Conductivity and anticorrosion performance of polyaniline/zinc composites: Investigation of zinc particle size and distribution effect

Polyaniline/zinc composites and nanocomposites were prepared using solution mixing method. Zinc (Zn) particles with an average particle size of 60 μm and zinc nanoparticles with an average particle size of 35 nm were used as fillers in polyaniline (PANI) matrix. Films and coatings of PANI/Zn composites and nanocomposites were prepared by the solution casting method. Electrical conductivity and anticorrosion properties of PANI/Zn composite and nanocomposite films and coatings with different zinc loadings were evaluated. According to the results, electrical conductivity and anticorrosion performances of both PANI/Zn composites and nanocomposites were increased by increasing the zinc loading. Also results showed that the PANI/Zn nanocomposite films and coatings have better electrical conductivity and corrosion protection effect on iron coupons compared to that of PANI/Zn composite.     

Non-Fourier heat conduction in a hollow sphere with periodic surface heat flux

Presented is the analysis of non-Fourier effect in a hollow sphere exposed to a periodic boundary heat flux. The problem is studied by deriving an analytical solution of the hyperbolic heat conduction equation. Using the obtained analytical expression, the temperature profiles at outer and inner surfaces of the sphere are evaluated for various thermal relaxation times. By comparing the results of non-Fourier model with those obtained from Fourier heat conduction equation, the transition process from parabolic model to hyperbolic one is shown. The phase difference and amplitude ratio of boundary surfaces are calculated as functions of the thermal relaxation time and the results are depicted graphically.     

An adaptive latency mitigation scheme for massively multiuser virtual environments

As massively multiuser virtual environments (MMVEs) expand in terms of size and user population, they tend toward using P2P architectures as a way to provide scalability without the need for large centralized resources. Distributed hash table (DHT)-based networks have been introduced as a promising option for overlay-based distributed massively multiuser virtual environment applications. However, overlay latency stretch seriously affects MMVE performance where QoS is crucial for real-time user collaboration. This work includes a series of efforts in the alleviation of such undesired latency. Our approach to latency mitigation consists of two phases. First, we propose a position-based ID assignment approach to minimize message hop-count by exploiting the clustered pattern of traffic exchange among MMVE users. Second, we introduce a new ant-based distributed neighbor selection scheme that can be used by MMVE users to select the best neighbors within their areas of interest. In order to evaluate the performance of this heuristic approach, we model the neighbor selection problem in the form of a network flow problem and use its solution as an optimality bound to compare the results. Simulation results demonstrate that the proposed algorithms will compensate for DHT latency stretch to a high extent and the performance of the resulting system would closely follow the optimal bound while communication overhead is negligible.     

Cost of transmission transactions: an introduction

Major technical issues are discussed related to the cost of transmission transactions provided by a vertically integrated electric utility company. The authors identify the components of the cost of a transmission transaction and present methodologies for evaluating them using simple examples. The major cost components of a transmission transaction are: operating cost; opportunity cost; reinforcement cost; and existing system cost. The evaluation of these components requires the development of new analytical and knowledge-based techniques     

A game attention model for efficient bit rate allocation in cloud gaming

The widespread availability of broadband internet access and the growth in server-based processing have provided an opportunity to run games away from the player into the cloud and offer a new promising service known as cloud gaming. The concept of cloud gaming is to render a game in the cloud and stream the resulting game scenes to the player as a video sequence over a broadband connection. To meet the stringent network bandwidth requirements of cloud gaming and support more players, efficient bit rate reduction techniques are needed. In this paper, we introduce the concept of game attention model (GAM), which is basically a game context-based visual attention model, as a means for reducing the bit rate of the streaming video more efficiently. GAM estimates the importance of each macro-block in a game frame from the player’s perspective and allows encoding the less important macro-blocks with lower bit rate. We have evaluated nine game video sequences, covering a wide range of game genre and a spectrum of scene content in terms of details, motion and brightness. Our subjective assessment shows that by integrating this model into the cloud gaming framework, it is possible to decrease the required bit rate by nearly 25 % on average, while maintaining a relatively high user quality of experience. This clearly enables players with limited communication resources to benefit from cloud gaming with acceptable quality.     

Java multimedia telecollaboration

Using Java-based tools in multimedia collaborative environments accessed over the Internet can increase an application's client base. Most operating systems support Java, and its "compile once-run everywhere" architecture is easy to maintain and update. The Java-based tools presented here let users share Internet resources, including resources originally designed for single use.     

A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods

Artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have an extensive range of applications in water resources management. Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels. The objective of this research is to compare several data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS models have been compared to evaluate their performances to forecast groundwater level with 1, 2, 3 and 4 months ahead under two case studies in two sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error method. The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series. The prediction of these models is more accurate for 1 and 2 months ahead (for example RMSE?=?0.12, E?=?0.93 and R ²?=?0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE?=?2.07, E?=?0.63 and R ²?=?0.91 for wavelet-ANFIS model for 4 months ahead).     

Extension of Fuzzy Delphi AHP Based on Interval-Valued Fuzzy Sets and its Application in Water Resource Rating Problems

Water resource management problems are complex by nature and are often accompanied by many uncertainties, requiring suitable decision-making tools to solve. If decision makers cannot agree on a method of defining linguistic variables based on the fuzzy sets, favorable results and more accurate modeling can be achieved by using interval-valued fuzzy sets (IVFSs), which provide an additional degree of freedom to represent the uncertainty and fuzziness of the real world. Accordingly, this study is aimed to extend a fuzzy Delphi analytic hierarchy process (AHP) based on IVFSs (Interval-Valued Fuzzy Delphi AHP) and its application to large-scale rating problems related to water resource management. The proposed method is subsequently applied to select an optimal strategy for the rural water supply of Nohoor Village in northeast Iran, as a case study and actual water resource rating problem. According to sensitivity analyses of the results and a comparison of the results with a real project, the proposed method offers good outcomes for water resource rating problems.     

Group-based spatio-temporal video analysis and abstraction using wavelet parameters

In this paper, we present a spatio-temporal event-based approach to video signal analysis and abstraction employing wavelet transform features. The video signal is assumed to be a sequence of overlapping independent visual components called events, which typically are temporally overlapping compact functions that describe temporal evolution of a given set of the spatial parameters of the video signal. We utilize event-based temporal decomposition technique to resolve the overlapping arrangement of the video signal that is known to be one of the main concerns in video analysis via conventional frame-based schemes. In our method, a set of spatial parameters, extracted from the video, is expressed as a linear combination of a set of temporally overlapping compact functions, called events, through an optimization process. First, to reduce computational complexity, the video sequence is divided into overlapped groups. Next, Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform subbands, are used as the spatial parameters. Temporal decomposition is then applied to the GGD parameters, structured as a frame-based matrix of GGD vectors, to compute the event functions and associated orthogonal GGD parameters. Frames located at event centroids, which are much smaller in number than the number of frames in the original video, are taken as candidates for the keyframes that are selected based on a distance criterion in the feature space. Our contribution is that this still image video abstraction scheme does not need shot or cluster boundary detection, unlike current methods. Experimental results confirm the efficiency and accuracy of our approach.