A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

Matching-Pursuits Dissimilarity Measure for Shape-Based Comparison and Classification of High-Dimensional Data

In this paper, a new matching pursuits dissimilarity measure (MPDM) is presented that compares two signals using the information provided by their matching pursuits (MP) approximations, without requiring any prior domain knowledge. MPDM is a flexible and differentiable measure that can be used to perform shape-based comparisons and fuzzy clustering of very high-dimensional, possibly compressed, data. A novel prototype based classification algorithm, which is termed the computer aided minimization procedure (CAMP), is also proposed. The CAMP algorithm uses the MPDM with the competitive agglomeration (CA) fuzzy clustering algorithm to build reliable shape based prototypes for classification. MP is a well known sparse signal approximation technique, which is commonly used for video and image coding. The dictionary and coefficient information produced by MP has previously been used to define features to build discrimination and prototype based classifiers. However, existing MP based classification applications are quite problem domain specific, thus making their generalization to other problems quite difficult. The proposed CAMP algorithm is the first MP based classification system that requires no assumptions about the problem domain and builds a bridge between the MP and fuzzy clustering algorithms. Experimental results also show that the CAMP algorithm is more resilient to outliers in test data than the multilayer perceptron (MLP) and support-vector-machine (SVM) classifiers, as well as prototype-based classifiers using the Euclidean distance as their dissimilarity measure.     

水声网络全双工定向碰撞避免媒体接入控制协议

近年来水声网络(UAN)技术飞速发展,但仍然面临诸多严峻挑战,能量效率成为水声网络的首要考虑因素。此外,水声信道传播时延大且可用带宽受限,严重制约了水声通信技术的可靠性和有效性,进而限制了水声网络的整体性能。定向通信技术可以有效改善上述情况,通过波束聚焦能力将声波能量聚集在一定角度范围内,获得比全向通信更高的通信范围和信噪比,提升整个网络的能耗效率和空间复用率。但该技术需要对目的节点的位置具备先验知识,且会面临“聋节点”问题,因此该文提出一个水声网络全双工定向碰撞避免(FDDCA)媒体接入控制(MAC)协议,通过装备两个分别处于不同工作频带的全向换能器和定向换能器解决“聋节点”问题,并通过降低节点的冲突域解决了“暴露终端”问题。仿真结果表明,与水下Aloha(UW-Aloha)和时隙地面多址接入(S-FAMA)协议相比,FDDCA在多汇聚节点的网络拓扑下吞吐量分别提升了140%和400%,网络能效上节省了90%和94%。     

Optimization of a proton exchange membrane fuel cell membrane electrode assembly

A computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon’s algorithm and an adaptive finite element method in order to achieve near quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve the optimization problem of finding the optimal MEA composition for maximizing performance. To solve the optimization problem a gradient-based optimization algorithm is used in conjunction with analytical sensitivities. By using a gradient-based method and analytical sensitivities, the framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 min, making it a viable alternative for solving large-scale fuel cell problems.     

Learning from demonstration in robots: Experimental comparison of neural architectures

Robots have played an important role in the automation of computer aided manufacturing. The classical robot control implementation involves an expensive key step of model-based programming. An intuitive way to reduce this expensive exercise is to replace programming with machine learning of robot actions from demonstration where a (learner) robot learns an action by observing a demonstrator robot performing the same. To achieve this learning from demonstration (LFD) different machine learning techniques such as Artificial Neural Networks (ANN), Genetic Algorithms, Hidden Markov Models, Support Vector Machines, etc. can be used. This piece of work focuses exclusively on ANNs. Since ANNs have many standard architectural variations divided into two basic computational categories namely the recurrent networks and feed-forward networks, representative networks from each have been selected for study, i.e. Feed Forward Multilayer Perceptron (FF) network for feed-forward networks category and Elman (EL), and Nonlinear Autoregressive Exogenous Model (NARX) networks for the recurrent networks category. The main objective of this work is to identify the most suitable neural architecture for application of LFD in learning different robot actions. The sensor and actuator streams of demonstrated action are used as training data for ANN learning. Consequently, the learning capability is measured by comparing the error between demonstrator and corresponding learner streams. To achieve fairness in comparison three steps have been taken. First, Dynamic Time Warping is used to measure the error between demonstrator and learner streams, which gives resilience against translation in time. Second, comparison statistics are drawn between the best, instead of weight-equal, configurations of competing architectures so that learning capability of any architecture is not forced handicap. Third, each configuration's error is calculated as the average of ten trials of all possible learning sequences with random weight initialization so that the error value is independent of a particular sequence of learning or a particular set of initial weights. Six experiments are conducted to get a performance pattern of each architecture. In each experiment, a total of nine different robot actions were tested. Error statistics thus obtained have shown that NARX architecture is most suitable for this learning problem whereas Elman architecture has shown the worst suitability. Interestingly the computationally lesser MLP gives much lower and slightly higher error statistics compared to the computationally superior Elman and NARX neural architectures, respectively.     

Recent developments in catalyst synthesis using DBD plasma for reforming applications

The catalyst has a significant role in gas processing applications such as reforming technologies for H₂ and syngas production. The stable catalyst is requisite for any industrial catalysis application to make it commercially viable. Several methods are employed to synthesize the catalysts. However, there is still a challenge to achieve a controlled morphology and pure catalyst which majorly influences the catalytic activity in reforming applications. The conventional methods are expansive, and the removal of the impurities are major challenges. Nevertheless, it is not straightforward to achieve the desired structure and stability. Therefore, significant interest has been developed on the advanced techniques to take control of the physicochemical properties of the catalyst through non-thermal plasma (NTP) techniques. In this review, the systematic evolution of the catalyst synthesis using NTP technique is elucidated. The emerging DBD plasma to synthesized and effective surface treatment is reviewed. DBD plasma synthesized catalyst performance in reforming application for H₂ and syngas production is summarised. Furthermore, the status of DBD plasma for catalyst synthesis and proposed future avenues to design environmentally suitable and cost-effective synthesis techniques are discussed.     

A robust weakly compressible SPH method and its comparison with an incompressible SPH

This paper presents a comparative study for the weakly compressible (WCSPH) and incompressible (ISPH) smoothed particle hydrodynamics methods by providing numerical solutions for fluid flows over an airfoil and a square obstacle. Improved WCSPH and ISPH techniques are used to solve these two bluff body flow problems. It is shown that both approaches can handle complex geometries using the multiple boundary tangents (MBT) method, and eliminate particle clustering‐induced instabilities with the implementation of a particle fracture repair procedure as well as the corrected SPH discretization scheme. WCSPH and ISPH simulation results are compared and validated with those of a finite element method (FEM). The quantitative comparisons of WCSPH, ISPH and FEM results in terms of Strouhal number for the square obstacle test case, and the pressure envelope, surface traction forces, and velocity gradients on the airfoil boundaries as well as the lift and drag values for the airfoil geometry indicate that the WCSPH method with the suggested implementation produces numerical results as accurate and reliable as those of the ISPH and FEM methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Conducting polymers inducing catalysis: Enhanced formic acid electro-oxidation at a Pt/polyaniline nanocatalyst

Research is moving rapidly to sustain convenient energy resources fulfilling the global climate legislations. Herein, a novel catalyst of platinum nanoparticles (PtNPs) dispersed onto polyaniline (PANi) is recommended for formic acid electro-oxidation (FAO); the fundamental anodic reaction in direct formic acid fuel cells (DFAFCs). The catalyst's preparation scheme allows a sequential electrodeposition of fibril PANi and spherical PtNPs (ca. 65 nm in size) on a glassy carbon (GC) substrate and permits a precise control over the deposition sequence and loading. Interestingly, incorporation of PANi into the catalyst's ingredients can significantly (ca. 16 times) improve the catalytic activity of the catalyst towards FAO by shifting the mechanism towards the desirable dehydrogenation pathway and mitigating the undesirable poisoning dehydration pathway. The catalytic efficiency is tuned by manipulating the deposition order and loading of different catalyst's ingredients. Several techniques are employed to confirm the successful deposition of the catalyst and to evaluate its morphology, composition and crystal structure. While PtNPs are essential for FA adsorption, PANi improves the dispersion of PtNPs and mediates FAO to facilitate the charge transfer and mitigate CO poisoning. A promising catalytic stability is achieved in a long continuous (150 CVs) electrolysis experiment.     

Progress on Epoxy/Polyamide and Inorganic Nanofiller-Based Hybrids: Introduction,Application, and Future Potential

An efficient method to obtain better properties of epoxy-based nanocomposites is to introduce thermoplastic polymer such as polyamide into thermosetting resin. Combined effect of both polymers provides extra-bonding sites for nanofiller dispersion. This review mainly covers inorganic nanofiller dispersed epoxy/polyamide nanocomposite and their applications. To understand interaction between thermoset epoxy and thermoplastic polyamide, knowledge of structure, synthesis, and categorization is worth important. Addition of inorganic nanofiller such as layered silicate and metal oxide results in enhanced thermomechanical, physiochemical, and anticorrosive properties of resultant nanocomposite. These nanocomposites have applications as protective coatings, adhesives, insulators in electrical devices, and in aerospace industries.     

Bioaugmentation of floating treatment wetlands for the remediation of textile effluent

This pilot study evaluated the effects of bacterial augmentation on the efficiency of floating treatment wetlands (FTWs) to remediate textile wastewater. Two wetland plants, Phragmites australis and Typha domingensis, were used to develop FTWs, which were then augmented with a bacterial consortium of three strains (Acinetobacter junii, Pseudomonas indoloxydans, and Rhodococcus sp.). Results showed that both plant species removed colour, organic matter, toxicity, and heavy metals from textile wastewater and their removal efficiency was further enhanced by augmentation with bacteria. The maximum removal efficiencies of colour, COD and BOD after an 8‐day period were 97, 87 and 92%, respectively, by FTWs carrying P. australis inoculated with the bacterial consortium. Furthermore, the same combination showed 87–99% reduction of heavy metals in the textile wastewater as well. These results indicate that FTWs can be used for the treatment of textile effluent and their working efficiency can be improved by plant‐bacterial synergism.