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Eng. Proc., 2025, IEEE ECICE 2024

2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering

Yunlin, Taiwan| 15–17 November 2024

Volume Editors:
Teen-Hang Meen, National Formosa University, Taiwan
Chi-Ting Ho, National Formosa University, Taiwan
Cheng-Fu Yang, National University of Kaohsiung, Taiwan

Number of Papers: 36
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Cover Story (view full-size image): The 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering (IEEE ECICE 2024) was held in Yunlin, Taiwan, on 15–17 November 2024. It offered researchers, engineers, and [...] Read more.
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6 pages, 630 KiB  
Proceeding Paper
Analysis of One-Degree-of-Freedom Spring-Mass-Damper System with Nonlinear Spring Using Runge–Kutta Method
by Kuan-Bo Lin and Tzu-Li Tien
Eng. Proc. 2025, 92(1), 1; https://doi.org/10.3390/engproc2025092001 - 10 Apr 2025
Viewed by 177
Abstract
Most engineering problems are described using differential equations, yet only a few can be solved analytically. Nonlinear differential equations are generally difficult to solve. The goal of numerical analysis is to minimize the difference between the numerical solution and the exact solution as [...] Read more.
Most engineering problems are described using differential equations, yet only a few can be solved analytically. Nonlinear differential equations are generally difficult to solve. The goal of numerical analysis is to minimize the difference between the numerical solution and the exact solution as much as possible. The Runge–Kutta method, particularly the fourth-order Runge–Kutta method (RK4), is a highly accurate numerical analysis technique. We applied the RK4 method to the analysis of a spring-mass-damper system with a nonlinear spring. The results show that the numerical solution of the displacement time response function of the spring-mass-damper system is accurate and precise, with six significant figures. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 785 KiB  
Proceeding Paper
Calculating Percentiles of T-Distribution Using Gaussian Integration Method
by Tzu-Li Tien
Eng. Proc. 2025, 92(1), 2; https://doi.org/10.3390/engproc2025092002 - 10 Apr 2025
Viewed by 117
Abstract
Statistical inference is used to estimate population parameters based on sample information and to quantify the sampling error based on the probability narrative. The population mean is inferred by its sample mean, but when using sample variance, the population variance is needed. In [...] Read more.
Statistical inference is used to estimate population parameters based on sample information and to quantify the sampling error based on the probability narrative. The population mean is inferred by its sample mean, but when using sample variance, the population variance is needed. In the quantitative analysis of the sampling error, the t-distribution is used. To determine the percentiles of the t-distribution, the cumulative probability density function is necessary. However, the analytic expression does not exist for the cumulative probability density function of the t-distribution. Its values are obtained using numerical integration. However, the percentiles of the t-distribution are not listed for degrees of freedom over 30, while only listed for every 10 data points in probability theory or mathematical statistics. This is inconvenient for research. Therefore, the cumulative probability density function of t-distribution was calculated using the Gaussian integration method in this study. The results show that the percentiles of the t-distribution are accurately estimated using the algorithm developed in this study. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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9 pages, 470 KiB  
Proceeding Paper
Applying a Parameterized Quantum Circuit to Anomaly Detection
by Jehn-Ruey Jiang and Jyun-Sian Li
Eng. Proc. 2025, 92(1), 3; https://doi.org/10.3390/engproc2025092003 - 10 Apr 2025
Viewed by 189
Abstract
In this study, a parameterized quantum circuit (PQC) is applied for anomaly detection, a crucial process to identify unusual patterns or outliers in data. PQC is a quantum circuit with trainable parameters linked to quantum gates, which are iteratively optimized by classical optimizers [...] Read more.
In this study, a parameterized quantum circuit (PQC) is applied for anomaly detection, a crucial process to identify unusual patterns or outliers in data. PQC is a quantum circuit with trainable parameters linked to quantum gates, which are iteratively optimized by classical optimizers to ensure that the circuit’s output fulfills its objectives. This is analogous to the way of using trainable parameters, such as weights adjusted in classical machine learning and neural network models. We used the amplitude−embedding mechanism with classical data into quantum states of qubits. These states are fed into PQC, which contains strongly entangled layers, and the circuit is trained to determine whether an anomaly exists. As anomaly detection datasets are often imbalanced, resampling techniques, such as random oversampling, the synthetic minority oversampling technique (SMOTE), random undersampling, and Tomek-Link undersampling, are applied to reduce the imbalance. The proposed PQC and various resampling techniques were compared using the public Musk dataset for anomaly detection. Their combination was also compared with the combination of the classical autoencoder and the classical isolation forest model in terms of the F1 score. By analyzing the comparison results, the advantages and disadvantages of PQC for future research studies were determined. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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9 pages, 652 KiB  
Proceeding Paper
Indirect Measurement of Tensile Strength of Materials by Grey Prediction Models GMC(1,n) and GM(1,n)
by Tzu-Li Tien
Eng. Proc. 2025, 92(1), 4; https://doi.org/10.3390/engproc2025092004 - 10 Apr 2025
Viewed by 98
Abstract
Grey theory is applied to forecasting, decision-making, and control as this theory is appropriate for predictive analysis. Incomplete information is a primary characteristic of the grey system, necessitating the supplementation of information to transform the relationships between various information elements from grey to [...] Read more.
Grey theory is applied to forecasting, decision-making, and control as this theory is appropriate for predictive analysis. Incomplete information is a primary characteristic of the grey system, necessitating the supplementation of information to transform the relationships between various information elements from grey to white and improve the accuracy of predictive models. However, for the first-order grey prediction model with n variables, specifically the traditional GM(1,n) model, modelling values are derived using a rough approximation method. It is assumed in this method that the elements of the one-order accumulated generating series of each associated series are constant, leading to an unreasonable relationship between the forecast series and the associated series, which is fundamentally an incorrect model. The elements of a non-negative series’s one-order accumulated generating series cannot be constants; even if they are constant series, this is not true. Consequently, the traditional GM(1,n) model yields significant errors. There have been few papers addressing the errors of this model. To improve the GM(1,n) model, correct algorithms must be used by incorporating convolution algorithms or fitting system action quantities with basic functions to derive particular solutions. The modelling procedure of the grey convolution prediction model GMC(1,n) demonstrates that the traditional grey prediction model GM(1,n) is incorrect. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 726 KiB  
Proceeding Paper
Menstruation-Related Physical Condition Management for Women Using an Underwear-Type Wearable Biosensor
by Takuto Nishi, Yuki Aikawa, Kyosuke Kato, Miki Kaneko and Ken Kiyono
Eng. Proc. 2025, 92(1), 5; https://doi.org/10.3390/engproc2025092005 - 10 Apr 2025
Viewed by 184
Abstract
Many females experience physical problems caused by menstruation, such as menstrual cramps and premenstrual syndrome, which disrupt their daily lives and work. Knowing when menstruation begins is essential for managing such physical conditions. However, menstrual periods are not always cyclic and can be [...] Read more.
Many females experience physical problems caused by menstruation, such as menstrual cramps and premenstrual syndrome, which disrupt their daily lives and work. Knowing when menstruation begins is essential for managing such physical conditions. However, menstrual periods are not always cyclic and can be extended by physical and mental stress. Currently used menstrual management applications rely on self-reported cycle length and basal body temperature (BBT), which makes it challenging to predict irregular periods. Advances in smart wearables have made continuous, non-invasive health monitoring accessible, such as heart rate variability (HRV). HRV characteristics reflect autonomic nervous system activity and are used as physical and mental health status indices. This study aims to explore the relationship between HRV indices and the menstrual cycle using smart wearables. A total of 13 females aged from 18 to 20 participated in this study and measured their indices using an underwear-type wearable device for six months. The device measured HRV and body acceleration. Participants recorded their BBT every morning and answered questionnaires about their physical and mental status every morning and evening. They also reported the start and end dates of menstruation. The HRV data were split into sleep and wake phases using acceleration and calculated time- and frequency-domain HRV indices. Cross-correlation and regression analysis were conducted to assess the relation between the menstrual cycle and phases, such as follicular and luteal, and the HRV indices. A significant relationship between HRV indices and the menstrual cycle length was found, particularly in the difference between the follicular and luteal phases of HRV indices. This difference showed a relatively high association with menstrual cycle length. Importantly, the regression analysis results suggested that HRV indices can be used to predict the length of the menstrual cycle and potential physical and mental disorders. These findings significantly contributed to menstrual health management and the Femtech industry. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 1998 KiB  
Proceeding Paper
Monitoring Leg Muscle Strength Symmetry via Electromyography
by Fu-Jung Wang, Liang-Sian Lin, Chun-Kai Tseng, Cheng-Hsiang Chan, Zhe-Yu Lee and Ting-An Yeh
Eng. Proc. 2025, 92(1), 6; https://doi.org/10.3390/engproc2025092006 - 14 Apr 2025
Viewed by 160
Abstract
Many movements of the human body’s muscles rely on the leg muscles for power or weight-bearing. However, leg muscle symmetry is often ignored. Therefore, it is necessary to monitor uneven or asymmetric muscle strength between the legs. We developed a system using electromyography [...] Read more.
Many movements of the human body’s muscles rely on the leg muscles for power or weight-bearing. However, leg muscle symmetry is often ignored. Therefore, it is necessary to monitor uneven or asymmetric muscle strength between the legs. We developed a system using electromyography (EMG) and an HW827 sensor for detecting leg muscles and monitoring the heart rate. In the system, the data are displayed on the Node-RED dashboard and are stored in the SQLite database. These experimental results show that for two subjects at a moderate level of exercise intensity, their non-dominant leg EMG values are higher than those for the dominant leg. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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10 pages, 3359 KiB  
Proceeding Paper
Guarded Diagnosis: Preserving Privacy in Cervical Cancer Detection with Convolutional Neural Networks on Pap Smear Images
by Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Kabilan Saravanan and Bruhathi Sathyanarayanan
Eng. Proc. 2025, 92(1), 7; https://doi.org/10.3390/engproc2025092007 - 11 Apr 2025
Viewed by 106
Abstract
Advancements in image processing have advanced medical diagnostics, especially in image classification, impacting healthcare by offering faster and more accurate analyses of magnetic resonance imaging (MRI) and X-rays. The manual examination of these images is slow, error-prone, and costly. Therefore, we propose a [...] Read more.
Advancements in image processing have advanced medical diagnostics, especially in image classification, impacting healthcare by offering faster and more accurate analyses of magnetic resonance imaging (MRI) and X-rays. The manual examination of these images is slow, error-prone, and costly. Therefore, we propose a new method focusing on the Pap smear exam for early cervical cancer detection. Using a convolutional neural network (CNN) and the SIPaKMeD dataset, cervical cells are classified into normal, precancerous, and benign cells after segmentation. The CNN’s architecture is simple yet efficient, achieving a 91.29% accuracy. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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11 pages, 4387 KiB  
Proceeding Paper
Revolutionizing Prenatal Care: Harnessing Machine Learning for Gestational Diabetes Anticipation
by Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Bruhathi Sathyanarayanan and Kabilan Saravanan
Eng. Proc. 2025, 92(1), 8; https://doi.org/10.3390/engproc2025092008 - 11 Apr 2025
Viewed by 128
Abstract
We implemented a robust framework for diabetes prediction, leveraging a diverse array of machine learning algorithms. Through an analysis of diabetes-related characteristics, we identified the most accurate classifier. Diverse algorithms were tested to compare their accuracies with the complexities of data: K-nearest neighbors [...] Read more.
We implemented a robust framework for diabetes prediction, leveraging a diverse array of machine learning algorithms. Through an analysis of diabetes-related characteristics, we identified the most accurate classifier. Diverse algorithms were tested to compare their accuracies with the complexities of data: K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), and decision tree (DT). The decision tree algorithm demonstrated the best accuracy in predicting diabetes. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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8 pages, 4426 KiB  
Proceeding Paper
Application of Image Analysis Technology in Detecting and Diagnosing Liver Tumors
by Van-Khang Nguyen, Chiung-An Chen, Cheng-Yu Hsu and Bo-Yi Li
Eng. Proc. 2025, 92(1), 9; https://doi.org/10.3390/engproc2025092009 - 16 Apr 2025
Viewed by 629
Abstract
We applied processing technology to detect and diagnose liver tumors in patients. The cancer imaging archive (TCIA) was used as it contains images of patients diagnosed with liver tumors by medical experts. These images were analyzed to detect and segment liver tumors using [...] Read more.
We applied processing technology to detect and diagnose liver tumors in patients. The cancer imaging archive (TCIA) was used as it contains images of patients diagnosed with liver tumors by medical experts. These images were analyzed to detect and segment liver tumors using advanced segmentation techniques. Following segmentation, the images were converted into binary images for the automatic detection of the liver’s shape. The tumors within the liver were then localized and measured. By employing these image segmentation techniques, we accurately determined the size of the tumors. The application of medical image processing techniques significantly aids medical experts in identifying liver tumors more efficiently. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 1709 KiB  
Proceeding Paper
Developing Frugal Internet of Things with Backpropagation Neural Network for Predicting Impact of Gemini Artificial Intelligence on Student Meditation and Relaxation
by Chun-Kai Tseng, Cheng-Hsiang Chan, Liang-Sian Lin, Fu-Jung Wang, Kai-Hsuan Yao and Chao-Wei Hsu
Eng. Proc. 2025, 92(1), 10; https://doi.org/10.3390/engproc2025092010 - 17 Apr 2025
Viewed by 96
Abstract
With the rapid development of generative artificial intelligence (AI) technologies, large language models have been developed and used in education. In this study, we employ the Google Gemini AI tool (version 1.0) to annotate teachers’ programming of teaching materials. When students learned these [...] Read more.
With the rapid development of generative artificial intelligence (AI) technologies, large language models have been developed and used in education. In this study, we employ the Google Gemini AI tool (version 1.0) to annotate teachers’ programming of teaching materials. When students learned these annotated teaching materials, the ThinkGear ASIC module (TGAM) and galvanic skin response (GSR) sensors were deployed to measure student mindfulness meditation, relaxation levels, and learning stress. We constructed a backpropagation neural network (BPNN) model with three hidden layers to predict student concentration and relaxation levels using GSR data and the time that students spent answering questions. In the developed system, we deployed a Node-Red dashboard to monitor all sensing data and predict results for mindfulness meditation and relaxation levels. The results were stored in an SQLite database. The BPNN model effectively predicted students’ mindfulness meditation and relaxation levels. For multiple-choice questions about teaching materials, the mean absolute error (MAE) of the BPNN model was 14.29 for mindfulness meditation and 10.54 for relaxation. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 891 KiB  
Proceeding Paper
Networked Symphony Orchestra in Internet of Things Courses
by Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Julio Barzola-Monteses and Lorenzo Cevallos-Torres
Eng. Proc. 2025, 92(1), 11; https://doi.org/10.3390/engproc2025092011 - 23 Apr 2025
Viewed by 139
Abstract
Internet of Things (IoT) education is hindered by a deficiency of dynamic and interactive courses, in addition to a lack of components and difficulty in device configuration. These difficulties diminish students’ enthusiasm for IoT initiatives and reduce their drive and involvement. We designed [...] Read more.
Internet of Things (IoT) education is hindered by a deficiency of dynamic and interactive courses, in addition to a lack of components and difficulty in device configuration. These difficulties diminish students’ enthusiasm for IoT initiatives and reduce their drive and involvement. We designed and constructed a networked symphony orchestra using the Lego Mindstorms EV3 package as a project belonging to the IoT subject. Lego Mindstorms EV3 was selected due to its easy configuration. In this study, the knowledge obtained during the subject was utilized. In IoT courses at the University of Guayaquil, there is strong encouragement to apply the studied material to new initiatives. Through the design, the assessment of multiple technologies, and the final implementation of the project described within this paper, students were motivated for the practical application of concepts related to IoT. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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8 pages, 2046 KiB  
Proceeding Paper
Classification of Salmon Freshness In Situ Using Convolutional Neural Network
by Juan Miguel L. Valeriano and Carlos C. Hortinela IV
Eng. Proc. 2025, 92(1), 12; https://doi.org/10.3390/engproc2025092012 - 23 Apr 2025
Viewed by 151
Abstract
Fish is an important food resource, an economic contributor, and a staple food for Filipinos. For the safety and satisfaction of consumers, fish freshness must be determined. Using the convolutional neural network (CNN) algorithm, we determined salmon fillet freshness in this study. In [...] Read more.
Fish is an important food resource, an economic contributor, and a staple food for Filipinos. For the safety and satisfaction of consumers, fish freshness must be determined. Using the convolutional neural network (CNN) algorithm, we determined salmon fillet freshness in this study. In total, 7000 images were used for training and 40 for testing the CNN model. The deep learning technique, specifically ResNet50 architecture, was used with Raspberry Pi 4B, and Raspberry Pi camera V2 was employed to take images of fish. The model showed a 92.5% accuracy, highlighting the CNN model’s accurate evaluation of seafood quality. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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8 pages, 1840 KiB  
Proceeding Paper
Image Descriptions for Visually Impaired Individuals to Locate Restroom Facilities
by Cheng-Si He, Nan-Kai Lo, Yu-Huan Chien and Siao-Si Lin
Eng. Proc. 2025, 92(1), 13; https://doi.org/10.3390/engproc2025092013 - 25 Apr 2025
Viewed by 80
Abstract
Since visually impaired individuals cannot observe their surroundings, they face challenges in accurately locating objects. Particularly in restrooms, where various facilities are spread across a limited space, the risk of tripping and being injured significantly increases. To prevent such accidents, individuals with visual [...] Read more.
Since visually impaired individuals cannot observe their surroundings, they face challenges in accurately locating objects. Particularly in restrooms, where various facilities are spread across a limited space, the risk of tripping and being injured significantly increases. To prevent such accidents, individuals with visual impairments need help to navigate these facilities. Therefore, we designed a head-mounted device that utilized artificial intelligence (AI) to enhance its functionality. The ESP32-CAM was implemented to capture and transmit images to a computer. The images were then converted into a model-compatible format for the bootstrapping language-image pre-training (BLIP) model to process and generate English descriptions (i.e., written captions). Then, Google Text-to-Speech (gTTS) was employed to convert these descriptions into speech, which was delivered audibly through a speaker. The SacreBLEU and MOS scores indicated that the developed device produced relatively accurate, natural, and intelligible spoken directions. The device assists visually impaired individuals in navigating and locating the restroom facilities to a satisfactory level. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 3962 KiB  
Proceeding Paper
Assessing Impact of Seasonal Lighting Variation on Visual Positioning of Drones
by Che-Cheng Chang, Bo-Yu Liu, Bo-Ren Chen and Po-Ting Wu
Eng. Proc. 2025, 92(1), 14; https://doi.org/10.3390/engproc2025092014 - 25 Apr 2025
Viewed by 72
Abstract
Positioning systems and algorithms are essential for drones. The global positioning system (GPS) is the most common method for drone positioning, but the GPS is not always precise or available. For visual-based positioning, convolutional neural networks (CNNs) are often used to match geometric [...] Read more.
Positioning systems and algorithms are essential for drones. The global positioning system (GPS) is the most common method for drone positioning, but the GPS is not always precise or available. For visual-based positioning, convolutional neural networks (CNNs) are often used to match geometric features in drone positioning. However, seasonal lighting is not considered, although its changes can affect the results. Hence, by incorporating critical components into a CNN, a new architecture is designed to position a drone accurately despite seasonal lighting variations. The experimental results show that the developed method solves issues in drone positioning with high accuracy and stability. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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9 pages, 3054 KiB  
Proceeding Paper
Simulated Adversarial Attacks on Traffic Sign Recognition of Autonomous Vehicles
by Chu-Hsing Lin, Chao-Ting Yu, Yan-Ling Chen, Yo-Yu Lin and Hsin-Ta Chiao
Eng. Proc. 2025, 92(1), 15; https://doi.org/10.3390/engproc2025092015 - 25 Apr 2025
Viewed by 71
Abstract
With the development and application of artificial intelligence (AI) technology, autonomous driving systems are gradually being applied on the road. However, people still have requirements for the safety and reliability of unmanned vehicles. Autonomous driving systems in today’s unmanned vehicles also have to [...] Read more.
With the development and application of artificial intelligence (AI) technology, autonomous driving systems are gradually being applied on the road. However, people still have requirements for the safety and reliability of unmanned vehicles. Autonomous driving systems in today’s unmanned vehicles also have to respond to information security attacks. If they cannot defend against such attacks, traffic accidents might be caused, leaving passengers exposed to risks. Therefore, we investigated adversarial attacks on the traffic sign recognition of autonomous vehicles in this study. We used You Look Only Once (YOLO) to build a machine learning model for traffic sign recognition and simulated attacks on traffic signs. The simulated attacks included LED light strobes, color-light flash, and Gaussian noise. Regarding LED strobes and color-light flash, translucent images were used to overlay the original traffic sign images to simulate corresponding attack scenarios. In the Gaussian noise attack, Python 3.11.10 was used to add noise to the original image. Different attack methods interfered with the original machine learning model to a certain extent, hindering autonomous vehicles from recognizing traffic signs and detecting them accurately. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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9 pages, 2243 KiB  
Proceeding Paper
Classification of Flavored Filipino Vinegars Using Electronic Nose
by Jon Laurman Palanas, Michael Irvin C. Peña and Meo Vincent C. Caya
Eng. Proc. 2025, 92(1), 16; https://doi.org/10.3390/engproc2025092016 - 25 Apr 2025
Viewed by 69
Abstract
Condiments such as vinegar are made and fermented manually with the help of the human nose. We developed an electronic nose to classify pure Filipino vinegar varieties for automated vinegar classification. MQ sensors were used to determine the sensitivity of gas content of [...] Read more.
Condiments such as vinegar are made and fermented manually with the help of the human nose. We developed an electronic nose to classify pure Filipino vinegar varieties for automated vinegar classification. MQ sensors were used to determine the sensitivity of gas content of different vinegar flavors, namely, Sinamak, Pinakurat, and Iloko. Linear discriminant analysis was conducted for dimensionality reduction. A support vector machine (SVM) was employed to utilize the data gathered and accurately identify the varieties. 360 samples were included in the training dataset, while 108 samples were included in the testing datasets. The accuracy was 78.7%. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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6 pages, 167 KiB  
Proceeding Paper
Classification of Artificial Intelligence-Generated Product Reviews on Amazon
by Jia-Luen Yang
Eng. Proc. 2025, 92(1), 17; https://doi.org/10.3390/engproc2025092017 - 25 Apr 2025
Viewed by 74
Abstract
Amazon has been flooded with artificial intelligence (AI)-generated product reviews that offer minimal value to customers. These AI reviews merely echo the given product descriptions without providing any authentic information on how buyers feel when using the products. Therefore, an AI review-identifying method [...] Read more.
Amazon has been flooded with artificial intelligence (AI)-generated product reviews that offer minimal value to customers. These AI reviews merely echo the given product descriptions without providing any authentic information on how buyers feel when using the products. Therefore, an AI review-identifying method was developed to enhance the quality of the review-reading experience in this study. A dataset of 6217 Amazon reviews was compiled including 1116 identified as AI-generated ones. They were classified with a 99.25% F1 score on the test data using the term frequency–inverse document frequency (TF–IDF) and support vector classifier (SVC). The developed method enables the detection of AI-generated reviews on the internet, fostering an authentic and reliable platform. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
7 pages, 768 KiB  
Proceeding Paper
Effectiveness of Active Learning in Flipped Classroom in ICT Course
by Min-Bin Chen
Eng. Proc. 2025, 92(1), 18; https://doi.org/10.3390/engproc2025092018 - 25 Apr 2025
Viewed by 75
Abstract
In this study, an ICT course is redesigned with a blended learning concept. This course aims to teach an introduction to game technology in the following three main topics: ‘Introduction to Computer’, ‘Game software technology’, and ‘Game art technology’. Basic computer science concepts [...] Read more.
In this study, an ICT course is redesigned with a blended learning concept. This course aims to teach an introduction to game technology in the following three main topics: ‘Introduction to Computer’, ‘Game software technology’, and ‘Game art technology’. Basic computer science concepts such as binary numbers, algebra, vectors, data structure, computer graphics, and artificial intelligence (AI) are introduced in this course. In the flipped classroom, insufficient preparation of students before class and an increased workload of students and teachers are the challenges to overcome. Active learning is carried out in the classroom, as it enhances students’ concentration in the classroom. The pre- and post-test was used to investigate the effects of in-class and out-of-class activities in this method. In this study, active learning was applied to flipped classrooms in this course, and its learning effects were compared with that of the traditional method. The learning outcomes of active learning were significantly improved. In-class activity had significant effects on the outcome quantitatively and qualitatively. The learning outcomes of out-of-class activities for which students were usually insufficiently prepared were also improved. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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8 pages, 2677 KiB  
Proceeding Paper
A Magnetic Deburring Method for Hypodermic Needles Used in Human Bodies
by Yanhua Zou
Eng. Proc. 2025, 92(1), 19; https://doi.org/10.3390/engproc2025092019 - 25 Apr 2025
Viewed by 58
Abstract
In the manufacturing process of precision micro parts, burrs generated in cutting and grinding processes cause various problems. Shot blasting was used in the deburring technology of cutting and grinding burr in the process of manufacturing hypodermic needles for the human body. However, [...] Read more.
In the manufacturing process of precision micro parts, burrs generated in cutting and grinding processes cause various problems. Shot blasting was used in the deburring technology of cutting and grinding burr in the process of manufacturing hypodermic needles for the human body. However, we found that a secondary burr facing the inside occurs on the chin part of the needle during the blasting process. The existence of burrs on a hypodermic needle also causes several problems. We developed a new deburring method by using a vibration magnetic abrasive machining process. Our experimental results validated the effectiveness of the magnetic deburring method. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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12 pages, 1011 KiB  
Proceeding Paper
Educational Effectiveness of Using Big Data Based and Its Evaluation with Cluster Analysis and Qualification Framework in Financial Services and Management
by Yujie Jiao, Ruiting Zhang and Ying Zhu
Eng. Proc. 2025, 92(1), 20; https://doi.org/10.3390/engproc2025092020 - 25 Apr 2025
Viewed by 66
Abstract
We evaluated and predicted the quality of financial services and professional management using cluster analysis. Using K-prototype clustering analysis and TF-IDF word frequency methods, the differences in different evaluations of job positions and vocational skill requirements of college graduates were analyzed. The graduates [...] Read more.
We evaluated and predicted the quality of financial services and professional management using cluster analysis. Using K-prototype clustering analysis and TF-IDF word frequency methods, the differences in different evaluations of job positions and vocational skill requirements of college graduates were analyzed. The graduates with better school curricula and higher rationality tended to have more knowledge-based skills. Professional knowledge learning ability, theoretical knowledge level, project execution ability, and organizational coordination ability were important in learning skill requirements. The ability to analyze data and conduct research and development is important in the development of digital finance technology. It is necessary to build a professional foundation, teach workplace skills, keep up with recent technology, and optimize the standards to improve educational effectiveness in educating financial services and management. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 734 KiB  
Proceeding Paper
Fuzzy Decision Support System for Science and Technology Project Management
by Minhui Tong, Jianhua Cheng, Ying Liu and Yuhang Ye
Eng. Proc. 2025, 92(1), 21; https://doi.org/10.3390/engproc2025092021 - 26 Apr 2025
Viewed by 38
Abstract
To improve the accuracy and scientific of science and technology project management, a fuzzy decision support system was developed in this study. We designed the overall deployment architecture of the system, which consists of the system access layer, system core layer, system service [...] Read more.
To improve the accuracy and scientific of science and technology project management, a fuzzy decision support system was developed in this study. We designed the overall deployment architecture of the system, which consists of the system access layer, system core layer, system service layer, and basic platform layer. A Web server was used to reduce the response time of the system. The indices of science and technology projects were sorted by using the fuzzy decision support process and the expert’s weight matrix. To improve evaluation accuracy, a program and the storage process of the results were established at each stage of the evaluation. The developed system spent less time querying evaluation results. The query error rate was low, indicating improved efficiency of science and technology project management. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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5 pages, 1134 KiB  
Proceeding Paper
vFerryman: An Artificial Intelligence-Driven Personalized Companion Providing Calming Visuals and Social Interaction for Emotional Well-Being
by Wei-Ji Wang
Eng. Proc. 2025, 92(1), 22; https://doi.org/10.3390/engproc2025092022 - 26 Apr 2025
Viewed by 41
Abstract
As awareness of mental health issues grows, there is an increasing demand for innovative tools that provide personalized emotional support. By introducing vFerryman, an AI-driven companion system was designed to enhance emotional well-being in this study. The system integrates advanced natural language processing [...] Read more.
As awareness of mental health issues grows, there is an increasing demand for innovative tools that provide personalized emotional support. By introducing vFerryman, an AI-driven companion system was designed to enhance emotional well-being in this study. The system integrates advanced natural language processing and machine learning technologies into the CrewAI framework. Multiple AI agents were used to deliver personalized, real-time emotional responses. By utilizing large language model operations (LLMOps), vFerryman optimizes the performance of large language models to dynamically adapt to users’ emotional feedback. A key feature of the system is its calming aquarium module, which offers a soothing visual environment to alleviate stress and anxiety. Additionally, vFerryman includes a social interaction platform that fosters emotional connections and shared experiences among users. The effectiveness of vFerryman in improving emotional well-being and facilitating social interaction was evaluated while identifying areas for further technical enhancement and practical applications in emotional support systems. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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8 pages, 3671 KiB  
Proceeding Paper
The Implementation of the Physical Unclonable Function in a Field-Programmable Gate Array for Enhancing Hardware Security
by Kuang-Hao Lin, Wei-Hao Wang and I-Chen Wang
Eng. Proc. 2025, 92(1), 23; https://doi.org/10.3390/engproc2025092023 - 27 Apr 2025
Viewed by 57
Abstract
The integrated circuit (IC) industry has rapidly developed, with chip hardware security assuming a critical role in IC design. The physical unclonable function (PUF) exploits semiconductor process variation differences to generate unique responses randomly. Due to its non-replicability, PUF has emerged as one [...] Read more.
The integrated circuit (IC) industry has rapidly developed, with chip hardware security assuming a critical role in IC design. The physical unclonable function (PUF) exploits semiconductor process variation differences to generate unique responses randomly. Due to its non-replicability, PUF has emerged as one of the most commonly employed methods in hardware security. We propose PUF implementation employing an automatic scan selector to toggle between eight sets of multiplexers. With an 8-bit selector, 256 state inputs are automatically generated, and the PUF architecture enables a 256-bit unique identification code for the chip. Finally, the generated identification code is outputted either serially or in parallel and implemented on a field-programmable gate array platform. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 1981 KiB  
Proceeding Paper
Development of Proportional-Integral-Derivative Based Self-Balancing Robot Using ESP32 for STEM Education
by Cheng-Tiao Hsieh
Eng. Proc. 2025, 92(1), 24; https://doi.org/10.3390/engproc2025092024 - 27 Apr 2025
Viewed by 63
Abstract
A STEM education provides students with a friendly and efficient environment for learning science, technology, engineering, and math. According to the needs of STEM programs and activities, humanoid, biped, and quadruped robots have been developed. Those robots are used as a learning tool [...] Read more.
A STEM education provides students with a friendly and efficient environment for learning science, technology, engineering, and math. According to the needs of STEM programs and activities, humanoid, biped, and quadruped robots have been developed. Those robots are used as a learning tool supporting students in exploring the principles and theory of robotics and their related applications. In addition, those robots adapt open sources to provide free instructions for the students to build their own low-cost robots. To enhance the effects, a low-cost, two-wheel robot was created in this study. Unlike other robots, two-wheel robots usually require a gyroscope sensor and a motion controller to keep them balanced. The developed robot is an integrated system including hardware and software. Its hardware consists of an ESP32 microcontroller, a pair of DC motors, a gyroscope sensor MPU6050, and a driver for DC motors. The robot receives signals “angle” from the gyroscope, and then depends on the PID approach to drive the DC motors precisely in order to achieve balanced and smooth motions. The results of this study present the design of the robot, sensor calibration methods, and proportional-integral-derivative tuning. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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10 pages, 3195 KiB  
Proceeding Paper
Evaluation of Peltier Cooling Vest
by Vin Klein A. Talamayan, Mharlon Jefferson S. A. Yalung and Jessie R. Balbin
Eng. Proc. 2025, 92(1), 25; https://doi.org/10.3390/engproc2025092025 - 27 Apr 2025
Viewed by 57
Abstract
We incorporated a Peltier cooling system into vests for personal comfort and applications in various workplaces. We tested the Peltier cooling vest using temperature sensors and evaluated the vest’s performance. The developed Peltier cooling vest included thermoelectric cooler modules to improve cooling efficiency [...] Read more.
We incorporated a Peltier cooling system into vests for personal comfort and applications in various workplaces. We tested the Peltier cooling vest using temperature sensors and evaluated the vest’s performance. The developed Peltier cooling vest included thermoelectric cooler modules to improve cooling efficiency and comfort by using water’s heat transfer and thermal conductivity. Through testing and subjective assessments, the effectiveness of the wearable cooling system and its potential for widespread adoption were validated. Furthermore, an intelligent control algorithm was developed to maintain target temperatures. The built-in temperature sensor enabled temperature stability in the set temperature range. The average cooling response time of the Peltier cooling vest was 9.42 min. In a lower temperature range of 16 to 24 °C, the vest maintained a stable temperature. A correlation between temperature and power consumption was observed. To improve the performance, built-in Bluetooth and a graphic user interface need to be integrated. Then, the Peltier cooling vest and its technology can be used in medical and industrial settings. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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8 pages, 1055 KiB  
Proceeding Paper
Applying Artificial Intelligence in Software Development Education
by Emanuel S. Grant, Sicong Shao, Qinxuan Shi and Mark Arinaitwe
Eng. Proc. 2025, 92(1), 26; https://doi.org/10.3390/engproc2025092026 - 28 Apr 2025
Abstract
Artificial intelligence (AI) is applied at a pace that challenges the verification of its suitability to the domains of application. This situation arises from the proliferation of AI development being conducted from a data science point of view rather than a software engineering [...] Read more.
Artificial intelligence (AI) is applied at a pace that challenges the verification of its suitability to the domains of application. This situation arises from the proliferation of AI development being conducted from a data science point of view rather than a software engineering approach. The situation leads to the question of whether software development course curricula are addressing the necessary educational needs for graduates to respond to the challenges of applying AI development in emerging domains. The challenge has two parts: the first is the use of AI in developing software systems, and the second is the development of AI systems. By looking at the first part of this challenge, we propose a pedagogy for introducing AI tools in software engineering education and structuring a methodology for AI application development to establish software engineering principles. This article is exploratory. We reviewed the existing literature to identify the commonalities of approaches to select a required set of topics, course outcomes, and structure for a curriculum on AI in software development. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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6 pages, 544 KiB  
Proceeding Paper
Real-Time Super Resolution Utilizing Dilation and Depthwise Separable Convolution
by Che-Cheng Chang, Wen-Pin Chen, Yi-Wei Lin, Yu-Jhan Lin and Po-Jui Pan
Eng. Proc. 2025, 92(1), 27; https://doi.org/10.3390/engproc2025092027 - 28 Apr 2025
Viewed by 52
Abstract
Computer vision applications require high-quality reproductions of original images, typically demanding complex models with many trainable parameters and floating-point operations. This increases computational load and restricts deployment on resource-constrained devices. Therefore, we designed a new dilation depthwise super-resolution (DDSR) model that is composed [...] Read more.
Computer vision applications require high-quality reproductions of original images, typically demanding complex models with many trainable parameters and floating-point operations. This increases computational load and restricts deployment on resource-constrained devices. Therefore, we designed a new dilation depthwise super-resolution (DDSR) model that is composed of dilation convolution, depthwise separable convolution, and residual connection, to overcome the predicaments. Compared with the well-known model, fast super-resolution convolutional neural network (FSRCNN), the developed DDSR shows better performance in evaluations and You Only Look Once (YOLO v8) confidence scores. Most importantly, the architecture of the developed DDSR has 55% trainable parameters, 19% floating-point operations per second (FLOPs) of one-channel FSRCNN, 27% of the trainable parameters, and 8% of the FLOPs of three-channel FSRCNN. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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11 pages, 4345 KiB  
Proceeding Paper
Deep Learning Approach to Cassava Disease Detection Using EfficientNetB0 and Image Augmentation
by Jazon Andrei G. Alejandro, James Harvey M. Mausisa and Charmaine C. Paglinawan
Eng. Proc. 2025, 92(1), 28; https://doi.org/10.3390/engproc2025092028 - 28 Apr 2025
Abstract
Cassava, a vital crop in the Philippines and other tropical regions, is highly susceptible to various diseases that drastically reduce its yield. Traditional inspection methods for detecting these diseases are manual, time-consuming, expensive, and prone to inaccuracies. While recent advances enable improved detection, [...] Read more.
Cassava, a vital crop in the Philippines and other tropical regions, is highly susceptible to various diseases that drastically reduce its yield. Traditional inspection methods for detecting these diseases are manual, time-consuming, expensive, and prone to inaccuracies. While recent advances enable improved detection, many approaches focus primarily on leaves and stems, overlooking tubers—one of the most critical parts of the plant. Since tubers are the harvested portion of the cassava and a direct source of food and income, early disease detection in this part is crucial for preventing severe yield losses. Furthermore, symptoms often manifest in the tubers before becoming visible in other parts, making their monitoring essential for timely intervention. To address these challenges and improve accuracy, we employed EfficientNetB0 and data augmentation techniques to enhance disease detection across multiple parts of the cassava plant. The developed system integrates a Raspberry Pi 4B with a camera module LCD screen enclosed in a 3D-printed casing for ease of use by farmers, and this showed detection accuracies of 94% for leaves, 90% for stems, and 92% for tubers. The system’s reliability was validated with p-values at a 0.05 significance level. By reducing the need for expensive manual inspections, the system offers a robust solution for early disease detection, particularly in the tubers, to mitigate yield losses. Its proven accuracy and practical design support better disease management practices, thereby improving crop health while enhancing food security and supporting the livelihoods of cassava farmers. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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11 pages, 4392 KiB  
Proceeding Paper
Implementation of Autonomous Navigation for Solar-Panel-Cleaning Vehicle Based on YOLOv4-Tiny
by Wen-Chang Cheng and Xu-Dong Chen
Eng. Proc. 2025, 92(1), 31; https://doi.org/10.3390/engproc2025092031 - 28 Apr 2025
Abstract
We developed an autonomous navigation system for a solar-panel-cleaning vehicle. The system utilizes the YOLOv4-Tiny object detection model to detect white lines on the solar panels and combines the model with a proportional–integral–derivative (PID) controller to achieve autonomous navigation functionality. The main system [...] Read more.
We developed an autonomous navigation system for a solar-panel-cleaning vehicle. The system utilizes the YOLOv4-Tiny object detection model to detect white lines on the solar panels and combines the model with a proportional–integral–derivative (PID) controller to achieve autonomous navigation functionality. The main system platform was built on Raspberry Pi, and the Intel Neural Compute Stick 2 (NCS2) was used for hardware acceleration, which boosted the model’s inference speed from 2 to 8 frames per second (FPS), significantly enhancing the system’s real-time performance. By tuning the PID controller parameters, the system achieved an optimal performance, with KP = 11, Ki = 0.01, and Kd = 30, maintaining the average value of the error e(t) at −0.0412 and the standard deviation at 0.1826 and improving the inference speed. The system autonomously followed the white lines on the solar panels and automatically turned when reaching the boundaries. The system also autonomously cleaned itself. The developed autonomous navigation system effectively improved the efficiency and convenience of solar panel cleaning. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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8 pages, 3697 KiB  
Proceeding Paper
Pansharpening Remote Sensing Images Using Generative Adversarial Networks
by Bo-Hsien Chung, Jui-Hsiang Jung, Yih-Shyh Chiou, Mu-Jan Shih and Fuan Tsai
Eng. Proc. 2025, 92(1), 32; https://doi.org/10.3390/engproc2025092032 - 28 Apr 2025
Abstract
Pansharpening is a remote sensing image fusion technique that combines a high-resolution (HR) panchromatic (PAN) image with a low-resolution (LR) multispectral (MS) image to produce an HR MS image. The primary challenge in pansharpening lies in preserving the spatial details of the PAN [...] Read more.
Pansharpening is a remote sensing image fusion technique that combines a high-resolution (HR) panchromatic (PAN) image with a low-resolution (LR) multispectral (MS) image to produce an HR MS image. The primary challenge in pansharpening lies in preserving the spatial details of the PAN image while maintaining the spectral integrity of the MS image. To address this, this article presents a generative adversarial network (GAN)-based approach to pansharpening. The GAN discriminator facilitated matching the generated image’s intensity to the HR PAN image and preserving the spectral characteristics of the LR MS image. The performance in generating images was evaluated using the peak signal-to-noise ratio (PSNR). For the experiment, original LR MS and HR PAN satellite images were partitioned into smaller patches, and the GAN model was validated using an 80:20 training-to-testing data ratio. The results illustrated that the super-resolution images generated by the SRGAN model achieved a PSNR of 31 dB. These results demonstrated the developed model’s ability to reconstruct the geometric, textural, and spectral information from the images. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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9 pages, 2525 KiB  
Proceeding Paper
High-Speed-Recognition Artificial Intelligence Chip Based on ARM+FPGA Platform
by Chin-Hsiung Shen, Yu-Hsien Wu, Shu-Jung Chen and Chuan-Yin Yu
Eng. Proc. 2025, 92(1), 33; https://doi.org/10.3390/engproc2025092033 - 29 Apr 2025
Abstract
We developed a license plate recognition platform based on the Zynq-7000 SoC. A field-programmable gate array (FPGA) was used to build a low-power, high-speed neural network. The system leveraged the ARM processor for initial image processing and used standard license plate characters as [...] Read more.
We developed a license plate recognition platform based on the Zynq-7000 SoC. A field-programmable gate array (FPGA) was used to build a low-power, high-speed neural network. The system leveraged the ARM processor for initial image processing and used standard license plate characters as a training dataset. After filtering and processing, the images were resized to 28 × 28 pixels in the grayscale format and then transmitted to the FPGA for high-speed recognition. The digital circuit in the FPGA was implemented using Verilog in a deep learning neural network architecture, with the neurons configured as (57, 12, 57, 36) in a hidden layer. The model was trained for 60 epochs. The neural network was also trained with a dataset consisting of 26 English alphabet characters and 10 digits, augmented using image dilation and erosion. Recognition accuracy was 83.33%. Using Vivado, the system was successfully deployed on the Zynq-7000 SoC, demonstrating its potential in intelligent applications. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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11 pages, 584 KiB  
Proceeding Paper
Evaluation and Enhancement of Power System Resilience Under Weather Events
by Yuan-Kang Wu, Duc-Tung Trinh and Chun-Hung Li
Eng. Proc. 2025, 92(1), 34; https://doi.org/10.3390/engproc2025092034 - 29 Apr 2025
Abstract
Extreme weather events might harm power system equipment. Although these events are infrequent, their impact is substantial, making the power system and its modern grids vulnerable to weather-related conditions. In this study, we reviewed weather-related resilience metrics and appropriate methods for assessing power [...] Read more.
Extreme weather events might harm power system equipment. Although these events are infrequent, their impact is substantial, making the power system and its modern grids vulnerable to weather-related conditions. In this study, we reviewed weather-related resilience metrics and appropriate methods for assessing power system resilience. These metrics were derived from various resilience curves. We also compiled data from different countries on resilience evaluation and methods to improve power system resilience. Potential metrics, evaluation methods, operational experiences, and strategies for enhancing power system resilience were proposed based on the results. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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9 pages, 471 KiB  
Proceeding Paper
Performance Analysis of Japanese Electric Vehicle Manufacturers in Environmental, Social, and Governance Using Text Mining and Predictive Methods
by Meihui Zhao
Eng. Proc. 2025, 92(1), 35; https://doi.org/10.3390/engproc2025092035 - 29 Apr 2025
Abstract
The value of environmental, social, and governance (ESG) has been increasingly emphasized across various industries, particularly in the automotive sector, where its importance has become especially prominent. In this study, the environmental initiatives of Japanese electric vehicle (EV) manufacturers were evaluated from an [...] Read more.
The value of environmental, social, and governance (ESG) has been increasingly emphasized across various industries, particularly in the automotive sector, where its importance has become especially prominent. In this study, the environmental initiatives of Japanese electric vehicle (EV) manufacturers were evaluated from an ESG perspective. Leading Japanese companies in EV production, such as Toyota, Nissan, and Honda, were included in the analysis. Using text mining techniques on sustainability and CSR reports from the past five years, key environmental keywords were extracted, and word clouds were generated to visualize the trends in each company’s environmental efforts. A correlation analysis was conducted between the frequency of environmental keywords and CO2 emissions data. Based on past trends in keywords and emissions data, predictive analysis was performed to analyze the potential for future emissions reductions and the strategic direction of each company’s sustainability initiatives. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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11 pages, 2770 KiB  
Proceeding Paper
Adaptive Smart System for Energy-Saving Campus
by Ziling Chen, Ray-I Chang and Quincy Wu
Eng. Proc. 2025, 92(1), 36; https://doi.org/10.3390/engproc2025092036 - 29 Apr 2025
Abstract
Due to the increasing severity of global warming and climate change, more attention is being paid to environmental problems caused by human activities. Although energy saving and carbon reduction have become a global ambition, the implementation of energy-saving mechanisms remains limited. To address [...] Read more.
Due to the increasing severity of global warming and climate change, more attention is being paid to environmental problems caused by human activities. Although energy saving and carbon reduction have become a global ambition, the implementation of energy-saving mechanisms remains limited. To address this, an adaptive smart energy-saving campus system is developed in this study to improve students’ electricity usage habits. In this system, the Internet of Things (IoT) with control interfaces is integrated to enhance convenience. Using expert system rules, the system regulates the operation of the IoT for the efficient energy-saving control of a classroom. Additionally, by incorporating a random forest classifier, the system learns users’ electricity usage habits to create a tailored energy-saving environment. Gamification is also introduced to create a reward system that stimulates users’ desire to achieve goals, thus promoting autonomous energy saving. An experiment was conducted on 62 students. In total, 59 out of 62 participants responded with a sampling error of ±2.8% at a 95% confidence level. The average system usability scale (SUS) score reached 84, surpassing the cross-industry average standard, indicating that the system is user-friendly. The average self-efficacy score for energy saving reached 4.28 (σ = 3). The system significantly impacted the participant’s motivation to enhance energy saving. The net promoter score (NPS) was 29. This indicated that, although users are generally satisfied with the system, there is still room for improvement. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 8590 KiB  
Proceeding Paper
Design and Implementation of Environmental Monitoring System Using Flask-Based Web Application
by Rong-Tai Hong
Eng. Proc. 2025, 92(1), 37; https://doi.org/10.3390/engproc2025092037 - 29 Apr 2025
Abstract
A low-cost, real-time environmental monitoring system is proposed in this study. The system integrates the Internet of Things (IoT) technology and a micro-framework Flask-based web application. The star topology of Bluetooth devices is adopted to connect the master server and multiple sensor nodes. [...] Read more.
A low-cost, real-time environmental monitoring system is proposed in this study. The system integrates the Internet of Things (IoT) technology and a micro-framework Flask-based web application. The star topology of Bluetooth devices is adopted to connect the master server and multiple sensor nodes. The system employs a Raspberry Pi 4 model B as the master server running a micro-framework web application and an Arduino UNO as the sensor nodes connected to multiple sensors and actuators. Since the sensor data need to be consecutively and continuous in real-time, multiple tasks are executed simultaneously to complete the process; therefore, thread-based parallelism is used. The proposed system enables real-time environmental monitoring with low maintenance costs by leveraging the micro-framework web application and ad hoc network. Furthermore, the proposed system is scalable, as its components are commercial off-the-shelf commodities available on the market, and the number of sensor nodes and sensors used can be increased based on the requirements of the desired system. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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7 pages, 25197 KiB  
Proceeding Paper
Identifying Barong Tagalog Textile Using Convolutional Neural Network and Support Vector Machine with Structural Pattern Segmentation
by Jeff B. Totesora, Edward C. Torralba and Cyrel O. Manlises
Eng. Proc. 2025, 92(1), 2029; https://doi.org/10.3390/engproc2025092029 (registering DOI) - 28 Apr 2025
Abstract
The Barong Tagalog is a formal attire traditionally worn by men for special occasions. Despite its cultural significance, distinguishing between the Cocoon silk, Jusi, and Piña-silk types of Philippine Barong Tagalog is challenging due to their similar colors. Although these textiles share similar [...] Read more.
The Barong Tagalog is a formal attire traditionally worn by men for special occasions. Despite its cultural significance, distinguishing between the Cocoon silk, Jusi, and Piña-silk types of Philippine Barong Tagalog is challenging due to their similar colors. Although these textiles share similar hues, their patterns and textures differ significantly, leading to potential misidentification by individuals. To identify structural patterns in textile classification, machine learning was used. Especially convolutional neural networks (CNNs) and support vector machines (SVMs) were used. The system employed a Raspberry Pi (RPI) V4 as the microprocessor and an RPI Camera V2 for image capture. The system performance was validated involving 30 sample images per classification and an additional 30 unknown samples. The system correctly classified 64 out of 90 sample images with an accuracy of 71.1%. For evaluation, a confusion matrix was determined. By combining CNN V1 and SVM V2, the textile analysis using image processing was conducted precisely to identify Barong Tagalog textiles. Full article
(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)
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