Cloud providers seek to maximize their market share. Traditionally, they deploy datacenters with sufficient capacity to accommodate their entire computing demand while maintaining geographical affinity to its customers. Achieving these goals by a single cloud provider is increasingly unrealistic from a cost of ownership perspective. Moreover, the carbon emissions from underutilized datacenters place an increasing demand on electricity and is a growing factor in the cost of cloud provider datacenters. Cloud-based systems may be classified into two categories: serving systems and analytical systems. We studied two primary workload types, on-demand video streaming as a serving system and MapReduce jobs as an analytical systems and suggested two unique energy mix usage for processing that workloads. The recognition that on-demand video streaming now constitutes the bulk portion of traffic to Internet consumers provides a path to mitigate rising energy demand. On-demand video is usually served through Content Delivery Networks (CDN), often scheduled in backend and edge datacenters. This publication describes a CDN deployment solution that utilizes green energy to supply on-demand streaming workload. A cross-cloud provider collaboration will allow cloud providers to both operate near their customers and reduce operational costs, primarily by lowering the datacenter deployments per provider ratio. Our approach optimizes cross-datacenters deployment. Specifically, we model an optimized CDN-edge instance allocation system that maximizes, under a set of realistic constraints, green energy utilization. The architecture of this cross-cloud coordinator service is based on Ubernetes, an open source container cluster manager that is a federation of Kubernetes clusters. It is shown how, under reasonable constraints, it can reduce the projected datacenter’s carbon emissions growth by 22% from the currently reported consumption. We also suggest operating datacenters using energy mix sources as a VoltDB-based fast data system to process offline workloads such as MapReduce jobs. We show how cross-cloud coordinator service can reduce the projected data- centers carbon emissions growth by 21% from the currently expected trajectory when processing offline MapReduce jobs.

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Cognitive Radio (CR) encompasses a number of technologies which enable adaptive self-programing of systems at different levels to provide more effective use of the increasingly congested radio spectrum. CRs have potential to use spectrum allocated to TV services, which is not used by the primary user (TV), without causing disruptive interference to licensed users by using appropriate propagation modelling in TV White Spaces (TVWS). In this paper we address two related aspects of channel occupancy prediction for cognitive radio. Firstly, we continue to investigate the best propagation model among three propagation models (Extended-Hata, Davidson-Hata and Egli) for use in the TV band, whilst also finding the optimum terrain data resolution to use (1000, 100 or 30 m). We compare modelled results with measurements taken in randomly-selected locations around Hull UK, using the two comparison criteria of implementation time and accuracy, when used for predicting TVWS system performance. Secondly, we describe how such models can be integrated into a database-driven tool for CR channel selection within the TVWS environment by creating a flexible simulation system for creating a TVWS database.

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This research work is novel technique to control and optimize SSSC (Subsynchronous Series Controller) functions with degree of precision in between Qatar and Kingdom of Saudi Arabia. The SSSC model developed and simulated in order to identify and determine its control and functioning parameters by introducing new tuning parameters based on that the SSSC can be adjusted stringently to witness desired results lead to address outstanding reactive power management issue. The proposed new parameters are contributing significantly to control SSSC functions in multiple directions in a power system network in between QATAR and Kingdom of Saudi Arabia at different time-based transmission contingencies on the GCC Electrical-power grid. Strategically, the SSSC capacity and capability can be utilized fully in between Qatar and Kingdom of Saudi Arabia by introducing and optimizing its control and tuning parameters more tangibly under both steady and dynamic states.

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IEEE 802.11 WLAN indoor networks face major inherent and environmental issues such as interference, noise, and obstacles. At the same time, they must provide a maximal service performance in highly changing radio environments and conformance to various applications’ requirements. For this purpose, they require a solid design approach that considers both inputs from the radio interface and the upper-layer services at every design step. The modelization of radio area coverage is a key component in this process and must build on feasible work hypotheses. It should be able also to interpret highly varying characteristics of dense indoor environments, technology advances, service design best practices, end-to-end integration with other network parts: Local Area Network (LAN), Wide Area Network (WAN) or Data Center Network (DCN). This work focuses on Radio Resource Management (RRM) as a key tool to achieve a solid design in WLAN indoor environments by planning frequency channel assignment, transmit directions and corresponding power levels. Its scope is limited to tackle co-channel interference but can be easily extended to address cross-channel ones. In this paper, we consider beamforming and costing techniques to augment conventional RRM’s Transmit Power Control (TPC) procedures that market-leading vendors has implemented and related research has worked on. We present a novel approach of radio coverage modelization and prove its additions to the cited related-work’s models. Our solution model runs three algorithms to evaluate transmission opportunities of Wireless Devices (WD) under the coverage area. It builds on realistic hypotheses and a thorough system operation’s understanding to evaluate such an opportunity to transmit, overcomes limitations from compared related-work’s models, and integrates a hierarchical costing system to match Service Level Agreement (SLA) expectations. The term “opportunity” in this context relates also to the new transmission’s possibilities that related-work misses often or overestimates.

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New multimedia applications such as mobile video, high-quality Internet video or digital television requires high-performance encoding of video signals to meet technical constraints such as runtime, bandwidth or latency. Video coding standard h.265 HEVC (High Efficiency Video Coding) was developed by JCT-VC to replace the MPEG-2, MPEG-4 and h.264 codecs and to respond to these new functional constraints. Currently, there are several implementations of this standard. Some implementations are based on software acceleration techniques; Others, on techniques of purely hardware acceleration and some others combine the two techniques. In software implementations, several techniques are used in order to decrease the video coding and decoding time. We quote data parallelism, tasks parallelism and combined solutions. In the other hand, In order to fulfill the computational demands of the new standard, HEVC includes several coding tools that allow dividing each picture into several partitions that can be processed in parallel, without degrading neither the quality nor the bitrate.
In this paper, we adapt one of these approaches, the Tile coding tool to propose a pipeline execution approach of the HEVC / h265 decoder application in its version HM Test model. This approach is based on a fine profiling by using code injection techniques supported by standard profiling tools such as Gprof and Valgrind. Profiling allowed us to divide functions into four groups according to three criteria: the first criterion is based on the minimization of communication between the different functions groups in order to have minimal intergroup communication and maximum intragroup communication. The second criterion is the load balancing between processors. The third criterion is the parallelism between functions. Experiments carried out in this paper are based on the Zedboard platform, which integrates a chip Zynq xilinx with a dual core ARM A9. We start with a purely sequential version to reach a version that use the pipeline techniques applied to the functional blocks that can run in parallel on the two processors of the experimental Platform. Results show that a gain of 30% is achieved compared to the sequential implementation.

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Metal housing has been used extensively on portable communication devices such as on mobile phones and tablets. The choice of metal housing ranges from metallic rim to metallic back cover.  This metal housing tends to improve the outlook appearance of the mobile devices, and add mechanical strength towards the mobile devices.  However, from the aspect of the communication antenna, the metal housing often posts great challenges towards the flexibility in antenna design and reduction in antenna performance.  This paper presents an approach to overcome the challenges by integrating the metal housing of the mobile phone as part of the antenna, along with the introduction of tunable antenna concept to provide different forms of Aperture Tuning to the Cellular Main Antenna, to satisfy its wide frequency band coverages for the 2^nd, 3^rd and 4^th Generation (2G, 3G and 4G) mobile network.  

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A new coupled structure identification of Multi-Input Multi-Output (MIMO) Hammerstein models with separate nonlinearities is proposed. It is based on the use of the Recursive Least Squares (RLS) algorithm. A comparative study between a decoupled and coupled structures identification of MIMO Hammerstein models is discussed. A quadruple-tank process is used to illustrate the effectiveness of the new structure.

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This paper deals with the design of a new reconfigurable beam antenna used to improve the efficiency of spatial telemetry links on Nano-Satellite. This agile beam antenna is not built on the well-known array concept AESA (Agile Electronically Scanned Array) but using a new one called ARMA (Agile Matrix Radiating Antenna); MARPEM in French. In this paper there is the design of the circularly polarized matrix antenna, the generation of two beam forming modes, the design of the distribution circuit and the polarization circuits.

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Real estate pressures in modern electronics have resulted in the need for electrically small antennas, which have subsequently garnered interest amongst researchers and industry alike. These antennas are characterized by their largest dimensions translating to a fraction of the operating wavelength; such a diminutive size comes at the expense of reduced gain and efficiency, and a worse overall match to a corresponding power source. In order to compensate for this deterioration in performance, antenna designers must turn towards increasingly complex and voluminous geometries, well beyond the capabilities of traditional manufacturing techniques. We present voluminous metal antennas, based on a novel inverted-F design, and fabricated using the emergent selective electron beam melting manufacturing technique, a type of powder bed fusion process. As predicted by small antenna theory, simulation results presented show in increase in the antenna’s efficiency as it is voluminously expanded into the third dimension. Measurement results illustrate that key trends observed from simulations are upheld; however, further understanding of the electromagnetic properties of raw materials, in particular how these change during the printing process, is needed. Nevertheless, this type of additive manufacturing technique is suitable for rapid prototyping of novel and complex antenna geometries, and is a promising avenue for further research and maturation.

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This paper proposes extended-range high-speed control for an IPMSM drive system. A simple real-time tuning flux-weakening control algorithm is proposed and implemented to control an IPMSM drive system in a wide variable speed range, from 3 r/min up to 2700 r/min. This flux-weakening control algorithm does not require any motor parameters and only needs simple mathematical computations. The proposed drive system adjusts the angle between the d-axis and q-axis current to reach flux-weakening control. In addition, a multiple sampling predictive controller is implemented to enhance the dynamic responses of the proposed drive system, which yields improved overall transient responses, superior load responses, and good tracking responses. A detailed analysis of the proposed drive system’s stability is discussed as well. A 32-bit digital signal processor, TMS-320F-28335, is used to execute the predictive controller and the flux-weakening control algorithm for the IPMSM drive system. Experimental results can validate the theoretical analysis.

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The current advances in proteomic and transcriptomic technologies produced huge amounts of high-throughput data that spans multiple biological processes and characteristics in different organisms. One of the important directions in today’s bioinformatics research is to discover patterns of genes that have interesting properties. These groups of genes can be referred to as functional modules. Detecting functional modules can be accomplished by the deep analysis of protein-protein interaction (PPI) networks, gene expression profiles, or both. In this work the focus will be on Human protein-protein interaction network and genes expression data that represents genes behavior in a group of diseases. Two of the most well-established clustering methods that target the interaction networks and the expression data will be used in this analysis. In addition, and to have more insights, genes molecular functionality will be studied. Finally, I will introduce the relation of the extracted modules on biological pathways. This study mainly illustrates the importance of including protein interaction activities as part of any study that aims at discovering meaningful knowledge about the biological scene where many actors play different roles.

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This paper presents the modelling and analysis of permanent magnet synchronous generator (PMSG) which are used in direct driven small scale wind turbines. The 3 kW PM generator which is driven directly without gear system is analyzed by Ansoft Maxwell 2D RMxprt. The performance analysis of generator includes the cogging torque in two teeth, induced coil voltages under load, winding current under load, airgap flux density distribution and so on. The modelling analysis is based on the 2D finite element techniques. In an electrical machine, an accurate determination of the geometry parameters is a vital role. The proper performance results of 3kW PMSG in small scale wind turbine can be seen in this paper.

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A switched capacitor low-pass filter employing folded-cascode CMOS OP Amps with a dynamic switching bias circuit capable of processing video signals, which enables low power consumption, and operation in wide bandwidths and low power supply voltages, is proposed. In this filter, charge transfer operations through two-phase clock pulses during the on-state period of the OP Amps and a non-charge transfer operation during their remaining off-state period are separated. Through simulations, it was shown that the low-pass filter with an OP Amp switching duty ratio of 50 % is able to operate at a 14.3 MHz high-speed dynamic switching rate, allowing processing video signals, and a dissipated power of 68 % of that observed in the static operation of the OP Amps and a full charge transfer operation without separation of a cycle period. The gain below -31 dB in the frequency response, which is suitable, was obtained at over 6 MHz within a stop-band. Especially high attenuation in 5 MHz was achieved under the optimized condition of load capacitances (4 pF) of OP Amps.

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In acronym Captology – Computers as Persuasive Technology, a persuasive component (lat. persuasibilibus – enticing) refers to the persuasive stimulation by intelligent technologies. Latter being transitive and interactive as intelligent systems, they have imposed, by their persuasivity, a ‘cult of information’, after which information has become a type of goods that as a utilitarian resource must be exploited quickly and efficiently. Such a widely accepted fact resulted as hype, presenting a perspective that the approach to a large amount of information and faster ‘digestion’ of their content will enable users to quickly get desired knowledge.
Recent investigations about persuasion processes have shown its dependence on intelligent technology factors (design, interactive computer products, web, desktop and others). Such technologies are also used to influence people’s attitudes, beliefs, learning, and behaviour. Development strategies for global computer production and sales head in that direction and confirm latter statement with the promoted 3-P model: persuasive, permissive and pervasive components. Cognitive level of human integrated development is increasingly overshadowed by the contribution of artificial intelligence through its products, i.e. ‘smart’ creations, and by the array of shortcomings and problems that the same interactive technology brings. This paper presents a parallel between captological component of intelligent and interactive technologies on one side and illustrates examples of captological influences proved by confirmed trials within cognitive science through computer simulations of human thinking on the other side. Many studies have shown that the success of persuasion depends on the factors which have been exposed by cognitive cybernetics. Next to it, people’s behavior system is transforming through the very development of society. Therefore, the influence of latter can be either positive or negative, while its extremes are already escalating in direction of a new trans-singularity and post humanism theories based on the principles of extropy.

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This paper proposes the modeling and control of a Solid-State Transformer using a three-stage conversion topology. First, a rectification stage is used, where a three-phase high-voltage AC signal is converted to a DC level; this stage is then followed by a DC-DC converter, and finally an inverter is used to convert the DC into a three-phase low-voltage AC signal. The adopted topology is modeled using a simplified model for each stage, useful to design their controllers. Based on these models, the controllers are tuned to obtain a good performance to sudden load changes. This performance is tested through simulations.

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In this paper, a hybrid system solar cell antenna was studied. Our contribution lies in the combination of both optical and radio-frequency signals in a solar cell antenna. Hence, we propose a new method to merge antenna and solar cell. The leaf- shaped patch solar cell antenna was dedicated at a time to the energy harvesting and the RF transmission. A performance analysis of the solar cell antenna was conducted using Advanced Design System (ADS) software. Simulation results showed a resonance at a frequency of 15.77 GHz with an effective return loss of -27.62dB and a gain of 5.77dBi.

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Combinatorial optimization is an area of great importance since many of the real-world problems have discrete parameters which are part of the objective function to be optimized. Development of combinatorial optimization algorithms is guided by the empirical study of the candidate ideas and their performance over a wide range of settings or scenarios to infer general conclusions. Number of scenarios can be overwhelming, especially when modeling uncertainty in some of the problem’s parameters. Since the process is also iterative and many ideas and hypotheses may be tested, execution time of each experiment has an important role in the efficiency and successfulness. Structure of such experiments allows for significant execution time improvement by distributing the computation. We focus on the cloud computing as a cost-efficient solution in these circumstances. In this paper we present a system for validating and comparing stochastic combinatorial optimization algorithms. The system also deals with selection of the optimal settings for computational nodes and number of nodes in terms of performance-cost tradeoff. We present applications of the system on a new class of project scheduling problem. We show that we can optimize the selection over cloud service providers as one of the settings and, according to the model, it resulted in a substantial cost-savings while meeting the deadline.

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Environmental sound recognition (ESR) has become a hot topic in recent years. ESR is mainly based on machine learning (ML) and ML algorithms require first a training database. This database must comprise the sounds to be recognized and other related sounds. An ESR system needs the database during training, testing and in the production stage. In this paper, we present the design and pilot establishment of a database which will assists all researchers who want to establish an ESR system. This database employs relational database model which is not used for this task before. We explain in this paper design and implementation details of the database, data collection and load process. Besides we explain the tools and developed graphical user interface for a desktop application and for the WEB.

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In modern powers systems, one of the most considerable topics is economic load dispatch (ELD). ELD is non-linear problem and it became non-convex and non-smooth problem with some constraints such as valve point loading effect. ELD is very crucial for energy generation and distribution in power systems. For solving ELD problem, a lot of methods were developed and used at different power systems. Vortex search algorithm (VSA) is proposed and applied for solving ELD problem in this paper. VSA method was developed in the form of stirring liquids. Transmission line losses, valve point loading effect, ramp rate limits and prohibited zones constraints were used to make the results of ELD problem the closest to the truth. The results which are obtained from VSA compared with PSO, CPSO, WIPSO, MFO, GA and MRPSO techniques. It can be clearly seen that VSA gave minimum cost values with optimum generator powers so it is very effective and useful method and it gave the best solutions for ELD.

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The automotive industry has focused to use polymer materials in order to increase energy efficiency. So, the industry pays attention to use 3D printing technologies using several polymers. Among several 3D printer technologies, fused deposition modeling (FDM) is one of the popular 3D printing technologies due to an inexpensive extrusion machine and multi-material printing. FDM could use thermoplastics such as ABS, PLA, ULTEM so on. However, it has a problem related to the post-processing because FDM has relatively poor layer resolution. In this study, the mechanical properties of ABS parts fabricated by FDM were measured. The ABS parts were divided into one with vapor smoothing process and the other without the vapor smoothing process which is one of the post-processing methods. Using dynamic mechanical analysis (DMA) and dilatometer, temperature-dependent storage modulus and CTE for ABS specimens were measured. Based on the measured thermo-mechanical properties of ABS parts, finite element analysis was performed for an automotive bumper made of ABS. Moreover, response surface methodology was applied to study relationships among design parameters of thickness of the bumper, ambient temperature, and application of the vapor smoothing process. In result, a design guideline for a ABS product could be provided without time-consuming experiments

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The paper shows a method of transforming an asynchronously feedbacked Mealy machine into a Moore machine. The transformation is done in dual-rail logic under the use of the RS-buffer. The transformed machine stabilizes itself and is safe to use. The transformation is visualized via KV-diagrams and calculated with formulas. We will present three use-cases for a better understanding. To underpin the stated transformation a simulation is also presented.

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When we drive a car, we often want to know the circumstances of roads between the current location and the destination. Ishihara et al. proposed a Vehicular ad-hoc NETworks (VANET) -based information sharing system, called the Real-Time Visual Car Navigation System, in which a driver can obtain Location-Dependent Information (LDI), such as photographs or videos of his/her Point of Interest (PoI), by telling the onboard device, e.g., a car navigation device, the PoI. The simplest way to provide LDI to vehicles is to flood the LDI to all vehicles, but unneeded LDI may be disseminated, and network resources may be wasted. To disseminate LDI to many vehicles that demand it with low communication traffic, we have proposed a data dissemination scheme based on Demand maps (Dmaps). In this scheme, each vehicle has a Dmap, a data set representing the geographical distribution of the strength of demands for LDI. To keep the Dmap up to date, each vehicle exchanges a subset of data constituting a Dmap (Dmap Information: DMI) with other vehicles. Vehicles preferentially send new LDI or forwarded LDI strongly demanded to the area where the LDI is frequently demanded based on Dmaps. If vehicles frequently send DMI, the accuracy of the Dmap becomes high, however, communication traffic becomes large. In this paper, we propose strategies for controlling the frequency of sending DMI and strategies for selecting DMI to be sent to improve the Dmap accuracy with low communication traffic. Simulation results show that one of the proposed strategies, the LDI Transfer Zone (LTZ) strategy, achieves highly accurate Dmaps with a small amount of communication traffic.

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This paper deals with the problem of Fault Tolerant Control (FTC) using PID-type Fuzzy Logic Controller (FLC) for an Electronic Throttle Valve (ETV) described by a switched discrete-time system with input disturbances and actuator faults. In order to detect the faults, Unknown Input Observers (UIOs) are designed and formulated in terms of Lyapunov theory and Linear Matrix Inequalities (LMIs). This approach is designed in order to minimize the error between the desired flat trajectory generated using the flatness property and the estimated state provided from differents UIOs and to maintain asymptotic stability under an arbitrary switching signal, even in the presence of actuator faults. The simulation results have shown the effectiveness of the proposed approach.

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Radio frequency Identification (RFID) is currently considered as one of the most used technologies for an automatic identification of objects or people. Based on a combination of tags and readers, RFID technology has widely been applied in various areas including supply chain, production and traffic control systems. However, despite of its numerous advantages, the technology brings out many challenges and concerns still not being attracting more and more researchers especially the security and privacy issues. In this paper, we review some of the recent research works using RFID solutions and dealing with security and privacy issues, we define our specific parameters and requirements allowing us to classify for each work which part of the RFID system is being secured, the solutions and the techniques used besides the conformity to RFID standards. Finally, we present briefly a solution that consists of combining RFID with smartcard based biometric to enhance security especially in access control scenarios. Hence the result of our study aims to give a clear vision of available solutions and techniques used to prevent and secure the RFID system from specific threats and attacks.

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This paper presents an analysis of a parallel connection of one synchronous generator and one self-excited induction generator that feeds a resistive load and an Induction Motor. The system voltage and frequency are controlled by a voltage control loop and a speed control loop connected to synchronous generator. The induction generator speed is controlled by its primary machine that is fed by an autotransformer and a diodes bridge. Through by voltages applied by an adjustable tap autotransformer connected to induction generator’s primary machine, it is possible widen the range of its shaft speed if compared with the shaft speed caused by only field flux variation method. Then, by the autotransformer method is possible to widen the speed and power limits from the induction generator what increases the induction generator contributions and relieves the power supply from synchronous generator. Analysis of generators power supply and its interactions in various operational scenarios are shown. The results enable comparisons of the two methods of induction generator speed control, either by auto-transformer method or by field flux variation method. The first results in larger range of speed and power from the induction generator. Therefore, it has more representative features of actual field conditions.

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In remote measurements on large-sized objects main focus is on measuring deformations of bridge surfaces and determination of the dynamic amplification factor in particular. The proposed method estimates deflections of entire lower surface of a bridge due to placement of secondary radiators on this surface. Antenna array directivity pattern (ADP) is distorted during deflection of the bridge surface. ADP distortions contain the information about deflections of the individual points of the surface. Minimization of the distance in a functional space with a quadratic metric (the functional) between a distorted measured ADP and theoretical one allows us to determine these deflections. The main problem is measurement of the secondary radiators system ADP in far zone which often cannot be achieved for real bridges in practical situations. Therefore, a new method for determining surface deflections based only on field amplitude measurements in near-field zone of a secondary radiators system is considered in the article. This amplitude of field strength depends on the bridge surface deflection. The modulus of difference between the normalized amplitude distribution of the measured field at the outputs of the receiving array elements, which is created by the radiators system of an unloaded bridge, and the similar amplitude field distribution of a loaded bridge is minimized and deflection of the bridge at the points of the radiating elements placement is calculated.

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Two technology trends – a move toward software defined capabilities and toward networked devices – support both unprecedented innovations and requirements for security. A fundamental aspect of security is user authentication, which allows devices and software applications to establish their user’s identity and identity is in turn used to establish which of its capabilities the user is authorized to access. While multiple authentication steps, known as multifactor authentication, are being used more widely throughout the military, government, businesses, and consumer sectors, the selection and implementation of which authentication factors to require is typically defined by security policy. Security policy is in turn typically established by a security organization that may have no formal metrics or means to guide its selection of authentication factors. This paper will present a taxonomy for describing authentication factors including important attributes that characterize authentication robustness to aid in the selection of factors that are consistent with the user’s mission. One particular authentication factor that I have developed will be discussed in the context of this taxonomy to motivate the need to broaden current definitions and security policies. The ultimate goal of this paper is to inspire the development of standards for authentication technologies to both support mission aware authentication innovation and to inform decision making about security policies concerning user authentication and authorization. Further, this paper aims to demonstrate that such an approach will fundamentally enhance both security and usability of increasingly networked, software-defined devices, equipment and software applications.

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Cn(R) denotes circulant graph Cn(r1; r2; : : : ; rk) of order n for a set R = fr1; r2; : : : ; rkg where 1 _ r1 < r2 < : : : < rk _ h n2 I : Circulant graph Cn(R) is said to have the Cayley Isomorphism (CI) property if whenever Cn(S) is isomorphic to Cn(R); there is some a 2 Z_ n for which S = aR. In this paper, isomorphic properties of circulant graphs that includes (i) Self-complementary circulant graphs; (ii) Type-2 isomorphism, a new type of isomorphism other than already known Adam’s isomorphism of circulant graphs and (iii) Cartesian product and factorization of circulant graphs similar to the theory of product and factorization of natural numbers are studied. New abelian groups are obtained from these isomorphic circulant graphs. Type-2 isomorphic circulant graphs have the property that they are isomorphic graphs without Cayley Isomorphism (CI) property and thereby new families.

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The efficiency of a plasma-aided finishing process, namely capillary impregnation, can be predicted only by setting two basic parameters of the real wood porous surface and the actual impregnating solution – surface free energy and surface tension. In general, the following processing efficiency parameter was found and the rule is true: “The plasma aided or enhanced finishing of a porous media will be more successful and this media will be more susceptible to it as the difference between its surface free energy and the surface tension or the so-called penetration-spreading parameter is positive: PSP = (?S – ?L) > 0. If not, wetting, wicking and finishing problems will occur.

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The aim of this paper is to develop a hybrid device for voltage stability enhancement using two kinds of FACTS (Flexible AC Transmission System) namely SVC (Static Var Compensator) and TCSC (Thyristor Controlled Series Capacitor). The idea behind the proposed method is to maintain safe and satisfactory power system operation in a lesser costing manner by taking advantage of the performances of SVC and TCSC at the same time. We propose to evaluate the efficacy of the combined device to UPFC, as it is a hybrid FACTS and it is the most versatile compensator. For purpose of identifying the placement of the devices, we opt for a heuristic based approach. The methodology is tested with the IEEE 14-Bus system using the software EUROSTAG, and the simulation results reveal the efficiency of the proposed method for enhancing voltage stability.

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This paper proposed the identification of the proper placement of UPFC (Unified Power Flow Controller) using a series of methodological numeric simulations to improve voltage stability. For this purpose, the critical zone of the system is determined, then, comparative analyses depending on different emplacements of UPFC are performed. The dynamic model of UPFC and the proposed method are tested on the IEEE 14-bus system. The finding shows that the proper location of UPFC helps in improving voltage profiles and increasing the maximum loading capacity.

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This paper is an extension of work originally presented in 2016 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE). A 2×1 microstrip patch array antenna integrated with photonic bandgap (PBG) and stubs is designed and analyzed. The performance of the PBG and stubs structure are explained and analyzed in terms of the elimination of the resonance at the harmonic frequencies of the antenna. The proposed antenna is designed on FR-4 substrate with thickness of 1.6 mm and operated at 2.45 GHz frequency suitable for rectenna design application. From the simulated result, the first harmonic frequency (5.4 GHz), the second harmonic frequency (6.6 GHz) and the third harmonic frequency (7.8 GHz) are successfully suppressed. For instance, the radiation to the forward of the stubs-PBG antenna is suppressed at more than 15 dB at the second and third harmonic frequencies.

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This article is devoted to the problem of blocking the propagation of sound in a gas stream. This can be useful in the problems of pipeline acoustics and the design of automotive silencers, when it is necessary to ensure the opacity of the boundary (cross section of the pipe) for sound simultaneously with the free (ideally) flow through this boundary. The tool for solving this problem is the rapid periodic overlapping of waveguide (gas-conducting) channels in a system of parallel waveguides. The design of the proposed device assumes the following technological requirements: high mechanical rigidity of the elements, high accuracy of their manufacture and high rotor speed. Fulfillment of these conditions allows creating an effective device for blocking sound in the gas flow. Such a device may have small wave dimensions (with respect to the wavelength of sound) and a small expenditure of mechanical power to push gas through it very wide frequency range of silencing.

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With the abundance of Internet and electronic devices bullying has moved its place from schools and backyards into cyberspace; to be now known as Cyberbullying. Cyberbullying is affecting a lot of children around the world, especially Arab countries. Thus concerns from cyberbullying are rising. A lot of research is ongoing with the purpose of diminishing cyberbullying. The current research efforts are focused around detection and mitigation of cyberbullying. Previously, researches dealt with the psychological effects of cyberbullying on the victim and the predator. A lot of research work proposed solutions for detecting cyberbullying in English language and a few more languages, but none till now covered cyberbullying in Arabic language. Several techniques contribute in cyberbullying detection, mainly Machine Learning (ML) and Natural Language Processing (NLP). This journal extends on a previous paper to elaborate on a solution for detecting and stopping cyberbullying. It first presents a thorough survey for the previous work done in cyberbullying detection. Then a solution that focuses on detecting cyberbullying in Arabic content is displayed and assessed.

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Water quality assessment and prediction is a more and more important issue. Traditional ways either take lots of time or they can only do assessments. In this research, by applying machine learning algorithm to a long period time of water attributes’ data; we can generate a decision tree so that it can predict the future day’s water quality in an easy and efficient way. The idea is to combine the traditional ways and the computer algorithms together. Using machine learning algorithms, the assessment of water quality will be far more efficient, and by generating the decision tree, the prediction will be quite accurate. The drawback of the machine learning modeling is that the execution takes quite long time, especially when we employ a better accuracy but more time-consuming algorithm in clustering. Therefore, we applied the high performance computing (HPC) System to deal with this problem. Up to now, the pilot experiments have achieved very promising preliminary results. The visualized water quality assessment and prediction obtained from this project would be published in an interactive website so that the public and the environmental managers could use the information for their decision making.

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The goal of this study was to use underwater robots instead of a diver’s observations to monitor and record the condition of an obstructed seabed in a shallow area. It is difficult to investigate marine resources that exist in deep water shaded by rock due to large and/or small rocks on the seabed. To solve these problems, we newly constructed a flexible lengthening-shortening arm with a small camera unit for an underwater robot to assist in the management of fishery resources. In this paper, we describe the concept and configuration of the newly developed arm mechanism using a sliding screw mechanism to overcome obstacles by changing arm posture in a two-dimensional plane, and we demonstrate the experimental results of a path-tracing controller for the rear links. The results were that the maximum deviations between the target path and the tracing path were less than 4.0% of the total width of the arm mechanism. These results suggest that the newly developed path-tracing algorithm is effective for our flexible lengthening-shortening arm mechanism.

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In this paper, the performance and reliability of oxide-based Resistive RAM (ReRAM) memory is investigated in a 28nm FDSOI technology versus interconnects resistivity combined with device variability. Indeed, common problems with ReRAM are related to high variability in operating conditions and low yield. At a cell level ReRAMs suffer from variability. At an array level, ReRAMs suffer from different voltage drops seen across the cells due to line resistances. Although research has taken steps to resolve these issues, variability combined with resistive paths remain an important characteristic for ReRAMs. In this context, a deeper understanding of the impact of these characteristics on ReRAM performances is needed to propose variability tolerant designs to ensure the robustness of the technology. The presented study addresses the memory cell, the memory word up to the memory matrix.

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This paper is an extension of work originally presented in WITS-2017 CONF. We extend our previous works by improving the Risk calculation formula, and risk assessment of an alert cluster instead of every single alert. Also, we presented the initial results of the implementation of our model based on risk assessment and alerts prioritization. The idea focuses on a new approach to estimate the risk of each alert and a cluster of alerts. This approach uses indicators such as priority, reliability and asset value as decision factors to calculate alert’s risk. The objective is to determine the impact of alerts generated by Intrusion detection system (IDS) on the security status of an information system, and also improve the detection of intrusions using snort IDS by classifying the most critical alerts by their levels of risk. Thus, only alerts that present a real threat will be displayed to the security administrator. The implementation of this approach will reduce the number of false alerts and improve the performance of the IDS.

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In today’s information based world, it is increasingly important to safeguard the data owned by any organization, be it intellectual property or personal information. With ever increasing sophistication of malware, it is imperative to come up with an automated and advanced methods of attack vector recognition and isolation. Existing methods are not dynamic enough to adapt to the behavioral complexity of new malware. Widely used operating systems, especially Linux, have a popular perception of being more secure than other operating systems (e.g. Windows), but this is not necessarily true. The open source nature of the Linux operating system is a double edge sword; malicious actors having full access to the kernel code does not reassure the IT world of Linux’s vulnerabilities. Recent widely reported hacking attacks on reputable organizations have mostly been on Linux servers. Most new malwares are able to neutralize existing defenses on the Linux operating system. A radical solution for malware detection is needed – one which cannot be detected and damaged by malicious code. In this paper, we propose a novel framework design that uses virtualization to isolate and monitor Linux environments. The framework uses the well-known Xen hypervisor to host server environments and uses a Virtual Memory Introspection framework to capture process behavior. The behavioral data is analyzed using sophisticated machine learning algorithms to flag potential cyber threats. The framework can be enhanced to have self-healing properties: any compromised hosts are immediately replaced by their uncompromised versions, limiting the exposure to the wider enterprise network.

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To advance Java programming educations, we have developed the Java Programming Learning Assistant System (JPLAS) that provides the code writing problem. This problem asks a student to write a source code to satisfy the specification of a given assignment, where the correctness is verified by running test code on JUnit. For a novice student, a code of implementing the standard input/output with the exception handling should be mastered at the early stage as the first step programming for human interfaces. However, for a teacher, it is not easy to write the test code for it. In this paper, we propose a test code generation method to generate the test code using the reference source code for the assignment. In the evaluation of this proposal, all the students completed the codes using the generated test codes for exception handling, although the use of exception handling functions was sometimes insucient or incorrect.

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Ensuring the compliance of developed software with general and application-specific security requirements is a challenging task due to the lack of automatic and formal means to lead this verification. In this paper, we present our approach that aims at integrating the formal specification and verification of security guidelines in early stages of the development lifecycle by combining both the model checking analysis together with information flow analysis. We present our framework that is based on an extension of LTS (labelled transition Systems) by data dependence information to cover the end-to-end specification and verification of security guidelines.

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Quality assurance of modern-day safety-critical systems is continually facing new challenges with the increase in both the level of functionality they provide and their degree of interaction with their environment. We propose a novel selection method for black-box regression testing on the basis of machine learning techniques for increasing testing efficiency. Risk-aware selection decisions are performed on the basis of reliability estimations calculated during an online training session. In this way, significant reductions in testing time can be achieved in industrial projects without uncontrolled reduction in the quality of the regression test for assessing the actual system reliability.

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Quality assurance of modern-day safety-critical systems is continually facing new challenges with the increase in both the level of functionality they provide and their degree of interaction with their environment. We propose a novel selection method for black-box regression testing on the basis of machine learning techniques for increasing testing efficiency. Risk-aware selection decisions are performed on the basis of reliability estimations calculated during an online training session. In this way, significant reductions in testing time can be achieved in industrial projects without uncontrolled reduction in the quality of the regression test for assessing the actual system reliability.

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Microelectromechanical Systems (MEMS) are devices made up of several electrical and mechanical components. They consist of mechanical functions (sensing, thermal, inertial) and electrical functions (switching, decision making) on a single chip made by microfabrication methods. These chips exhibit combined properties of the two functions. The size of system has characteristic dimensions less than 1mm but more than 1?m. The configuration of these components determine the final deliverables of the switch. MEMS can be designed to meet user requirements on any level from microbiological application such as biomedical transducers or tissue engineering, to mechanical systems such as microfluidic diagnoses or chemical fuel cells. The low cost, small mass and minimal power consumption of the MEMS makes it possible to readily integrate to any kind of system in any environment. MEMS are faster, better and cheaper. They offer excellent electrical performances. MEMS working at Radio frequencies are RF MEMS. RF-MEMS switches find huge market in the modern telecommunication networks, biological, automobiles, satellites and defense systems because of their lower power consumptions at relatively higher frequencies and better electrical performances. But the reliability is the major hurdle in the fate of RF MEMS switches. Reliability mainly arises due to the presence of residual stresses, charging current, fatigue and creep and contact degradation. The presence of residual stresses in switches the S-Parameters of the switches are affected badly and the residual stress affects the final planarity of the fabricated structure. Design and simulation of an RF-MEMS switch is proposed considering the residual stresses in both on and off state. The operating frequency band is being optimized and the best possible feasible fabrication technique for the proposed switch design is being analyzed. S-Parameters are calculated and a comparison for the switches with stress and without stress is performed in FEM based HFSS software.

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Microelectromechanical Systems (MEMS) are devices made up of several electrical and mechanical components. They consist of mechanical functions (sensing, thermal, inertial) and electrical functions (switching, decision making) on a single chip made by microfabrication methods. These chips exhibit combined properties of the two functions. The size of system has characteristic dimensions less than 1mm but more than 1?m. The configuration of these components determine the final deliverables of the switch. MEMS can be designed to meet user requirements on any level from microbiological application such as biomedical transducers or tissue engineering, to mechanical systems such as microfluidic diagnoses or chemical fuel cells. The low cost, small mass and minimal power consumption of the MEMS makes it possible to readily integrate to any kind of system in any environment. MEMS are faster, better and cheaper. They offer excellent electrical performances. MEMS working at Radio frequencies are RF MEMS. RF-MEMS switches find huge market in the modern telecommunication networks, biological, automobiles, satellites and defense systems because of their lower power consumptions at relatively higher frequencies and better electrical performances. But the reliability is the major hurdle in the fate of RF MEMS switches. Reliability mainly arises due to the presence of residual stresses, charging current, fatigue and creep and contact degradation. The presence of residual stresses in switches the S-Parameters of the switches are affected badly and the residual stress affects the final planarity of the fabricated structure. Design and simulation of an RF-MEMS switch is proposed considering the residual stresses in both on and off state. The operating frequency band is being optimized and the best possible feasible fabrication technique for the proposed switch design is being analyzed. S-Parameters are calculated and a comparison for the switches with stress and without stress is performed in FEM based HFSS software.

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Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning, which depends on training the system on recognising and picking the best features that match the application. The main concept of feature learning is to create a model that is expected to be able to learn the best features without any human intervention instead of recourse the traditional methods for feature extraction or reduction and avoid dealing with feature extraction that depends on researcher experience. In this paper, Auto-Encoder will be utilised as a feature learning algorithm to practice the recommended model to excerpt the useful features from the surface electromyography signal. Deep learning method will be suggested by using Auto-Encoder to learn features. Wavelet Packet, Spectrogram, and Wavelet will be employed to represent the surface electromyography signal in our recommended model. Then, the newly represented bio-signal will be fed to stacked autoencoder (2 stages) to learn features and finally, the behaviour of the proposed algorithm will be estimated by hiring different classifiers such as Extreme Learning Machine, Support Vector Machine, and SoftMax Layer. The Rectified Linear Unit (ReLU) will be created as an activation function for extreme learning machine classifier besides existing functions such as sigmoid and radial basis function. ReLU will show a better classification ability than sigmoid and Radial basis function (RBF) for wavelet, Wavelet scale 5 and wavelet packet signal representations implemented techniques. ReLU will illustrate better classification ability, as an activation function, than sigmoid and poorer than RBF for spectrogram signal representation. Both confidence interval and Analysis of Variance will be estimated for different classifiers. Classifier fusion layer will be implemented to glean the classifier which will progress the best accuracies’ values for both testing and training to develop the results. Classifier fusion layer brought an encouraging value for both accuracies either training or testing ones.

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A new statistical approach to predict the gain bandwidth of Phoenix-cell based reflectarrays is proposed. It combines the effects of both main factors that limit the bandwidth of reflectarrays: spatial phase delays and intrinsic bandwidth of radiating cells. As an illustration, the proposed approach is successfully applied to two reflectarrays based on new Phoenix cells.

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Through-Silicon-Vias (TSVs) are utilized for high density 3D integration, which induce crosstalk problems and impact signal integrity. This paper focuses on TSV crosstalk characterization in 3D integrated circuits, where several TSV physical and environmental configurations are investigated. In particular, this work shows a detailed study on the influence of signal-ground TSV locations, distances and their structural configurations on crosstalk. Embedded 3D testing circuits are also presented to evaluate the coupling effects between adjacent TSVs such as crosstalk induced delay and glitches for different crosstalk modes. Additionally, A 3D parallel Ring Oscillators testing structure is proposed to provide crosstalk strength coupling indicator between adjacent TSVs. Simulation results are conducted using a 3D electromagnetic field solver (HFSS) from Ansoft Corporation and a Spice-like simulator (ADS) from Keysight Technologies Corporation based on MIT 0.15µm 3DFDSOI process technology.

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The United Arab Emirates (UAE) is dedicated to establishing the best teaching and learning environment for students and staff across all higher education institutions. To heed this call, the engineering division adopted the Learning-by-Doing (LBD) pedagogical philosophy for 21st Century education at the heart of its strategic directions. This study intends to explore how LBD is understood and practiced in UAE colleges and how 21st Century skills can be explicitly incorporated into its engineering curriculum by using constructive alignment as a pattern for instructional design. This work intends to investigate the general question: “What constructively aligned Learning-by-Doing pedagogical model, with the incorporation of 21st Century skills, needs to be developed to effectively teach and prepare engineering students at the Higher College of Technology, UAE for successful long-term employment in the global working economy?”. Using mixed method research design and input from its major stakeholders, student survey questionnaires, engineering instructors and dean structured interviews, overt class observation and syllabi examination have all been utilized for data triangulation. In conclusion, the study developed a collaborative LBD model tailor-fitted to the institution’s engineering program and to UAE’s culture.

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This paper reports the results of an empirical, 3D full wave inversion (FWI) numerical analysis which provides an estimation of the performance of multi-static handheld ground penetrating radar (GPR,) compared to a bi-static system, for landmine detection using FWI imaging. The experiments are based on simulated data and provide a more realistic evaluation of the performance of multi-static handheld GPR for the landmine problem based on FWI, than previous studies based on a 2D analysis.. Furthermore, a novel method of estimating a parameter set that is closer to the global minimum for an iterative FWI optimization is introduced.

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This article describes the modeling procedure of a direct resistive heat model of the molybdenum sheet and its simulation with electrodes made from various materials. The main characteristic of the proposed model is its dynamic behavior, because model considers the movement of the molybdenum sheet. The simulation results are mutually compared and the optimal material parameters are selected. The development of the model, in which the movement of molybdenum sheet is considered is used for determination of optimal movement speed of the molybdenum sheet in order to achieve requested temperature. The presented model can be also used for determination of optimal electrode materials and its geometrical properties/shape.

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This paper critically evaluates the operational perspective of Virtual Power Plants (VPP) in Germany by analyzing key factors to replace conventional power plants in the future power system. Therefore, its necessity for the secure operation as well as the technical and economic benefits for the German power system are pointed out. The single sections describe in detail how the requirements on technical functions and standardized communication can be reconciled with the increasing challenges on volatile generation. Furthermore, different operation concepts (profit maximization, intra-day schedule loyalty and system services maximization) are described with respect to their mathematical algorithms and their practical feasibility under consideration of given circumstances and future developments. The impact on the power system by the different possible VPP-operational concepts are exemplary pointed out in a case study with use of a medium voltage Cigre-Benchmark Network. The results indicate a high impact on hand-over points by VPP operational concepts.

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Reliable operation of a power transformer with a certain load depends on the technical condition of individual construction parts and the ability to prevent defects that can cause a failure. During the lifecycle of a transformer, valuable data is constantly accumulated, which forms the basis for technical or risk assessment of the equipment. Therefore it also serves as a ground for the decisions on further operation, or repairs, or replacement. To achieve this goal, data need to be systematized. Since technical condition indexing allows combining various types of data including results of diagnostic tests is used within the framework of this research.
As part of a larger risk assessment methodology, algorithms for two indicators are proposed in this paper, and they are based on results of electrical measurements and analysis of oil parameters, respectively. The novelty of the algorithms for indicators introduced in this paper is based on analysis of features specific to the power system in Latvia such as large proportion of aged transformers, low loading level, significant variation in oil volumes, and statistics on typical faults. Proposed limits of parameters are verified with data from operation history. Taking into account the differences in the measurement periodicity, the indicator that is based on electrical measurements assesses the individual constructive parts of the transformer (windings and core, bushing and on-load tap changer) separately, whereas the other, indicator combines the results of oil parameters into a single assessment. These indicators were verified by using 30 transformers from the Latvian power system and the obtained results coincide well with the operation history.

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This paper is an extension of work originally presented in EIC-2017. It deals with influence of the supply frequency for dissipation factor measurements, mainly for tests under power frequency and low frequency. After a theoretical reminder, we present some experiments on single coils and stators of high voltage motors. Finally, we discuss the results and the desirability of choosing one or the other of these methods.

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The main target of this article is to perform the multidimensional analysis of multipath propagation in an indoor and outdoor environment at higher frequencies i.e. 15 GHz, 28 GHz and 60 GHz, using “sAGA” a 3D ray tracing tool. A real world outdoor Line of Sight (LOS) microcellular environment from the Yokusuka city of Japan is considered for the analysis. The simulation data acquired from the 3D ray tracing tool includes the received signal strength, power angular spectrum and the power delay profile. The different propagation mechanisms were closely analyzed. The simulation results show the difference of propagation in indoor and outdoor environment at higher frequencies and draw a special attention on the impact of diffuse scattering at 28 GHz and 60 GHz. In a simple outdoor microcellular environment with a valid LOS link between the transmitter and a receiver, the mean received signal at 28 GHz and 60 GHz was found around 5.7 dB and 13 dB inferior in comparison with signal level at 15 GHz. Whereas the difference in received signal levels at higher frequencies were further extended in an indoor environment due to higher building penetration loss. However, the propagation and penetration loss at higher frequency can be compensated by using the antenna with narrow beamwidth and larger gain.

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The article considers linear algebra as an alternative mathematical tool of logic synthesis of digital structures to Boolean algebra and synthesis methods of digital electronic component base (ECB) on its ground. The methods of solving the applied problems of logic synthesis are shown, including the expansion of an arbitrary logic function by means of monotonic functions. The proposed mathematical apparatus actually provides the creation of digital structures on the principles of analog circuitry. It can find application in the design of multivalued digital ECB, specialized system-on-chip and analog-digital sensors with current output. The examples of synthesis of the combinational and sequential two-valued and multivalued digital devices are given. In conclusion, the advantages of linear algebra in comparison with Boolean algebra are formulated.

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This paper is the extended reflection of work originally presented in conference of Electrical, Computer and Communication Engineering (ECCE)-CUET 2017, entitled “Automated Anti Collision System for Automobiles”. Automated collision avoidance system is a trending technology of science in automobile engineering. The aim of this paper is to design a system which will prevent collision from the front as well as the back for automobiles. This paper gives an overview of secure and smooth journey of car (vehicles) as well as the certainty of human life. This system is controlled by microcontroller ATMEGA32. Two Sharp distance sensors are used to detect object within the danger range where one is for front detection and other is for back detection. A crystal oscillator is used to produce the oscillation and generates the clock pulse of the microcontroller. An LCD and a GLCD are used to give information about the safe distance for front and rear respectively, and a buzzer is used as alarm. An actuator is used as automatic brake and inside the actuator there is a motor driver that runs the actuator. For coding “microC PRO for PIC” is used and “Proteus Design Suite Version 8 Software” is used for simulation.

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CdTe-based photovoltaic (PV) cells provide the lowest EBPT (energy payback time) and emit less amount of GHG (green house gases) among different types of PV cells. Thus, it is very essential to enhance the efficiency of CdTe-based solar cells. A high efficiency CdTe/CdS p-i-n heterostructure solar cell is designed and the performance of the cell is investigated. All the simulations have been done using AMPS 1D (Analysis of Microelectronic and Photonic Structures) simulator. The cell consists of a transparent conducting oxide (TCO) layer (ZnO), a window layer (n-type CdS), an intrinsic layer (CdTe), an absorber layer (p-type CdTe) and a highly doped CdTe and Si as back surface field (BSF) layer. After the optimization of layer thicknesses and doping densities of different layers, we obtained a conversion efficiency of 26.01% for a total cell thickness of 1.8 ?m. It is also found that the conversion efficiency can be increased by simply increasing the thickness of intrinsic layer. At 1.5 AM solar irradiance, the proposed cell structure attained a Voc of 1.09 V, a Jsc of 26.78 mA/cm2, and a fill factor of 89%, reaching an overall conversion efficiency of 26.01% with CdTe as BSF layer.

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Sentiment analysis is one of the most popular information extraction tasks both from business and research prospective. From the standpoint of research, sentiment analysis relies on the methods developed for natural language processing and information extraction. One of the key aspects of it is the opinion word lexicon. Product’s feature from online reviews is an important and challenging task in opinion mining. Opinion Mining or Sentiment Analysis is a Natural Language Processing and Information Extraction task that identifies the user’s views or opinions. In this paper, we developed an approach to extract domain independent product features and opinions without using training examples i.e, lexicon-based approach. Noun phrases are extracted using not only dependency rules but also textblob noun phrase extraction tool. Dependency rules are predefined according to dependency patterns of words in the sentences. StandfordCoreNlp Dependency parser is used to identify the relations between words. The orientation of words is classified by using lexicon-based approach. According to the experimental results the system gets good performance in six different domains.

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This paper addresses the motion planning problem of Reeds-Shepp-type car-like mobile robots moving among static and dynamic obstacles. If the positions and the velocity vectors of the obstacles are known or well estimated, the Velocity Obstacles (VO) method and its non-linear version (NLVO) can be used to plan a collision-free trajectory for a robot in the dynamic environment. VO and NLVO algorithms determine a velocity vector for the robot which corresponds not necessarily to the orientation of the robot, hence a nonholonomic car-like mobile robot cannot apply it exactly. Previously, the NLVO method was adopted for Dubins-like mobile robots, which are able to move forward only. In this paper, a method similar to NLVO is presented, but it results motions feasible for Reeds-Shepp-type robots, which are able to drive both forward and backward. Longitudinal velocities and curvatures of turning circles are calculated, which ensure collision-free motion if the arbitrary movement of the obstacles are known for some time-horizon.

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This paper presents various concepts related to the application of a microgrid pilot project in a residential condominium at Fortaleza / CE – Brazil, such as battery energy system, renewable and distributed generation, islanding recloser and all different units using interface based on power electronics. This papers main objective is to create information about microgrid operation and the interaction between its main equipment, such as power converters, utility energy distribution system and control units responsible for algorithms and changes in microgrids operation mode. This information is important for understanding the need for a test setup construction. To perform the test procedures, a temporary setup in a controlled environment within the microgrid is proposed. During the test periods, intentional power outages are required to evaluate the operating mode switching on each unit. The test setup described in this paper aims to mitigate the tests effects on other residential units inside the condominium.

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Log-periodic antennas have high electrical characteristics over a wide frequency range. The large length of the antennas is their significant disadvantage caused by the fact that each active region consisting of several radiators operates in a narrow frequency range. To extend this range, it is proposed to use radiators with concentrated capacitive loads, providing in-phase currents with a linear amplitude distribution law. An approximate calculation of the fields and directional characteristics of a complex multielement radiating structure is performed using a new method of electrostatic analogy based on the assumption that the ratio of emf in the radiator centers is equal to the charge ratio on the conductors of the electrically neutral system. The problem of optimization of considered antennas by methods of mathematical programming is formulated.

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Model driven development is an important role in software engineering. It consists of multiple transformation functions. This development is a paradigm for writing and implementing computer program quickly, effectively, at minimum cost and reducing development efforts because it transforms design model to object-oriented code. Our approach is rule-based model driven development in which textual Umple model is used as primary artifact and transformed to mobile applications. In this model driven development, evaluation of quality of transformation is critical. This paper has presented a set of metrics to assess the quality attribute of modifiability and evaluated using these object-oriented metrics. Results represent our approach achieves high efficiency in quality of modifiability.

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Constant Envelope Discrete Cosine Transform based Orthogonal Frequency Division Multiplexing (CE-DCT-OFDM) system with M-ary Pulse Amplitude Modulation (PAM) mapper is considered. In the system phase modulation is used to achieve constant envelope signals that have 0 dB Peak-to-Average-Power Ratio (PAPR). Transmission of such signals permit high power amplifiers in the system to operate with maximum power efficiency. The performance of CE-DCT-OFDM system is examined over Additive White Gaussian Noise (AWGN) and over fading channels. Closed-form expressions for Bit Error Rate (BER) over Ricean and Rayleigh channels are derived. The performances of CE-DCTOFDM and conventional DCT-OFDM systems are compared as a function of Input power Back-Off (IBO) and Signal-to-Noise Ratio (SNR) for the Traveling-Wave Tube Amplifier (TWTA) model. Results show that CE-DCT-OFDM system offers superior BER performance compared to DCT-OFDM system and has other advantages as well.

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Chosen-ciphertext attacks (CCA) are typical threat on public-key encryption schemes. We show direct chosen-ciphertext security modification in the case of attribute-based encryption (ABE), where an ABE scheme secure against chosen-plaintext attacks (CPA) is converted into an ABE scheme secure against CCA by individual techniques. Our modification works in the setting that the Diffie-Hellman tuple to be verified in decryption is in the target group of a bilinear map. The employed techniques result in expansion of the secret-key length and the decryption cost by a factor of four, while the public-key and the ciphertext lengths and the encryption cost remain almost the same.

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The world’s pollution rates have been increasing exponentially due to the many reckless lifestyle practices of human beings as well as their choices of contaminating power sources. Eventually, this lead to a worldwide awareness on the risks of those power sources, and in turn, a movement towards the exploration and deployment of several green technologies emerged.
Proton Exchange Membrane Fuel cells (PEMFCs) are one of those green technologies. However, in order to be able to successfully and efficiently deploy PEMFC systems, a solid fault diagnosis scheme is needed. The development of accurate model based fault diagnosis schemes has been imposing a lot of challenge and difficulty on researchers due to the high complexity of the PEMFC system. Furthermore, confidentiality issues with the manufacturer can also impose further constraints on the model development of a commercial PEMFC system. In this work, an approach to develop an accurate PEMFC system model despite the lack of crucial system information is presented through the use of Siemens LMS AMESim software. The developed model is then used to develop a fault diagnosis scheme that is able to detect and isolate five system faults.

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Due to the increasing bandwidth requirement of modern wireless communication systems, developing antenna having wider bandwidth have been receiving significant attention in the recent years. In this paper, a comparative analysis of the contacting feed (micro strip line and coaxial probe) and non-contacting feed mechanism (both Aperture Coupling and Proximity Coupling) in micro strip patch antenna has been done. In case of contacting feed, RF power is fed directly to the radiating patch using a connecting element such as a micro strip line whereas in case of non-contacting feeds, electromagnetic field coupling is done to transfer power between the micro strip line and the radiating patch. As per the latest research, ultra wide band technology is used in the frequency range from 3 GHz to 10 GHz. We have analyzed and compared the return loss and corresponding bandwidth of these four types of antenna at 5.853GHz so that this antenna may be used in medical as well as wireless applications.

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This paper, presents the design of a temperature sensor based on RO (Ring Oscillator) in order to make a thermal study for the detection and localization of thermal peaks in a complex system. In this work, a simulation and FPGA implementation of a fully digital temperature sensor features a number of exact inverters that can be dynamically inserted. Before the transition to the implementation in FPGA board, the use of VHDL code is necessary to describe the exact number of inverters that form a single ring oscillator, in order to verify and validate the results obtained. This paper offers a solution to thermally induced stress and local overheating in complex system design which has been a major concern for the designers during the design of integrated circuit. In this paper a DE1 FPGA board cyclone V family 5CSEMA5F31C6 is used for the implementation.

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This article proposes a new MAC scheme for Vehicle-to-Infrastructure (V2I) communications that dynamically reallocates unused TDMA slots. By maintaining a balanced waiting time, the proposed TDMA based scheduling scheme allocates TDMA slots in a rational way to minimize merging and one-hop neighboring collision. The proposed scheme ensures dynamic reallocation of unused slots by using “time slot reassignment” mechanism. The scheme has been simulated in VEINS framework of OMNET++ network simulator and its performance has been compared with other conventional protocols. Experimental results show that our scheme performs better than existing schemes in terms of successfully transmitted data packets.

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This paper investigates the efficiency benefits of replacing the Silicon diodes of a commercial IGBT module for the main inverter application of an electric vehicle with Silicon Carbide diodes, leaving the package, operating conditions and the system unchanged. This ensures that the comparison is directly between the chip technologies without any scope for discrepancies arising out of differences in the packaging, gatedriver circuit etc. A behavioral power loss calculation model is used to investigate the performance of the two modules for various drive cycles (Artemis, WLTP, NEDC). The behavioral power loss model is experimentally validated using two independent measurement methods, namely, power analyser based electrical input output method, and a calorimetric method which was developed especially for the low lossy light load condition. Furthermore, it is shown that the electrical method has close to 30% inaccuracy making it unsuitable for the main inverter applications, especially for comparing two different chip technologies, e.g., Silicon versus Silicon Carbide. The developed calorimetric method in contrast offers lower than 3% uncertainty.

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In the current industrial development, an increasing number of sensors is applied for monitoring of any kind of appliances and machines. A predictive mathematical model allows for realistic assessment of the health state of the appliance, indication of service requirements, as well as control of the appliance, serving as so-called digital twin on a control device.
Completely analogous modeling can be used for virtual testing, i.e. a (partial) substitution of physical experiments. The software-in-the-loop model provides realistic feedback to the physical specimen on the test rig and helps to increase the representativeness of the experiment and to reduce costs.
In a joint research project partially sponsored by the German Federal Ministry of Economics and Energy, the authors developed an approach for meta-modeling of dynamic systems. While the modeling process is fed by results from sophisticated simulations, or even test results as input data, the resulting model can be used for fast stochastic analyses as well as software-in-the-loop in dynamic real-time experiments. The approach was verified on tests of an aircraft high lift system.

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This work describes computerized data acquisition system for current-voltage (I-V) characteristics of diodes. The proposed system consist of a personal computer pre-installed with LabVIEW, two units of power supply, an interface board, current and voltage sensors. Two devices under test (DUTs) are selected namely 1N4007 silicon and 1N34 germanium diodes. The current flow through the circuit and voltage across the diode is measured and I-V characteristic is successfully plotted. When data acquisition interrupted by the user, real time I-V curve with least-square fitting is plotted. The LabVIEW was employed primarily to display data from sensors and storing collected data for further post-measurement processing. Estimated forward voltage drop from plotted I-V curve was then compared with the published value from manufacturer’s datasheet. Comparison revealed that developed system has been successfully characterized both DUTs base on the forward voltage drop obtained from the plotted I-V curve. Furthermore, proposed system architecture offers extra flexibility where stored data can be manipulated with minimum programming efforts. The application of the proposed system can also be extended for in-situ device characterization in various circuits.

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In this study, we use a new proposed framework to develop the Hamiltonian circuit learning games for college students. The framework is for enhancing learners’ activities with learning reflexiveness. The design of these games is based on this framework to achieve the targeted learning outcomes. In recent years, the game-based learning is a very popular research topic. The Hamiltonian circuit is an important concepts for learning many computer science and electric engineering topics, such as IC design routing algorithm. The developed games use guiding rules to enable students to learn the Hamiltonian circuit in complicate graph problem. After the game, the learners are given a reviewing test which using the animation film for explaining the knowledge. This design concept is different from the previous studies. Through this new design, the outcome gets the better learning results under the effect of reflection. The students will have a deeper impression on the subject, and through self-learning and active thinking, in the game will have a deeper experience.

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Solving the Unit Commitment problem (UCP) optimizes the combination of production units operations and determines the appropriate operational scheduling of each production units to satisfy the expected consumption which varies from one day to one month. Besides, each production unit is conducted to constraints that render this problem complex, combinatorial and nonlinear. In this paper, we proposed a new strategy based on the combination three optimization methods: Tabu search, Particle swarm optimization and Lagrangian relaxation methods in order to develop a proper unit commitment scheduling of the production units while reducing the production cost during a definite period. The proposed strategy has been implemented on a the IEEE 9 bus test system containing 3 production unit and the results were promising compared to strategies based on meta-heuristic and deterministic methods.

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To avoid biopsies, doctors use non invasive medical techniques such as the computed tomography. Even that, the detection of the liver remains a big challenge because of the gray level and shape variations which depend on patients and acquisition modalites. In this work, we propose to create a 3D liver model in the training phase of 3D active shape model algorithm. This training model will be deformed according to any given 3D data for liver segmentation. The contribution of our work is the use of the Non-rigid registration with a B-spline registration on the training phase. We tested our method on an open access database (”3DIRCADb”) and on our database obtained from the radiology department of the National Oncology Institute of Tunis. Both data-sets showed the reliability of the method with an accuracy equal to 69.98% and 71.18% respectively for our database and ”3D-IRCADb”.

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This paper deals with the design of adaptive observer for a class of nonlinear systems with time delays. Within this work, we develop an adaptive observer for a bilinear time delay system, then we extend those results in the presence of Lipschitz nonlinear functions in the system’s dynamics. In the stability analysis of the estimation errors, we combine a Linear Parameter Varying (LPV) approach in the presence of time delays with a polytopic approach. The obtained stability conditions are given in terms of the solvability of Linear Matrix Inequalities (LMIs) on the vertices of a convex polytope. Numerical examples are finally given to show the effectiveness and feasibility of our results.

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One of the prominent challenges of the automotive-transportation system is represented by the integration of security and safety properties within protocols, applications and connectivity mechanisms. A joint safety/security design can sometimes expose to trade-offs, since their requirements may not match perfectly or even be incompatible. This paper analyses an example of security and safety design, by combining integrity with encryption considering the constraints of a typical CAN protocol and real-time traffic. The analysis is presented modelling differently attackers, packet fragmentation issues and the residual probability of error of the combined scheme.

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In this paper, we investigate the decentralized control problem for largescale interconnected systems. The synthesis of the decentralized controller consists in determining gains which ensure the stability of the global system. To calculate these gains, three approaches are presented. Our main contribution is to develop a new decentralized stabilization approach which the decentralized local gains are calculated and formulated via the resolution of linear matrix inequalities (LMIs) problem. A numerical simulation comparison of the three methods is performed on an interconnected double-parallel inverted pendulum.

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The occurrence of many failures in the power system can lead to power instability and affects the system parameters to go beyond its operating limits. It may lead to obstructing the secure operations and reliability of power systems. Ensuring power system security needs proper actions to be taken for the undesirable contingency. Thus, security analysis is important tasks in modern energy management systems. This paper proposes an approach based on the Newton Raphson power flow method for power system security analysis. Firstly, the contingencies will be specified to assess their impact on the transient stability. Secondly, the selected contingencies will be classified in the order of severity. In addition, the integration of the Unified Power Flow Controller (UPFC) to enhance the transient stability of the power system is considered. The proposed method is implemented on the IEEE-14 bus system. We performed this case study using the well-known software EUROSTAG.

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Examples of analytical calculations of the system characteristics of the hardware and time complexity of the correlation system based on a certain number of microphones and the corresponding number of interrelations are presented. The structural solutions of the hardware special processor implementation of such class of multichannel devices for recognition and identification of types and the spatial location of sources of acoustic signals are developed. The structural model of the spatial identification of sources of acoustic signals in Cartesian coordinates of a two-dimensional Hamming space with the priority placement of microphones as receivers of acoustic signals is proposed.

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Engineering schools must adopt or develop their own systems and processes for graduate attribute assessment. In this paper, we take a systems engineering approach to graduate attribute assessment and propose a system architecture and tool-supported continuous improvement process with key algorithms and mathematical analysis to process the data and provide performance management reporting. Over several iterations, we have introduced and evaluated improved systems support in a collection of tools called the Graduate Attribute Information Analysis system (GAIA). GAIA integrates course indicators as well as external indicators from a variety of sources. It provides a tool-supported continuous improvement process with templates and notifications for all deliverables. There are sound algorithms and tool support and built-in mathematical analysis for data collection and reporting that includes quantitative and qualitative data; weighted grading; historic trend analysis; improved visualization of results; and standardized reports at both the course level and the program level that can be used either for accreditation or to inform program improvement.

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Fully Homomorphic Encryption (FHE) is considered as a key cryptographic tool in building a secure cloud computing environment since it allows computing arbitrary functions directly on encrypted data. However, existing FHE implementations remain impractical due to very high time and resource costs. These costs are essentially due to the computationally intensive modular polynomial multiplication. In this paper, we present a software/hardware co-designed modular polynomial multiplier in order to accelerate homomorphic schemes. The hardware part is implemented through a High-Level Synthesis (HLS) flow. Experimental results show competitive latencies when compared with hand-made designs, while maintaining large advantages on resources. Moreover, we show that our high-level description can be easily configured with different parameters and very large sizes in negligible time, generating new designs for numerous applications.

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Drug abuse problem is not a new problem, but it is still serious in Hong Kong. The emergence of hidden drug abuse youth has brought more difficulties to them and their parents. The usual ways of providing anti-drug abuse assistance are not sufficient to these types of young people. Intelligent methods have been developed to assist anti-drug workers. In this paper, we present three mobile applications with a set of intelligent tools for parents and social workers to help our young people. The applications have an alerting tool to detect the possibility of drug abuse of a young student, and to alert corresponding parents and social workers for remedial actions. Another function is the nearby notification tool to help social workers in identifying people who may need assistance. For the alerting function, a number of data mining and text analysis techniques have been adopted for behavior analysis. A supporting instant messaging has been co-developed for communicating alerting messages.

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This paper is an extension of work originally presented in conference name. The goal is to propose new fault detection and fault isolation techniques for a polytypic linear parameter-varying system (LPV). In this work, an adaptive observer design is formulated for a given polyquadratic Lyapunov function. Subsequently, new sufficient conditions are given in terms of Linear Matrix Inequalities (LMIs). To show the effectiveness of the proposed algorithm, an illustrative example is included.

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In recent years, a number of studies have been conducted exploring the potential of digital tour guides, that is, multimedia components (e.g., 2D graphic, 3D models, and sound effects) that can be integrated into digital storytelling with location-based services. This study uses component-based software engineering to develop the content of game-based tourism information services. The results of this study are combined with 3D VR/AR technology to implement the digital 2D/3D interactive tour guide and show all the attractions’ information on a service platform for the gamification of cultural tourism. Nine kinds of game templates have been built in the component module. Five locations have completed indoor or external 3D VR real scenes and provide online visitors with a virtual tour of the indoor or outdoor attractions. The AR interactive work has three logos. The interactive digital guide includes animated tour guides, interactive guided tours, directions and interactive guides. Based on the usage analysis of the component databases built by this study, VR game types are suited to object-oriented game templates, such as the puzzle game template and the treasure hunt game template. Background music is the database component required for each game. The icons and cue tones are the most commonly used components in 2D graphics and sound effects, but the icons are gathered in different directions to approximate the shape of the component to be consistent. This study built a vivid story of a scene tour for online visitors to enhance the interactive digital guide. However, the developer can rapidly build new digital guides by rearranging the components of the modules to shorten the development time by taking advantage of the usage frequency of various databases that have been built by this study to effectively continue to build and expand the database components. Therefore, more game-based digital tour guides can be created to make better defined high-quality heritage attractions understood.

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This paper describes the design and analysis of a regenerative braking system for a permanent magnet synchronous motor (PMSM) drive for electric vehicle (EV) applications. First studied is the principle for electric braking control of a PMSM motor under field-oriented control (FOC). Next, the maximum braking torque in the regeneration mode as well as the braking torque for the maximum regeneration power, respectively, are deduced. Additionally, an optimum switching scheme for the inverter is developed with the objective of maximizing energy recovery during regenerative braking to the DC-bus capacitor. The integration of an ultra-capacitor module with the battery allows for the efficient and high power transfer under regenerative braking. It was important to manage the power flow to the DC-bus as this is a key issue that affects the efficiency of the overall system. Finally, the amounts of braking energy that can be recovered, and the efficiency with which it can be returned to the battery/ultra-capacitor, is analyzed for a PMSM coupled with a DC motor as the load. The results of the analysis are validated through experimentation.

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Detect and tracking of moving weak targets is a complicated dynamic state estimation problem whose difficulty is increased in case of high clutter conditions or low signal to noise ratio (SNR). In this case, the track-before-detect filter (TBDF) that uses unthresholded measurements considers as an effective method for detecting and tracking a single target under low SNR conditions. In this paper, a particle filter based track-before-detect (PF-TBD) method is proposed to address the problem of detection and tracking with unthersholded data and a binary variable of the existence of the target for two motion models. Simulation results using image measurements based on TBD scenarios are also presented to demonstrate the capability of the proposed approach.

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In this work, a representative combinational circuit is visualized in various ways. It is abstracted (concretized) from transistor level to gate level and a structure-preserving transition is carried out into a signal flow graph. For creating a signal flow plan it is necessary to swap the nodes and the edges in the signal flow graph. After having executed this action the result is a signal flow plan. A value table exhibits the coding of the whole circuit. Then the so called module view is used to get the familiar compact and directed display and neighborhood relations are repeated once more, the resolution method is used. It is observed that undefined results can occur in digital circuits. But, these must be avoided in safety critical circuits. These events have to be secured in practice by costly and expensive verification and testing. In order to deal with the problem now, the structure-preserving modeling has to be understood, since this is the only way to achieve a onepurpose, qualitative and cost effective search for errors.

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Cloud computing allows users and enterprises to process their data in high performance servers, thus reducing the need for advanced hardware at the client side. Although local processing is viable in many cases, collecting data from multiple clients and processing them in a server gives the best possible performance in terms of processing rate. In this work, the implementation of a high performance cloud computing engine for recognizing handwritten digits is presented. The engine exploits the benefits of cloud and uses a powerful hardware accelerator in order to classify the images received concurrently from multiple clients. The accelerator implements a number of neural networks, operating in parallel, resulting to a processing rate of more than 10 MImages/sec.

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In this modern age, information technology (IT) plays a role in a number of different fields. And therefore, the role of security is very important to control and assist the flow of activities over the network. Intrusion detection (ID) is a kind of security management system for computers and networks. There are many approaches and methods used in ID. Each approach has merits and demerits. Therefore this paper highlights the similar distribution of attacks nature by using K-means and also the effective accuracy of Random Forest algorithm in detecting intrusions. This paper describes full pattern recognition and machine learning algorithm performance for the four attack categories, such as Denial-of-Service (DoS) attacks (deny legitimate request to a system), Probing attacks (information gathering attacks), user-to-root (U2R) attacks (unauthorized access to local super-user), and remote-to-local (R2L) attacks (unauthorized local access from a remote machine) shown in the KDD 99 Cup intrusion detection dataset.

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This paper is devoted to the analysis of aircraft dynamics in the cruise flight phase under windshear conditions. The study is conducted with reference to a point-mass aircraft model restricted to move in a vertical plane. We formulate the problem as a differential game against the wind disturbances: The first player, autopilot, manages, via additional smoothing filters, the aircraft’s angle of attack and power setting. The second player, wind, produces disturbances that are transferred, also via smoothing filters, into most dangerous wind gusts. The state variables of the game are subject to state constraints representing aircraft safety conditions related, for example, to the altitude, path inclination and velocity. Viability theory is used to compute the so-called viability kernels, the maximal subsets of state constraints where an appropriate feedback strategy of the first player can keep aircraft trajectories arbitrary long for all admissible disturbances generated by the second player. A grid method is utilized, and challenging computations in seven dimensions are conducted on a supercomputer system.

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Mobile broadband has gained momentum with the growing demand of user data rates. Long Term Evolution (LTE) technology is the step in mobile communications evolution, developed to satisfy high data rate demand, and meet better spectral efficiency requirements. Effective radio resource management and inter cell interferences are the major challenges. Fractional Frequency Reuse (FFR) is one of the effective interference avoidance mechanisms applied to LTE networks to yield optimal throughput. In this extended paper, we propose a novel performance metric, weighted throughput on user satisfaction, and evaluate an existing adaptation process that dynamically adjusts to optimal network performance determined by FFR mechanism with mobile users. The performance of FFR mechanism with mobility model, adaptation process, and femtocell densification is evaluated and optimized for proposed metric and other metrics. Results optimized by proposed metric show comparatively higher average throughput and lower variance among user throughput.

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Cloud computing has become a compelling paradigm built on compute and storage virtualization technologies. The current virtualization solution in the Cloud widely relies on hypervisor-based technologies. Given the recent booming of the container ecosystem, the container-based virtualization starts receiving more attention for being a promising alternative. Although the container technologies are generally considered to be lightweight, no virtualization solution is ideally resource-free, and the corresponding performance overheads will lead to negative impacts on the quality of Cloud services. To facilitate understanding container technologies from the performance engineering’s perspective, we conducted two-stage performance investigations into Docker containers as a concrete example. At the first stage, we used a physical machine with “just-enough” resource as a baseline to investigate the performance overhead of a standalone Docker container against a standalone virtual machine (VM). With findings contrary to the related work, our evaluation results show that the virtualization’s performance overhead could vary not only on a feature-by-feature basis but also on a job-to-job basis. Moreover, the hypervisor-based technology does not come with higher performance overhead in every case. For example, Docker containers particularly exhibit lower QoS in terms of storage transaction speed. At the ongoing second stage, we employed a physical machine with “fair-enough” resource to implement a container-based MapReduce application and try to optimize its performance. In fact, this machine failed in affording VM-based MapReduce clusters in the same scale. The performance tuning results show that the effects of different optimization strategies could largely be related to the data characteristics. For example, LZO compression can bring the most significant performance improvement when dealing with text data in our case.

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Hereby in this paper, we are going to refer image classification. The main issue in image classification is features extraction and image vector representation. We expose the Bag of Features method used to find image representation. Class prediction accuracy of varying classifiers algorithms is measured on Caltech 101 images. For feature extraction functions we evaluate the use of the classical Speed Up Robust Features technique against global color feature extraction. The purpose of our work is to guess the best machine learning framework techniques to recognize the stop sign images. The trained model will be integrated into a robotic system in a future work.

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In the present article, we develop multilayer perceptron model for identification of some possible genes mediating different leukemia. The procedure involves grouping of gene based correlation coefficient and finally select of some possible genes. The procedure has been successfully applied three human leukemia gene expression data sets. The superiority of the procedure has been demonstrated seven existing gene selection methods like Support Vector Machine (SVM), Signal-to-Noise Ratio (SNR), Significance Analysis of Microarray (SAM), Bayesian Regularization (BR), Neighborhood Analysis (NA), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) is demonstrated, in terms of the affluence of each Go attribute of the important genes based on p-value statistics. The result are properly validated by before analysis, t-test, gene expression profile plots. The proposed procedure has been capable to select genes that are more biologically significant for mediating of leukemia then those obtained by existing methods.

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Exploring communities and outliers in Social Network is based on considering of some nodes have overlapped neighbor node within the same group as well as some nodes have no any link to the other node or have no any overlapped value. The existing approaches are based on the overlapping community detection method were only defined the overlap nodes or group of overlap nodes without thinking of which nodes might have individual communities or which nodes are outliers. Detecting communities can be used the similarity measure based on neighborhood overlapping of nodes and identified nodes so called outliers which cannot be grouped into any of the communities. This paper proposed method to detect communities and outliers from Edge Structure with neighborhood overlap by using nodes similarity. The result implies the best quality with modularity measurement which leads to more accurate communities as well as improved their density after removing outliers in the network structure.

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Convolutional Neural Networks (CNNs) have gained attention in recent years for their ability to perform complex machine learning tasks with high accuracy and resilient to noise of inputs. The time-consuming convolution operations of CNNs pose great challenges to both software as well as hardware designers. To achieve superior performance, a design involves careful concerns between exposing the massive computation parallelism and exploiting data reuse in complex data accesses. Existing designs lack comprehensive analysis on design techniques and decisions. The analytical discussion and quantitative proof behind the design criterion, such as choosing proper dimensions to parallelize, are not well studied. This paper performs a series of qualitative and quantitative studies on both the programming techniques and their implications on the GPU architecture. The observations reveal comprehensive understanding on the correlation between the design techniques and the resulting performance. Based on the analyses, we pinpoint the two major performance bottlenecks of CNN on GPGPU: performing computation and loading data from global memory. Software and hardware enhancements are proposed in this paper to alleviate these issues. Experimental results on a cycle-accurate GPGPU simulator have demonstrated up to 4.4x performance enhancement when compared with the reference design.

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Brain tumor is such an abnormality of brain tissue that causes brain hemorrhage. Therefore, apposite detections of brain tumor, its size, and position are the foremost condition for the remedy. To obtain better performance in brain tumor and its stages detection as well as its position in MRI images, this research work proposes an advanced hybrid algorithm combining statistical procedures and machine learning based system Support Vector Machine (SVM) and Artificial Neural Network (ANN). This proposal is initiated with the enhancement of the brain MRI images which are obtained from oncology department of University of Maryland Medical Center. An improved version of conventional K-means with Fuzzy C-means algorithm and temper based K-means & modified Fuzzy C-means (TKFCM) clustering are used to segment the MRI images. The value of K in the proposed method is more than the conventional K-means. Automatically updated membership of FCM eradicates the contouring problem in detection of tumor region. The set of statistical features obtained from the segmented images are used to detect and isolate tumor from normal brain MRI images by SVM. There is a second set of region based features extracted from segmented images those are used to classify the tumors into benign and four stages of the malignant tumor by ANN. Besides, the classified tumor images provide a feature like orientation that ensures exact tumor position in brain lobe. The classifying accuracy of the proposed method is up to 97.37% with Bit Error Rate (BER) of 0.0294 within 2 minutes which proves the proposal better than the others.

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The Internet of Things (IOT) is a revolutionary communication technology which enables numerous heterogeneous objects to be inter-connected. In such a wireless system, interference management between the operating devices is an important challenge. Cognitive Radio (CR) seems to be a promising enabler transmission technology for the 5G-IOT system. The “sense-and-adapt” smart transmission strategy in CR systems can help to overcome the problem of multiple access interference (MAI) in IOT systems. In this paper, a 5G-IOT smart infrastructure system is arranged in the form of CR based virtual antenna array (VAA) system. In VAA based wireless system, knowledge of users’ locations can help the transmitter to achieve interference avoidance by steering the main beam towards the intended recipient. This idea has been applied to the VAA-IOT system, where smart antenna array based location aware beamforming are applied at both transmitter and receiver cluster of smart sensors with the help of spatial modulation principle. The waveform of choice for the CR-IOT clusters is Generalized Frequency Division Multiplexing (GFDM) while corresponding waveform for the primary user (PU) cluster is conventional Orthogonal Frequency Division Multiplexing (OFDM). Computer simulation shows that under multipath fading conditions, the implemented system can reduce the interference to the primary user (PU) system, leading to better coexistence.

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The main target of this article is to study the provision of indoor service (coverage) using outdoor base station at higher frequencies i.e. 10 GHz, 30 GHz and 60 GHz. In an outdoor to indoor propagation, an angular wall loss model is used in the General Building Penetration (GBP) model for estimating the additional loss at the intercept point of the building exterior wall. A novel angular wall loss model based on a separate incidence angle in azimuth and elevation plane is proposed in this paper. In the second part of this study, an Extended Building Penetration (EBP) model is proposed, and the performance of EBP model is compared with the GBP model. In EBP model, the additional fifth path known as the “Direct path” is proposed to be included in the GBP model. Based on the evaluation results, the impact of the direct path is found significant for the indoor users having the same or closed by height as that of the height of the transmitter. For the indoor users located far away from the exterior wall of building, a modified and enhanced approach of ray tracing type is proposed in this article. In the light of acquired simulation results, the impact of a modified ray tracing approach is emphasized.

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Probabilities topic models are active research area in text mining, machine learning, information retrieval, etc. Most of the current statistical topic modeling methods, such as Probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA). They are used to build models from unstructured text and produce a term-based representation to describe a topic by choosing single words from multinomial word distribution. There are two main weaknesses. First, popular or common words are different topics, often causing ambiguity for understanding the topics; Second, lack of consistent semantics for single words to be represented correctly. To address these problems, this paper proposes a model (A Two-Phase Method for Constructing Topic Model, TPMTM) that combines statistical modeling (LDA) with frequent pattern mining and produces better presentations of rich topics and semantics. Empirical evaluation shows that the results of the proposed model are better than LDA.

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The research reported in this paper proposes an Advanced Process Control system, denoted “i.Process | Steel – RHF”, oriented to energy efficiency improvement in a pusher type billets reheating furnace located in an Italian steel plant. A tailored control method based on a two-layer Model Predictive Control strategy has been created that involves cooperating modules. Different types of linear models have been combined and an overall furnace global linear model has been developed and included in the controller formulation. The developed controller allows handling all furnace conditions, guaranteeing the fulfillment of the defined specifications. The reliability of the proposed approach has been tested through significant simulation scenarios. The controller has been installed on the considered real plant, replacing local standalone controllers manually conducted by plant operators. Very satisfactory field results have been achieved, both on process control and energy efficiency improvement. Optimized trade-offs between energy saving, environmental impact decreasing, product quality improvement and production maximization have been guaranteed. Consequently, Italian energy efficiency certificates have been obtained. The formulated steel industry reheating furnaces control method has been patented.

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On-ramping is being widely used as e method to increase the freeway operational efficiency. The main traffic parameter that must be taken in consideration for the implementation of the feedback control strategies for the on-ramp metering is density on the main section of road. In this paper is given discretized model of traffic which is then improved by a recursive technique called Kalman-Filter with the aid of which is possible to predict the density, by only having the traffic flow measured on the start and end road section. Kalman Filter is based on linear relationship of flow and density. By minimizing the square of error between of the measurements and the estimated values of flows, a gain is derived which then is applied to the densities of the model in order to obtain the greatest accuracy of these values.

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Recently, the number of industrial accidents of chemical plants has been increasing in Japan. The fault detection system is required to keep chemical plant safely. In this study, a fault detection system for a chemical plant using agent framework and negative selection algorithm was proposed. The negative selection algorithm is one of artificial immune systems. The artificial immune system is an imitative mechanism of vital actions to discriminate self/nonself to protect itself. The method was implemented and applied to a complicated chemical plant—which is a boiler plant virtually operated using a dynamic plant simulator. The simulations of fault detection were carried out. And also, the results of simulations are presented in this paper.

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A bistatic radar breadboard for space surveillance has been developed by ONERA for the European Space Agency. This item was operated during the launch of the ExoMars mission on March 14th 2016. The spacecraft, attached to the Proton launcher, was well detected in real-time during its passage over South Europe. This paper presents the setting up of an experiment to detect this particular type of targets with the radar breadboard. The results of its operation as space surveillance system as well as a specific kinematic analysis of the ExoMars spacecraft as viewed from the radar.

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The necessity of navigation in people and mobile robots (MR) through specific environments (indoors or outdoors) has become more and more relevant nowadays. For indoors, generally speaking, the positioning systems can be divided into 2D (two dimensions) or 3D (three dimensions) approaches, where Ultrasonic Local Positioning Systems (ULPS) are often a common solution for MRs in 2D. This work proposes the extension of an already developed 2D ULPS to a 3D ULPS, where the compact design and the suitable performance of the initial 2D ULPS have been maintained. The ultrasonic beacons have been re-arranged to avoid co-planarity, then improving the third coordinate estimation. Furthermore, this work proposes the use of up to four ULPSs together to cover the 3D region of interest. Two configurations have actually been considered, one involving three ULPSs and another based on four. A heuristic Position Dilution of Precision (PDOP) estimation has been carried out, by taking into account two ways of obtaining the 3D-position: a) all beacons from the three different ULPSs are processed simultaneously, so all measurements are considered in the same set of positioning equations; and b) every ULPS is detected and considered separately and, later, the different estimated positions are merged. The second option is more likely to happen in a real scenario and, furthermore, the fusion of the independent positions obtained from each one of the ULPS improves the final position accuracy.

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Nowadays, text annotation plays an important role within real-time social media mining. Social media analysis provides actionable information to its users in times of natural disasters. This paper presents an approach to a real-time two layer text annotation system for social media stream to the domain of natural disasters. The proposed system annotates raw tweets from Twitter into two types such as Informative or Not Informative as first layer. And then it annotates again five information types based on Informative tweets only as second layer. Based on the first and second layer annotation results, this system provides the tweets with user desired informative type in real time. In this system, annotation is done at tweet level by using word and phrase level features with LibLinear classifier. All features are in the form of Ngram nature based on part of speech (POS) tag, Sentiment Lexicon and especially created Disaster Lexicon. The validation of this system is performed based on different disaster related datasets and new Myanmar_Earthquake_2016 dataset derived from Twitter. The annotated datasets generated from this work can also be used by interested research communities to study the social media natural disaster related research.

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Safety-critical systems are well documented and standardized (e.g. IEC 61508, RTCA DO-178B) within system design cycles. However in Defence and Security, systems that are critical to the success of a Mission are not defined within the literature nor are there any guidelines in defining criticality in their design or operational capabilities. When it comes to Vetronics (Vehicle Electronics), a mission-critical system, is a system with much complexity and mixed criticality levels that is a part of the overall platform (military vehicle) offering integrated system capabilities. In this paper, a framework is presented, providing guidelines in designing efficiently and effectively mission-critical systems considering principles of Interoperable Open Architectures (IOA), mission-critical integrity levels and following new standardization activities such as NATO Generic Vehicle Architecture (NGVA). A Defensive Aid Suite (DAS) system is used as a case study to illustrate how this framework can be exploited. The indention of this extension is to provide an approach to precisely estimate threats in order to de-risk missions in the very early stages.

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In this paper an efficient feedback quantization technic for beamforming in MIMO systems is presented. The proposed technic named time domain quantization TD-Q is based on the feedback of time domain parameters necessary for the reproduction of the beamforming matrix at the transmitter. This TD-Q presents the same performance than the conventional Givens rotation quantization GR-Q approach which is adopted in IEEE 802.11ac standardand. The performance and amount of feedback of the proposed TD-Q are studied and compared with the GR-Q in IEEE 802.11ac context.

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The energy marathon is becoming increasingly based on renewable sources, whereas the continuous decrease on the cost of energy production has supported in last decade, for instance, the development of large-scale offshore wind applications. The consistency and availability of the AC-working equipment portfolio is only limited by physical application boundaries, which are quite evident in this sort of accomplishments. The focus of this article is to present a tool developed under the MATLAB environment which allows for a quick and real-time analysis of HVAC links, assessing the impact of voltage, current, power factor or distance conditions. The conclusions are drawn directly by means of a stress-point and operational acceptance range.

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Patient generated data, or personal clinical data, is considered an important aspect in improving patient outcomes. However, personal clinical data is difficult to collect and manage due to its distributed nature. For example, they can be located in multiple places such as doctors’ offices, radiology centers, hospitals, or some clinics. Another factor that can make personal clinical data difficult to manage is that it can be heterogeneous data types such as text, images, charts, or paper-based documents. In case of emergencies, this situation makes personal clinical data retrieval very difficult. In addition, since the amount and types of personal clinical data continue to grow, finding relevant clinical data when needed is getting more difficult if no action is taken. In response to such scenarios, we propose an untethered patient health record system that manages personal health data by utilizing meta-data that enables easy retrieval of clinical data. We incorporate cloud-based storage for easy access and sharing with caregivers to implement continuity of care and evidence-based treatment. In emergency cases, we make critical medical information such as current medications and allergies available to relevant caregivers with valid license numbers only. Clinical data needs to be stored or made accessible from one place for easy sharing and retrieval. Well-managed personal cloud space could outlive the lifetime of personal health records system (PHRS) since the discontinuity of the service does not affect the data stored in the cloud space. In our approach, we separate the clinical data from applications in order to make the data independent from the application. Also, the users can have alternative applications for their clinical data. Such independence motivates users to use PHRS with flexibility.

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Tenants associated with a cloud-hosted application seek to reduce running costs and minimize resource consumption by sharing components and resources. However, despite the benefits, sharing resources can affect tenant’s access and overall performance if one tenant abruptly experiences a significant workload, particularly if the application fails to accommodate this sudden increase in workload. In cases where a there is a higher or varying degree of isolation between components, this issue can become severe. This paper aims to present novel solutions for deploying components of a cloud-hosted application with the purpose of guaranteeing the required degree of multitenancy isolation through a mathematical optimization model and metaheuristic algorithm. Research conducted through this paper demonstrates that, when compared, optimal solutions achieved through the model had low variability levels and percent deviation. This paper additionally provides areas of application of our optimization model as well as challenges and recommendations for deploying components associated with varying degrees of isolation.

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Excellence in hospitals supply management results in better quality, best prices, and good deliveries. One of the questions that come up as healthcare organization to capture the economies of scale in purchasing prices and process costs is whether their purchasing activities should be centralized or decentralized. In most cases, centralization strategy usually gives good supplier’s service with a lower cost, but the consideration of supplier’s cost in the hospital sector are mainly limited to visible ones. The high levels of hidden quality costs generated by suppliers and their unknown presence have serious consequences on the decisions made by the managers. However, the existence of this kind of costs has not been considered yet. Therefore, the main objective of this paper is to propose a decision-making-approach, integrating a new method of measuring supplier’s hidden quality costs, in order to help managers to choose the appropriate purchasing organizational structure in the hospital sector.

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This application developing for the project OCEMA comes as result of architecture building in Android, then the development of a professional modeling in Talend Open Studio for Big Data, which enabled the integration of data from many data sources. One of its uses is the quick identification of people found in areas affected by natural disasters. The application identifies the persons who do not have an ID card, or another identification document, by using identification through fingerprint or personal number. OCEMA Application has access to all the agencies involved in natural disasters managing, such as ISK, HMIK, MIK, FSK and CRA. This application is developed to connect to web applications as well, such as applications, which gather real time information on earthquakes and weather in the world.
The study of literature and the actual work with these systems has shown some important components of success for DWH systems, DataMart and mobile application development.

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This application developing for the project OCEMA comes as result of architecture building in Android, then the development of a professional modeling in Talend Open Studio for Big Data, which enabled the integration of data from many data sources. One of its uses is the quick identification of people found in areas affected by natural disasters. The application identifies the persons who do not have an ID card, or another identification document, by using identification through fingerprint or personal number. OCEMA Application has access to all the agencies involved in natural disasters managing, such as ISK, HMIK, MIK, FSK and CRA. This application is developed to connect to web applications as well, such as applications, which gather real time information on earthquakes and weather in the world.
The study of literature and the actual work with these systems has shown some important components of success for DWH systems, DataMart and mobile application development.

Read more…