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2021 International Conference on Biomedical engineering, ICoBE 2021

作者： Halim, S.F. Abdul Zakaria, M.H. Zakaria, Z. Norali, A.N. Noor, A. Mohd Ahmed, A.A. Pusppanathan, J. Rahiman, M.H. Fazalul Muji, S.Z. Mohd Rahim, R. Abdul Biomedical Electronic Engineering Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Arau Perlis 02600 Malaysia Universiti Teknologi Malaysia Skudai Johor81310 Malaysia Mechatronic Engineering Program Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Malaysia Electronic Engineering Department Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Skudai Johor81310 Malaysia

Monitoring of fetal condition during labor could save hundred lives in a single year. During labor, fetus is at critical condition as acidosis may occur suddenly without any early symptoms. Invasive method such as Fetal Blood Sampling (FBS) has been used to detect the decline in pH level of fetus. However, fetal loss rate after FBS may range from 1.4% up to 25%. In this paper, magnetic field induction spectroscopy was implemented to determine fetal acidosis by using primary magnetic field cancellation technique. Magnetic Induction Spectroscopy (MIS) probe was design where transmitter coil (TX) is perpendicular to receiver coil (RX). The result shows that the secondary magnetic field produced have been successfully measured without any interruption from primary magnetic field. By using transmitter input 1A, it shows that voltage is inversely proportional to the blood pH due to the conductivity properties of blood. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Spectroscopy

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Sales forecasting for life insurance on primary and supplementary policies using seasonal and trend methods

Sales forecasting for life insurance on primary and suppleme...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Panrawe Boonsom Chanin Wongyai Duang-Arthit Srimoon Student in Master of Electrical and Computer Engineering Program College of Engineering Rangsit University Pathumthani Lak-Hok Thailand Faculty of Computer Engineering College of Engineering Rangsit University Pathumthani Lak-Hok Thailand

ISBN: (数字)9798350318098

ISBN: (纸本)9798350318104

Sales of insurance are collected monthly or yearly as statistics which most insurance companies haven’t estimated the sales for the next year. The current sales of insurance make it difficult to evaluate the market and organize various campaigns for customers. Therefore, this research has collected sales of life insurance from the website of the Office of Insurance Commission from the year 2018 - 2022. The forecasting of sales for life insurance using 4 forecasting methods which are Holt Winters' Additive, Holt Winters' Multiplicative, Simple Exponential Smoothing, and Double Exponential Smoothing. These forecasting methods are used to forecast insurance premiums one year ahead from the year 2021. The computation of total sales for 3 insurance types which are Primary-General, Primary, and Additional found that the Holt Winters' Multiplicative method is the best forecasting method with an accuracy percentage for forecasting methods of 97.56%.

关键词： Smoothing methods Additives Accuracy Measurement uncertainty Insurance Companies Market research

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Demonstration of a clock distribution method using switching matrices and a two-phase clock signal on an FPGA

Demonstration of a clock distribution method using switching...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Ogura, Ayumu Watanabe, Minoru Watanabe, Nobuya Information Technology Track Information Technology Electrical Engineering and Mathematical and Data Sciences Program Faculty of Engineering Okayama University 3-1-1 Tushima-naka Kita-ku Okayama700-8530 Japan

ISBN: (纸本)9798331521165

Very large-scale Integrations (VLSIs) are vulnerable to radiation, and VLSIs used in high radiation environments such as space environment and nuclear power plants have a short lifetime. Therefore, we are developing radiation-hardened optically reconfigurable gate arrays. Conventional optically reconfigurable gate array VLSIs have a weak point in clock distribution. This problem can be solved by a new clock distribution exploiting reconfigurable switching matrices. However, in this case, the clock skew increases. Therefore, to solve this problem, this paper proposes a new clock distribution method using a two-phase clock signal and switching matrices on an optically reconfigurable gate array VLSI. The proposed clock distribution method has been demonstrated on a Cyclone V FPGA by using a 4-bit counter circuit. This paper presents the demonstration results. © 2025 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Multiplayer Ludo Game Application Based on Mixed Reality Using UrhoSharp and RabbitMQ Message Broker

Multiplayer Ludo Game Application Based on Mixed Reality Usi...

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International Conference on Wireless and Telematics (ICWT)

作者： Agus Sukoco Aghil Alfhauzhi Apriliansyah Rin Rin Nurmalasari Vitradisa Pratama Ary Setijadi Prihatmanto Agus Juhana School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Indonesia Multimedia Education Study Program Universitas Pendidikan Indonesia Bandung Indonesia

This paper describes the design and implementation of the Ludo game on Microsoft Hololens to create a mixed reality-based application with Hololens technology connected to many services. The Ludo game is made for two players, with player one playing on Hololens and player two sending and receiving player one's data via the console. The Ludo game with Hololens is one of the results of the development of Hololens technology and the RabbitMQ Messaging Server. The Ludo game is made on Visual Studio software using UrhoSharp Framework and RabbitMQ Messaging Server service with the AMQP protocol. As application frameworks, Hololens and UrhoSharp function as devices for visualization and running games. At the same time, the RabbitMQ Messaging Server and AMQP protocol serve as services that connect two players from the Hololens and console by sending the data needed by the application. The test results show that Hololens is running successfully and communicating with other console players. Thus, the results obtained are the purpose of this paper, which is to create a mixed reality-based application with Hololens technology connected to many services.

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Performance Analysis for Glaucoma Diagnosis Using Transfer Learning on the PAPILA Dataset

Performance Analysis for Glaucoma Diagnosis Using Transfer L...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Etik Irijanti Hanung Adi Nugroho Igi Ardiyanto Electrical and Information Engineering Department Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia Information Technology Program Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

Glaucoma, a serious vision illness that can result in irreversible vision loss, has a huge influence on the health of people all over the world because it causes damage to the optic nerve. The purpose of this research is to improve the accuracy and efficiency of screenings and lessen the impact of the condition on a global scale. Convolutional Neural Networks (CNNs) are utilized in this investigation to improve early glaucoma identification. Several different CNN architectures and data pre-processing methods were investigated using the PAPILA dataset. These methods and architectures included VGG19, VGG16, InceptionV3, Xception, EfficientNetB0, ResNet50, and DenseNet121. It is recommended to use VGG16 for generic detection that does not require any specific pre-processing. According to the study’s findings, EfficientNetB0 attained a remarkable area under the curve (AUC) of 78.29% (0.78), highlighting its potential utility in increasing glaucoma detection.

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Radiation degradation evaluation of the dynamic configuration circuit on an optically reconfigurable gate array VLSI

Radiation degradation evaluation of the dynamic configuratio...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Ohashi, Seiji Watanabe, Minoru Watanabe, Nobuya Information Technology Track Information Technology Electrical Engineering and Mathematical and Data Sciences Program Faculty of Engineering Okayama University 3-1-1 Tushima-naka Kita-ku Okayama700-8530 Japan

ISBN: (纸本)9798331521165

The Fukushima Daiichi Nuclear Power Plant was melted down by the earthquake and tsunami in 2011. The decommissioning tasks must be executed under a high radiation intensity. For the tasks, we have been developing a radiation-hardened optically reconfigurable gate array with 1 Grad total-ionizing-dose tolerance. In this device, dynamic optical parallel configuration architecture was introduced to increase the radiation tolerance. However, the radiation degradation evaluation has never been done up to now. This paper presents the first evaluation results of the dynamic optical parallel configuration circuit on an optically reconfigurable gate array VLSI. © 2025 IEEE.

关键词： Field programmable gate arrays (FPGA)

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3d Aerodynamic Blade Performance of a Novel Plant Airfoil

SSRN

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SSRN 2023年

作者： Rosa, Firlya Soetikno, Priyono Suweca, I. Wayan Moelyadi, Mochammad Agoes Mechanical Engineering Study Program Faculty of Mechanical and Aerospace Engineering Bandung Institute of Technology Bandung40132 Indonesia Department of Mechanical Engineering Faculty of Engineering Universitas Bangka Belitung 33172 Indonesia Fluid Machinery Laboratory Faculty of Mechanical and Aerospace Engineering New and Renewable Research Bandung Institute of Technology Bandung40132 Indonesia

Based on the morphology of the banana leaf's midrib, this study investigates, designs, models, and analyzes the performance of a 3D airfoil in terms of aerodynamics. The objective is to discover if the banana leaf midrib airfoil is suitable for a low-wind-velocity horizontal-axis wind turbine. Model the aerodynamic analysis wind velocity at 3, 4, 5, 6, and 7 meters per second applying QBLADE and ANSYS Fluent. The selected tip speed ratio is 6 for a blade with a radius of 12.5 m and a chord length of 3.180 m. The investigation is conducted in 3D with QBLADE and ANSYS Fluent. Single Reference Frame (SRF) approach with periodic boundary conditions in ANSYS Fluent. According to the data, the coefficient of power was calculated to be 0.46 when QBLADE was used. The average margin of error was 16.4 percentage points lower when using ANSYS FLUENT software instead of QBLADE. Blades with a banana leaf midrib airfoil profile can offer good power even at low wind speeds, as shown by both programs. © 2023, The Authors. All rights reserved.

关键词： Morphology

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction 3

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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A Comprehensive Analysis of Mask Detection Using Convolutional Neural Networks (CNN) and Single Shot Multibox Detector (SSD) Approach

A Comprehensive Analysis of Mask Detection Using Convolution...

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Mukti, Bayu Krisna Novita, Mega Amirudin, Muhammad Singh, Sanjay Kumar Harjanto, Imadudin Bhardwaj, Priyanka Kusmantoro, Adhi Roy, Manidipa Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Electrical Engineering Jl. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Jl. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia ABES Engineering College Department of Electronics & Communication Engineering 19th KM Stone NH-09 Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

The Covid-19 pandemic has emphasized the critical role of mask-wearing in reducing virus transmission. However, declining public awareness necessitates effective monitoring solutions. This study explores the Single Shot Multibox Detector (SSD) approach, a Convolutional Neural Networks (CNN)-based object detection method, for mask detection. Specifically, we investigate the performance of the SSD model with the ResNet50 architecture as the base network. Through the analysis of a dataset comprising 1000 images of individuals wearing and not wearing masks, the SSD model with ResNet50 achieves an impressive accuracy of 98.23%, surpassing the VGG16-based model's accuracy of 93.56%. These findings underscore the superiority of the CNN-based SSD model with ResNet50 for mask detection. Moreover, they have significant implications for real-time monitoring and enforcing mask-wearing protocols, emphasizing the importance of public awareness and adherence. Leveraging advanced AI techniques, such as CNN-based object detection with SSD, strengthens public health initiatives. The development of robust and efficient mask detection systems contributes to creating a safer environment amid the ongoing Covid-19 pandemic. This research highlights the value of harnessing these advanced technologies to effectively address public health crises. By employing CNN-based SSD with ResNet50, society can enhance its ability to mitigate risks and ensure public safety. © 2023 IEEE.

关键词： Object detection

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Comparative Analysis of Normal and ASD EEG Data Using Hjorth Parameters

Comparative Analysis of Normal and ASD EEG Data Using Hjorth...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muliyadi Muliyadi Melinda Melinda Yuwaldi Away Syahrul Gazali Doctoral Program School of Engineering Post Graduate Program Universitas Syiah Kuala Banda Aceh Indonesia Electrical Engineering Department Politeknik Negeri Lhokseumawe Aceh Indonesia Dept. Electrical Engineering and Computer Engineering Faculty Universitas Syiah Kuala Banda Aceh Indonesia Dept. Neurology Universitas Syiah Kuala Banda Aceh Indonesia Mahar Mardjono National Brain Center Hospital Jakarta Indonesia

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

The increasing prevalence of autism spectrum disorder (ASD) in recent decades has underscored the need for more accurate and objective diagnostic methods. Electroencephalography (EEG) holds promise as a tool in ASD neurobiological research due to its ability to capture neural oscillatory activity with high temporal resolution. This study aims to analyze the neurophysiological differences between individuals with ASD and neurotypical populations by examining EEG data using Hjorth parameters. The preprocessing stage involved using a Butterworth filter to enhance EEG signal quality by eliminating noise in the frequency range of 4-40 Hz. The Hjorth parameters, including Activity, Mobility, and Complexity, were used to assess the statistical characteristics of the EEG signals. The analysis was conducted on EEG data collected from 5 typical individuals and 5 ASD ASD subjects. Our findings reveal that the Hjorth Activity parameter is notably lower in the ASD group across all regions, with the most significant differences observed in the left and right frontal areas, where average activity measures 0.236 μV 2 and 0.060 μV 2 in ASD individuals, compared to 0.512 μV 2 and 0.138 μV 2 in the neurotypical group. However, the Hjorth Complexity and Mobility values remained consistent between the two groups, implying that the fundamental structure of the EEG signals is relatively preserved. These findings offer insights into the neurobiological mechanisms of ASD and suggest the potential use of Hjorth parameters in developing more targeted interventions for individuals with ASD.

关键词： Human computer interaction Autism Frontal lobe Noise Neural activity Electroencephalography Complexity theory Signal resolution Robots Optimization

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