bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploiting 2-Dimensional Source Correlation in Channel Decoding with Parameter Estimation
Authors: Amir Mohd Nor Muhammad Izzat , Mohd Izhar Mohd Azri , Ahmad Norulhusna , Kaidi Hazilah Md.
Published: January 2019

Traditionally, it is assumed that source coding is perfect and therefore, the redundancy of the source encoded bit-stream is zero. However, in reality, this is not the case as the existing source encoders are imperfect and yield residual redundancy at the output. The residual redundancy can be exploited by using Joint Source Channel Coding (JSCC) with Markov chain as the source. In several studies, the statistical knowledge of the sources has been assumed to be perfectly available at the receiver. Although the result was better in terms of the BER performance, practically, the source correlation knowledge were not always available at the receiver and thus, this could affect the reliability of the outcome. The source correlation on all rows and columns of the 2D sources were well exploited by using a modified Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm in the decoder. A parameter estimation technique was used jointly with the decoder to estimate the source correlation knowledge. Hence, this research aims to investigate the parameter estimation for 2D JSCC system which reflects a practical scenario where the source correlation knowledge are not always available. We compare the performance of the proposed joint decoding and estimation technique with the ideal 2D JSCC system with perfect knowledge of the source correlation knowledge. Simulation results reveal that our proposed coding scheme performs very close to the ideal 2D JSCC system.
Hysteresis-based Voltage and Current Control Techniques for Grid Connected Solar Photovoltaic Systems: Comparative Study
Authors: Raja Mohamed S. , Jeyanthy P. Aruna , Devaraj D.
Published: January 2019

Solar PV system development and integration with existing grid is very fast in recent years all over the world, as they require limited maintenance, pollution free and simple structure. When observing the factors affecting the performance of the grid connected solar photovoltaic system, the inverter output voltage with harmonics add with the harmonics generated due to the non-linear loads, retain a bigger challenge to maintain power quality in the grid. To maintain grid power quality, better inverter control technique should be developed. This paper presents the two control techniques for grid-tied inverters. This study developed the hysteresis controller for the inverter. Hysteresis controller used in this work two way (i) Voltage control mode (ii) Current control mode. Matlab/Simulink model is developed for the proposed system. Further the study presents the comparative evaluation of the performance of both control techniques based on the percentage of total harmonic distortion (THD) with the limits specified by the standards such as IEEE 1547 and IEC 61727 and IEEE Std 519-2014
BB84 with Both Several Cloning and Intercept-resend Attacks
Authors: Dehmani Mustapha , Salmani El Mehdi , Ez-Zahraouy Hamid , Benyoussef Abdelilah
Published: January 2019

The goal of the protocol QKD BB84 is to allow a transmitter and a receiver which uses a quantum channel to exchange their keys and to detect the presence of eavesdropping attacks. In the present research, we investigate the effect of several eavesdroppers with both intercept-resend and cloning attacks. We will propose the different possible cases of the positioning of the eavesdroppers and their strategies of attacks; also we will calculate the mutual information for each case. The explicit expressions of the mutual information and quantum error clearly show that the security of the exchanged information depends on the numbers of the eavesdroppers and their attacks parameters on the quantum channel.
Web Application to Build Information System of National Seminar Alerts in Indonesia
Authors: Erlin Erlin , Andesa Khusaeri , Agustin Agustin
Published: January 2019

The output of research in the form of a proceeding published through a national seminar or an international conference has become a routine activity performed by lecturers as a researcher. The escalating atmosphere of disseminating the results of a study is directly proportional to the increasing number of universities, associations, or non-educational institutions organizing either a national seminar or an international conference. Each seminar organizer posts their seminar relevant information through official sites released by each organizer. However, to date, there has never been storage that can compile all the information pertinent to all national seminars held in Indonesia. This situation has resulted in the search done by search engines such as Google, Yahoo Search, Bing and others have not been efficient nor effective. Design of a web-based national seminar alert system is built to address this issue. This information system can display all the seminars held in Indonesia in the current year in storage and can be broken down based on the choices of topic, location, and date. Other additional facilities are that the application can send email messages to the application users as an early warning on the upcoming national seminar. Researchers can track each existing national seminar and always up-to-date on the most current national seminar.
Towards Benchmarking User Stories Estimation with COSMIC Function Points-A Case Example of Participant Observatio
Authors: Angara Jayasri , Prasad Srinivas , Sridevi Gutta
Published: January 2019

Shorter time-to-market and unstable requirements is leading to introduction of Agile and DevOps practices. Story point estimation is becoming handier in Agile/DevOps setting. However, developing user stories and defining sizing unit in terms of story point is subjective process. It lacks benchmarking/standardization in terms of sizing measure and productivity of historical data. COSMIC has been considered as FSM (Functional Size Method). It has flexibility to receive requirements as User stories (popular Agile/Devops method) and derive COSMIC functional units using parametric approach. COSMIC method reduces the subjectivity and populates the productivity parameter for benchmarking. It standardizes the estimation process and can be easily deployed in Agile or DevOps setting. This paper presents the related work on linkages between User stories, COSMIC methods and traditional function point methods. It also presents the outcome of the industry survey conducted on 49 practitioners working in 10 different domains with respective to parametric estimation process adoption and presents 9 real-time case studies developed to demonstrate the usage of COSMIC method in various domains. This paper also attempts to derive mapping process of COSMIC functional process with User Stories with the help of 2 real-time industry case studies.
Analysis of Blackhole Attack in AODV and DS
Authors: Panda Niranjan , Pattanayak Binod Kumar
Published: January 2019

Mobile Ad-Hoc Networks (MANETs) are supreme ruler and demoralization wireless scheme. MANETs are infrastructure less i.e. their structure is not fixed, and the nodes be able to move about and can leave the network whenever they want. The nodes are to perform as more over router and host. In MANETs, the node can be in contact with every node as their configuration is not fixed and the nodes starts transmitting the packets to each other for the establishment of the connection. To hitch the link, the nodes make use of some routing protocols like Ad-Hoc On Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and DestinationSequenced Distance Vector (DSDV). Security in MANET is the key matter meant for the fundamental utility of network. There are many attacks caused in MANET. Blackhole attack is one that occurs in MANET. A Black hole attack is an attack where the node, which is malicious advertise itself as having the optimal route to the destination and drops all the packets instead of forwarding further to the destination. Here, we have shown the blackhole attack in AODV and DSR. Through simulation we evaluate the performance of the two above protocols under blackhole attack.
Motorcycle Movement Model Based on Markov Chain Process in Mixed Traffic
Authors: Mardiati Rina , R Trilaksono Bambang , S Gondokaryono Yudi , S Wibowo Sony
Published: January 2019

Mixed traffic systems are dynamically complex since there are many parameters and variables that influence the interactions between the different kinds of vehicles. Modeling the behavior of vehicles, especially motorcycle which has erratic behavior is still being developed continuously, especially in developing countries which have heterogeneous traffic. To get a better understanding of motorcycle behavior, one can look at maneuvers performed by drivers. In this research, we tried to build a model of motorcycle movement which only focused on maneuver action to avoid the obstacle along with the trajectories using a Markov Chain approach. In Markov Chain, the maneuver of motorcycle will described by state transition. The state transition model is depend on probability function which will use for determine what action will be executed next. The maneuver of motorcycle using Markov Chain model was validated by comparing the analytical result with the naturalistic data, with similarity is calculated using MSE. In order to know how good our proposed method can describe the maneuver of motorcycle, we try to compare the MSE of the trajectory based on Markov Chain model with those using polynomial approach. The MSE results showed the performance of Markov Chain Model give the smallest MSE which 0.7666 about 0.24 better than 4th order polynomial.
Optimization of Backpropagation for Early Detection of Diabetes Mellitu
Authors: Sofiana Rosita , Sutikno Sutikno
Published: January 2019

Diabetes mellitus is one of the urgent health problems in the world. Diabetes is a condition primarily defined by the level of hyperglycemia giving rise to risk of micro vascular damage. Those who suffer from this disease generally do not realize and tend to overlook the early symptoms. Late recognition of these early symptoms may drive the disease to a more concerning level. One solution to solve this problem is to create an application that may perform early detection of diabetes mellitus so that it does not grow larger. In this article, a new method in performing early detection of diabetes mellitus is suggested. This method is backpropagation with three optimization namely early initialization with Nguyen-Widrow algorithm, learning rate adaptive determination, and determination of weight change by applying momentum coefficient. The observation is conducted by collecting 150 data consisting of 79 diabetes mellitus patient and 71 non diabetes mellitus patient data. The result of this study is the suggested algorithm succeeds in detecting diabetes mellitus with accuracy rate of 99.33%. Optimized backpropagation algorithm may allow the training process goes 12.4 times faster than standard backpropagation.
WiRoTip: an IoT-based Wireless Sensor Network for Water Pipeline Monitoring
Authors: Karray Fatma , Triki Mariem , Wassim Jmal Mohamed , Abid Mohamed , M. Obeid Abdulfattah
Published: January 2019

One of the key components of the Internet of Things (IoT) is the Wireless Sensor Network (WSN). WSN is an effective and efficient technology. It consists of senor nodes; smart devices that allows data collection and pre-processing wirelessly from real world. However, issues related to power consumption and computational performance still persist in classical
wireless nodes since power is not always available in application like pipeline monitoring. Moreover, they could not be usually suitable and adequate for this kind of application due to memory shortage and performance constraints. Designing new IoT WSN system that matches the application specific requirements is extremely important. In this paper, we
present WiRoTip, a WSN node prototype for water pipeline application. An experimental and a comparative studies have been performed for the different node’s components to achieve a final adequate design.
New Approach for Detecting and Tracking a Moving Object
Authors: Hatimi H. , Fakir M. , Chabi M. , Najimi M.
Published: January 2019

This article presents the implementation of a tracking system for a moving target using a fixed camera. The objective of this work is the ability to detect a moving object and locate their positions. In picture processing, tracking moving objects in a known or unknown environment is commonly studied. It is based on invariance properties of objects of interest. The invariance can affect the geometry of the scene or the objects. The proposed approach is composed of several steps; the first is the extraction of points of interest in the current image. Then, these points will be tracked in the following image by using techniques for calculating the optical flow. After this step, the static points will be removed to focus on moving objects, That is to say, there is only the characteristic points belonging to moving objects. Now, to detect moving targets using images of the video, the background is first extracted from the successive images. In our approach, a method of the average values of every pixel has been developed for modeling background. The last step which stays before switching to tracking moving object is the segmentation which allows identifying every moving object. And by using the characteristic points in the previous steps.
Performance Analysis of Mesh-based NoC’s on Routing Algorithms
Authors: R Anala M. , Subrahmanya Amit N. , D’Souza Allbright
Published: January 2019

The advent of System-on-Chip (SoCs), has brought about a need to increase the scale of multi-core chip networks. Bus Based communications have proved to be limited in terms of performance and ease of scalability, the solution to both bus – based and Point-to-Point (P2P) communication systems is to use a communication infrastructure called Network-on-Chip (NoC). Performance of NoC depends on various factors such as network topology, routing strategy and switching technique and traffic patterns. In this paper, we have taken the initiative to compile together a comparative analysis of different Network on Chip infrastructures based on the classification of routing algorithm, switching technique, and traffic patterns. The goal is to show how varied combinations of the three factors perform differently based on the size of the mesh network, using NOXIM, an open source SystemC Simulator of mesh-based NoC. The analysis has shown tenable evidence highlighting the novelty of XY routing algorithm.
Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System
Authors: Al-Mekhlafi Mohammed A. A. , Wahid Herman , Aziz Azian Abd
Published: January 2019

The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.
Epidemic Alert System: A Web-based Grassroots Model
Authors: Osaghae Etinosa Noma , Okokpujie Kennedy , Ndujiuba Charles , Okesola Olatunji , Okokpujie Imhade P.
Published: January 2019

Most web-based disease surveillance systems that give epidemic alerts are based on very large and unstructured data from various news sources, social media and online queries that are parsed by complex algorithms. This has the tendency to generate results that are so diverse and non-specific. When considered along with the fact that there are no existing standards for mining and analyzing data from the internet, the results or decisions reached based on internet sources have been classified as low-quality. This paper proposes a web-based grassroots epidemic alert system that is based on data collected specifically from primary health centers, hospitals and registered laboratories. It takes a more traditional approach to indicator-based disease surveillance as a step towards standardizing web-based disease surveillance. It makes use of a threshold value that is based on the third quartile (75th percentile) to determine the need to trigger the alarm for the onset of an epidemic. It also includes, for deeper analysis, demographic information.
IT Service Management System Measurement using ISO20000-1 and ISO15504-8: Developing a Solution-Mediated Process Assessment Tool to Enable Transparent and SMS Process Assessment
Authors: Asrowardi Imam , Putra Septafiansyah Dwi , Subyantoro Eko , Daud Norzaidi Haji Mohd
Published: January 2019

Information technology is about not only hardware, software, communication infrastructure and communication infrastructure but also how to manage services. Information technology plays an increasingly important role in developing the structure and functions of public and private sectors. Service measurement plays an important role in IT service management (ITSM) that is one of the subfields of Services Computing science. ITSM is a big part of service science, a science field that combines computer science, operation research engineering, business strategy, management science, and organizational theory. Performance measurement from each of IT services is absolutely needed and is important in the continuous development of ITSM. These research provide good technical knowledge about the measuring ITSM with some requirements. In this paper we suggest the metrics in each service processes enables organizations to predict a direction for active process enhancement and to identify if the goal of process can achieve. This objective process metrics based on ISO/IEC 15504-8 and PRM ISO/IEC 20000-4 refinement. The output of this research, in the form of metrics and tools for any type organizational use.
Simple Measurement System for Biological Signal Using a Smartphone
Authors: Miyauchi Ryoichi , Tanno Koichi , Tamura Hiroki
Published: January 2019

This paper describes simple measurement system for biological signal using smartphone. The proposed system consists of an instrumentation amplifier, a filter and an AC/DC converter. The biological signal is converted to the digital data through the microphone terminal with A/D converter in the smartphone. In many cases, the circuits require the power sources such as the cell batteries, however, the proposed system is supplied the power through the earphone terminal of the smartphone. Therefore, the proposed system no require the batteries. The software of this system parallelizes the processing so that the earphone output and the microphone terminal can be executed at the same time. The proposed system was verified through the measurement of surface electromyogram using discrete parts and iOS. Results of experimentation, the proposed system was operating correctly.
A Novel Technique for Tuning PI -controller In Switched Reluctance Motor Drive for Transportation Systems
Authors: Yaich Mohamed , Ghariani Moez
Published: January 2019

This paper presents, an optimal basic speed controller for switched reluctance motor (SRM) based on ant colony optimization (ACO) with the presence of good accuracies and performances. The control mechanism consists of proportional-integral (PI) speed controller in the outer loop and hysteresis current controller in the inner loop for the three phases, 6/4 switched reluctance motor. Because of nonlinear characteristics of a SRM, ACO algorithm is employed to tune coefficients of PI speed controller by minimizing the time domain objective function. Simulations of ACO based control of SRM are carried out using MATLAB /SIMULINK software. The behavior of the proposed ACO has been estimated with the classical Ziegler- Nichols (ZN) method in order to prove the proposed approach is able to improve the parameters of PI chosen by ZN method. Simulations results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on classical Ziegler-Nichols method.
Profit Driven Decision Assist System to Select Efficient IaaS Providers
Authors: MK Mohan Murthy , HA Sanjay , BM Supreeth
Published: January 2019

IaaS providers provide infrastructure to the end users with various pricing schemes and models. They provide different types of virtual machines (small, medium, large, etc.). Since each IaaS provider uses their own pricing schemes and models, price varies from one provider to the other for the same requirements. To select a best IaaS provider, the end users need to consider various parameters such as SLA, pricing models/schemes, VM heterogeneity, etc. Since many parameters are involved, selecting an efficient IaaS provider is a challenging job for an end user. To address this issue, in this work we have designed, implemented and tested a decision-assist system which assists the end users to select efficient IaaS provider(s). Our decision-assist system consists of an analytical model to calculate the cost and decision strategies to assist the end user in selecting the efficient IaaS provider(s). The decision assist system considers various relevant parameters such as VM configuration, price, availability, etc. to decide the efficient IaaS provider(s). Rigorous experiments have been conducted by emulating various IaaS providers, and we have observed that our DAS successfully suggests the efficient IaaS provider/ providers by considering the input parameters given by the user.
A Defense-in-depth Cybersecurity for Smart Substations
Authors: Dazahra M. N. , Elmariami F. , Belfqih A. , Boukherouaa J.
Published: January 2019

The increase of cyber-attacks on industrial and power systems in the recent years make the cybersecurity of supervisory control and data acquisition and substation automation systemsa high important engineering issue. This paper proposes a defense in depth cybersecurity solution for smart substations in different layers of the substation automation system. In fact, it presents possible vulnerabilities in the substation automation system and propose a multiple layer solution based on best practice in cyber security such as the hardening ofdevices, whitelisting, network configuration, network segmentation, role-based account management and cyber security management and deployement.
A Novel Intelligence-based e-Procurement System to offer Maximum Fairness Index in Ongoing Auction Process
Authors: Madhusudan N. , Rao L. Manjunatha
Published: January 2019

A perfect auction policy is one of the most strategic elements that contribute to success factor for any e-Procurement system. An auction policy can be only term as an effective if it really offer win-win situation to both the bidder as well as to the merchant. After reviewing existing studies on e-Procurement system, it is found that there isno effective research work focusing on this point and maximum research contribution has limited its scope to certain application or case studis. Hence, the proposed system introduces a novel e-Procurement system which is equipped by an itelligence-building process for performing predictive analysis of ongoing auction process. A mathematical modelling is implemented where all teh variables have been formed using practical implementation of auction system and followed by optimization process using regression-based approach. The study outcome shows that proposed system offers better response time and higher predictive accuracy in contrast to existing approaches.
A Novel Design of Voltage Controlled Oscillator By using the Method of Negative Resistance
Authors: Malki Ayoub , Abdellaoui Larbi El , Zbitou Jamal , Errkik A. , Tajmouati A. , Latrach Mohamed
Published: January 2019

The objective of this paper is to develop a new design of a voltage controlled microwave oscillator by using the method of negative resistance in order to fabricate VCO with very good performance in terms of tuning rang, phase noise, output power and stability. The use of hybrid microwave integrated circuit technology’s (HMIC) offers a lot of advantage for our structure concerning size, cost, productivity, and Q factor. This VCO is designed at [480MHz; 1.4GHz] frequency for applications in the phase locked loop (PLL) for signal tracking, FM demodulation, frequency modulation, mobile communication, etc. The different steps of studied voltage controlled oscillator’s design are thoroughly described. Initially designed at a fixed frequency meanwhile the use of a varactor allow us to tune the frequency of the second design. It has been optimized especially regarding tuning bandwidth, power, phase noise, consumption and size of the whole circuit. The achieved results and proposed amendment are the product of theoretical study and predictive simulations with advanced design system microwave design software. A micro-strip VCO with low phase noise based on high gain ultra low noise RF transistor BFP 740 has been designed, fabricated, and characterized. The VCO delivers a sinusoidal signal at the frequency 480 MHz with tuning bandwidth 920 MHz, spectrum power of 12.62 dBm into 50 Ω load and phase noise of -108 dBc/Hz at 100 Hz offset. Measurement results and simulation are in good agreement. Circuit is designed on FR4 substrate which includes integrated resonators and passive components.
Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules
Authors: Prakash R. Vijaya , Sarma S. S. V. N. , Sheshikala M.
Published: January 2019

Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.
Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation
Authors: Erwin Erwin , Saparudin Saparudin , Saputri Wulandari
Published: January 2019

This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy.
Trust Enhanced Role Based Access Control Using Genetic Algorithm
Authors: Mowla Saleh , Sinha Niharika , Ganiga Raghavendra , Shetty Nisha P.
Published: January 2019

Improvements in technological innovations have become a boon for business organizations, firms, institutions, etc. System applications are being developed for organizations whether small-scale or large-scale. Taking into consideration the hierarchical nature of large organizations, security is an important factor which needs to be taken into account. For any healthcare organization, maintaining the confidentiality and integrity of the patients’ records is of utmost importance while ensuring that they are only available to the authorized personnel. The paper discusses the technique of Role-Based Access Control (RBAC) and its different aspects. The paper also suggests a trust enhanced model of RBAC implemented with selection and mutation only ‘Genetic Algorithm’. A practical scenario involving healthcare organization has also been considered. A model has been developed to consider the policies of different health departments and how it affects the permissions of a particular role. The purpose of the algorithm is to allocate tasks for every employee in an automated manner and ensures that they are not over-burdened with the work assigned. In addition, the trust records of the employees ensure that malicious users do not gain access to confidential patient data.
Advanced SOM & K Mean Method for Load Curve Clustering
Authors: Binh Phan Thi Thanh , Le Trong Nghia , Xuan Nui Pham
Published: January 2019

From the load curve classification for one customer, the main features such as the seasonal factors, the weekday factors influencing on the electricity consumption may be extracted. By this way some utilities can make decision on the tariff by seasons or by day in week. The popular clustering techniques are the SOM & K-mean or Fuzzy K-mean. SOM &Kmean is a prominent approach for clustering with a two-level approach: first, the data set will be clustered using the SOM and in the second level, the SOM will be clustered by K-mean. In the first level, two training algorithms were examined: sequential and batch training. For the second level, the K-mean has the results that are strongly depended on the initial values of the centers. To overcome this, this paper used the subtractive clustering approach proposed by Chiu in 1994 to determine the centers. Because the effective radius in Chiu’s method has some influence on the number of centers, the paper applied the PSO technique to find the optimum radius. To valid the proposed approach, the test on well-known data samples is carried out. The applications for daily load curves of one Southern utility are presented.
Shape and Level Bottles Detection Using Local Standard Deviation and Hough Transform
Authors: Abdul Rahman Nor Nabilah Syazana , Saad Norhashimah Mohd , Abdullah Abdul Rahim
Published: January 2019

This paper presents shape and level analysis using local standard deviation and Hough transform technique to detect the shape and level of the bottle.A 155 sample images are used as a test product to detect shape and level. Local standard deviation is used contrast gain technique to segment the shape of the bottle by enhancing the contrast of the image. The ratio of the area is calculated from the extent parameter. The maximum and minimum water level is created by using Hough transform technique to identify the level of the water. Decision tree is applied to classify the shape and level of the bottle either good or defect condition. From experimental result, 97% and 93% accuracy of shape and level is achieved which shows that the proposed analysis technique is potential to be applied for beverages product inspection system.
Convolutional Neural Network and Feature Transformation for Distant Speech Recognition
Authors: Pardede Hilman F. , Yuliani Asri R. , Sustika Rika
Published: January 2019

In many applications, speech recognition must operate in conditions where there are some distances between speakers and the microphones. This is called distant speech recognition (DSR). In this condition, speech recognition must deal with reverberation. Nowadays, deep learning technologies are becoming the the main technologies for speech recognition. Deep Neural Network (DNN) in hybrid with Hidden Markov Model (HMM) is the commonly used architecture. However, this system is still not robust against reverberation. Previous studies use Convolutional Neural Networks (CNN), which is a variation of neural network, to improve the robustness of speech recognition against noise. CNN has the properties of pooling which is used to find local correlation between neighboring dimensions in the features. With this property, CNN could be used as feature learning emphasizing the information on neighboring frames. In this study we use CNN to deal with reverberation. We also propose to use feature transformation techniques: linear discriminat analysis (LDA) and maximum likelihood linear transformation (MLLT), on mel frequency cepstral coefficient (MFCC) before feeding them to CNN. We argue that transforming features could produce more discriminative features for CNN, and hence improve the robustness of speech recognition against reverberation. Our evaluations on Meeting Recorder Digits (MRD) subset of Aurora-5 database confirm that the use of LDA and MLLT transformations improve the robustness of speech recognition. It is better by 20% relative error reduction on compared to a standard DNN based speech recognition using the same number of hidden layers.
Twitter Sentiment Analysis on 2013 Curriculum Using Ensemble Features and K-Nearest Neighbor
Authors: Irfan M. Rizzo , Fauzi M. Ali , Tibyani Tibyani , Mentari Nurul Dyah
Published: January 2019

2013 curriculum is a new curriculum in the Indonesian education system which has been enacted by the government to replace KTSP curriculum. The implementation of this curriculum in the last few years has sparked various opinions among students, teachers, and public in general, especially on social media twitter. In this study, a sentimental analysis on 2013 curriculum is conducted. Ensemble of several feature sets were used twitter specific features, textual features, Parts of Speech (POS) features, lexicon based features, and Bag of Words (BOW) features for the sentiment classification using K-Nearest Neighbor method. The experiment result showed that the the ensemble features have the best performance of sentiment classification compared to only using individual features. The best accuracy using ensemble features is 96% when k=5 is used.
Credit Scoring Using Classification and Regression Tree (CART) Algorithm and Binary Particle Swarm Optimization
Authors: Malik Reza Firsandaya , Hermawan Hermawan
Published: January 2019

Credit scoring is a procedure that exists in every financial institution. A way to predict whether the debtor was qualified to be given the loan or not and has been a major concern in the overall steps of the loan process. Almost all banks and other financial institutions have their own credit scoring methods. Nowadays, data mining approach has been accepted to be one of the well-known methods. Certainly, accuracy was also a major issue in this approach. This research proposed a hybrid method using CART algorithm and Binary Particle Swarm Optimization. Performance indicators that are used in this research are classification accuracy, error rate, sensitivity, specificity, and precision. Experimental results based on the public dataset showed that the proposed method accuracy is 78 %. In compare to several popular algorithms, such as neural network, logistic regression and support vector machine, the proposed method showed an outstanding performance. 
Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs
Authors: Chandran Nisha , Gangodkar Durgaprasad , Mittal Ankush
Published: January 2019

Pattern based texture descriptors are widely used in Content Based Image Retrieval (CBIR) for efficient retrieval of matching images. Local Derivative Pattern (LDP), a higher order local pattern operator, originally proposed for face recognition, encodes the distinctive spatial relationships contained in a local region of an image as the feature vector. LDP efficiently extracts finer details and provides efficient retrieval however, it was proposed for images of limited resolution. Over the period of time the development in the digital image sensors had paid way for capturing images at a very high resolution. LDP algorithm though very efficient in content-based image retrieval did not scale well when capturing features from such high-resolution images as it becomes computationally very expensive. This paper proposes how to efficiently extract parallelism from the LDP algorithm and strategies for optimally implementing it by exploiting some inherent General-Purpose Graphics Processing Unit (GPGPU) characteristics. By optimally configuring the GPGPU kernels, image retrieval was performed at a much faster rate. The LDP algorithm was ported on to Compute Unified Device Architecture (CUDA) supported GPGPU and a maximum speed up of around 240x was achieved as compared to its sequential counterpart.
Pornography Addiction Detection based on Neurophysiological Computational Approach
Authors: Kamaruddin Norhaslinda , Abdul Rahman Abdul Wahab , Handiyani Dini
Published: January 2019

The rise of Internet access, social media and availability of smart phones intensify the epidemic of pornography addiction especially among younger teenagers. Such scenario may offer many side effects to the individual such as alteration of the behavior, changes in moral value and rejection to normal community convention. Hence, it is imperative to detect pornography addiction as early as possible. In this paper, a method of using brain signal from frontal area captured using EEG is proposed to detect whether the participant may have porn addiction or otherwise. It acts as a complementary approach to common psychological questionnaire. Experimental results show that the addicted participants had low alpha waves activity in the frontal brain region compared to non-addicted participants. It can be observed using power spectra computed using Low Resolution Electromagnetic Tomography (LORETA). The theta band also show there is disparity between addicted and non-addicted. However, the distinction is not as obvious as alpha band. Subsequently, more work need to be conducted to further test the validity of the hypothesis. It is envisaged that with more participants and further investigation, the proposed method will be the initial step to groundbreaking way of understanding the way porn addiction affects the brain.
Multi-Level of Feature Extraction and Classification for X-Ray Medical Image
Authors: Abdulrazzaq Mohammed Muayad , FT Yaseen Imad , Noah SA , A. Fadhil Moayad
Published: January 2019

There has been a rise in demand for digitized medical images over the last two decades. Medical images' pivotal role in surgical planning is also an essential source of information for diseases and as medical reference as well as for the purpose of research and training. Therefore, effective techniques for medical image retrieval and classification are required to provide accurate search through substantial amount of images in a timely manner. Given the amount of images that are required to deal with, it is a non-viable practice to manually annotate these medical images. Additionally, retrieving and indexing them with image visual feature cannot capture high level of semantic concepts, which are necessary for accurate retrieval and effective classification of medical images. Therefore, an automatic mechanism is required to address these limitations. Addressing this, this study formulated an effective classification for X-ray medical images using different feature extractions and classification techniques. Specifically, this study proposed pertinent feature extraction algorithm for X-ray medical images and determined machine learning methods for automatic X-ray medical image classification. This study also evaluated different image features (chiefly global, local, and combined) and classifiers. Consequently, the obtained results from this study improved results obtained from previous related studies.
Classification of Leaf Disease from Image Processing Technique
Authors: Md Kamal Mahanijah , Ikhwan Masazhar Ahmad Nor , Abdul Rahman Farah
Published: January 2019

Disease in palm oil sector is one of the major concerns because it affects the production and economy losses to Malaysia. Diseases appear as spots on the leaf and if not treated on time, cause the growth of the palm oil tree. This work presents the use of digital image processing technique for classification oil palm leaf disease sympthoms. Chimaera and Anthracnose is the most common symtoms infected the oil palm leaf in nursery stage. Here, support vector machine (SVM) acts as a classifier where there are four stages involved. The stages are image acquisition, image enhancement, clustering and classification. The classification shows that SVM achieves accuracy of 97% for Chimaera and 95% for Anthracnose.
Vehicle Accident Report Application for Solving Traffic Problems and Reduce the Ratio of Pollution using Case Study: Kuwait City
Authors: Alkandari Abdulrahman , Moein Samer
Published: January 2019

Minor traffic accidents have become a major problem facing the road users in the recent years, according to the statistics from the Ministry of Interior (MOI) in Kuwait there were recorded 80,388 accidents by the year 2014. Accidents not only affect the mobility but also contribute to air pollution and slow down economic growth. These effects are the result of the seriously extended trips travel time due to accumulated vehicles queue. In some accidents cases, the lost time waiting for the arrival of the traffic officers and filling up the accident report could take up to 45 minutes. The new idea of Vehicle Accident Report application (I-VAR) concept developed by the research team would reduce the waiting time up to 3 minutes (93% savings), which would increase the level of service of the segment of a roadway. In addition, the study will be discussed four major situations on some of the busiest roads in Kuwait. Specifically, gas emissions and cost estimation. Improve the pollution obviously, by using the (I-VAR) application for the minor accidents there is an amount of 360,776,460 K.D would be saved yearly from the Kuwait government funds. It is a consequence of the huge savings in alleviating traffic congestion and generally produces more saver and efficient travel conditions.
Adopting Hardware-In-the-Loop for Testing Vehicle Instrument Panel using Economical Approach
Authors: Wan Jamaludin Wan Shahmisufi , Wei Ren Tan , Affendi Rosdi Bakhtiar , Ishak Dahaman , Hafizi Hanafi Noor , Nasiruddin Mahyuddin Muhammad
Published: January 2019

An economical approach for testing Vehicle Instrument Panel is proposed in this paper due to high expenditure of purchasing the available Commercial Off-The-Shelf Hardware-In-The-Loop. Vehicle Instrument Panel is designated as the Device-Under-Test in this paper. The Hardware-In-The-Loop, designated as the test equipment, will simulate the assigned input signals controllable via designed Graphical User Interface. The resulting display is shown on the Graphical User Interface and the Device-Under-Test. The speedometer gauge measurement showed the highest disparity of 4 km/h which is within the tolerance of the pre-determined specification of the Device-Under-Test.
An Analysis of a Flexible Dry Surface Electrodes
Authors: Wong Azman Amelia , Azman Muhammad Farhan , Ariff Siti Mohd , Mustafah Yasir Mohd , Mohd Ramli Huda Adibah , Alam AHM Zahirul , Habaebi Mohamed Hadi
Published: January 2019

In the medical field, electrodes are commonly used either to retrieve signals or to conduct current. Most of the off-the-shelf surface electrodes are made from metal or rigid substrates. This paper presents a work on designing a new flexible dry electrodes using poly(3,4-ethylenedioxythiophene) polystyrene sulfonate and silver by means of dispenser printing technology. The polyester cotton fabric was selected as the substrate in this electrode designed. To analyse the new proposed composites of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate and silver, different mixtures have been applied.  Results from the experiment show that the conductivity of the proposed flexible electrode is comparable with the commercialized pre-gelled electrode when applied to an electrical stimulator device. Eight out of ten subjects under test described no difference in comfort between the proposed electrodes and pre-gelled electrodes.
Type-2 Fuzzy Logic in Pair Formation
Authors: Shrinivasan Lakshmi , R. Rao J. l
Published: January 2019

This paper gives an overview of Type-2 Fuzzy sets (T2FSs) and Type-2 fuzzy Logic system (T2FLS) considering one aviation scenario. The existing type-1 Fuzzy system has limited capability to handle the uncertainty directly. In order to overcome the limitations of Type-1 fuzzy Logic system (T1FLS), a next level of fuzzy set is introduced, that is known as T2FSs. Here we will discuss about: Type-2 fuzzy sets, type-2 membership functions, inference engine, type reduction and defuzzification. Pair formation is the undertaken aviation scenario which is very critical in a fighting situation. Crisp data are taken by the sensors of aircraft and with the techniques of data fusion, a constant decision is passed whether two aircrafts can achieve pair formation or not. Experiments are evaluated and performance is compared with ground truth and existing T1FLS, which proves better in terms of decision making while a certain amount of uncertainty is present.
IoT: Electrocardiogram (ECG) Monitoring System
Authors: Ortiz Kristine Joyce P. , Davalos John Peter O. , Eusebio Elora S. , Tucay Dominic M.
Published: January 2019

Internet of Things (IoT) has many applications in the medical field. With remote-information gathering, healthcare professionals can evaluate, diagnose and treat patients in remote locations using telecommunications technology. This study aimed to develop a small-scale electrocardiogram (ECG) monitoring device that will measure heart rates and waveforms and send the data in a database and a web server. An ECG acquisition device was developed using a single-lead heart rate monitor sensor and an Arduino microcontroller. A program, which will process, analyze and upload the ECG data is coded using MATLAB and C# programs. The collected information is viewed in a Graphical User Interface (GUI) display, coded using C# and in a webpage. Rapid Application Technology (RAD) was used in the methodology, which began with a quick design of the system. The hardware and software systems underwent a prototyping cycle for development. Once finished, the integration of the system is conducted to construct a complete IoT-based ECG monitoring system. For testing using t-test, a sample size of 18 and a a= 0.05 is used. Testing resulted into t-test values that lie in the non-critical zone for all ECG parameters, denoting that there is no significant difference between the gathered data. The device’s percent reliability in detecting ECG conditions such as normal sinus rhythm, sinus tachycardia, sinus bradycardia and flatline, is 83.33%. The percent difference for the heart rate is 0.35 %, which falls within the acceptable medical standard of 99% accuracy. The device was deemed functional and reliable.