bluuebunny/crossref_metadata_2025 huggingface dataset:
Hadoop Security Challenges and Its Solution Using KNOX
Authors: N Sirisha , K V D Kiran , Karthik R
Published: January 2019

Big Data is a new technology and architecture. It can work on a very large volume of a variety of data with high-velocity, discovery, and/or analysis. Big Data is about the fast-growing sources of data such as web logics, Sensor networks, Social media, Internet text and documents, Internet pages, Search Index data, scientific research. Big data also formally introduces a complex range of analysis. Big data can evaluate mixed data (structured and unstructured) from multiple sources. As there are some security issues in big data which are no longer solved using the hashing techniques on large amount of data, this paper shows an idea of new approach of designing a Knox’ified Hadoop cluster.
An Accurate Medium-Term Load Forecasting based on Hybrid Technique
Authors: Yasin Z.M. , Aziz N.F.A. , Salim N.A. , Wahab N.A. , Rahmat N.A.
Published: January 2019

An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM.
The Embedding Performance of StegSVM Model in Image Steganography
Authors: Shaker Hussain Hanizan , Din Roshidi , Hanif Ali Mohd , Balqis Nor
Published: January 2019

This paper focuses on one of the areas of information hiding which is image steganography. It proposes the StegSVM model as an embedding technique in steganography that has exploited human visual system through Shifted LSB that shows an expected performance. The performance of this technique evaluation is based on imperceptibility and robustness of the technique compared to the other previous models in image steganography doamin. Thus, the result shows that the proposed StegSVM model is promising. For further work, it is suggested that the other image domain through other intelligent methods should be investigated.
Validation of S-parameter Power Cable Joint Model on Two Cable Defect Sections
Authors: Mei Kuan Tze , Sulaiman Suhaila , Mohd. Ariffin Azrul , Ridzuan Radhi Mohd Amjad
Published: January 2019

Electricity disruption due to power cable failure is a major challenge to power utility companies worldwide. A technique which provides quick response with accurate result is needed to detect defects along the cable to reduce the disruption time. This paper discusses the application of time domain reflectometry (TDR) technique to identify and locate the defect along a cable. A cable system is modelled in MATLAB/Simulink to replicate the cable configuration with two joints using the scattering parameter (S-parameter). This research studies the fault identification for two degraded sections in a cable by analyzing the reflected signal from the TDR technique. The characteristics of the reflected signal which indicates the defect cable section are discussed in detail in this paper. Results from MATLAB/Simulink simulations are also compared to the experimental results of the same cable configuration. This study shows that the signal reflection characteristics from MATLAB/Simulink simulations are similar to the experimental results. This indicates that the cable model system built using MATLAB/Simulink replicates the actual cable system accurately which can be used for further analysis of cable fault localization.
Modelling of a Low Frequency Based Rectangular Shape Piezoelectric Cantilever Beam for Energy Harvesting Applications
Authors: Mohamed Ramizi , Sarker Mahidur R. , Mohamed Azah
Published: January 2019

Harvesting few amount of charge from environmental ambient sources namely, wind, thermal, heat, vibration, solar utilizing micro scale energy harvesting devices, offers vast view of powering for numerous portable low power electronic devices. However, power harvesting using piezoelectric crystal from low power ambient source nowdays has increasing popularity with the advantages of low cost, long life time, stability and clean energy.  Recent trends have shown that most researchers are interested in designing a low resonance frequency vibration based energy harvesting devices despite of its challenges ahead. In this paper, a low frequency based rectangular shape piezoelectric cantilever beam has been developed for energy harvesting applications. The proposed vibration based low frequency cantilever beam using piezoelectric element has been developed by finite element analysis (FEA) employing COMSOL Multiphysics platform. The main goal of the study is to analyze the outcome of geometric model of a piezoelectric cantilever beam and to calculate the resonance frequency of the structure according to its length. The material of PZT-5H, has been considered to enhance the efficiency of the low frequency based cantilever beam. Finally, the proposed result is compared with other existing works.
Voltage & Current Magnitude Pattern Recognization by Using Fuzzy Logic Toolbox for Fault Types Classification
Authors: Awalin Lilik J. , Fatini Fatini , Abdullah M. N. , Tay L.T. , Ab. Hamid M. Fairuz , Ismail Bazilah
Published: January 2019

This research introduces the appropriate input pattern of Fuzzy Logic design for fault type classification of Single Line to Ground Fault at distribution network. The proposed design is solely using Fuzzy Logic as the research technique with input data from PSCAD simulation. PSCAD software simulate the circuit configuration for fault disturbance at the distribution network. The research technique was applied with multiples input values of voltage and current that extracted from the PSCAD simulation. This research testifies the output result by using different fault resistance values; 0.01Ω, 10Ω, 30Ω, 50Ω and 70Ω. Voltage sag and current swell of phase a, b and c that were obtained from the PSCAD simulation have been used as the input variables for Fuzzy Logic design. The acquired results that represented in average accuracy shown that voltage sag and current swell can draw a satisfying accuracy in classifying the fault type.
Intrinsic ZnO/Al-doped ZnO Homojunction: Structural and Optical Properties
Authors: Ismail A.S. , Mamat M.H. , Malek M.F. , Yusoff M.M. , Md. Sin N.D. , Shariffudin S.S. , Zoolfakar A.S. , Suriani A.B. , Ahmad M.K. , Shameem Banu I. B. , Rusop M.
Published: January 2019

Intrinsic zinc oxide (ZnO)/Al-doped ZnO (AZO) homojunction film was prepared using two-step immersion processes. The film was characterized using field emission scanning electron microscopy, X-ray diffraction (XRD), Raman spectroscopy, and ultraviolet–visible spectrophotometer to investigate their structural and optical properties. The surface morphology image displays that the ZnO deposited on the nanorod surfaces in layer form with average diameter of nanorods about 95 nm. The structural properties of XRD pattern demonstrate that the film possessed good crystallinity with the preferred orientation at (002) plane. The film also possessed excellent absorption in the ultraviolet (UV) region with optical band gap energy of 3.22 eV. These results indicate that the film has a good potential for optical-based device such a UV sensor.
Power Cable with Two Joints Experimental Analysis for Defect Assessments
Authors: Kuan Tze Mei , Ariffin Azrul Mohd.
Published: January 2019

Time domain reflectometry (TDR) is an easy technique that provides quick response which is ideal for power utility companies to conduct cable diagnostics on-site. Electricity disruption due to power cable failure is a major challenge for power utility companies due to the long length of cable installed with joints. The long time taken to diagnose the defect along the cable before electricity can be restored has not only jeopardized the reputation of power utility companies but also brings losses to the economy. Hence, this study conducts experimental analysis on cable with two joints with the application of TDR technique to reduce the electricity disruption time. This research is divided into two stages where stage 1 conducts experiments on cable with one degraded section while stage 2 looks into cable with two degraded sections. The TDR reflection characteristics are studied from stage 1 experiments and applied to stage 2 experiments to verify the consistency of the TDR reflection characteristics. The cable conditions of stage 2 experiments are predicted using the reflection characteristics from stage 1 observations and are then validated by comparing these predictions with the actual cable configuration. Results obtained from these experiments have proven that the TDR reflection characteristics are consistent and accurate which can be used to sectionalize the degraded cable section. Detail findings of all experiments conducted with the TDR application are discussed in part three of this paper.
A Simulation of Energy Recycling Concept in Automotive Application Using Hybrid Approach
Authors: Samsudin Noor Azah , Syariff Aripin Muhammad , Ahmad Khalid Shamsul Kamal , Amri Nordin Nor Amirul
Published: January 2019

This paper presents development of a simulation to demonstrate a relatively new hybrid approach in improving energy resources that is applicable in automotive industry. The existing hybrid approach in automotive industry is considerably efficient in terms of energy saving by switching between fuel and electricity for energy resources. However, both energy resources confront various challenges. While the electricity resources require recharging, the fuel resources are scarce and expensive. Therefore, in this paper we aim to propose a relatively new hybrid approach, referred to as energy recycling concept equipped with coordination algorithm. To simulate the proposed energy recycling concept, a prototype of Electrical Control Unit (ECU) car is built. Then, an algorithm that coordinates battery charging is developed and integrated with the ECU. Finally, the simulation of the proposed energy recycling concept equipped with the coordination algorithm is evaluated on the prototype of the ECU car. The results show that the proposed energy recycling concept that allows switching between two sources of energy is applicable to operate the ECU car prototype. 
Impact of Combine Dithering and Modulators to Mitigate Noise in Radio Over Fiber System
Authors: Hario Fakhriy , Maulana Eka , Suyono Hadi , Hasanah Rini N , Pramono Sholeh H
Published: January 2019

A well-prepared Radio over Fiber (RoF) is a technology that combines two transmission technologies, radio and optical fiber transmissions. The study focused on the characteristics and problems of the optical fiber medium. One of the problems in the optical fiber is the effect of nonlinear characteristic, which caused by the high light intensity in the optical fiber core with extended interaction area in a single mode fiber (SMF). This characteristic reduces the output width and creates a pulse broadening. The nonlinear characteristics discussed in this study focused on SPM (self-phase modulation) and GVD (Group Velocity Dispersion). To overcome the nonlinear problems, this study presented a method to make the noise-resistant transmitted signal and improve the optical fiber power range. The fundamental of this study was developing similarities of previous studies regarding nonlinearity in the optical fiber. The results show that the use of two modulators combined with the amplification generated the signal with smoother spectrum, which means that the spectrum distribution was more uniform. There was 61.5 % increase of the peak power of the output signal after amplification using an optical amplifier.
Robust Visual Multi Target Trackers: A Review
Authors: E Pavan Kumar , Rajgopal Manojkumar
Published: January 2019

In this review paper, we address on the state-of-art process with visual object tracking in video surveillance, medical and military applications. In the present scenario number of algorithms and methods are used to track the object in the different scene, a robust visual object tracking remains a critical challenge. The challenges arise due to object motion from frame to frame with a change in appearance, structures, illumination, and occlusion. In this paper, at last, we outline the different algorithms, dataset, strength, and weakness of the different object tracker.
Theoretical Analysis and Empirical Comparison of Different Population Initialization Techniques for Evolutionary Algorithms
Authors: K Devika , Jeyakumar Guruswamy
Published: January 2019

Evolutionary Algorithms (EAs) are the potential tools for solving optimization problems. The EAs are the population based algorithms and they search for the optimal solution(s) from an initial set of candidates solutions known as population. This population is to be initialized at first before the evolution of the algorithm starts. There exists different ways to initialize this population. Understanding and choosing the right population initialization technique for the given problem is a difficult task for the researchers and problem solvers. To alleviate this issue, this paper is framed with two objectives. The first objective is to present the details of various Population Initialization (PI) techniques of EAs, for the readers to give brief description of all the PI techniques. The second objective is to present the steps and empirical comparison of the results of two different PI techniques implemented for Differential Evolution (DE) algorithm. Theoretical insights and empirical results of the PI techniques are presented in this paper.
Celebrity Face Recognition using Deep Learning
Authors: Binti Mat Kasim Nur Ateqah , Binti Abd Rahman Nur Hidayah , Ibrahim Zaidah , Abu Mangshor Nur Nabilah
Published: January 2019

Face recognition is one of the well studied problems by researchers in computer visions.  Among the challenges of this task are the occurrence of different facial expressions like happy or sad, and different views of the images such as front and side views.  This paper experiments a publicly available dataset that consists of 200,000 images of celebrity faces. Deep Learning technique is gaining its popularity in computer vision and this paper applies this technique for face recognition problem.  One of the techniques under deep learning is Convolutional Neural Network (CNN).  There is also pre-trained CNN models that are AlexNet and GoogLeNet, which produce excellent accuracy results.  The experimental results indicate that AlexNet is better than basic CNN and GoogLeNet for face recognition.
Insulation Properties of PMMA:TiO2 Nanodielectric Film for High Voltage Applications
Authors: Welan Mathew Melvin , Nyl Ismail Lyly , Sihab Norsabrina , Abd Samad Ahmad Asri , Ashida Salim Nur
Published: January 2019

This research investigates the insulation properties of poly methyl methacrylate with titanium dioxide (PMMA:TiO2) nanodielectric film to be used as insulation layer in high voltage (HV) applications. AC and DC breakdown test has been conducted to investigate the insulation properties of PMMA:TiO2 nanodielectric to be used as solid insulation in HV. In this research, the PMMA:TiO2 nanodielectric film were prepared by varying the annealing temperature (60°C up to 180°C) and spin coating techniques wwere used to form the PMMA:TiO2 nanocomposite film. The insulation properties of the nanocomposite film were characterized using high voltage test (DC and AC breakdown strength) to identify which nanocompoiste film has the good insulation properties. In addition, different types of electrodes are also used to to measure the insulation properties of nanocompoiste film. The structural properties of PMMA:TiO2 nanocomposite films were characterized using atomic force microscopy (AFM). Results from HV test shows that the annealing temperature affected the insulation properties PMMA:TiO2 nanocomposite film. By increasing the annealing temperature, the insulation properties decreased. Film anneal at 120 oC has the good insulation compare to other temperature. 120 oC are the optimize temperature to prepared the PMMA:TiO2 nanocomposite films. Result from different types of electrodes show that sharp tip has high DC breakdown strength compare to spherical electrodes.
Brain Developmental Disorders’ Modelling based on Preschoolers Neuro-Physiological Profiling
Authors: Wahab Abdul , Kamaruddin Norhaslinda
Published: January 2019

Frequently misunderstood by their teachers as being low performers, children with learning disabilities (LDs) such as dyslexia, ADHD, and Asperger’s Syndrome develop low self-confidence and poor self-esteem that may lead to the risk of developing psychological and emotional problems. On contrary, research has shown that a substantial number of these children are capable of learning, and hence, are high-functioning. Therefore, there is a need to provide for the early detection of LDs and instruction that focuses on their needs based on their profiles. Profiling is normally done through observations on the psychological manifestations of LDs by parents and teachers as third-party observers. The first party experience, which is reflected through brain manifestations, is often overlooked. Hence the aim of this paper is to present an alternative solution to profile young children with LDs using electroencephalogram (EEG) that capture brain signals to measure brain functionalities and correlate them with the different LDs. Studies on neurophysiological signals and their relationship to LDs are used to develop Computational Neuro-Physiological (CN-P) model to be an alternative in quantifying the children brain activation function related to learning experience. It is envisaged that such model can profile children with learning disabilities to provide effective intervention in timely manner which can help teachers to provide differentiated instruction for children with LDs. This is in line with the thrust of the Education National Key Result Area (NKRA), the Malaysia Education Blueprint 2013-2025, and the Special Education Regulations 2013.
Comparison of Weak Load Bus Detection using LQP_LT Index with PV and QV Analysis of PSS/E
Authors: Verayiah Renuga , Mohamed Azah
Published: January 2019

Identification of weak load buses which contributes to voltage instability problem is crucial in order for an appropriate mitigation action to be executed. The current power system transmission is not only stressed to deliver high load demand at the receiving end but also facing new challenges brought by the penetratrion of renewable energy sources. This new scenario requires power system operation and analysis to be robust and fast in detecting the accurate weak load bus for correction action. Due to this, many online indices to detect weak load bus during power system contingency have been developed. Nevertheless, LQP_LT is of the latest index developed which ultimately has the reactive power tracing capability for weak load bus detection and generate priority ranking list of the weak load buses. This index was tested on IEEE 14 bus test system for different contingency scenarios. The results obtained from the LQP_LT index is compared and validated with the PV and QV analyses obtained using industrial graded PSS/E software. It was concluded that the LQP_LT index is found to be robust, efficient and need less computation time as compared to the execution of voltage stability analysis using the PSS/E Tool.
Techno-economic Influence on Malaysia’s Solar Thermal Installation for Heating Process
Authors: Sabirin Baharom Anis , Yenita Dahlan Nofri
Published: January 2019

This paper presents System Dynamics simulation and Techno-Economic analysis to estimate the techno-economic benefits and reliabilities of solar heating. The study focuses only on industrial heat process in Malaysia. The main objective of the current work was to investigate the solar thermal installation feasibility by Malaysian industrial heat process for next 20 years. The solar thermal installation was measured through the techno-economic feasibility (TEF) study by developing the TEF model using System Dynamics Simulation (SDS). subsidy, net solar thermal cost, and NPV are used as economic influence factors. System performance, solar thermal life time, energy, solar fraction and size of collector area are used as technical influence parameters. Results have shown that the economic factor has a bigger influence on solar thermal installation capacity in Malaysia. The technical factors only contribute small percentage to the installation percentage. Policy maker could benefit from the outcomes obtained as a guideline.
Information Structure Framework for ISMS Planning and Certification: Malaysian Data
Authors: Shamala Palaniappan , Chinniah Muruga , Mohd Foozy Cik Feresa , Chai Wen Chuah , Mustapha Aida , Ahmad Rabiah
Published: January 2019

Information security are becoming an important aspect of organizations. Organisations also are progressively conscious of its important in their business strategy. The awareness make organisations are currently applying for information security management system (ISMS) to effectively manage their information assets. Therefore, this research aims to provide an Information Structure Framework for ISMS planning and certification. Then Likert structured questionnaire was distributed and the findings have been analyzed using Rasch Measurement Model (RMM). The results from this study, managed to develop Information Structure Framework for ISMS. The proposed framework consists of information structure focuses on providing the information outline which is structured in a way, in which the components are put together to form a meaningful structure which can be navigated at any time. The framework contributes to the field of ISMS and certification area. The framework provides an awareness on knowing beforehand what to do and to what extent they are already conquering the information needed for getting clear direction and to develop ISMS.
A Modular and Extensible Framework for Human Resource System
Authors: Abdul Halim Muhammad Zabir , Mohd Rosli Marshima
Published: January 2019

Human Resource is an essential part for an organization and integration of technology would just enhance the effectiveness. One of the scope that Human Resource must take note is performance and productivity. However, there is still no infallible or highly reliable way to measure productivity and performance of an organization and its employees for current industrial Human Resource systems. There is still possible improvement that can be made to the system in the current time. The purpose of this study is to propose a modular and extensible framework of Human Resource systems that used to measure the performance and productivity of an organization. This study compares the different criteria of existing Human Resource systems to ensure the proposed framework would surpassed the current real world industrial system. The results of the review provide insights for important criteria in HR system to increase the accuracy of the performance review. This study also constructs Entity Relationship Diagram (ERD) to demonstrate the logical structure of the proposed framework. The ERD will form the foundation of the proposed framework to improve the Human Resource system for evaluating the productivity and performance of an organization.
Automated Calibration Of Greenhouse Energy Model Using Hybrid Evolutionary Programming (EP)-Energy Plus
Authors: Dahlan NY , Z. Sakimin S. , Faizwan M. , Ajmain N. , A. Aris A.
Published: January 2019

This paper presents an optimization approach of calibrating a tomato greenhouse energy model using hybrid Evolutionary Programming (EP)-EnergyPlus. The proposed methodology applies automated simulation-based approach by coupling Matlab and EnergyPlus to perform building energy simulation and obtain the best variables configuration with minimal error between the simulated and measured energy of the greenhouse. The proposed method is tested using a tomato greenhouse system located in Universiti Putra Malaysia (UPM). The greenhouse envelope is built using 0.15mm thick Transparency Plastic Film. Meanwhile, the electrical loads in the greenhouse consists of 6 exhaust fans, 2 axial fans, 5 fluorescent lamps and 1 irrigation pump. An Evolutionary Programming (EP) algorithm is chosen and programmed in Matlab to find the best configurations for optimum calibration of the greenhouse energy model. Three variables were chosen to find the best configuration which are the operating hours of Exhaust Fan, Axial Fan and Water Pump. The EP optimization algorithm in Matlab is coupled with building energy simulator, EnergyPlus using BCVTB as the coupling tool. Result shows that the EnergyPlus-EP model can provide NMBE and CV(RMSE) within the range recommended by the IPMVP protocol. The proposed method is not only requiring less computation time but also effective in searching for the best variables configuration with minimal error.
Link Adaptation Algorithm for IEEE 802.11 Wireless Local Area Netwoks in Fading Channel
Authors: W Muhamad Wan Norsyafizan , Seroja Sarnin Suzi , Idris Azlina , Syahira Aliya
Published: January 2019

This paper proposes an enhance version of the dual parameter based cost function algorithm for time-varying environments. For ideal channel condition, the proposed cost function algorithm adaptively change the transmission data rate according to traffic load condition. However, similar approach is not well-suited for time-varying environment. For time-varying condition, transmission of packet at lower transmission data rate is much preferred in order to achieve reliable transmission. In addition to that, packet transmission at lower data rate contributes to low energy consumption due to lower transmit power used to transmit packet. This paper presents a combination of cost function algorithm with adaptive rate/power. Adaptation of transmission data rate and transmit power for time varying condition are carried out based on received power values. An OPNET based simulation model was developed to analyses the performance of the propose algorithm. Simulation results show the proposed algorithm can significantly improve the energy efficiency of a WLAN while maintaining the network QoS in fading channel environments.
Optical Sensor Based on Dye Sensitized Solar Cell (DSSC)
Authors: Rahmadwati Rahmadwati , Nainy Sari Sapriesty , Maulana Eka , Sabarudin Akhmad
Published: January 2019

An optical sensor is designed to convert a number of light energy in to electrical energy. The sensor hasbeen successfully measured using light illuminance to achieve electric parameters as the sensor output. In this research the optical sensor design was characterized according to the voltage and current output with the stimulus from mercury lamp. The sensor is customized from Dye-Sensitized Solar Cell with photo-electrode and photo-catalysator of Titanium Dioxide and extracted tobacco chlorophyll dye. Spin coating method was conducted to fabricate the thick layer deposition using selected material. Based on the absorbance measurement, it shows that tobacco dye has the characteristics of visible light absorption in the wavelength of 300-000 nm. The result of this research revealed that from 2 variation of optical sensor design square with active area of (2 cm x 2 cm) and (1 cm x 1 cm). Analitycal result shows that the sensor has wide linear characteristic in certain light illuminance both of output current and voltage.
Prediction of Solar Radiation Intensity using Extreme Learning Machine
Authors: Suyono Hadi , Santoso Hari , Nur Hasanah Rini , Wibawa Unggul , Musirin Ismail
Published: January 2019

The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m2 and 84.04 W/m2, while using the MLR method they are 141.18 W/m2 and 104.87 W/m2 respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.
Neural Network Prediction for Efficient Waste Management in Malaysia
Authors: Hajar Yusoff Siti , Abdullah Din Ummi Nur Kamilah , Mansor Hasmah , Shahida Midi Nur , Azra Zaini Syasya
Published: January 2019

Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on population growth factor. This study uses Malaysian population as sample size and the data for weight is acquired via authorized Malaysia statistics’ websites. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with ten and five nodes were used respectively. The result portrayed that there will be an increase of 29.03 percent of SWG in year 2031 compared to 2012. The limitation to this study is that the data was not based on real time as it was restricted by the government.
Design of Smart Waste Bin and Prediction Algorithm for Waste Management in Household Area
Authors: Hajar Yusoff Siti , Abdullah Din Ummi Nur Kamilah , Mansor Hasmah , Shahida Midi Nur , Azra Zaini Syasya
Published: January 2019

Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on household size factor. Kulliyyah of Engineering (KOE) in International Islamic University Malaysia (IIUM) has been chosen as the sample size for household size factor. A smart waste bin has been developed that can measure the weight, detect the emptiness level of the waste bin, stores information and have direct communication between waste bin and collector crews. This study uses the information obtained from the smart waste bin for the waste weight while the sample size of KOE has been obtained through KOE’s department. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with five and ten nodes were used respectively. The result portrayed that the average rate of increment of waste weight is 2.05 percent from week one until week twenty. The limitation to this study is that the amount of smart waste bin should be replicated more so that all data for waste weight is directly collected from the smart waste bin.
Application-based Smart Parking System using CAN Bus
Authors: Patil Rahul Muppanagouda , Vinay N R , D Pratiba
Published: January 2019

We have witnessed a rapid growth in the Internet of Things and its contribution to the growth of Smart cities in recent years. Consistent efforts have been undertaken by the Government of India to elevate the growth of this industry. The increase in population and vehicular densities have led to congested roads, inadequate parking facilities and poor infrastructure has called for a technology-driven solution known com-monly as a Smart Parking System (SPS). The solution proposed in this paper is a client-based parking slot reservation system that is implemented using micro-controllers interfaced with sensors for parking vacancy detection. They are also connected to cloud services in order to produce a complete IoT solution, with each micro-controller acting as a node in a network. Additionally, the CAN protocol is deployed for communication between nodes of this network in case of failure of the nodes in the wireless network. This is a solution de-veloped that caters to users mainly residing in Smart Cities and addresses the issue of network node failure.
Ideal Huffman Code for Lossless Image Compression for Ubiquitous Access
Authors: Kavitha T , Jayasankar K.
Published: January 2019

Compression technique is adopted to solve various big data problems such as storage and transmission. The growth of cloud computing and smart phone industries has led to generation of huge volume of digital data. Digital data can be in various forms as audio, video, images and documents. These digital data are generally compressed and stored in cloud storage environment. Efficient storing and retrieval mechanism of digital data by adopting good compression technique will result in reducing cost. The compression technique is composed of lossy and lossless compression technique. Here we consider Lossless image compression technique, minimizing the number of bits for encoding will aid in improving the coding efficiency and high compression. Fixed length coding cannot assure in minimizing bit length. In order to minimize the bits variable Length codes with prefix-free codes nature are preferred. However the existing compression model presented induce high computing overhead, to address this issue, this work presents an ideal and efficient modified Huffman technique that improves compression factor up to 33.44% for Bi-level images and 32.578% for Half-tone Images. The average computation time both encoding and decoding shows an improvement of 20.73% for Bi-level images and 28.71% for Half-tone images. The proposed work has achieved overall 2% increase in coding efficiency, reduced memory usage of 0.435% for Bi-level images and 0.19% for Half-tone Images. The overall result achieved shows that the proposed model can be adopted to support ubiquitous access to digital data.
Object Recognition Inspiring HVS
Authors: Akbarpour Mohammadesmaeil , Mehrshad Nasser , Razavi Seyyed-Mohammad
Published: January 2019

Human recognize objects in complex natural images very fast within a fraction of a second. Many computational object recognition models inspired from this powerful ability of human. The Human Visual System (HVS) recognizes object in several processing layers which we know them as hierarchically model. Due to amazing complexity of HVS and the connections in visual pathway, computational modeling of HVS directly from its physiology is not possible. So it considered as a some blocks and each block modeled separately. One models inspiring of HVS is HMAX which its main problem is selecting patches in random way. As HMAX is a hierarchical model, HMAX can enhanced with enhancing each layer separately. In this paper instead of random patch extraction, Desirable Patches for HMAX (DPHMAX) will extracted.  HVS for extracting patch first selected patches with more information. For simulating this block patches with more variance will be selected. Then HVS will chose patches with more similarity in a class. For simulating this block one algorithm is used. For evaluating proposed method, Caltech 5 and Caltech101 datasets are used. Results show that the proposed method (DPMAX) provides a significant performance over HMAX and other models with the same framework.
Radio Access Technology (RAT) Selection Mechanism using TOPSIS Method in Heterogeneous Wireless Networks (HWN)
Authors: Anwar Farhat , Masud Mosharrof , Islam Burhan Ul , Olanrewaju Rashidah Funke
Published: January 2019

In next-generation wireless networks, a Multi-Mode Device (MMD) can be connected with available Radio Access Technology (RAT) in a Heterogeneous Wireless Network (HWN). The appropriate RAT selection is essential to achieve expected Quality of Service (QoS) in HWN. There are many factors to select an appropriate RAT in HWN including Data rate, Power consumption, Security, Network delay, Service price, etc. Nowadays, the MMDs are capable to handle with multiple types of services like voice, file downloading, video streaming. Considering numerous factors and multiple types of services, it is a great challenge for MMDs to select the appropriate RAT. A Multi-Attribute Decision Making (MADM) method to deal with numerous attributes to achieve the expected goal is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This research utilized TOPSIS method to evaluate its proposed algorithm to choose the proper RAT for single and dual call services. The algorithm applies users' preference of a specific RAT that varies for diverse categories of calls. It also aggregates the assigned call weight and call priority to choose the RAT for group call admission for different scenarios. The highest closeness coefficient has been considered the appropriate networks among other networks. 100 call admission into three networks has been simulated and has been observed.
Impact of MRAI Timer on BGP Updates and Convergence Time
Authors: Devikar Rohit Nilkanth , Patil Dipak V. , Chandra Prakash V
Published: January 2019

BGP is a vital routing protocol for the communication amongst autonomous systems in the internet and has been broadly applied in all categories of large scale network. The inter-domain routing protocol (BGP) shows slow convergence, which effects on many internet applications due to its high convergence delay. The network operators broadly use different MRAI timers in BGP routers to deal with the issue of growing convergence time of the network. The variation in MRAI timer and its impact on network convergence and update messages has been broadly studied over the years. The increasing size of autonomous systems leads to rise in number of MRAI timers. Hence, the optimum use of MRAI timers can decrease the problem of slow convergence and necessity of huge number of MRAI timers. The proposed system uses the ckle minimum route advertisement interval timer (FMRAI) for fast update of routing table, which leads to reduce the convergence time of a network. In comparison with static MRAI timer of 30s the FMRAI timer leads to better result in terms of convergence time and number of update messages.
Elliptical BandPassThree Dimensional Frequency Selective Surface with Multiple Transmission Zeros
Authors: Dutta Bimal Raj , Kanaujia Binod Kumar , Dalela Chhaya
Published: January 2019

An elliptic band pass response three-dimensional Frequency Selective Surface (3D FSS) is designed from a single unit cell of 2D array of two shielded microstrip lines. The designed FSS provides pseudo-elliptic band-pass frequency response (5.4 – 9.6) GHz with its application in long-distance radio telecommunications and space communications etc. The four transmission zeros at 5.4GHz, 9.6GHz, 12.4GHz and 15GHz provides wide out-of-band frequency rejection. The 3D FSS is independent of the variations in the incident angle of the plane wave up to 60 degree. Each unit cell is a combination of two shielded microstrip lines with one having an air gap and the other one having in between rectangular metallic plate. When a TE polarized plane wave incidents perpendicular to the perfect electric conductor (PEC) boundary walls shielded microstrip lines, it results in two quasi-TEM modes namely air and substrate mode. The 3D FSS consists of multiple resonators with a multimode cavity having number of propagating modes. These resonating modes in phase provide transmission poles and when out of phase give transmission zeros. The 3D FSS structure is simulated using Ansys HFSS software with improved performance over 2DFSS, for many practical applications such as antenna sub-reflector, radomes and spatial filters.
Adaptive Data Structure Based Oversampling Algorithm for Ordinal Classification
Authors: Kasiraja Dhanalakshmi , Vijendran Anna Saro
Published: January 2019

The main objective of this research is to improve the predictive accuracy of classification in ordinal multiclass imbalanced scenario. The methodology attempts to uplift the classifier performance through synthesizing sophisticated objects of immature classes.  A novel Adaptive Data Structure based oversampling algorithm is proposed to create synthetic objects and Extreme Learning Machine for Ordinal Regression (ELMOP) classifier is adopted to validate our work.   The proposed method generating new objects by analyzing the characteristics and intricacy of immature class objects. On the whole, the data set is divided into training and test data. Training data set is updated with new synthetic objects.  The experimental analysis is performed on testing data set to check the efficiency of the proposed methodology by comparing it with the existing work.    The performance evaluation is conducted in terms of the parameters called Mean Absolute Error, Maximum Mean Absolute Error, Geometric Mean, Kappa and Average Accuracy.  The measures prove that the proposed methodology can produce authentic synthetic objects than the existing techniques.  The Proposed technique can synthesize the new effective objects through evaluating the structure of immature class.  It boosts the global precision and class wise precision especially preserves rank order of the classes.
PAPR Reduction at Large Multi-User-MIMO-OFDM using Adaptive Data Detection Algorithm
Authors: Praba N , Ravikumar K. M.
Published: January 2019

Wireless communication in present era contains large-scale MIMO network architecture that need to deliver an optimize-QoS to multi-user (MU).
 The optimize data rate transmission in massive MU-MIMO wireless systems is one of the most difficult task due to the extremely high implementation complexity. The practical wireless system channels generally exhibits the PAPR and frequency selective fading, it is also necessary to have a precoding solution in PAPR for the selected desirable channels. A solution for the designed problem of a noble error-correcting code for OFDM process with a low PAPR, in the case of impulse noise should be considered. In this paper, Adaptive-Data-detection (ADD) algorithm is proposed to obtain lower-complexity data-detection that corresponds to high throughput design and impulse noise removal for large MUI-MIMO wireless systems by the OFDM modulation technique. That contains some steps such as; initialization,
 pre-processing and equalization steps in order to get no performance loss and to minimalize the recurrent amount at each iterations during operation. In order to use simplify model, here we assume suitably perfect synchronization, large cyclic prefix and perfect-CSI (channel-state-information) which has been developed through the pilot depended training. Simulation results analysis show the proposed method substantial improvement over the existing algorithm in terms of both ‘Error-rate’ minimization and PAPR reduction.
Empowering E-governance with E-voting
Authors: Singh Amarjeet , P Ramakanth Kumar , G Cholli Nagaraj
Published: January 2019

Advances in Information and Communications Technology (ICT) have impacted the society in many ways. Be it education, healthcare, media or governance, the transformation is visible. As individual entities like education or healthcare systems have already implemented ICT to certain extent, e-governance is yet to make a significant progress even though several initiatives are undertaken. E-governance means activities like voting, administration, financial transaction etc., are enabled using automation systems.  This paper discusses one part of the e-governance; e-voting. As e-voting empowers government, there are lot of challenges in implementing the e-voting system considering security threats and confidentiality involved in it. Online voting (e-voting) needs to be deployed as more convenient, relatively secure and less resource consuming system.   E-voting system should ensure convenience for people to be able to access the system from personal or public computer with security and confidentiality. The presence of e-voting system with all the necessary security measures and convenience can be a potential solution for low voter turnout at the polls.  The presented work demonstrates an online e-voting prototype system called SecureV. The proposed model achieves specific tasks namely, maintaining the anonymity of the voter, encryption of the vote, integrity check and avoids second time voting.
Integration of Linux Containers in OpenStack: An Introspection
Authors: Lingayat Ashish , Badre Ranjana R. , Gupta Anil Kumar
Published: January 2019

In cloud computing, sharing of resources is supported using heavy weighted traditional virtualization techniques. Such techniques involve hypervisors to emulate hardware for creating virtual machines. The inclusion of an additional layer of hypervisor over host operating system depreciates the performance of the virtual machine. Recent evolution is a lightweight alternative to the virtual machine called containers which have gained
popularity among developers and administrators. Container Based virtualization has proven very efficient regarding performance, and many industries are now migrating their virtualized environment to run on Linux containers. Containers use host operating systems kernel and isolate each container by encapsulating them with their required services and packages. Linux kernel is very beneficial in implementing containers, which is the reason for the existence of Linux containers. Linux containers utilize less storage space and consume optimal computational power, giving a hike in performance. Having them integrated into the cloud surely benefits consumer and cloud provider. Many projects have extended their support in incorporating containers in the cloud. In this paper, we will discuss various Linux containers and their management tools along with cloud computing software, OpenStack, including projects undertaken by OpenStack for integrating containers in the cloud.