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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 115 Documents

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Search results for , issue "Vol 10, No 4: August 2020" : 115 Documents clear

Design of probe for NQR/NMR detection Preeti Hemnani; A. K. Rajarajan; Gopal Joshi; S. V. G. Ravindranath
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Nuclear Magnetic Resonance (NMR) is a RF technique that is able to detect any compound by sensing the excited resonance signals from atomic nuclei having non-zero spin. NQR is similar to NMR but the only difference is NMR needs a DC magnetic field and due to this its application in the field is limited. A FPGA based NQR spectrometer is designed using a single FPGA chip to perform the digital tasks required for NQR spectrometer. Design of Probe for NMR/NQR spectrometer is researched. Parallel tuned and series tuned Probes are discussed and simulated.14N NQR from NaNO2 is observed from spectrometer designed with parallel tuned probe.

A feasibility study of electrical energy generation from municipal solid waste in Iraq: Najaf case study Othman M. Anssari; Esam A. Alkaldy; Naseem Almudhaffar; Abbas Nasir AlTaee; Nabeel Salih Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

In several developing countries, the electricity crisis obstructs both socio-economic and technological sustainable evolution. Also, it leads to reducing job availability due to shut down several industries or relocate to neighbouring countries to such an issue. A Najaf City is an important holy and tourist city in the middle of Iraq country. Indeed, waste management in An Najaf City needs to be reconsidered to be used as an energy source. In this article, we investigated and listed the waste quantity which produced recently (one year) respect to waste types and types of content. Data collected from the waste products for one year and are used as a key factor to study the feasibility of generating electrical energy from collected MSWs. The proposed model was simulated and tested respect to cost analysis factor of the suggested power plant by Homer pro simulation software. Results were very encouraging and competitive to the current energy production cost based on the production cost of the Kwh prospective among the conventional methods in Iraq. The proposed scenario provide proper and secure waste proposal technique with low-cost.

Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data Santosh Jankatti; Raghavendra B. K.; Raghavendra S.; Meenakshi Meenakshi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Big data is the biggest challenges as we need huge processing power system and good algorithms to make an decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar.

Design of smart wireless changeover for continuous electric current feeding from power sources of variable capacities Haider A. H. Alobaidy; Hikmat N. Abdullah; Tariq M. Salman
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Electric power has become a vital element for life today. Despite this importance, electric power consumers in Iraq suffer from the problem of noncontinuity and daily electric power supply interruption. This problem led to the use of various sources of electric power as an alternative to compensate for the shortage of electric power provided by the Iraqi national grid. In this work, a smart wireless changeover device is designed using wireless sensor networks technology aiming to solve problem caused by the multiplicity of power sources received at home and governmental buildings in Iraq by controlling operation of some electrical devices (which consume high current) in the home or workplace automatically when changing source of electricity from one to another. This solution will help to ensure the continuity of electric current feeding from power sources of variable capacities, also, to rationalize power consumption by assigning an operation priority to electric devices. Furthermore, a statistical measurement as a case study was performed in a building with a total power consumption of 160.8 KW/h. The result showed that the device functions effectively and it is capable of achieving an average saving in power of about 50% to 86% depending on the applied priorities and case study scenario.

Speaker specific feature based clustering and its applications in language independent forensic speaker recognition Satyanand Singh; Pragya Singh
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Forensic speaker recognition (FSR) is the process of determining whether the source of a questioned voice recording (trace) is a specific individual (suspected speaker). The role of the forensic expert is to testify by using, if possible, a quantitative measure of this value to the value of the voice evidence. Using this information as an aid in their judgments and decisions are up to the judge and/or the jury. Most existing methods measure inter-utterance similarities directly based on spectrum-based characteristics, the resulting clusters may not be well related to speaker’s, but rather to different acoustic classes. This research addresses this deficiency by projecting language-independent utterances into a reference space equipped to cover the standard voice features underlying the entire utterance set. The resulting projection vectors naturally represent the language-independent voice-like relationships among all the utterances and are therefore more robust against non-speaker interference. Then a clustering approach is proposed based on the peak approximation in order to maximize the similarities between language-independent utterances within all clusters. This method uses a K-medoid, Fuzzy C-means, Gustafson and Kessel and Gath-Geva algorithm to evaluate the cluster to which each utterance should be allocated, overcoming the disadvantage of traditional hierarchical clustering that the ultimate outcome can only hit the optimum recognition efficiency. The recognition efficiency of K-medoid, Fuzzy C-means, Gustafson and Kessel and Gath-Geva clustering algorithms are 95.2%, 97.3%, 98.5% and 99.7% and EER are 3.62%, 2.91 %, 2.82%, and 2.61% respectively. The EER improvement of the Gath-Geva technique based FSRsystem compared with Gustafson and Kessel and Fuzzy C-means is 8.04% and 11.49% respectively

Deep-learning based single object tracker for night surveillance Zulaikha Kadim; Mohd Asyraf Zulkifley; Nabilah Hamzah
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Tracking an object in night surveillance video is a challenging task as the quality of the captured image is normally poor with low brightness and contrast. The task becomes harder for a small object as fewer features are apparent. Traditional approach is based on improving the image quality before tracking is performed. In this paper, a single object tracking algorithm based on deep-learning approach is proposed to exploit its outstanding capability of modelling object’s appearance even during night. The algorithm uses pre-trained convolutional neural networks coupled with fully connected layers, which are trained online during the tracking so that it is able to cater for appearance changes as the object moves around. Various learning hyperparameters for the optimization function, learning rate and ratio of training samples are tested to find optimal setup for tracking in night scenarios. Fourteen night surveillance videos are collected for validation purpose, which are captured from three viewing angles. The results show that the best accuracy is obtained by using Adam optimizer with learning rate of 0.00075 and sampling ratio of 2:1 for positive and negative training data. This algorithm is suitable to be implemented in higher level surveillance applications such as abnormal behavioral recognition.

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.

A NURBS-optimized dRRM solution in a mono-channel condition for IEEE 802.11 entreprise Wlan networks Mehdi Guessous; Lahbib Zenkouar
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Dynamic radio resource management, RRM, is an essential design block in the functional architecture of any Wifi controller in IEEE 802.11 indoor dense enterprise Wlans. In a mono-channel condition, it helps tackle co-channel interference problem and enrich end-to-end Wifi clients experience. In this work, we present our dRRM solution: WLCx, and demonstrate its performance over related-work and vendor approaches. Our solution is built on a novel and realistic per-Beam coverage representation approach. Unlike the other RRM solutions, WLCx is dynamic: even the calculation system parameters are processed. This processing comes at price in terms of processing time. To overcome this limitation, we constructed and implemented a NURBS surface-based optimization to our RRM solution. Our NURBS optimized WLCx, N-WLCx, solution achieves almost 92.58% time reduction in comparison with basic WLCx. Furthermore, our optimization could easily be extended to enhance others, vendors and research, RRM solutions.

Feature selection for multiple water quality status: Integrated bootstrapping and SMOTE approach in imbalance classes Shofwatul Uyun; Eka Sulistyowati
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

STORET is one method to determine the river water quality into four classes (very good , good, medium and bad) based on the data of water for each attribute or feature. The success of the formation of pattern recognition model much depends on the quality of data. There are two issues as the concern of this research as follows: the data having disproportionate amount among the classes (imbalance class) and the finding of noise on its attribute. Therefore, this research integrates the SMOTE Technique and bootstrapping to handle the problem of imbalance class. While an experiment is conducted to eliminate the noise on the attribute by using some feature selection algorithms with filter approach (information gain, rule, derivation, correlation and chi square). This research has some stages as follows: data understanding, pre-processing, imbalance class, feature selection, classification and performance evaluation. Based on the result of testing using 10-fold cross validation, it shows that the use of the SMOTE-bootstrapping technique is able to increase the accurate value from 83.3% to be 98.8%. While the process of noise elimination on the data attribute is also able to increase the accuracy to be 99.5% (the use of feature subset produced by the information gain algorithm and the decision tree classification algorithm).

Test platform for electronic control units of high-performance safety-critical multi actuator systems Giovanni Bucci; Fabrizio Ciancetta; Edoardo Fiorucci; Simone Mari
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

In this paper we are mostly concerned with the problem of testing electronic control units of synchronized electric power actuators. This task is particularly complex for safety critical applications, where it is crucial that the control system properly reacts in response to the faults, that are hard to reproduce and verify. A cost-effective flexible and reconfigurable test platform is proposed, discussing its architecture and implementation. The proposed system facilitates the phase of definition and development of the electronic control unit, allowing the interfacing towards both hydraulic and electromechanical actuators, and having a high flexibility as regards the I/O signals. Some results, obtained during the laboratory test activity, are also presented.

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Tole Sutikno

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ijece@iaesjournal.com

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ijece@iaesjournal.com

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Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

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

Abstract

Abstract

Abstract

Abstract

Abstract

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Abstract

Abstract

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