC is the best feature among the others. Consequently, local MFCC features is extracted from the framed signal and used to both the proposed models. The result shows that the local based MFCC improved the accuracy of the CNN based model.

Let $mathcal A$ be a Banach algebra and $sigma$ and $tau$ be continuous endomorphisms on $mathcal A$. In this paper, we investigate $(sigma, tau)-$amenability and$(sigma, tau)-$weak amenability for unitization of Banach algebras, and also the relation between of them.We introduce and study the concepts $(sigma, tau)-$trace extention property, $(sigma, tau)-I-$weak amenability and $(sigma, tau)-$ideal amenability for $mathcal A$ and its unitization, where $I$ is a closed two-sided ideal in $mathcal A$.

This work presented here is the solution of the one-dimensional Bratu’s problem. The nonlinear Bratu’s problem is first linearised using the quasi-linearization method and then solved by the spectral element method. We use the Legendre polynomials for interpolation. Finally we show the results with a numerical example.

This paper aims to investigate the stability and numerical approximation of the Sivashinsky equations. We apply the Galerkin meshfree method based on the radial basis functions (RBFs) to discretize the spatial variables and use a group presenting scheme for the time discretization. Because the RBFs do not generally vanish on the boundary, they can not directly approximate a Dirichlet boundary problem by Galerkin method. To avoid this difficulty, an auxiliary parametrized technique is used to convert a Dirichlet boundary condition to a Robin one. In addition, we extend a stability theorem on the higher order elliptic equations such as the biharmonic equation by the eigenfunction expansion.Some experimental results will be presented to show the performance of the proposed method.

In this paper, the notation of almost conjugate Jordan homomorphism between Fr$acute{mathbf{text{e}}}$chet algebras is introduced. It is proven that, under special hypotheses, every  almost conjugate Jordan homomorphism on  Fr$acute{mathbf{text{e}}}$chet algebras is an (weakly) almost homomorphism. Also, the automatic continuity of them is generalized.

With modern technology development, functional data analysis (FDA) has received considerable recent attention in many scientific fields. The estimation of mean in FDA is of interest, because it is not only important by itself but it is a prelude to other issues such as dimension reduction and modeling of functional dataIn this paper we construct a two-sample Behrens-Fisher problem when data are functions and obtain the asymptotic properties of the test statistic as data dimension increases with the sample size. The power of the proposed test is also investigated.The proposed test used for inference about the differences in the mean temperature functions of the Western and the South-western weather stations of Iran.

Our main aim is to compute eigenvaluesof generalized shift isomorphism $sigma_varphi:V^GammatoV^Gamma$ with $sigma_varphi((x_alpha)_{alphainGamma})=(x_{varphi(alpha)})_{alphainGamma}$ ($(x_alpha)_{alphainGamma}in V^Gamma$) where $V$ is avector space (over field $F$), $Gamma$ is a nonempty arbitrary set and $varphi:GammatoGamma$ is an arbitrary bijection.

Assume we have $k$ immediate (due)-annuities with different interest rates. Let ${bf i}=(i_1,i_2,...,i_k)$ and ${bf i^*}=(i^*_1,i^*_2,...,i^*_k)$ be two vectors of interest rates such that ${bf i^*}$ is majorized by ${bf i}$. It's shown that sum of present and accumulated value of annuities-immediate with interest rate ${bf i}$ is grater than sum of present value of annuities-immediate with interest rate ${bf i^*}$. We also prove the similar results for annuities-due.

Traditional logistic regression  is plugged with degenerates and violent behavior in high-dimensional classification, because of   the problem of non-invertible matrices in estimating model parameters. In this paper, to overcome the high-dimensionality of data,  we introduce  two new algorithms. First, we  improve the efficiency of finite population Bayesian bootstrapping logistic regression classifier by using the rule of  majority vote.  Second, using simple random sampling without replacement to select a smaller number of covariates rather than the sample size and applying traditional logistic regression, we introduce the other new algorithm  for high-dimensional binary classification.   We compare the proposed algorithms with the regularized logistic regression  models and two other  classification algorithms, i.e., naive Bayes and K-nearest neighbors using both simulated and real data.

The analysis of complex networks become more popular through the easily access of huge network data resources in the last years. Researchers have developed techniques and models to help understanding and predicting the behaviour of complex network systems. This advanced analysis is not possible without proper softwares and tools. A large number of tools are available with specific features for analysing and visualizing network systems and we can use a software or a set of suitable tools based on these features and capabilities for the project. Understanding the features of tools and softwares help to achieve better results from network analysis. In this paper, first we review the structure of different types of networks. Based on Wenjun paper, the complex networks are divided into four categories: information networks; social networks; Biological networks and Technological networks [23]. Then we define some functional indicators including: Basic Functionalities, Graph type Support, File Formats Support, Indicator Supports, Visualization Layouts Support, and Community Detection Support. In the next step, by using analytic hierarchical processing (AHP) and truly definable criteria try to evaluate main complex network analysis (CNA) softwares. Eventually, an opportunity is provided using AHP to identify, understand, and evaluate completely four main CNA softwares objectively before identifying and selecting the most efficient CNA software.