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International Journal of Electrical and Computer Engineering

IJECE
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Published by Institute of Advanced Engineering and Science

ISSN : 20888708 EISSN : 27222578 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering

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.

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 6,301 Documents

287 288 289 290 291

A Comparative Study between DPC and DPC-SVM Controllers Using dSPACE (DS1104) Adel Mehdi; Salah-eddine Rezgui; Houssam Medouce; Hocine Benalla
International Journal of Electrical and Computer Engineering (IJECE) Vol 4, No 3: June 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (984.63 KB)

The aim of this paper is to compare two different control structures. The Simple Direct Power Control (DPC) and the Direct Power Control with Space Vector Modulation (DPC- SVM) for two level converter applications. The ﬁrst strategy (DPC) has been developed to control the instantaneous active and reactive power directly by selecting the optimum switching state of the converter. Applied to the Pulse Width Modulation (PWM) converter its main feature is to improve the total power factor and efﬁciency, even harmonics components existence. In the second structure, the active and reactive powers are used as (PWM) control vari- ables instead of the three-phase line currents usually used in other techniques. It is shown that DPC-SVM exhibits several properties; good dynamic response, constant switching fre- quency, and in particular it provides a sinusoidal line currents. Simulation and experimental results has shown that both control structures achieve good performances.DOI:http://dx.doi.org/10.11591/ijece.v4i3.6074

An IOT based smart metering development for energy management system S.G Priyadharshini; C. Subramani; J. Preetha Roselyn
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

The worldwide energy demand is increasing and hence necessity measures need to be taken to reduce the energy wastage with proper metering infrastructure in the buildings. A Smart meter can be used to monitor electricity consumption of customers in the smart grid technology. For allocating the available resources proper energy demand management is required. During the past years, various methods are being utilized for energy demand management to precisely calculate the requirements of energy that is yet to come. A large system presents a potential esteem to execute energy conservation as well as additional services linked to energy services, extended as a competent with end user is executed. The supervising system at the utilities determines the interface of devices with significant advantages, while the communication with the household is frequently proposing particular structures for appropriate buyer-oriented implementation of a smart meter network. Also, this paper concentrates on the estimation of vitality utilization. In this paper energy is measured in units and also product arrangement is given to create bill for energy consumption and implementing in LabVIEW software. An IOT based platform is created for remote monitoring of the metering infrastructure in the real time. The data visualization is also carried out in webpage and the data packet loss is investigated in the remote monitoring of the parameters.

Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach Zakaria Boulouard; Amine El Haddadi; Fadwa Bouhafer; Anass El Haddadi; Lahcen Koutti; Bernard Dousset
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 2: April 2018
Publisher : Institute of Advanced Engineering and Science

Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, “Bat-Cluster (BC).” This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values.

Reversible Multiple Image Secret Sharing Using Discrete Haar Wavelet Transform Ashwaq T. Hashim; Suhad A. Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Multiple Secret Image Sharing scheme is a protected approach to transmit more than one secret image over a communication channel. Conventionally, only single secret image is shared over a channel at a time. But as technology grew up, there is a need to share more than one secret image. A fast (r, n) multiple secret image sharing scheme based on discrete haar wavelet transform has been proposed to encrypt m secret images into n noisy images that are stored over different servers. To recover m secret images r noise images are required. Haar Discrete Wavelet Transform (DWT) is employed as reduction process of each secret image to its quarter size (i.e., LL subband). The LL subbands for all secrets have been combined in one secret that will be split later into r subblocks randomly using proposed high pseudo random generator. Finally, a developed (r, n) threshold multiple image secret sharing based one linear system has been used to generate unrelated shares. The experimental results showed that the generated shares are more secure and unrelated. The size reductions of generated shares were 1:4r of the size of each of original image. Also, the randomness test shows a good degree of randomness and security.

Performance Analysis of Post Compensated Long Haul High Speed Coherent Optical OFDM System Divya Dhawan; Neena Gupta
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 1: February 2017
Publisher : Institute of Advanced Engineering and Science

This paper addresses the performance analysis of OFDM transmission system based on coherent detection over high speed long haul optical links with high spectral efficiency modulation formats such as Quadrature Amplitude Modulation (QAM) as a mapping method prior to the OFDM multicarrier representation. Post compensation is used to compensate for phase noise effects. Coherent detection for signal transmitted at bit rate of 40 Gbps is successfully achieved up to distance of 3200km. Performance is analyzed in terms of Symbol Error Rate and Error Vector Magnitude by varying Optical Signal to Noise Ratio (OSNR) and varying the length of the fiber i.e transmission distance. Transmission performance is also observed through constellation diagrams at different transmission distances and different OSNRs.

Performance evaluation of random forest with feature selection methods in prediction of diabetes Raghavendra S; Santosh Kumar J
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes.

Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Scheduling Gibet Tani Hicham; El Amrani Chaker; Elaachak Lotfi
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 6: December 2017
Publisher : Institute of Advanced Engineering and Science

Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.

Enhancenig OLSR routing protocol using K-means clustering in MANETs Y. Hamzaoui; M. Amnai; A. Choukri; Y. Fakhri
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

The design of robust routing protocol schemes for MANETs is quite complex, due to the characteristics and structural constraints of this network. A numerous variety of protocol schemes have been proposed in literature. Most of them are based on traditional method of routing, which doesn’t guarantee basic levels of Qos, when the network becomes larger, denser and dynamic. To solve this problem we use one of the most popular methods named clustering. In this work we try to improve the Qos in MANETs. We propose an algorithm of clustering based in the new mobility metric and K-Means method to distribute the nodes into several clusters; it is implemented to standard OLSR protocol giving birth a new protocol named OLSR Kmeans-SDE. The simulations showed that the results obtained by OLSR Kmeans-SDE exceed those obtained by standard OLSR Kmeans and OLSR Kmed+ in terms of, traffic Control, delay and packet delivery ratio.

Implementation of Electronic Nose in Omni-directional Robot Harianto Harianto; Muhammad Rivai; Djoko Purwanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 3, No 3: June 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (699.925 KB)

Electronic nose (E-nose) is a device detecting odors which is designed to resemble the ability of the human nose. E-nose can identifying chemical elements that contained in the odors. E-nose is made of arrays of gas sensor, each of it could detect certain chemical element. When detects gases, each sensor will generate a specific pattern for each gas. These patterns could be classified using neural network algorithm. Neural network is a computational method based on mathematical models which has the structure and operation of neural networks which imitate the human brain. Neural network consists of a group of neurons conected to each other with a connection named weight. The weights will determine wether neural networks could compute given inputs to produce a specified output. To generate the appropriate weight, the neural network needs to be trained using a number of gasoline and alcohol samples. The training process to generate appropriate weights is done by using back propagation algorithm on a personal computer. The appropriate weight then transferred to omni-directional robot equipped with e-nose. The result shows that the robot could identify the trained gas.DOI:http://dx.doi.org/10.11591/ijece.v3i3.2531

Energy optimization of 6T SRAM cell using low-voltage and high-performance inverter structures M. Madhusudhan Reddy; M. Sailaja; K. Babulu
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

The performance of the cell deteriorates, when static random access memory (SRAM) cell is operated below 1V supply voltage with continuous scale down of the complementary metal oxide semiconductor (CMOS) technology. The conventional 6T, 8T-SRAM cells suffer writeability and read static noise margins (SNM) at low-voltages leads to degradation of cell stability. To improve the cell stability and reduce the dynamic power dissipation at low- voltages of the SRAM cell, we proposed four SRAM cells based on inverter structures with less energy consumption using voltage divider bias current sink/source inverter and NOR/NAND gate using a pseudo-nMOS inverter. The design and implementation of SRAM cell using proposed inverter structures are compared with standard 6T, 8T and ST-11T SRAM cells for different supply voltages at 22-nm CMOS technology exhibit better performance of the cell. The read/write static noise margin of the cell significantly increases due to voltage divider bias network built with larger cell-ratio during read path. The load capacitance of the cell is reduced with minimized switching transitions of the devices during high-to-low and low- to-high of the pull-up and pull-down networks from VDD to ground leads to on an average 54% of dynamic power consumption. When compared with the existing ones, the read/write power of the proposed cells is reduced to 30%. The static power gets reduced by 24% due to stacking of transistors takes place in the proposed SRAM cells as compare to existing ones. The layout of the proposed cells is drawn at a 45-nm technology, and occupies an area of 1.5 times greater and 1.8 times greater as compared with 6T-SRAM cell.

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Home Page

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Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

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Contact Name
Tole Sutikno

Contact Email
ijece@iaesjournal.com

Phone
-

Journal Mail Official
ijece@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA