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A Gaze-Based Intelligent Textbook Manager 1

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Klasnja-Milicevic, Aleksandra Ivanovic, Mirjana Porta, Marco Faculty of Sciences University of Novi Sad Novi Sad21000 Serbia Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

IT technologies and their rapid development can greatly support and significantly improve Human–computer Interaction, defining new communication methods for a fast and direct user experience. One very promising technology nowadays is eye tracking. The main contribution of our research is to propose a gaze-based intelligent textbook manager that will support complete document analysis. Our solution includes tools providing translation support (single or entire sentences), keywords annotations, and the creation of document summaries, that contain all the areas that the user has read, with their time and words of major interest. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Eye tracking

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Modified Isolated SEPIC Converter for EV Charging and High Voltage Applications

Modified Isolated SEPIC Converter for EV Charging and High V...

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2023 International Conference on Power, Instrumentation, Energy and Control, PIECON 2023

作者： Ahmad, Javed Lin, Chang-Hua Khan, Mohammad Suhail Liu, Hwa-Dong Fahad, Mohammad National Taiwan University of Science and Technology College of Electrical Engineering and Computer Science Department of Electrical Engineering Taipei Taiwan National Taiwan Normal University Undergraduate Program of Vehicle and Energy Engineering Taipei Taiwan

ISBN: (纸本)9798350399769

This research article presents a modified isolated SEPIC converter suitable for high-power applications. It uses a transformer instead of coupled inductor for isolation between the input power source and the output load. The steady-state analysis is explained in detail. Thermal loss analysis is performed by using PLECS software. Further, a hardware prototype is developed to verify the performance aspects of the proposed converter. © 2023 IEEE.

关键词： High frequency transformers

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Safe Transportation System using IoT based Alcohol Detection 7

Safe Transportation System using IoT based Alcohol Detection

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Pradeepkumar, G. Vijayakumar, P. Chandrasekaran, N. Rohith Bhat, C. Senthilkumar, C. Kumar, Neelam Sanjeev KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Saveetha School of Engineering Department of Computer Science and Engineering SIM ATS Chennai India SIMATS University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Biomedical Engineering SIMATS Chennai India

ISBN: (纸本)9798350397253

Every year, drunk driving injures and sometimes kills a number of people. It is critical to investigate an IoT-based alcohol detection system for safe mobility. Because it uses sensors and a microprocessor to monitor the driver's BAC and cuts off the car's ignition if it suspects that the driver has been drinking while driving, this system can detect and prevent drunk driving. The proposed system will use IoT to remotely monitor and analyze the driver's Blood Alcohol Level (BAC). A communication module, a microprocessor, and an alcohol sensor comprise the system. The communication component sends real-time data to a BAC server for monitoring and analysis. The proposed strategy reduces the likelihood of accidents, improves driver and passenger safety, and lowers the likelihood of transportation companies having to pay for injuries or damages. IoT-based technologies that detect alcohol can aid in the prevention of drunk driving. By monitoring drivers' BACs in real time and discouraging drunk driving, this device has the potential to significantly reduce traffic-related injuries and fatalities. © 2023 IEEE.

关键词： Internet of things

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Assessing a Personalized, Hybrid, and Generic Approach for Glucose Prediction in Type 1 Diabetes

Assessing a Personalized, Hybrid, and Generic Approach for G...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bosoni, Pietro Jensen, Morten Hasselstrom Bellazzi, Riccardo Cichosz, Simon Lebech University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy Aalborg University Data Science Department of Health Science and Technology Aalborg Denmark Aalborg University Department of Health Science and Technology Aalborg Denmark

ISBN: (纸本)9798350386226

This study investigates the potential of using generic, hybrid, and personalized neural network models for glucose prediction in individuals with Type 1 Diabetes (T1D). Data from 194 participants in the Wireless Innovations for Seniors with Diabetes Mellitus (WISDM) study, totaling over 46 million minutes of Continuous Glucose Monitoring (CGM), were used to develop and evaluate the models. A baseline reference model, Last Observation Carried Forward (LOCF), was also included for comparison. Models were trained using data from 70% of the participants and tested on the remaining 30%, with prediction horizons (PH) set at 30 and 60 minutes. At the 30-minute PH, the generic model achieved a Root Mean Square Error (RMSE) of 19.6 mg/dL and a Mean Absolute Relative Difference (MARD) of 9.2%. These results were slightly worse than those of the hybrid and personalized models, which yielded RMSEs of 19.4 mg/dL and 19.5 mg/dL, respectively, and MARDs of 9.6% for both. However, the differences were not statistically significant. At the 60-minute PH, the generic model showed the best performance, with an RMSE of 34.9 mg/dL and MARD of 16.2%. The hybrid and personalized models exhibited slightly higher RMSEs (35.3 mg/dL and 35.5 mg/dL, respectively) and MARDs (17.8% and 18.1%, respectively). These findings suggest that both generic and individualized models can provide satisfactory glucose forecasting results based solely on CGM data. Nonetheless, the potential benefits of individualized approaches deserve further investigation, particularly when substantial training data are available. © 2024 IEEE.

关键词： Prediction models

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System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture

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IEEE Journal of biomedical and Health Informatics 2023年第7期27卷 3549-3558页

作者： Lin, Bor-Shyh Peng, Chih-Wei Lee, I-Jung Hsu, Hung-Kai Lin, Bor-Shing National Yang Ming Chiao Tung University Institute of Imaging and Biomedical Photonics Tainan71150 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei11031 Taiwan Taipei Medical University School of Gerontology and Long-term Care College of Nursing Taipei11031 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237303 Taiwan

Bedside falls and pressure ulcers are crucial issues in geriatric care. Although many bedside monitoring systems have been proposed, they are limited by the computational complexity of their algorithms. Moreover, most of the data collected by the sensors of these systems must be transmitted to a back-end server for calculation. With an increase in the demand for the Internet of Things, problems such as higher cost of bandwidth and overload of server computing are faced when using the aforementioned systems. To reduce the server workload, certain computing tasks must be offloaded from cloud servers to edge computing platforms. In this study, a bedside monitoring system based on neuromorphic computing hardware was developed to detect bedside falls and sleeping posture. The artificial intelligence neural network executed on the back-end server was simplified and used on an edge computing platform. An integer 8-bit-precision neural network model was deployed on the edge computing platform to process the thermal image captured by the thermopile array sensing element to conduct sleep posture classification and bed position detection. The bounding box of the bed was then converted into the features for posture classification correction to correct the posture. In an experimental evaluation, the accuracy rate, inferencing speed, and power consumption of the developed system were 94.56%, 5.28 frames per second, and 1.5 W, respectively. All the calculations of the developed system are conducted on an edge computing platform, and the developed system only transmits fall events to the back-end server through Wi-Fi and protects user privacy. © 2013 IEEE.

关键词： Feature extraction

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Active Power Control Strategy with Load Impedance Estimation Method for IH Cooktop Considering Utilization of Double-bottom Pot

Active Power Control Strategy with Load Impedance Estimation...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Man Jae Kwon Seung Hyun Kang Yun Seong Hwang Hyeon Soo Kim Dong-Wook Yoo Byoung Kuk Lee Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Department of Electrical and Biomedical Engineering Hanyang University Seoul Korea

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

For conventional domestic induction cooktop using series resonant inverter, the reference power of system can be controlled by frequency variation. In this case, the controller only monitors the input power condition to satisfy target power level. However, when the double-bottom pot is located on a working coil, system can be damaged by large magnitude of output current under the same target input power since the equivalent impedance of load highly decreases compared to ferromagnetic pot. Therefore, in this digest, active power control strategy is established with mathematical analysis of load impedance without obtaining input power curve data. Through experimental results using 3.2 kW test bed, the validity of proposed method is evaluated.

关键词： Coils Heating systems Power control Mathematical analysis Estimation Resonant frequency Impedance Monitoring Frequency control Resonant inverters

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Breaking Language Barriers for Real-Time Translation Solutions in Tourism Using Cloud Computing and AI 3

Breaking Language Barriers for Real-Time Translation Solutio...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Jame, S. Lourdu Nageswari, C. Shobana Jayaprakash, S. Punarselvam, E. Meenakshi, R. Muthulekshmi, M. SRM Institute of Science and Technology Kattankulathur Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Namakkal India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

To improve tourism experiences and enable cultural communication in today's worldwide tourism industry, it is essential to communicate effectively across linguistic boundaries. Cloud computing and artificial intelligence (AI) are the focus of this paper to create tourism-specific real-time translation systems. These solutions use cloud computing and advanced AI algorithms to better connect visitors with local businesses and provide accurate and contextually aware translations. Important technical parts include cloud computing for scalability and accessibility, machine learning models for language comprehension, and natural language processing (NLP). It uses empirical research or case study techniques to assess the solutions' practicability, performance, and user acceptability. Through the efficient and effective removal of language barriers, the results highlight the promise of cloud-based AI translation systems to revolutionize the tourist industry. This research investigates real-time translation innovations, highlighting scalability, context awareness, and multilingual support, with implications for future advances in AI and cloud-based tourist applications. Also covered are potential avenues for further study and application, with the growth of the tourist industry's continuous development and use of AI-driven solutions. The proposed approach enhances real-time translation using cloud computing and AI, enabling scalability and context-aware translations. However, there are other challenges, such as overcoming latency and advanced, idiomatic expressions. © 2025 IEEE.

关键词： Tourism

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Digitally-defined ultrathin transparent wireless sensor network for room-scale imperceptible ambient intelligence

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npj Flexible Electronics 2024年第1期8卷 869-878页

作者： Yunxia Jin Mengxia Yu Dat T.Nguyen Xin Yang Zhipeng Li Ze Xiong Chenhui Li Yuxin Liu Yong Lin Kong John S.Ho Institute for Health Innovation and Technology National University of SingaporeSingapore 117599Singapore Department of Biomedical Engineering National University of SingaporeSingapore 119276Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Integrative Sciences and Engineering Programme National University of Singapore Graduate SchoolSingapore 119077Singapore The N.1 Institute for Health National University of SingaporeSingapore 117456Singapore Institute of Materials Research and Engineering Agency for Science Technology and ResearchSingapore 138634Singapore Department of Mechanical Engineering University of UtahSalt Lake CityUT 84112USA

Wireless and battery-free radio-frequency(RF)sensors can be used to create physical spaces that ambiently sense and respond to human *** such sensors ultra-flexible and transparent is important to preserve the aesthetics of living environments,accommodate daily activities,and functionally integrate with ***,existing RF sensors are unable to simultaneously achieve high transparency,flexibility,and the electrical conductivity required for remote room-scale ***,we report 4.5μm RF tag sensors achieving transparency exceeding 90%that provide capabilities in room-scale ambient wireless *** develop a laser-assisted water-based adhesion-reversion process to digitally realize computer-aided RF design at *** individually tagging multiple objects and regions of the human body,we demonstrate multiplexed wireless tracking of humanenvironment interactions and physiological signals at a range of up to *** radio-frequency identification sensors open opportunities for non-intrusive wireless sensing of daily living spaces for applications in health monitoring and elderly care.

关键词： network ambient transparent

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Optimized Design Strategy of CLLC converter Considering High Efficiency Operation in 22kW Bidirectional On-Board Charger

Optimized Design Strategy of CLLC converter Considering High...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Dong Hyeon Sim Ju-A Lee Hyeonu Jo Chae-Lyn Kim Dong-Wook Yoo Byoung Kuk Lee Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Department of Electrical and Biomedical Engineering Hanyang University Seoul Korea

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper proposes a high-efficiency design methodology for a CLLC converter within a 22kW bidirectional On-Board Charger (OBC). Further analysis determines the converter's operational range in relation to the transformer turn ratio for different operational modes. Following the design of a resonance network that aligns with these turn ratios, the study identifies an optimal design strategy for high-efficiency operation through a comparative loss analysis. Experimental verification on a CLLC converter testbed confirms the effectiveness and validity of the proposed design method.

关键词： Design methodology Energy conversion Transformers Resonance

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Estimating Heart Rate from Seismocardiogram Signal using a Novel Deep Dominant Frequency Regressor and Domain Adversarial Training

Estimating Heart Rate from Seismocardiogram Signal using a N...

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2022 IEEE biomedical Circuits and Systems Conference, BioCAS 2022

作者： Chan, Michael Gazi, Asim H. Soliman, Moamen Richardson, Kristine L. Abdallah, Calvin A. Ozmen, Goktug C. Nikbakht, Mohammad Inan, Omer T. Georgia Institute of Technology Coulter Department of Biomedical Engineering AtlantaGA United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States Georgia Institute of Technology School of Electrical and Computer Engineering Coulter Department of Biomedical Engineering AtlantaGA United States

ISBN: (数字)9781665469173

ISBN: (纸本)9781665469173

Estimating heart rate (HR) from the seismocardiogram (SCG) signal can provide a more unobtrusive alternative for long-term HR monitoring where the gold standard electrocardiogram (ECG) signal is less favorable. Deep learning (DL) approaches have demonstrated promise in accomplishing this and are attractive due to their flexible and data-driven nature. However, current dense layer-based DL approaches lack a carefully designed regressor to estimate the HR in the SCG input and may overfit in low-data regime. It is also uncertain how well most of these DL approaches can generalize to unseen subjects, as evaluation has primarily taken place with no separation of subjects between training and testing data. In this work, we address these limitations by designing a DL approach for SCG HR estimation that leverages our proposed Dominant Frequency Regressor (DFR) with a Fast Fourier Transform layer and test the model performance using leave-one-subject-out cross validation. Specifically, we measure ECG and SCG from 19 subjects using our custom-built wearable patch. Here, ECG is used for training and regularization only, and SCG is used for training and inference. These signals are bandpass filtered, segmented into 60s windows, and normalized. Next, the proposed DFR-based DL model is applied and regularized by domain adversarial training. We report a mean absolute error (MAE) of 1.42±1.66 beats per minute (bpm) for HR with a range of 63-104 bpm. The proposed method can augment existing methods or be adapted for other problems of similar nature owning to its superiority to the dense layers-based alternatives. This work can lead to accurate, real-time algorithms for estimating 60s HR from a single-chest worn accelerometer only, which could be embedded in textiles. © 2022 IEEE.

关键词： Fast Fourier transforms

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