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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 63 Documents
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Search results for , issue "Vol 14, No 1: April 2019" : 63 Documents clear
High gain boost converter with modified voltage multiplier for stand alone PV system Getzial Anbu Mani; A. K. Parvathy
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp185-192

Abstract

Boost converters of high gain are used for photo voltaic systems to obtain high efficiency. These high gain Boost converters gives increased output voltage for a low input produces high outputs for low input voltage. The High gain boost converters have the following merits. Conduction losses input current ripple and stress across the switches is reduced while the efficiency is increases. The high gain of the converters with the above said merits is obtained by changing the duty cycle of switches accordingly .In this paper a boost converter working with interleaved concept along with a additional Nstage voltage Multiplier has been carried out by simulation using MATLAB/ simulink and the mathematical modeling of various parameters is also done.
Multilayer neural network synchronized secured session key based encryption in wireless communication Arindam Sarkar; Joydeep Dey; Anirban Bhowmik
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp169-177

Abstract

Energy computation concept of multilayer neural network synchronized on derived transmission key based encryption system has been proposed for wireless transactions. Multilayer perceptron transmitting machines accepted same input array, which in turn generate a resultant bit and the networks were trained accordingly to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights of hidden units of this selected hidden layer help to form a secret session key. A novel approach to generate a transmission key has been explained in this proposed methodology. The last thirty two bits of the session key were taken into consideration to construct the transmission key. Inverse operations were carried out by the destination perceptron to decipher the data. Floating frequency analysis of the proposed encrypted stream of bits has yielded better degree of security results. Energy computation of the processed nodes inside multi layered networks can be done using this proposed frame of work.
Convolutional neural network vs bag of features for bambara groundnut leaf disease recognition Hafizatul Hanin Hamzah; Nurbaity Sabri; Zaidah Ibrahim; Dino Isa
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp368-374

Abstract

This paper investigates bambara groundnut leaf disease recognition using two popular techniques known as Convolutional Neural Network (CNN) and Bag of Features (BOF) with Speeded-up Robust Feature (SURF) and Support Vector Machine (SVM) classifier.  Leaf disease recognition has attracted many researchers because the outcome is useful for farmers. One of the crops that provide high income for farmers is bambara groundnut but the leaves are easily infected with diseases especially after the rain.  This could affect the crop productivity.  Thus, automatic disease recognition is crucial.  A new dataset that consists of 400 images of the infected and non-infected leaves of bambara groundnut has been constructed. The experimental results indicate that both of these techniques produce excellent leaf disease recognition accuracy.
Hybrid enhanced ICA & KSVM based brain tumor image segmentation Thrivikram Bathini; Baswaraj Gadgay
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp478-489

Abstract

Medical image processing is an important aspect in diagnosis and treatment strategy. The tremendous volume of medical data has accelerated the need for automated analysis of this image, more so in the case Magnetic Resonance Imaging (MRI). An improved K-means algorithm and EM algorithm have been combined in the proposed approach to produce a hybrid strategy for better clustering and segmentation using Enhanced ICA. A classifier for based on Support Vector Machine (SVM) has been formulated and employed for the classification of brain tumors in Magnetic Resonance Images (MRI). The proposed SVM classifier used a kernel in the form of Gaussian radial basis function kernel (GRB kernel) to improve the classifier performance. The performance of the classifier has been validated through expert clinical opinion and calculation of performance measures. The results amply illustrate the suitability of the proposed classifier.
Research on neutral-point potential control of a three-level inverter Guangjie Fu; Xinpeng Li
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp20-28

Abstract

Diode-clamped three-level inverters have been widely used in high voltage and high power fields because of their unique advantages. Nowadays, diode-clamped three-level inverters have become a research hotspot. In order to reduce the content of energy harmonics injected into the power grid by the inverter system, the neutral point potential needs to be controlled. This paper proposes a control method based on a proportional controller. The voltage sector was redefined and the design of the proportional controller was completed. In combination with the introduction of a new PWM technology, a smooth control of the midpoint potential was achieved. The effectiveness of the method is verified by simulation in MATLAB
Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition Nik Noor Akmal Abdul Hamid; Rabiatul Adawiya Razali; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp333-339

Abstract

This paper presents a comparative study between Bag of Features (BoF), Conventional Convolutional Neural Network (CNN) and Alexnet for fruit recognition.  Automatic fruit recognition can minimize human intervention in their fruit harvesting operations, operation time and harvesting cost.  On the other hand, this task is very challenging because of the similarities in shapes, colours and textures among various types of fruits. Thus, a robust technique that can produce good result is necessary. Due to the outstanding performance of deep learning like CNN and its pre-trained models like AlexNet in image recognition, this paper investigates the accuracy of conventional CNN, and Alexnet in recognizing thirty different types of fruits from a publicly available dataset.  Besides that, the recognition performance of BoF is also examined since it is one of the machine learning techniques that achieves good result in object recognition.   The experimental results indicate that all of these three techniques produce excellent recognition accuracy. Furthermore, conventional CNN achieves the fastest recognition result compared to BoF, and Alexnet.
Parametric studies of ring and parallel coupled line resonators for matched bandstop filter design Abdullah Mohammed Zobilah; Adib Othman; Noor Azwan Shairi; Zahriladha Zakaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp29-37

Abstract

Recently, matched bandstop filters had become a substantial part of modern RF and microwave systems. However, in these types of filters, the key problem in the couplings of any microstrip resonators to transmission microstrip line is the variation or tolerance of the coupling gap. It was found that the bandstop response is very sensitive to the gap size of the coupled line. Therefore, this paper presents parametric studies of dual-mode parallel coupled line and ring resonator for matched bandstop filter design. For parallel coupled line resonator, it was found that with careful design and proper circuit parametric study on the coupling spacing, very high notch and matched return loss response were obtained. In contrast, for ring resonator, based on the simulated result, it was found that a very high notch and matched return loss response were obtained with careful design and proper circuit parametric study on the coupling spacing, width at coupling lines, and perturbed stub length.
Linearity improvement of differential CMOS low noise amplifier Maizan Muhamad; Norhayati Soin; Harikrishnan Ramiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp407-412

Abstract

This paper presents the linearity improvement of differential CMOS low noise amplifier integrated circuit using 0.13um CMOS technology. In this study, inductively degenerated common source topology is adopted for wireless LAN application. The linearity of the single-ended LNA was improved by using differential structures with optimum biasing technique. This technique achieved better LNA and linearity performance compare with single-ended structure. Simulation was made by using the cadence spectre RF tool. Consuming 5.8mA current at 1.2V supply voltage, the designed LNA exhibits S21 gain of 18.56 dB, noise figure (NF) of 1.85 dB, S11 of −27.63 dB, S22 of -34.33 dB, S12 of −37.09 dB and IIP3 of -7.79 dBm.
A novel approach for selective feature mechanism for two-phase intrusion detection system B Narendra Kumar; M S V Sivarama Bhadri Raju; B Vishnu Vardhan
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp101-112

Abstract

Intrusion Detection is an important aspect to secure the computing systems from different intrusions. To improve the accuracy and to reduce the computational time, this paper proposes a two-phase hybrid method based on the SVM and RNN. In addition, this paper also had a proposal to obtain a few sets of features with a feature selection technique in which the detection performance increases. For the two-phase system, two different feature selection techniques were proposed which solves both the linear dependency and non-linear dependency between the features. In the first phase, the RNN combines with the proposed Joint Mutual Information Maximization (JMIM) based feature selection and in the second phase, the Support Vector Machine (SVM) combines with correlation based feature selection. Extensive simulations are carried out over the proposed system using two different datasets, NSL-KDD and Kyoto2006+. The performance is measured through the performance metrics such as Detection Rate (DR), Precision, False Alarm Rate (FAR), Accuracy and F-Score. Furthermore, a comparative analysis with few recent hybrid frameworks is also enumerated. The obtained results signify the effectiveness of proposed method.
Spatial domain image enhancement techniques for acute myeloid leukemia (M1,M4,M5,M7) A.S. A.Salam; M.N. M.Isa; M. I. Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp250-257

Abstract

In this paper, several techniques of image enhancement spatial domain is elucidated and analyzed for the purpose of enhancing Acute Myeloid Leukemia (AML) subtype of M1, M4, M5 and M7. The techniques involved contrast stretching of greyscale images, image subtraction and image sharpening. The three methods compared with one another to achieve the highest PSNR value for the suitability technique of AML subtypes (M1, M4, M5 and M7). Firstly, subtypes images converted into grayscale. Then, each four images tested with contrast stretching techniques followed by image subtraction and image sharpening. The performances were evaluated based on Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Due to its higher value obtained, image sharpening is a good enhancement techniques for Acute Myeloid Leukemia with 68.2083 dB and the lowest MSE achieved of 0.0103.

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