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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications"

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Interactive Effects of Water Molecules Across A Suspended Double-Layer Graphene with Electro-Modulation

Interactive Effects of Water Molecules Across A Suspended Do...

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IEEE International conference on Micro Electro Mechanical systems

作者： Yu-Xuan Lu Wei-Yu Long Cheng-Yu Lin Chih-Ting Lin Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Graduate School of Advanced Technology National Taiwan University Taipei Taiwan

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

Numerous graphene-based applications correspond with the graphene-water interface. However, limited researches focus on the impact of water molecules across the graphene interface in mechanistic perspective. This work first reports experimental investigations of surface water molecular configuration and water-water interaction across double-layer suspended graphene. It is achieved by carrier transportation measurement of suspended graphene in water environment with electro-modulation. This investigation shows the relationship between interfacial molecular arrangement and electrical properties of the graphene-water interface. It also reveals the fundamental mechanisms of conductivity improvement and highlights the potential for graphene-based applications in aqueous environments.

关键词： Water Micromechanical devices Mechanical sensors Graphene Transportation Interference Logic gates sensor systems Mechanical factors Transistors

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Vehicle Dynamical sensor Monitoring Toward Forest Soil Moisture Measuring System

Vehicle Dynamical Sensor Monitoring Toward Forest Soil Moist...

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IEEE International Workshop on Intelligent Data Acquisition and advanced Computing systems: Technology and applications

作者： Yoshinori Kiga Takumi Tazawa Masami Iwase Department of Robot Mechatronics School of Science and Technology for Future Life Tokyo Denki University Tokyo JAPAN

In this research, we develop a sensor monitoring system for vehicle dynamics aimed at developing a forest soil moisture measurement system for landslide disaster prevention. The identification of landslide hazard warning areas is necessary to prevent landslide disasters. Soil moisture is one of the factors. Common methods of surveying this soil moisture content include borings and satellites. However, borehole investigations may omit areas that truly need to be investigated without a priori knowledge of the locations to be measured. In addition, it is difficult to specify the timing of measurements in satellite surveys. Therefore, this research aims to establish a system that is safe, wide-ranging, and capable of setting the timing and range of measurement as needed. One approach to meet these requirements is to mount sensors on remotely operated vehicles to measure soil moisture content. In general, vehicles for such purposes need to travel in rough environments, and in order to travel well in rough environments, it is necessary to detect abnormal driving conditions such as slipping or skidding. For this purpose, we develop a sensor monitoring for the vehicle traveling in rough terrain and through forest.

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Smart Automated Parking System using IoT 2

Smart Automated Parking System using IoT

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2nd International conference on Sustainable Computing and Data Communication systems, ICSCDS 2023

作者： Archana, M. Raju, S. Shanmuga Preethi, D. Mydhili, S.K. Vinothkumar, U. Dr.N.G.P. Institute of Technology Coimbatore India Kgisl Institute of Technology Coimbatore India

ISBN: (纸本)9781665491990

The growing urbanization, in conjunction with the more congested urban areas and the greatly increased number of metropolitan vehicles, has resulted in the demand for guarded pull-up locations at schools, shopping malls, and other organizations. In this paper, an autonomous IR sensor based secure and smart parking without the intervention of human beings is discussed by utilizing advanced technologies and low-cost smart devices. this ensures the mission of smart cities to improve the economic growth as well as to improve the quality of people's lives. The suggested architecture incorporates gizmos like RFID readers and labels, servomotors as an entrance, personal computers, operating systems that are programmed code, and light-emitting diodes as components. As a result, the framework is used in order to take the functioning boundaries and dazzling Light-Emitting Diodes in hand grasp for a variety of applications. The envisioned architecture is adaptable to any setting imaginable. The suggested system would restrict access to the parking places to just those members who have been given permission to do so. Node MCU is an open-source Internet-of-Things (IoT) platform, and the inclusion of this component in the proposed system gives it a vital part to play in the transmission of data. When the RFID scanner reads a vehicle's tag, the Node MCU transmits information about available slots to the Arduino micro-controller. The micro-controller then assigns a slot to the user, and this information is shown on the LCD screen. The RFID scanner reads the tag on the car. By using this architecture, the school can reduce the risk of a car being broken into. People who are members of the system, such as students are able to have peace of mind about the security of their automobiles. A system that manages the entrance and departure of registered vehicles, users automatically will also make it possible for individual users' vehicles to be secure. © 2023 IEEE.

关键词： Computer operating systems

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A Self-Assembled Flexible Strain and Temperature sensor based on the Microstructure of Pyramids with High Elasticity, Temperature Sensitivity and Wide Range

A Self-Assembled Flexible Strain and Temperature Sensor base...

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IEEE International conference on Micro Electro Mechanical systems

作者： Yangtao Yu Bo Yan Wenbo Cui Mengqiu Li Chenyuan Li Faheng Zang Zhuoqing Yang National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

Polymers have been extensively studied in the field of flexible sensors for wearable electronics. However, polymer-based flexible sensors still face great challenges in terms of sensitivity, sensing range and stretchable properties. This paper reports a novel self-assembled flexible strain and temperature sensor, constructed from NiO molecular spheres assembled between MXene flakes. This sensor exhibits exceptional temperature sensitivity, with a sensitivity up to -15.11%°C −1 . It operates a broad temperature range(-20~60°C) and boasts a high measurement gauge factor(GF) of 6.57, along with a significant elongation at break exceeding 572%. Additionally, the sensor demonstrates an ultrahigh tensile strength of over 45 MPa. The microstructure of the sensor, featuring pyramidal formations, facilitates rapid testing capabilities. Furtherly, breathing cycle test under 0°C was performed, showing robust reliability. Furthermore, an array sensing circuit has been developed and designed to precisely measure spatial temperature distributions, enhancing utility of the sensor in various applications.

关键词： Temperature sensors Temperature measurement Temperature distribution Sensitivity sensor systems sensors Microstructure Wearable sensors sensor arrays Strain

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Machine Learning-Enhanced Dual Axis Solar Tracking with Weather Monitoring for Rural Electrification

Machine Learning-Enhanced Dual Axis Solar Tracking with Weat...

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advanced Computing Technologies (ICoACT), International conference on

作者： Logesh S Jeevashree J Arun Kumar P Jeevitha Kandasamy Jaisiva S Harish R Electrical and Electronics Engineering Sri Krishna College of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The proposed dual-axis solar tracking system integrates real-time weather monitoring and machine learning to enhance solar energy efficiency. Machine learning algorithms analyze historical weather data alongside real-time sensor readings, which is crucial for optimizing solar energy capture, as demonstrated in various studies on solar tracking systems. The integration of a mobile application interface allows users to access real-time data and receive alerts, facilitating immediate adjustments to the system settings. This aligns with advancements in remote monitoring technologies, such as those used in microalgal productivity prediction, which leverage real-time sensor data for operational forecasting. Furthermore, the use of the Blynk IoT platform for remote control enhances user engagement and system responsiveness, reflecting a growing trend in smart energy management solutions. Overall, this project exemplifies the convergence of renewable energy technology, machine learning, and IoT, promising improved operational reliability and energy efficiency.

关键词： Renewable energy sources Machine learning Solar energy Solar tracking Market research Real-time systems Energy efficiency Mobile applications Remote monitoring Meteorology

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Cross sensor Finger Vein Recognition 21

Cross Sensor Finger Vein Recognition

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Annual International conference of the Biometrics-Special-Interest-Group (BIOSIG) of the of the Gesellschaft-fur0Informatik (GI) e-V

作者： Prommegger, Bernhard Wimmer, Georg Uhl, Andreas Univ Salzburg Dept Art Intelligence & Human Interfaces Salzburg Austria

ISBN: (数字)9781665476669

ISBN: (纸本)9781665476669

If biometric systems are rolled out on a large scale, it will not always be guaranteed that all capturing devices are of exactly the same type. It is therefore important to ensure that the biometric system also works across multiple capturing devices. In finger vein biometry, there is almost no published work in this regard. The biggest problem here is certainly that there are only very few datasets (recorded by different institutions) that usually have no overlap at all with the test persons contained. In a first approach, this article tries to examine how well cross-sensor finger vein recognition works. For the investigation, four publicly available datasets, which were acquired with four different devices in three different scenarios, were evaluated. Using three different finger vein recognition approaches, we will show that the results distinctly deteriorate in cross-sensor recognition scenarios compared to the recognition results using only images from the same device, even more so for image data from contact-less and contact-based capturing devices.

关键词： Finger Vein Recognition Cross-sensor Recognition

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Online Pattern Recognition of Time-series Gas sensor Data with Adaptive 2D-CNN Ensemble 11

Online Pattern Recognition of Time-series Gas Sensor Data wi...

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11th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems - Technology and applications (IDAACS)

作者： Pareek, Vishakha Chaudhury, Santanu Singh, Sanjay CSIR Cent Elect Engn Res Inst Pilani 333031 Rajasthan India Acad Sci & Innovat Res AcSIR Ghaziabad 201002 India Indian Inst Technol Delhi Delhi 110016 India

ISBN: (纸本)9781665426053

Intelligent sensor system design demands the efficient pattern recognition method that uses raw time-series sensor data to identify the target gas with fast and accurate results. The presented work uses gas sensor array response (open sampling setting) for 10 bio-marker gases. The responses are obtained at different concentrations. We propose a 2D convolution neural network (2D-CNN) based adaptive ensemble network for gas identification. The Spatio-temporal correlation of sensor array responses inspired us to design deep-learning-based gas identification networks. The network uses raw time-series gas sensor array data and identifies the target gas mixtures with improved accuracy despite sensor drift. Experimental results show that the proposed methods are an effective technique with identification accuracy approximately 91% for identifying gas mixture for smart sensor system application. The proposed method outperforms and provides higher identification accuracy than comparable various machine learning and deep learning methods.

关键词： 2D-CNN Pattern Recognition Ensemble Gas sensor

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Acceleration of a CNN-based Heart Sound Segmenter: Implementation on Different Platforms Targeting a Wearable Device 26

Acceleration of a CNN-based Heart Sound Segmenter: Implement...

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26th Euromicro conference on Digital System Design (DSD) / 49th Euromicro conference on Software Engineering and advanced applications (SEAA)

作者： Ragusa, Domenico Rodriguez-Almeida, Antonio J. Nolting, Stephan Torti, Emanuele Fabelo, Himar Hoyer, Ingo Utz, Alexander Callico, Gustavo M. Leporati, Francesco Univ Pavia Dept Elect Comp & Biomed Engn Pavia Italy Univ Las Palmas Gran Canaria Inst Appl Microelect IUMA Las Palmas Gran Canaria Spain Fraunhofer IMS Smart Sensor Syst Duisburg Germany

ISBN: (纸本)9798350344196

Cardiovascular diseases (CVDs) are currently one of the leading causes of death worldwide. Being able to detect their symptoms at early stages, even the most hidden ones, is crucial to shorten the diagnosis time and facilitate an early treatment. Currently, the use of continuous tracking systems, mainly based on wearable devices that analyze data using artificial intelligence (AI) algorithms, is being explored to automatically identify, in real time, CVDs symptoms. This could be especially relevant in low-income countries where there is a shortage of specialized doctors. Therefore, this work focuses on analyzing the real-time execution of the state-of-the-art convolutional neural network (CNN) for heart sound segmentation (HSS) on platforms such as traditional CPU/GPU and the Fraunhofer IMS (c) AIRISC Core Complex (a RISC-V processor developed for AI). Results revealed that, while all implementations exploiting the CPU/GPU platform proved to be useful in real-time diagnosis from a fixed location, the AIRISC demonstrated its goodness, as a system on a chip (SoC) for a real-time wearable application, when executing a quantized version of the CNN.

关键词： Cardiovascular diseases Convolutional Neural Networks Quantization RISC-V Wearable devices Phonocardiogram

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Review of Non-invasive, Wearable Biomedical sensor and Imaging IC

Review of Non-invasive, Wearable Biomedical Sensor and Imagi...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Jaeeun Jang Hoi-Jun Yoo School of Semiconductor Systems Engineering Kwangwoon University Seoul Republic of Korea Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

Technological innovation in biomedical science, driven by semiconductor circuit and system engineering, has enabled faster diagnosis, continuous health monitoring, and advanced treatment approaches. Recent advances in noninvasive, wearable biosensor interfaces and imaging have significantly improved performance and reduced costs, enhancing accessibility to medical technology. These sensors, benefiting from semiconductor technology, offer convenience, low infection risk, and cost-effective solutions for medical monitoring. This paper explores the principles, challenges, and advancements in non-invasive sensor technology, including electrodes, optical elements, and Electrical Impedance Tomography (EIT). The integration of semiconductor technology in these applications is paving the way for personalized medicine and point-of-care solutions, with promising potential to improve patient care and healthcare outcomes.

关键词： Electrical impedance tomography Technological innovation Precision medicine Point of care Optical variables measurement Optical sensors Biosensors Biomedical monitoring Medical diagnostic imaging Monitoring

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Applied Machine Learning for Securing the Internet of Medical Things in Healthcare 37th

Applied Machine Learning for Securing the Internet of Medica...

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37th International conference on advanced Information Networking and applications, AINA 2023

作者： Lu, Wei Department of Computer Science Keene State College USNH KeeneNH United States

ISBN: (纸本)9783031284502

Integrating mobile computing technologies and human health activities using IoMT devices can accelerate biomedical discovery and improve the efficiency of healthcare research and delivery. However, the quality of the collected health data is critical for the success of these efforts. In this paper, a hybrid intrusion detection system is proposed to identify cyberattacks in real time on medical devices. The system combines a logistic regression-based detector using network traffic features with a gradient-boosted tree-based detector using medical sensor features. Evaluation of the system using a publicly available dataset shows an accuracy score of 95.4% using only 11 features, compared to the current best accuracy of 92.98% achieved by artificial neural networks using 40 features. Additionally, by combining the decisions of the two individual detection systems, the number of attacks detected is increased from 111 to 305 out of a total of 423 attack instances, improving the sensitivity score and addressing the challenge of effectively and efficiently integrating different detection technologies in a hybrid intrusion detection system. To the best of the authors’ knowledge, this is the first attempt to combine multiple misuse detection models in a hybrid system to secure both IoMT devices and their networking equipment across the entire healthcare spectrum of the IoMT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hybrid systems

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