Volume 3, Issue 5

Finding similarity of textual data is very important task in natural language processing. In this article we present approach to finding similarity of words, paragraphs, sentences and documents. Semantic similarity is one of the central tasks in many applications, including text summarization, Intelligent Tutoring Systems (ITS) etc. In ITS sentence similarity is used to compare the student’s response with the correct answer. The result is used to gain information about student’s level of knowledge. We propose three different methods that measure text to text semantic relatedness. There are multiple approaches to finding the right measure to determine the similarity of the sentences. Some measure the alignment of characters, and other measure semantic similarity between sentences. In this work we present and evaluate methods for finding not just similarity of sentences but even also similarity of whole paragraphs and documents. We have evaluated these methods using the data from the Yahoo Question and Answer of the Non-Factual Data Set.

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As the contribution from renewable energy sources increase, the power quality degradation has become a challenge for the power system operator. There are major two factors causing power quality deterioration. First is the current harmonic generation by the power electronic converters used in renewable energy sources, and second is the magnification of harmonic voltage due to harmonic resonance condition educed by power factor correction capacitors. The motivation behind this work is power quality related problems, observed recently in the field. To analyze the problem a comprehensive approach is adopted. A practical wind farm configuration is considered for study. Using an eigenvalue analysis method, critical resonance modes are determined for different value of capacitor connected to different buses. To check the effect of change in network parameter on eigenvalue, the sensitivity analysis is carried out and how resonant point is varied with the capacitor value and connection point is discussed by simulating different cases. The results are useful in determining the filter value and location for maximizing its effectiveness in curbing the harmonic problems.

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This paper first simplifies the GA-optimized filter structure developed by us. Then the filter structure is modified by introducing slot and conductor stubs, based on electric fields and magnetic currents at resonant and transmission zero points. As a result, return losses in two passband regions are suppressed more than 28 dB, and also insertion loss in stopband dividing two passbands is suppressed more than 20 dB. Finally, two kinds of the proposed filters are fabricated, and effectiveness of them is confirmed from comparison between the measured and the numerical results.

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This work introduces the design and prototype development of a novel optical-based fingertip force sensor, which is integrated in a bio-mimetic finger for robotic and prosthetic manipulation. Given the optical nature of the sensor, the proposed device is free of any electrical and metal components. Accordingly, the design improves manipulation of objects with high electromagnetic compatibility performance, reducing sensor’s susceptibility in the presence of these disturbances. The sensor has inherently high SNR and low-power consumption. The concept of using simplified and low cost optical based fingertip force sensor for bio-mimetic anthropomorphic fingers has not been widely implemented so far. The sensor is based on a cantilever design combined with a couple of fiber optics and a reflective surface, it is integrated on the distal phalange of a novel bio-mimetic and anthropomorphic robotic finger. Sensor design was performed and optimized throughout a 3D printing process and Finite Element Analysis. The sensor’s sensitivity (0.098V/N), resolution (0.01N), accuracy (3% of full scale output (FS)) and hysteresis (9.24% of FS) were characterized through a calibration processes. Various applications like surgical manipulation or handling in harsh environment (i.e. high voltage, high temperature and explosive environments) will benefit from the proposed sensor’s performance, reliability, simplicity and bio-compatibility.

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As a result, outdoor travelers, tourists and businessman who utilize portable electronic devices often experience battery depletion of their devices without the means of charging their device. The number of Wi- Fi access point continues to increase. There are many occasions where it is desirable to acquire energy from many radio frequency stations, while charging any electronics device through RF-DC circuit with high efficiency, for example, Wi-Fi hotspot access points. We focus on ambient radio frequency signals and power available in the ambient. This research explain, how can we charge portable devices without electrical source but means of propagating signal generated by Wireless-Fidelity (Wi-Fi) router. The research will show all the activities addressed to design a system to recover energy from electromagnetic sources presents around us likeWi-Fi routers. An energy acquiring circuit includes one loop antenna to detect RF signals and electronic board for converting RF energy to DC current. We can use to charge the battery of our portable devices by acquiring the electromagnetic energy around us.

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In this work, we discuss how the insertion of a LaSiOx layer in between an in-house IL passivation layer and the high-k has moved the III-V gate stack into the target window for future technology nodes. The insertion of this LaSiOx layer in the gate stack has reduced the Dit and Nbt below the target level of 5×1011 /eV.cm2 and 3×1010 /cm2 (target at 10 years operation: ?Vfb<30mV at 125°C) respectively. From physical analysis, it was found that LaSiOx can stabilize the interaction of the IL layer with the InGaAs substrate. An implant free In0.53Ga0.47As n-MOSFET was fabricated with this gate stack and for the first time, a III-V gate stack meets the reliability target for advanced technology nodes with a max operating Vov of 0.6 V. In addition, an excellent electron mobility (µeff,peak=3531 cm2/V-s), low SSlin=71 mV/dec and an EOT of 1.15 nm were obtained. We also report the scaling potential of this stack to 1 nm EOT without any loss in performance, reliability and further reduction of the sub-threshold swing (SSlin=68 mV/dec).

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This paper presents a novel parametric co-variance assignment strategy for multi-variable stochastic uncertain systems. Based upon the explicit parametric design and reduced-order closed-form co-variance model, the variances and co-variances of the system outputs can be assigned artificially using output feedback while the effect of the system uncertainties can be minimized by optimizing the free parameters. In addition, the stability of the closed-loop system has been analyzed and an illustrative numerical example is given to demonstrate the effectiveness of the presented strategy. As a summary, the contributions of this paper include the reduced-order co-variance model, the co-variance error based performance criterion and the parametric control design with stability analysis.

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A lot of research reported that the active learning, especially the flipped classroom learning has higher performance than the lecture-based learning called the traditional learning. Therefore, this research is experimented on the object-oriented analysis and design course. The objectives are to try the flipped classroom learning with the workshop activity in order to analyze learners’ behavior and to evaluate the effectiveness of the learning system. The aim of the research is to determine the suitability of the flipped classroom learning on the object-oriented analysis and design course, and to find the advantages and limitations of such learning method. The scope of this research is to test on the object-oriented analysis and design course, Faculty of Science, Chulalongkorn University. CourseVille developed by Chulalongkorn University is used as the learning management system. Moreover, the learning materials are Thai lecture videos, and English documents, such as English PowerPoint slides. Furthermore, the examples of activities in the classroom are Thai discussion and group activities, including the learner presentation. The research mythology is to collect data from learners composed of the behavior of the learners, learners’ test scores, the result of the workshop activity and the output of term project. In addition, the collected data from the questionnaires about learner opinions with the classroom observation result are used for analyzing the learner attitude of the flipped classroom learning, and the learners’ scores in the flipped classroom learning are compared to those in the traditional learning. In conclusion, the research results show that the 90 percentage of learners agree that the flipped classroom learning is suitable for the object-oriented analysis and design course, and it has higher performance that the traditional lecture-based learning system.

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A low-complexity dual-band chirp FSK, direct conversion receiver is described in this paper. The receiver is dedicated to be used in the transceiver unit of a medical implantable wireless sensor. The system uses the RF band between 402 and 405 MHz. Two sub-bands frequencies employing chirped pulses are assigned for both binary information. The novelty of this work is the use of a Binary FSK LFM modulator, a direct conversion receiver and a simple and low power non-coherent BFSK envelope detection demodulator. Receiver performances are evaluated for all the input power dynamic range. Receiver front-end parameters are optimized using harmonic balance simulation. In order to improve receiver sensitivity, a low pass filter with controllable bandwidth between 40 and 300 KHz is used to avoid in-band interference. The receiver is able to achieve a noise figure of 5.5 dB, a receiver sensitivity of -93 dBm and a maximum data rate of 100Kbps. The simulated IIP3 and P-1dB are 12.6 dBm and 22.1 dBm respectively. A simple non coherent binary dual band FSK demodulator was used which is based on an envelope detector, integrate & dump, a sampling & hold and a liming circuit. The receiver was co-simulated with the dual band non coherent demodulator. The proposed receiver has a sensitivity of -93 dBm and a BER less than 10-3.

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There are more than 7billion people in the world where there are around 500 million people in the world who are denied from normal lifestyle due to physical and mental issue. It is completely fair to say that every person deserves to enjoy a normal lifestyle. While physically and mentally challenged people find suitable way to surpass their limits, thus become able in other ways, researchers always try to find solutions better than the existing one. A complete remedial of such issue is included in advanced medical science, and the amelioration of such issue to a better extent is the challenge for the engineers.
In this work we have focused on hand gestures. Hand gestures are created using the movement of hand and arm, using fingers to create different shapes, using fingers and palm to create different angles. Single or both hands can be used to create different expressions. The main objective of this work is to generate an algorithm that can recognize different patterns of hand gestures with notable accuracy. American Sign Language is one possible reference model that can be used. Images of different hand signs are taken as inputs using a webcam, followed by segmentation of the images using polygon approximation and approximate convex decomposition. Feature extraction is done by recording the unique feature among the various convex segments of the hand. The resultant singularities are then used as extracted feature vectors. This involves training with the obtained features which are approximately unique for different hand gestures. Hence, we will be able to identify sign languages and successively make disabled individuals socially acceptable. This work is an extension of the work entitled “A Machine Learning Framework Using Distinctive Feature Extraction for Hand Gesture Recognition” in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (IEEE-CCWC).

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The Internet of Things (IoT) is a new concept that has a great appeal for researchers, businesses and the ordinary tech user. It presents new possibilities of connection between devices and people and it stimulates our need and desire to interact, to exchange ideas and to communicate with the surrounding environment. This paper briefly explores aspects of the IoT that may be important for future developments and focuses on the impact of creative methodologies, such as user centered design (UCD) in a smart parking, IoT-based system prototype. It aspires to provide an alternative perspective for an improved user experience (UX) and aims to contribute to the discussion about the challenges, findings and perspectives when merging science and creativity to maintain continuous progress in the IoT domain.

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Animal movement is inherent in the marketing between the rural productive units, establishing space-time connections between them. The relational nature of such information is kept in the Animal Transit Guides (GTA), a mandatory issuance in Brazil. When evaluating such set of information, this work aimed at characterizing the bovine movement network in the state of Pará through the application of concepts from Social Network and Network Theory analysis. Where the nodes and edges that structure the network are respectively the rural properties and the number of cattle moved between them. The results obtained characterize the bovine movement in the state, which show distinct patterns such as the movement of a great number of animals rambling and a clear seasonality due to commercialization purposes. The evaluation of the network components’ modularity also shows the subdivision of the state in four commercial clusters, where the central measures of its components define municipalities of economic and epidemiological interest. The results described herein help in the support of health surveillance and control measures, as well as the adoption of economic strategies and policies.

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The latest design approach for Data Centers (DCs) follows the direction of exploiting optical switching to connect Top-of-Rack (ToR) switches that serve thousands of data storing and computing devices. A ToR’s usual function is the Virtual Output Queues (VOQs), which is the prevalent solution for the head-of-line blocking problem of the DC switches. An effective VOQs architecture improves the DC’s performance by reducing the frames communication latency and it is efficient with respect to the implementation cost. The current paper introduces a VOQs architecture for the ToRs of DCs that function with Time Division Multiple Access (TDMA). The proposed VOQ architecture contains a bounded number of queues at each input port supporting the active destinations and forwarding the input Ethernet frames to a shared memory. An efficient mechanism of low latency grants each queue to an active destination. The VOQs constitutes a module of a ToR development, which is based on a commercially available Ethernet switch and two FPGA Xilinx boards, the Virtex VC707 and the Xilinx NetFPGA. The VOQs architecture’s implementation and validation took place on the NetFPGA board.

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This paper introduces an electroencephalogram (EEG) analysis method to detect preferences for particular sounds. Our study aims to create novel brain–computer interfaces (BMIs) to control human mental (NBMICM), which are used to detect human mental conditions i.e., preferences, thinking, and consciousness, choose stimuli to control these mental conditions, and evaluate these choices. It is important to detect the preferences on stimuli. If the stimuli related to the preference can be detected, the NBCIMC can provide stimuli to the user based on their emotions by detecting their favorite stimuli. The proposed method adopted EEG recording technique, extraction techniques of EEG features and detection methods of preferences. EEG recording employs a simple electroencephalograph, for which the measurement position is the left frontal lobe (Fp1) of the brain. We assume that the differences of the EEG activities on the patterns of preference are expressed in the association between the changes of the power spectra on each frequency band of the EEG. To calculate the association, we employ the gray theory model. The EEG feature is extracted by calculating the gray association degree, then, the preferences are detect using a support vector machine (SVM). Experiments are conducted to test the effectiveness of this method, which is validated by a mean accuracy rate >88% on the favorite sound detection. These results suggest that the detection of subject’s favorite sounds becomes easy when the EEG signals are analyzed while the gray associate degrees are used as the EEG feature and the SVM is used as the classifier.

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In this work, we investigate the effects of variation of processing parameters on the quality of dispersion of polycarbonate compound. In order to achieve appropriate pigments dispersion, we performed compounding process parameters optimizations, by investigating three processing parameters, temperature, screw speed, and feed rate. We utilized experimental design for the optimization of process parameters based upon three levels full factorial response surface methodology was utilized. The experimental designs, statistical and numerical optimization were performed using design expert software. Statistical equation was developed to understand individual parameters interactions on the values of color. The model was established as statistically significant based on diagnostic tests performed. Our analysis of variance (ANOVA) illustrates that the parameters of color (dL*, da* and db*) are affected by the three investigated parameters. The process parameters required to attain color values in a minimum desirable deviation dE* of 0.8 were found through optimization to be equal to 245.26 oC, 741.27 rpm, and 24.72 kg/hr. Furthermore, we also demonstrate variations of the processing variables while other parameters remained constant (General Trends). Both strategies generated process parameters that were statistically significant.

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This work presents new pipeline-parallel, generic and configurable parallel hardware architectures for the Direct/Inverse Wavelet Packet Transform (DWPT/IDWPT) independent of any specific family of wavelets, implemented in FPGA technology using a parallel architecture of direct FIR filter. We propose in the following paper, new P-parallel structures for the DWPT and IDWPT transforms based on the Mallat binary tree algorithm. Therewith, we developed a P-parallel/modified direct FIR filter architecture using pipeline-parallelize and hardware resource sharing, which provides not only ultra-high speed data processing but also a limited amount of hardware as resources are shared between filters and the bidirectional wavelet packet transformation. This model follows two important strategies: I) a powerful structure pipeline/P-parallel using strict data management and interleaving, II) sharing hardware at different levels in the transformation and between the two DWPT/IDWPT transformations. These architectures are modeled in VHDL at RTL modeling level. They are generic and fully configurable: at synthesis and post-synthesis.
The simulation results show an acceleration of data processing to an approximate value of P-Parallel multiplied by frequency with lower used resources. Furthermore, the impact of tree depth and filters order on throughput is very light due to the linearize architecture of our model. The synthesis was achieved using the Intel Quartus Prime Lite Edition software and targeting the Intel Altera Cyclone FPGA technology.

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Industrial estate firms have important roles in national development through the industrial development. In the course of their life, industrial estate firms ought to follow the firm life cycle pattern. The common pattern shows that the period, in which the firm experiences profit increase, will be directly followed by consistent profit and profit decrease. Should the strategy realization are not precisely implemented during each stage, especially the decreases period, it will be difficult for the firm to survive.
A strategy that can be implemented by industrial estate firms in order to be sustained is to manage Product-Service System (PSS). It is a well-known concept in creating opportunities for a new business model in which firms can offer bundles of products and services to their customers instead of merely tangible products. PSS can be distinguished into three types of categorization consist of different compositions of products and services and different business activities. Each type of PSS and business activities can be applied at the stages of the firm life cycle in accordance with its condition to maintain the company’s sustainability at every stage of its life cycle. In this article, we construct a conceptual framework to help Indonesian industrial estate firm to be alert on the need to focuses on providing services instead of only selling products. This article also shows that different PSS management would be needed in particular stages of firm life cycle. Applying suitable PSS and business activities based on PSS in each stage of the firm life cycle can lead to gaining a competitive advantage and sustainable industrial estate firms. This article’s novelty is to combine the PSS concept concerning each stage of firm life cycle at industrial estate firms.

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With the increasing need for effective storage management due to ever-growing content-generation over the Internet, Distributed Storage Systems (DSS) has arisen as a valuable tool. Although DSS has considerably improved in the past years, it still leverages legacy techniques in its networking. To cope with the demanding requirements, Software Defined Networking (SDN) has revolutionized the way we manage networks and can significantly help in improving DSS network management. In this paper, we propose an SDN-based network control method that is capable of handling DSS network management and improving its performance. This paper presents the design, implementation, and evaluation using an emulated environment of a typical Data Center Network (DCN) deployment. The experiment results show that by applying the proposed method, DSS can increase the performance and service resilience compared to existing solutions.

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Fifth generation of mobile networks (5G) comes to cope with the context of the new mobile telecommunications era. Edge computing is a new collaborative technology under standardization utilizing end-user devices or near-end-user edge devices to operate processing, communication, or control operations. The achievement of efficient error control is a very critical aspect in the successful development of 5G Next Generation Mobile Networks (NGMN). Especially utilizing Forward Error Correction (FEC) codes on the application level is an efficient approach on improving error control on NGMN since it is adopted in several mobile multicast standards. FEC is a feedback free error control technique where redundant data are introduced with the source data to enable the recipient recovering from packet losses with an interesting approach on applying AL-FEC error protection introduces deterministic or randomized online algorithms. Based on this we present a novel online scheme on applying AL-FEC application on the context of the AL-FEC policy online problem. In this work we present an online algorithm based on feedback received from mobile end-users which adapts the introduced protection based on this. Another aspect of this work is the utilization of AL-FEC protection on the edge level based on RaptorQ FEC codes.

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Robot contests are suitable for bachelor courses to gather hands-on experience in engineering diciplines such as mechanics, electronics and embedded software. After summarizing a concept of a robot contest for distance education (robot sumo) as presented in earlier publications this paper describes the implementation and first results of this concept. The contest is seperated into different tasks such as line detection and opponent location. As an example for transferring theoretical knowledge into a working prototype a simple sensor design for robot sumo is described and tested. Additionally a first student concept of a self designed robot is presented.

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In this study, torrefaction pretreatment on palm kernel shell (PKS) was investigated using fixed bed reactor. The PKS was torrefied at the temperatures of 210, 230, 250, 270 and 290 °C. The characteristics between untreated and torrefied PKS were compared. The results showed that, the mass and energy yield lessened, while the calorific value augmented with the increasing torrefaction temperature. Furthermore, with the rise of temperature, the oxygen composition, O/C ratio, oxygenated compounds and volatile matter of torrefied PKS decreased, but, the carbon and fixed carbon content increased. The composition of carbon in torrefied PKS was toward coal where equivalent calorific values was achieved. The gasification of torrefied PKS enhanced the product yield which produced higher gas, lower tar and char yield than the gasification of untreated PKS. Gasification of torrefied PKS increased the gas yield by 16.9 % than the untreated PKS. The tar and char yield of torrefied PKS decreased by 19.4 % and 25.9 %, respectively than the untreated PKS. Therefore, the torrefied PKS, by which their physical and chemical properties have been improved through torrefaction pretreatment is more suitable to be used in gasification and co-gasification as their influences are significant than the untreated PKS.

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Digitalization changes the way people work to a considerable extent. It alters business models and process organizations of whole industries. The ensuing market dynamics and faster innovation cycles cause an increase in complexity. In this article, the interconnection of digitalization and complexity in work systems is analyzed. For this purpose, a framework for comparing relevant complexity definitions is developed. Moreover, complexity drivers in digitalized labor systems in six different organizational dimensions (process organization, organizational structure, technology, working conditions, product and personnel) are explored. 23 experts from the academic and industrial sector were interviewed using semi-structured interviews. The results of a qualitative content analysis show that the consideration of complexity and digitalization has extensive impact what becomes evident in interdependent relations amongst the organizational dimensions. Furthermore, complexity drivers in digitalized work systems are determined as a result of the analysis procedure. Finally, the implications of the expert interviews for cooperative forms of work are discussed. The concept of a “task complexity mountain range” is presented to explain the effect of task complexity on performance and motivation in the context of work groups.

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Network motif analysis has several applications in many different fields such as biological study and social network modeling, yet motif detection tools are still limited by the intensive computation. Currently, there are two categories for network motif detection method: network-centric and motif-centric approach. While most network-centric algorithms excel in enumerating all potential motifs of a given size, the runtime is infeasible for larger size of motifs. Researchers who are interested in larger motifs and have established a set of potential motif patterns could utilize motif-centric tools to check whether such patterns are truly network motifs by mapping them to the target network and counting their frequency. In the paper, we present NemoMap (Network Motif Mapping algorithm) which is an improvement of the motif-centric algorithm, GK (by Grochow and Kellis) and MODA (Motif Detection Algorithm). Experimental results on three different protein-protein interaction networks show that NemoMap is more efficient in mapping complex motif patterns, while GK and MODA is much faster in analyzing simpler patterns with fewer edges. We also compare the performance of NemoMap and ParaMODA (introduced previously to improve MODA), and the result shows that NemoMap yields better runtime due to the implementation of Grochow-Kellis’ symmetry-breaking technique and the better node selection process.

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Conceptually, flood modeling can be done based on process and data availability (data driven model). The implementation of flood mitigation requires accurate data, which can be obtained from high resolution satellite imagery, vertical air photos and high-resolution Digital Elevation Model (DEM). Modeling using high accuracy DEM data can detect landforms in more detail. The objective of this research was to develop flood risk assessment using Topographic Wetness Index (TWI) method. Areas with high humidity indicated that the area was a flood hazard area. Flood spatial risk analysis used hazard mapping methods, vulnerability mapping and capacity assessment. The results obtained indicated that 8,838 ha area was in high flood hazard, while 9,780 ha was in moderate flood hazard and 7,652 ha was in low flood hazard. When incorporating the risk factors, 12,898 ha areas was in high risk, 9,357 ha was in moderate risk, and 4,015 ha was in low risk.

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An experimental investigation of reconfigurable ultra-wideband (UWB) modified octagonal microstrip monopole patch antenna with switchable and tunable band-notched characteristic is presented in this paper. Notched-bands are obtained by connecting or disconnecting the open shunt stubs. The switching is achieved by using PIN diodes. Additionally, more notch-bands are created by adding additional stubs to the original design and tuning is achieved by employing a varactor diode in the circuit. The proposed antenna has more freedom to suppress the interfering bands because of tuning nature. The proposed designs are simulated, fabricated and then tested. The proposed structures are simple in geometry and useful to operate in the rich electromagnetic scattering environment.

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Recently, several studies focus on a GaN material system that exhibits a significant probability of use in power devices including wide-gap semiconductors. However, the GaN-HEMT is also a structure that easily leads to crystal defects in AlGaN and i-GaN heterojunction. The aim of the study involved investigating the cause of the current collapse in GaN-HEMT after device construction. A GaN-HEMT with a field plate structure was subject to an environmental temperature change from 300 K to 400 K. A pulse voltage was applied to the gate electrode, and the transient response characteristic of the drain current was analyzed. Given the application of the pulse voltage on the gate electrode, charging and discharging of 2 DEG carriers was repeated with respect to crystal defects near the gate electrode. The charge / discharge reduction was observed via a sampling oscilloscope as a transient response. The transient response exhibited an evident dependence on temperature change. The dependence indicated a time constant change, and thus it was possible to calculate the activation energy of crystal defects trapping carriers. The results suggested that the crystal defect evaluation of GaN-HEMT was possible via transient response analysis of 2DEG carrier by using the proposed method.

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In this research paper, the development and evaluating of a smart controller for electrical powered wheelchairs are presented. The controller aimed to assist quadriplegic, paralyzed, and handicap patients who cannot use their hands to drive an electrical wheelchair by using a joystick controller. Two sub control units have been combined in one hybrid system to create the current version which are voice, and head tilt controllers. They are activated and operated in parallel at the same time to allow the wheelchair user to choose the preferred control method. The voice controller is activated by the user’s voice command to control the wheelchair instead of a joystick. The head tilt controller uses the user’s head motions to create control commands instead of the joystick controller. The head tilt controller design is based on using two embedded MEMS orientation detection modules as input measurement units. The system uses a modern low power consumption microcontroller to analyze the received information and data from inputs and creating the prompt control commands and send it to the wheelchair motors driver as an output unit.

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At present, the wireless charging and data transmission systems developed by major car manufacturers rely heavily on additional transmission equipment. This paper presents a newly developed wireless power and bidirectional data transmission scheme without adding any radio frequency (RF) devices. The battery charge status and vehicle related information can be transmitted bidirectionally in the wireless manner by the compensation capacitor between two isolated units. Based on the function, this system can quickly inform the user when an emergency event occurs during power transmission. An inverter is used on the primary side of the system, and the generated AC power is transmitted to the load at the secondary side through mutual inductance; the secondary side of the system adjusts the load current to transmit data back. The primary side uses the zero voltage switching (ZVS) method to receive data, and the trimming of the current curve to transmit commands; the secondary side of the system receives commands and uses carrier cycles for decoding. Experimental verification shows applicability of the proposed system.

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Cloud Computing is considered nowadays an attractive solution to serve the Big Data storage, processing, and analytics needs. Given the high complexity of Big Data workflows and their contingent requirements, a single cloud provider might not be able alone to satisfy these needs. A multitude of cloud providers that offer myriad of cloud services and resources can be selected. However, such selection is not straightforward since it has to deal with the scaling of Big Data requirements, and the dynamic cloud resources fluctuation. This work proposes a novel cloud service selection approach which evaluates Big Data requirements, matches them in real time to most suitable cloud services, after which suggests the best matching services satisfying various Big Data processing requests. Our proposed selection scheme is performed throughout three phases: 1) capture Big Data workflow requirements using a Big Data task profile and map these to a set of QoS attributes, and prioritize cloud service providers (CSPs) that best fulfil these requirements, 2) rely on the pool of selected providers by phase 1 to then choose the suitable cloud services from a single provider to satisfy the Big Data task requirements, and 3) implement multiple providers selection to better satisfy requirements of Big Data workflow composed of multiples tasks. To cope with the multi-criteria selection problem, we extended the Analytic Hierarchy Process (AHP) to better provide more accurate rankings. We develop a set of experimental scenarios to evaluate our 3-phase selection schemes while verifying key properties such as scalability and selection accuracy. We also compared our selection approach to well-known selection schemes in the literature. The obtained results demonstrate that our approach perform very well compared to the other approaches and efficiently select the most suitable cloud services that guarantee Big Data tasks and workflow QoS requirements.

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The monitoring of environmental parameters is a critical topic in occupational medicine. With advances in technology, during the recent years, the wearable sensors are widely used to evaluate the ambient parameters. In this paper a compact, small, light-weighted, and configurable sensor node in three physical layers for prolonged chemical ambient monitoring is described. The device is designed and demonstrated by using MLMS (Multi-Layer Multi-Sensor) concept. The device application is not only limited to hazardous gas (CO & NO2) monitoring but physical ambient elements (air pressure, air humidity and temperature) and motion tracking are tracked, too. The device consist of integrated (ambient physical and motion tracking) and add-on sensors (hazardous gases) and enables centralized data processing. The integrated sensors are distributed on the middle physical layer on both sides (host platform). The host platform also includes micro controller and antenna for data transmission to the smartphone. The gas sensor along with the gas sensor driver board form the gas sensor node which for easy gas exposure is located at the top of the device and stuck at the top of host platform. The gas sensor driver is designed universally to be compatible with 3-lead gas sensors with low power consumption. This layer is replaceable and can be substituted with one more target gas (e.g. NO2 instead of CO). The sensor configuration and initialization is performed via sending commands from the smartphone to the device by the wearer. In addition, the device is equipped with the notification system (beeper and vibrating motor) for early user warning in an abnormal detected. The notification driver layer is located at the bottom of the proposed device. All physical layers are stuck on top of each other appropriately through a board to board connector. The collected data from integrated and add-on sensors are transmitted in various time interval to the smartphone for screening and logging. Filter design and calibration process are also briefly described in this work.

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This paper is an extension of work originally presented in the 19th International Conference on Computer Supported Education and Information Technology. This paper identifies motivational factors that ensure the initiation and effective implementation of mathematical activity. These factors are in subordination to each other, forming a hierarchical dependence. At the heart of this hierarchy is the practical need for solving problems from real-life practice. In order to actualize this need, a number of approaches are proposed, each of which manifests itself differently at various stages of teaching mathematics at school. At the first stage, the task material is intended, in the main, only to stimulate the consideration of certain mathematical problems. It also initiates to some extent the activity procedures inherent in reality through observation and experiment. At the same time, the emphasis in teaching is on solving problems of calculation, measurement, tracing, construction, cutting, etc. At the next stage, the main emphasis is on the possibility of using a mathematical tools in the study of related disciplines. It introduces elements of mathematical modelling of real-life states and processes, which can be carried out on the basis of solving various textual problems. At the final stage, the dominant focus the vocational guidance function of practice, the mathematical knowledge for successful implementation of future professional activity. Such awareness can be provided, in particular, with the help of pseudo-real applications from the relevant professional field. In this paper the authors also provides examples supporting each stage.

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Data deduplication technology is used to improve the spatial efficiency of cloud storage. This technology is used for storing data on a cloud and omitting data uploading if the data are already present. However, various security threats may occur during the deduplication process. These security threats include poison attacks and user identity exposure through ownership. In addition, in an environment in which ownership changes in real time, there is a problem in renewing ownership information that has already been issued. Therefore, various studies have been conducted to solve these problems. In this study, a poison attack, real-time ownership management, and ownership anonymization are provided through MLE and dynamic ownership management.

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Language skill is a rule-like operation, based on generalized connections. The main property of language skills is awareness. They are formed with conscious mastery of the language means of communication (phonetic, lexical and grammatical). Language acquisition is a complex process which includes a large number of different parameters. Therefore, the study and improvement of language learning and teaching require creative collaboration between experts from different domains. We propose to transfer knowledge about the structure of the language from the verbal to the visual form, thereby creating the opportunity to use them as an indicative basis for planning, managing, controlling and correcting the training of primary language skills both by the teacher and by the student himself. The proposed method allows to develop the ability to organize sentences to convey meaning by means of Visual Models and to describe the sequential steps to choose the most effective ways for working with audio to improve listening skills. The use of visual tools for the analysis of abstract systems and theories makes it possible to discover new patterns and simplify their understanding. The aim of our study is to put principles for building a new effective system using new methods for acquiring language skills.

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DC power is apparently attempting to breach into the power system – the system which it once lost to the AC power paradigm. DC is already present in the generation, transmission and utilization sides of the system, leaving distribution as the only area where it has not shown any significant presence – this may be regarded as still in research phase. The current work aims to investigate efficiency enhancement of a DC power distribution system for residential buildings via the use of a modular approach for the DC/DC solid state transformers (SSTs). Specifically, DC distribution system shows reduced efficiency at light loads because of the lowering in efficiency of its constituent SSTs. Via the use of modular approach, a single SST is composed of smaller modules which may be turned on or off individually, for the purpose of improving the converter efficiency. In this paper, we utilize two methods of load division upon SST converter modules. As the efficiency of an SST is increased, the overall efficiency of the system increases because bulk power passes through these transformers.

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The contents of this article consist of an extension original work presented at the 4th International Conference on Control, Automation and Robotics (ICCAR 2018) and aims to develop the systematic model of an effective triggering system of production Kanban size towards Just-In-Time (JIT) production system. The developed model was introduced based on the philosophy of continuous improvement program by selection of case study industry in automotive manufacturing floor and it has been carried out through the development of an effective material Kanban size model using triggering system towards achieving a JIT production system. This developed model was analysed and verified by implementing through three phases which are the investigation of the Kanban size, analysis the production idling time and analysis of the time management for material replenishment process. The implementation was carried out to ensure the main objectives of the developed model in minimizing the waste of waiting through effective Kanban size for production system will be achieved by focusing on the communication between material incoming warehouse and production floor. The significant results were obtained during the verification process where the dual material feeders were identified for every process in the production floor to ensure the effectiveness of the production system. Throughout the implementation of the developed model, the production losses time caused by miscommunication between the production floor and warehouse had been identified and it was successfully minimized and the waiting time was reduced by 36.64%.

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The effect of rotation and translation of an electromagnetic (EM) scatterer upon the scattered field is studied for the case of TM polarization. The Franklin and Lorenz transformations are used to analyze the phase and the magnitude of the backscattered field during the movement of the scatterer. Also, a new model is presented that simulates rotation and translation using static data. This stationary data is generated using FEKO. These simulation results of the exact analytical solution are compared with the results of the proposed model using static data. Finally, a comparison between the phase and magnitude of the backscattered field of a scatterer undergoing both translation and rotation for the two different modes (TM and TE) is presented.

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Nowadays, many companies and organizations use various encryption techniques to secure their private data before send it through the unsecure network. However, many attackers try to store private data by using various attacks. Most of the organizations use password based encryption algorithm (PBE) to protect their private data. However, the existing PBE methods are vulnerable in brute-force attacks because of user-generated weak or repeated passwords. The problem of weak password based encryption algorithm prompted us to introduce honey encryption (HE). Honey encryption and honeywords help to minimize this vulnerability of password based encryption algorithm and they can prevent the text based messages from brute face attacks in order to make messaging communication more secure and efficient. However, the conventional HE has message space limitation in Distribution Transforming Encoder (DTE) process and storage overhead problem in honeywords generation process. Therefore, our proposed honey encryption algorithm uses discrete distribution function in DTE process to solve message space limitation instead of using cumulative distribution function. Storage overhead and typo safety problem can overcome by using our proposed honeywords generation method in honey encryption algorithm. Furthermore, we also propose hashing and salting algorithm for securing password and key expansion. In this paper, we describe the case studies as a calculation of DTE and hashing and salting algorithm. And then, we design and implement the improved honey encryption mechanisms and honeywords generation algorithm in message transmission process. Finally, we show the comparison results using mathematical model in DTE process and security analysis of our system.

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The proposed solution is intended for virtual universities wishing to improve their distance learning platforms. These enhancements include the instructional content model, how to deliver courses to students, and how to evaluate students’ knowledge. With this solution that improves the functionality of Moodle, virtual classes now include new features such as the transfer of heavy files, control of remote screens, video, audio, chat without degradation of the quality of service. The solution will be particularly useful for language teachers who have difficulty in assessing the speaking skills of their students. Indeed, the platform with its audio / video features, integrates a tool to assess the knowledge and improve the speaking skills of students. Teachers of technical subjects such as programming or computer networks will also be able to use this solution to improve the way they deliver courses, including making it easier for students to do practical work.
On the technical side, the solution is based on the MSRP-RELAY, MSRP and Websocket protocols. It is therefore compatible with smartphones, tablets and computers. Tests were conducted with teachers of languages and STEM subjects to evaluate their students in direct synchronous encounters without necessarily going through multiple choice questions.

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Intelligent Transportation Systems (ITS) applications can take big advantage of Context Awareness approaches. Parameters such as user mobility, passengers comfort reaction and pollution emission levels (CO2) can enrich such applications during the decision making phase. Moreover, the expanding in ITS services offers great opportunities for travelers to find the best route to reach their destinations with the lowest or fair costs. It can offer a selecting methodology for optimal route that adapted with some processing parameters like CO2 level, ticket cost, waiting or connection times and the overall traveling time plus the comfortability reaction for each means of transportation) in real time environment using Machine Learning (ML) tools like Q-Learning or SVM: Support Vector Machines. This paper aims at conducting a comparison study for green ITS routes (i.e. the lowest CO2 levels). The study compares between Q-Learning and SVM techniques for identifying different variety of routes between two stops as ranked routes from best to lowest based on some traces gathered from some known transportation traces. Reinforcement Q-Learning is applied to validate the first phase in our approach to recommend the best means and SVM is used to validate the prediction phase about the best route among different routes built based on three means of transportation (metro, train and bus).

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This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner.

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This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner.

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A sequential pre-treatment of demineralization and torrefaction, was carried out on palm empty fruit bunches (EFB). EFB and demineralized EFB (DEFB) were torrefied in a vertical tubular reactor in the temperature range of 200 to 280 °C for 30 mins under nitrogen (flow rate:100 mL/min. The pretreated samples were characterized using proximate and ultimate analyses, fuel properties, and Fourier-transform infrared (FTIR) spectroscopy techniques. The thermal and kinetic study on the torrefied samples were carried out using thermogravimetric analysis. The results showed that sequential pre-treatment enhances the properties of solid EFB by increasing the carbon content and reducing the oxygen content with increasing the calorific value. Fuel properties of torrefied samples showed the mass and energy yield decreased, with an increase in energy density. In addition, the FTIR spectra showed the decomposition of hemicellulose occurring for torrefied samples as evidenced by the disappearance of the vibrational features belonging to hydroxyl and carbonyl groups. The kinetic study carried out using Coats-Redfern method on torrefied samples suggested that the activation energy can be transferred by the sequential pre-treatment, indicating that the abundant energy it has can be converted into bio oil of high quality. Apparently, torrefied samples bear high potential to be used as biofuel feedstock when exposed to further thermal decomposition and pyrolysis processes.

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This paper presents the input-output feedback linearization and decoupling algorithm for control of nonlinear Multi-input Multi-output MIMO systems. The studied analysis was motivated through its application to a robot manipulator with six degrees of freedom. The nonlinear MIMO system was transformed into six independent single-input single-output SISO linear local systems. We added PD linear controller to each subsystem for purposes of stabilization and tracking reference trajectories, the obtained results in different simulations shown that this technique has been successfully implemented.

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The physically-based environmental model is a crucial tool used in many scientific inquiries. With physical modeling, different models are used to simulate real world phenomena and most environmental scientists use their own devices to execute the models. A complex simulation can be time-consuming with limited computing power. Also, sharing a scientific model with other researchers can difficult, which means the same model is rebuilt multiple times for similar problems. A web-service-based framework to expose models as services is proposed in this paper to address these problems. The main functions of the framework include model executions in cloud environments, NetCDF file format transmission, model resource management with various web services. As proof of concept, a prototype is introduced,implemented and compared against existing similar software packages. Through a feature comparison with equivalent software, we demonstrate that the Virtual Watershed System (VWS) provides superior customization through its APIs. We also indicate that the VWS uniquely provides friendly, usable UIs enabling researchers to execute models.

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A design of elliptical printed dipole antenna based on neural network approach is presented in this paper for recent WiMAX, Bluetooth, WLAN, LTE, and future 5G applications. The dipole patch is printed on top of substrate which has relative permittivity of 2.2 and 0.787mm of thickness. The elliptical dipole antenna is single band and the operational frequency is controlled by the major and minor axes of the ellipse. Neural network composed of one input layer, one hidden layer, and one output layer is implemented using simulated dataset of 24 samples collected using the electromagnetic simulator CST Microwave Studio. The neural network is trained using Levenberg-Marquardt algorithm and we divided the data set into 90% for training, 5% for testing, and 5% for validation. The error, described by the difference between the target data and expected output, is less than 0.005. The neural network model is a promising candidate to implement the design of elliptical dipole antenna to eliminate the simulation time taken by the EM simulator and reduce the cost of using large computing cluster.

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Nd:YAG laser has shown some potential to be used in dental practice replacing the conventional method. In particular, it can be used to modify the tooth surface by the ablation process. The laser provides an ability to accurately deliver a significant amount of energy into a confined region. Thus, alteration of the sample surface and composition may occur during the process. Additionally, the use of a laser in ablation procedure is also associated with heat generation and potential thermal injury that may be experienced by a patient. The wet condition of the samples is expected to reduce the thermal effect. In this paper, the changes of enamel surface and elemental composition following laser irradiation of Nd:YAG laser are discussed. The teeth samples were irradiated at 1.5 Hz of pulse rate, 100 ms of pulse width, and range of fluences of 80-120 J/cm2. Field Emission Scanning Microscopy (FESEM) and Energy Dispersive X-ray (EDX) were used to analyze the morphology and composition of the teeth samples. Samples were compared before and after laser irradiation. The percentages of carbon increased after laser irradiation, while oxygen decreased for most of the samples. The morphologies of the samples were varied with a more pronounced effect on the sample surface at higher fluence. In addition, the effect of wet sample condition is also investigated and discussed. It is demonstrated that the ablation in wet condition produced less damage to the enamel surface compared to dry sample. However, no remarkable difference between the elemental composition of wet and dry samples.

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Transport planners and engineers frequently face the challenge to determine the best design for a specific junction. Many road design manuals provide guidelines for the design and evaluation of different junction alternatives, however these mostly refer to specialized software in which the performances of design alternatives can be modelled. In the first stage of the design process, such assessments of many alternatives are undesirable due to time and budget constraints. There is a need for quick design rules which need limited input data. Although some of these rules exist, their usability is limited due to inconsistencies in rules and non-transparency in combination with objectives. In this paper, we present an approach by which consistent and transparent junction design rules can be determined. The resulting rules can be used to predict a set of viable junction design alternatives for the first stage of the junction design assessment process. The predicted set is in fact the Pareto optimal set of solutions for multiple objectives, e.g. regarding operational, safety and/or environmental impact. The Pareto optimal set of solutions always contains the best solution, whatever set of weights is used for different objectives in a later stage of the assessment process, thus handling multiple objectives in a straightforward manner. The rules are derived from a dataset by using decision tree data mining techniques. For this, a large dataset is first generated, using performance models, with Pareto optimal sets of junction design alternatives for a large amount of, randomly generated, traffic volumes. The approach is applied and evaluated on cases for two different countries. Results show that for over 90% of the situations the Pareto optimal set can be predicted by the new rules, whereas existing rules hardly reach 33%. The new rules provide junction design alternatives with a better performance.Nd:YAG laser has shown some potential to be used in dental practice replacing the conventional method. In particular, it can be used to modify the tooth surface by the ablation process. The laser provides an ability to accurately deliver a significant amount of energy into a confined region. Thus, alteration of the sample surface and composition may occur during the process. Additionally, the use of a laser in ablation procedure is also associated with heat generation and potential thermal injury that may be experienced by a patient. The wet condition of the samples is expected to reduce the thermal effect. In this paper, the changes of enamel surface and elemental composition following laser irradiation of Nd:YAG laser are discussed. The teeth samples were irradiated at 1.5 Hz of pulse rate, 100 ms of pulse width, and range of fluences of 80-120 J/cm2. Field Emission Scanning Microscopy (FESEM) and Energy Dispersive X-ray (EDX) were used to analyze the morphology and composition of the teeth samples. Samples were compared before and after laser irradiation. The percentages of carbon increased after laser irradiation, while oxygen decreased for most of the samples. The morphologies of the samples were varied with a more pronounced effect on the sample surface at higher fluence. In addition, the effect of wet sample condition is also investigated and discussed. It is demonstrated that the ablation in wet condition produced less damage to the enamel surface compared to dry sample. However, no remarkable difference between the elemental composition of wet and dry samples.

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This paper is the extension of the work originally presented in 2nd International Conference on Man and Machine Interfacing (MAMI 2017) and the paper of Yue et al. The present study aims at observing the effect of various parameters on temperature distribution profiles at various environmental temperature, tissue thermal conductivities, metabolic rates, blood perfusion rates, and heat transfer coefficients. The analytic solution of Pennes’ bioheat equation in the steady-state case is obtained by using the Modified Bessel’s equation incorporating the effect of sweating and non-sweating state of the body. From the study, it was observed that the variation of atmospheric temperature and heat transfer coefficients have a significant effect for the temperature distribution in the body towards the skin surface.

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In most industrial high-power applications, the brushless DC (BLDC) motor is connected to multi-level inverters specially the three-level NPC inverters (B12). Torque ripple is a critical issue in BLDC motor drives. Accordingly, minimizing the ripple produced in the torque is necessary to enhance the BLDC motor drive performances. This paper aims to develop two direct torque control (DTC) schemes. To evaluate the proposed strategies performances, three DTC schemes, namely DTC- 1, DTC- 2, and DTC-3, are treated. The DTC-1 scheme is inspired from the Takahashi’s strategy proposed in induction motor drives. This scheme is penalized by the high torque dips. In view of that, the DTC-2 is proposed to address the DTC-1 limitations. Simulation evaluation proves that difference between the slopes of the incoming and outgoing phases currents procreates commutation torque dips penalizing the DTC-2 scheme. This difference is more serious at high speed. To overcome the DTC-2 strategy drawbacks, a DTC-3 scheme, based on the compensation of this difference, is proposed. Taking into account the increase of this difference with the increase of the rotation speed, the vector selection table design of this strategy is synthesized to minimize the torque ripple as far as possible.

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Usually, after properly biasing an analog circuit we need to linearize its transistors in order to linearize the entire circuit for performance analysis and design. This linearization is done through modeling of devices, which is becoming more and more complicated as the electronic technology advances. This is not of course a major issue for circuit simulators that use the latest device models and fast computers to deal with them. However, it makes it difficult when manual computation and understanding of the circuit behavior is in question. This is particularly important in teaching and training electrical engineering students.
What the proposed method offers is to simplify the case in two steps. First, adopt a most recent model with nominal parameter values for the transistors and run the circuit. Second, compare the circuit responses with those obtained through simulators with the most advanced device models. Next, with the help of one or more nullors try to modify the manually selected model parameters so that the two responses become close enough together. Several examples demonstrate the way the technique works and how close we can get to the simulated responses.

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This research is part of a study concerned with building an integrated framework for development of sustainable urban planning in Iraqi provinces. Seven Iraqi middle provinces are covered in this study. The aim of the study is to determine, measure and evaluate economic and social sustainability in the Iraqi middle provinces.
The stages to achieve this research were based on establishing a two levels hierarchy framework (a level for categories and a level for indicators). In this study, the Economic dimension comprised of 6 categories and 18 indicators and the Social dimension comprised of 8 categories and 41 indicators. Through using the Multi Criteria Analysis method and the Analytic Hierarchy Process, the Sustainability Index has been determined and used to measure and evaluate the economic and social sustainability in the Iraqi middle provinces

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Flow measurements are important for numerous industries as it directly relates to proper equipment functioning and product quality. This work demonstrates a novel thermal energy based compact flow measuring sensor. The sensor injects thermal energy into the core which is then extracted by the moving flow through the center of the core. Temperature changes produced by thermal energy exchange is evaluated over time and then used to calibrate and measure flows. The sensor can detect extremely low energy changes therefore measure relatively low flow rates as well. Being minimally intrusive, it does not disturb the flow eliminating any additional resistance commonly introduced by intrusive type sensors. It is made from inexpensive components and is easy to assemble therefore making it affordable. The simple geometry allows for it to be easily incorporated into compact systems and can be used in miniature devices to measure low flows. We declare this minimally intrusive only due to breaking the flow path with the sensor element otherwise it is non-intrusive.

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In this paper a chemical substance reporting system is presented to enable industry to assess the impacts of increasing chemical regulations. Chemical regulations impose the need to monitor, control and restrict the use of hazardous substances. As chemical substances become more regulated, industry requires mechanisms to identify potential business continuity risks, posed by increased regulation. A chemical reporting system is one such mechanism that identifies chemical substances used on their (i) own, (ii) in mixtures, (iii) in materials, (iv) in internally defined articles (products) and (v) from articles (products) procured from the supply chain. The chemical reporting system will contrast product related information against chemical regulation substance lists, identifying the applicable reporting obligations and potential supply chain risks.

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Plastic is highly polluting nowadays and is found in many disposable elements used for people. Plastic materials are disposed inadequately and large quantities of them become pollution in the ground, water, and air, affecting plants and animals. Most of the plastic is Polyethylene Terephthalate (PET) which is used in many activities. Resistance, support capacity, and flexibility are some characteristics that make PET useful in road engineering. This research shows a numerical simulation of three kinds of structures for pavements in PET. The simulation uses 3D models that were analyzed in the software SAP2000. The material characteristics of the recycling PET were taken from industries and analyzed in the laboratory. The model used in the simulation was composed of vertical loads for traffic type A. Loads were applied in the border, 0.75 meters and 1.5 meters from the border. Three types of structures were analyzed with three, four and five supports respectively. Structure type 2 with four supports displayed the best results regarding vertical displacements in all the elements. Structure type 2 was simulated with six different thicknesses (0.06, 0.08, 0.10, 0.12, 0.14 and 0.16 meters). Optimization of this material and the resistance of the structure showed the 0.10 meters thickness as the best due to its admissible vertical displacements and required the lowest quantity of material for producing it.

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Mental health disorder is becoming a social problem, and there is a need for technology that can easily check for states of stress and depression as a countermeasure. Conventional methods of diagnostic support and screening include self-administered psychological tests and use of biomarkers. However, there are problems such as burden on subjects, examination costs, dedicated reagents and equipment required for examinations, and reporting bias. On the other hand, voice-based evaluations are advantageous in terms of providing diagnostic support for physicians. They are non-invasive, do not require special and exclusive equipment, and can therefore be easily conducted remotely. We are pursuing the research and development of the Mind Monitoring System (MIMOSYS), which estimates the state of mental health from voice. Recorded audio is often compressed for efficient storage and transmission. However, there are concerns regarding the effects of deterioration of sound quality on analysis by MIMOSYS. Therefore, this study aims to verify the influence of the deterioration of voice quality due to coding on MIMOSYS analysis. As a verification experiment, coding was applied on the recording of 704 subjects reading 17 fixed phrases, assuming compression for transmission and storage. Then, the results of MIMOSYS analysis before and after encoding were compared. A strong correlation was observed before and after encoding, suggesting that MIMOSYS analysis is also valid for voice to which coding was applied.

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Identity and Access Management (IAM) and Single Sign on (SSO) are two security concepts that are related to each other. IAM governs the user access in an organization whereas SSO facilitates the user by authenticating to one centralized application and not having to re-authenticate when trying to access other applications. This paper addresses the different benefits that an IAM and SSO tool can provide to reduce the security risk within an organization. Since, authentication and authorization are one of the major concerns in the cyber security; this paper analyzes common problems that are faced during authentication and authorization. We have also analyzed prior researches that have been done in the IAM and SSO space along with conducting a survey to understand the different issues and benefits of IAM and SSO. From the survey that has been conducted, we have addressed different issues when implementing IAM and SSO solutions along with understanding the architecture, in which these solutions have been deployed into. The surveys conducted have been compared with prior researches done in IAM and SSO space to understand the benefits that the solution provides. The results from the survey have been analyzed to provide the best practices when implementing IAM and SSO solutions along with the benefits provided by the solution.

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Predicting the impact of suicide on incidental witnesses at an early stage helps to avert the possible side effect. When suicide is committed in public, incidental observers are left to grapple with it. In many cases, these incidental witnesses tend to experience the emotional side effect with time. In this study, we employed a Machine learning algorithms to predict the impact of suicide and suicidal attempt on incidental witnesses. This prediction was based on the accounts of suicide given by selected participants who have witnessed the act. The accounts, which was pre-processed into a corpus, were manually annotated with predefined emotion categories. While sadness emerged as the most salient emotional impact on the witnesses, fear was found as the lowest of the emotional impact on the witnesses. However, the machine learning prediction yielded highest in predicting depression with insignificant variations in the other emotional categories. This nonetheless shows that people who have witnessed suicide or suicidal attempts are inherently affected by some form of emotions that may require urgent attention to alleviate. By evaluating the performance of the Machine learning algorithms, the Support Vector Machine was superior, in terms its prediction, then the Multinomial Naïve Bayes algorithm. The outcome of the study contributes to the pool of research that sought to advocate the use of Machine Learning for predicting social phenomenon.

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