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Open AccessEditorial

Preface: The 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data (IEEE ICEIB 2025)

by Teen-Hang Meen, Shu-Han Liao and Cheng-Fu Yang

Eng. Proc. 2025, 108(1), 51; https://doi.org/10.3390/engproc2025108051 - 25 Sep 2025

Abstract

This volume presents the proceedings of the 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things, and Big Data (IEEE ICEIB 2025) [...] Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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2 pages, 262 KB

Open AccessEditorial

Statement of Peer Review

by Teen-Hang Meen, Shu-Han Liao and Cheng-Fu Yang

Eng. Proc. 2025, 108(1), 52; https://doi.org/10.3390/engproc2025108052 - 25 Sep 2025

Abstract

In submitting conference proceedings of the 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data (IEEE ICEIB 2025) to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this [...] Read more.

In submitting conference proceedings of the 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data (IEEE ICEIB 2025) to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...] Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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Other

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10 pages, 2239 KB

Open AccessProceeding Paper

Combining Forgetting Factor Recursive Least Squares and Adaptive Extended Kalman Filter Techniques for Dynamic Estimation of Lithium Battery State of Charge

by En-Jui Liu, Cai-Chun Ting, Wei-Hsuan Hsu, Pei-Zhang Chen, Wei-Hua Hong and Hung-Chih Ku

Eng. Proc. 2025, 108(1), 1; https://doi.org/10.3390/engproc2025108001 - 28 Aug 2025

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Abstract

For electric vehicles widely used recently, lithium-ion batteries serve as the primary energy storage units, affecting the vehicles’ performance, safety, and lifespan. Accurate state of charge (SOC) estimation is pivotal for the battery management system (BMS) to enhance the predictability of the vehicle’s [...] Read more.

For electric vehicles widely used recently, lithium-ion batteries serve as the primary energy storage units, affecting the vehicles’ performance, safety, and lifespan. Accurate state of charge (SOC) estimation is pivotal for the battery management system (BMS) to enhance the predictability of the vehicle’s range and avert thermal runaway due to improper charging methods. In this study, an adaptive SOC estimation methodology was developed using parameter identification with forgetting factor recursive least squares (FFRLS). These parameters are then incorporated into a dual adaptive extended Kalman filter (DAEKF) for SOC estimation under varying load conditions. DAEKF is used to dynamically adjust the covariance matrices for process and measurement noises, significantly enhancing the filter’s adaptability and precision. The integration of FFRLS and DAEKF enables a robust SOC estimation of electric vehicles, featuring rapid computation speeds, high accuracy, and excellent adaptability, positioning them as ideal candidates for enhancements in battery management system technology. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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9 pages, 844 KB

Open AccessProceeding Paper

Adaptive Wavelet Thresholding for Multiresolution Denoising Based on Image Size

by Chih-Yu Tsai and Jian-Jiun Ding

Eng. Proc. 2025, 108(1), 2; https://doi.org/10.3390/engproc2025108002 - 28 Aug 2025

Viewed by 745

Abstract

Hard thresholding is a common method for denoising wavelet coefficients. However, determining an optimal threshold remains challenging. Therefore, we propose an adaptive hard threshold that accounts for the size of the noisy image based on the statistical properties of the wavelet coefficient. Regression [...] Read more.

Hard thresholding is a common method for denoising wavelet coefficients. However, determining an optimal threshold remains challenging. Therefore, we propose an adaptive hard threshold that accounts for the size of the noisy image based on the statistical properties of the wavelet coefficient. Regression analysis is used to determine the adaptive threshold to ensure effective performance with multiresolution wavelet coefficients. The adaptive threshold enables a balance between denoising and high-frequency detail preservation, and excellent denoising performance. Multiple segmentations are conducted using the multiresolution properties of the wavelet transform, based on both image size and noise level, leading to the best denoising results. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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11 pages, 2319 KB

Open AccessProceeding Paper

Creating a Drawing Werewolf Game and Building an Artificial Intelligence Player Using SketchRNN

by Nodoka Okamoto, Sota Nishiguchi and Shun Nishide

Eng. Proc. 2025, 108(1), 3; https://doi.org/10.3390/engproc2025108003 - 28 Aug 2025

Viewed by 1095

Abstract

We developed an artificial intelligence (AI) player for Drawing Werewolf, a multiplayer game involving collaborative sketching and hidden roles. Players contribute to a shared drawing while identifying a secret “Werewolf” unaware of the theme. AI, trained with SketchRNN on the “Quick, Draw!” dataset, [...] Read more.

We developed an artificial intelligence (AI) player for Drawing Werewolf, a multiplayer game involving collaborative sketching and hidden roles. Players contribute to a shared drawing while identifying a secret “Werewolf” unaware of the theme. AI, trained with SketchRNN on the “Quick, Draw!” dataset, mimics human behavior by generating theme-relevant sketches. The game was built with C++ and supports real-time human–AI interaction via sockets. Initial experiment results confirmed AI’s ability to participate and draw appropriately. The remaining challenges, including improving strategic voting, theme inference, and context-aware drawing, need to be addressed. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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5 pages, 970 KB

Open AccessProceeding Paper

Application of Artificial Intelligence in Graphical Discrimination of Structural Cracks

by Ren-Jwo Tsay

Eng. Proc. 2025, 108(1), 4; https://doi.org/10.3390/engproc2025108004 - 28 Aug 2025

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Abstract

For the detection of structural cracks, engineers need to conduct measurements on-site. However, structural cracks are widely distributed and are not easy to access for measurement. Therefore, photography is commonly used for crack detection. Although the crack is observed in the photos, its [...] Read more.

For the detection of structural cracks, engineers need to conduct measurements on-site. However, structural cracks are widely distributed and are not easy to access for measurement. Therefore, photography is commonly used for crack detection. Although the crack is observed in the photos, its location and size need to be clearly defined. Artificial intelligence (AI) and databases are used in graphic processing methods to perform structural crack analysis, in which the Python 3.12 program is used as the main tool. Using the Python program, an AI image analysis program was developed for crack analysis and evaluation. ChatGPT 3.5 was also used for the analysis of crack length and width. Using AI considerably increased credibility in detecting and measuring structural cracks. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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9 pages, 1441 KB

Open AccessProceeding Paper

Application of Machine Learning for Optimizing Chemical Vapor Deposition Quality

by Chen-Yu Lin, Chun-Wei Chen, Jung-Hsing Wang, Chung-Ying Wang, Wei-Lin Wang and Hao-Kai Tu

Eng. Proc. 2025, 108(1), 5; https://doi.org/10.3390/engproc2025108005 - 29 Aug 2025

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Abstract

Chemical vapor deposition (CVD) is a high-precision thin-film fabrication technique that is widely applied in semiconductor manufacturing, optical component manufacturing, and materials science. The performance of the deposition process plays a critical role in determining the quality of the final product. However, multiple [...] Read more.

Chemical vapor deposition (CVD) is a high-precision thin-film fabrication technique that is widely applied in semiconductor manufacturing, optical component manufacturing, and materials science. The performance of the deposition process plays a critical role in determining the quality of the final product. However, multiple variables in CVD processes have a highly nonlinear nature that involves complex interactions. Therefore, conventional experimental methods exhibit limitations in quality control and process optimization for CVD. In this study, we developed a predictive model based on process parameters and quality indicators using machine learning techniques to analyze and optimize the CVD processes. Through data collection, feature selection, model training, and model validation, the developed machine-learning algorithms were tested and evaluated. The adopted machine learning algorithms effectively captured the nonlinear relationships between multiple variables in CVD processes, accurately predicted thin-film quality indicators, and provided data for optimizing process parameters. In addition, the analysis results of feature importance revealed the effect of each key parameter on product quality, offering a basis for process improvement. Overall, the results of this study highlight the capability of machine learning algorithms for quality control and optimization in CVD processes for future advancements in smart manufacturing. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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9 pages, 1286 KB

Open AccessProceeding Paper

Grid and Refinement Double-Stage-Based Tumor Detection Using Ultrasonic Images

by Daisuke Osako and Jian-Jiun Ding

Eng. Proc. 2025, 108(1), 6; https://doi.org/10.3390/engproc2025108006 - 29 Aug 2025

Viewed by 276

Abstract

Accurate tumor segmentation is crucial for cancer diagnosis and treatment planning. We developed a hybrid framework combining complementary convolutional neural network (CNN) models and advanced post-processing techniques for robust segmentation. Model 1 uses contrast-limited adaptive histogram equalization preprocessing, CNN predictions, and active contour [...] Read more.

Accurate tumor segmentation is crucial for cancer diagnosis and treatment planning. We developed a hybrid framework combining complementary convolutional neural network (CNN) models and advanced post-processing techniques for robust segmentation. Model 1 uses contrast-limited adaptive histogram equalization preprocessing, CNN predictions, and active contour refinement, but struggles with complex tumor boundaries. Model 2 applies noise-augmented preprocessing and iterative detection to enhance the segmentation of subtle and irregular regions. The outputs of both models are merged and refined with edge correction, size filtering, and a spatial intensity metric (SIM) expansion to improve under-segmented areas, an approach that achieves higher F1 scores and intersection over union scores. The developed framework highlights the potential in combining machine learning and image-processing techniques to develop reliable diagnostic tools. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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8 pages, 767 KB

Open AccessProceeding Paper

Artificial Intelligence-Driven Analytics for Monitoring and Mitigating Climate Change Impacts

by Wai Yie Leong

Eng. Proc. 2025, 108(1), 7; https://doi.org/10.3390/engproc2025108007 - 29 Aug 2025

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Abstract

Artificial intelligence (AI) and big data analytics are transforming the fight against climate change by enabling advanced monitoring, predictive modeling, and actionable insights. This study aims to examine how AI-driven analytics enhance the understanding of climate systems, support mitigation strategies, and inform policy [...] Read more.

Artificial intelligence (AI) and big data analytics are transforming the fight against climate change by enabling advanced monitoring, predictive modeling, and actionable insights. This study aims to examine how AI-driven analytics enhance the understanding of climate systems, support mitigation strategies, and inform policy decisions. By processing vast datasets from satellites, sensors, and climate models, AI algorithms identify patterns, predict extreme weather events, and quantify the impacts of human activities on ecosystems. Applications, such as real-time greenhouse gas monitoring, precision agriculture, and energy optimization, showcase AI’s potential to reduce emissions and enhance sustainability. Challenges, including data gaps, algorithmic biases, and ethical considerations, must be addressed to fully realize AI’s transformative potential. AI and big data contribute to the accelerating global efforts to mitigate climate change and build resilience against its adverse effects. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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10 pages, 217 KB

Open AccessProceeding Paper

Identification of Factors Influencing Consumers’ Use of Virtual Try-On Technology Based on UTAUT2 Model

by Jen-Ying Shih and Chia-Chieh Yeh

Eng. Proc. 2025, 108(1), 8; https://doi.org/10.3390/engproc2025108008 - 29 Aug 2025

Viewed by 297

Abstract

We explored the adoption of virtual try-on (VTO) technology in Taiwan’s fashion retail sector, which has gained prominence as consumer behavior online has changed since the COVID-19 pandemic. Using an extended unified theory of acceptance and use of technology 2 (UTAUT2) model, we [...] Read more.

We explored the adoption of virtual try-on (VTO) technology in Taiwan’s fashion retail sector, which has gained prominence as consumer behavior online has changed since the COVID-19 pandemic. Using an extended unified theory of acceptance and use of technology 2 (UTAUT2) model, we examined the behavioral intentions and actual usage of VTO. The original framework of the UTAUT2 model was modified by excluding experience and incorporating personality traits as moderating variables. Based on 257 valid survey responses analyzed using SmartPLS 4.1, influencing factors were identified, revealing that gender was a significant moderator in VTO adoption. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

8 pages, 1120 KB

Open AccessProceeding Paper

Interactive System Design for Sustainable Enterprise Management: A Case Study of Chazence Technology Company

by Hui-Ting Ma, Peng-Wei Hsiao and Qi-Fan Huang

Eng. Proc. 2025, 108(1), 9; https://doi.org/10.3390/engproc2025108009 - 1 Sep 2025

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Abstract

Chazence is a subsidiary of Zence Object Technology Company in the Greater Bay Area of China. It is a sustainable enterprise that combines tea industry consumables (tea residue) with fiber composite technology to replace traditional materials and conduct product practices. Their core philosophy [...] Read more.

Chazence is a subsidiary of Zence Object Technology Company in the Greater Bay Area of China. It is a sustainable enterprise that combines tea industry consumables (tea residue) with fiber composite technology to replace traditional materials and conduct product practices. Their core philosophy aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly Goals 9 and 12, emphasizing industrial innovation, the sustainable management of natural resources, and the promotion of sustainable consumption and production patterns. However, the current system of tea recycling is extensive and requires precise data management and back-end human resource allocation to ensure efficient collaboration between professionals and grassroots staff. Currently, the system does not have a user-friendly interface for human resource allocation, data management, resource management, and visual information. Therefore, we optimized the interface and functional design of the warehouse system to improve the efficiency of resource management of Chazence by understanding its approach to tea recycling. Through surveys and interviews, employee needs and user experiences were analyzed, and the results guide the design of a sustainable enterprise management system from a user experience (UX) perspective. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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10 pages, 5667 KB

Open AccessProceeding Paper

Advanced Machine Learning Method for Watermelon Identification and Yield Estimation

by Memoona Farooq, Chih-Yuan Chen and Cheng-Pin Wang

Eng. Proc. 2025, 108(1), 10; https://doi.org/10.3390/engproc2025108010 - 1 Sep 2025

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Abstract

Watermelon is a popular fruit, predominantly cultivated in Asian countries. However, the production and harvesting processes present several challenges. Due to its size and weight, manually harvesting watermelons is labor-intensive and costly. In the future, technology is expected to enable robots to harvest [...] Read more.

Watermelon is a popular fruit, predominantly cultivated in Asian countries. However, the production and harvesting processes present several challenges. Due to its size and weight, manually harvesting watermelons is labor-intensive and costly. In the future, technology is expected to enable robots to harvest watermelons. Therefore, it becomes essential to introduce intelligent systems to effectively identify and locate watermelons in harvesting. This research aims to develop an advanced methodology for watermelon identification and location using You Look Only Once (YOLO)v8 and YOLOv8-oriented bounding box (OBB) algorithms. Furthermore, the simple online and real-time tracking (SORT) algorithm was employed to track and count watermelons and estimate yield. The performance of YOLOv8-OBB was better than that of YOLOv8 and the highest precision (0.938) was achieved by YOLOv8s-OBB. Additionally, the size of each watermelon was measured with both models. The models help farmers find the optimal watermelons for harvest. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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6 pages, 310 KB

Open AccessProceeding Paper

Simulated Attacks and Defenses Using Traffic Sign Recognition Machine Learning Models

by Chu-Hsing Lin, Chao-Ting Yu and Yan-Ling Chen

Eng. Proc. 2025, 108(1), 11; https://doi.org/10.3390/engproc2025108011 - 1 Sep 2025

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Abstract

Physically simulated attack experiments were conducted using LED lights of different colors, the You Look Only Once (YOLO) v5 model, and the German Traffic Sign Recognition Benchmark (GTSRB) dataset. We attacked and interfered with the traffic sign detection model and tested the model’s [...] Read more.

Physically simulated attack experiments were conducted using LED lights of different colors, the You Look Only Once (YOLO) v5 model, and the German Traffic Sign Recognition Benchmark (GTSRB) dataset. We attacked and interfered with the traffic sign detection model and tested the model’s recognition performance when it was interfered with by LED lights. The model’s accuracy in identifying objects was calculated with the interference. We conducted a series of experiments to test the interference effects of colored lighting. The attack with different colored lights caused a certain degree of interference to the machine learning model, which affected the self-driving vehicle’s ability to recognize traffic signs. It caused the self-driving system to fail to detect the existence of the traffic sign or commit recognition errors. To defend from this attack, we fed back the traffic signs into the training dataset and re-trained the machine learning model. This enabled the machine learning model to resist related attacks and avoid disturbance. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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9 pages, 1283 KB

Open AccessProceeding Paper

Improving Effectiveness of Energy Baseline Using Deep Learning

by Chun-Wei Chen, Chen-Yu Lin, Jung-Hsing Wang and Hao-Kai Tu

Eng. Proc. 2025, 108(1), 12; https://doi.org/10.3390/engproc2025108012 - 1 Sep 2025

Viewed by 226

Abstract

Energy conservation and carbon reduction are critical in energy policies. Therefore, numerous energy-saving methods, such as the introduction of new technologies and the replacement of outdated equipment, have been proposed. To determine whether these methods are effective in energy conservation and carbon reduction, [...] Read more.

Energy conservation and carbon reduction are critical in energy policies. Therefore, numerous energy-saving methods, such as the introduction of new technologies and the replacement of outdated equipment, have been proposed. To determine whether these methods are effective in energy conservation and carbon reduction, scientific validation is required. The most common validation method is energy baseline. An energy baseline refers to the use of data measured before energy-saving improvements. It is used to construct a mathematical model that describes energy consumption. Using the baseline, the energy consumption during the baseline period after improvements is calculated. By subtracting the measured consumption from the value, the amount of energy saved is estimated. Traditionally, linear regression is used to establish energy baseline prediction. However, linear regression has limitations with complex energy data. Therefore, we used deep learning models to handle nonlinear data in the air compression system for comparative analysis. The developed long-short-term memory (LSTM) model showed superior capabilities for processing nonlinear data, aligning with the actual data distribution, and reducing errors. Compared with linear regression models, the LSTM model reduced uncertainty, risk, and cost by 40.3%. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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8 pages, 1211 KB

Open AccessProceeding Paper

Application of Whiteboard Animation in Engineering Mathematics Education Based on YouTube OpenCourseWare

by John C.-C. Lu

Eng. Proc. 2025, 108(1), 13; https://doi.org/10.3390/engproc2025108013 - 1 Sep 2025

Viewed by 492

Abstract

With advancements in digital technology, OpenCourseWare (OCW) has become a crucial tool for enhancing learning outcomes. Therefore, the innovative application of whiteboard animation in engineering mathematics education was explored, utilizing YouTube as a platform as a free digital learning resource. More than 300 [...] Read more.

With advancements in digital technology, OpenCourseWare (OCW) has become a crucial tool for enhancing learning outcomes. Therefore, the innovative application of whiteboard animation in engineering mathematics education was explored, utilizing YouTube as a platform as a free digital learning resource. More than 300 animated teaching materials and implemented interactive mechanisms were developed to improve students’ study effectiveness and self-directed learning abilities. Pre-tests and post-tests were conducted in “Engineering Mathematics I” and “Engineering Mathematics II” to assess the educational effectiveness of whiteboard animation. The results presented significant improvements in students’ performance. The average scores of Engineering Mathematics I and II increased from 64.37 and 63.73 in the pre-test to 87.03 and 92.39 in the post-test. Whiteboard animation effectively enhanced the learning experience in engineering mathematics, improving students’ comprehension and motivation. Such results provide a reference for the development of digital education technologies in engineering. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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10 pages, 1019 KB

Open AccessProceeding Paper

Classification of Infectious and Parasitic Diseases by Smart Healthcare System

by Junwei Yang, Teerawat Simmachan, Subij Shakya and Pichit Boonkrong

Eng. Proc. 2025, 108(1), 14; https://doi.org/10.3390/engproc2025108014 - 1 Sep 2025

Viewed by 876

Abstract

We developed a machine-learning model for the International Classification of Diseases, 10th Revision (ICD-10) classification using data from 5108 patients. Nine features, including age, gender, BMI, and vital signs, were extracted to classify the top three ICD-10 categories: intestinal infections, tuberculosis, and other [...] Read more.

We developed a machine-learning model for the International Classification of Diseases, 10th Revision (ICD-10) classification using data from 5108 patients. Nine features, including age, gender, BMI, and vital signs, were extracted to classify the top three ICD-10 categories: intestinal infections, tuberculosis, and other bacterial diseases. Decision trees, random forest, and XGBoost models were tested using the synthetic minority over-sampling technique (SMOTE) and class weights to minimize class imbalance. Five-fold cross-validation was used using the training and testing datasets in a data ratio of 80:20. The random forest model with class weights showed the best performance. Shapley additive explanations (SHAP) analysis highlighted body-mass index (BMI), gender, and pulse as key features. The developed model showed potential for enhancing ICD-10 classification through real-time and personalized medical applications. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 1157 KB

Open AccessProceeding Paper

Quantum Random Forest Regression for Indoor Localization

by Hanas Subakti and Jehn-Ruey Jiang

Eng. Proc. 2025, 108(1), 15; https://doi.org/10.3390/engproc2025108015 - 1 Sep 2025

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Abstract

Accurate indoor localization is vital for smart environments and the Internet of Things (IoT) applications. Received signal strength indicator (RSSI)-based methods suffer from multipath fading, signal attenuation, and missing data. To address these issues, we developed quantum random forest indoor localization (QRF-IL), a [...] Read more.

Accurate indoor localization is vital for smart environments and the Internet of Things (IoT) applications. Received signal strength indicator (RSSI)-based methods suffer from multipath fading, signal attenuation, and missing data. To address these issues, we developed quantum random forest indoor localization (QRF-IL), a quantum-inspired machine learning method that combines quantum random forests (QRFs) with weighted centroid regression. Each quantum decision tree in QRF uses a quantum support vector machine (QSVM) with Nyström quantum kernel estimation for efficient and accurate learning. On a public dataset, QRF-IL showed an average localization error of 2.3 m, which was reduced by 9% over a standalone QRF model and 21% over an adaptive path loss model (ADAM). Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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9 pages, 970 KB

Open AccessProceeding Paper

Virtual Reality in Phobia Treatment and Emotional Resilience

by Wai Yie Leong

Eng. Proc. 2025, 108(1), 16; https://doi.org/10.3390/engproc2025108016 - 1 Sep 2025

Viewed by 805

Abstract

Virtual reality (VR) has emerged as a transformative tool in the treatment of phobias and the cultivation of emotional resilience. This study aims to explore the potential of VR to create controlled, immersive environments that facilitate exposure therapy, enabling individuals to confront and [...] Read more.

Virtual reality (VR) has emerged as a transformative tool in the treatment of phobias and the cultivation of emotional resilience. This study aims to explore the potential of VR to create controlled, immersive environments that facilitate exposure therapy, enabling individuals to confront and desensitize themselves to their fears in a safe and personalized manner. The flexibility of VR systems allows therapists to tailor scenarios to the unique needs of patients, addressing specific phobias such as acrophobia, arachnophobia, and social anxiety disorders. Beyond phobia treatment, VR’s capacity to simulate challenging or stress-inducing scenarios presents opportunities for fostering emotional resilience by building adaptive coping mechanisms and reducing stress responses over time. The integration of biofeedback and machine learning further enhances VR applications, enabling real-time adjustments based on physiological and psychological responses. In this article, the current advancements, underlying mechanisms, and challenges in leveraging VR technology for therapeutic purposes are discussed with a focus on its implications for mental health care. By combining immersive technology with evidence-based practices, VR offers a promising pathway for improving mental health outcomes and expanding the accessibility of therapeutic interventions. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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8 pages, 1928 KB

Open AccessProceeding Paper

Innovative Design of Internet of Things-Based Intelligent Teaching Tool with Application Using Quality Function Deployment

by Hsu-Chan Hsiao, Meng-Dar Shieh, Chi-Hua Wu, Yu-Ting Hsiao and Jui-Feng Chang

Eng. Proc. 2025, 108(1), 17; https://doi.org/10.3390/engproc2025108017 - 1 Sep 2025

Viewed by 785

Abstract

With globalization and technology advancement, traditional teaching models are facing challenges due to the diverse needs of modern learners. It is necessary to enhance learner engagement and motivation, and incorporating Internet of Things (IoT)-assisted teaching tools has become a major concern for educators. [...] Read more.

With globalization and technology advancement, traditional teaching models are facing challenges due to the diverse needs of modern learners. It is necessary to enhance learner engagement and motivation, and incorporating Internet of Things (IoT)-assisted teaching tools has become a major concern for educators. However, the time it takes to develop new teaching tools and integrate IoT technology must be shortened by combining educational content with game mechanics seamlessly. Therefore, we developed a gamified teaching model by incorporating IoT technology. We used the “System, Indicators, Criteria” framework to develop a three-tiered board game evaluation and development model. Based on this framework, a teaching tool was designed to provide personalized learning experiences with IoT technology. The tool provides abstract knowledge, fosters interaction and collaboration among learners, and thus enhances engagement. To ensure a rigorous design and evaluation process, we employed quality function deployment (QFD), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). The developed model facilitates the integration of IoT technology with innovative design concepts and enhances the application value of teaching tools in education. The model also enhances intelligence, interactivity, and creativity for traditional education to revitalize learning experiences. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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6 pages, 662 KB

Open AccessProceeding Paper

Lightweight Model for Weather Prediction

by Po-Ting Wu, Ting-Yu Tsai and Che-Cheng Chang

Eng. Proc. 2025, 108(1), 18; https://doi.org/10.3390/engproc2025108018 - 1 Sep 2025

Viewed by 360

Abstract

Autonomous driving technology is developing rapidly, particularly in vision-based approaches that rely on cameras to monitor the environment. However, one of the critical challenges for autonomous vehicles is the ability to adapt to different weather conditions, as environmental factors such as clouds, fog, [...] Read more.

Autonomous driving technology is developing rapidly, particularly in vision-based approaches that rely on cameras to monitor the environment. However, one of the critical challenges for autonomous vehicles is the ability to adapt to different weather conditions, as environmental factors such as clouds, fog, rain, sand, shine, snow, and the sunrise significantly impact their perceptual capabilities. Control strategies of the vehicles must be dynamically adjusted based on real-time weather conditions to ensure safe and efficient driving. For the strategies, we developed a novel weather perception model to improve the adaptability of autonomous driving systems. The model is more lightweight than an existing study, as it is computationally efficient with enhanced performance. Moreover, the model detects a weather type, improving its robustness, and providing reliable weather awareness for autonomous driving. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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9 pages, 594 KB

Open AccessProceeding Paper

Stress and Temperature Monitoring of Bridge Structures Based on Data Fusion Analysis

by Zhensong Ni, Shuri Cai, Cairong Ni, Baojia Lin and Liyao Li

Eng. Proc. 2025, 108(1), 19; https://doi.org/10.3390/engproc2025108019 - 1 Sep 2025

Viewed by 332

Abstract

Structural parameters, such as strain or deflection, were collected by sensors and analyzed to assess the bridge’s structural condition and obtain a reliable reference for bridge maintenance. In the data acquisition and transmission process, sensor data inevitably contains noise and interference, resulting in [...] Read more.

Structural parameters, such as strain or deflection, were collected by sensors and analyzed to assess the bridge’s structural condition and obtain a reliable reference for bridge maintenance. In the data acquisition and transmission process, sensor data inevitably contains noise and interference, resulting in anomalies, especially data distortion during wireless transmission. These anomalies significantly impact data analysis and structural evaluation. To mitigate the effects of these abnormalities, we conducted the cause analysis. The Sanxia Viaduct was used to design a strain monitoring method as a bridge model. We analyzed vibrating string sensor data collected in the cold environment using the Nair method to eliminate outlier data. The analysis results of strain and temperature trends showed that the data fusion method developed in this study showed high precision and stability and effectively reduced the impact of noise and data anomalies. By monitoring actual bridges, the effectiveness and practicality of the method were validated. The model provides significant information on the development and application of bridge health monitoring technology. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 2528 KB

Open AccessProceeding Paper

Integration of Radio Frequency Identification Interface for Enhanced Controller Area Network Bus

by Fuh-Liang Wen, Ching-Hsu Chan and Chu-Po Wen

Eng. Proc. 2025, 108(1), 20; https://doi.org/10.3390/engproc2025108020 - 1 Sep 2025

Viewed by 372

Abstract

The radiofrequency identification (RFID) interface is used in a controller area network (CAN) bus system to enhance the performance of stacked fuel cells. In addition, a personal computer base logic analyzer (LA) is utilized to monitor and analyze data transmitted over the CAN [...] Read more.

The radiofrequency identification (RFID) interface is used in a controller area network (CAN) bus system to enhance the performance of stacked fuel cells. In addition, a personal computer base logic analyzer (LA) is utilized to monitor and analyze data transmitted over the CAN bus. The LA enables the visualization of digital signals, identification of data patterns, and troubleshooting of communication protocols. The combination of the CAN bus with the RFID interface and LA provides an effective solution for testing and monitoring digital communication systems. The result of this study proves that LA is applied in series-connected fuel cells. The advantages of the RFID CAN bus are validated by modern communication protocols. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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6 pages, 1720 KB

Open AccessProceeding Paper

Enhanced Photovoltaic Performance of TiO₂ Thin Films Dye-Sensitized Solar Cells by Incorporating TiO₂ Nanoparticles

by Ming-Cheng Kao, Kai-Huang Chen and Cheng-Shu Hsiao

Eng. Proc. 2025, 108(1), 21; https://doi.org/10.3390/engproc2025108021 - 1 Sep 2025

Viewed by 299

Abstract

We fabricated TiO₂ thin films using the sol–gel method, incorporating TiO₂ nanoparticle sizes of 25 nm on the fluorine-doped tin oxide (FTO) substrates by spin coating and annelation at 600 °C. The influence of incorporating TiO₂ particles on the surface [...] Read more.

We fabricated TiO₂ thin films using the sol–gel method, incorporating TiO₂ nanoparticle sizes of 25 nm on the fluorine-doped tin oxide (FTO) substrates by spin coating and annelation at 600 °C. The influence of incorporating TiO₂ particles on the surface morphology, optical properties, and photovoltaic performance of TiO₂ thin-film dye-sensitized solar cells (DSSC) was examined. Structural characterization was analyzed using X-ray diffraction (XRD), while the morphologies were analyzed using scanning electron microscopy (SEM). The transmittance and absorbance of films were measured using an ultraviolet (UV)–visible (VIS)–near-infrared (NIR) spectrophotometer. The current–voltage (I-V) property was evaluated under simulated solar irradiation. The results demonstrated that the incorporation of TiO₂ particles enhanced the efficiency of DSSCs. The photovoltaic performance of DSSCs was improved with TiO₂ nanoparticle incorporation. The optimized DSSC incorporated TiO₂ films (TIFNA). TIFNA achieved a Jsc of 14.49 mA/cm², Voc of 0.69 V, fill factor of 60.5%, and efficiency of 6.05%, compared to 4.23% for the DSSC with unincorporated TiO₂ thin film. The improved performance was attributed to increased dye adsorption, better crystallinity, and enhanced electron transport. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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10 pages, 2931 KB

Open AccessProceeding Paper

Dynamic Hand Gesture Recognition Using MediaPipe and Transformer

by Hsin-Hua Li and Chen-Chiung Hsieh

Eng. Proc. 2025, 108(1), 22; https://doi.org/10.3390/engproc2025108022 - 3 Sep 2025

Viewed by 1354

Abstract

We developed a low-cost, high-performance gesture recognition system with a dynamic hand gesture recognition technique based on the Transformer model combined with MediaPipe. The technique accurately extracts hand gesture key points. The system was designed with eight primary gestures: swipe up, swipe down, [...] Read more.

We developed a low-cost, high-performance gesture recognition system with a dynamic hand gesture recognition technique based on the Transformer model combined with MediaPipe. The technique accurately extracts hand gesture key points. The system was designed with eight primary gestures: swipe up, swipe down, swipe left, swipe right, thumbs up, OK, click, and enlarge. These gestures serve as alternatives to mouse and keyboard operations, simplifying human–computer interaction interfaces to meet the needs of media system control and presentation switching. The experiment results demonstrated that training deep learning models using the Transformer achieved over 99% accuracy, effectively enhancing recognition performance. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 1498 KB

Open AccessProceeding Paper

AI and Big Data for Assessing Carbon Emission in Tourism Areas: A Pilot Study in Phuket City

by Pawita Boonrat, Voravika Wattanasoontorn, Kanruthay Ruktaengam, Konthee Boonmeeprakob and Napatsakorn Roswhan

Eng. Proc. 2025, 108(1), 23; https://doi.org/10.3390/engproc2025108023 - 1 Sep 2025

Viewed by 354

Abstract

Artificial intelligence (AI) and big data technology were applied to assess carbon emissions in a high-tourism area in this study. In the study site, the Thalang Road in Phuket Old Town, Thailand, visitors and vehicles (including cars, motorcycles, trucks, vans, and Tuktuks) were [...] Read more.

Artificial intelligence (AI) and big data technology were applied to assess carbon emissions in a high-tourism area in this study. In the study site, the Thalang Road in Phuket Old Town, Thailand, visitors and vehicles (including cars, motorcycles, trucks, vans, and Tuktuks) were counted using closed-circuit television (CCTV) footage and classified via the real-time detection transformer (RT-DETR) algorithm. The data were combined with records of electricity usage. From March to October 2024, 20,000 visitors per month visited the site. Electricity was the main source of carbon emissions, averaging 88 ± 11 tCO₂-eq monthly. Transport accounted for 500 ± 14 kg CO₂-eq. The average emission per visitor was calculated as 4.2 ± 0.4 kg CO₂-eq. The results showed how sustainable tourism policies and urban planning strategies need to be developed in Phuket. Based on the results, indirect emissions from the site need to be estimated. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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10 pages, 1818 KB

Open AccessProceeding Paper

Challenges and Optimization of Message Queuing Telemetry Transport-Resource Discovery Operation

by An-Tong Shih, Hung-Yu Chien and Yuh-Ming Huang

Eng. Proc. 2025, 108(1), 24; https://doi.org/10.3390/engproc2025108024 - 2 Sep 2025

Viewed by 292

Abstract

With the rapid development of the Internet of Things (IoT) applications, the ability to automatically discover and retrieve resource information has become increasingly enhanced. Despite being one of the most commonly used IoT communication protocols, Message Queuing Telemetry Transport (MQTT) does not natively [...] Read more.

With the rapid development of the Internet of Things (IoT) applications, the ability to automatically discover and retrieve resource information has become increasingly enhanced. Despite being one of the most commonly used IoT communication protocols, Message Queuing Telemetry Transport (MQTT) does not natively support resource discovery. To address this limitation, MQTT-resource discovery (MQTT-RD), a resource discovery mechanism based on MQTT, has been used for resource management. In this study, we tested and evaluated MQTT-RD using the Sniffer system that manages the resource directory and synchronizes data via MQTT. When too many Sniffers are activated, the MQTT-RD system becomes unsustainable. However, the experimental results in this study revealed that frequent updates to the resource directory (RD) and high-frequency heartbeat messages (pingalive) significantly increase network traffic and system load. In this study, we identified performance and stability issues to propose improvement strategies, including refining the topic design, reducing message transmission frequency, and improving the synchronization mechanism. Additionally, the feasibility of incorporating centralized management was explored to enhance system efficiency. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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6 pages, 793 KB

Open AccessProceeding Paper

Hands-On Training Framework for Prompt Injection Exploits in Large Language Models

by Sin-Wun Chen, Kuan-Lin Chen, Jung-Shian Li and I-Hsien Liu

Eng. Proc. 2025, 108(1), 25; https://doi.org/10.3390/engproc2025108025 - 3 Sep 2025

Viewed by 1369

Abstract

With the increasing deployment of large language models (LLMs) in diverse applications, security vulnerability attacks pose significant risks, such as prompt injection. Despite growing awareness, structured, hands-on educational platforms for systematically studying these threats are lacking. In this study, we present an interactive [...] Read more.

With the increasing deployment of large language models (LLMs) in diverse applications, security vulnerability attacks pose significant risks, such as prompt injection. Despite growing awareness, structured, hands-on educational platforms for systematically studying these threats are lacking. In this study, we present an interactive training framework designed to teach, assess, and mitigate prompt injection attacks through a structured, challenge-based approach. The platform provides progressively complex scenarios that allow users to exploit and analyze LLM vulnerabilities using both rule-based adversarial testing and Open Worldwide Application Security Project-inspired methodologies, specifically focusing on the LLM01:2025 prompt injection risk. By integrating attack simulations and guided defensive mechanisms, this platform equips security professionals, artificial intelligence researchers, and educators to understand, detect, and prevent adversarial prompt manipulations. The platform highlights the effectiveness of experiential learning in AI security, emphasizing the need for robust defenses against evolving LLM threats. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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10 pages, 1058 KB

Open AccessProceeding Paper

Risk Factors in Males and Females for Disease Classification Based on International Classification of Diseases, 10th Revision Codes

by Pichit Boonkrong, Subij Shakya, Junwei Yang and Teerawat Simmachan

Eng. Proc. 2025, 108(1), 26; https://doi.org/10.3390/engproc2025108026 - 3 Sep 2025

Viewed by 567

Abstract

We developed a machine learning model for disease classification based on the International Classification of Diseases, 10th Revision (ICD-10) codes, analyzing male and female groups using seven features. The three most prevalent ICD-10 classes covered over 98% of the data. Features were selected [...] Read more.

We developed a machine learning model for disease classification based on the International Classification of Diseases, 10th Revision (ICD-10) codes, analyzing male and female groups using seven features. The three most prevalent ICD-10 classes covered over 98% of the data. Features were selected using the least absolute shrinkage and selection operator, ridge, and elastic net, followed by the mean decrease in accuracy and impurity. A random forest classifier with five-fold cross-validation showed improved performance with more features. Using Shapley additive explanations, age, BMI, respiratory rate, and body temperature were identified as key predictors, with gender-specific variations. Integrating gender-specific insights into predictive modeling supports personalized medicine and enhances early diagnosis and healthcare resource allocation. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 571 KB

Open AccessProceeding Paper

Key Drivers of Environmental, Social, and Governance Practices in Taiwan’s Manufacturing Industry: Digital Supply Chain by Hybrid Delphi Technique and Analytical Hierarchy Process

by Hsueh-Lin Chang, Riana Magdalena Silitonga, Yung-Tsan Jou, Ronald Sukwadi, Stefani Prima Dias Kristiana and Agustinus Silalahi

Eng. Proc. 2025, 108(1), 27; https://doi.org/10.3390/engproc2025108027 - 3 Sep 2025

Viewed by 1012

Abstract

Environmental, social, and governance (ESG) has become a concern for companies, investors, and regulators. Its significance cannot be underestimated, as stakeholders increasingly demand accountability and transparency regarding corporate practices in these areas. Government agencies enforce laws mandating companies adhere to established ESG standards [...] Read more.

Environmental, social, and governance (ESG) has become a concern for companies, investors, and regulators. Its significance cannot be underestimated, as stakeholders increasingly demand accountability and transparency regarding corporate practices in these areas. Government agencies enforce laws mandating companies adhere to established ESG standards in response. However, despite these regulatory pressures, several obstacles have hindered organizations from effectively implementing sustainability initiatives, often resulting in lackluster outcomes. In this study, we developed a framework to implement ESG principles across various companies, utilizing the critical success factor (CSF) theory. By incorporating the perspectives of stakeholders, we identified the essential elements to achieve ESG. The developed framework in ESG studies employed the hybrid Delphi technique and the analytical hierarchy process (AHP), a structured method for organizing and analyzing complex decisions. Based on the results obtained from targeted questions, variables that influence ESG performance were identified. The effectiveness of different sustainability initiatives was also assessed to understand stakeholder engagement strategies and evaluate the impact of organizational culture on ESG adoption. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 1959 KB

Open AccessProceeding Paper

Big Data Analytics for Construction Progress Management

by Hsu-Hua Lee and Chi-Feng Chen

Eng. Proc. 2025, 108(1), 28; https://doi.org/10.3390/engproc2025108028 - 3 Sep 2025

Viewed by 174

Abstract

Construction projects face growing public environmental awareness, a shortage of skilled workers, and increasingly stringent legal regulations. Although construction technologies are advancing, construction progress management remains context-dependent, involving a complex construction process with numerous variables and uncertainties. Therefore, an effective management model is [...] Read more.

Construction projects face growing public environmental awareness, a shortage of skilled workers, and increasingly stringent legal regulations. Although construction technologies are advancing, construction progress management remains context-dependent, involving a complex construction process with numerous variables and uncertainties. Therefore, an effective management model is necessary to control construction progress. By applying statistical methods, we enhanced the efficiency and accuracy of construction progress management. By utilizing artificial intelligence and big data analytics, we established a management program that empowers construction management teams to predict construction progress conveniently, manage human resources effectively, identify risk management issues, and control costs. The program improves the success rate of on-time project completion and the efficient use of resources. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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8 pages, 1497 KB

Open AccessProceeding Paper

Parameter-Based Finite Element Modeling of Functionally Graded Rotating Disks Subject to Thermal Loadings

by Chieh-Jung Lu and Wen-Feng Lin

Eng. Proc. 2025, 108(1), 29; https://doi.org/10.3390/engproc2025108029 - 1 Sep 2025

Viewed by 136

Abstract

A parametric-based finite element model was developed to analyze the behavior of rotating disks made from functionally graded materials (FGMs) subjected to thermal loads. The model enabled the rapid determination of critical speeds to prevent slip and plastic deformation, while also enabling the [...] Read more.

A parametric-based finite element model was developed to analyze the behavior of rotating disks made from functionally graded materials (FGMs) subjected to thermal loads. The model enabled the rapid determination of critical speeds to prevent slip and plastic deformation, while also enabling the analysis of disk performance under varying operational conditions, such as different rotational speeds, disk diameters, and material gradients. The model is a highly efficient tool for design engineers to assess stress and deformation in rotating disks and facilitates the optimization of FGM parameters, offering valuable support to FGM designers. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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4 pages, 429 KB

Open AccessProceeding Paper

Optical-Flow-Based Algorithm of Depth Estimation with Model-Free Control Policy on Autonomous Nano Quadcopters for Obstacle Avoidance

by Jia-Jun Lai, Sheng-Qian Li, Fang-Kai Hsiao, Jheng-Lin Lin, Jhin-Hao Lai, Chen-Fu Yeh, Chung-Chuan Lo and Ya-Tang Yang

Eng. Proc. 2025, 108(1), 30; https://doi.org/10.3390/engproc2025108030 - 4 Sep 2025

Viewed by 487

Abstract

Nano quadcopters are small, agile, and cost-effective Internet of Things platforms, especially appropriate for narrow and cluttered environments. We developed a model-free control policy combined with FlowDep, an efficient optical flow depth estimation algorithm that computes object depth information using vision. FlowDep was [...] Read more.

Nano quadcopters are small, agile, and cost-effective Internet of Things platforms, especially appropriate for narrow and cluttered environments. We developed a model-free control policy combined with FlowDep, an efficient optical flow depth estimation algorithm that computes object depth information using vision. FlowDep was successfully deployed on the Bitcraze Crazyflie 2.1 (with weight ~34 g) using its monocular camera for obstacle avoidance. FlowDep calculated depth information from images and use multizone scheme for control policy. Successful obstacle avoidance is demonstrated. The developed policy showed its potential for future applications in complex environment exploration to enhance the autonomous flight and perception abilities of drones. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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11 pages, 4573 KB

Open AccessProceeding Paper

Markov Modelling and Cluster-Based Analysis of Transport Layer Traffic Using Quick User Datagram Protocol Internet Connections

by Zoltan Gal, Marcell B. Gal and Gyorgy Terdik

Eng. Proc. 2025, 108(1), 31; https://doi.org/10.3390/engproc2025108031 - 5 Sep 2025

Viewed by 5019

Abstract

Quick User Datagram Protocol Internet Connection (QUIC) is a modern transport protocol leveraging the User Datagram Protocol (UDP) to improve latency, security, and mobility. In this study, we analyzed QUIC traffic by uploading a 10 MB file under varied maximum transmission unit (MTU), [...] Read more.

Quick User Datagram Protocol Internet Connection (QUIC) is a modern transport protocol leveraging the User Datagram Protocol (UDP) to improve latency, security, and mobility. In this study, we analyzed QUIC traffic by uploading a 10 MB file under varied maximum transmission unit (MTU), bandwidth, and segment size conditions. Interarrival times (IAT) at both client and server were captured and analyzed using ordering points to identify the clustering structure (OPTICS) clustering and Markov modelling. Transition matrices and eigenvalue spectra revealed steady states, convergence behavior, and spectral gaps. The results showed that parameter variations significantly affected the traffic state diversity and flow dynamics, optimizing QUIC performance in real-world deployments. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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12 pages, 1813 KB

Open AccessProceeding Paper

An Efficient Approach for Mining High Average-Utility Itemsets in Incremental Database

by Ye-In Chang, Chen-Chang Wu and Hsiang-En Kuo

Eng. Proc. 2025, 108(1), 32; https://doi.org/10.3390/engproc2025108032 - 5 Sep 2025

Viewed by 4589

Abstract

Traditional high-utility itemset (HUI) mining methods tend to overestimate utility for long itemsets, leading to biased results. High average-utility itemset (HAUI) mining addresses this problem by normalizing utility with itemset length. However, uniform utility thresholds fail to account for varying item importance. Recently, [...] Read more.

Traditional high-utility itemset (HUI) mining methods tend to overestimate utility for long itemsets, leading to biased results. High average-utility itemset (HAUI) mining addresses this problem by normalizing utility with itemset length. However, uniform utility thresholds fail to account for varying item importance. Recently, HAUI mining with multiple minimum utility thresholds (MMU) has been used for flexible utility evaluation. While the generalized HAUIM (GHAUIM) algorithm performs well, it requires two database scans and is limited to static datasets. Therefore, we developed a novel tree-based method that scans the database only once to improve efficiency by reducing storage and eliminating costly join operations. Additionally, pruning strategies and incremental updates were introduced to enhance scalability. The developed method outperformed GHAIM in efficiency. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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10 pages, 7119 KB

Open AccessProceeding Paper

Identification and Optimization of Components of University Campus Space

by Yue Sun and Yifei Ouyang

Eng. Proc. 2025, 108(1), 33; https://doi.org/10.3390/engproc2025108033 - 5 Sep 2025

Viewed by 163

Abstract

Amid expanding higher education and enhancing spatial quality, modern university campuses face challenges including inefficient space utilization and a disconnect from human-centered design. We developed a coupled model that integrates the analytic hierarchy process (AHP) with space syntax theory to identify and address [...] Read more.

Amid expanding higher education and enhancing spatial quality, modern university campuses face challenges including inefficient space utilization and a disconnect from human-centered design. We developed a coupled model that integrates the analytic hierarchy process (AHP) with space syntax theory to identify and address functional fragmentation, limited accessibility, and diminished spatial vitality. The Delphi method was employed to determine weights on visual and traffic influence factors. Through spatial quantitative analysis using Depthmap software, we estimated spatial-efficiency discrepancies across 11 component types, including school gates, teaching buildings, and libraries. A case study was conducted at a university located in the hilly terrain of Conghua District, Guangzhou, China which revealed significant contradictions between subjective evaluations and objective data at components, such as the administrative building and gymnasium. These contradictions led to poor visual permeability, excessive path redundancy, and imbalanced functional layouts. Based on the results of this study, targeted optimization strategies were proposed, including permeable interface designs, path network reconfiguration, and the implementation of dynamic functional modules. These interventions were tailored to accommodate the humid subtropical climate, balancing shading, ventilation, and visual transparency. In this study, methodological support for the renovation of existing campus infrastructure was provided as theoretical and technical references for space renewal in tropical and subtropical academic environments and the enhancement of the quality and resilience of campus spaces. The results also broadened the application of interdisciplinary methods in university planning. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 1397 KB

Open AccessProceeding Paper

Event-Triggered Robust Fuzzy Controller Design for a Quadcopter Under Network Bandwidth Constraints

by Ti-Hung Chen

Eng. Proc. 2025, 108(1), 34; https://doi.org/10.3390/engproc2025108034 - 8 Sep 2025

Viewed by 157

Abstract

Quadcopter drones have been extensively researched due to their flexibility and suitability for diverse tasks. In this study, a control strategy tailored for scenarios with restricted network bandwidth is developed. An event-triggered control approach was used to minimize network bandwidth load. Also, a [...] Read more.

Quadcopter drones have been extensively researched due to their flexibility and suitability for diverse tasks. In this study, a control strategy tailored for scenarios with restricted network bandwidth is developed. An event-triggered control approach was used to minimize network bandwidth load. Also, a robust fuzzy controller was integrated to enhance the system’s resilience and efficiency. The simulation results confirmed that the developed control strategy fosters stable performance, even under constrained network conditions. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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9 pages, 1622 KB

Open AccessProceeding Paper

Development of Artificial Intelligence-Based Product Size Detection System

by Ari Aharari and Shuntaro Tanaka

Eng. Proc. 2025, 108(1), 35; https://doi.org/10.3390/engproc2025108035 - 8 Sep 2025

Viewed by 1540

Abstract

We developed an artificial intelligence (AI)-based size detection system by combining image recognition and weight analysis to address inconsistent sizing in manually processed sashimi products. Using a custom imaging setup and YOLOv11_obb, the system detects sashimi pieces and accounts for orientation variations. It [...] Read more.

We developed an artificial intelligence (AI)-based size detection system by combining image recognition and weight analysis to address inconsistent sizing in manually processed sashimi products. Using a custom imaging setup and YOLOv11_obb, the system detects sashimi pieces and accounts for orientation variations. It measures their size based on their area and weight, ensuring compliance with quality standards. This system reduces human error, identifies out-of-spec products at an early stage, and prevents defective shipments. The developed system demonstrated high detection accuracy, although its classification precision needs to be enhanced. The system is a promising tool for enhancing efficiency and quality control in seafood processing environments. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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9 pages, 2933 KB

Open AccessProceeding Paper

Synergic Impact of Reinforcement Learning and Swarm Intelligence in Wireless Sensor Network Services

by Levente Filep and Zoltán Gál

Eng. Proc. 2025, 108(1), 36; https://doi.org/10.3390/engproc2025108036 - 8 Sep 2025

Viewed by 130

Abstract

Wireless sensor networks (WSNs) consist of distributed sensor nodes deployed for real-time monitoring and data collection. Optimizing sensor energy consumption is critical for extending the overall network lifespan. In large-scale WSNs, clustering techniques are required to reduce energy consumption. Many effective clustering methods [...] Read more.

Wireless sensor networks (WSNs) consist of distributed sensor nodes deployed for real-time monitoring and data collection. Optimizing sensor energy consumption is critical for extending the overall network lifespan. In large-scale WSNs, clustering techniques are required to reduce energy consumption. Many effective clustering methods have been proposed, but finding the optimal number of clusters in an energy-efficient manner remains challenging. Swarm intelligence (SI) algorithms help solve this problem, but testing all possible cluster configurations is computationally expensive. Neural networks excel in identifying hidden patterns in data, making them a promising tool for this task. However, training an AI agent to accurately predict both the number of cluster heads (CHs) and their locations is difficult. In this study, we developed a synergic method by employing a reinforcement learning (RL) model to predict the number of CHs while utilizing an SI algorithm to identify the most appropriate nodes to become CHs. This approach minimizes transmission energy and prolongs the lifespan of WSNs and their services. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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6 pages, 788 KB

Open AccessProceeding Paper

Deadlock Prevention Policy for Flexible Manufacturing Systems: Petri Net-Based Approach Utilizing Iterative Synthesis and Places Invariant

by Shih-Chih Lee, Jui-Fu Cheng and Ter-Chan Row

Eng. Proc. 2025, 108(1), 37; https://doi.org/10.3390/engproc2025108037 - 8 Sep 2025

Viewed by 1466

Abstract

An iterative method was developed in this study within a Petri net system (PNS) for flexible manufacturing systems (FMSs) to eliminate deadlocks. The algorithm employs variant tokens, ranging from a few to many, in idle places to identify subnet deadlocks through reachability states. [...] Read more.

An iterative method was developed in this study within a Petri net system (PNS) for flexible manufacturing systems (FMSs) to eliminate deadlocks. The algorithm employs variant tokens, ranging from a few to many, in idle places to identify subnet deadlocks through reachability states. Additionally, it utilizes the place invariant (PI) to resolve deadlocks. An algorithm with a simple example was developed and applied using a complex scenario. The developed algorithm is user-friendly and effectively eliminates deadlocks in the PNS of FMSs, producing optimal results. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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8 pages, 654 KB

Open AccessProceeding Paper

Rapid Temperature Annealing Effect on Bipolar Switching and Electrical Properties of SiC Thin Film-Resistant Random-Access Memory Devices

by Kai-Huang Chen, Ming-Cheng Kao, Yao-Chin Wang, Hsin-Chin Chen and Chin-Chueh Huang Kao

Eng. Proc. 2025, 108(1), 38; https://doi.org/10.3390/engproc2025108038 - 8 Sep 2025

Viewed by 532

Abstract

In this study, silicon carbide (SiC) thin films for resistive random-access memory (RRAM) devices were successfully prepared using the radio-frequency magnetron sputtering method at deposition powers of 50 and 75 W for 1 h. The aluminum (Al) top electrode of the RRAM devices [...] Read more.

In this study, silicon carbide (SiC) thin films for resistive random-access memory (RRAM) devices were successfully prepared using the radio-frequency magnetron sputtering method at deposition powers of 50 and 75 W for 1 h. The aluminum (Al) top electrode of the RRAM devices was also fabricated using thermal evaporator deposition. Additionally, the electrical properties of the SiC thin film RRAM devices were determined using a B2902A mechanism. The current–voltage (I–V) curves of the as-deposited SiC thin films at 50 and 75 W power levels were measured and analyzed. Specifically, the set and reset voltages for the RRAM devices deposited at 50 and 75 W were approximately 1.2 and −1.5 V, respectively. For the annealed samples, the memory windows of the 75 W SiC thin film RRAM devices treated at 300 °C were found to be around 10⁵. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 872 KB

Open AccessProceeding Paper

Smart Cushion System Based on Machine Learning and Pressure Sensing

by Mei-Chen Lee and Ching-Fen Jiang

Eng. Proc. 2025, 108(1), 39; https://doi.org/10.3390/engproc2025108039 - 8 Sep 2025

Viewed by 1468

Abstract

Prolonged poor sitting posture increases the risk of musculoskeletal disorders and chronic diseases. We developed a smart cushion system that integrated pressure sensing and machine learning for posture recognition. Nine FSR406 sensors were used to measure pressure distribution on the system. A calibration [...] Read more.

Prolonged poor sitting posture increases the risk of musculoskeletal disorders and chronic diseases. We developed a smart cushion system that integrated pressure sensing and machine learning for posture recognition. Nine FSR406 sensors were used to measure pressure distribution on the system. A calibration and normalization process improves data consistency, and a heatmap visualizes the result. Among the five machine learning models evaluated, the narrow neural network achieved the best performance, with a validation accuracy of 97.63% and a test accuracy of 91.73%. When body mass index (BMI) was included as an additional input feature, the test accuracy improved to 95.49%, indicating that BMI positively impacts recognition performance. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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11 pages, 1012 KB

Open AccessProceeding Paper

Design and Implementation of Wireless Detection Network for Bridge Inspection

by Zhensong Ni, Shuri Cai, Cairong Ni, Baojia Lin and Liyao Li

Eng. Proc. 2025, 108(1), 40; https://doi.org/10.3390/engproc2025108040 - 9 Sep 2025

Viewed by 380

Abstract

The construction of a wireless detection network for bridge inspection is important in intelligent infrastructure management. Advanced wireless communication technology and a sensor network enable the real-time remote and accurate monitoring of bridge structure health. We designed a protocol and implemented it in [...] Read more.

The construction of a wireless detection network for bridge inspection is important in intelligent infrastructure management. Advanced wireless communication technology and a sensor network enable the real-time remote and accurate monitoring of bridge structure health. We designed a protocol and implemented it in a wireless detection network to overcome the limitations of traditional bridge health monitoring methods. The network improves the efficiency and accuracy of monitoring and ensures safe bridge maintenance. We analyzed the requirements of bridge monitoring, including the strict requirements for high-precision data acquisition, low delay transmission, energy efficiency and network reliability. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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12 pages, 1668 KB

Open AccessProceeding Paper

Artificial Intelligence Model for Predicting Power Consumption in Semiconductor Coating Process

by Jung-Hsing Wang, Chun-Wei Chen and Chen-Yu Lin

Eng. Proc. 2025, 108(1), 41; https://doi.org/10.3390/engproc2025108041 - 5 Sep 2025

Viewed by 139

Abstract

We developed an artificial intelligence (AI) model to optimize the time efficiency, yield, and energy efficiency of the semiconductor coating process. A random forest-based model was developed for rapid modeling and analysis of the semiconductor coating process, thus allowing designers and operation managers [...] Read more.

We developed an artificial intelligence (AI) model to optimize the time efficiency, yield, and energy efficiency of the semiconductor coating process. A random forest-based model was developed for rapid modeling and analysis of the semiconductor coating process, thus allowing designers and operation managers to conduct an efficient and effective process. The developed AI model offers an objective and accurate basis for decision-making, thereby ensuring that each unit is operated energy-efficiently, stably, and reliably in the minimized operation time. The developed model assists Taiwan’s semiconductor industry in transitioning from engineer experience to data-driven approaches, thus accelerating the technological optimization of semiconductor factories and adding value to customers. This model considerably reduces the material, energy, resource, time, labor, and costs of thin film deposition. The model allows the semiconductor industry of Taiwan to consolidate its competitive advantage by achieving net-zero carbon emissions and sustainability. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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8 pages, 1746 KB

Open AccessProceeding Paper

Graphic Quick-Response Codes Without Finder Patterns

by Chih-Ting Hsu and Hsi-Chun Wang

Eng. Proc. 2025, 108(1), 42; https://doi.org/10.3390/engproc2025108042 - 11 Sep 2025

Viewed by 228

Abstract

Quick-response (QR) codes are widely used for information transmission. However, their black-and-white dot structure often lacks visual appeal. Therefore, we developed novel graphic QR codes that remove traditional corner finder patterns and conceal data within redesigned modules. These codes can be scanned, positioned, [...] Read more.

Quick-response (QR) codes are widely used for information transmission. However, their black-and-white dot structure often lacks visual appeal. Therefore, we developed novel graphic QR codes that remove traditional corner finder patterns and conceal data within redesigned modules. These codes can be scanned, positioned, and decoded directly in software, while paper-based versions can be instantly decoded by overlaying a transparency with printed finder patterns. Experimental results across varying grayscale levels and devices confirmed reliable readability. The proposed QR codes provide enhanced esthetics and security, offering a new direction for future QR code applications. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 726 KB

Open AccessProceeding Paper

Enhancing Fault Detection in Industry 4.0 by Introducing a Power and Fault Behavior Monitoring Tool for Programmable Logic Controllers with Validation Through a Virtual Manufacturing System

by Kuan-Chun Huang, Tzu-Hsuan Chuang, Mathieu Bodin, Wei-Nung Huang and Hsiao-Tse Lin

Eng. Proc. 2025, 108(1), 43; https://doi.org/10.3390/engproc2025108043 - 11 Sep 2025

Viewed by 185

Abstract

As manufacturing technology advances, the shift toward smart solutions makes programmable logic controllers (PLCs) essential due to their reliability and scalability. Operational failures cause major disruptions if not detected early. Therefore, we developed an improved fault and behavior monitoring tool for programmable logic [...] Read more.

As manufacturing technology advances, the shift toward smart solutions makes programmable logic controllers (PLCs) essential due to their reliability and scalability. Operational failures cause major disruptions if not detected early. Therefore, we developed an improved fault and behavior monitoring tool for programmable logic controllers (IFBMTP) to detect Type I and II errors using Boolean and analog signals. The tool addresses the problems caused by power load variations and complex power signals. The developed PFBMTP enables accurate power signal analysis and fault detection. We simulated different systems to model various fault scenarios, enabling early-stage detection through current and voltage monitoring. This approach overcame the limitations of physical hardware testing, allowing efficient, repeated validation across dynamic manufacturing environments. By integrating multiple technologies on a virtual platform, PFBMTP enhanced diagnostic accuracy, saved costs, and ensured process reliability in deployment. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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10 pages, 2783 KB

Open AccessProceeding Paper

Design and Implementation of Controller Area Network-Based Monitoring and Control System with Arduino UNO and Logic Analyzer

by Ching-Hsu Chan, Fuh-Liang Wen and Sheng-Jen Wen

Eng. Proc. 2025, 108(1), 44; https://doi.org/10.3390/engproc2025108044 - 12 Sep 2025

Viewed by 216

Abstract

We developed and evaluated a monitoring and control system based on the controller area network (CAN) bus with a microprocessor of Arduino UNOs and a logic analyzer as auxiliary tools. We implemented a CAN bus communication system using Arduino UNO to control servo [...] Read more.

We developed and evaluated a monitoring and control system based on the controller area network (CAN) bus with a microprocessor of Arduino UNOs and a logic analyzer as auxiliary tools. We implemented a CAN bus communication system using Arduino UNO to control servo movements and collected data from ultrasonic sensors, infrared (IR) sensors, or DHT11 sensors that measure temperature and humidity. The CAN node received the data to control the servo motor and to display the information on the liquid crystal display. While the IR sensor detects an object, the ultrasonic measurement is stopped, and the servo is set to the home position at 0°. The CAN bus communication operated effectively, enabling real-time control of the servo motor following the command from sensor data. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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9 pages, 586 KB

Open AccessProceeding Paper

An Efficient Algorithm for Mining Top-k High-On-Shelf-Utility Itemsets with Positive/Negative Profits of Local/Global Minimum Count

by Ye-In Chang, Po-Chun Chuang, Yu-Hao Liao, Po-Yu Hu and Ting-Wei Chen

Eng. Proc. 2025, 108(1), 45; https://doi.org/10.3390/engproc2025108045 - 16 Sep 2025

Viewed by 167

Abstract

High-utility itemset mining (HUIM) utilizes the threshold value to extract HUI from the transactional database. However, it is difficult to define an optimal threshold value, since it depends on the domain knowledge of the application. Therefore, top-k HUIM is used to solve the [...] Read more.

High-utility itemset mining (HUIM) utilizes the threshold value to extract HUI from the transactional database. However, it is difficult to define an optimal threshold value, since it depends on the domain knowledge of the application. Therefore, top-k HUIM is used to solve the problem of setting a threshold. A user can define a k value, which represents the number of HUIs. Moreover, there exist itemsets occurring at a specific time interval, which can become HUI. Since the traditional HUIM algorithm does not consider the transaction with the time interval, the HUIM algorithm cannot be used directly. Therefore, high-on-shelf-utility itemset mining (HOUIM) is used to address the above problem in this study. The proportion of the utility value of the item in all of the time intervals with the itemset is used for determining whether the itemset is HOUI or not. In the top-k HOUIM, the KOSHU algorithm is used based on the data structure, ignoring the item with the negative profit in overestimating the utility of the itemset. The KOSHU algorithm needs less processing time. However, the KOSHU algorithm has to scan the database twice and sort the database once. Therefore, we developed an efficient algorithm based on the TIPN table to mine top-k HOUIs. The developed data structures include TIPN and MINC tables, IO Bitmap, and TIUL. In the TIPN table, we recorded positive items, positive utilities, negative items, and negative counts. The MINC table is used for storing the local/global counts of all of the items with negative profits. In the algorithm, we scanned the database only once. The developed algorithm is more efficient than the KOSHU algorithm. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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15 pages, 6691 KB

Open AccessProceeding Paper

Smart Customizable Spinning System

by Wei-Chuan Lin, Yu-Wen Hsu and Wan-Lin Yu

Eng. Proc. 2025, 108(1), 46; https://doi.org/10.3390/engproc2025108046 - 12 Sep 2025

Viewed by 96

Abstract

As global obesity rates rise, cardiovascular diseases increase, and stress-related issues become more severe. This increases the public awareness of health and exercise. However, existing spinning fitness equipment lacks personalized customization for individual needs. To address this, we developed a smart customizable spinning [...] Read more.

As global obesity rates rise, cardiovascular diseases increase, and stress-related issues become more severe. This increases the public awareness of health and exercise. However, existing spinning fitness equipment lacks personalized customization for individual needs. To address this, we developed a smart customizable spinning system that enables health monitoring, central computation, flywheel, voice interaction, notification, and query subsystems. Users can set fitness goals based on their personal needs, monitor workout data via sensors, and utilize voice interaction and control to track their exercise status in real time. The system notifies users of workout progress through a buzzer and message queuing telemetry transport, while the Web interface provides access to past workouts and health records. Additionally, the system supports bilingual functionality (Chinese and English), allowing users to operate it in their preferred language, enhancing global usability. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 1072 KB

Open AccessProceeding Paper

Selective Intersection Flow: A Lightweight Optical Flow Algorithm for Micro Drones

by Che Liu, Chen-Fu Yeh and Chung-Chuan Lo

Eng. Proc. 2025, 108(1), 47; https://doi.org/10.3390/engproc2025108047 - 22 Sep 2025

Abstract

In this study, selective intersection flow (SIF), a lightweight optical flow algorithm, was used to enhance efficiency and accuracy by filtering out non-contributive pixels. SIF, derived from the differential category of algorithms, is used to compute optical flow by analyzing intersections of equations [...] Read more.

In this study, selective intersection flow (SIF), a lightweight optical flow algorithm, was used to enhance efficiency and accuracy by filtering out non-contributive pixels. SIF, derived from the differential category of algorithms, is used to compute optical flow by analyzing intersections of equations from selected pixels rather than solving for all pixels. It replaces warping with a minimal computational procedure for initial flow estimate and employs a sliding window for optimized single-core performance. SIF runs 1.7–1.8 times faster and achieves 1.2–1.4 higher accuracy than the single iteration of the Lucas–Kanade method, showing promise for real-time micro drone navigation. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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8 pages, 2234 KB

Open AccessProceeding Paper

Thermal Impacts of Solar Panels on Environment Surrounded by Medium- and High-Rise Buildings

by Ying-Ming Su and Po-Chun Yang

Eng. Proc. 2025, 108(1), 48; https://doi.org/10.3390/engproc2025108048 - 22 Sep 2025

Abstract

By employing computational fluid dynamics (CFD) software (Ansys Fluent), we examined the thermal effects of vertical solar panels on the south-facing facades of mid-rise (50 m) and high-rise (100 m) buildings in a 5 × 5 idealized urban environment. Four configurations were analyzed [...] Read more.

By employing computational fluid dynamics (CFD) software (Ansys Fluent), we examined the thermal effects of vertical solar panels on the south-facing facades of mid-rise (50 m) and high-rise (100 m) buildings in a 5 × 5 idealized urban environment. Four configurations were analyzed in this study: no installation, evenly distributed, concentrated, and panels on both sides. Vertical solar panel installations were affected by building temperatures and pedestrian-level thermal environments. In the leeward streets of central buildings, pedestrian-level temperatures progressively increased by row, with the even distribution showing significant heat accumulation. For 100 m buildings, the average temperature under the even distribution configuration increased from 35.6 °C (no panels) to 39.5 °C (a difference of 3.9 °C, approximately 10.96%). Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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8 pages, 765 KB

Open AccessProceeding Paper

Integrating Internet with Long-Term Care Management Policy with the Internet

by Chi-Shiuan Lee, Ming-Hsun Yeh and Hai-Wu Lee

Eng. Proc. 2025, 108(1), 49; https://doi.org/10.3390/engproc2025108049 - 23 Sep 2025

Abstract

With the advancement of medical care technology, the aging population has become a serious problem, and long-term care for the elderly is a major concern facing today’s society. Long-term care institutions take care of people with dysfunction or difficulties and provide them with [...] Read more.

With the advancement of medical care technology, the aging population has become a serious problem, and long-term care for the elderly is a major concern facing today’s society. Long-term care institutions take care of people with dysfunction or difficulties and provide them with continuous assistance. However, the shortage of specialists and the relative increase in costs have affected the burden on families. Long-term care has developed from traditional approaches to advanced ones at well-equipped facilities. We combine network technology with long-term care service with sensors that have alarm functions according to diverse needs, so that the elderly can receive complete care. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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7 pages, 1431 KB

Open AccessProceeding Paper

Application of Vision Language Models in the Shoe Industry

by Hsin-Ming Tseng and Hsueh-Ting Chu

Eng. Proc. 2025, 108(1), 50; https://doi.org/10.3390/engproc2025108050 - 24 Sep 2025

Abstract

The confluence of computer vision and natural language processing has yielded powerful vision language models (VLMs) capable of multimodal understanding. We applied state-of-the-art VLMs for quality monitoring of the shoe assembly industry. By leveraging the ability of VLMs to jointly process visual and [...] Read more.

The confluence of computer vision and natural language processing has yielded powerful vision language models (VLMs) capable of multimodal understanding. We applied state-of-the-art VLMs for quality monitoring of the shoe assembly industry. By leveraging the ability of VLMs to jointly process visual and textual data, we developed a system for automated defect detection and contextualized feedback generation to enhance the efficiency and consistency of quality assurance processes. We conducted an empirical evaluation by evaluating the effectiveness of the developed VLM system in identifying standard procedures for assembly, using the video data from a shoe assembly line. The experimental results validated the potential of the VLM system in detecting the quality of footwear assembly, highlighting the feasibility of future practical deployment in industrial quality control scenarios. Full article

(This article belongs to the Proceedings of 2025 IEEE 5th International Conference on Electronic Communications, Internet of Things and Big Data)

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