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ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy

Sustainable Computing: Informatics and Systems 2024年 41卷

作者： Masdari, Mohammad Band, Shahab S. Qasem, Sultan Noman Sayed, Biju Theruvil Pai, Hao-Ting Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Riyadh11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz6803 Yemen Department of Computer Science Dhofar University PO Box 2509 SalalahPCode 211 Oman Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research. © 2023 Elsevier Inc.

关键词： Authentication

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Constructing a Minimized Connected Dominating Set Based on the Decentralized Nomination Algorithm in Mobile Ad Hoc Networks

SSRN

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SSRN 2023年

作者： Hsieh, Hui-Ching Chiang, Mao-Lun Lin, Tzu-Ling Liao, Lu-Wen Chen, Ze-Yu Department of New Media Communication Hsing Wu University New Taipei City Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung City Taiwan Department of Business Management National Taichung University of Science and Technology Taichung404336 Taiwan Department of Intelligent Production Engineering National Taichung University of Science and Technology Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung City Taiwan

Inspired by the backbone architecture of wired networks, in this study, we applied the concept of a virtual backbone to the routing scheme in a mobile ad hoc network (MANET) to develop an effective routing method. Using connected dominating set (CDS)-based methods to construct a virtual backbone network can effectively improve routing performance in a MANET. The CDS virtual backbone network we developed in the present study can respond quickly and identify routing paths through these CDS members as the network topology changes. Meanwhile, the nodes that are not CDS members can enter the power-saving mode to minimize unnecessary power consumption. Wireless nodes in a MANET can move freely within the network;therefore, CDS virtual backbones are easy to deconstruct. Researchers have developed centralized and decentralized CDS algorithms to reconstruct network topologies. When previously developed centralized CDS algorithms are applied, the system requires the information of all nodes in the network to reconstruct the virtual backbone. Therefore, the information exchange costs of such algorithms before refactoring are high. By contrast, decentralized CDS algorithms can use local information to reconstruct virtual backbones quickly. Yet, decentralized algorithms produce larger CDSs than centralized algorithms. To improve upon the performance of previously developed decentralized algorithms, we developed and herein present a new decentralized CDS algorithm called APP-CDS that can quickly construct a virtual backbone with the smallest possible CDS for use in MANET environments. © 2023, The Authors. All rights reserved.

关键词： Routing algorithms

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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Perceived Risk and Reward Factors on Residential Solar Pv Adoption in the Philippines an Integration of Utaut, Tpb and Tam

SSRN

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SSRN 2024年

作者： Ragragio, Titus James Reynon Borres, Rianina D. Persada, Satria Fadil Young, Michael Nayat Ong, Ardvin Kester S. Prasetyo, Yogi Tri Nadlifatin, Reny Zulvia, Ferani Eva School of Industrial Engineering and Engineering Management Mapua University 658 Muralla St. Intramuros Manila1002 Philippines Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Malang65154 Indonesia International Bachelor Program in Engineering Yuan Ze University 135 Yuan-Tung Road Chung-Li32003 Taiwan Department of Industrial Engineering and Management Yuan Ze University 135 Yuan-Tung Road Chung-Li32003 Taiwan Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia National Taiwan University of Science and Technology No.43 Keelung Rd. Sec.4 Da'an Dist. Taipei City106335 Taiwan

This study delves into the perception of homeowners in the Philippines regarding the utilization of Residential Rooftop Solar Photovoltaic Systems (RSPVS), focusing on their intention to adopt this renewable energy technology. The research is driven by the advantageous conditions in the Philippines, including high irradiance levels, supportive solar PV policies, and decreasing system costs. Understanding prosumers' (producers and consumers) perspectives is crucial to accelerating the adoption of RSPVS, which can significantly contribute to decentralizing and greening the electrical production landscape in the country. Building on previous studies, which highlight a keen interest in solar technology among homeowners but cite high installation costs and a lack of understanding as major barriers, this research aims to shed light on the factors influencing prosumers' intentions to use RSPVS. Employing a theoretical framework combining the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), Unified Theory of Acceptance and Use of Technology (UTAUT), and integrating Technophilia and Perceived Risk as supplementary constructs, this study seeks to uncover the drivers and inhibitors of RSPVS adoption. The study employs an online questionnaire distributed in key cities where Residential RSPVS is available, with a sample size of 239 respondents. The data collected are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between variables and to test the hypothesized model. The results indicate that factors such as Technophilia, Price Value, and Social Influence are significant determinants of prosumers' intent to adopt RSPVS. Notably, Technophilia, reflecting enthusiasm for new technology, emerged as a key factor, suggesting that prosumers are motivated by the allure of solar energy. Additionally, the study found that Perceived Risk was not a significant inhibitor, indicating a high level of confidence and tr

关键词： Solar energy

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Deep convolutional neural network model for short-term electricity price forecasting

arXiv

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arXiv 2020年

作者： Cheng, Hsu-Yung Kuo, Ping-Huan Shen, Yamin Huang, Chiou-Jye Department of Computer Science and Information Engineering National Central University Computer and Intelligent Robot Program for Bachelor Degree National Pingtung University School of Electrical Engineering and Automation Jiangxi University of Science and Technology

In the modern power market, electricity trading is an extremely competitive industry. More accurate price forecast is crucial to help electricity producers and traders make better decisions. In this paper, a novel method of convolutional neural network (CNN) is proposed to rapidly provide hourly forecasting in the energy market. To improve prediction accuracy, we divide the annual electricity price data into four categories by seasons and conduct training and forecasting for each category respectively. By comparing the proposed method with other existing methods, we find that the proposed model has achieved outstanding results, the mean absolute percentage error (MAPE) and root mean square error (RMSE) for each category are about 5.5% and 3, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Power markets

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Develop intelligent waste bin prototype based on fusion feature recognition of sounds and RGB images

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Waste Management 2025年 204卷

作者： Wei-Chun Chen Chieh-Feng Chiang Yu-Hao Lin Hao-Jun Hsieh Bachelor Program in Industrial Technology National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan Department of Computer Science and Information Engineering Vanung University No.1 Wanneng Rd. Zhongli Dist. Taoyuan City 320676 Taiwan Department of Environmental Engineering National Chung Hsing University 145 Xingda Rd. Taichung 402 Taiwan Environmental Education and Sustainable Technology Development Center National Chung Hsing University 145 Xingda Rd. Taichung 402 Taiwan

Sorting municipal solid waste (MSW) at the source is a critical first step toward achieving a circular economy. Previous research has primarily focused on vision-based intelligent algorithms for MSW classification using red–green–blue (RGB) images. Some studies have also explored sound-based classification; however, only a few studies have employed multimodal deep learning methods (MDLMs) that integrate both image and sound data for MSW identification. To address this gap, the present study developed an innovative intelligent waste bin (IWB) prototype that utilizes MDLM and multimodal data fusion to enhance MSW classification. First, the sorting bin was constructed and an MSW dataset was constructed containing both audio signals and RGB images. The audio data were converted into Mel-frequency cepstral coefficients (MFCCs), and feature extraction from RGB images and MFCCs was performed using ResNet-101 and a Long Short-Term Memory network (LSTM) network, respectively. These features were then concatenated and fused within the MDLM model to classify MSW, which in turn controlled servomotors and rotatable boards in the IWB to automatically sort the waste. The results of this study clearly indicate that using sound modality alone is more effective for MSW identification than using RGB images. Based on fivefold cross-validation results, the MDLM model generally achieved higher average accuracy than the LSTM model, which used only sound features. However, the improvement was relatively modest, as the LSTM already achieved a high accuracy of approximately 90%. In the actual MSW sorting test using the IWB, the MDLM model also outperformed the LSTM, further confirming the advantages of multimodal fusion. Future research could explore the inclusion of a larger number of RGB images to develop more robust fusion features and further enhance the performance of the MDLM model.

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Left ventricular hypertrophy detection using electrocardiographic signal

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Scientific reports 2023年第1期13卷 2556页

作者： Cheng-Wei Liu Fu-Hsing Wu Yu-Lun Hu Ren-Hao Pan Chuen-Horng Lin Yung-Fu Chen Guo-Shiang Tseng Yung-Kuan Chan Ching-Lin Wang Division of Cardiology Department of Internal Medicine Tri-Service General Hospital Songshan Branch National Defense Medical Center Taipei Taiwan. Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. La Vida Tec. Co. Ltd. Taichung Taiwan. Preventive Medicine Center National Yang Ming Chiao Tung University Taipei Taiwan. Department of Information Management Tunghai University Taichung Taiwan. Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan. Department of Dental Technology and Materials Science Central Taiwan University of Science and Technology Taichung Taiwan. Division of Cardiology Department of Internal Medicine Taoyuan Armed Force General Hospital Hsinchu Branch Hsinchu Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. ykchan@nchu.edu.tw. Department of Information Management National Chin-Yi University of Technology Taichung Taiwan. clwang@ncut.edu.tw.

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

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Implementing exchanged hypercube communication patterns on ring-connected WDM optical networks

Implementing exchanged hypercube communication patterns on r...

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作者： Liu, Yu-Liang Wu, Ruey-Chyi Department of Computer Science and Information Engineering Aletheia University New Taipei City25103 Taiwan Bachelor Degree Program of Digital Marketing National Taipei University Taipei10478 Taiwan

The exchanged hypercube, denoted by EH(s, t), is a graph obtained by systematically removing edges from the corresponding hypercube, while preserving many of the hypercube’s attractive properties. Moreover, ring-connected topology is one of the most promising topologies in Wavelength Division Multiplexing (WDM) optical networks. Let Rn denote a ring-connected topology. In this paper, we address the routing and wavelength assignment problem for implementing the EH(s, t) communication pattern on Rn, where n = s + t + 1. We design an embedding scheme. Based on the embedding scheme, a near-optimal wavelength assignment algorithm using 2s+t−2 + 2t/3 wavelengths is proposed. We also show that the wavelength assignment algorithm uses no more than an additional 25 percent of (or 2t−1/3) wavelengths, compared to the optimal wavelength assignment algorithm. Copyright © 2017 The Institute of Electronics, information and Communication Engineers.

关键词： Topology

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Getting Undergraduates Ready for China’s Belt and Road Initiative (BRI) Through an Overseas Experiential Learning Project

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China and the World 2021年第3期4卷

作者： Andrew Min Han Chin Thomas Menkhoff Hans-Dieter Evers Hoong Hui Daniel Gn Kevin Koh Chester Wey Lee Patrick H. M. Loh Linda Low Sebastian Tan Teng Seng Teo Natalie Yap Lee Kong Chian School of Business Singapore Management University (SMU) 81 Victoria Street Singapore 188065 Singapore Andrew Chin Min Han graduated from Tsinghua University in Beijing China with a Tsinghua–MIT Sloan International Master of Business Administration degree under the Singapore Government’s Asian Business Fellowship (ABF) scholarship program. He is the Program Director of Singapore Management University (SMU) Executive Certificate in China Business and of the SMU Internationalisation Series. He teaches as an Adjunct Faculty of Strategy and Organisation at the SMU Lee Kong Chian School of Business since January 2017. He teaches effectively in English and Chinese. With over 23 years of experience in education and publishing industry in Asia Andrew Chin’s multi-sector career has enabled him to experience the full spectrum of the education industry in his roles as the Singapore EDB Officer handling the World-Class University portfolio and the Specialist Information and Publishing Industry portfolio Chief Representative and Country Manager (China) at Thomson Learning (Fortune 500 company) and Co-founder and CEO for an early childhood education business. Andrew was a Business Development Director (Universities) at Sodexo Group (Fortune 500 company) for the Asia-Pacific region. After working 10 years in Beijing China Andrew returned to Singapore in 2012 to work closely with the top leadership at Singapore Management University on the SMU China & East Asia Initiatives. Thomas Menkhoff is the Professor of Organisational Behaviour & Human Resources (Education) at the Lee Kong Chian School of Business Singapore Management University (SMU). Two of his recent publications include: (i) Menkhoff Thomas Ning Kan Siew Evers Hans-Dieter and Chay Yue Wah eds. Living in Smart Cities: Innovation and Sustainability (New Jersey: World Scientific Publishing 2018) and (ii) Chay Yue Wah Menkhoff Thomas and Low Linda eds. China’s Belt and Road Initiative — Understanding the Dynamics of a Global Transfor

In this paper, we explain how an experiential learning course and study tour to Gansu Province (People’s Republic of China) enabled undergraduates at the Singapore Management University (SMU) to acquire 21st-century competencies and higher-order thinking skills by analyzing and evaluating specific aspects of China’s Belt and Road Initiative (BRI) and China–Singapore (Chongqing) Connectivity Initiative — New International Land–Sea Trade Corridor (CCI-ILSTC) with emphasis on developing viable Go-To-Market (GTM) strategies aimed at selling Gansu produce in four Southeast Asian markets. We share how the course was designed to support the attainment of key learning goals and discuss how we turned pedagogical aspirations into concrete learning outcomes. We introduce key aspects of the so-called “SMU-XO” project that the students conducted in partnership with an industry partner, Pacific International Lines (PIL), and discuss how the project work helped learners to gain global competency by (i) examining critical issues related to BRI such as multi-modal infrastructure connectivity, (ii) appreciating the local perspectives of project stakeholders in Lanzhou and Shanghai and (iii) successfully interacting with people from different cultures, namely China, Indonesia, Vietnam, Malaysia and Thailand.

关键词： Experiential learning Singapore China Belt and Road Initiative (BRI)

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A PSO method for optimal design of PID controller in motion planning of a mobile robot

A PSO method for optimal design of PID controller in motion ...

引用

International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Li-Chun Lai Yen-Ching Chang Jin-Tsong Jeng Guan-Ming Huang Wan-Ning Li Yi-Shan Zhang Bachelor Program in Robotics National Pingtung University of Education Pingtung Taiwan ROC Department of Applied Information Sciences Chung Shan Medical University Taichung Taiwan ROC Department of Computer Science and Information Engineering National Formosa University Yunlin Taiwan ROC

Based on particle swarm optimization (PSO) algorithm for optimal design of PID controller, a method is proposed to navigate of a two-wheeled mobile robot from a given initial configuration to a desired final configuration in an unknown environment filled with obstacles. The mobile robot can follow a wall with PID control to adjust motor speed and avoid obstacles with moving square method in an unknown environment. The PSO algorithm is an optimization technique which is inspired by the social-like behavior, such as bird flocking or fish utilized it to solve optimization problems. It finds the global best solution by simply adjusting the trajectory of each particle its own best particle and toward the best particle of the entire swarm at each generation. The PSO algorithm becomes very popular due to it can be easily implemented and quickly find a good solution. Then the PSO algorithm can be used to adjust parameters of PID to control wheel velocities in an unknown environment filled with obstacles. To show the feasibility of the proposed method, simulation results and experiments are included for illustration.

关键词： Mobile robots Educational institutions Wheels Navigation Planning Electronic mail

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