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6 pages, 630 KiB

Open AccessProceeding 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 363

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

Open AccessProceeding 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 173

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

Open AccessProceeding 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 336

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

Open AccessProceeding 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 138

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

Open AccessProceeding Paper

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 288

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

Open AccessProceeding 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 265

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

Open AccessProceeding 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 225

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

Open AccessProceeding 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 218

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

Open AccessProceeding 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 1003

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

Open AccessProceeding 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 187

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

Open AccessProceeding 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 243

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

Open AccessProceeding 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 324

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

Open AccessProceeding 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 185

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

Open AccessProceeding 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 150

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

Open AccessProceeding 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 237

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

Open AccessProceeding 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 218

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

Open AccessProceeding 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 331

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

Open AccessProceeding 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 207

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

Open AccessProceeding 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 146

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

Open AccessProceeding 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 189

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

Open AccessProceeding 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 139

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

Open AccessProceeding 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 275

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

Open AccessProceeding 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 208

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

Open AccessProceeding 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 298

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

Open AccessProceeding 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 357

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

Open AccessProceeding 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

Viewed by 266

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

Open AccessProceeding 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 214

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

Open AccessProceeding 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

Viewed by 242

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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10 pages, 2080 KiB

Open AccessProceeding Paper

Tunnel Traffic Enforcement Using Visual Computing and Field-Programmable Gate Array-Based Vehicle Detection and Tracking

by Yi-Chen Lin and Rey-Sern Lin

Eng. Proc. 2025, 92(1), 30; https://doi.org/10.3390/engproc2025092030 - 25 Apr 2025

Viewed by 142

Abstract

Tunnels are commonly found in small and enclosed environments on highways, roads, or city streets. They are constructed to pass through mountains or beneath crowded urban areas. To prevent accidents in these confined environments, lane changes, slow driving, or speeding are prohibited on [...] Read more.

Tunnels are commonly found in small and enclosed environments on highways, roads, or city streets. They are constructed to pass through mountains or beneath crowded urban areas. To prevent accidents in these confined environments, lane changes, slow driving, or speeding are prohibited on single- or multi-lane one-way roads. We developed a foreground detection algorithm based on the K-nearest neighbor (KNN) and Gaussian mixture model and 400 collected images. The KNN was used to gather the first 200 image data, which were processed to remove differences and estimate a high-quality background. Once the background was obtained, new images were extracted without the background image to extract the vehicle’s foreground. The background image was processed using Canny edge detection and the Hough transform to calculate road lines. At the same time, the oriented FAST and rotated BRIEF (ORB) algorithm was employed to track vehicles in the foreground image and determine positions and lane deviations. This method enables the calculation of traffic flow and abnormal movements. We accelerated image processing using xfOpenCV on the PYNQ-Z2 and FPGA Xilinx platforms. The developed algorithm does not require pre-labeled training models and can be used during the daytime to automatically collect the required footage. For real-time monitoring, the proposed algorithm increases the computation speed ten times compared with YOLO-v2-tiny. Additionally, it uses less than 1% of YOLO’s storage space. The proposed algorithm operates stably on the PYNQ-Z2 platform with existing surveillance cameras, without additional hardware setup. These advantages make the system more appropriate for smart traffic management than the existing framework. 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

Open AccessProceeding 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

Viewed by 204

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

K_{P}

= 11,

K_{i}

= 0.01, and

K_{d}

= 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

Open AccessProceeding 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

Viewed by 181

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

Open AccessProceeding 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

Viewed by 252

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

Open AccessProceeding 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

Viewed by 238

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

Open AccessProceeding 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

Viewed by 195

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

Open AccessProceeding 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

Viewed by 252

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

Open AccessProceeding 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

Viewed by 362

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, 1414 KiB

Open AccessProceeding Paper

Improved Low Complexity Predictor for Block-Based Lossless Image Compression

by Huang-Chun Hsu, Jian-Jiun Ding and De-Yan Lu

Eng. Proc. 2025, 92(1), 38; https://doi.org/10.3390/engproc2025092038 - 30 Apr 2025

Viewed by 178

Abstract

Lossless image compression has been studied and widely applied, particularly in medicine, space exploration, aerial photography, and satellite communication. In this study, we proposed a low-complexity lossless compression for image (LOCO-I) predictor based on the joint photographic expert group–lossless standard (JPEG-LS). We analyzed [...] Read more.

Lossless image compression has been studied and widely applied, particularly in medicine, space exploration, aerial photography, and satellite communication. In this study, we proposed a low-complexity lossless compression for image (LOCO-I) predictor based on the joint photographic expert group–lossless standard (JPEG-LS). We analyzed the nature of the LOCO-I predictor and offered possible solutions. The improved LOCO-I outperformed LOCO-I by a reduction of 2.26% in entropy for the full image size and reductions of 2.70, 2.81, and 2.89% for 32 × 32, 16 × 16, and 8 × 8 block-based compression, respectively. In addition, we suggested vertical/horizontal flip for block-based compression, which requires extra bits to record and decreases the entropy. Compared with other state-of-the-art (SOTA) lossless image compression predictors, the proposed method has low computation complexity as it is multiplication- and division-free. The model is also better suited for hardware implementation. As the predictor exploits no inter-block relation, it enables parallel processing and random access if encoded by fix-length coding (FLC). 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, 858 KiB

Open AccessProceeding Paper

Real-Time Visual Identification System to Assess Maturity, Size, and Defects in Dragon Fruits

by Lambert Marc A. Cometa, Robert Kobe T. Garcia and Mary Ann E. Latina

Eng. Proc. 2025, 92(1), 39; https://doi.org/10.3390/engproc2025092039 - 30 Apr 2025

Viewed by 196

Abstract

In the Philippines, dragon fruit has become an essential, high-value crop and is important to the country’s economy. However, due to inefficient manual inspection methods, farmers need help with quality control and market preparation. Therefore, we developed an automated, real-time visual identification system [...] Read more.

In the Philippines, dragon fruit has become an essential, high-value crop and is important to the country’s economy. However, due to inefficient manual inspection methods, farmers need help with quality control and market preparation. Therefore, we developed an automated, real-time visual identification system to detect the maturity, size, and defects of dragon fruits. Advanced deep learning models (EfficientNet and YOLOV8) were trained on a diverse dataset of dragon fruit images collected from online sources and a local farm using DSLR and smartphone cameras. A Raspberry Pi 4B with an HQ camera and wide-angle lens was used as a cost-effective and accessible device for farmers. The developed system showed an accuracy of 93.33% for maturity and size classification, 96.67% for defect detection, and an overall accuracy of 83.33%. Regarding accuracy and reliability, the developed method presents a technological advancement for dragon fruit identification and classification. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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13 pages, 3623 KiB

Open AccessProceeding Paper

Development and Evaluation of Learning Portfolio Query System Based on LangChain Framework

by Nien-Lin Hsueh and Wei-Ting Wang

Eng. Proc. 2025, 92(1), 40; https://doi.org/10.3390/engproc2025092040 - 30 Apr 2025

Viewed by 274

Abstract

With the increasing popularity of online education platforms, the use frequency of students and teachers has gradually increased. A large volume of data is generated and analyzed daily on these platforms including course information and student learning status. However, traditional analysis methods often [...] Read more.

With the increasing popularity of online education platforms, the use frequency of students and teachers has gradually increased. A large volume of data is generated and analyzed daily on these platforms including course information and student learning status. However, traditional analysis methods often require substantial manpower and expertise. Large language models Chat GPT-4 offer a potential solution to this problem. This study aims to address this challenge by utilizing the large-scale language model framework LangChain and the database of the OpenEdu online education platform. We designed an interface capable of querying educational data in natural language. When a user queries in natural language, the large language model generates structured query language to query the database and converts the query back into natural language to respond to the user’s question. Two different query methods were developed based on LangChain components: the sequential query version and the Agent query version. Based on these methods, four different versions of prompt and model combinations were created. The accuracy in converting natural language to SQL was estimated, and error type analysis was conducted to enhance the system’s performance and accuracy. The execution accuracy reached up to 85.7%, with the primary error type in natural language-generated SQL being Schema Linking. By integrating large-scale language models into conversational query systems, a promising approach was developed for handling large-scale data queries on educational platforms. 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, 1828 KiB

Open AccessProceeding Paper

Dog Activity Recognition Using Convolutional Neural Network

by Evenizer Nolasco, Jr., Anton Caesar Aldea and Jocelyn Villaverde

Eng. Proc. 2025, 92(1), 41; https://doi.org/10.3390/engproc2025092041 - 30 Apr 2025

Viewed by 323

Abstract

We classified common dog activities, such as sitting, standing, and lying down, which are crucial for monitoring the well-being of pets. To create a new model, we used convolutional neural networks (CNNs) on a Raspberry Pi platform and the InceptionV3 model, optimized on [...] Read more.

We classified common dog activities, such as sitting, standing, and lying down, which are crucial for monitoring the well-being of pets. To create a new model, we used convolutional neural networks (CNNs) on a Raspberry Pi platform and the InceptionV3 model, optimized on a dataset of Siberian Husky photos. The accuracy was 88% on a test set of 50 samples. In the developed model, TensorFlow Keras was used, while the OpenCV library was also used for system interaction with the Raspberry Pi and its Camera module. The model was effective for the image classification of dog behaviors in various environmental circumstances. The model substantially contributes to the development of pet welfare monitoring systems and improves the care for beloved animal companions. 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, 3609 KiB

Open AccessProceeding Paper

Abaca Blend Fabric Classification Using Yolov8 Architecture

by Cedrick D. Cinco, Leopoldo Malabanan R. Dominguez and Jocelyn F. Villaverde

Eng. Proc. 2025, 92(1), 42; https://doi.org/10.3390/engproc2025092042 - 30 Apr 2025

Viewed by 164

Abstract

Advanced deep learning has assisted in various operations in different industries. In the textile industry, the professional must be trained and experienced in fabric classification. Fabrics such as Abaca are difficult to classify as the same base material is intertwined with a different [...] Read more.

Advanced deep learning has assisted in various operations in different industries. In the textile industry, the professional must be trained and experienced in fabric classification. Fabrics such as Abaca are difficult to classify as the same base material is intertwined with a different material. The versatile nature of Abaca is used in various products including paper bills, ropes, handwoven handicrafts, and fabric. Abaca fabric is an unsought product of fabric due to its rough texture. Blended Abaca fabrics are traditionally mixed with cotton, silk, and polyester. Due to the combination of the characteristics of the materials, the fabric classification is prone to human error. Therefore, we created a device capable of classifying blends of Abaca fabric using YOLOv8 architecture. We used a Raspberry Pi 4B with camera module v3 to capture images for classification. The dataset consisted of four blends, specifically Abaca, Cotton Abaca, Polyester Abaca, and Silk Abaca. A total 500 images were used to test the model’s performance, and the performance accuracy was 94.6%. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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13 pages, 7433 KiB

Open AccessProceeding Paper

Real-Time Head Orientation and Eye-Tracking Algorithm Using Adaptive Feature Extraction and Refinement Mechanisms

by Ming-Chang Ye and Jian-Jiun Ding

Eng. Proc. 2025, 92(1), 43; https://doi.org/10.3390/engproc2025092043 - 30 Apr 2025

Viewed by 126

Abstract

We propose a fast eye-tracking method that takes the depth image and the gray-scale infrared (IR) image using a traditional image processing algorithm. As an IR image contains one face and the corresponding depth image, the method locates the real coordinate of the [...] Read more.

We propose a fast eye-tracking method that takes the depth image and the gray-scale infrared (IR) image using a traditional image processing algorithm. As an IR image contains one face and the corresponding depth image, the method locates the real coordinate of the camera with a high speed (>90 frames per second) and with and acceptable error. The method takes advantage of the depth information to quickly locate the face by shrinking the eyeballs. The method decreases the error rate but accelerates the operation speed. After finding the face region, less complicated computer vision algorithms are used at a high execution speed. Refinement mechanisms for extracting features and determining edge distribution are used to locate the eyeball’s position and transform the pixel coordinate of the image to the real coordinate. 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, 2329 KiB

Open AccessProceeding Paper

Cotton T-Shirt Size Estimation Using Convolutional Neural Network

by John King D. Alfonso, Ckyle Joshua G. Casumpang and Jocelyn F. Villaverde

Eng. Proc. 2025, 92(1), 44; https://doi.org/10.3390/engproc2025092044 - 30 Apr 2025

Viewed by 171

Abstract

Online shopping has become popular due to its convenience and potential cost savings. However, clothing size cannot be accurately estimated, particularly when buying shirts. Many shoppers provide size choices but with inaccurate fits. To assist users in selecting the correct size when purchasing [...] Read more.

Online shopping has become popular due to its convenience and potential cost savings. However, clothing size cannot be accurately estimated, particularly when buying shirts. Many shoppers provide size choices but with inaccurate fits. To assist users in selecting the correct size when purchasing t-shirts online, we estimated shirt size using calculated upper body dimensions. Computer vision algorithms, including YOLO, PoseNet, body contour detection, and a trained convolutional neural network (CNN) model were employed to estimate shirt sizes from 2D images. The model was tested using images of 30 participants taken at a distance of 180–185 cm away from a Raspberry Pi camera. The estimation accuracy was 70%. Inaccurate predictions were attributed to the precision of body measurements from computer vision and image quality, which needs to be solved in further studies. 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, 4404 KiB

Open AccessProceeding Paper

Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity

by Wai Yie Leong

Eng. Proc. 2025, 92(1), 45; https://doi.org/10.3390/engproc2025092045 - 30 Apr 2025

Viewed by 273

Abstract

Machine learning (ML) has played an increasingly pivotal role in shaping and evolving artistic expression, leading to new forms of algorithmic creativity. In this study, we explore how ML models, particularly deep learning algorithms such as generative adversarial networks (GANs), have contributed to [...] Read more.

Machine learning (ML) has played an increasingly pivotal role in shaping and evolving artistic expression, leading to new forms of algorithmic creativity. In this study, we explore how ML models, particularly deep learning algorithms such as generative adversarial networks (GANs), have contributed to evolving art styles by learning from vast datasets of historical and contemporary artworks. These algorithms mimic artistic techniques, generate new styles, and even create novel art forms that blend or deviate from traditional artistic boundaries. The challenges of algorithmic creativity, such as concerns about authorship, originality, and the potential loss of human touch in art are also highlighted. The role of machine learning in art raises important philosophical and ethical questions about the nature of creativity and the evolving relationship between human artists and machines. Machine learning has become a powerful tool in expanding the possibilities of artistic expression. While AI-generated art challenges traditional notions of creativity, it also opens up new horizons for collaboration and innovation in art, potentially leading to entirely new art styles in the digital age. 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, 1446 KiB

Open AccessProceeding Paper

Linear Quadratic Regulator Control of Rotary Inverted Pendulum Using Elvis III Embedded Platform

by Ming-Hung Lin, Jun-Qi Huang, Yao-Hung Tsai, Chun-Chieh Chang and Cheng-Yi Chen

Eng. Proc. 2025, 92(1), 46; https://doi.org/10.3390/engproc2025092046 - 2 May 2025

Viewed by 200

Abstract

Modern education is characterized by diversity and the need for extensibility. Educational experimental platforms are rapidly evolving according to these factors. However, software and hardware are provided by major domestic manufacturers, which imposes limitations on the development of teaching materials. We investigate the [...] Read more.

Modern education is characterized by diversity and the need for extensibility. Educational experimental platforms are rapidly evolving according to these factors. However, software and hardware are provided by major domestic manufacturers, which imposes limitations on the development of teaching materials. We investigate the implementation of a rotational inverted pendulum control system within the NI ELVIS III embedded system. The mathematical model of the rotational inverted pendulum is constructed using Lagrangian equations and then represented in matrix form. Following linearization of the nonlinear state equations, the linear quadratic regulator (LQR) controller of the rotational inverted pendulum apparatus is designed and implemented on the NI ELVIS III embedded system by using LabVIEW graphical programming software. Illustrations are generated to compare the continuous tracking performance of LQR and PID controllers with preset target values. The results are then analyzed to evaluate and contrast the effectiveness of both control strategies in tracking the target values. The findings of this study enhance the development of educational content related to the ELVIS III embedded system’s experimental platform. 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, 42434 KiB

Open AccessProceeding Paper

Enhanced Adaptive Wiener Filtering for Frequency-Varying Noise with Convolutional Neural Network-Based Feature Extraction

by Chun-Lin Liao, Jian-Jiun Ding and De-Yan Lu

Eng. Proc. 2025, 92(1), 47; https://doi.org/10.3390/engproc2025092047 - 2 May 2025

Viewed by 221

Abstract

Denoising has long been a challenge in image processing. Noise appears in various forms, such as additive white Gaussian noise (AWGN) and Poisson noise across different frequencies. This study aims to denoise images without prior knowledge of the noise distribution. First, we estimate [...] Read more.

Denoising has long been a challenge in image processing. Noise appears in various forms, such as additive white Gaussian noise (AWGN) and Poisson noise across different frequencies. This study aims to denoise images without prior knowledge of the noise distribution. First, we estimate the noise power in the frequency domain to approximate the local signal-to-noise ratio (SNR) and guide an adaptive Wiener filter. The initial denoised result is obtained by assembling the locally filtered patches. However, since the Wiener filter is a low-pass filter, it can remove fine details along with the noise. To overcome this limitation, we post-process the noise and interpolate it between the denoised and original noisy patches to enhance the denoised image. We also mask the frequency domain to avoid grid-like artifacts. Additionally, we introduce a convolutional neural network-based refinement technique to the spatial domain to recover latent textures lost during denoising. The method presents the effectiveness of masking and feature extraction. 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, 1182 KiB

Open AccessProceeding Paper

A Decentralized Framework for the Detection and Prevention of Distributed Denial of Service Attacks Using Federated Learning and Blockchain Technology

by Mao-Hsiu Hsu and Chia-Chun Liu

Eng. Proc. 2025, 92(1), 48; https://doi.org/10.3390/engproc2025092048 - 6 May 2025

Viewed by 286

Abstract

With the rapid development of the internet of things (IoT) and smart cities, the risk of network attacks, particularly distributed denial of service (DDoS) attacks, has significantly increased. Traditional centralized security systems struggle to address large-scale attacks while simultaneously safeguarding privacy. In this [...] Read more.

With the rapid development of the internet of things (IoT) and smart cities, the risk of network attacks, particularly distributed denial of service (DDoS) attacks, has significantly increased. Traditional centralized security systems struggle to address large-scale attacks while simultaneously safeguarding privacy. In this study, we created a decentralized security framework that integrates federated learning (FL) with blockchain technology for DDoS attack detection and prevention. Federated learning enables devices to collaboratively learn without sharing raw data and ensures data privacy, while blockchain provides immutable event logging and distributed monitoring to enhance the overall security of the system. The created framework leverages multi-layer encryption and Hashgraph technology for event recording, ensuring data integrity and efficiency. Additionally, software-defined networking (SDN) was employed for dynamic resource management and rapid responses to attacks. This system improves the accuracy of DDoS detection and effectively reduces communication costs and resource consumption. It has significant potential for large-scale attack defense in IoT and smart city environments. 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, 1726 KiB

Open AccessProceeding Paper

New (t, n)-Threshold P-VSS Schemes Using Fewer Types of Polarizers

by Cang-Wei Huang and Justie Su-Tzu Juan

Eng. Proc. 2025, 92(1), 49; https://doi.org/10.3390/engproc2025092049 - 6 May 2025

Viewed by 125

Abstract

As network demands increase, encryption becomes increasingly important. Visual secret sharing (VSS) encrypts a secret image into multiple images (called shares) and then superimposes these shares so that the original image can be directly identified by humans. In traditional VSS, black represents 1 [...] Read more.

As network demands increase, encryption becomes increasingly important. Visual secret sharing (VSS) encrypts a secret image into multiple images (called shares) and then superimposes these shares so that the original image can be directly identified by humans. In traditional VSS, black represents 1 and white represents 0. Therefore, traditional VSS decryption methods are viewed as logical OR operations. In 1997, the VSS scheme based on the polarization of the light wave was proposed. It utilizes the fact that light passes through a unidirectional polarizer but cannot pass through two mutually perpendicular polarizers. This scheme is relevant to a logical XOR operation in decryption. However, this scheme does not explain the situation when more than two shares are decrypted. Therefore, a new (t, n)-threshold VSS scheme based on polarization (called (t, n)-threshold P-VSS) was proposed to solve the above problem. In that (t, n)-threshold P-VSS scheme, four types of polarizers were used to design the scheme. In this study, we propose three (t, n)-threshold P-VSS schemes, each using only two or three types of polarizers to effectively reduce the proportion of black pixels in the shares compared with previous work. Experimental results and theoretical analysis prove the safety and effectiveness of these schemes. 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, 1425 KiB

Open AccessProceeding Paper

Development of Educational System for Buddhism and Meditation Using Virtual Reality Technology

by Yuan-Chin Hsu, Ming-Feng Wang and Chen-Shih Lu

Eng. Proc. 2025, 92(1), 50; https://doi.org/10.3390/engproc2025092050 - 6 May 2025

Viewed by 272

Abstract

In Taiwan, professional training for Buddhist meditation demands significant time and space. These limitations reduce the effectiveness of learners’ practice. Therefore, metaverse technology was developed in this study to enable students to deeply engage with Buddhism and meditation and offer the opportunity to [...] Read more.

In Taiwan, professional training for Buddhist meditation demands significant time and space. These limitations reduce the effectiveness of learners’ practice. Therefore, metaverse technology was developed in this study to enable students to deeply engage with Buddhism and meditation and offer the opportunity to learn virtual reality (VR) technology. The developed system also guides them to develop a VR system for other meditation practices. The developed virtual space for Buddhist studies provides practitioners with an immersive environment for meditation. Through in-depth learning of Buddhist culture, students can develop creative meditation training models under the guidance of experienced practitioners. This system enables (1) the creation of a virtual space for Buddhist studies and meditation, (2) the provision of educational and training models for Buddhist practice, and (3) the ability to meditate through VR at any time and place. The system has proven applicability in VR environments for Buddhist culture, promoting cultural heritage through digital technology and metaverse technology. 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, 2866 KiB

Open AccessProceeding Paper

Road Wetness Estimation Using Deep Learning Model

by Marc Samuel C. Cruz, Lawrence A. Ong and Analyn N. Yumang

Eng. Proc. 2025, 92(1), 51; https://doi.org/10.3390/engproc2025092051 - 6 May 2025

Viewed by 204

Abstract

Accurately identifying road conditions, particularly wetness, is crucial for ensuring road safety and enhancing vehicle performance. We conducted road surface classification and road wetness estimation using state-of-the-art deep learning models in this study. Raspberry Pi Model 4 was used to classify road surfaces [...] Read more.

Accurately identifying road conditions, particularly wetness, is crucial for ensuring road safety and enhancing vehicle performance. We conducted road surface classification and road wetness estimation using state-of-the-art deep learning models in this study. Raspberry Pi Model 4 was used to classify road surfaces and estimate road wetness. SqueezeNet, a lightweight convolutional neural network, was used to recognize wet and dry road surfaces with an accuracy of 90%. The ENet model, known for its efficiency in semantic segmentation tasks, was used to estimate the degree of wetness, categorizing roads into damp, wet, and very wet roads. The ENet model showed an accuracy of 90.48%. The efficiency of the deep learning models in road surface wetness monitoring was validated using a confusion matrix created with the margin classifier. A total of 300 images per category were used for training, amounting to 1200 in total. A total of 20 testing images were used for road surface classification and 21 for road wetness estimation. The results highlighted the robustness and applicability of SqueezeNet and ENet models in estimating diverse environmental road conditions. 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, 1224 KiB

Open AccessProceeding Paper

Multi-Feature Long Short-Term Memory Facial Recognition for Real-Time Automated Drowsiness Observation of Automobile Drivers with Raspberry Pi 4

by Michael Julius R. Moredo, James Dion S. Celino and Joseph Bryan G. Ibarra

Eng. Proc. 2025, 92(1), 52; https://doi.org/10.3390/engproc2025092052 - 6 May 2025

Viewed by 166

Abstract

We developed a multi-feature drowsiness detection model employing eye aspect ratio (EAR), mouth aspect ratio (MAR), head pose angles (yaw, pitch, and roll), and a Raspberry Pi 4 for real-time applications. The model was trained on the NTHU-DDD dataset and optimized using long [...] Read more.

We developed a multi-feature drowsiness detection model employing eye aspect ratio (EAR), mouth aspect ratio (MAR), head pose angles (yaw, pitch, and roll), and a Raspberry Pi 4 for real-time applications. The model was trained on the NTHU-DDD dataset and optimized using long short-term memory (LSTM) deep learning algorithms implemented using TensorFlow version 2.14.0. The model enabled robust drowsiness detection at a rate of 10 frames per second (FPS). The system embedded with the model was constructed for live image capture. The camera placement was adjusted for optimal positioning in the system. Various features were determined under diverse conditions (day, night, and with and without glasses). After training, the model showed an accuracy of 95.23%, while the accuracy ranged from 91.81 to 95.82% in validation. In stationary and moving vehicles, the detection accuracy ranged between 51.85 and 85.71%. Single-feature configurations exhibited an accuracy of 51.85 to 72.22%, while in dual features, the accuracy ranged from 66.67 to 75%. An accuracy of 80.95 to 85.71% was attained with the integration of all features. Challenges in the drowsiness included diminished accuracy with MAR alone and delayed prediction during transitions from non-drowsy to drowsy status. These findings underscore the model’s applicability in detecting drowsiness while highlighting the necessity for refinement. Through algorithm optimization, dataset expansion, and the integration of additional features and feedback mechanisms, the model can be improved in terms of performance and reliability. 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, 5139 KiB

Open AccessProceeding Paper

You Only Look Once v8 Cattle Identification Based on Muzzle Print Pattern Using ORB and Fast Library for Approximate Nearest Neighbor Algorithms

by Allan Josef Balderas, Kaila Mae A. Pangilinan and Meo Vincent C. Caya

Eng. Proc. 2025, 92(1), 53; https://doi.org/10.3390/engproc2025092053 - 7 May 2025

Viewed by 170

Abstract

Cattle identification is important in livestock management, and advanced techniques are required to identify cattle without ear tagging, branding, or any identification method that harms the cattle. This study aims to develop computer vision techniques to identify cattle based on their unique muzzle [...] Read more.

Cattle identification is important in livestock management, and advanced techniques are required to identify cattle without ear tagging, branding, or any identification method that harms the cattle. This study aims to develop computer vision techniques to identify cattle based on their unique muzzle print features. The developed method employed the YOLOv8 object detection model to detect the cattle’s muzzle. Following detection, the captured muzzle image underwent image processing. Contrast-limited adaptive histogram equalization (CLAHE) was used to enhance the image quality and obtain a prominent and detailed image of the muzzle print. Feature extraction algorithm-oriented FAST and rotated BRIEF (ORB) was applied to extract key points and detect descriptors that are crucial for the cattle identification process. The fast library for approximate nearest neighbor (FLANN) was also employed to identify individual cattle by comparing descriptors of query images from those stored in the database. To validate the developed method, its performance was evaluated on 25 different cattle. In total, 22 out of 25 were correctly identified, resulting in an overall accuracy of 88%. 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, 2122 KiB

Open AccessProceeding Paper

Classification of Ocimum basilicum Using a Convolutional Neural Network

by Mary Angel N. Perlas, John Isaac B. Santosildes and Jocelyn F. Villaverde

Eng. Proc. 2025, 92(1), 54; https://doi.org/10.3390/engproc2025092054 - 7 May 2025

Viewed by 147

Abstract

Basil varieties were classified using a convolutional neural network (CNN) with VGG16 architecture. The developed system in this study identified and classified the variety of basil images. The system applied the contrast-limited adaptive histogram equalization (CLAHE) algorithm to the basil image in the [...] Read more.

Basil varieties were classified using a convolutional neural network (CNN) with VGG16 architecture. The developed system in this study identified and classified the variety of basil images. The system applied the contrast-limited adaptive histogram equalization (CLAHE) algorithm to the basil image in the architecture VGG16 to extract features and classify the images. The system was tested using 50 images, and the confusion matrix showed an 82.00% accuracy. An inaccurate output was caused by the wrong positions or the size of the leaf. Of the 50 basil images, 41 were correctly classified. Two models were created in the study for epochs of eight and 10. The best model study was chosen based on accuracy. The best model showed an accuracy of 97% in training for 10 epochs. 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, 2588 KiB

Open AccessProceeding Paper

Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference

by Chi-Hung Lien, Ya-Ching Tu, Sheng-Lian Liao, Juei-Chi Chu, Chia-Yu Hsieh and Jyun-Jia Jhang

Eng. Proc. 2025, 92(1), 55; https://doi.org/10.3390/engproc2025092055 - 7 May 2025

Viewed by 113

Abstract

Recently, the increasing frequency of cybersecurity incidents has raised concerns about communication security and personal privacy. In a zero-trust network environment, it is critically important to protect communication content and ensure that it is not intercepted, recorded, or stored without proper authorization. End-to-end [...] Read more.

Recently, the increasing frequency of cybersecurity incidents has raised concerns about communication security and personal privacy. In a zero-trust network environment, it is critically important to protect communication content and ensure that it is not intercepted, recorded, or stored without proper authorization. End-to-end encryption (E2EE) is a reliable solution for this purpose. The COVID-19 pandemic has accelerated the adoption of remote work and virtual meetings, making the security of voice conferences a critical issue. This study aims to explore the application of end-to-end encryption technology in voice conferences. We designed and implemented an end-to-end encrypted voice conferencing system based on terminal-side mixing to ensure security while also being applicable in low-bandwidth network environments. The developed system effectively prevented man-in-the-middle attacks and data wiretaps, while maintaining high performance and low latency. It can be used in low-bandwidth scenarios such as satellite networks. The end-to-end encryption technology, when combined with terminal-side voice mixing, significantly enhances the security and usability of voice conferences as a new solution for secure communication in the future. 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, 2975 KiB

Open AccessProceeding Paper

Classification of Non-Frozen and Frozen–Thawed Pork with Adaptive Support Vector Machine and Electronic Nose

by Paul Christian E. Artista, Abraham M. Mendoza and Dionis A. Padilla

Eng. Proc. 2025, 92(1), 56; https://doi.org/10.3390/engproc2025092056 - 7 May 2025

Viewed by 156

Abstract

The quality of raw meat is important for community health as its freshness is crucial to preventing foodborne illnesses. In the United States, the related illness cases were 9.4 million cases with 55,961 hospital admissions and 1351 deaths annually. This underscores the urgent [...] Read more.

The quality of raw meat is important for community health as its freshness is crucial to preventing foodborne illnesses. In the United States, the related illness cases were 9.4 million cases with 55,961 hospital admissions and 1351 deaths annually. This underscores the urgent need for improved meat quality monitoring. This study aims to develop an electronic nose (E-nose) that can differentiate between frozen–thawed and fresh pork meat samples, thereby enhancing food safety. We designed the E-nose using MQ series gas sensor array with temperature and humidity sensors, and an Arduino Uno microcontroller. Sensors were calibrated for accurate data collection. An adaptive support vector machine (ASVM) was used for data classification. We evaluated the model’s accuracy using a confusion matrix. The ASVM model exhibited robust performance, achieving an accuracy of 88%. Its performance was evaluated with recall, F1 scores, and precision. To further enhance the model’s performance, future studies are mandated to integrate additional gas sensors, increase sample sizes, advance data preprocessing techniques, and explore different machine learning algorithms or ensemble methods. 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, 3506 KiB

Open AccessProceeding Paper

Automated Monitoring and Control System of Solar Greenhouse Using ESP32 and Blynk Application

by Meridith Lindsey Q. Galon, Michael Vincent R. Tumaliwan and Marianne M. Sejera

Eng. Proc. 2025, 92(1), 57; https://doi.org/10.3390/engproc2025092057 - 7 May 2025

Viewed by 258

Abstract

Greenhouse farming has brought a revolution in agriculture as it provides a climate favorable to crops all year round. Besides securing the production of foods of higher quality, it also extends the growing seasons and protects crops from pests and harsh weather. The [...] Read more.

Greenhouse farming has brought a revolution in agriculture as it provides a climate favorable to crops all year round. Besides securing the production of foods of higher quality, it also extends the growing seasons and protects crops from pests and harsh weather. The greenhouse is centrally controlled by the user due to the technological advancements of devices such as cell phones and a control system of temperature, which is important for the plant. To realize remote real-time automated monitoring of the greenhouse based on the user settings, an Android app was 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, 2059 KiB

Open AccessProceeding Paper

Reliability Assessment of Power Distribution System in Freeport Area of Bataan

by Jomel R. Cristobal and Ronald Vincent M. Santiago

Eng. Proc. 2025, 92(1), 58; https://doi.org/10.3390/engproc2025092058 - 8 May 2025

Viewed by 221

Abstract

The continuous distribution ability of electricity is defined as the effectiveness of the computation of reliability indices. Therefore, we conducted a reliability assessment and evaluated the performance of the distribution system in the Freeport Area of Bataan (FAB). For reliability assessment, software was [...] Read more.

The continuous distribution ability of electricity is defined as the effectiveness of the computation of reliability indices. Therefore, we conducted a reliability assessment and evaluated the performance of the distribution system in the Freeport Area of Bataan (FAB). For reliability assessment, software was developed to automate the computation of indices, including system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption frequency index (CAIFI), and customer average interruption duration index (CAIDI). Through reliability assessment and evaluation, the low-performing distribution network of the FAB was successfully identified. After the identification of the low-performing network, reconductoring and redundant feeder line projects were proposed to alleviate and reduce the occurrence of power interruptions. An analysis of its economy was also conducted, and the result showed that line reconductoring from bare conductor to insulated cable was the most feasible option since it resulted in a high benefit–cost ratio (BCR) and a positive net present value (NPV) for all evaluated cases. 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, 8167 KiB

Open AccessProceeding Paper

Integrating Sustainable Concepts into Blended Learning and Interactive Game System Design

by Peng-Wei Hsiao and Zheng-Qing Wang

Eng. Proc. 2025, 92(1), 59; https://doi.org/10.3390/engproc2025092059 - 8 May 2025

Viewed by 348

Abstract

Food is essential for sustaining human life. While people love delicious food, they often neglect the care for it. One of the most commonly wasted foods is bread. There has not been much research on bread waste. Carbon emissions from bread are not [...] Read more.

Food is essential for sustaining human life. While people love delicious food, they often neglect the care for it. One of the most commonly wasted foods is bread. There has not been much research on bread waste. Carbon emissions from bread are not less than those from meat products. Therefore, it is necessary to integrate sustainable concepts with mixed learning approaches into a mixed reality (MR) interactive system, focusing on bakeries. We conducted field research and observations of leftover bread from eight local bakeries, categorizing and photographing them. We combined knowledge and teaching about carbon emissions with interactive games to help users understand the relationship between bread and carbon emissions. Users can learn about relevant knowledge and content by playing the MR game. The interactive game provides a reference for sustainability research in the future. 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, 3580 KiB

Open AccessProceeding Paper

Speech Delay Assistive Device for Speech-to-Text Transcription Based on Machine Learning

by Maria Kristina C. Rodriguez, Gheciel Mayce M. Santos, Jennifer C. Dela Cruz and Jmi C. Dela Cruz

Eng. Proc. 2025, 92(1), 60; https://doi.org/10.3390/engproc2025092060 - 8 May 2025

Viewed by 227

Abstract

Despite advances by major companies, existing technologies often misinterpret speech from individuals with speech delays. To address this challenge, a portable machine learning (ML) speech-to-text assistive device was developed for speech-delayed children. The device is composed of a Raspberry Pi 4 and Google [...] Read more.

Despite advances by major companies, existing technologies often misinterpret speech from individuals with speech delays. To address this challenge, a portable machine learning (ML) speech-to-text assistive device was developed for speech-delayed children. The device is composed of a Raspberry Pi 4 and Google Web Speech API and enables the accurate transcription of challenging speech sounds of children aged 6 to 14 years old. The device performs noise reduction and digital transcription. Its performance was validated by speech language pathologists (SLPs). The device achieved 94% word accuracy, 92% sentence accuracy, and a word error rate (WER) of 0 to 14%. The ML-based device is a significant improvement on existing speech therapy tools, offering an accessible solution for speech-delayed children. 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, 5014 KiB

Open AccessProceeding Paper

Internet of Things for Enhancing Public Safety, Disaster Response, and Emergency Management

by Waiyie Leong

Eng. Proc. 2025, 92(1), 61; https://doi.org/10.3390/engproc2025092061 - 2 May 2025

Viewed by 391

Abstract

The Internet of Things (IoT) offers transformative capabilities in enhancing public safety, disaster response, and emergency management by leveraging interconnected devices and real-time data. Through the IoT, smart sensors and networks are deployed across cities and environments to monitor critical parameters including air [...] Read more.

The Internet of Things (IoT) offers transformative capabilities in enhancing public safety, disaster response, and emergency management by leveraging interconnected devices and real-time data. Through the IoT, smart sensors and networks are deployed across cities and environments to monitor critical parameters including air quality, structural integrity, and environmental changes. These systems provide early warnings for natural disasters such as earthquakes, floods, and wildfires, enabling authorities to respond proactively. In emergency management, IoT devices help coordinate resources and improve situational awareness during crises. Real-time data from wearable devices, smart infrastructure, and communication systems allow responders to track people, manage evacuations, and deploy resources more effectively. For example, IoT-enabled drones and autonomous vehicles are used to deliver supplies or assess damage in hazardous areas without risking human lives. IoT technologies improve post-disaster recovery by continuously monitoring areas for safety hazards and supporting infrastructure restoration. Smart traffic management systems assist in controlling traffic flow for emergency vehicles, while IoT-based communication networks ensure connectivity when traditional systems fail. The IoT significantly enhances the speed, accuracy, and effectiveness of disaster response and public safety operations, leading to the better protection of communities and faster recovery from emergencies. 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, 2190 KiB

Open AccessProceeding Paper

Shoe Recommendation System Integrating Generative Artificial Intelligence and Convolutional Neural Networks for Image Recognition

by Chin-Chih Chang, Chi-Hung Wei, Ray-Nan Liao, Sean Hsiao and Chyuan-Huei Thomas Yang

Eng. Proc. 2025, 92(1), 62; https://doi.org/10.3390/engproc2025092062 - 8 May 2025

Viewed by 252

Abstract

We developed a shoe recommendation system that integrates generative artificial intelligence (AI) and convolutional neural networks (CNNs) to enhance image recognition and personalize recommendations. The system utilizes CNNs to accurately identify shoe types from user-uploaded images. Utilizing the capabilities of generative AI, the [...] Read more.

We developed a shoe recommendation system that integrates generative artificial intelligence (AI) and convolutional neural networks (CNNs) to enhance image recognition and personalize recommendations. The system utilizes CNNs to accurately identify shoe types from user-uploaded images. Utilizing the capabilities of generative AI, the system generates custom shoe suggestions based on weather and location. The proposed system minimizes the need for manual searching but enhances user experience by providing an efficient, automated, and visually driven solution for selecting shoes. The effectiveness of integrating image recognition and generative techniques paves the way for advancements in AI-driven fashion recommendation systems. The developed method offers a powerful tool for increasing customer engagement and satisfaction by delivering personalized and fashion-forward shoe recommendations. 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, 3282 KiB

Open AccessProceeding Paper

Design and Development of 35–4.4 GHz Inset-Fed 2 × 2 Phased Array Microstrip Patch Antenna for Intentional Electromagnetic Interference Testing

by John Joshua O. Gutierrez, Jervin D. Louis and Jennifer C. Dela Cruz

Eng. Proc. 2025, 92(1), 63; https://doi.org/10.3390/engproc2025092063 - 12 May 2025

Viewed by 201

Abstract

Communication devices are frequency-operating electronics equipment that utilizes analog modulation, frequency modulation, shortwave frequency, and even higher frequencies in telecommunications. We designed an antenna to transmit interfering frequencies for testing equipment and components based on the effects and conditions of achieving electromagnetic interference. [...] Read more.

Communication devices are frequency-operating electronics equipment that utilizes analog modulation, frequency modulation, shortwave frequency, and even higher frequencies in telecommunications. We designed an antenna to transmit interfering frequencies for testing equipment and components based on the effects and conditions of achieving electromagnetic interference. Ansys 2024 was used to design the 35 to 4.4 GHz 2 × 2 patch antennas and simulate the response using a sample frequency of 35 MHz to determine the antenna’s polarization. The polarization was circular, in contrast to the results of the phases Phi and Theta observed in the radial field 3D polar plot, which are completely out of phase and different in magnitude by 5.4 in Phi and 5402.01 in Theta. The measurements from Ansys were congruent to the 2D model dimensions in AutoCAD 2024. The antenna was fabricated under a double-layered photosensitive FR-4 copper board. The antenna connected to the signal generator ADF 4351 effectively was interfered with by a frequency near the actual frequency with a maximum distance of 7.5 m in a room. The frequencies that interfered were from 91.5 to 102.7 MHz. Strong electromagnetic waves for interference disrupted frequency-operating devices due to high signal power achieving destructive interference. 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, 3295 KiB

Open AccessProceeding Paper

Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security

by Mao-Hsiu Hsu and Ying-Hong Shi

Eng. Proc. 2025, 92(1), 64; https://doi.org/10.3390/engproc2025092064 - 13 May 2025

Viewed by 151

Abstract

Biometric systems such as fingerprint recognition encounter significant challenges under wet conditions or small fingerprints, where noise degrades recognition accuracy. These challenges increase false acceptance rates (FARs) and false rejection rates (FRRs) as conventional denoising models designed for larger fingerprints cannot handle the [...] Read more.

Biometric systems such as fingerprint recognition encounter significant challenges under wet conditions or small fingerprints, where noise degrades recognition accuracy. These challenges increase false acceptance rates (FARs) and false rejection rates (FRRs) as conventional denoising models designed for larger fingerprints cannot handle the smaller and noisier samples in portable and embedded devices. In this study, we collected 71,188 wet–dry fingerprints using a capacitive sensor. Fingerprints in sizes of 176 × 36, 88 × 88, and 80 × 100 pixels were preprocessed by padding and cropping them to a uniform size of 48 × 48 pixels. Preprocessing was conducted to standardize and augment the data and enhance the model’s ability to generalize across diverse data types. We developed a wet fingerprint denoising network (WFDN), a multi-stage neural network designed to improve wet fingerprint quality for small and large samples. By integrating scale-invariant feature transform, WFDN effectively restores critical minutiae and significantly enhances feature preservation compared with existing models. The network also incorporates an automatic label classifier and cyclic multi-variate functions to reduce noise. Despite its compact architecture, WFDN demonstrates superior performance, reducing the FRR from 19.6 to 8.4% for small fingerprints. Moreover, assessment results using NIST fingerprint image quality 2.0 (NFIQ2) for larger fingerprints show notable improvements in system reliability. The proposed model improves biometric processing significantly. WFDN represents a significant advancement in fingerprint-based identification, offering improved performance and robustness in challenging conditions. 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, 5251 KiB

Open AccessProceeding Paper

Soft Robotics: Engineering Flexible Automation for Complex Environments

by Wai Yie Leong

Eng. Proc. 2025, 92(1), 65; https://doi.org/10.3390/engproc2025092065 - 13 May 2025

Viewed by 293

Abstract

Soft robotics represents a transformative approach to automation, focusing on the development of robots constructed from flexible, compliant materials that mimic biological systems. Being different from traditional rigid robots, soft robots are engineered to adapt and operate efficiently in complex, unstructured environments, making [...] Read more.

Soft robotics represents a transformative approach to automation, focusing on the development of robots constructed from flexible, compliant materials that mimic biological systems. Being different from traditional rigid robots, soft robots are engineered to adapt and operate efficiently in complex, unstructured environments, making them highly appropriate for applications that require delicate manipulation, safe human–robot interaction, and mobility on unstable terrain. The key principles, materials, and fabrication techniques of soft robotics are explored in this study, highlighting their versatility in industries such as healthcare, agriculture, and search-and-rescue operations. The essence of soft robotic systems lies in their ability to deform and respond to environmental stimuli. The system enables new paradigms in automation for tasks that demand flexibility, such as handling fragile objects, navigating narrow spaces, or interacting with humans. Emerging materials, such as elastomers, hydrogels, and shape-memory alloys, are driving innovations in actuation and sensing mechanisms, expanding the capabilities of soft robots in applications. We also examine the challenges associated with the control and energy efficiency of soft robots, as well as opportunities for integrating artificial intelligence and advanced sensing to enhance autonomous decision-making. Through case studies and experimental data, the potential of soft robotics is reviewed to revolutionize sectors requiring adaptive automation, ultimately contributing to safer, more efficient, and sustainable technological advancements than present robots. 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, 991 KiB

Open AccessProceeding Paper

Automated Dance Scoring Algorithm Using Alignment and Least Square Approximation with Fractional Power of Joint Features

by Chen-Jhen Fan, Han-Hui Jeng, Bing-Ze Li and Jian-Jiun Ding

Eng. Proc. 2025, 92(1), 66; https://doi.org/10.3390/engproc2025092066 - 13 May 2025

Viewed by 215

Abstract

Automated motion evaluation has become popular in exercise training and entertainment. In this study, an advanced automatic dance scoring algorithm is proposed. First, to avoid misjudgment from misalignment, space and time alignment are assessed. Then, instead of using the whole video frames as [...] Read more.

Automated motion evaluation has become popular in exercise training and entertainment. In this study, an advanced automatic dance scoring algorithm is proposed. First, to avoid misjudgment from misalignment, space and time alignment are assessed. Then, instead of using the whole video frames as the input, we apply the joint information, including the relative locations, the moving velocities, the orientations, and the areas between the joint lines. To make the features more flexible and magnify the detail difference, we take the fractional powers on input features. The correlation coefficients are calculated for feature selection, and a nonlinear analysis is introduced to determine the angle difference. The least mean square error approximation is also applied to determine the linear combination coefficients of the features. The difference between the ground truth and the interpolated results from the regression line is minimized using the input features. The proposed algorithm accurately predicts dancing scores. 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, 3153 KiB

Open AccessProceeding Paper

Systematic Review on Automation of Central Tire Inflation System Based on Terrain Conditions

by Carl Luis C. Ledesma, Charlothe John I. Tablizo, Marites B. Tabanao, Emmanuel A. Salcedo, Emmy Grace T. Requillo and John Paul T. Cruz

Eng. Proc. 2025, 92(1), 67; https://doi.org/10.3390/engproc2025092067 - 13 May 2025

Viewed by 211

Abstract

Incorrect vehicle tire pressure affects vehicle dynamics, fuel efficiency, and driver safety across different terrain conditions. The current central tire inflation system (CTIS) alleviates this issue by adjusting the tire pressure to a predetermined reference level. However, the existing CTIS only adjusts the [...] Read more.

Incorrect vehicle tire pressure affects vehicle dynamics, fuel efficiency, and driver safety across different terrain conditions. The current central tire inflation system (CTIS) alleviates this issue by adjusting the tire pressure to a predetermined reference level. However, the existing CTIS only adjusts the pressure based on load conditions through manual input for terrain types and lacks advanced intelligence for optimal automation. Integrating the recognition result of terrain conditions enables real-time adjustments of tire pressure and enhances driving performance and efficiency. This study aims to integrate the terrain recognition component using a convolutional neural network (CNN) by reviewing previous terrain-detection models. The CTIS was enhanced to classify and detect terrain conditions and apply the correct tire pressure level. We employed a systematic literature review (SLR) to assess the development procedures for integrating the intelligent component with the basic CTIS. ResNet-18 was used as the most appropriate CNN model to classify the terrain conditions on a gathered local dataset. A single-wheel testbed using the enhanced CTIS is appropriate for laboratory testing and system integration tests. 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, 214 KiB

Open AccessProceeding Paper

Platform-Based Design of a Smart 12-Lead Electrocardiogram Device by Using Multiple Criteria Decision-Making Methods

by Chi-Yo Huang, Ping-Jui Chen and Jeng-Chieh Cheng

Eng. Proc. 2025, 92(1), 68; https://doi.org/10.3390/engproc2025092068 - 14 May 2025

Viewed by 166

Abstract

Smart telemedicine represents an innovative application of information and communication technology within the healthcare sector, encompassing healthcare delivery, disease management, public health surveillance, education, and research. The commercialization of 5G and the extensive adoption of the Internet of Things (IoT) enable smart telemedicine [...] Read more.

Smart telemedicine represents an innovative application of information and communication technology within the healthcare sector, encompassing healthcare delivery, disease management, public health surveillance, education, and research. The commercialization of 5G and the extensive adoption of the Internet of Things (IoT) enable smart telemedicine devices to mitigate geographical and transmission delays, hence enhancing the quality of treatment provided to individuals. Although intelligent medicine is significant, previous studies emphasize the implementation and adoption of systems or technologies with few studies conducted on the platform of smart telemedicine equipment. This study aims to address the research gap by forecasting future developments and delineating smart telemedicine device designs utilizing platform-based design. We introduce a hybrid multi-criteria model that delineates the components of the intelligent medical platform. A portable 12-lead electrocardiogram (ECG) system is used by a global telemedicine technology company to assess the viability of the suggested framework. The portable 12-lead ECG device integrates artificial intelligence (AI), cloud computing, and 6G technology. The results of this study provide a basis for product creation by other smart telemedicine companies, while the platform-based analytical methodology can be employed for future product design. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

6 pages, 904 KiB

Open AccessProceeding Paper

Lightweight Depthwise Pooling Transformer for Enhanced Coffee Bean Recognition

by Liang-Ying Ke, Pin-Feng Lin and Chih-Hsien Hsia

Eng. Proc. 2025, 92(1), 69; https://doi.org/10.3390/engproc2025092069 - 14 May 2025

Viewed by 143

Abstract

As global trade networks rapidly expand, coffee production and consumption have increased globally, profoundly influencing modern lifestyles. However, the coffee production process still demands substantial labor, especially in the selection and processing of coffee beans. The high implementation costs have impeded its widespread [...] Read more.

As global trade networks rapidly expand, coffee production and consumption have increased globally, profoundly influencing modern lifestyles. However, the coffee production process still demands substantial labor, especially in the selection and processing of coffee beans. The high implementation costs have impeded its widespread adoption. Therefore, we developed a defect detection and roasting level recognition method using a lightweight vision transformer (ViT) based on the deep learning (DL) method to extract features from coffee bean images. The developed method effectively reduces the overall cost of the coffee production process, showing a recognition accuracy of 98.49% for the Coffee Cobra database and 99.68% for the Roasting Coffee Bean database. The number of the model parameters was only 0.13 M, making it appropriate to deploy to low-cost embedded platforms. 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, 944 KiB

Open AccessProceeding Paper

Improving Facial Expression Recognition with a Focal Transformer and Partial Feature Masking Augmentation

by Liang-Ying Ke, Chia-Yu Liao and Chih-Hsien Hsia

Eng. Proc. 2025, 92(1), 70; https://doi.org/10.3390/engproc2025092070 - 14 May 2025

Viewed by 234

Abstract

With the advancement of deep learning (DL) and computer vision (CV) technologies, significant progress has been made in facial expression identification FER for real-world applications. However, FER still faces challenges such as occlusion and head pose variations, which make it difficult for FER [...] Read more.

With the advancement of deep learning (DL) and computer vision (CV) technologies, significant progress has been made in facial expression identification FER for real-world applications. However, FER still faces challenges such as occlusion and head pose variations, which make it difficult for FER models to maintain stability and accuracy. In this study, we introduced a focal vision transformer (FViT) with partial feature masking (PFM) into FER. This method was found to efficiently simulate the challenges posed by occlusion and head pose variations by introducing PFM data augmentation. Parts of the image were randomly masked while preserving key facial expressions. The proposed FViT showed an accuracy of 89.08% on the real-world affective faces database, which includes scenarios with occlusion and head pose variations. PFM enhanced the model’s performance, too. The developed method effectively addresses the challenges of occlusion and head pose variations in FER. 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, 3787 KiB

Open AccessProceeding Paper

Powder-Mixed Micro Electrical Discharge Machining-Assisted Surface Modification of Ti-35Nb-7Zr-5Ta Alloy in Biomedical Applications

by Altair Kossymbayev, Shahid Ali, Didier Talamona and Asma Perveen

Eng. Proc. 2025, 92(1), 71; https://doi.org/10.3390/engproc2025092071 - 16 May 2025

Viewed by 84

Abstract

One of the most popular alloys for biomedical applications is TiAl6V4. Even though TiAl6V4 is widely used, it faces several challenges. Firstly, TiAl6V4 is prone to stress shielding caused by the difference in Young’s moduli of the alloy (110 GPa) and human bones [...] Read more.

One of the most popular alloys for biomedical applications is TiAl6V4. Even though TiAl6V4 is widely used, it faces several challenges. Firstly, TiAl6V4 is prone to stress shielding caused by the difference in Young’s moduli of the alloy (110 GPa) and human bones (20–30 GPa). Secondly, there is the presence of cytotoxic elements, aluminum and vanadium. Researchers have proposed Ti-35Nb-7Zr-5Ta (TNZT) alloy to overcome these disadvantages, an excellent substitute for natural human bones. This alloy offers a lower elastic modulus (up to 81 GPa), much closer to human bones than TiAl6V4 alloy. Also, TNZT alloy contains no cytotoxic elements and has excellent biocompatibility and high corrosion resistance. Given the positive outcomes on powder-mixed micro electro-discharge machining (PM-μ-EDM) of Ti alloy using hydroxyapatite (HA) powder, we studied the machinability of TNZT alloy using HA powder mixed-μ-EDM by changing the HA powder concentration (0, 5, and 10 g/L), gap voltage (90, 100, and 110 V), and capacitance (10, 100, and 400 nF) according to the Taguchi L9 method. Machining performance metrics such as material removal rate (MRR), overcut, and circularity were examined using a tungsten carbide tool of 237 µm diameter. The results showed an overcut of 10.33 µm, circularity of 8.47 µm, and MRR of 6030.89 µm³/s for the lowest energy setup. 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, 1163 KiB

Open AccessProceeding Paper

Real-Time Detection and Process Status Integration System for High-Pressure Gas Leakage

by Nian-Ze Hu, Hao-Lun Huang, Chun-Min Tsai, Yen-Yu Wu, You-Xin Lin, Chih-Chen Lin and Po-Han Lu

Eng. Proc. 2025, 92(1), 72; https://doi.org/10.3390/engproc2025092072 - 19 May 2025

Viewed by 211

Abstract

This study aims to develop a real-time gas leak detection system for application in gas cylinder filling machines. To promptly recover gas during leakage incidents, the efficiency of the gas filling process was improved by reducing resource wastage. The system utilized a Raspberry [...] Read more.

This study aims to develop a real-time gas leak detection system for application in gas cylinder filling machines. To promptly recover gas during leakage incidents, the efficiency of the gas filling process was improved by reducing resource wastage. The system utilized a Raspberry Pi with a camera for image-based detection and employed the dark channel prior method to detect the presence of gas. The message queue system was used for the real-time data transmission of gas leak status, temperature, and humidity data. The system sent data to a central server via message queuing telemetry transport (MTQQ). Node-RED was used for data visualization and anomaly alerts. Machine learning methods such as support vector machines (SVMs) and decision trees were applied to analyze the correlation between gas leaks and other environmental parameters to predict leak incidents. This system effectively detected gas leakage and transmitted and analyzed the data, significantly improving the operational efficiency of the gas cylinder filling process. 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, 1075 KiB

Open AccessProceeding Paper

Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms

by Nian-Ze Hu, Bo-An Lin, Yen-Yu Wu, Hao-Lun Huang, You-Xin Lin, Chih-Chen Lin and Po-Han Lu

Eng. Proc. 2025, 92(1), 74; https://doi.org/10.3390/engproc2025092074 - 19 May 2025

Viewed by 200

Abstract

By integrating visual recognition technology and multi-object recognition into robotic arms, the flexibility and automation of the production process were improved in this study. By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control the robotic [...] Read more.

By integrating visual recognition technology and multi-object recognition into robotic arms, the flexibility and automation of the production process were improved in this study. By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control the robotic arms and identified objects precisely on the production line, with ultra-low energy consumption. The developed system in this study included the SparkFun Edge development board and Raspberry Pi Camera Module 3, as edge devices for data processing, image recognition, and robotic arm control. By utilizing the Edge Impulse platform for data collection, model training, and optimization, edge devices and models for use in resource-limited environments were successfully generated. Using Edge Impulse’s automated toolchain, real-time image processing and object recognition were realized. The system achieved improved recognition accuracy and operational speed, demonstrating the potential of TinyML in enhancing the intelligence of robotic arms. MariaDB was chosen for data storage. Grafana was used to design a user-friendly web interface for real-time data monitoring and visualization and immediate data analysis and system monitoring. The developed system presented a success rate of 99% during actual operation. The feasibility of combining advanced image processing technology with robotic arms in intelligent manufacturing was verified in this study. The potential of integrating machine learning and automation technologies was also confirmed for the development of future manufacturing technologies. The model provides a technical reference and ideas for future factories that require high levels of automation and intelligence. 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, 4438 KiB

Open AccessProceeding Paper

Application of a Convolutional Neural Network in a Terrain-Based Tire Pressure Management System

by Carl Luis C. Ledesma, Charlothe John I. Tablizo, Emmanuel A. Salcedo, Marites B. Tabanao, Emmy Grace T. Requillo and John Paul T. Cruz

Eng. Proc. 2025, 92(1), 75; https://doi.org/10.3390/engproc2025092075 - 20 May 2025

Viewed by 144

Abstract

Improper car tire pressure affects dynamics, fuel economy, and driver safety. Current central tire inflation systems (CTISs) regulate tire pressure relative to its reference value. However, the current CTIS is limited in its automation, as the system requires the loading of present conditions [...] Read more.

Improper car tire pressure affects dynamics, fuel economy, and driver safety. Current central tire inflation systems (CTISs) regulate tire pressure relative to its reference value. However, the current CTIS is limited in its automation, as the system requires the loading of present conditions and the manual input of terrain conditions. Therefore, the system lacks intelligent components which would increase its efficiency. Adding a terrain recognition feature to the current CTIS technology, the tire pressure management system (TPMS) described in this paper enhances the capability to adjust to the ideal tire pressure according to the terrain condition. In this study, we integrate a terrain recognition component which uses a convolutional neural network (CNN), specifically, ResNet-18, into the TPMS to classify and detect terrain conditions and apply the correct pressure level. A one-tire terrain-based TPMS model was developed through system integration. The system was tested under flat, uneven, and soft terrain conditions. The CNN model demonstrated 95% accuracy in classifying the chosen terrains, with demonstrated adaptability to nighttime environments. Inflation and deflation tests were conducted at varying speeds and terrains, and the results showed longer inflation times at higher pressure ranges, while deflation times remained consistent regardless of pressure range. A negligible impact on inflation and deflation speed was observed at speeds below 15 km/h. Instantaneous response time between the microcontrollers increases efficiency in the overall CTIS process. 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, 693 KiB

Open AccessProceeding Paper

Fabrication and Characterization of Tungsten-Modified TiO₂ as a Photo-Anode in a Dye-Sensitized Solar Cell

by Ming-Cheng Kao, Jun-Hong Weng, Chih-Hung Chiang, Kai-Huang Chen, Der-Yuh Lin and Tsung-Kuei Kang

Eng. Proc. 2025, 92(1), 76; https://doi.org/10.3390/engproc2025092076 - 21 May 2025

Viewed by 171

Abstract

The tungsten (W)-modified TiO₂ films were fabricated on the fluorine-doped TiO₂ substrates using the sol–gel process. The influences of W dopant on the photovoltaic properties of the tungsten-modified TiO₂ DSSC were analyzed, too. The crystallization and dye absorption of tungsten-modified [...] Read more.

The tungsten (W)-modified TiO₂ films were fabricated on the fluorine-doped TiO₂ substrates using the sol–gel process. The influences of W dopant on the photovoltaic properties of the tungsten-modified TiO₂ DSSC were analyzed, too. The crystallization and dye absorption of tungsten-modified TiO₂ thin films increased more than those of the undoped TiO₂ thin films. Furthermore, the optimal performances of the V_oc, J_sc, fill factor, and efficiency of the DSSC with tungsten-modified TiO₂ thin films were 0.68 V, 16.28 mA/cm², 65.5%, and 7.03%, respectively. The enhancement was mainly due to the improved crystallinity and increased dye adsorption of the tungsten-modified TiO₂ thin films, which contributed to improving the efficiency of the dye-sensitized solar cell. 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, 1236 KiB

Open AccessProceeding Paper

Analysis of Compressed Air Energy Storage System and Evaluation of Financial Feasibility—A Case Study

by Ming-Hong Chen, Yan-Ting Lin, Pin-Hsuan Liu and Ching-Chang Cho

Eng. Proc. 2025, 92(1), 77; https://doi.org/10.3390/engproc2025092077 - 21 May 2025

Viewed by 133

Abstract

We analyzed the performance and financial feasibility of a compressed air energy storage (CAES) system in a potential region in Miaoli County, Taiwan, with the aquifer in the underground structure. We conducted a performance analysis of the system using the commercial software Flownex [...] Read more.

We analyzed the performance and financial feasibility of a compressed air energy storage (CAES) system in a potential region in Miaoli County, Taiwan, with the aquifer in the underground structure. We conducted a performance analysis of the system using the commercial software Flownex 9.0. Initially, a model for the Huntorf case in Germany was built, and its performance was compared with others for validation. The calculation results showed a deviation of about 1% in terms of efficiency, confirming the analytical capabilities and accuracy of the model. After verifying the system performance, the scale of output power was adjusted to 2 MW for initial development and subsequent planning. Then, geological characteristics were analyzed using COMSOL to establish a multiphase flow analysis model. This model evaluated the flow rate and pressure required for the operation of the CAES system. Lastly, a financial analysis was conducted based on the obtained results. The cost for system components was estimated, and the levelized cost of the proposed CAES system was evaluated. A comparison with other energy storage technologies was conducted to assess the financial feasibility of the analyzed CAES system. 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, 1768 KiB

Open AccessProceeding Paper

Real-Time Detection and Counting of Melted Spatter Particles During Deposition of Biomedical-Grade Co-Cr-Mo-4Ti Powder Using the Micro-Plasma Transferred Arc Additive Manufacturing Process

by Sagar Nikam, Sonya Coleman, Dermot Kerr, Neelesh Kumar Jain, Yash Panchal and Deepika Nikam

Eng. Proc. 2025, 92(1), 78; https://doi.org/10.3390/engproc2025092078 (registering DOI) - 21 May 2025

Viewed by 139

Abstract

Spatters in the powder-based metal additive manufacturing processes influence deposition quality, part surface quality, and internal defects. We developed a novel video analysis method to monitor and count the melted spatter particles of biomedical-grade Co-Cr-Mo-4Ti powder particles in depositing layers using a micro-plasma [...] Read more.

Spatters in the powder-based metal additive manufacturing processes influence deposition quality, part surface quality, and internal defects. We developed a novel video analysis method to monitor and count the melted spatter particles of biomedical-grade Co-Cr-Mo-4Ti powder particles in depositing layers using a micro-plasma transferred arc additive manufacturing (M-PTAAM) process. We captured the spatters using a weld-monitoring camera and building datasets of videos and monitored different combinations of M-PTAAM process parameters. We captured videos of the melted spatter particles and counted the melted spatter particles in real time using a Kalman filter. The weld-monitoring camera captured the melted spatter particles and the fumes generated by the evaporated spatter particles. The video processing algorithm was developed in this study to accurately capture melted spatter particles. In images without fumes, nearly all melted spatter particles were successfully detected. Even in images with the presence of fumes, the algorithm maintained a detection accuracy of 90%. The real-time melted spatter count particle exhibited a powder feed rate changing from 30 to 35 g/min and then to 50 g/min. The melted spatter particle count was lowest at a powder feed rate of 30 g/min and increased with the increasing powder feed rate. 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, 1884 KiB

Open AccessProceeding Paper

Simulation and Fault Diagnosis Using Current-Voltage Characteristics of Photovoltaic Systems—A Case Study

by Jhih-Hao Lin and Yuan-Kang Wu

Eng. Proc. 2025, 92(1), 79; https://doi.org/10.3390/engproc2025092079 (registering DOI) - 22 May 2025

Viewed by 165

Abstract

The I-V characteristics of a photovoltaic (PV) system reveal its actual state and performance, and are used for fault detection and diagnosis in PV systems. We reviewed modeling methods for common faults using MATLAB/Simulink R2021a software, including module degradation, open-circuit faults, short-circuit faults, [...] Read more.

The I-V characteristics of a photovoltaic (PV) system reveal its actual state and performance, and are used for fault detection and diagnosis in PV systems. We reviewed modeling methods for common faults using MATLAB/Simulink R2021a software, including module degradation, open-circuit faults, short-circuit faults, shading faults, and hotspot faults, in this study. A detailed analysis was conducted regarding how these faults impact I-V characteristics. Taking a real PV system as an example, a fault diagnosis case study was carried out. By fitting the measured I-V curves from the PV system and diagnosing potential faults and their severity based on the fitted model parameters, the approach proposed in this study offers a cost-free, simple, and effective detection method. This method can be used by researchers and engineers in the PV field for the advanced fault detection and diagnosis of PV systems. 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, 398 KiB

Open AccessProceeding Paper

Enhancing Real Estate Listings Through Image Classification and Enhancement: A Comparative Study

by Eyüp Tolunay Küp, Melih Sözdinler, Ali Hakan Işık, Yalçın Doksanbir and Gökhan Akpınar

Eng. Proc. 2025, 92(1), 80; https://doi.org/10.3390/engproc2025092080 (registering DOI) - 22 May 2025

Viewed by 205

Abstract

We extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s appropriateness level and increasing the advertisements’ visual appeal. [...] Read more.

We extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s appropriateness level and increasing the advertisements’ visual appeal. A dataset of 3000 labeled images was utilized to compare different image classification models, including convolutional neural networks (CNNs), VGG16, residual networks (ResNets), and the LLaVA large language model (LLM). Each model’s performance and benchmark results were measured to identify the most effective method. In addition, the classification pipeline was expanded using image enhancement with contrastive unsupervised representation learning (CURL). This method assessed the impact of improved image quality on classification accuracy and the overall attractiveness of property listings. For each classification model, the performance was evaluated in binary conditions, with and without the application of CURL. The results showed that applying image enhancement with CURL enhances image quality and improves classification performance, particularly in models such as CNN and ResNet. The study results enable a better visual representation of real estate properties, resulting in higher-quality and engaging user listings. They also underscore the importance of combining advanced image processing techniques with classification models to optimize image presentation and categorization in the real estate industry. The extended platform offers information on the role of machine learning models and image enhancement methods in technology for the real estate industry. Also, an alternative solution that can be integrated into intelligent listing systems is proposed in this study to improve user experience and information accuracy. The platform proves that artificial intelligence and machine learning can be integrated for cloud-distributed services, paving the way for future innovations in the real estate sector and intelligent marketplace platforms. 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, 1831 KiB

Open AccessProceeding Paper

Voltage Regulation of Data Strobe Inputs in Mobile Dynamic Random Access Memory to Prevent Unintended Activations

by Yao-Zhong Zhang, Chiung-An Chen, Powen Hsiao, Bo-Yi Li and Van-Khang Nguyen

Eng. Proc. 2025, 92(1), 81; https://doi.org/10.3390/engproc2025092081 - 23 May 2025

Viewed by 125

Abstract

In mobile dynamic random access memory (DRAM) receivers, the data strobe complement (DQS_c) and data strobe true (DQS_t) signals must be maintained at high and low voltage levels in the write data strobe off (WDQS_OFF) mode. Therefore, we developed a voltage regulation circuit [...] Read more.

In mobile dynamic random access memory (DRAM) receivers, the data strobe complement (DQS_c) and data strobe true (DQS_t) signals must be maintained at high and low voltage levels in the write data strobe off (WDQS_OFF) mode. Therefore, we developed a voltage regulation circuit to optimize the differential voltage signals of DQS_c and DQS_t, ensuring a high voltage level above 0.9 V and a low voltage level below 0.3 V. Experimental results showed that the circuit stably maintained DQS_c above 0.9 V and DQS_t below 0.3 V before the write preamble time (tWPRE) and in WDQS_OFF mode. This configuration effectively prevents unintended activation in the mobile DRAM DQS input receiver. 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, 559 KiB

Open AccessProceeding Paper

Review of Microgrids to Enhance Power System Resilience

by Jian-Hua He and Jhih-Hao Lin

Eng. Proc. 2025, 92(1), 82; https://doi.org/10.3390/engproc2025092082 - 27 May 2025

Viewed by 160

Abstract

As the frequency of extreme events keeps increasing, large-scale power system interruption is also increasing. Natural disasters cause more extensive damage than typical power outages or failures, and the system demands a longer recovery period. Accordingly, it is crucial and urgent for the [...] Read more.

As the frequency of extreme events keeps increasing, large-scale power system interruption is also increasing. Natural disasters cause more extensive damage than typical power outages or failures, and the system demands a longer recovery period. Accordingly, it is crucial and urgent for the power system to have resilience in addition to possessing strong robustness and reliability. For the power system resilience, time is a critical factor. The microgrid (MG) can be connected to the main grid or operate independently to significantly improve the flexibility of the system with great potential in enhancing the power system resilience. We summarize the important concepts of power system resilience and MGs to improve power system resilience. Useful references are provided in this article for power-related practitioners regarding efficient design schemes to improve the application of MGs in enhancing resilience. 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, 697 KiB

Open AccessProceeding Paper

Construction of Fully Automated Key Production Line

by Guo-Cheng Lee, Yi-Hsuan Chiu and Kuang-Chyi Lee

Eng. Proc. 2025, 92(1), 83; https://doi.org/10.3390/engproc2025092083 - 27 May 2025

Viewed by 76

Abstract

We developed a fully automated key production line for smart manufacturing technologies based on the Internet of Things (IoT) and automatic optical inspection (AOI) to enable efficient and consistent production. The production line consists of seven processing stations: raw materials uploading, groove milling, [...] Read more.

We developed a fully automated key production line for smart manufacturing technologies based on the Internet of Things (IoT) and automatic optical inspection (AOI) to enable efficient and consistent production. The production line consists of seven processing stations: raw materials uploading, groove milling, laser marking, key tooth cutting, deburring, defects inspection, and a discharge station. IoT technology enables real-time monitoring and data transmission through a visual panel that displays the operational status of each station and provides immediate alerts in case of abnormalities for quick intervention. The defects inspection station ensures comprehensive quality checks, automatically stops the production line for detected defects, and prevents defective products from proceeding to subsequent stages. Chronological data are used to support predictive maintenance, production parameter optimization, and energy efficiency improvements. Overall, the system effectively integrates automation, real-time monitoring, and quality control to ensure stable production and high product 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, 713 KiB

Open AccessProceeding Paper

Characterization of Six Common Household Pollutants in Multilayered Indoor Air Quality System for Monitoring and Reducing Volatile Organic Compounds and PM2.5

by Glenn V. Magwili, Mathew G. Bandiez and Jobert A. Carbon

Eng. Proc. 2025, 92(1), 84; https://doi.org/10.3390/engproc2025092084 - 27 May 2025

Viewed by 91

Abstract

Air pollution is a significant health concern identified by the World Health Organization (WHO) as it poses serious health risks and climate impacts. WHO indicates that 99% of the global population breathes air with pollutant levels exceeding safe guidelines. Indoor particulate level (IPL) [...] Read more.

Air pollution is a significant health concern identified by the World Health Organization (WHO) as it poses serious health risks and climate impacts. WHO indicates that 99% of the global population breathes air with pollutant levels exceeding safe guidelines. Indoor particulate level (IPL) is approximately 20% higher in naturally ventilated buildings than mechanically ventilated ones. Volatile organic compounds (VOCs), found in products such as pesticides and gasoline, and pollutants including PM2.5 and PM10 contribute to these health risks. This study aims to characterize six common household pollutants, focusing on their concentrations and potential health impacts indoor environments. By understanding the characteristics of the pollutants, indoor air quality can be improved to mitigate associated health risks. The results showed that VOC showed the highest level of concentration as 23.8% was filtered while vape showed the highest concentration of PM2.5 with 83.3% filtered. No significant difference was observed among the VOC concentrations of candles, mosquito coils, and cigarettes. For PM2.5, frying and LPG had the same levels of concentration while the other groups had similar levels. 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, 1584 KiB

Open AccessProceeding Paper

Identification of Grass Weed Species Using YOLO5 Algorithm

by Charlene Grace Rabulan, John Alfred Gascon and Noel Linsangan

Eng. Proc. 2025, 92(1), 86; https://doi.org/10.3390/engproc2025092086 - 27 May 2025

Viewed by 47

Abstract

Grass weeds are considered one of the major pests that pose a challenge to agricultural activity as they consume nutrients, space, and water. With advancements in technology, these pests can be identified and removed. Using computer vision techniques, we developed a grass weed [...] Read more.

Grass weeds are considered one of the major pests that pose a challenge to agricultural activity as they consume nutrients, space, and water. With advancements in technology, these pests can be identified and removed. Using computer vision techniques, we developed a grass weed management and control method. Identifying the species of grass weeds enables the correct selection of weed control measures and decreases the use of herbicides and weedicides. The YOLOv5 algorithm was used in this study. It was trained using training images that were also captured as part of this study. These images were then augmented, and Raspberry Pi was adopted to create a portable system. By successfully training the YOLOv5 algorithm on four different types of grass weeds, the system achieved an overall accuracy rate of 95.31% in detecting and identifying the target objects. The developed system detects and identifies the four main types of weeds, contributing to the improvement of weed control 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, 952 KiB

Open AccessProceeding Paper

Development of Automatic Inspection and Optimization Platform for Computer Numerical Control Machining Using Automatic Optical Inspection and Artificial Intelligence

by Qi-Ren Lin, Bo-Cing Hu, Liang-Yin Kuo and Ting-Yi Shen

Eng. Proc. 2025, 92(1), 87; https://doi.org/10.3390/engproc2025092087 - 27 May 2025

Viewed by 30

Abstract

We developed an automatic optical inspection (AOI) system for detecting defects in finished workpieces and determining the parameters for CNC machining. The system addresses quality control issues in CNC machining using image processing, machine learning, and G-code analysis techniques. The accuracy and efficiency [...] Read more.

We developed an automatic optical inspection (AOI) system for detecting defects in finished workpieces and determining the parameters for CNC machining. The system addresses quality control issues in CNC machining using image processing, machine learning, and G-code analysis techniques. The accuracy and efficiency of CNC machining were improved by reducing manual inspection tasks, minimizing production downtime, and achieving higher precision in defect detection and correction. Experiments were conducted in a pre-planned CNC machining environment to validate the effectiveness of the proposed AOI system. The system was tested on metals and composites and CNC lathes and milling machines. The AOI system significantly improved defect detection accuracy, exceeding 95% across different defect types. The proposed machining parameters enabled a reduction in the recurrence rate of defects by approximately 80%, demonstrating the potential to enhance overall machining quality. By developing AOI recognition and optimizing CNC machining parameters, an automated and intelligent defect detection and correction solution was realized. The reliability and accuracy of CNC processes were improved, and data-driven automated manufacturing and process optimization were achieved, meeting the goals of intelligent manufacturing and Industry 4.0. 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, 5625 KiB

Open AccessProceeding Paper

Molding Characteristics and Impact Strength of Polypropylene with Different Numbers of Recycling Cycles

by Hui-Mei Zheng, Jui-Chan Li, Yen-Kai Wang, Kai-Fu Liew and Hsin-Shu Peng

Eng. Proc. 2025, 92(1), 88; https://doi.org/10.3390/engproc2025092088 - 29 May 2025

Viewed by 90

Abstract

We analyzed the changes in the molding properties of polypropylene (PP) resin in the process of recycling after multiple plasticization, injection, and crushing processes. We also explored the changes in the material properties and characteristics with the ASTM-D256 impact test specimen and the [...] Read more.

We analyzed the changes in the molding properties of polypropylene (PP) resin in the process of recycling after multiple plasticization, injection, and crushing processes. We also explored the changes in the material properties and characteristics with the ASTM-D256 impact test specimen and the number of recycling cycles. After the material is injected and crushed, it is recycled to produce the material required for re-injection, and a pressure sensor is installed at the nozzle position to observe the effects of material properties and impact characteristics in recycling. Injecting and pulverizing PP several times results in looser molecular spacing, increasing the fluidity of the material. After several recycling cycles, the fluidity of the material gradually decreased. Its crystallinity fluctuated depending on the crystallinity and crystallization rates. Recycled PP materials in various molding processes were influenced by melt temperature, screw speed, back pressure, and injection speed, which also affected nozzle pressure and strength. As the melt temperature increased, the effect on the nozzle pressure and impact strength became more evident. 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, 2147 KiB

Open AccessProceeding Paper

Design of Magnetron for Beamforming

by Chun-Hsi Su and Meng-Xun Ku

Eng. Proc. 2025, 92(1), 89; https://doi.org/10.3390/engproc2025092089 - 29 May 2025

Viewed by 74

Abstract

The design of a vane-type magnetron with a resonant frequency in the megahertz range is presented in this article. The initial dimensions are determined based on magnetron empirical formulas. CST Studio Suite was used to simulate a series of magnetron behaviors, including eigenmode [...] Read more.

The design of a vane-type magnetron with a resonant frequency in the megahertz range is presented in this article. The initial dimensions are determined based on magnetron empirical formulas. CST Studio Suite was used to simulate a series of magnetron behaviors, including eigenmode analysis and particle-in-cell (PIC) simulations. The relationship between volume and frequency results in a significantly large magnetron size for MHz frequencies. Considering manufacturing convenience and cost factors, the magnetron was simplified from its initial design, and the operating frequencies were compared, revealing a difference of about 40 MHz. The simulated frequency was 193 MHz, and the magnetron field patterns were simulated using CST. In the simulation, a horn antenna-like structure was employed to reduce the magnetron’s half-power beamwidth (HPBW), narrowing it from 101 to 50°. The result of this study can be used for beam focusing. 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, 2145 KiB

Open AccessProceeding Paper

Tunnel Oxide Passivated Contact and Passivated Emitter Rear Cell Solar Module Testing

by Tzong-Jiy Tsai, Jun-You Lu and Ming-Hung Lin

Eng. Proc. 2025, 92(1), 90; https://doi.org/10.3390/engproc2025092090 - 3 Jun 2025

Abstract

The tunnel oxide passivated contact (TOPCon) solar cell utilizes an ultra-thin tunnel oxide layer in its passivation layer structure. The performance difference between TOPCon and passivated emitter and rear cell (PERC) solar cells is obvious due to differences in their structure and operational [...] Read more.

The tunnel oxide passivated contact (TOPCon) solar cell utilizes an ultra-thin tunnel oxide layer in its passivation layer structure. The performance difference between TOPCon and passivated emitter and rear cell (PERC) solar cells is obvious due to differences in their structure and operational characteristics. Compared with PERC, TOPCon involves additional processes such as boron diffusion, tunnel oxide deposition, polysilicon doping, and cleaning, while eliminating the need for laser grooving. PERC production lines can be converted to TOPCon production lines which reduces equipment investment costs. Therefore, it is beneficial to replace PERC products in the future. On two different manufacturing technologies for TOPCon and PERC solar modules, we conducted electroluminescence (EL) tests to analyze power degradation in the solar modules. 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

Open AccessProceeding 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 - 28 Apr 2025

Viewed by 274

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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10 pages, 1935 KiB

Open AccessProceeding Paper

Signal Enhancement and Interference Reduction with Minimum-Variance Distortionless Response Algorithm Using MATLAB and GNU Radio Simulations

by Tuan-Khanh Nguyen, Nguyen Do Nguyen, Huy Quang Nguyen and Khang Thai Viet Nguyen

Eng. Proc. 2025, 92(1), 2073; https://doi.org/10.3390/engproc2025092073 - 16 May 2025

Viewed by 154

Abstract

We improved signal reception by minimizing interference in dynamic communication environments with a minimum-variance distortionless response (MVDR) algorithm. The conditions of the MVDR algorithm were simulated using MATLAB and GNU Radio to enhance its capabilities in noise and interference suppression. Through a MATLAB [...] Read more.

We improved signal reception by minimizing interference in dynamic communication environments with a minimum-variance distortionless response (MVDR) algorithm. The conditions of the MVDR algorithm were simulated using MATLAB and GNU Radio to enhance its capabilities in noise and interference suppression. Through a MATLAB simulation, the adaptive beamforming performance of MVDR was examined and compared with that of conventional beamforming techniques to identify the advantages of beam steering for obtaining the desired signals. MVDR was effective in interference reduction and the improvement of signal clarity, with superiority over conventional approaches in cases with complex interference patterns. Based on the results of the MATLAB simulations, GNU Radio was used in a complete software-defined radio (SDR) environment that enabled the replication of real-world conditions to study MVDR. We simulated real-world applications by integrating GNU Radio to ensure the robustness and adaptability of the algorithm in live signal processing. The results from these two simulations prove the potential of MVDR as a strong dynamic interference suppressor that enables superior signal reception. The results enable the implementation of the MVDR algorithm in communication 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, 2696 KiB

Open AccessProceeding Paper

Anomalous Weapon Detection for Armed Robbery Using Yolo V8

by Adrian Lester E. Reyes and Jennifer C. Dela Cruz

Eng. Proc. 2025, 92(1), 2085; https://doi.org/10.3390/engproc2025092085 - 27 May 2025

Viewed by 161

Abstract

Improved surveillance systems provide early warnings and improve public safety. Such systems are desperately needed in light of the rising number of armed robberies in private and public places. A YOLOv8-based system specifically intended for CCTV-based armed robbery detection was developed to meet [...] Read more.

Improved surveillance systems provide early warnings and improve public safety. Such systems are desperately needed in light of the rising number of armed robberies in private and public places. A YOLOv8-based system specifically intended for CCTV-based armed robbery detection was developed to meet this demand in this study. The system identified weapons such as handguns, assault weapons, shotguns, and others in real-time, utilizing a custom-trained model. The system demonstrated a strong performance with an overall anomaly detection accuracy of 87.50%. The confidence level was 1.2 m (58.79) and 2 m (59.74) in determining the optimal height and distance considering the positioning of the CCTV camera. The low confidence level was attributed to the mixture of images from a general database from the Internet along with self-captured images that resulted in the overfitting of the datasets. Although improvements are needed to increase the confidence level by using real guns in training the model and reducing false negatives, the potential of YOLOv8 to enhance public safety has been confirmed by providing early warnings of armed robberies. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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