bluuebunny/crossref_metadata_2025 huggingface dataset:
An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding
Authors: Hore Sirshendu , Chakraborty Souvik , Chatterjee Sankhadeep , Dey Nilanjan , S. Ashour Amira , Chung Le Van , Le Dac-Nhuong
Published: October 2019

Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.
Face Recognition using Multi Region Prominent LBP Representation
Authors: Konda Srinivasa Reddy , Kumar V Vijaya , Krishna Venkata
Published: October 2019

Various face recognition methods are derived using local features among them the Local Binary Pattern (LBP) approach is very famous. The histogram techniques based on LBP is a complex task. Later Uniform Local Binary Pattern (ULBP) is derived on LBP, based on the bitwise transitions and ULBP’s are treated as the fundamental property of texture. The ULBP approach treated all Non-Uniform Local Binary Patterns’ (NULBP) into one miscellaneous label. Recently we have derived Prominent LBP (PLBP), Maximum PLBP (MPLBP) and Smallest PLBP (SPLBP). The PLBP consists of the majority of the ULBP’s and some of the NULBP’s. The basic disadvantage of these various variants of LBP’s  is they are basically local approaches and completely failed in representing features derived from large regions or macrostructures, which are very much essential for faces. This paper derives PLBP’s on the large region. The rectangular region of this paper is assumed with a size of multiples of three and PLBPs are evaluated on dividing each region into multiple regions. The proposed Multi Region-PLBP (MR-PLBP) approach is tested on three facial databases namely Yale, Indian and AT&T ORL. The experimental results show the proposed approach significantly outperforms the other LBP based face recognition methods.
Effective Computer-Assisted Automatic Cervical Vertebrae Extraction with Rehabilitative Ultrasound Imaging by using K-means Clustering
Authors: Lee Hae-Jung , Song Doo Heon , Kim Kwang Baek
Published: October 2019

Neck pain is one of most common musculoskeletal condition resulting in significant clinical, social and economic costs. Muscles around cervical spine including deep neck flexors play a key role to support and control its stability, thus monitoring such muscles near cervical vertebrae is important. In this paper, we propose a fully automated computer assisted method to detect cervical vertebrae with K-means pixel clustering from ultrasonography. The method also applies a series of image processing algorithms to remove unnecessary organs and noises in the process. The experiment verifies that our approach is consistent with human medical experts’ decision to locate key measuring point for muscle analysis and successful in detecting cervical vertebrae accurately – successful in 48 out of 50 test cases (96%).
Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera
Authors: Winarno Edy , Harjoko Agus , Arymurthy Aniati Murni , Winarko Edi
Published: October 2019

The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux).
Evaluation of Various Digital Controllers for Forward Converter with Active Clamp
Authors: Janga Ravindra , Malaji Sushama
Published: October 2019

This research paper presents the design issues of digital Controllers for Forward Converter with Active Clamp circuit (ACFC). Brief review of Working Principle and mathematical modelling of the converter is first given. The importance of Digital Controllers for forward converter and design procedure is described in detail. And the validity of designed controllers and achievement of desired compensation is verified by the results of implemented digital controllers to Active clamp forward Converter (ACFC). Finallly results are analyzed by applying disturbances at both supply end and Load ends of the converter. And the conclusions are made based on obtained results of Voltage Mode Controller (VMC) by comparing with the results of the Current Mode Controller (CMC).
Pursuit Algorithm for Robot Trash Can Based on Fuzzy-Cell Decomposition
Authors: Padang Tunggal Tatiya , Supriyanto Andi , Zaidatur Rochman Nur Mukhammad , Faishal Ibnu , Pambudi Imam , Iswanto Iswanto
Published: October 2019

Scooby Smart Trash can is a trash can equipped with artificial intelligence algorithms that is able to capture and clean up garbages thrown by people who do not care about the environment. The can is called smart because it acts like scoobydoo in a children's cartoon in that the can will react if there is garbage thrown and it catches and cleans them up. This paper presents pursuit algorithm that uses cell decomposition algorithm in which algorithms are used to create a map of the robot's path and fuzzy algorithm as one of the artificial intelligence algorithm for robot path planning. By using the combined algorithms, the robot is able to pursuit and chases the trash carelessly discarded, but it has not been able to find the shortest distance. Therefore, this paper considers a second modification of the algorithm by adding a potential field algorithm used to add weight values on the map, so that the robot can pursue trash by finding the shortest path. The proposed algorithm shows that the robot can avoid obstacles and find the shortest path so that the time required to get to the destination point is fast.
Characterizing Multi-radio Energy Consumption in Cellular/Wi-Fi Smartphones
Authors: Bhutto Zuhaibuddin , Park Jun-Hyuk , Yoon Wonyong
Published: October 2019

Cellular networks evolved to meet the ever increasing traffic demand by way of offloading mobile traffic to Wi-Fi network elements. Exploiting multi-radio interfaces on a smartphone has recently been examined with regards to heterogeneous bandwidth aggregation and radio switching. However, how a smartphone consumes its energy in driving cellular and Wi-Fi multi-radio interfaces, is not well understood. In this paper, we revealed the energy consumption behavior of 3G cellular and Wi-Fi multi-radio operations of a smartphone. We modified smartphone’s firmware to enable multi-radios operations simultaneously and we performed extensive measurements of multi-radio energy consumption in a real commercial network. From the measured data set, we established a realistic multi-radio energy consumption model and it gave 98% stability from the derived coefficients.
Adaptive Online Module Prototype for Learning Unified Modelling Language (UML)
Authors: Hidayat Arief , Utomo Victor Gayuh
Published: October 2019

Every student has their own learning style. Some students may prefer to read the learning material while some other students may prefer to discuss with their colleagues to help them understanding the information more effectively. One of the learning materials is Unified Modeling Language or UML. UML is an industry standard language to specify, visualize, construct, and document the artifacts of software systems. Today, many UML online learning sites are designed to give added information to students other than face-to-face interaction in the classroom. However, most of UML materials provided by the online learning resources are limited to text-based material which is suitable only for students with verbal learning styles. This research aims to identify and analyze the needs before developing a prototype of an adaptive online module. The research is also driven by the need to develop a prototype of adaptive online module that is based on the student's learning style categories in order to help students understand UML better. The result of this research is a prototype of adaptive online module which will identify students' learning styles and lead the students to learning environment that suits their learning style.
Implementation of Population Algorithms to Minimize Power Losses and Cable Cross-Section in Power Supply System
Authors: Manusov V. Z. , Matrenin P. V. , Tretiakova E. S.
Published: October 2019

The article dues to the arrangement of the reactive power sources in the power grid to reduce the active power losses in transmission lines and minimize cable cross-sections of the lines. The optimal arrangement is considered from two points of view. In the first case, it is possible to minimize the active power losses only. In the second case, it is possible to change the cross-sections of the supply lines to minimize both the active power losses and the volume of the cable lines. The sum of the financial cost of the active power losses, the capital investment to install the deep reactive power compensation, and cost of the cable volume is introduced as the single optimization criterion. To reduce the losses, the deep compensation of reactive power sources in nodes of the grid are proposed. This optimization problem was solved by the Genetic algorithm and the Particle Swarm optimization algorithm. It was found out that the deep compensation allows minimizing active power losses the cable cross-section. The cost-effectiveness of the suggested method is shown. It was found out that optimal allocation of the reactive power sources allows increasing from 9% to 20% the financial expenses for the enterprise considered.
A Hybrid Cryptographic System for Secured Device to Device Communication
Authors: Krishna A. Rama , Chakravarthy A. S. N. , Sastry A. S. C. S.
Published: October 2019

It is general fact that even after enormous expansion of wireless communication there are still dead regions that hampers the effective communication. With exponential rise in the smart phones, a new layer of communication has evolved that could address the concerns of dead regions and capacity barriers. D2D is the evolving communication technology which focuses on short distance hops between the public devices to reach the destination. The major drawback of this technology is that most of the devices are public hence trustworthiness of the entire channel needs to be addressed in order to make it a viable solution. In this paper, we introduce a novel hybrid cryptographic approach that could address multiple eavesdroppers’ scenario. This approach incorporates both Huffman coding and Binary coding to enhance the crypto benefits for the information transmitted over D2D channel that consists of several public devices. The dual-crypto nature of the proposed algorithm offers higher efficiency, better security and improved key transmission.  Thus, the proposed hybrid cryptographic approach is robust in nature while easy and simple to operate. In addition, the proposed approach could recover the original information without any distortion from the encrypted data making the approach lossless in nature. Further simulation results prove that the proposed offers confidentiality to the transmitted to data while addressing the network capacity crunch.
Energy Efficient Frame Structure for Gigabit Passive Optical Networks
Authors: Butt Rizwan Aslam , Idrus Sevia Mahdaliza , Radzi Raja Zahilah , Qureshi Kashif Naseer
Published: October 2019

Increasing power consumption in information and communication access networks is one of the major cause of greenhouse gas emissions. These emissions are harmful to life on earth. Passive Optical Networks (PONs) are energy efficient but the broadcast nature of downstream traffic may cause of huge unnecessary processing of frames by the optical network units and result in significant energy wastage. Bi-PON technique tried to solve this problem by changing the XGPON / GPON frame structure to an interleaved pattern but also required additional hardware changings at the optical network units.  In this study, we have tried to achieve the same objective by making a few changings in the GPON frame structure without modifying the existing hardware structure. The simulation results show that 25.25% processing energy of an ONU can be saved by incorporating these changes.
Location Based Services with Location Centric Profiles
Authors: Ukrit M.Ferni , Venkatesh B. , Suman Swetabh
Published: October 2019

In the advancing world of technology, Mobile applications are a rapidly growing segment of the global mobile market. Mobile applications are evolving at a meteor pace to give users a rich and fast user experience. Android operating system supports most applications in today’s technical world. It is an open source operating system which highly satisfies the user’s needs. In this paper, the applications that are location based i.e. the applications that make use of the Global Positioning System (GPS) is discussed. It is a space based satellite navigation system which provides details of time and location in all weather conditions anywhere on or near earth. We introduce PROFILER, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of collocated users. In our System we find the corresponding location using Global Positioning and Service Provider also. This is to change the profile mode automatically and alarm notification enable automatically when you are going to reach corresponding location or Regular Location.
Goal Model to Business Process Model: A Methodology for Enterprise Government Tourism System Development
Authors: Fajar Ahmad Nurul , Shofi Imam Marzuki
Published: October 2019

The critcal factor in successfully in system development is the requirement phase. The requirement should meets with its purpose. In order to achieve it, the methodology for requrement analysis is needed. Nowdays, the complexity of e-governemnt applications is grown significantly in the government environment. E-government applications should be developed based on regulations in order to achieve the goal model of government entitties. However, the goal model could not used directly to make business process model. In order to solve this problem,this paper presents and proposed a Methodology to extract goal model into business process model that called GBPM Methodology. It can support the requirement analysis phase, especially in enterprise government tourism system. This methodology consists of two methods, there are (1) Method for convert goal model to business use case diagram, (2) Method for convert business use case diagram to activity diagram. We do the experiment in e-government applications domain. This methodology suitable with enterprise government tourism system development.
Issues of K Means Clustering While Migrating to Map Reduce Paradigm with Big Data: A Survey
Authors: Nirmal Khyati R , Satyanarayana K.V.V.
Published: October 2019

In recent times Big Data Analysis are imminent as essential area in the field of Computer Science. Taking out of significant information from Big Data by separating the data in to distinct group is crucial task and it is beyond the scope of commonly used personal machine. It is necessary to adopt the distributed environment similar to map reduce paradigm and migrate the data mining algorithm using it. In Data Mining the partition based K Means Clustering is one of the broadly used algorithms for grouping data according to the degree of similarities between data. It requires the number of K and initial centroid of cluster as input. By surveying the parameters preferred by algorithm or opted by user influence the functionality of Algorithm. It is the necessity to migrate the K means Clustering on MapReduce and predicts the value of k using machine learning approach. For selecting the initial cluster the efficient method is to be devised and united with it. This paper is comprised the survey of several methods for predicting the value of K in K means Clustering and also contains the survey of different methodologies to find out initial center of the cluster. Along with initial value of k and initial centroid selection the objective of proposed work is to compact with analysis of categorical data.
Using the Fuzzy Logic to Find Optimal Centers of Clusters of K-means
Authors: Kadhim Oleiwi Wed
Published: October 2019

Techniques of data mining that used in the medical diagnosis a number of diseases like cancer, diabetes, stroke, and heart disease. The great importance emerging fields for providing diagnosis and a profounder understanding of medical data, its coms from Data mining in medical field .researcher attempts to solve real world health problems in the prognosis and treatment of diseases, by using Healthcare data mining. In this research, the algorithm of k-means is used for grouping medical data, the problem of k-means is to find optimal centers of clusters so, and fuzzy logic is used to get optimal centers of clusters.
A Model Driven Approach based on Interaction Flow Modeling Language to Generate Rich Internet Applications
Authors: Roubi Sarra , Erramdani Mohammed , Mbarki Samir
Published: October 2019

A Rich Internet Applications (RIAs) combine the simplicity of the hypertext paradigm with the flexibility of desktop interfaces. These appliations were proposed as a solution to follow the rapid growth and evolution of the Graphical User Interfaces. However, RIAs are complex applications and their development requires designing and implementation which are time-consuming and the available tools are specialized in manual design. In this paper, we present a model driven approach to generat GUI for Rich Internet Application. The approach exploits the new language IFML recently adopted by the Object Management Group. We used frameworks and technologies known to Model-Driven Engineering, such as Eclipse Modeling Framework (EMF) for Meta-modeling, Query View Transformation (QVT) for model transformations and Acceleo for code generation. The approach allows to quickly and efficiently generating a RIA focusing on the graphical aspect of the application.
Study on Improving the Network Life Time Maximazation for Wireless Sensor Network using Cross Layer Approach
Authors: R Bhandari Rajiv , Rajasekhar K
Published: October 2019

In recent the espousal of Wireless Sensor Networks has been broadly augmented in numerous divisions. Battery operated Sensor nodes in the wireless network accomplish main task of capturing and responding to the surroundings. The lifetime of the network depends on the energy consumption of the sensor nodes. This paper contributes the survey on how the energy consumption should be managed for maximizing the life time of network and how to improve the efficiency of Network by using Cross layer architecture. The traditional MAC Layer, Network Layer & Transport for WLAN having their own downsides just by modifying those we can achieve the network life time maximization goal. This paper represents analytical study for Energy efficiency by modifying Scheduling algorithm, by modifying traditional AODV routing algorithm for efficient packet transmission and by effectively using TCP for End to End Delivery of Data.
Localization of Cooperative WSN using Distributed PSO with Optimum References
Authors: Janapati Ravichander , Balaswamy Ch. , Soundararajan K.
Published: October 2019

In indoor environment WSN nodes are deployed randomly and do not know the accurate position. Find the node position with the help of anchor nodes is known as localization. CRB algorithm selects the best anchor nodes which gives high accuracy. In this paper distributed PSO algorithm with optimum selection of reference nodes using CRB is proposed to find accurate node position. The proposed method performs better in comparison with other algorithms like PSO, RLS, LMS and GPS in terms of position accuracy, latency and complexity.
WWLLN Data Cluster Analysis Methods for Lightning-Caused Forest Fires Monitoring
Authors: Nikolay Baranovskiy , Svetlana Krechetova , Marina Belikova , Anton Perelygin
Published: October 2019

Storm activity is the main reason for forest fires to occur in remote forested territories. The current article presents the results for cluster analysis of WWLLN data on lightning discharges. It provides the description for clusterization algorithms of lightning discharges over the controlled territory. Research area is Timiryazevskiy forestry of the Tomsk region (Siberia, Russia). We analyzed the applicability of cluster analysis results for monitoring of the forest fire danger caused by storm activity. As a result of the conducted research, we established that the following characteristics of storm activity can be included in deterministic-probabilistic criterion to assess the forest fire danger. The article gives the recommendations how to create new generation information-computer and geoinformation systems for monitoring of the forest fire danger caused by storm activity in the controlled forested territory.
Reducing Routing Overhead in Random Walk Protocol under MP2P Network
Authors: Arunachalam Ajay , Sornil Ohm
Published: October 2019

Due to network dynamics in self-organizing networks the resource discovery effort increases. To discover objects in unstructured peer-to-peer network, peers rely on traditional methods like flooding, random walk and probabilistic forwarding methods. With inadequate knowledge of paths, the peers have to flood the query message which creates incredible network traffic and overhead. Many of the previous works based on random walk were done in wired network. In this context random walk was better than flooding. But under MANETs random walk approach behaved differently increasing the overhead, due to frequent link failures incurred by mobility. Decentralized applications based on peer-to-peer computing are best candidates to run over such dynamic network. Issues of P2P service discovery in wired networks have been well addressed in several earlier works. This article evaluates the performance of random walk based resource discovery protocol over P2P Mobile Adhoc Network (MP2P) and suggests an improved scheme to suit MANET. Our version reduces the network overhead, lowers the battery power consumption, minimizes the query delay while providing equally good success rate. The protocol is validated through extensive NS-2 simulations. It is clear from the results that our proposed scheme is an alternative to the existing ones for such highly dynamic mobile network scenario.
EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder
Authors: Narejo Sanam , Pasero Eros , Kulsoom Farzana
Published: October 2019

A Brain-Computer Interface (BCI) provides an alternative communication interface between the human brain and a computer. The Electroencephalogram (EEG) signals are acquired, processed and machine learning algorithms are further applied to extract useful information.  During  EEG acquisition,   artifacts  are induced due to involuntary eye movements or eye blink, casting adverse effects  on system performance. The aim of this research is to predict eye states from EEG signals using Deep learning architectures and present improved classifier models. Recent studies reflect that Deep Neural Networks are trending state of the art Machine learning approaches. Therefore, the current work presents the implementation of  Deep Belief Network (DBN) and Stacked AutoEncoders (SAE) as Classifiers with encouraging performance accuracy.  One of the designed  SAE models outperforms the  performance of DBN and the models presented in existing research by an impressive error rate of 1.1% on the test set bearing accuracy of 98.9%. The findings in this study,  may provide a contribution towards the state of  the  art performance on the problem of  EEG based eye state classification.
Development of Automatic Mold Shot Measurement and Management System for Smart Factory
Authors: Shin Hyun-Jun , Kim Sung-Jin , Jeon Min-Ho , Oh Chang-Heon
Published: October 2019

Many small- and medium-sized car-part manufacturers are either still managing their mold manually or rarely managing it, and therefore, experience significant manufacturing cost and loss in time. In such a situation, a module has been developed in the present work which can count the number of mold used. Such a module is extremely important for small and medium-sized enterprises (SMEs) applying which in the production line they will be able to manage the mold life cycle and improve product quality. This is expected to have both direct and indirect effects on their business activities. The developed system uses a photo sensor, distance measurement sensor, Atmega128 MCU, tablet pc and Bluetooth communication module. The actual module developed in this study was set up on a molding equipment for test and data were collected using an existing tablet PC. The test showed that the number of shots increased when the upper mold touched the lower mold. The maximum and minimum value between the upper and lower molds could be adjusted with the automatic mold shot measurement and management system. Therefore, any molding equipment with various upper-lower gaps will be able to apply the newly developed system.
Texture Fusion for Batik Motif Retrieval System
Authors: Nurhaida Ida , Wei Hong , A. M. Zen Remmy , Manurung Ruli , M. Arymurthy Aniati
Published: October 2019

This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object.
Evaluation of the Forward-Backward Sweep Load Flow Method using the Contraction Mapping Principle
Authors: Issicaba Diego , Coelho Jorge
Published: October 2019

This paper presents an assessment of the forward-backward sweep load flow method to distribution system analysis. The method is formally assessed using fixed-point concepts and the contraction mapping theorem. The existence and uniqueness of the load flow feasible solution is supported by an alternative argument from those obtained in the literature. Also, the closed-form of the convergence rate of the method is deduced and the convergence dependence of loading is assessed. Finally, boundaries for error values per iteration between iterates and feasible solution are obtained. Theoretical results have been tested in several numerical simulations, some of them presented in this paper, thus fostering discussions about applications and future works.
A Fault Tolerant Control for Sensor and Actuator Failures of an Non Linear Hybrid System
Authors: Hanene Chalandi , Slimane Jihane Ben , Moufida Ksouri
Published: October 2019

We focused in this work on a fault tolerant control of a non linear hybrid system class based on diagnosis method (determine and locate the defects and their types) and on the faults reconfiguration method.In literature we can found many important research activities over the fault-tolerant control of non linear systems and linear Hybridsystems. But it dosen´t exist too many for the non linear hybridsystem.The main idea in this paper is to consider a new approach to improve the reconfiguration performance of the non linear hybrid system by using hammerstein method which is designed to works only for linear systems. This method compensated the effect of the faults and guarantees the closed-loop system stable. The proposed method is simulated with a hydraulic system of two tanks with 4 modes.
An Effective PSO-inspired Algorithm for Workflow Scheduling
Authors: Thanh Toan Phan , The Loc Nguyen , Elnaffar Said , Doan Cuong Nguyen , Quoc Huu Dang
Published: October 2019

The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers and storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on maximizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. This entails the need to adopt an effective scheduling algorithm in order to minimize task completion time (makespan). Although workflow scheduling has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this paper, we propose the LPSO, a novel algorithm for workflow scheduling problem that is based on the Particle Swarm Optimization method. Our proposed algorithm not only ensures a fast convergence but also prevents getting trapped in local extrema. We ran realistic scenarios using CloudSim and found that LPSO is superior to previously proposed algorithms and noticed that the deviation between the solution found by LPSO and the optimal solution is negligible.
Gamification for Elementary Mathematics Learning in Indonesia
Authors: Udjaja Yogi , Guizot Vincent Sadino , Chandra Natalia
Published: October 2019

The purpose of this research is to combine multimedia elements and mathematics learning material to a mathematic learning interactive application. Research and design methodology that used is Game Development Life Cycle (GDLC) which consist of initiation, pre-production, production, testing and release. Content inside the game is made using gamification and expert system concept. The result of this research is an interactive learning game to support student to understand mathematic materials. The purpose of this application is to help student to learn mathematic in an interactive and interesting way, to deliver mathematic material easily.
Design of a Monitoring-combined Siting Scheme for Electric Vehicle Chargers
Authors: Lee Junghoon , Park Gyung-Leen
Published: October 2019

This paper designs a siting scheme for public electric vehicle chargers based on a genetic algorithm working on charger monitoring streams. The monitoring-combined allocation scheme runs on a long-term basis, iterating the process of collecting data, analyzing demand, and selecting candidates. The analysis of spatio-temporal archives, acquired from the fast chargers currently in operation, focuses on the per-charger hot hour and proximity effect to justify demand balancing in geographic cluster level. It leads to the definition of a fitness function representing the standard deviation of per-charger load and cluster-by-cluster distribution. In a chromosome, each binary integer is associated with a candidate and its static fields include the index to the cluster to which it is belonging. The performance result obtained from a prototype implementation reveals that the proposed scheme can stably distribute the charging load with an addition of a new charger, achieving the reduction of standard deviation from 8.7 % to 4.7 % in the real-world scenario.
Efficient energy, cost reduction, and QoS based routing protocol for wireless sensor networks
Authors: Samara Ghassan , Aljaidi Mohammad
Published: October 2019

Recent developments and widespread in wireless sensor network have led to many routing protocols, many of these protocols consider the efficiency of energy as the ultimate factor to maximize the WSN lifetime. The quality of Service (QoS) requirements for different applications of wireless sensor networks has posed additional challenges. Imaging and data transmission needs both QoS aware routing and energy to ensure the efficient use of sensors. In this paper, we propose an Efficient, Energy-Aware, Least Cost, (ECQSR) quality of service routing protocol for sensor networks which can run efficiently with best-effort traffic processing. The protocol aims to maximize the lifetime of the network out of balancing energy consumption across multiple nodes, by using the concept of service differentiation, finding lower cost by finding the shortest path using nearest neighbor algorithm (NN), also put certain constraints on the delay of the path for real-time data from where link cost that captures energy nodes reserve, energy of the transmission, error rate and other parameters. The results show that the proposed protocol improves the network lifetime and low power consumption.
Application of the Least Square Support Vector Machine for point-to-point forecasting of the PV Power
Authors: Farhadi Mahdi , Mollayi Nader
Published: October 2019

In today's industrial world, the growing capacity of renewable energy sources is a crucial factor for sustainable power generation. The application of solar photovoltaic (PV) energy sources, as a clean and safe renewable energy resource has found great attention among the consumers in the recent decades. Accurate forecasting of the generated PV power is an important task for scheduling the generators and planning the consumption patterns of customers to save electricity costs. To this end, it is necessary to develop a global model of the generated power based on the effective factors which are mainly the solar radiation intensity and the ambient weather temperature. As a result of the wide numerical range of these parameters and various weather conditions, a large training database must be used for developing the models, which results in high-computational complexity of the algorithms used for training the models. In this paper, a novel algorithm for point to point prediction of the generated power based on the least squares support vector machine (LS-SVM) has been proposed which can handle the large training database with a very fewer deal of computation and benefits from reasonable accuracy and generalization capability. 
Evaluation of Wind Power for Electrical Energy Generation in the Mediterranean Coast of Palestine for 14 Years
Authors: Badawi Ahmed S A , Hasbullaha Nurul Fadzlin , Yusoff Siti , Khan Sheroz , Hashim Aisha , Zyoud Alhareth , Elamassie Mohammed
Published: October 2019

The generation, distributionand transmission of electricity in Palestine have recently emerged as major issues. This study comprehensively assesses the production of wind energy and the estimation of wind energy potential in Palestine’s south coastal plain. The goal is to evaluate Palestine’s wind energy production by studying wind data and calculating energy and power. This study analyses two actual time series datasets. Findings are elaborated to determine the wind energy conversion per 1 m2. The wind speed data from January 1996 to December 2006 in Gaza and from January 2012 to December 2015 in Ashqelon are selected as the data sample. This study is crucial given the need for clean and renewable energy, the power shortage in the Gaza Strip and the limited number of wind energy studies conducted in the south coastal plain of Palestine, especially Gaza Strip. This study estimates the wind energy potential of the Gaza Strip to determine the wind potential. The annual mean wind speed and power are 4.11 ms-1 and 903.4 Wm-2, respectively. Moreover, the study clarifies the electrical energy generation in the Gaza Strip using small-scale turbines and offers a feasible alternative to existing schemes.
An AC-AC High Frequency Pulse Density Modulated Full Bridge Series Resonant Converter for Induction Heating Application
Authors: Geetha V , Sivachidambaranathan V
Published: October 2019

This paper deals with a new AC – AC single stage full bridge series resonant converter for induction heating application. The proposed system is simulated with high power density modulation technique to produce a output power of 1.8Kw.The main feature of the proposed converter is to reduce the total harmonic distortion produced in the output current for the switching frequency of 29Khz.Asymmetrical pulse density modulation technique is proposed to achieve maximum output power with less harmonic content. It is found that the THD of the proposed high power density modulated converter is 13.43%. Here, the validity of the proposed converter for domestic induction heating application is compared and summarized.
A New Design of Capacitive Power Transfer Based on Hybrid Approach for Biomedical Implantable Device
Authors: Mustapa Zaki , Saat Shakir , Yusof Yusmarnita
Published: October 2019

This paper presents the development of a new design method of capacitive power transfer (CPT) which is based on hybrid concept for Biomedical Implants. This method is able to improve various issues found in the widely used CPT system that is bipolar CPT method. Based on the ability of this purposed, the simulation of the CPT system has been designed to prove an amount of power transferred through a layer of tissue. The design used to validate the suggested model which to powering implanted device, and it was performed with 3cm square plates, which have a layer of beef with the 5mm thickness in between 2 coupling plate. Power signal was generated by Class E zero voltage switching. The Class E zero voltage switching has been designed to generating alternate current with the 1MHz frequency appropriate to the hybrid CPT system specification.

 
Classification of Palm Oil Fresh Fruit Bunch using Multiband Optical Sensors
Authors: Setiawan Agung Wahyu , Mengko Richard , Putri Ayu Paranitha H. , Danudirdjo Donny , Ananda Alfie Rizky
Published: October 2019

This study investigated optical sensor system consist of sixteen light emitting diode (LED) in visible/near infrared region to detect palm oil fresh fruit bunch (FFB) quality. Practically, experience grader assessed FFB quality by its ripeness based on external features such as colour and number of detached fruitlets. However, different seed and plantation management resulting in FFB quality variation. Same external features not linearly correlate with FFB oil content that corresponding with industrial needs. The 660 nm LED is choosen to be used to estimate the oil content of FFB. Using linear discriminant analysis (LDA) with Mahalanobis distance, the accuracy of the systems is 79.8% and 88.2%. From 33 FFB oil content measurement, grader misclassified 4 out of 17 FFB as ripe FFB but with low oil content (<17.5%) and misclassified 7 out of 16 FFB as unripe but with high oil content (>=17.5%). Classifying model build from FFB from main plantation then tested to evaluate FFB from smallholder. Classification model generated from FFB oil content data showed more accurate result compared to model generated from visual inspection 66.7% compared to 52.1%. Model accuracies attained by Discriminant Analysis (DA) and k-Nearest Neighbors (k-NN) were 79.8% and 80.7%, respectively based on grader evaluation. Model accuracies based on FFB oil content was 88.2% for both classifying algorithms.
High Level Speaker Specific Features as an Efficiency Enhancing Parameters in Speaker Recognition System
Authors: Singh Satyanand
Published: October 2019

In this paper, I present high-level speaker specific feature extraction considering intonation, linguistics rhythm, linguistics stress, prosodic features directly from speech signals. I assume that the rhythm is related to language units such as syllables and appears as changes in measurable parameters such as fundamental frequency (  ), duration, and energy. In this work, the syllable type features are selected as the basic unit for expressing the prosodic features. The approximate segmentation of continuous speech to syllable units is achieved by automatically locating the vowel starting point. The knowledge of high-level speaker’s specific speakers is used as a reference for extracting the prosodic features of the speech signal. High-level speaker-specific features extracted using this method may be useful in applications such as speaker recognition where explicit phoneme/syllable boundaries are not readily available. The efficiency of the particular characteristics of the specific features used for automatic speaker recognition was evaluated on TIMIT and HTIMIT corpora initially sampled in the TIMIT at 16 kHz to 8 kHz. In summary, the experiment, the basic discriminating system, and the HMM system are formed on TIMIT corpus with a set of 48 phonemes. Proposed ASR system shows 1.99%, 2.10%,  2.16%  and  2.19 % of efficiency improvements compared to traditional ASR system for and of 16KHz TIMIT utterances.
Crowd Recognition System Based on Optical Flow Along with SVM classifier
Authors: K Shreedarshan , Selvi S.Sethu
Published: October 2019

The manuscript discusses about abnormalities in a crowded scenario. To prevent the mishap at a public place, there is no much mechanism which could prevent or alert the concerned authority about suspects in a crowd. Usually in a crowded scene, there are chances of some mishap like a terrorist attack or a crime. Our target is finding techniques to identify such activities and to possibly prevent them. If the crowd members exhibit abnormal behavior, we could identify and say that this particular person is a suspect and then the concerned authority would look into the matter. There are various methods to identify the abnormal behavior. The proposed approach is based on optical flow model. It has an ability to detect the sudden changes in motion of an individual among the crowd. First, the main region of motion is extracted by the help of motion heat map. Harris corner detector is used for extracting point of interest of extracted motion area. Based on the point of interest an optical flow is estimated here. After analyzing this optical flow model, a threshold value is fixed. Basically optical flow is an energy level of individual frame. The threshold value is forwarded to SVM classifier, which produces a better result with 99.71% accuracy. This approach is very useful in real time video surveillance system where a machine can monitor unwanted crowd activity. 
Area & Power Efficient VLSI Architecture of Mode Decision in Integer Motion Estimation for HEVC Video Coding Standard
Authors: Abdessamad EL Ansari , Bahri Nejmeddine , Mansouri Anass , Masmoud Nouri , Ali Ahaitouf
Published: October 2019

In this paper, we propose a new parallel hardware architecture for the mode decision algorithm, that it is based on the Sum Absolute of the Difference (SAD) for compute the motion estimation, which is the most critical algorithm in the recent video encoding standard HEVC. In fact, this standard introduced new large variable block sizes for the motion estimation algorithm and therefore the SAD requires a more reduced execution time in order to achieve the real time processing even for the ultra-high resolution sequences. The proposed accelerator executes the SAD algorithm in a parallel way for all sub-block prediction units (PUs) and coding unit (CU) whatever their sizes, which turns in a huge improvements in the performances, given that all the block sizes, PUs in each CU, are supported and processed in the same time. The Xilinx Artix-7 (Zynq-7000) FPGA is used for the prototyping and the synthesis of the proposed accelerator. The mode decision for motion estimation scheme is implemented with 32K LUTs, 50K registers and 108Kb BRAMs. The implementation results show that our hardware architecture can achieve 30 frames per second of the 4K (3840 × 2160) resolutions in real time processing at 115.15MHz.
Design Nonlinear Model Reference with Fuzzy Controller for Nonlinear SISO Second Order Systems
Authors: Karam Ekhlas H. , Al-Awad Nasir A. , Abdul-Jaleel Noor Safaa
Published: October 2019

Model reference controller is considering as one of the most useful controller to specific performance of systems where the desired output is produced for a given input. This system used the difference between the outputs of the plant and the desired model by comparing them to produce the signals of the control. This paper focus on design a model reference controller (MRC) combined with (type-1 and interval type-2) fuzzy control scheme for single input-single output (SISO) systems under uncertainty and external disturbance. The model reference controller is designed firstly without fuzzy scheme based on an optimal desired model and Lyapunov stability theory. Then a (type-1 and Interval type-2) fuzzy controller Takagi-Sugeno type is combine with the suggested MRC in order to enhance the performer of it, the common parts between the two fuzzy systems such as: fuzziﬁer, inference engine, fuzzy rule-base and defuzziﬁer are illustrated. In this paper the proposed controller is applied to controla (SISO) inverted pendulum sustem and the Matlab R2015 software is used to carry out two simulation cases for the overall controlled scheme. The obtained results for the two cases show that the proposed MRC with both fuzzy control schemes have acceptable performance, but it have better performance with the interval type-2 fuzzy scheme.