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检索条件"任意字段=Intelligent Sensors, Sensor Networks and Information Processing Conference"

共 10630 条记录，以下是151-160 订阅

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Harshith Pallapothu Gopi Krishna G Digvijay Sunil Teja Yadav S Nagasundari S Dept. of Computer Science & Engineering PES University Bengaluru India Dept. of Computer Science & Engineering Research Center for Information Security Forensics and Cyber Resilience PES University Bengaluru India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs.

关键词： Cloud computing Connected vehicles Web services Vehicle safety Propagation losses Real-time systems Reliability Autonomous vehicles Payloads Predictive maintenance

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Model-Driven Learning Approach for Robust WiFi-based Fall Detection

Model-Driven Learning Approach for Robust WiFi-based Fall De...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Sai Deepika Regani Beibei Wang K. J. Ray Liu Origin Research Rockville MD USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Indoor falls often lead to fatalities due to delayed assistance. Current approaches to detecting indoor falls, such as cameras and wearables, intrude on privacy and are inconvenient. Radar-based device-free sensing has a limited range and requires dense deployment, leading to overhead costs. WiFi-based solutions, while promising, are currently either environment-dependent or insufficiently tested. In this work, we propose a fusion approach that leverages signal processing techniques to extract environment-independent features from the Channel State information (CSI) in commercial WiFi devices. We then use a neural network to detect differentiating patterns from these features. Our lightweight LSTM network, with just 21,000 parameters, has been tested on 2,400 fall events from over 25 volunteers in 5 environments. It has also undergone 21 months of false alarm testing in 6 diverse settings. The system achieves a 94.1% detection rate and fewer than 5 false alarms per month in single-person homes.

关键词： Wireless communication Wireless sensor networks Signal processing Feature extraction sensors Fall detection Wearable devices Speech processing Wireless fidelity Testing

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A Resolution-Preserving Multi-Scale Network for Spectrogram-Based Semantic Segmentation of 5G-LTE Signals in Spectrum Sensing

A Resolution-Preserving Multi-Scale Network for Spectrogram-...

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International conference on Ubiquitous information Management and Communication (IMCOM)

作者： Huu-Tai Nguyen Hai-Trang Phuoc Dang Thien Huynh-The Department of Computer and Communications Engineering HCMC University of Technology and Education Ho Chi Minh City Vietnam

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

In this paper, we propose a novel semantic segmentation model designed to enhance the accuracy of 5G New Radio (NR) and Long-Term Evolution (LTE) signal segmentation in spectrogram images for intelligent spectrum sensing. To address the challenge of preserving essential signal information in spectral feature learning, we developed Resolution-Preserving Multi-Scale Network (RPMSN), an effective deep network architecture for semantic segmentation. This model maintains input resolution and extracts features at multiple scales by exploiting two components: a resolution-preserving architecture and a multi-scale block, consequently making it particularly effective for segmenting wide band spectrograms. In addition to significantly outperforming several state-of-the-art architectures, including U-Net, U-Net++, DeepLabV3, DeepLabV3+, FPN, and PSPNet, RPMSN achieved impressive performance with a mean accuracy of 97.22%, mean IoU of 95.42%, mean F1 Score of 97.64% under the presence of different channel impairments while keeping the model size efficient at 14.5M parameters.

关键词： Wireless sensor networks Accuracy Image resolution Semantic segmentation Simulation Feature extraction sensors Spectrogram Signal resolution Long Term Evolution

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A 94.8nW Battery-Free intelligent Silicon Platform Enabling Distributed, Adaptive, and Event-Driven Multimodal Sensing at the Edge

A 94.8nW Battery-Free Intelligent Silicon Platform Enabling ...

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IEEE International conference on Solid-State Circuits (ISSCC)

作者： Haochen Zhang Wei-Han Yu Zhongyu Zhao Zhizhan Yang Ka-Fai Un Jun Yin Rui P. Martins Pui-In Mak University of Macau Macau China Instituto Superior Tecnico/University of Lisboa Lisbon Portugal

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

sensor nodes with machine learning (ML) are adept at analyzing intricate environmental and physiological data patterns at the edge [1]–[9]. The design of such ultra-low-power (ULP) devices strives to reduce power consumption, which ensures continuous and energy-harvested operation even with fluctuating ambient available energy levels down to 100nW [10]–[11]. Consequently, ML capabilities on such ULP devices are constrained to perform lightweight detection for events such as voice activity [3] [6], arrhythmia [4] [8], and bearing anomalies [9]. Yet, these isolated, monomodal sensing paradigms suffer from low task complexity and accuracy for overlooking fused information from spatially distributed sensor networks. Additionally, real-world applications subject to data distribution drifts necessitate model adaptability to maintain accuracy in ever-changing environments [12]. Also, current ULP neural network (NN) accelerators consume significant power, limiting their ability to expand the network size, while impeding the overall ML performance.

关键词： Voice activity detection Performance evaluation Accuracy Power demand Limiting Multimodal sensors Machine learning Silicon Physiology Solid state circuits

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IoT-Enhanced Airborne Pathogen Surveillance in Healthcare Environments using Deep Generative networks

IoT-Enhanced Airborne Pathogen Surveillance in Healthcare En...

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2025 International conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Prabhu, V.S. Sekar, Satheeshkumar Dhanalakshmi, S. Mohankumar, N. Gopi, B. Murugan, S. R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Mphasis Corporation Project Manager ChandlerAZ United States Sri Muthukumaran Institute of Technology Department of Information Technology Tamil Nadu Chennai India Nagpur Campus Pune India Innovation Center Sona College of Technology Department of Biomedical Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

In hospital settings, airborne pathogens present significant risks to the health and safety of workers and patients. This research proposes an enhanced method for airborne pathogen detection by integrating deep generative networks (DGNs) with Internet of Things (IoT) technologies. The system uses a network of IoT sensors placed in hospitals to continuously monitor environmental factors, including humidity, temperature, and particulate matter, to identify pathogens. By analyzing the data collected by the sensors, DGNs can accurately detect the amount and kind of airborne pathogens by modeling complicated patterns. The system's deep generative method enhances the model's capacity to identify minute variations in pathogen levels by producing artificial data that complements sensor data. The possibility of spreading infection may be greatly reduced with real-time data processing and analysis, which enables quick alerts and responses. The proposed system's capacity to enhance infection control measures and sustain a safer hospital environment is shown via simulations and real implementations, proving its efficiency. Integrating IoT real-time monitoring capabilities with DGNs' complex analytical capability signifies a notable leap forward in pathogen surveillance. © 2025 IEEE.

关键词： Deep Generative networks Environmental sensors Infection Control Internet of Things (IoT) Pathogen Detection Real-Time Monitoring

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On Predicting Emotion From Commercial Smartwatch - A Big Data Narrative

On Predicting Emotion From Commercial Smartwatch - A Big Dat...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Partha Pratim Sarmah Partha Sarathi Paul Krishnandu Hazra Dept. of CSE IIITG Assam India School of Computer Engineering KIIT University Bhubaneswar India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Smart wearable devices such as smartwatches and smart bands are all-pervasive nowadays. They are accompanied by various sensors like heart rate monitors, oximetry sensors, ambient light and temperature sensors, etc. that collect different physiological data about the individuals while wearing. Such data can effectively be used to detect the activities of the user. Here, one relevant intriguing question arises: Is it feasible to predict the emotional status of an individual by utilizing such physiological data from such devices? To address this question, no such work utilizes commercial smartwatches. Therefore, to fill this gap, in this work, we analyze and predict emotions by utilizing the physiological data collected by the commercial Fitbit smartwatch. Here, we have utilized the Lifesnaps Fitbit dataset, a large public dataset of size 9.72 GB that contains both physiological sensor data and psychological survey data collected from 71 volunteers from different demographic areas using commercially available Fitbit smartwatches between May 2021 and January 2022. Here we investigate the correlation between emotional state and physiological parameters using data collected from the Lifesnaps dataset. We perform comprehensive data pre-processing such as data cleaning, data imputation, and scaling to ensure the data integration and usability of the dataset. We apply various non-neural machine learning algorithms (Support Vector Machine, Logistic Regression, Decision Trees, LightGBM, XGBoost, and Random Forest) and achieve an accuracy of up to 78.75% in predicting emotions. We further enhanced the performance of the prediction through efficiently hyper-tuning two neural network approaches (i.e., Feedforward neural network, and BiLSTM) and achieved a maximum accuracy of 97% in the Feedforward neural network. Our experimental evaluation shows a significant potential in commercial smartwatches for mental health and emotion detection.

关键词： Temperature sensors Temperature measurement Wearable Health Monitoring Systems Correlation Accuracy Big Data Feedforward neural networks Biomedical monitoring Usability intelligent sensors

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Ceramic-Based High-Q Retroreflectors for Sub-mm Localization in High-Temperature Environments

Ceramic-Based High-Q Retroreflectors for Sub-mm Localization...

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International Workshop on Mobile Terahertz Systems (IWMTS)

作者： Alejandro Jiménez-Sáez Masoud Sakaki Jesús Sánchez-Pastor Martin Schüßler Rolf Jakoby Niels Benson Technical University of Darmstadt Darmstadt Germany University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

This scientific paper is an outcome of the MARIE project (for more information, see ***) C09. This research addresses the development of innovative frequency-coded tags for localization, sensing, and ranging in harsh environments. The study focuses on enhancing orientation-insensitive THz retroreflectors through advanced approaches, including multi-notch high Radar Cross-Section (RCS) Corner Reflectors (CR) and Quasi Conformal Transformation Optics (QCTO)-enabled designs. Core innovations involve the integration of Luneburg lenses with Photonic Crystal (PhC)-based coding particles, fabricated using advanced materials such as Deep Reactive Ion Etched (DRIE) High-Resistive Silicon (HR-Si) and Lithography-based Ceramic Additive Manufacturing (LCM) with Alumina (Al₂O₃). These designs enable high-Q resonators for effective signal detection in extreme conditions up to 1200 °C, as in an indoor fire scenario. Achievements include wide-field-of-view (FoV) retroreflectors, frequency-coded lens reflectors, and wireless readouts capable of sub-mm localization. Future work will explore multi-material additive manufacturing and Liquid Crystal (LC)-based Reconfigurable intelligent Surfaces (RIS) to advance state-of-the-art localization systems for autonomous rescue applications. These findings establish a foundation for robust, high-temperature sensor fusion technologies.

关键词： Location awareness Wireless communication Wireless sensor networks Ultraviolet sources Reconfigurable intelligent surfaces Three-dimensional printing sensors Retroreflectors Resonators Lenses

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A Comparative Study of Validation Methods for sensor-Based Human Activity Recognition Using Deep Learning Models

A Comparative Study of Validation Methods for Sensor-Based H...

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International ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Narit Hnoohom Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Computer Engineering Faculty of Engineering Image Information and Intelligence Laboratory Mahidol University Nakhon Pathom Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

With the rise of wearable sensors and smart devices, human activity recognition (HAR) has become a vital research area in ubiquitous computing. Although many studies report high accuracy using k-fold cross-validation, these results often do not reflect actual generalization interpretation due to subject-dependent data leakage, where models test on activities from the subjects they were trained. This study compares traditional k-fold cross-validation with leave-one-subject-out (LOSO) validation using the WISDM dataset, highlighting the importance of proper validation techniques in HAR systems. We implemented and evaluated five advanced deep learning models – convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) – on windowed raw sensor data. Our experiments showed significant differences between validation methods. The BiGRU model achieved 97.91% accuracy with k-fold cross-validation and 98.02% with LOSO validation, while the CNN model achieved only 92.55% and 93.44%, respectively. These results underscore the impact of both model architecture and validation approach on performance. Our findings stress the need for subject-independent validation strategies like LOSO to develop truly generalizable HAR systems.

关键词： Deep learning Accuracy Bidirectional long short term memory Ubiquitous computing Data models Human activity recognition Convolutional neural networks Smart devices Wearable sensors Stress

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Wireless Fire Safety Monitoring System

Wireless Fire Safety Monitoring System

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International Russian Smart Industry conference (SmartIndustryCon)

作者： A. Y. Trofimov K. G. Gajiyev M. A. Portnov A. A. Merkulov National Research University “Moscow Power Engineering Institute” Moscow Russia MIREA — Russian Technological university Moscow Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The development of modern industrial systems and their protection from emergencies is currently associated with the integration of wireless radio-electronic modules for monitoring the state of processes and devices that implement production processes. The use of wireless device communication technologies and fast data processing helps to increase the reliability of industrial systems, which is achieved by scanning devices and evaluating their parameters in real time. The use of IoT modules for wireless device management in human life safety systems is a priority, minimizing the impact of human error during emergencies in industrial IoT systems, as well as for special ones, such as those with a closed ecosystem. In this article, the authors analyzed the algorithms of microprocessor-based human life safety systems and the use of IoT solutions in security systems. A system for monitoring the condition of fire detectors has been developed. The intelligent module that scans the status of fire safety sensors is based on the K1986VE91T controller running on the cortex-m3 core. Data processing and statistics collection were implemented on a separate module based on the STM32F407VGT6 controller, with which the data collection device is in contact via wireless communication. Experimental data obtained on practical equipment confirmed the effectiveness of the proposed algorithms and the possibility of integration with existing fire safety systems.

关键词： Wireless communication Wireless sensor networks Microcontrollers Transmission line measurements Software Communication system security Software measurement Monitoring Fire safety Industrial Internet of Things

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Task Offloading Scheduling and Privacy Protection Optimization in Vehicular Edge Computing Based on Double Deep Q-Network 18th

Task Offloading Scheduling and Privacy Protection Optimizat...

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18th China conference on Wireless sensor networks, CWSN 2024

作者： Weifeng, Lu Saijun, Yang Jia, Xu Lijie, Xu Lingyun, Jiang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Xianlin Street Jiangsu Nanjing210023 China

ISBN: (纸本)9789819621859

As the applications and services within vehicular networking systems become increasingly diverse, vehicles with limited computing resources face challenges in handling these computationally intensive and latency-sensitive tasks. In this paper, we propose an improved Task Offloading Scheduling Strategy based on a Double Deep Q-Network (TOSDDQN) for a dynamic multi-vehicle, multi-edge unit task offloading environment in vehicular networking. This strategy designs communication models and computational models aimed at minimizing the total system overhead in processing tasks. To further protect the privacy of vehicle users, a Distributed Training Algorithm based on Federated Learning (DTAFL) is proposed. Comparative experimental results indicate that, compared to three other task offloading schemes, our approach reduces the average system overhead in processing tasks by 11%–36%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computation offloading

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