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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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Robot movement controller based on dynamic facial pattern recognition Siti Nurmaini; Ahmad Zarkasi; Deris Stiawan; Bhakti Yudho Suprapto; Sri Desy Siswanti; Huda Ubaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp733-743

Abstract

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).
Enhancing PAPR reduction for FBMC-OQAM systems by joint both tone reservation and companding methods Salima Senhadji; Yassine Mohammed Bendimerad; Fathi Tarik Bendimerad
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp919-926

Abstract

One of the major problems that faces any wireless communication system that apply multicarrier modulation technology is large Peak-to-Average Power Ratio (PAPR). There are divers PAPR reduction methods to solve this problem. Tone Reservation (TR) scheme is one of the most famous PAPR reduction techniques in which a peak cancelling signal is added to the original one in such a way that PAPR will reduce. Companding is another easy PAPR reduction technique in which compression of large amplitude samples and expansion of low one. In this paper, we suggest a new PAPR reduction scheme based on combining tone reservation and companding techniques for FBMC-OQAM systems. The simulation results show that the new scheme (TR&Compd) presents better result in term of PAPR reduction compared to TR and Companding methods taken separately.
Internet of things and multi-class deep feature-fusion based classification of tomato leaf disease Rina mahakud; Binod Kumar Pattanayak; Bibudhendu Pati
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp995-1002

Abstract

A deep transfer learning (deep-TL) classification model has been proposed to diagnose tomato leaf disease. The main challenge of inaccurate classification of a convolution neural network (CNN) model was the availability of the small-sized dataset. This model deals with the challenges like availability of small-sized and imbalanced datasets. The proposed Alex support vector machine (SVM) fused hybrid classification (ASFHC) model is based on fully fusion technology that avoids overfitting to classify the type of disease in tomato leaves. The proposed model achieves the best performance in terms of accuracy by data augmentation of the training data. It uses a pre-trained network for feature extraction with the modification of architecture by concatenating two layers FC6 and FC7 (fully connected layer), plus a linear SVM classifier for classification of the disease. The uniqueness of the research is although the dataset is not balanced, the performance of the model has achieved the maximum. Compared with VGG 16 and VGG 19, the proposed model (ASFHC) has been evaluated using different measuring parameters, indicating remarkable computation time for implementation in the internet of things (IoT) domain. The overall accuracy attained by the model is 99.62%.
Stability and performance evaluation of the speed control of DC motor using state-feedback controller Saad A. Salman; Zeyad Assi Obaid; Haider Salim Hameed
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1372-1378

Abstract

Direct current (DC) motor are widely used in many applications due to its accurate control of speed and position. However, a proper control and operation is still required and might be a challenge for control designers. This paper presents the design of a state-feedback control to evaluate the performance of the speed control of DC motor for different applications. The simulation results were carried out with and without disturbance applied to the system. The proposed control method showed a stable system response with both cases of disturbances. Therefore, it can be used to solidate the control of DC motor in the real application.
Optimal design of CMOS current mode instrumentation amplifier using bio-inspired method for biomedical applications Issa Sabiri; Hamid Bouyghf; Abdelhadi Raihani; Brahim Ouacha
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp120-129

Abstract

Analog integrated circuits for biomedical applications require good performance. This paper presents an instrumentation amplifier (IA) design based on three complementary metal oxide semiconductor (CMOS) conveyors with an active resistor. This circuit offers the possibility to control the gain by voltage and current. We have designed the IA to minimize the parasitic resistance (Rx) with large bandwidth and high common mode rejection ratio (CMRR) using the artificial bee colony algorithm (ABC). The topology is simulated using 0.35µm CMOS technology parameters. The optimization problem is represented by an objective function that will be implemented using MATLAB script. The results were approved by the simulation using the advanced design system (ADS) tool. The simulation results were compared to the characteristics of some other instrumentation amplifiers exsisting in the literature. The circuit has a higher CMRR than other topologies.
Pedestrian age estimation based on deep learning Nawal Younis Abdullah; Mohammed Talal Ghazal; Najwan Waisi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1548-1555

Abstract

The large-scale distribution of camera networks in the traffic area resulted in the increasing popularity of video surveillance systems. As pedestrian detection and tracking are the critical monitoring targets in traffic surveillance, many studies focus on pedestrian detection algorithms across cameras. This paper addressed the effect of using the age estimation based on deep convolution neural network (CNN) as a convenience for pedestrian monitoring who is crossing at intersections. Two popular deep convolutional neural networks (DCNNs) pre-trained models have been used in this work, which have recently achieved the best performance in facial features extraction tasks: VGG-Face and ResNet-50. We combined these two models to increase the efficiency of the proposed system. We ran our experiments to evaluate the system based on the VGGFace2 dataset consisting of 3.31 million face images. From the experimental results, we observed a gap in the detection performances between those age groups: children from (00-10) years and elderly with 55 years and more. Moreover, it noted that the proposed pedestrian age estimation model performance is high, also a good result can be obtained by using the model for new purpose.
Power quality mitigation and transient analysis in AC/DC hybrid microgrid for electric vehicle charging Sumana Sreenivasa Rao; Dhanalakshmi Rangaswamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1315-1322

Abstract

The usage of electric vehicles (EV) increased in recent years as the vehicles design and performances are nearly similar to petrol vehicles. The main source of energy for EV is taken from the grid for charging. So, the penetration of EVs in alternating current (AC) grid creates more power quality issues like voltage sag, swell and harmonics in the current. This energy can also be produced from the renewable energy resources like photovoltaic (PV) power generation. This PV energy can also be used as direct current (DC) grid. The electric vehicle chargers which come with intelligent grid operation is considered as load in this paper. This paper is an attempt to discuss the penetration of EVs in AC/DC hybrid micro grid which causes the power quality problems, and the power quality problem is mitigated by using the unified power quality conditioner (UPQC). The results are analyzed for three cases and four scenarios which is based on the function of UPQC and the action of smart charger in grid connected as well as autonomous mode operation of the AC/DC micro grid when the load is considered as dynamic load. The simulation is carried out in MATLAB2017b environment
Basic FBG apodization functions effects on the filtered optical acoustic signal Mahmoud M. A. Eid; Ahmed Nabih Zaki Rashed
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp287-296

Abstract

This article clarified the basic fiber Bragg grating (FBG) apodization functions effects on the filtered optical acoustic signal (AS). Max optical acoustic power variations after acousto optic filter is clarified with the spectral wavelength variations. FBG apodization functions are uniform, Gaussian, and Tanh. We have tested the max optical acoustic power variations after various apodization FBG functions with the spectral wavelength variations. The max AS power amplitude after the electrical combiner is reported based various Apodization FBG functions. The max optical AS after FBG is studied with various FBG lengths for various FBG apodization functions at the central wavelength of 1.55 μm. The max electrical power after power combiner unit is demonstrated with different FBG lengths for various FBG apodization functions at the central frequency of 193.1 THz. 
Duobinary modulation/predistortion techniques effects on high bit rate radio over fiber systems Mahmoud M. A. Eid; Ashraf S. Seliem; Ahmed Nabih Zaki Rashed; Abd El-Naser A. Mohammed; Mohamed Yassin Ali; Shaimaa S. Abaza
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp978-986

Abstract

The work has presented duobinary modulation and predistortion techniques for the radio over fiber system enhancement for achieving security level. Duobinary modulation technique has more compact modulated spectral linewidth with standard non return to zero modulation code. Different NRZ/RZ rectangle shape employed that are namely exponential rectangle shape (ERS), and Gaussian rectangle shape (GRS) for different transmission bit rates. Switching bias voltage, and switching RF voltage based LiNbO3 modulator are changed to measure the performance parameters of the radio over fiber (RoF) system. Predistortion technique improves the linearity of transmitter amplifiers and it is considered as a power efficiency technique. The optimum values of the Q-factor, data error rate (BER), electrical power, signal gain, noise figure, and light signal/noise ratio are achieved with 8 Volt for both switching biases/switching RF signal at 100 GHz. Signal quality/BER and electrical power after the receiver enhancement ratio by using this technique at different RF signal frequencies. 
An artificial intelligence solution for crop recommendation N., Varshitha D.; Choudhary, Savita
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1688-1695

Abstract

Agriculture is the major occupation in India. The development of India is in the hands of farmers. Farmers are said to be our nation’s backbone, so there is a need to support our farmers technologically so that the difficulties of traditional agricultural practices would be overcome and also there will be positive impact on the yield, harvest, healthy crop output and the income of the farmers. Farmer needs awareness about his soil and the methods to improve his soil to grow the healthy crops. We propose an approach which involves deep learning and some IOT features to help our farmers. Soil parameters such as nitrogen, phosphorous, potassium (NPK), pH, organic carbon, moisture content and few more things are considered for predicting the fertility of the soil and also to predict the right crops to be grown and nutrition required for it. We have developed a deep neural network model to predict the crop which can be suitably grown in the soil. We have also implemented the other machine learning classifiers on the same collected dataset to test the accuracies of each classifier and our deep neural network model.

Page 632 of 918 | Total Record : 9174

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