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<![CDATA[DESIGN AND SIMULATION HYBRID FILTER FOR 17 LEVEL MULTILEVEL INVERTER ]]>
<![CDATA[Hutabarat, Marshal Andrea]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.890
<![CDATA[Increased use of renewable energy such as solar cells, wind power, ocean thermal and technology HVDC (high voltage direct current) causes an increase in the use of the inverter. Harmonics that generated by the inverter has a negative impact on the electrical equipment, it can cause excessive heat and may shorten the life of electrical equipment. A multilevel inverter is an arranged in a stack of several inverters in cascaded form which it aims to reduce THD (total harmonic distortion). In this work, the designed 17 levels of a single phase cascaded multilevel inverteris proposed. With the aid of PSIM, the THD of this inverter were compared to THD produced by single pulse width modulation (SPWM) inverter and the hybrid filter to reduce its THD also proposed. The results show that the THD produced by cascaded multilevel inverter much smaller than SPWM one. The increasing of load tend to cause the THD values also increased. Therefore, it is a requirement to install the filters to reduce the value of THD to comply it with IEEE 519-2014 standard. Installation of hybrid filter able to fix a maximum of 0.23% THDv and a maximum of 1.05% THDi.]]>
<![CDATA[Performance analysis of low-complexity welch power spectral density for automatic frequency analyser ]]>
<![CDATA[Teh Yi Jun]]>;
<![CDATA[Asral Bahari Jambek]]>;
<![CDATA[Uda Hashim]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1393
<![CDATA[The aim of this paper is to investigate the performance of the Low Complexity Welch Power Spectral Density Computation (PSDC). This algorithm is an improvement from Welch PSDC method to reduce the computational complexity of the method. The effect of the sampling rate and the input frequency toward to accuracy of frequency detection is being evaluated. From the experiment results, sampling rate nearest to the twice of the input frequency provides the highest accuracy which achieved 99%. The ability of the algorithm to perform complex signal also has been investigated.]]>
<![CDATA[Performance comparison of automatic peak detection for signal analyser ]]>
<![CDATA[Teh Yi Jun]]>;
<![CDATA[Asral Bahari Jambek]]>;
<![CDATA[Uda Hashim]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1394
<![CDATA[The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD. For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.]]>
<![CDATA[SOC integration for video processing application ]]>
<![CDATA[Chan Boon Cheng]]>;
<![CDATA[Asral Bahari Jambek]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1396
<![CDATA[Video processing is an additional system that can improve the functionality of video surveillance. Integration of a simple video processing system into a complete camera system with a field-programmable gate array (FPGA) is an important step for research, to further improve the tracking process. This paper presents the integration of greyscale conversion into a complete camera system using Nios II software build tools for Eclipse. The camera system architecture is designed using the Nios II soft-core embedded processor from Altera. The proposed greyscale conversion system is designed using the C programming language in Eclipse. Parts of the architecture design in the camera system are important if greyscale conversion is to take place in the processing, such as synchronous dynamic random-access memory (SDRAM) and a video decoder driver. The image or video is captured using a Terasic TRDB-D5M camera and the data are converted to RGB format using the video decoder driver. The converted data are shown in binary format and the greyscale conversion system extracts and processes the data. The processed data are stored in the SDRAM before being sent to a VGA monitor. The camera system and greyscale conversion system were developed using the Altera DE2-70 development platform. The data from the video decoder driver and SDRAM were examined to confirm that the data conversion matched greyscale conversion formulae. The converted data in the SDRAM correctly displayed the greyscale image on a VGA monitor.]]>
<![CDATA[High-gain PDMS-magnetite zero refractive index metamaterial antenna for Vehicle-to-Vehicle communications ]]>
<![CDATA[Noorlindawaty Md. Jizat]]>;
<![CDATA[Nazihah Ahmad]]>;
<![CDATA[Zubaida Yusoff]]>;
<![CDATA[Mohd Faizal Jamlos]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1397
<![CDATA[This paper presents the simulation design of a high-gain antenna using zero refractive index fishnet metamaterial (MTM) perforated on PDMS-Magnetite substrate for vehicle-to-vehicle (V2V) communications. In order to design the MTM, magnetite nanoparticles, 10-nm iron oxide (Fe3O4) are dispersed into polydimethylsiloxane (PDMS) matrix. Subsequently, the unit cell is designed by removing the circular hole with radius of 3.69 mm on the PDMS-Magnetite substrate layer and arranged in 5×5 array fishnet configuration. This optimized MTM is inserted between the antenna design and pure PDMS substrate to improve the gain. The characteristic of the respective unit-cell is investigated to operate at 5.9 GHz and the effectiveness of MTM is performed by comparing the antenna performance with and without MTM. The unique characteristics of zero refractive index transform the diverging wave into plane wave for perfectly parallel wave impact on the design to improve the directivity and gain of the antenna. The proposed MTM into design improves the antenna gain to 7.36 dB without having to compromise other antenna parameters of return loss, Voltage Standing Wave Ratio (VSWR), gain, directivity, efficiency, current distribution, radiation pattern and bandwidth. These advantages has made proposed antenna as a suitable candidate for V2V in Dedicated Short Range Communication (DSRC) application since high-gain directional antenna is required to increase the sensitivity towards signals coming from certain direction.]]>
<![CDATA[Design & investigation of 10x10 gbit/s MDM over hybrid FSO link under different weather conditions and fiber to the home ]]>
<![CDATA[Alaan Ghazi]]>;
<![CDATA[S. A. Aljunid]]>;
<![CDATA[Awab Noori]]>;
<![CDATA[Syed Zulkarnain Syed Idrus]]>;
<![CDATA[C. B. M. Rashidi]]>;
<![CDATA[Aras Al-Dawoodi]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1398
<![CDATA[In this paper, we design and investigate 10-channels of mode division multiplexer (MDM) over hybrid free-space optics (FSO) link in several weather conditions to achieve the maximum possible medium range and fiber to the home (FTTH) for high bandwidth access networks. System capacity can be effectively increased with the use of MDM over hybrid FSO-FTTH. In this study, a 10-channel MDM over FSO-FTTH system has been analyzed in different weather conditions that operate at 1550 nm wavelength. The simulated system has transmitted 100 Gbit/s up for a distance of 3200 meters FSO in superbly clear weather condition. It also transmitted 100 Gbit/s up for a distance of 650 meters FSO during heavy rain. The validation of this study is measures based on eye diagrams bit-error rates (BER) that have been analyzed.]]>
<![CDATA[Dynamic evolving neural fuzzy inference system equalization scheme in mode division multiplexer for optical fiber transmission ]]>
<![CDATA[Awab Noori]]>;
<![CDATA[Angela Amphawan]]>;
<![CDATA[Alaan Ghazi]]>;
<![CDATA[S. A. Aljunid Ghazi]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1399
<![CDATA[The performance of optical mode division multiplexer (MDM) is affected by inter-symbol interference (ISI), which arises from higher-order mode coupling and modal dispersion in multimode fiber (MMF). Existing equalization algorithms in MDM can mitigate linear channel impairments, but cannot tackle nonlinear channel impairments accurately. Therefore, mitigating the noise in the received signal of MDM in the presence of ISI to recover the transmitted signal is important issue. This paper aims at controlling the broadening of the signal from MDM and minimizing the undesirable noise among channels. A dynamic evolving neural fuzzy inference system (DENFIS) equalization scheme has been used to achieve this objective. Results illustrate that nonlinear DENFIS equalization scheme can improve the received distorted signal from an MDM with better accuracy than previous linear equalization schemes such as recursive‐least‐square (RLS) algorithm. Desirably, this effect allows faster data transmission rate in MDM. Additionally, the successful offline implementation of DENFIS equalization in MDM encourages future online implementation of DENFIS equalization in embedded optical systems.]]>
<![CDATA[A general framework for improving electrocardiography monitoring system with machine learning ]]>
<![CDATA[A. M. Khairuddin]]>;
<![CDATA[Ku Azir K. N. F.]]>;
<![CDATA[P. Eh Kan]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1400
<![CDATA[As one of the most important health monitoring systems, electrocardiography (ECG) is used to obtain information about the structure and functions of the human heart for detecting and preventing cardiovascular disease. Given its important role, it is vital that the ECG monitoring system provides relevant and accurate information about the heart. Over the years, numerous attempts were made to design and develop more effective ECG monitoring system. Nonetheless, the literature reveals not only several limitations in conventional ECG monitoring system but also emphasizes on the need to adopt new technology such as machine learning to improve the monitoring system as well as its medical applications. This paper reviews previous works on machine learning to explain its key features, capabilities as well as presents a general framework for improving ECG monitoring system.]]>
<![CDATA[Configurations of memristor-based APUF for improved performance ]]>
<![CDATA[Julius Han Loong Teo]]>;
<![CDATA[Noor Alia Noor Hashim]]>;
<![CDATA[Azrul Ghazali]]>;
<![CDATA[Fazrena Azlee Hamid]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1401
<![CDATA[The memristor-based arbiter PUF (APUF) has great potential to be used for hardware security purposes. Its advantage is in its challenge-dependent delays, which cannot be modeled by machine learning algorithms. In this paper, further improvement is proposed, which are circuit configurations to the memristor-based APUF. Two configuration aspects were introduced namely varying the number of memristor per transistor, and the number of challenge and response bits. The purpose of the configurations is to introduce additional variation to the PUF, thereby improve PUF performance in terms of uniqueness, uniformity, and bit-aliasing; as well as resistance against support vector machine (SVM). Monte Carlo simulations were carried out on 180 nm and 130 nm, where both CMOS technologies have produced uniqueness, uniformity, and bit-aliasing values close to the ideal 50%; as well as SVM prediction accuracies no higher than 52.3%, therefore indicating excellent PUF performance.]]>
<![CDATA[Motor imagery classification in Brain Computer Interface (BCI) based on EEG signal by using machine learning technique ]]>
<![CDATA[N. E. Md Isa]]>;
<![CDATA[A. Amir]]>;
<![CDATA[M. Z. Ilyas]]>;
<![CDATA[M. S. Razalli]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1402
<![CDATA[This paper focuses on classification of motor imagery in Brain Computer Interface (BCI) by using classifiers from machine learning technique. The BCI system consists of two main steps which are feature extraction and classification. The Fast Fourier Transform (FFT) features is extracted from the electroencephalography (EEG) signals to transform the signals into frequency domain. Due to the high dimensionality of data resulting from the feature extraction stage, the Linear Discriminant Analysis (LDA) is used to minimize the number of dimension by finding the feature subspace that optimizes class separability. Five classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree and Logistic Regression are used in the study. The performance was tested by using Dataset 1 from BCI Competition IV which consists of imaginary hand and foot movement EEG data. As a result, SVM, Logistic Regression and Naïve Bayes classifier achieved the highest accuracy with 89.09% in AUC measurement.]]>
