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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 6,255 Documents

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Effects of Electromagnetic Fields on Mammalian Cells Md Kamal Hosain
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 2: April 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (229.195 KB)

The characteristics of mammalian cells can be influenced by electromagnetic fields (EMFs). The electromagnetic fields have a number of physiological effects on cells and tissues such as alteration of gene expression, cells viability, proliferation, apoptosis, number of mammospheres, cells cycle phase, and invasion. The existing literature proves that the impact of EMFs on mammalian cells depends on the density and uniformity of the field, frequency range, exposure time, cell types, culture environment, and culcuremedium. This paper presents a review of the impacts of EMFs on mammalian cells in vitro culture. In this article, we reviewed the contemporary understanding of the various form of electromagnetic radiation effect on cultured mammalian cells in vitro, EMF exposing systems, and internal field mechanism in the cells.DOI:http://dx.doi.org/10.11591/ijece.v2i2.269

A Neural Network Approach to Identify Hyperspectral Image Content Puttaswamy Malali Rajegowda; Balamurugan P.
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

A Hyperspectral is the imaging technique that contains very large dimension data with the hundreds of channels. Meanwhile, the Hyperspectral Images (HISs) delivers the complete knowledge of imaging; therefore applying a classification algorithm is very important tool for practical uses. The HSIs are always having a large number of correlated and redundant feature, which causes the decrement in the classification accuracy; moreover, the features redundancy come up with some extra burden of computation that without adding any beneficial information to the classification accuracy. In this study, an unsupervised based Band Selection Algorithm (BSA) is considered with the Linear Projection (LP) that depends upon the metric-band similarities. Afterwards Monogenetic Binary Feature (MBF) has consider to perform the ‘texture analysis’ of the HSI, where three operational component represents the monogenetic signal such as; phase, amplitude and orientation. In post processing classification stage, feature-mapping function can provide important information, which help to adopt the Kernel based Neural Network (KNN) to optimize the generalization ability. However, an alternative method of multiclass application can be adopt through KNN, if we consider the multi-output nodes instead of taking single-output node.

Segmentation of Fingerprint Image Based on Gradient Magnitude and Coherence Saparudin Saparudin; Ghazali Sulong
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 5: October 2015
Publisher : Institute of Advanced Engineering and Science

Fingerprint image segmentation is an important pre-processing step in automatic fingerprint recognition system. A well-designed fingerprint segmentation technique can improve the accuracy in collecting clear fingerprint area and mark noise areas. The traditional grey variance segmentation method is widely and easily used, but it can hardly segment fingerprints with low contrast of high noise. To overcome the low image contrast, combining two-block feature; mean of gradient magnitude and coherence, where the fingerprint image is segmented into background, foreground or noisy regions, has been done. Except for the noisy regions in the foreground, there are still such noises existed in the background whose coherences are low, and are mistakenly assigned as foreground. A novel segmentation method based on combination local mean of grey-scale and local variance of gradient magnitude is presented in this paper. The proposed extraction begins with normalization of the fingerprint. Then, it is followed by foreground region separation from the background. Finally, the gradient coherence approach is used to detect the noise regions existed in the foreground. Experimental results on NIST-Database14 fingerprint images indicate that the proposed method gives the impressive results.

An overview of virtual machine live migration techniques Artan Mazrekaj; Shkelzen Nuza; Mimoza Zatriqi; Vlera Alimehaj
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

In a cloud computing the live migration of virtual machines shows a process of moving a running virtual machine from source physical machine to the destination, considering the CPU, memory, network, and storage states. Various performance metrics are tackled such as, downtime, total migration time, performance degradation, and amount of migrated data, which are affected when a virtual machine is migrated. This paper presents an overview and understanding of virtual machine live migration techniques, of the different works in literature that consider this issue, which might impact the work of professionals and researchers to further explore the challenges and provide optimal solutions.

A 300 GHz CMOS Transmitter Front-End for Ultrahigh-Speed Wireless Communications Tuan Anh Vu; Minoru Fujishima
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 4: August 2017
Publisher : Institute of Advanced Engineering and Science

This paper presents a 300 GHz transmitter front-end suitable for ultrahigh-speed wireless communications. The transmitter front-end realized in TSMC 40 nm CMOS consists of a common-source (CS) based doubler driven by a two-way D-band power amplifier (PA). Simulation results show that the two-way D-band PA obtains a peak gain of 21.6 dB over a -3 dB bandwidth from 132 GHz to 159 GHz. It exhibits a saturated power of 7.2 dBm and a power added efficiency (PAE) of 2.3%, all at 150 GHz. The CS based doubler results in an output power of 0.5 mW at 300 GHz. The transmitter front-end consumes a DC power of 205.8 mW from a 0.9 V supply voltage while it occupies an area of 2.1 mm2.

Compressed fuzzy logic based multi-criteria AODV routing in VANET environment Taqwa Oday Fahad; Abduladhem A. Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 1: February 2019
Publisher : Institute of Advanced Engineering and Science

Vehicular ad hoc networks (VANETs) are the core of intelligent transportation systems (ITS) to obtain safety, better transportation services, and improved traffic management. Providing more reliable and efficient on demand routing protocol is one of the main challenges in these networks research scope. This paper argues a compressed fuzzy logic based method to enhance Ad hoc on demand distance vector (AODV) routing decision by jointly considering number of relays, distance factor, direction angle, and vehicles speed variance. The proposed scheme is simulated in both freeway and urban scenarios with different number of vehicles using real time interaction between both OMNet++ and SUMO simulators. Simulation results show that the proposed approach can get better performance in terms of packet delivery ratio, throughput, mean delay, and number of sent control packets.

High level speaker specific features modeling in automatic speaker recognition system Satyanand Singh
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.

Academic Cloud ERP Quality Assessment Model Kridanto Surendro; Olivia Olivia
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 3: June 2016
Publisher : Institute of Advanced Engineering and Science

In the past few decades, educational institutions have been using conventional academic ERP system to integrate and optimize their business process. In this delivery model, each educational institutions are responsible of their own data, installation, and also maintenance. For some institutions, it might cause not only waste of resources, but also problems in management and financial aspects. Cloud-based Academic ERP, a SaaS-based ERP system, begin to come as a solution with is virtualization technology. It allows institutions to use only the needed ERP resources, without any specific installation, integration, or maintenance needs. As the implementation of Cloud ERP increases, problems arise on how to evaluate this system. Current evaluation approaches are either only evaluating the cloud computing aspects or only evaluating the software quality aspects. This paper proposes an assessment model for Cloud ERP system, considering both software quality characteristics and cloud computing attributes to help strategic decision makers evaluate academic Cloud ERP system.

An Unequal Cluster-based Routing Protocol Based on Data Controlling for Wireless Sensor Network Slaheddine Chelbi; Majed Abdouli; Mourad Kaddes; Claude Duvallet; Rafik Bouaziz
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 5: October 2016
Publisher : Institute of Advanced Engineering and Science

Wireless Sensor Networks (WSN) differ from traditional wireless communication networks in several characteristics. One of these characteristics is power awarness, due to the fact that the batteries of sensor nodes have a restricted lifetime and are difficult to be replaced. Therefore, all protocols must be designed to minimize energy consumption and preserve the longevity of the network. In this paper, we propose (i) to fairly balance the load among nodes. For this, we generate an unequal clusters size where the cluster heads (CH) election is based on energy availability, (ii) to reduce the energy consumption due to the transmission by using multiple metrics in the CH jointure process and taking into account the link cost, residual energy and number of cluster members to construct the routing tree and (iii) to minimize the number of transmissions by avoiding the unnecessary updates using sensitive data controller. Simulation results show that our Advanced Energy-Efficient Unequal Clustering (AEEUC) mechanism improves the fairness energy consumption among all sensor nodes and achieves an obvious improvement on the network lifetime.

Automatic Modulation Recognition for MFSK Using Modified Covariance Method Hanan M.Hamee; Jafer Wadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 3: June 2015
Publisher : Institute of Advanced Engineering and Science

This paper presents modulation classification method capable of classifyingMFSK digital signals without a priori information using modified covariancemethod. This method using for calculation features for FSK modulationshould have a good properties of sensitive with FSK modulation index andinsensitive with signal to noise ratio SNR variation. The numericalsimulations and investigation of the performance by the support vectorsmachine one against all (SVM-OAA) as a classifier for classifying 6 digitallymodulated signals which gives probability of correction classification up to85.85 at SNR=-15dB.

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Home Page

OAI Link

Editorial Team

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Contact Name Tole Sutikno

Contact Email ijece@iaesjournal.com

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Journal Mail Official ijece@iaesjournal.com

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Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

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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