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International Journal of Electrical and Computer Engineering
ISSN : 20888708     EISSN : 27222578     DOI : -
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Articles 90 Documents
Search results for , issue "Vol 6, No 6: December 2016" : 90 Documents clear
Study on Improving the Network Life Time Maximazation for Wireless Sensor Network using Cross Layer Approach Rajiv R Bhandari; K Rajasekhar
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.658 KB) | DOI: 10.11591/ijece.v6i6.pp3080-3086

Abstract

In recent the espousal of Wireless Sensor Networks has been broadly augmented in numerous divisions. Battery operated Sensor nodes in the wireless network accomplish main task of capturing and responding to the surroundings. The lifetime of the network depends on the energy consumption of the sensor nodes. This paper contributes the survey on how the energy consumption should be managed for maximizing the life time of network and how to improve the efficiency of Network by using Cross layer architecture. The traditional MAC Layer, Network Layer & Transport for WLAN having their own downsides just by modifying those we can achieve the network life time maximization goal. This paper represents analytical study for Energy efficiency by modifying Scheduling algorithm, by modifying traditional AODV routing algorithm for efficient packet transmission and by effectively using TCP for End to End Delivery of Data.
A New Paradigm for Development of Data Imputation Approach for Missing Value Estimation Madhu G; Nagachandrika G
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (36.423 KB) | DOI: 10.11591/ijece.v6i6.pp3222-3228

Abstract

Many real-world applications encountered a common issue in data analysis is the presence of missing data value and challenging task in many applications such as wireless sensor networks, medical applications and psychological domain and others. Learning and prediction in the presence of missing value can be treacherous in machine learning, data mining and statistical analysis. A missing value can signify important information about dataset in the mining process. Handling missing data value is a challenging task for the data mining process. In this paper, we propose new paradigm for the development of data imputation method for missing data value estimation based on centroids and the nearest neighbours. Firstly, identify clusters based on the k-means algorithm and calculate centroids and the nearest neighbour data records. Secondly, the nearest distances from complete dataset as well as incomplete dataset from the centroids and estimated the nearest data record which tends to be curse dimensionality. Finally, impute the missing value based nearest neighbour record using statistical measure called z-score. The experimental study demonstrates strengthen of the proposed paradigm for the imputation of the missing data value estimation in dataset. Tests have been run using different types of datasets in order to validate our approach and compare the results with other imputation methods such as KNNI, SVMI, WKNNI, KMI and FKNNI. The proposed approach is geared towards maximizing the utility of imputation with respect to missing data value estimation.
Environment Friendly Voltage Up-gradation Model for Distribution Power Systems K. Nithiyananthan; Umasankar Umasankar
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (317.654 KB) | DOI: 10.11591/ijece.v6i6.pp2516-2525

Abstract

The main aim of this research work is to analyze and develop voltage up gradation procedure model for effective & economic power distribution in urban and suburban area. Voltage up gradation from 6.6KV to 11KV of the distribution power system network has been considered for the proposed research work. Electric power consumption has been increasing uninterruptedly, being this increase specially accelerated in the last few years. Nowadays electric lines are saturated; they are reaching critical values of ampere capacity and sag. Therefore, building new lines has been necessary to provide the ever increasing consumption.  The difficulty to find new corridors to construct new distribution lines, underground cables is increasing in cities, industrial areas and in many cases it is simply impossible. The construction of new electric lines is increasing difficulty, thus there is a need to look at alternatives that increases the power transfer capacity. Voltage up gradation of the existing electric cables/lines of the distribution system is the most viable solution and it stresses on the savings of power due to a reduction in system losses when the voltage is high. The proposed research work is to develop and analyze voltage up gradation procedures and protocols for converting 6.6KV network into 11KV network in a distributed system. It also takes into account the expenses incurred in the process and the various other important constraints.
Three Patterns Programmable Russian Form Functional Electrical Stimulator Abbas Orand; Genichi Tanino; Hiroyuki Miyasaka; Kotaro Takeda; Shigeru Sonoda
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (866.126 KB) | DOI: 10.11591/ijece.v6i6.pp2682-2687

Abstract

In this paper, a programmable, multi-pattern, wide frequency and duty cycle range electrical stimulator is presented. Using a programmable micro-controller, two waves of carrier and modulating sources are produced. By modulating the two sources, 3 bi-phasic charge-balanced rectangular, triangular and sinusoidal stimulating patterns are produced. The frequency range of the carrier is fixed at 2.5 kHz and the carrier source frequency can be adjusted between 1 and 500 Hz. The duty cycle of both sources can be adjusted between 10% and 90%.
Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera Edy Winarno; Agus Harjoko; Aniati Murni Arymurthy; Edi Winarko
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.818 KB) | DOI: 10.11591/ijece.v6i6.pp2818-2827

Abstract

The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux).
Big Data and MapReduce Challenges, Opportunities and Trends Sachin Arun Thanekar; K. Subrahmanyam; A. B. Bagwan
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (465.105 KB) | DOI: 10.11591/ijece.v6i6.pp2911-2919

Abstract

Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Big data generated may be structured data, Semi Structured data or unstructured data. For existing database and systems lot of difficulties are there to process, analyze, store and manage such a Big Data.  The Big Data challenges are Protection, Curation, Capture, Analysis, Searching, Visualization, Storage, Transfer and sharing. Map Reduce is a framework using which we can write applications to process huge amount of data, in parallel, on large clusters of commodity hardware in a reliable manner. Lot of efforts have been put by different researchers to make it simple, easy, effective and efficient. In our survey paper we emphasized on the working of Map Reduce, challenges, opportunities and recent trends so that researchers can think on further improvement.
Issues of K Means Clustering While Migrating to Map Reduce Paradigm with Big Data: A Survey Khyati R Nirmal; K.V.V. Satyanarayana
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (346.733 KB) | DOI: 10.11591/ijece.v6i6.pp3047-3051

Abstract

In recent times Big Data Analysis are imminent as essential area in the field of Computer Science. Taking out of significant information from Big Data by separating the data in to distinct group is crucial task and it is beyond the scope of commonly used personal machine. It is necessary to adopt the distributed environment similar to map reduce paradigm and migrate the data mining algorithm using it. In Data Mining the partition based K Means Clustering is one of the broadly used algorithms for grouping data according to the degree of similarities between data. It requires the number of K and initial centroid of cluster as input. By surveying the parameters preferred by algorithm or opted by user influence the functionality of Algorithm. It is the necessity to migrate the K means Clustering on MapReduce and predicts the value of k using machine learning approach. For selecting the initial cluster the efficient method is to be devised and united with it. This paper is comprised the survey of several methods for predicting the value of K in K means Clustering and also contains the survey of different methodologies to find out initial center of the cluster. Along with initial value of k and initial centroid selection the objective of proposed work is to compact with analysis of categorical data.
Texture Fusion for Batik Motif Retrieval System Ida Nurhaida; Hong Wei; Remmy A. M. Zen; Ruli Manurung; Aniati M. Arymurthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1442.028 KB) | DOI: 10.11591/ijece.v6i6.pp3174-3187

Abstract

This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object.
Power Quality Enhancement of Integration Photovoltaic Generator to Grid under Variable Solar Irradiance Level using MPPT-Fuzzy Amirullah Amirullah; Agus Kiswantono
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1044.067 KB) | DOI: 10.11591/ijece.v6i6.pp2629-2642

Abstract

The paper presents power quality enhancement on low voltage of three phase grid caused by PV generator integration under variabel solar irradiance level on constant temperature and load. MPPT Fuzzy helps to generate duty cycle to control DC/DC boost converter of PV generators. This model was expected to improve power quality due to unbalance voltage and current, low voltage and current harmonics, and low input power factor. There were eigth scenarios PV generator connected to three phase grid using MPPT Fuzzy and compared with MPPT P and O. The research results that application of two methods on different irradiance and PV generator integration level produces unbalanced voltage value stable at 0%. At the same conditions, the use of MPPT Fuzzy results unbalanced current was greater than MPPT P and O. On solar irradiance level fixed, the greater number of PV generator connected to three-phase grid, then value of average voltage and current harmonics (THD) will increases. At the level of solar radiation increases, average grid voltage and current THD also have increased. The average grid voltage and current THD was reduced after using MPPT Fuzzy. The application of MPPT Fuzzy was able to enhance profile of grid voltage and current THD due to integration of a number of PV generator to three phase grid corresponding with IEEE Standard 519-1992. MPPT Fuzzy was capable to improve input power factor better than MPPT P and O.
Face Recognition using Multi Region Prominent LBP Representation Srinivasa Reddy Konda; Vijaya Kumar V; Venkata Krishna
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (563.167 KB) | DOI: 10.11591/ijece.v6i6.pp2781-2788

Abstract

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

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