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Error Analysis of Current sensor Based on Fluxgate sensor Array 18

Error Analysis of Current Sensor Based on Fluxgate Sensor Ar...

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18th IEEE conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhao, Chuanfeng Yang, Shanglin Jiang, Lijin Liou, Juin Jei North Minzu University School of Eletrical and Information Engineering Yinchuan China Republic Polytechnic School of Engineering Singapore Singapore

ISBN: (纸本)9798350312201

With the development of non-contact online measurement in smart grid, AC/DC power supply, new energy, high-speed trains, intelligent buildings, and other fields, the performance requirements of current sensors are getting higher and higher. As a possible new idea, the magnetic sensor array current measurement method has theoretical advantages in accuracy, bandwidth, measurement range, anti-interference ability, and universality. The structure of the magnetic sensor array, measurement principle, and data processing method are introduced. Some factors that affect its measurement accuracy such as geomagnetism, wire position, and crosstalk. Simulation of the measured values under various interferences is carried out to observe its influence on the measured values. Using fluxgate sensor to make sensor array and test its measurement performance. This paper has reference value for understanding the current measurement method of the magnetic sensor array and improving the performance of the magnetic sensor. © 2023 IEEE.

关键词： Timing circuits

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Enhancing Chewing Detection with a CNN-BiLSTM Network Utilizing IMU and Photoplethysmography Data from an Earable Device 15

Enhancing Chewing Detection with a CNN-BiLSTM Network Utiliz...

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15th IEEE International conference on Software Engineering and Service Science (ICSESS)

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jeefoo, Phaisarn Jitpattanakul, Anuchit Univ Phayao Sch Informat & Commun Technol Dept Comp Engn Phayao Thailand King Mongkuts Univ Technol North Bangkok Dept Appl Stat Fac Sci Appl Res Grp Stat Learning & Inference Bangkok Thailand Univ Phayao Sch Informat & Commun Technol Geog Informat Syst Phayao Thailand King Mongkuts Univ Technol North Bangkok Dept Math Fac Sci Appl Intelligent & Nonlinear Dynam Innovat Res Ctr Bangkok Thailand

ISBN: (纸本)9798350389166;9798350389173

Detecting chewing is vital to tracking eating habits, which is necessary to preserve good well-being and health. Advancements in wearable sensors have recently enabled capturing a wide range of sensor data, including signals from inertial measurement units (IMUs) and photoplethysmography (PPG). This research presents a new deep-learning design that efficiently detects chewing occurrences employing IMU and PPG sensor data acquired from an early instrument. The design includes convolutional neural networks (CNN) and bidirectional long short-term memory (LSTM). The CNN-BiLSTM network presented in this study combines the advantages of both CNN and BiLSTM to capture spatial and temporal characteristics from the sensor data effectively. The CNN layers extract localized patterns and characteristics from the IMU and PPG data, whereas the BiLSTM layers represent the extended relationships and timebased changes in chewing behavior. By integrating these two formidable designs, our network attains a high level of resilience and precision in detecting chewing. We assess the efficacy of the CNN-BiLSTM network using a standardized dataset obtained from several individuals wearing wearable devices while engaging in eating events. The dataset comprises IMU and PPG sensor data that have been annotated with associated chewing sessions. The experimental findings clearly show that the suggested network outperforms the current leading approaches, obtaining an accuracy of 98.18% and an F1-score of 98.29%, the highest recorded. The CNN-BiLSTM network performs better than baseline models such as CNN, LSTM, BiLSTM, GRU, and BiGRU, emphasizing the efficacy of integrating spatial and temporal modeling for chewing identification.

关键词： chewing detection earable device deep learning IMU sensor

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Vi-Fi: Associating Moving Subjects across Vision and Wireless sensors 21

Vi-Fi: Associating Moving Subjects across Vision and Wireles...

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21st ACM/IEEE International conference on information processing in sensor networks (IPSN)

作者： Liu, Hansi Alali, Abrar Ibrahim, Mohamed Cao, Bryan Bo Meegan, Nicholas Li, Hongyu Gruteser, Marco Jain, Shubham Dana, Kristin Ashok, Ashwin Cheng, Bin Lu, Hongsheng Rutgers State Univ New Brunswick NJ 08901 USA Old Dominion Univ Norfolk VA 23529 USA Saudi Elect Univ Riyadh Saudi Arabia Carnegie Mellon Univ Pittsburgh PA 15213 USA SUNY Stony Brook Stony Brook NY 11794 USA Google Res Mountain View CA USA Georgia State Univ Atlanta GA 30303 USA Toyota Motor North Amer R&D InfoTech Labs Ann Arbor MI USA

ISBN: (纸本)9781665496247

In this paper, we present Vi-Fi, a multi-modal system that leverages a user's smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e.g. WiFi MAC address). Our approach uses a recurrent multi-modal deep neural network that exploits FTM and IMU measurements along with distance between user and camera (depth information) to learn affinity matrices. As a baseline method for comparison, we also present a traditional non deep learning approach that uses bipartite graph matching. To facilitate evaluation, we collected a multi-modal dataset that comprises camera videos with depth information (RGB-D), WiFi FTM and IMU measurements for multiple participants at diverse real-world settings. Using association accuracy as the key metric for evaluating the fidelity of Vi-Fi in associating human users on camera feed with their phone IDs, we show that Vi-Fi achieves between 81% (real-time) to 91% (offline) association accuracy.

关键词： Wireless communication Deep learning Wireless sensor networks Correlation Atmospheric measurements Network architecture Cameras

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RSITS: Road Safety intelligent Transport System in Deep Federated Learning Assisted Fog Cloud networks 19th

RSITS: Road Safety Intelligent Transport System in Deep Fed...

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Mobile Web and intelligent information Systems - 19th International conference, MobiWIS 2023, Proceedings

作者： Grønli, Tor-Morten Lakhan, Abdullah Younas, Muhammad Department of Technology Kristiania University College Kirkegata 24-26 Oslo0153 Norway School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom

ISBN: (纸本)9783031397639

This paper presents the road safety intelligent transport system (RSITS) based on deep federated learning-assisted fog cloud networks. RSITS offers mobile LiDAR sensors and vehicle LiDAR sensors-enabled applications to alert road safety mechanisms. To deal with the complex features of road safety, we trained the large pedestrian and vehicle detection dataset on different road safety fog servers and aggregated them on the centralized cloud. To ensure that constraints such as safety, the accuracy of alarms, response times, security, and deadlines are met, we present a deep federated learning scheduling scheme (DFLSS) that consists of different components: Initially, we bound all applications so that emergency tasks, such as moving an object within 5 m, should be processed locally with the minimum response time. Due to resource constraints and the limitations of devices, other tasks of applications are offloaded to the centralized cloud for processing. To ensure security, each computing node must encrypt and decrypt data before offloading and processing it in DFLSS. Simulation results show that the proposed DFLSS outperformed all existing approaches regarding accuracy, response time, and road safety application deadlines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fog

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Sink Trajectory Planning for Data Collection in Mobile Wireless sensor Network 11th

Sink Trajectory Planning for Data Collection in Mobile Wir...

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11th International conference on Communications, Signal processing, and Systems, CSPS 2022

作者： Tian, Xiaoqing Li, Siwei Liang, Jing School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819926527

In mobile wireless sensor networks, the efficient collection of sensors’ data is the key to ensuring the quality of space monitoring. In this paper, considering the trajectory length and energy consumption of network data collection, we propose the virtual force and improved LEACH (Low Energy Adaptive Clustering Hierarchy) based algorithm (V-ILEACH) to realize the trajectory planning of sink. Compared with the algorithm based on traveling salesman problem (TSP), V-ILEACH achieves significant reduction of trajectory length on the basis of ensuring complete data collection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wireless sensor networks

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A Composite intelligent Forecasting Method for Load Demand of Distribution networks Based on Deep Learning 3

A Composite Intelligent Forecasting Method for Load Demand o...

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3rd International conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2023

作者： Jiarula, Yasenjiang Zhang, Liyang Luo, Li Xinjiang University Urumqi830000 China

ISBN: (纸本)9798350303698

This paper proposes a composite intelligent prediction method for distribution network load demand based on deep learning. Firstly, the method preprocesses the distribution network operation information collected by sensors, supplements the missing values of data through cubic Spline interpolation fitting function, and realizes dimensionless data processing by standardization and interval scaling. Based on this, XGBoost algorithm is used to extract the characteristics of distribution network load demand data, and long-term and short-term memory networks in deep learning are used to build load demand prediction models, Complement the Dense layer in the long-term and short-term memory network to achieve composite prediction. The experimental results show that this method can effectively improve the accuracy and stability of load demand prediction. © 2023 IEEE.

关键词： Forecasting

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Implementation of Internet of Things in Wireless sensor networks for Environmental Monitoring 2

Implementation of Internet of Things in Wireless Sensor Netw...

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2nd IEEE International conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Vineela, M. Krishna, V. Rama Rani, K. Sandhya Nadh Singh, Bhaludra R. Sri Varsha, Kasarla Vaishnavi, Kura Venkata Sai Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Telangana Hyderabad India Department of Computer Science and Engineering Annamacharya institute of Technology and Sciences Telangana Hyderabad India Department of Computer Science and Engineering MallaReddy College of Engineering &Technology Telangana Hyderabad India Bhoj Reddy Engineering College for Women Telangana Hyderabad59 India

ISBN: (纸本)9798350315905

Data is collected by the end tags, which in turn send it to the router, which forwards it to the coordinator and provides services to multiple clients, one of which is the display of collected data. Finally, the collected data is sent to the cloud via the base station, which can be accessed remotely via Ethernet. These include sensors for heat, cold, vibration, pressure, humidity, light, and air and water pollution. Recently, attention has been focused on wireless technologies such Wi-Fi [8, 9], Bluetooth [7, 10], and Zig Bee [6, 7].Ultimate goal in writing this article is to provide a framework that can be utilised on a global scale to control variables in any ecosystem. All environmental data in real time is gathered by a Raspberry Pi and associated sensors, and then retrieved by a web server for display. Since this information is available online, the user is not restricted to their physical location. The Raspberry Pi serves as a hub that establishes a zigzag link between a network of dispersed sensor nodes. In several applications, including habitat monitoring, "Wireless sensor networks" (WSN) have been utilized to gather data on physical phenomena. The term "Internet of Things" (IoTs) refers to the interconnection and intelligent linking of items such as smartphones, Internet televisions, sensors, and actuators, which creates new channels of interaction between people and their belongings. © 2023 IEEE.

关键词： Internet of things

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SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems 21

SPARCS: A Sparse Recovery Approach for Integrated Communicat...

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21st ACM/IEEE International conference on information processing in sensor networks (IPSN)

作者： Pegoraro, Jacopo Lacruz, Jesus O. Rossi, Michele Widmer, Joerg Univ Padua Padua Italy IMDEA Networks Inst Madrid Spain

ISBN: (纸本)9781665496247

A well established method to detect and classify human movements using Millimeter-Wave (mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed micro-Doppler) of the different body parts, which requires a regularly spaced and dense sampling of the Channel Impulse Response (CIR). This is currently done in the literature either using special-purpose radar sensors, or interrupting communications to transmit dedicated sensing waveforms, entailing high overhead and channel utilization. In this work we present SPARCS, an integrated human sensing and communication solution for mmWave systems. SPARCS is the first method that reconstructs high quality signatures of human movement from irregular and sparse CIR samples, such as the ones obtained during communication traffic patterns. To accomplish this, we formulate the micro-Doppler extraction as a sparse recovery problem, which is critical to enable a smooth integration between communication and sensing. Moreover, if needed, our system can seamlessly inject short CIR estimation fields into the channel whenever communication traffic is absent or insufficient for the micro-Doppler extraction. SPARCS effectively leverages the intrinsic sparsity of the mmWave channel, thus drastically reducing the sensing overhead with respect to available approaches. We implemented SPARCS on an IEEE 802.11ay Software Defined Radio (SDR) platform working in the 60 GHz band, collecting standard-compliant CIR traces matching the traffic patterns of real WiFi access points. Our results show that the micro-Doppler signatures obtained by SPARCS enable a typical downstream application such as human activity recognition with more than 7 times lower overhead with respect to existing methods, while achieving better recognition performance.

关键词： Time-frequency analysis Estimation information processing Traffic control Millimeter wave radar sensors Millimeter wave communication

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Application of foundation models for autonomous driving: a survey of data synthesis 8

Application of foundation models for autonomous driving: a s...

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8th International conference on Traffic Engineering and Transportation System, ICTETS 2024

作者： Gao, Song Gao, Bolin Wei, Peng Guo, Jianpeng Yuan, Meng Han, Cheng Xu, Yueyun School of Vehicle and Mobility Tsinghua University China National Innovation Center of Intelligent and Connected Vehicles Beijing China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

With the evolution of data-driven autonomous driving technology, transferring driving responsibility from humans to machines is now feasible. Addressing the long-tail distribution problem in autonomous driving data is crucial for enhancing safety. Recent advances in foundational models, including large language models (LLMs) and generative AI models, have significantly improved data synthesis capabilities. This paper reviews the application of foundation models in three key areas: sensor data synthesis, traffic flow synthesis, and world models. © 2024 SPIE.

关键词： Data modeling 3D modeling Autonomous driving Diffusion RGB color model Image segmentation Video Point clouds 3D image processing sensors

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Design of intelligent Reflecting Surface-Assisted Wireless Computational Imaging 15

Design of Intelligent Reflecting Surface-Assisted Wireless C...

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15th IEEE International conference on Wireless Communications and Signal processing, WCSP 2023

作者： Li, Mili Chen, Xiaoming Zhejiang University College of Information Science and Electronic Engineering Hangzhou320027 China

ISBN: (纸本)9798350324662

Wireless imaging, as a typical method of wireless sensing, is a promising application of the sixth generation (6G) wireless networks. In particular, radio-frequency signals originally dedicated to communication are also used for imaging without extra detectors or sensors. Wireless imaging usually suffers from channel fading, interference and noise, resulting in low imaging accuracy. Hence, advanced signal processing approaches are employed to enhance the imaging performance, namely wireless computational imaging (WCI). In this paper, we propose to utilize intelligent reflecting surface (IRS) to further improve the performance of WCI in complex environments. By minimizing the mean square error (MSE) of the reflectivity of interested physical object, we design a reflection matrix optimization algorithm for IRS. Both theoretical analysis and simulation results show the effectiveness of the proposed algorithm for WCI in 6G cellular Internet of Things (IoT). © 2023 IEEE.

关键词： 6G intelligent reflecting surface Internet of Things wireless computational imaging

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