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

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Cross sensor Finger Vein Recognition 21

Cross Sensor Finger Vein Recognition

引用

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...

引用

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...

引用

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...

引用

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...

引用

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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Virtual sensor for Door Opening Detection and Anomaly Detection Using Machine Learning

Virtual Sensor for Door Opening Detection and Anomaly Detect...

引用

International conference on Intelligent systems applications to Power systems (ISAP)

作者： Almir Neto Luis Gomes Zita Vale Federal Institute of Education Science and Technology of Maranhão (IFMA) São Luís Brazil GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (P.PORTO) Porto Portugal

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

Currently, several studies are being developed using machine Learning with the aim of replacing real sensors with virtual sensors. One of the biggest challenges is obtaining quality data to enable the system modeling. This article proposes the replacement of a light dependent resistor sensor, applied to a refrigerator, which detects whether the door is open or closed and an anomaly test when the door is forgotten open for too long. The use of tiny machine learning is applied to ESP32 to enable the use of regression and classification models to detect the door opening and anomaly. The tested metrics for the regression were the root mean squared error. The comparative results between the virtual and real sensors are satisfactory with an accuracy of 99.59% for classification and a root mean squared error value of 0.01 for regression. Furthermore, a prototype was developed, and the model was embedded in the ESP32 microcontroller.

关键词： Temperature sensors Temperature measurement Microcontrollers Soft sensors Tiny machine learning Neural networks Prototypes Virtualization Intelligent sensors Anomaly detection

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Mathematical Model for Determining the Geometric Location of the Environmental Pollutant Based on sensor Data

Mathematical Model for Determining the Geometric Location of...

引用

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

作者： Vladyslav Khaidurov Bohdan Yailymov Andrii Shelestov National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine Space Research Institute NASU-SSAU Kyiv Ukraine

The practical result of this work is the development of a generalized mathematical model for determining the position of an environmental pollutant. Computer simulation was carried out in order to verify the developed mathematical model. A number of computational experiments were conducted to verify the mathematical model. In the future, the developed algorithm is proposed to be used for real ground air quality measurement stations with additional use of air quality information from satellite data and specify the location of the pollution source using the Urban Atlas.

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UiAbot: A Versatile Autonomous Mobile Robot Platform for Research and Education

UiAbot: A Versatile Autonomous Mobile Robot Platform for Res...

引用

IEEE conference on Industrial Electronics and applications (ICIEA)

作者： Daniel Hagen Martin Mæland Tarjei Skotterud Martin Dahlseng Hermansen Husam Haij Martin Sauar Wad Dep. of Engineering Sciences University of Agder Grimstad Norway

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

This paper presents UiAbot, an innovative autonomous mobile robot platform developed at the University of Agder to bridge the gap in hands-on robotics education. Using cost-effective sensors and the Robotic Operating System with the Navigation Stack, this internally developed platform facilitates advanced autonomous navigation, surpassing predecessors such as the TurtleBot3 in flexibility, functionality, and affordability. Integrated into master courses and research, the UiAbot demonstrates its effectiveness as an educational and research platform, showcasing the impact of open source technologies in democratizing robotics research and enhancing educational methodologies in autonomous systems.

关键词： Technological innovation Simultaneous localization and mapping Service robots Navigation Operating systems Education sensor systems

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Efficient Human Activity Recognition through Multi-sensor Data and Deep Learning Techniques

Efficient Human Activity Recognition through Multi-Sensor Da...

引用

advanced Computing Technologies (ICoACT), International conference on

作者： Rhenius A Anitha J Immanuel Alex Pandian S CSE KITS Coimbatore India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Extensive research takes place in the field of Human Activity Recognition (HAR) systems with wearable computing that helps in healthcare applications, fitness tracking and smart environments. sensor technology advances have propelled Human Activity Recognition systems to utilize innovative machine learning methods for accurate and robust activity classification. This work evaluates the effectiveness of four cutting-edge deep learning models-Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU)-through their implementation on publicly accessible accelerometer and gyroscope HAR datasets. These classification models underwent testing through different activation functions namely ReLU, Leaky ReLU, and Swish to assess their practicality for classification operations. Standard evaluation metrics including accuracy, precision, recall, ROC-AUC and F1-score demonstrate the best features of each implemented model while providing descriptive visual representations. TCN delivered 98.59% accuracy and exhibited superior training speed efficiency thus establishing itself as the suitable AI algorithm for real-time implementation. This research offers an extensive breakdown for choosing optimal models in real-time HAR systems which supports advances in wearable computing and activity recognition technologies.

关键词： Deep learning Training Visualization Accuracy Computational modeling Real-time systems Human activity recognition Convolutional neural networks Wearable devices Long short term memory

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A TENG Finger sensor Enabled Human-Machine Interface for VR applications

A TENG Finger Sensor Enabled Human-Machine Interface for VR ...

引用

IEEE Region 10 International conference TENCON

作者： Chuyang Wu Zhongda Sun School of Electrical and Information Engineering Tianjin University Tianjin China the Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

A wireless, triboelectric nanogenerator (TENG) sensor enabled handle-like human-machine interface (HMI) is proposed to capture finger motions and manipulate game objects in virtual reality. By utilizing advanced signal processing methods including logarithmic transform and integration, peak-like TENG signals are transformed into stage-like signals to achieve continuous control. Haptic feedback supported by vibrators can bring better game experiences to players. Finally, a gesture recognition system based on processed sensor signals is also developed to categorize 10 common gestures.

关键词： Wireless communication Wireless sensor networks Human-machine systems Games Transforms Gesture recognition Virtual reality Haptic interfaces Voltage control Wireless fidelity

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