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<![CDATA[Recyclable waste separation system based on material classification using weight and size of waste ]]>
<![CDATA[Nur Shahida Midi]]>;
<![CDATA[Muhammad Aizat Rahmad]]>;
<![CDATA[Siti Hajar Yusoff]]>;
<![CDATA[Sarah Yasmin Mohamad]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i2.1523
<![CDATA[Insufficient landfills problem had increased the needs to decrease the waste and recycling them. However, despite the efforts done by the government and local authorities on promoting recycling culture by introducing new laws and regulations, the awareness and willingness among the community is still low. One of the possible reasons to this is lack of effort to categorize the waste into the designated category which are paper, glass, plastic and metal. In order to address this problem, it is important to design a system that will ease the process of categorizing the waste. This can be achieve by the automation of the said process. In this work, a system consist of an algorithm and hardware to automatically categorize recyclable waste is proposed. The proposed system are utilizing weight sensor and ultrasonic sensors in order to capture the characteristics of the waste item, which are weight and size so that it can be categorized into paper, glass, plastic and metal. Here, a sytem to automatically separate household waste item is presented by combining an algorithm with a set of hardware consist of minimal number of sensors, conveyer belt and servor motors.]]>
<![CDATA[Classification of different types of metal from recyclable household waste for automatic waste separation system ]]>
<![CDATA[Siti Hajar Yusoff]]>;
<![CDATA[Sazali Mahat]]>;
<![CDATA[Nur Shahida Midi]]>;
<![CDATA[Sarah Yasmin Mohamad]]>;
<![CDATA[Syasya Azra Zaini]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i2.1488
<![CDATA[Nowadays, solid waste has become a major problem in Malaysia. However, most people in Malaysia are not aware and take this problem for granted. The rising number of population and massive development in recent years indirectly generated an enormous value of household waste, making the household waste the main generator for solid waste in Malaysia. It stated that only 5 percent of an average 30,000 tons of waste have been recycled in Malaysia. The purpose of the paper is to design a system to separate the metal recyclable household waste automatically and record the data waste collected. There are total of four detectors used to separate the non-metal, steel, copper and aluminum metal waste. The average time used to complete metal separation process by using the proposed prototype is 14.5 seconds. This paper includes a mechanical part, programming part, an electronic design and also the data collected from this proposed system. The system will be programmed using Arduino Mega as a microcontroller to control all the electronic component in the system.]]>
<![CDATA[Automatic household waste separation system based on resistance value and moisture content ]]>
<![CDATA[Nurul Nazihah Ahamad]]>;
<![CDATA[Sarah Yasmin Mohamad]]>;
<![CDATA[Nur Shahida Midi]]>;
<![CDATA[Siti Hajar Yusoff]]>;
<![CDATA[Faridah Abd Rahman]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i2.1491
<![CDATA[In the new era of globalization, waste disposal has turned into an immense concern among the nation. Nowadays, almost every part of the world is confronting a huge issue of improper disposal, segregation and recycling solid waste. Due to rapid growth in economy, industrialization and urbanization, there is also a rapid increase of capacity and volume of solid waste. As a result, improper management of solid waste lead to disturbance to the environment and human health. In this paper, a fully automated waste separation system to discriminate residual and recyclable waste is proposed. The system is designed to focus on household waste, since household waste ranks the highest volume of waste among others. It is designed to separate household waste into recyclable and residual waste according to the materials’ state, which is dry and wet, by employing a moisture sensor to the waste separation system.]]>
<![CDATA[Analysis of Near-infrared (NIR) spectroscopy for chlorophyll prediction in oil palm leaves ]]>
<![CDATA[Mohd. Shafiq Amirul Sabri]]>;
<![CDATA[R. Endut]]>;
<![CDATA[C. B. M. Rashidi]]>;
<![CDATA[A. R. Laili]]>;
<![CDATA[S. A. Aljunid]]>;
<![CDATA[N. Ali]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i2.1412
<![CDATA[Oil palm nutrient content is investigated with using chlorophyll as a representative factor correlated with NIR spectroscopy spectral absorbance. NIR spectroscopy method of sampling have been tested to overcome time consuming, complex chemical analysis procedure and invasive sampling method in order to identify chlorophyll content in an oil palm tree. Spectral absorbance data from range 900 nm to 1700 nm and chlorophyll data, then tested through five pre-processing methods which is Savitzky-Golay Smoothing (SGS), Multiplicative Scatter Correction (MSC), Single Normal Variation (SNV), First Derivative (1D) and also Second Derivative (2D) using Partial Least Square (PLS) regression prediction model to evaluate the correlation between both data. The overall results show, SGS has the best performance for preprocessing method with the results, the coefficient of determination (R2) values of 0.9998 and root mean square error (RMSE) values of 0.0639. In summary, correlation of NIR spectral absorbance data and chlorophyll can be achieved using a PLS regression model with SGS pre-processing technique. Thus, we can conclude that NIR spectroscopy method can be used to identify chlorophyll content in oil palm with using time saving, simple sampling and non-invasive method.]]>
<![CDATA[Evaluation of patient electrocardiogram datasets using signal quality indexing ]]>
<![CDATA[Nazrul Anuar Nayan]]>;
<![CDATA[Hafifah Ab Hamid]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i2.1289
<![CDATA[Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.]]>
<![CDATA[Analysis of wavelet-based full reference image quality assessment algorithm ]]>
<![CDATA[Faizah Mokhtar]]>;
<![CDATA[Ruzelita Ngadiran]]>;
<![CDATA[Taha Basheer]]>;
<![CDATA[Amir Nazren Abdul Rahim]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i1.1404
<![CDATA[Measurement of Image Quality plays an important role in numerous image processing applications such as forensic science, image enhancement, medical imaging, etc. In recent years, there is a growing interest among researchers in creating objective Image Quality Assessment (IQA) algorithms that can correlate well with perceived quality. A significant progress has been made for full reference (FR) IQA problem in the past decade. In this paper, we are comparing 5 selected FR IQA algorithms on TID2008 image datasets. The performance and evaluation results are shown in graphs and tables. The results of quantitative assessment showed wavelet-based IQA algorithm outperformed over the non-wavelet based IQA method except for WASH algorithm which the prediction value only outperformed for certain distortion types since it takes into account the essential structural data content of the image.]]>
<![CDATA[Hand motion pattern recognition analysis of forearm muscle using MMG signals ]]>
<![CDATA[M. R. Mohamad Ismail]]>;
<![CDATA[C. K. Lam]]>;
<![CDATA[K. Sundaraj]]>;
<![CDATA[M. H. F. Rahiman]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i2.1415
<![CDATA[Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.]]>
<![CDATA[Face recognition using assemble of low frequency of DCT features ]]>
<![CDATA[Raja Abdullah Raja Ahmad]]>;
<![CDATA[Muhammad Imran Ahmad]]>;
<![CDATA[Mohd Nazrin Md Isa]]>;
<![CDATA[Said Amirul Anwar]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i2.1417
<![CDATA[Face recognition is a challenge due to facial expression, direction, light, and scale variations. The system requires a suitable algorithm to perform recognition task in order to reduce the system complexity. This paper focuses on a development of a new local feature extraction in frequency domain to reduce dimension of feature space. In the propose method, assemble of DCT coefficients are used to extract important features and reduces the features vector. PCA is performed to further reduce feature dimension by using linear projection of original image. The proposed of assemble low frequency coefficients and features reduction method is able to increase discriminant power in low dimensional feature space. The classification is performed by using the Euclidean distance score between the projection of test and train images. The algorithm is implemented on DSP processor which has the same performance as PC based. The experiment is conducted using ORL standard face databases the best performance achieved by this method is 100%. The execution time to recognize 40 peoples is 0.3313 second when tested using DSP processor. The proposed method has a high degree of recognition accuracy and fast computational time when implemented in embedded platform such as DSP processor.]]>
<![CDATA[Improved wolf algorithm on document images detection using optimum mean technique ]]>
<![CDATA[Wan Azani Mustafa]]>;
<![CDATA[Mohamed Mydin M. Abdul Kader]]>;
<![CDATA[Zahereel Ishwar Abdul Khalib]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i2.1426
<![CDATA[Detection text from handwriting in historical documents provides high-level features for the challenging problem of handwriting recognition. Such handwriting often contains noise, faint or incomplete strokes, strokes with gaps, and competing lines when embedded in a table or form, making it unsuitable for local line following algorithms or associated binarization schemes. In this paper, a proposed method based on the optimum threshold value and namely as the Optimum Mean method was presented. Besides, Wolf method unsuccessful in order to detect the thin text in the non-uniform input image. However, the proposed method was suggested to overcome the Wolf method problem by suggesting a maximum threshold value using optimum mean. Based on the calculation, the proposed method obtained a higher F-measure (74.53), PSNR (14.77) and lowest NRM (0.11) compared to the Wolf method. In conclusion, the proposed method successful and effective to solve the wolf problem by producing a high-quality output image.]]>
<![CDATA[Energy efficient smart street light for smart city using sensors and controller ]]>
<![CDATA[Aziera Abdullah]]>;
<![CDATA[Siti Hajar Yusoff]]>;
<![CDATA[Syasya Azra Zaini]]>;
<![CDATA[Nur Shahida Midi]]>;
<![CDATA[Sarah Yasmin Mohamad]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v8i2.1527
<![CDATA[Smart street light is an intelligent control of street lights to optimize the problem of power consumption of the street, late in night. Conventional street lights are being replaced by Light Emitting Diode (LED) street lighting system, which reduces the power consumption. The focus of this project is to design a system of street lights controller to provide a reduction in power consumption. The prototype was designed by using Light Dependent Resistor (LDR), Infrared sensor (IR), battery and LED. The brightness of the lamps is being controlled in this project to reduce the power consumption. The dimming of the lamps depends on the speed of object motion detected such as pedestrians, cyclists and cars. The higher speed of moving object, the greater the level of intensity. For this idea, the innovation of street lights is not quite the same as conventional street lights that are controlled by timer switch or light sensor which automatically turns light on during sunset and off during sunrise. According to the study, motion detection devices may help to save up to 40% of energy per month.]]>
