A novel frequency-selective metamaterial to improve helix antenna

A novel frequency-selective metamaterial with negative permittivity and permeability for improving directivity and gain of a helix antenna is presented in this paper.The proposed metamaterial is composed of two Z-shape resonators printed on opposite sides of a dielectric substrate.Two forms of multilayered cells are found to be suitable for antennas and waveguides applications.In addition,a new method of designing a metamaterial-based helix antenna is presented with high directivity and gain.A comparison on radiation properties is given between the conventional and the new metamaterial-based helix antennas.Two comparisons on radiation properties are performed:(1) the effect of proposed Z-structure on monopole,dipole,and helix antennas;(2) the effect of OE3,split-ring resonator (SRR),and proposed Z-structure unit cells on the performance of helix antennas.The results show improvement of parameters such as directivity,gain,and radiation power of the new metamaterial-based helix antenna.Therefore,the combination of Z-structure with the helix antenna shows the best performance.     

Using mobile node speed changes for movement direction change prediction in a realistic category of mobility models

In order to evaluate performance of protocols for ad hoc networks, the protocols have to be tested under realistic conditions. These conditions may include a reasonable transmission range, a limited buffer size, and realistic movement of mobile users (mobility models). In this paper, we propose a new and realistic type of random mobility models in which the mobile node has to decelerate to reach the point of direction change and accelerates with a defined acceleration to reach its intended speed. This realistic mobility model is proposed based on random mobility models. In reality, mobile objects tend to change their speed when they are going to change their direction, i.e. decelerate when approaching a direction change point and accelerate when they start their movement in a new direction. Therefore, in this paper, we implement this behavior in random mobility models which lack such specification. In fact, this paper represents our effort to use this accelerated movement to anticipate a probable direction change of a mobile node with reasonable confidence. The simulation type of this paper is based on traces produced by a mobility trace generator tool. We use a data mining concept called association rule mining to find any possible correlations between accelerated movement of mobile node and the probability that mobile node wants to change its direction. We calculate confidence and lift parameters for this matter, and simulate this mobility model based on random mobility models. These simulations show a meaningful correlation between occurrence of an accelerated movement and event of mobile node's direction change.     

Visual sensor network lifetime maximization by prioritized scheduling of nodes

Visual surveillance of a designated air space can be achieved by a randomly distributed camera sensor network spread over a large area. The location and field of view of each battery operated sensor, after a calibration phase, will be known to a central processing node. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. As a sensor dies another sensor should be selected to compensate for the dead one and reestablish the complete coverage. This process should be continued until complete coverage is not achievable by the existing sensors. Thereafter, a graceful degradation of the coverage is desirable.The goal is to elongate the lifetime of the network while maintaining a maximum possible coverage of the designated air space. Since the selection of a subset of sensors for complete coverage of the target area is an NP-complete problem, we present a number of heuristics for this case. The proposed methods are categorized in two groups. In one category, the sensors are prioritized based on their visual and communicative properties and the selection is performed according to the prioritizing function. In the other group, we propose traditional evolutionary and swarm intelligence algorithms. The performance of the proposed methods is evaluated through extensive simulations.     

Application of artificial neural networks for prediction of coercivity of highly ordered cobalt nanowires synthesized by pulse electrodeposition

This study aims to predict the coercivity of cobalt nanowires fabricated by Alternating Current (AC) pulse. Coercivity is one of the most important properties of magnetic materials and its value shows the needed magnetic field in a way that magnetization of system is decreased to zero. There are many parameters such as pH of solution, oxidative and reductive times, oxidative and reductive voltages, interval between pulses (off-time), and concentration of deposition solution that have direct effect on materials magnetic properties of. Change of initial conditions to obtain the best results is very time consuming, therefore employing a method which can save both the time and cost is necessary. Hence, it this study Artificial Neural Network (ANN), which has numerous applications and has attracted many attentions in various fields, was applied. Through this study, an ANN was designed to present a template that is capable for predicting output data (coercivity) according to input data (pH, oxidative and reductive times, oxidative and reductive voltages, and off-time). Besides, in this research, the results for pH = 4 and 6 were investigated and the effect of off-time as well as the deposition time on coercivity were studied.     

Nanofiltration membranes for ionic liquid recovery

Nanofiltration membranes have been developed by interfacial polymerization using base PES ultrafiltration membranes. By varying the concentration of the reactive monomers present as well as the reaction conditions, the structure of the polymerized barrier layer has been modified. Here, the ability to concentrate low molecular weight sugars while allowing dissolved ionic liquids in aqueous solution to be recovered in the permeate has been investigated for application in biomass hydrolysis. The results obtained here indicate that the selectivity for 1-butyl-3-methylimidazoliumchloride (BmimCl) over glucose can be as high as 36.6. The membrane permeance was 2.31 L m^?2 h^?1 bar^?1.     

Integration of beamforming and uncertainty-of-observation techniques for robust ASR in multi-source environments

This paper presents a new approach for increasing the robustness of multi-channel automatic speech recognition in noisy and reverberant multi-source environments. The proposed method uses uncertainty propagation techniques to dynamically compensate the speech features and the acoustic models for the observation uncertainty determined at the beamforming stage. We present and analyze two methods that allow integrating classical multi-channel signal processing approaches like delay and sum beamformers or Zelinski-type Wiener filters, with uncertainty-of-observation techniques like uncertainty decoding or modified imputation. An analysis of the results on the PASCAL-CHiME task shows that this approach consistently outperforms conventional beamformers with a minimal increase in computational complexity. The use of dynamic compensation based on observation uncertainty also outperforms conventional static adaptation with no need of adaptation data.     

Modelling and evaluation of building integrated SOFC systems

This study presents the final results of a series of modelling steps which are undertaken for the performance assessment of the building cogeneration and polygeneration systems using solid oxide fuel cell (SOFC). Based on earlier work, generic SOFC cell stack and system models were developed and employed to analyze different SOFC systems configurations for optimal efficiencies, this SOFC system model is used to derive performance input data for transient whole-building and energy system simulation tools which contain simpler SOFC system models. These steps are shortly summarized here. Then the final step, the evaluation of building integrated co- and polygeneration SOFC systems in terms of primary energy demand and CO₂ emissions, employing such tools, is presented here for a polygeneration system with typical heating and cooling loads, and electricity demand profiles, for different SOFC systems, including a comparison to current standard technologies.     

Multi-constraint QoS routing using a new single mixed metrics

Multi-constraint quality-of-service (QoS) routing has become increasingly important as the Internet evolves to support real-time services. It is well known, however, that optimum multi-constraint QoS routing is computationally complex, and for this reason various heuristics have been proposed for routing in practical situations. Among these methods, those that use a single mixed metric are the most popular. Although mixed metric routing discards potentially useful information, this is compensated for by significant complexity reduction. Exploiting this tradeoff is becoming increasingly important where low complexity designs are desired, such as in battery operated wireless applications. In this paper, novel single mixed metrics for multi-constraint routing are introduced. The proposed techniques have similar complexity compared with existing low complexity methods. Simulation and analytical results are presented which show that it can obtain better performance than comparable techniques in terms of generating feasible routes.     

Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields

This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user's experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (P_b) and the oil formation volume factor at bubble point (B_ob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions.     

Fuzzy automated visual broken edge detection

This paper describes an automated machine vision-based inspection method for fast and accurate detection of broken edges on the machined surface of water pumps which are formed during casting. The paper proposes a collection of steps to inspect images taken in a noisy environment to identify broken edges. By developing three broken edge verification features and using fuzzy C-means clustering, we provide a fuzzy broken edge inspection model that classifies water pumps into three classes: pumps with significant (major) broken edges, pumps with insignificant (minor) broken edges, and pumps with no broken edges. The devised machine vision-based inspection method is efficient in terms of processing time and accuracy to recognize the parts with broken edges and to make decisions consistent with human knowledge. The fuzzy broken edge detection was carried out on a database of 150 gray-level images. The developed method properly identifies about 95 % of the broken edges in the entire database.