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International Journal of Electrical and Computer Engineering
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 24 Documents
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Search results for , issue "Vol 5, No 2: April 2015" : 24 Documents clear
Classification of Emotional Speech of Children Using Probabilistic Neural Network Hemanta Kumar Palo; Mihir Narayan Mohanty
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (331.801 KB) | DOI: 10.11591/ijece.v5i2.pp311-317

Abstract

Child emotions are highly flexible and overlapping. The recognition is a difficult task when single emotion conveys multiple informations. We analyze the relevance and importance of these features and use that information to design classifier architecture. Designing of a system for recognition of children emotions with reasonable accuracy is still a challenge specifically with reduced feature set. In this paper, Probabilistic neural network (PNN) has been designed for such task of classification. PNN has faster training ability with continuous class probability density functions. It provides better classification even with reduced feature set. LP_VQC and pH vectors are used as the features for the classifier. It has been attempted to design the PNN classifier with these features. Various emotions like angry, bore, sad and happy have been considered for this piece of work. All these emotions have been collected from children in three different languages as English, Hindi, and Odia. Result shows remarkable classification accuracy for these classes of emotions. It has been verified in standard databse EMO-DB to validate the result.
Recursive Least-Squares Estimation for the Joint Input-State Estimation of Linear Discrete Time Systems with Unknown Input Talel Bessaoudi; Fayçal Ben Hmida
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (128.429 KB) | DOI: 10.11591/ijece.v5i2.pp259-270

Abstract

This paper presents a recursive least-squares approach to estimate simultaneously the state and the unknown input of linear time varying discrete time systems with unknown input. The method is based on the assumption that no prior knowledge about the dynamical evolution of the input is available. The joint input and state estimation are obtained by recursive least-squares formulation by applying the inversion lemmas. The proposed filter is equivalent to recursive three step filter. To illustrate the performance of the proposed filter an example is given.
A Power System Stabilizer for Multi-Machine Power Based on Hybrid BF0A-PSO Mary Saranya; Rajapandiyan A; Fathima K.; Hema S; GeethaPriya S; Saravanan S
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (299.936 KB) | DOI: 10.11591/ijece.v5i2.pp213-220

Abstract

Bacterial Swarm Optimization (BSO) is used to design Power System Stabilizers in a multi machine power system. In BSO, the search directions of tumble behavior for each bacterium are oriented by the individual’s best location and the global best location of PSO. The hybrid BFOA-PSO algorithm has been applied to IEEE 14 bus test system under normal, light and heavy load conditions. Simulations results have revealed the strength of the BSO in tuning Power System Stabilizers under normal, light and heavy load conditions. The results present the effectiveness of the controller to improve the power system stability over a different range of loading conditions.
Multiple Processes for Least Mean Square Adaptive Algorithm on Roadway Noise Cancelling Sri Arttini Dwi Prasetyowati; Adhi Susanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (295.179 KB) | DOI: 10.11591/ijece.v5i2.pp355-360

Abstract

Noise is a problem often found in daily life. Noise also make people could not concentrate to do their work. Efforts to reduce noise have been proposed, but, due to variety of the noise’s characteristics, every noise problem requires different solution. This research aim to cancel  the vehicle’s noise while maintaining the information heard. These conditions happened in the hospitals classrooms, or work room near the roadway. The vehicle’s noise change very fast, so the adaptive system is the good solution candidate for solving this problem. On the beginning, the simulation process had the trouble with the iterations. Matlab software only can execute the certain range of iteration. It could not cancel the noise, even the information becomes criptic. The problem is how to cancell the vehicle’s noise with the restriction software and still manage the important information. This research will modify the LMS adaptive algorithm so that the iteration could be done by the system and the main goal of the research could be reached. The modification of the algorithm is based on the filter length (L) used to adapt with the noise. Therefore, this research conducted simulation of the Adaptive Noise Cancelling with two process steps. The output of the first adaptive process have the.same characteristics with the noise that would be cancelled, thus the first adaptive process have the error near to zero.  The second adaptive process changes the input by the output of the first process and mix the information into the noise. Error occured in the final process is the information heard as the dominant output.

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