User-story driven development of multi-agent systems: A process fragment for agile methods

Agile software development methods are mostly built as a set of managerial guidelines and development concepts on how to handle a software development but are not bounded to software development paradigms like object or agent orientation. Some methods, like eXtreme Programming and SCRUM are driven by operational requirements representation models called User Stories. These User Stories can be used as an anchoring point to agile methods; this means that we could take a User Stories set to drive a software transformation approach embedded in a particular development paradigm. This paper presents a process fragment for Multi-Agent Systems development with agile methods based on User Stories sets. The process fragment indeed takes advantage of an initial set of User Stories to build a reasoning model (called the Rationale Tree; typically several of these are built for a single project) that documents decompositions and means-end alternatives in scenarios for requirements realization. A Rationale Tree can then be aligned with a Multi-Agent design and implemented in an agent-oriented development language. In this paper the transformation is targeted to the JAVA Agent DEvelopment (JADE) framework. The process fragment (at least partially) covers the Requirements Analysis, Multi-Agent System Design and Multi-Agent System Implementation phases. Transformation from one phase to the other is overseen and illustrated on an example.     

A robust incremental clustering-based facial feature tracking

Emerging significance of person-independent, emotion specific facial feature tracking has been actively tracked in the machine vision society for decades. Among distinct methods, the Constrained Local Model (CLM) has shown significant results in person-independent feature tracking. In this paper, we propose an automatic, efficient, and robust method for emotion specific facial feature detection and tracking from image sequences. A novel tracking system along with 17-point feature model on the frontal face region has also been proposed to facilitate the tracking of human basic facial expressions. The proposed feature tracking system keeps patch images and face shapes till certain number of key frames incorporating CLM-based tracker. After that, incremental patch and shape clustering algorithms is applied to build appearance model and structure model of similar patches and similar shapes respectively. The clusters in each model are built and updated incrementally and online, controlled by amount of facial muscle movement. The overall performance of the proposed Robust Incremental Clustering-based Facial Feature Tracking (RICFFT) is evaluated on the FGnet database and the Extended Cohn-Kanade (CK+) database. RICFFT demonstrates mean tracking accuracy of 97.45% and 96.64% for FGnet and CK+ database respectively. Also, RICFFT is more robust by minimizing average shape distortion error of 0.20% and 1.86% for FGnet and CK+ (apex frame) database, as compared with classic method CLM.     

Adaptive green traffic signal controlling using vehicular communication

The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today’s metropolitan life cannot be overemphasized. The vehicular ad hoc network (VANET), as an integral component of intelligent transportation systems (ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems (TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses: (1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and (2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service (QoS). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is used. The V2V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network QoS criteria.     

Estimation of Wind-Driven Coastal Waves Near a Mangrove Forest Using Adaptive Neuro-Fuzzy Inference System

At the coastline of the Carey Island, mangroves provide natural protection against the wind-driven coastal waves. The area is located at the west Malaysia within the waters of the Straits of Malacca. Recently, its coastline has been exposed to increasing rates of coastal erosion due to mangrove deforestation. In order to provide mitigating measures, it is necessary to study wave characteristics in this region. For this purpose, we collected 5 years (2009 to 2013) of hourly measurements for wind direction, wave height, wind speed and wave period. Moreover, we used the adaptive neuro-fuzzy inference system (ANFIS) to estimate the wave period and height. The model was trained using the measured data. The validation of the model gave satisfactory R² values of 0.8484 and 0.9496 for wave height and wave period, respectively. The findings from this study suggest that fuzzy logic based technique satisfactorily predicts the differences between multiple inputs and single output in terms of non-linear relationship. The developed model can be used to further study the effect of non-linear wind-driven waves on the depleting coastal mangrove forests in similar tropical and sub-tropical areas. We suggest further research to test the model in different geographical locations, such as in deep-ocean, narrow straits and other coastal sites, which were not covered in this study.     

Work Function Tuning and Doping Optimization of 22-nm HKMG Raised SiGe/SiC Source–Drain FinFETs

The basic requirements on process design of extremely scaled devices involve appropriate work function and tight doping control due to their significant effect on the threshold voltage as well as other critical electrical parameters such as drive current and leakage. This paper presents a simulation study of 22-nm fin field-effect transistor (FinFET) performance based on various process design considerations including metal gate work function (WF), halo doping (N _halo), source/drain doping (N _sd), and substrate doping (N _sub). The simulations suggest that the n-type FinFET (nFinFET) operates effectively with lower metal gate WF while the p-type FinFET (pFinFET) operates effectively with high metal gate WF in 22-nm strained technology. Further investigation shows that the leakage reduces with increasing N _halo, decreasing N _sd, and increasing N _sub. Taguchi and Pareto analysis-of-variance approaches are applied using an L₂₇ orthogonal array combined with signal-to-noise ratio analysis to determine the best doping concentration combination for 22-nm FinFETs in terms of threshold voltage (V _t), saturation current (I _on), and off-state current (I _off). Since there is a tradeoff between I _on and I _off, the design with the nominal-is-best V _t characteristic is proposed, achieving nominal V _t of 0.259 V for the nFinFET and ?0.528 V for the pFinFET. Pareto analysis revealed N _halo and N _sub to be the dominant factor for nFinFET and pFinFET performance, respectively.     

Comparison study on microstructure and mechanical properties of Sn-10Bi and Sn-Ag-Cu solder alloys and joints

The comparison study of Sn-10Bi and Sn-3.0Ag-0.5Cu solder alloys and joints was conducted. The results showed that the liquidus of Sn-10Bi solder alloy was lower than that of Sn-Ag-Cu slightly. The interfacial IMCs layer growth of Sn-10Bi/Cu was slower than that of Sn-Ag-Cu/Cu during liquid/solid reaction. The higher strength and lower creep strain rate of Sn-10Bi comparing with that of Sn-Ag-Cu were contributed by the solid solution strengthening effect of Bi atom in β-Sn phase. The ultimate bending load of Sn-10Bi joint was higher than that of Sn-Ag-Cu joint as the high strength of Sn-10Bi solder alloy. Moreover, the thinner and more flat IMCs layer also ensured the stable maximum bending displacement of Sn-10Bi joint at a loading speed of 1 mm/s compared with that of Sn-Ag-Cu joint.     

Distortion adaptive Sobel filters for the gradient estimation of wide angle images

We introduce a set of distortion adaptive Sobel filters for the direct estimation of geometrically correct gradients of wide angle images. The definition of the filters is based on Sobel’s rationale and accounts for the geometric transformation undergone by wide angle images due to the presence of radial distortion. Moreover, we show that a local normalization of the filters magnitude is essential to achieve state-of-the-art results. To perform the experimental analysis, we propose an evaluation pipeline and a benchmark dataset of images belonging to different scene categories. Experiments on both, synthetic and real images, show that our approach outperforms the current state-of-the-art in both gradient estimation and keypoint matching for images characterized by large amounts of radial distortion. The collected dataset and the MATLAB code of the proposed method can be downloaded at our web page http://iplab.dmi.unict.it/DASF/.     

Modeling anomalous charge carrier transport in disordered organic semiconductors using the fractional drift-diffusion equation

Anomalous transport is ever-present in many disordered organic semiconductor materials. The long-tail behavior observed in the transient photocurrent is a manifestation of anomalous transport. Owing to the fact that anomalous transport has dispersive and non-Gaussian transport dynamics, thus anomalous transport cannot be adequately described by the standard drift-diffusion equation which is a framework commonly used to model normal diffusive transport. In this work, we generalized the standard drift-diffusion equation to time fractional drift-diffusion equation (TFDDE) using the fractional calculus approach to model the anomalous transport in the regio-random poly(3-hexylthiophene) (RRa-P3HT) and regio-regular poly(3-hexylthiophene) (RR-P3HT). Physical elucidation of TFDDE is given by stressing how the influence of the multiple-trapping mechanisms and energy disorder lead to the long-tail behavior in the transient photocurrent curves. TFDDE is solved numerically using finite difference scheme to obtain the profiles of charge carriers density evolution and hence to reproduce the corresponding transient photocurrents of RRa-P3HT and RR-P3HT. Poisson solver is also included in the model to account for the fluctuation of localized electric field due to the evolution of charge carriers. It is found that charge carriers acquire additional energy from high electric field that helps them to escape from the trap centers more easily and then propagating at higher velocity, which yields higher transient current. Higher concentration of charge carriers can be generated at higher light intensity and they can occupy energy levels close to the mobility edge, where charge carriers will encounter smaller capturing rate and hop at a longer length in each hopping event. Thus, the transport dynamic of charge carriers at high light intensity is less dispersive than that of the low light intensity. Besides, the transport dynamic of charge carriers in RR-P3HT is relatively less dispersive and has higher mobility than that of the RRa-P3HT since RR-P3HT has lower capturing rate and is less energy disordered.     

A review of ionic liquids as catalysts for transesterification reactions of biodiesel and glycerol carbonate production

Biodiesel production has been rapidly increasing due to the strong governmental policies and incentives provided leading to an oversupply of its by-product, glycerol. Therefore, finding ways of utilizing glycerol is essential to increase the net energy and sustainability of biodiesel. Ionic liquids have been used successfully as catalyst for both the production of biodiesel and the conversion of glycerol to glycerol carbonate. These catalysts are relatively environmentally friendly as they have the potential to enable sustainable processes. Herein, the prospect of using ionic liquids to catalyze transesterification triglycerides for the production of biodiesel and the conversion of glycerol to glycerol carbonate will be discussed. Elucidation of the reaction mechanism is expected to provide an in-depth understanding of the process with respect to the effects of cation and anion based on the reactions of interest.