Pub Date : 2023-09-25DOI: 10.52549/ijeei.v11i3.4855
Omkar Subhash Ghongade, S Kiran Sai Reddy, Yaswanth Chowdary Gavini, Srilatha Tokala, Murali Krishna Enduri
White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non- leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction.
{"title":"Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique","authors":"Omkar Subhash Ghongade, S Kiran Sai Reddy, Yaswanth Chowdary Gavini, Srilatha Tokala, Murali Krishna Enduri","doi":"10.52549/ijeei.v11i3.4855","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4855","url":null,"abstract":"White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non- leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135866637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-24DOI: 10.52549/ijeei.v11i3.4829
Pranav P Nair, Ajay James, Philomina Simon, Bhagyasree P V
The process of encoding an input text image into a machine-readable format is called optical character recognition (OCR). The difference in characteristics of each language makes it difficult to develop a universal method that will have high accuracy for all languages. A method that produces good results for one language may not necessarily produce the same results for another language. OCR for printed characters is easier than handwritten characters because of the uniformity that exists in printed characters. While conventional methods find it hard to improve the existing methods, Convolutional Neural Networks (CNN) has shown drastic improvement in classification and recognition of other languages. However, there is no OCR model using CNN for Malayalam characters. Our proposed system uses a new CNN architecture for feature extraction and softmax layer for classification of characters. This eliminates manual designing of features that is used in the conventional methods. P-ARTS Kayyezhuthu dataset is used for training the CNN and an accuracy of 99.75% is obtained for the testing dataset meanwhile a collection of 40 real time input images yielded an accuracy of 95%.
{"title":"Malayalam Handwritten Character Recognition using CNN Architecture","authors":"Pranav P Nair, Ajay James, Philomina Simon, Bhagyasree P V","doi":"10.52549/ijeei.v11i3.4829","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4829","url":null,"abstract":"The process of encoding an input text image into a machine-readable format is called optical character recognition (OCR). The difference in characteristics of each language makes it difficult to develop a universal method that will have high accuracy for all languages. A method that produces good results for one language may not necessarily produce the same results for another language. OCR for printed characters is easier than handwritten characters because of the uniformity that exists in printed characters. While conventional methods find it hard to improve the existing methods, Convolutional Neural Networks (CNN) has shown drastic improvement in classification and recognition of other languages. However, there is no OCR model using CNN for Malayalam characters. Our proposed system uses a new CNN architecture for feature extraction and softmax layer for classification of characters. This eliminates manual designing of features that is used in the conventional methods. P-ARTS Kayyezhuthu dataset is used for training the CNN and an accuracy of 99.75% is obtained for the testing dataset meanwhile a collection of 40 real time input images yielded an accuracy of 95%.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135927052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.52549/ijeei.v11i3.4858
Shradha Joshi-Bag, Archana Vyas
Agriculture is a backbone of any country. Farmers need to test the soil fertility and nutrients present in the soil for proper growth of the crops. In traditional system, the farmers collect soil sample and submit to soil testing labs for testing the soil nutrients and get the soil test reports manually. Farmers based on his experience and the season; decide which crop to be taken in the farm. Based on soil testing reports farmers decide which fertilizers required for the proper growth of the crop. This process is time consuming and human efforts are required and hence crop yield is affected. The recent technologies in cloud storage, wireless sensors, and AI based algorithms are very instrumental in decision making process of crop growth life cycle. Farmers can make use of mechanical automation tools for seeding, watering, supplying fertilizers, crop cutting etc. for proper growth of the crop. However, to observe the crop growth during the entire life cycle of crop farmer has to take lot of efforts to check need of water, any problem of disease to the crop, any specific fertilizers required or not and whether there is a need of harvesting. A proper decision support system is needed for helping the farmers in all such activities. Such support can be provided to a farmer so that he will be well updated about the growth of his crop in the farm. To reduce the human efforts and improve the crop yield, Artificial Intelligence and IOT based soil testing and Crop Recommendation system (AIoTST-CR) is designed and developed. AIoT based handheld soil testing system has pH, Nitrogen, Phosphorous, Potassium and Soil moisture sensing capability. A mobile application is developed to fetch the sensed data from AIoT system. A historical data is inputted to give training to ML models. Machine learning algorithm is used to predict and recommend the crop to be taken. The results show AIoTST-CR which is AIoT based soil testing and crop recommendation system provides effortless and accurate recommendations of crop. Our findings indicate that AIoT based system provides high accuracy, which outperforms existing commonly, used machine learning based crop recommendation systems.
{"title":"AIoTST-CR : AIoT Based Soil Testing and Crop Recommendation to Improve Yield","authors":"Shradha Joshi-Bag, Archana Vyas","doi":"10.52549/ijeei.v11i3.4858","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4858","url":null,"abstract":"Agriculture is a backbone of any country. Farmers need to test the soil fertility and nutrients present in the soil for proper growth of the crops. In traditional system, the farmers collect soil sample and submit to soil testing labs for testing the soil nutrients and get the soil test reports manually. Farmers based on his experience and the season; decide which crop to be taken in the farm. Based on soil testing reports farmers decide which fertilizers required for the proper growth of the crop. This process is time consuming and human efforts are required and hence crop yield is affected. The recent technologies in cloud storage, wireless sensors, and AI based algorithms are very instrumental in decision making process of crop growth life cycle. Farmers can make use of mechanical automation tools for seeding, watering, supplying fertilizers, crop cutting etc. for proper growth of the crop. However, to observe the crop growth during the entire life cycle of crop farmer has to take lot of efforts to check need of water, any problem of disease to the crop, any specific fertilizers required or not and whether there is a need of harvesting. A proper decision support system is needed for helping the farmers in all such activities. Such support can be provided to a farmer so that he will be well updated about the growth of his crop in the farm. To reduce the human efforts and improve the crop yield, Artificial Intelligence and IOT based soil testing and Crop Recommendation system (AIoTST-CR) is designed and developed. AIoT based handheld soil testing system has pH, Nitrogen, Phosphorous, Potassium and Soil moisture sensing capability. A mobile application is developed to fetch the sensed data from AIoT system. A historical data is inputted to give training to ML models. Machine learning algorithm is used to predict and recommend the crop to be taken. The results show AIoTST-CR which is AIoT based soil testing and crop recommendation system provides effortless and accurate recommendations of crop. Our findings indicate that AIoT based system provides high accuracy, which outperforms existing commonly, used machine learning based crop recommendation systems.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136100189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-17DOI: 10.52549/ijeei.v11i3.4516
Theresiawati Theresiawati, Tjahjanto Tjahjanto, Yuni Widiastiwi, Hamonangan Kinantan Prabu, Bambang Tri Wahyono, Wan Nor Shuhadah Wan Nik
Cloud-based service is a key area for growth in Indonesia, but there are still very few villages that have adopted a village information system based on cloud computing. This study investigates the factors influencing OpenSID adoption in cloud computing. The research was informed by the Technological Organizational Environmental (TOE) and combined two multi-criteria decision analysis methods, namely, AHP and TOPSIS to analyze the acceptance of cloud computing-based village information systems, the driving factors for adoption, and the selection of forms of OpenSID. The research focuses on the analysis of four dimensions namely organization, trust, innovation, and vendor. The sub-dimensions of each dimension include the organization (the technological readiness of actors, top management support, and firm size), Trust (security and privacy factors), innovation (compatibility, complexity, trialability, and relative advantage factors), and Vendor (vendor reputation, perceived price, and external support factors). Primary data was collected using a questionnaire and semi-structured interviews with respondents from the village government apparatus in Indramayu. The results of the study showed an open-source cloud-based village information system is the most suitable alternative solution for government at the village level in Indramayu, West Java Province. The results highlighted that the enablers that are critical for cloud adoption include Technology readiness, trust, technological innovation, and vendor. The barriers that are hindering cloud adoption are infrastructure readiness, understanding the use of cloud computing technology, low technical skills and knowledge, data integration issues, and data security. This research is a reference for developing a village information system based on cloud computing.
{"title":"Factors Influencing the Adoption of Cloud-based Village Information System: A Technology-Organization-Environment Framework and AHP–TOPSIS Integrated Model","authors":"Theresiawati Theresiawati, Tjahjanto Tjahjanto, Yuni Widiastiwi, Hamonangan Kinantan Prabu, Bambang Tri Wahyono, Wan Nor Shuhadah Wan Nik","doi":"10.52549/ijeei.v11i3.4516","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4516","url":null,"abstract":"Cloud-based service is a key area for growth in Indonesia, but there are still very few villages that have adopted a village information system based on cloud computing. This study investigates the factors influencing OpenSID adoption in cloud computing. The research was informed by the Technological Organizational Environmental (TOE) and combined two multi-criteria decision analysis methods, namely, AHP and TOPSIS to analyze the acceptance of cloud computing-based village information systems, the driving factors for adoption, and the selection of forms of OpenSID. The research focuses on the analysis of four dimensions namely organization, trust, innovation, and vendor. The sub-dimensions of each dimension include the organization (the technological readiness of actors, top management support, and firm size), Trust (security and privacy factors), innovation (compatibility, complexity, trialability, and relative advantage factors), and Vendor (vendor reputation, perceived price, and external support factors). Primary data was collected using a questionnaire and semi-structured interviews with respondents from the village government apparatus in Indramayu. The results of the study showed an open-source cloud-based village information system is the most suitable alternative solution for government at the village level in Indramayu, West Java Province. The results highlighted that the enablers that are critical for cloud adoption include Technology readiness, trust, technological innovation, and vendor. The barriers that are hindering cloud adoption are infrastructure readiness, understanding the use of cloud computing technology, low technical skills and knowledge, data integration issues, and data security. This research is a reference for developing a village information system based on cloud computing.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135304429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.52549/ijeei.v11i3.4329
Shahla Sohail, S Thenmozhi, Swetha Priyanka Jannu, R. Gayathiri
The use of medical images in diagnostic procedures is increasing, leadning to a significant rise in the memory and bandwidth requirements for preserving and transmitting these images. To address this issue, image compression techniques have garnered significant attention. These techniques are capable of reducing the data size necessary to represent an image, allowing for more efficient utilization of storage space and communication bandwidth by eliminating unnecessary information. Numerous research directions have focused on compressing medical images, but past approaches have been time-consuming and risked information loss. To trounce these limitations, this paper introduces an effiective method for reducing the size of medical images in telemedicine applications. The method utilizes Integer Wavelet Transform (IWT) and sophisticated algorithm. Primarily, input images undergo pre-processing with a circular median filter to eliminate noise and improve image quality. Subsequently, the pre-processed images are divided into multiple sub bands using IWT.Then, these sub bands are furhter divided into n X n non-overlapping matrices, and optimal coefficients are chosen by employing a modified grey wolf optimizer algorithm. Finally, the selected coefficients are encoded using Huffman coding for transmission. During decompression, the reverse process of image compression is applied. The introduced method is tested on various medical images, and the findings demonstrate its superior performance compared to previous methods, generating visually similar images with a smaller data size.
{"title":"Efficient Medical Image Compression Based on Wavelet Transform and Modified Gray Wolf Optimization","authors":"Shahla Sohail, S Thenmozhi, Swetha Priyanka Jannu, R. Gayathiri","doi":"10.52549/ijeei.v11i3.4329","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4329","url":null,"abstract":"The use of medical images in diagnostic procedures is increasing, leadning to a significant rise in the memory and bandwidth requirements for preserving and transmitting these images. To address this issue, image compression techniques have garnered significant attention. These techniques are capable of reducing the data size necessary to represent an image, allowing for more efficient utilization of storage space and communication bandwidth by eliminating unnecessary information. Numerous research directions have focused on compressing medical images, but past approaches have been time-consuming and risked information loss. To trounce these limitations, this paper introduces an effiective method for reducing the size of medical images in telemedicine applications. The method utilizes Integer Wavelet Transform (IWT) and sophisticated algorithm. Primarily, input images undergo pre-processing with a circular median filter to eliminate noise and improve image quality. Subsequently, the pre-processed images are divided into multiple sub bands using IWT.Then, these sub bands are furhter divided into n X n non-overlapping matrices, and optimal coefficients are chosen by employing a modified grey wolf optimizer algorithm. Finally, the selected coefficients are encoded using Huffman coding for transmission. During decompression, the reverse process of image compression is applied. The introduced method is tested on various medical images, and the findings demonstrate its superior performance compared to previous methods, generating visually similar images with a smaller data size.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136362928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-08DOI: 10.52549/ijeei.v11i3.4491
Muneera Altayeb
A robotic arm is any variety of programmable mechanical devices designed to operate items like a human arm and is one of the most beneficial innovations of the 20th century, quickly becoming a cornerstone of many industries. It can perform a variety of tasks and duties that may be time-consuming, difficult, or dangerous to humans. The gesture-based control interface offers many opportunities for more natural, configurable, and easy human-machine interaction. It can expand the capabilities of the GUI and command line interfaces that we use today with the mouse and keyboard. This work proposed changing the concept of remote controls for operating a hand-operated robotic arm to get rid of buttons and joysticks by replacing them with a more intuitive approach to controlling a robotic arm via the hand gestures of the user. The proposed system performs vision-based hand gesture recognition and a robot arm that can replicate the user's hand gestures using image processing. The system detects and recognizes hand gestures using Python and sends a command to the microcontroller which is the Arduino board connected to the robot arm to replicate the recognized gesture. Five servo motors are connected to the Arduino Nano to control the fingers of the robot arm; These servos are related to the robot arm prototype. It is worth noting that this system was able to repeat the user's hand gestures with an accuracy of up to 96%.
{"title":"Hand Gestures Replicating Robot Arm based on MediaPipe","authors":"Muneera Altayeb","doi":"10.52549/ijeei.v11i3.4491","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4491","url":null,"abstract":"A robotic arm is any variety of programmable mechanical devices designed to operate items like a human arm and is one of the most beneficial innovations of the 20th century, quickly becoming a cornerstone of many industries. It can perform a variety of tasks and duties that may be time-consuming, difficult, or dangerous to humans. The gesture-based control interface offers many opportunities for more natural, configurable, and easy human-machine interaction. It can expand the capabilities of the GUI and command line interfaces that we use today with the mouse and keyboard. This work proposed changing the concept of remote controls for operating a hand-operated robotic arm to get rid of buttons and joysticks by replacing them with a more intuitive approach to controlling a robotic arm via the hand gestures of the user. The proposed system performs vision-based hand gesture recognition and a robot arm that can replicate the user's hand gestures using image processing. The system detects and recognizes hand gestures using Python and sends a command to the microcontroller which is the Arduino board connected to the robot arm to replicate the recognized gesture. Five servo motors are connected to the Arduino Nano to control the fingers of the robot arm; These servos are related to the robot arm prototype. It is worth noting that this system was able to repeat the user's hand gestures with an accuracy of up to 96%.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136364019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-03DOI: 10.52549/ijeei.v11i3.4919
Augustus E. Ibhaze, Adekunle O. Gbadebo, Akinwumi A. Amusan, Samuel N. John
The introduction of Fiber Optics cables in broadband Internet distribution has been a game changer in bulk capacity delivery, speed, reliability and penetration. However, the uncurbed incessant existence of cuts and failures have threatened the growth of Internet connectivity as a whole. In this work, the impact of fiber cuts is investigated using a hybrid approach, encompassing both real-world data from a live GPON network and simulations using OptiSystem 12 for FTTH GPON scenarios. Fiber cuts and failures are emulated by introducing varying attenuation levels in the simulated network's feeder cable section within OptiSystem 12, while in the live GPON network, the attenuation is induced by introducing wrap bends in the last-mile patch cord. The findings reveal a consistent pattern in both simulated and live data for both downstream and upstream traffic scenarios. As attenuation levels increased, there was a corresponding decline in Q-factor, Eye Height, and optical power, coupled with a concurrent rise in the minimum BER. Thus, in the most severe scenario, fiber cuts can result in service degradation and eventual service outage. To mitigate this issue, the implementation of a typeB PON protection system with a wireless auto-failover technique is proposed. Adoption and deployment of the proposed technique and deliberate maintenance measures alongside thorough supervision are suggested to be possible solutions to fiber cuts in metropolitan parlance.
{"title":"Performance Analysis of Fiber Attenuation in Passive Optical Networks","authors":"Augustus E. Ibhaze, Adekunle O. Gbadebo, Akinwumi A. Amusan, Samuel N. John","doi":"10.52549/ijeei.v11i3.4919","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4919","url":null,"abstract":"The introduction of Fiber Optics cables in broadband Internet distribution has been a game changer in bulk capacity delivery, speed, reliability and penetration. However, the uncurbed incessant existence of cuts and failures have threatened the growth of Internet connectivity as a whole. In this work, the impact of fiber cuts is investigated using a hybrid approach, encompassing both real-world data from a live GPON network and simulations using OptiSystem 12 for FTTH GPON scenarios. Fiber cuts and failures are emulated by introducing varying attenuation levels in the simulated network's feeder cable section within OptiSystem 12, while in the live GPON network, the attenuation is induced by introducing wrap bends in the last-mile patch cord. The findings reveal a consistent pattern in both simulated and live data for both downstream and upstream traffic scenarios. As attenuation levels increased, there was a corresponding decline in Q-factor, Eye Height, and optical power, coupled with a concurrent rise in the minimum BER. Thus, in the most severe scenario, fiber cuts can result in service degradation and eventual service outage. To mitigate this issue, the implementation of a typeB PON protection system with a wireless auto-failover technique is proposed. Adoption and deployment of the proposed technique and deliberate maintenance measures alongside thorough supervision are suggested to be possible solutions to fiber cuts in metropolitan parlance.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134948644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-30DOI: 10.52549/ijeei.v11i3.4397
Triwiyanto Triwiyanto, Sari Luthfiyah, Wahyu Caesarendra, Abdussalam Ali Ahmed
EMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03%. Furthermore, the additional datasets show a significant difference in accuracy (p-value <0.05). Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition.
肌电信号具有随机性、非线性和非平稳的特点，需要选择合适的特征提取和分类器，将其应用于基于肌电模式识别的假手。本研究旨在在嵌入式树莓派系统上实现肌电模式识别，以识别手部运动，作为智能假手的初步研究。本研究的贡献在于可以将时域特征提取模型和分类器实现到树莓派嵌入式系统中。此外，机器学习训练和评估过程是在树莓派系统上在线进行的。在线训练过程通过使用Python编程将肌电信号数据采集硬件设备，时域特征，分类器和嵌入式机器学习上的电机控制集成在一起进行。这项研究涉及10名健康状况良好的受访者。肌电图信号采集于桡侧腕屈肌和指伸肌。采用时域特征(TDF)、平均绝对值(MAV)、均方根(RMS)、方差(VAR)提取肌电信号，窗长为100 ms。选择有监督机器学习决策树(DT)、支持向量机(SVM)和k近邻(KNN)算法结构简单、计算量少。最后，将TDF和分类器嵌入到Raspberry Pi 3 Model B+微型计算机中。实验结果表明，在开放类中获得了最高的准确率，为97.03%。此外，额外的数据集在准确性上有显著差异(p值<0.05)。基于所获得的评价结果，该嵌入式系统可以实现基于肌电模式识别的假手。
{"title":"Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion as Preliminary Study for Smart Prosthetic Hand","authors":"Triwiyanto Triwiyanto, Sari Luthfiyah, Wahyu Caesarendra, Abdussalam Ali Ahmed","doi":"10.52549/ijeei.v11i3.4397","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4397","url":null,"abstract":"EMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03%. Furthermore, the additional datasets show a significant difference in accuracy (p-value <0.05). Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136240767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-28DOI: 10.52549/ijeei.v11i3.4857
Nur Syazana Izzati Razali, Teddy Surya Gunawan, Siti Hajar Yusoff, Mohamed Hadi Habaebi, Saerahany Legori Ibrahim, Siti Nadiah Mohd Sapihie
The Voltage Regulating Distribution Transformer (VRDT) is a tap-changing transformer that regulates the voltage across all three phases. However, its application in the context of renewable energy penetration into low-voltage grids remains understudied. This paper addresses this research gap by presenting a refined voltage drop model tailored for the International Islamic University Malaysia (IIUM) distribution network. Based on a derived mathematical equation, the model is validated and analyzed using Simulink's modeling platform. Simulations are performed without and with the VRDT, revealing that renewable energy penetration can cause instability, leading to voltage deviations proportional to the injected renewable energy. Incorporating the VRDT in the low-voltage grid allows for voltage adjustment under loaded conditions, ensuring uninterrupted renewable energy injection. Voltage stability analysis is conducted using actual load consumption data from the IIUM network for 2020 and 2021, offering valuable insights despite assuming equal energy consumption across buildings. Most hostels exhibit stable distribution systems with solar energy, but instability arises when solar energy comprises 100% of the input for the Safiyyah and Zubair hostels' 11kV distribution transformers. Implementing the VRDT regulates this instability, restoring system stability. This study highlights the importance of VRDT integration in high renewable energy proportion low-voltage grids, enabling voltage regulation and stability under variable renewable energy injection scenarios. The findings demonstrate that VRDTs mitigate voltage instability caused by renewable energy, providing a reliable solution for incorporating renewables into low-voltage distribution networks. It contributes to understanding renewable energy's impact on distribution system stability and offers guidance for VRDT implementation in similar contexts.
{"title":"Voltage Instability and Voltage Regulating Distribution Transformer Assessment Under Renewable Energy Penetration For Low Voltage Distribution System","authors":"Nur Syazana Izzati Razali, Teddy Surya Gunawan, Siti Hajar Yusoff, Mohamed Hadi Habaebi, Saerahany Legori Ibrahim, Siti Nadiah Mohd Sapihie","doi":"10.52549/ijeei.v11i3.4857","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4857","url":null,"abstract":"The Voltage Regulating Distribution Transformer (VRDT) is a tap-changing transformer that regulates the voltage across all three phases. However, its application in the context of renewable energy penetration into low-voltage grids remains understudied. This paper addresses this research gap by presenting a refined voltage drop model tailored for the International Islamic University Malaysia (IIUM) distribution network. Based on a derived mathematical equation, the model is validated and analyzed using Simulink's modeling platform. Simulations are performed without and with the VRDT, revealing that renewable energy penetration can cause instability, leading to voltage deviations proportional to the injected renewable energy. Incorporating the VRDT in the low-voltage grid allows for voltage adjustment under loaded conditions, ensuring uninterrupted renewable energy injection. Voltage stability analysis is conducted using actual load consumption data from the IIUM network for 2020 and 2021, offering valuable insights despite assuming equal energy consumption across buildings. Most hostels exhibit stable distribution systems with solar energy, but instability arises when solar energy comprises 100% of the input for the Safiyyah and Zubair hostels' 11kV distribution transformers. Implementing the VRDT regulates this instability, restoring system stability. This study highlights the importance of VRDT integration in high renewable energy proportion low-voltage grids, enabling voltage regulation and stability under variable renewable energy injection scenarios. The findings demonstrate that VRDTs mitigate voltage instability caused by renewable energy, providing a reliable solution for incorporating renewables into low-voltage distribution networks. It contributes to understanding renewable energy's impact on distribution system stability and offers guidance for VRDT implementation in similar contexts.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135134933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-22DOI: 10.52549/ijeei.v11i3.4662
M. Sobhana, Gudapati Satya Dinesh Kumar, Yarramreddy Tejaswi, Pavithra Pakkiru
The rapid growth of urbanization is driving increased road infrastructure development. Detecting and monitoring changes in urban road areas is challenging for city planners. This research proposes using semantic segmentation and vector analysis on high-resolution images to identify road network changes. The U-Net model performs semantic segmentation, pre-trained on a Massachusetts road dataset, predicting labels for a specific area with temporal data and co-registration to reduce distortions. Predicted labels are converted to shapefiles for vector analysis. Satellite images from Google Earth archives demonstrate the change detection process. The outcome of this predictive phase was the transformation of projected labels into shapefiles, thereby facilitating vector analysis to pinpoint and characterize alterations.
{"title":"Detecting Urban Road Changes using Segmentation and Vector Analysis","authors":"M. Sobhana, Gudapati Satya Dinesh Kumar, Yarramreddy Tejaswi, Pavithra Pakkiru","doi":"10.52549/ijeei.v11i3.4662","DOIUrl":"https://doi.org/10.52549/ijeei.v11i3.4662","url":null,"abstract":"The rapid growth of urbanization is driving increased road infrastructure development. Detecting and monitoring changes in urban road areas is challenging for city planners. This research proposes using semantic segmentation and vector analysis on high-resolution images to identify road network changes. The U-Net model performs semantic segmentation, pre-trained on a Massachusetts road dataset, predicting labels for a specific area with temporal data and co-registration to reduce distortions. Predicted labels are converted to shapefiles for vector analysis. Satellite images from Google Earth archives demonstrate the change detection process. The outcome of this predictive phase was the transformation of projected labels into shapefiles, thereby facilitating vector analysis to pinpoint and characterize alterations.","PeriodicalId":37618,"journal":{"name":"Indonesian Journal of Electrical Engineering and Informatics","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135717106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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