Wideband and low‐loss waveguides of nonuniform cross‐section

Waveguides of nonuniform cross‐section are introduced, analyzed and then their cross‐section is optimized to reach wideband and low‐loss waveguides. The waveguide cross‐section is defined as a Fourier series whose unknown coefficients are optimized using genetic algorithm to reach a proper cross‐section for waveguide. A verified finite difference method is used in analysis stage of optimization. The results show that optimum designed nonuniform cross‐section waveguides have lower conductor attenuation constant in comparison with double ridged waveguides of the same bandwidth. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:115–121, 2015.     

Synthesis of concurrent multibeam and conical beam antenna arrays

An approach is presented to synthesize linear and planar antenna arrays to have concurrent multibeam radiation patterns as well as desired sidelobe levels. This approach is based on zeros perturbation of the pattern of linear arrays. Multibeam planar arrays are synthesized by both multiplying and transforming multibeam linear arrays. Besides, planar arrays having conical or ring type beam are synthesized by applying a special transformation to dual‐beam linear arrays. Some examples are brought to verify the effectiveness of the proposed approach for both linear and planar arrays.     

Dynamic bitrate allocation of interactive real-time streamed multi-view video with view-switch prediction

In multi-view video (MVV), the real-world scene is usually captured by more than two cameras positioned in an array. A viewer can consume MVV using either a non-interactive or an interactive transmission method. In the context of interactive MVV streaming, view switching may cause a long delay due to the frequent requests by the viewer. In this paper, we consider the use case of real-time interactive MVV (IMVV) streaming, where the view switching delay problem has a significant user experience impact. Our proposed method compress and send all the captured views using a dynamic bitrate allocation method. Also, a novel prediction algorithm has been used to choose possible views that the user might switch to. The predicted view switching is mapped to a hidden Markov model, and the transition probability is solved using Zipf distribution. The experimental results of the proposed method show a superior performance on an objective metric and view-switching delay for better viewing quality over the existing method.     

Minimum cost multiple multicast network coding with quantized rates

In this paper, we consider multiple multicast sessions with intra-session network coding where rates over all links are integer multiples of a basic rate. Although having quantized rates over communication links is quite common, conventional minimum cost network coding problem cannot generally result in quantized solutions. In this research, the problem of finding minimum cost transmission for multiple multicast sessions with network coding is addressed. It is assumed that the rate of coded packet injection at every link of each session takes quantized values. First, this problem is formulated as a mixed integer linear programming problem, and then it is proved that this problem is strongly NP-hard on general graphs. In order to obtain an exact solution for the problem, an effective and efficient scheme based on Benders decomposition is developed. Using this scheme the problem is decomposed into a master integer programming problem and several linear programming sub-problems. The efficiency of the proposed scheme is subsequently evaluated by numerical results on random networks.     

Usage and impact of technology enabled job learning

Individuals use technology to experiment with new ways of carrying out their tasks and in doing so they learn more about their jobs. The current study examines the role of technology enabled job learning as a key component in the complex relationship between information systems use and technology outcomes. Data from 308 end-users were analyzed to evaluate the relationships between system use and technology enabled job learning, and technology enabled job learning and technology outcomes. Technology enabled job learning was conceptualized in terms of how computer applications helped individuals learn and better perform their jobs. System use was conceptualized in terms of decision support, work integration, and customer service. Technology outcomes were conceptualized in terms of management control, task innovation, task productivity, and customer satisfaction. Results suggest that systems use has a significant, positive effect on job learning, and that job learning has a significant, positive effect on technology outcomes. Post hoc analyses were then conducted to examine the potential mediating role of job learning between systems use and technology outcomes. The findings from this research lead to a greater understanding of how patterns of systems use influence organizationally relevant outcomes through technology-enabled job learning.     

Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels

In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900–1,100°C) and subsequent cooling rate (0.15–227°C/s) and initial austenite grain size (5–130 μm) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness.     

Artificial Neural Networks for Hardness Prediction of HAZ with Chemical Composition and Tensile Test of X70 Pipeline Steels

A neural network with feed-forward topology and back propagation algorithm was used to predict the effects of chemical composition and tensile test parameters on hardness of heat affected zone(HAZ)in X70 pipeline steels.The mass percent of chemical compositions(i.e.carbon equivalent based upon the International Institute of Welding equation(CEIIW),the carbon equivalent based upon the chemical portion of the Ito-Bessyo carbon equivalent equation(CEPcm),the sum of the niobium,vanadium and titanium concentrations(CVTiNb),the sum of the niobium and vanadium concentrations(CNbV),the sum of the chromium,molybdenum,nickel and copper concentrations(CCrMoNiCu)),yield strength(YS)at 0.005 offset,ultimate tensile strength(UTS)and percent elongation(El)were considered as input parameters to the network,while Vickers microhardness with 10 Nload was considered as its output.For the purpose of constructing this model,104 different data were gathered from the experimental results.Scatter diagrams and two statistical criteria,i.e.absolute fraction of variance(R2)and mean relative error(MRE),were used to evaluate the prediction performance of the developed model.The developed model can be further used in practical applications of alloy and thermo-mechanical schedule design in manufacturing process of pipeline steels.