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2nd IEEE International conference on Control, Electronics and Computer Technology, ICCECT 2024

作者： Mo, Zixiao School of Accounting Chongqing Finance and Economic College Chongqing China

ISBN: (纸本)9798350380958

With the development of information technology, more and more big data and Internet of Things related technologies have begun to integrate into the construction of campus information, and the construction of campus security information identification system is developing in the direction of automatic, intelligent, sharing and linkage. In this study, the intelligent campus security information needs are investigated, and the campus security system based on the Internet of Things is designed, and the overall design scheme is proposed. CC2530 chip is selected to be responsible for data processing and communication coordination. The main idea is to sense the environment through various terminal modules, that is, temperature sensors, smoke detection sensors, etc. sensor nodes and coordination nodes independently establish a wireless local area network based on ZigBee protocol, and the sensor module in the sensor nodes distributed on the campus begins to power on and collect environment and security information continuously. The sensing node sends the detected information to the coordination node through multicast mode, and then the coordination node sends the detected information to the back-end control gateway through the serial port. STM32 displays security detection information through the TFT LCD module, which is processed by the system administrator;When the monitoring indicators exceed the normal range, the remote alarm function will be activated, and the back-end control gateway will send alarm information to the Android client. © 2024 IEEE.

关键词： Zigbee

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External Interaction Estimation of 6-PSS Parallel Robots with Embodied Mechanical Intelligence

External Interaction Estimation of 6-PSS Parallel Robots wit...

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2024 International conference on intelligent Robots and Systems

作者： Xia, Jingyuan Lin, Zecai Ai, Xiaojie Yu, Guangjun Gao, Anzhu Shanghai Jiao Tong Univ Inst Med Robot Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Chinese Univ Hong Kong Affiliated Hosp 2 Shenzhen 518172 Guangdong Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Traditional interaction perception of parallel robots relies on a six-dimensional force sensor for contact sensing at their distal end. However, the sensor body occupies the space of moving platform and also increases the load on the robot actuations. To enable both minimization and embodied intelligence, this paper proposes an external interaction estimation method with embodied mechanical intelligence by embedding two single-axis force sensors in each leg of 6-PSS parallel robot. The method uses a backward propagation neural network optimized by sparrow search algorithm, and it can simultaneously estimate the external force and its position using information from multiple single-axis force sensors and the encoder of driving motor. The experimental platform is established to collect the data and train the network. The result shows that the force estimation mean error is 2.4% and the position estimation error is 2.9%. A demonstration with a virtual display interface showing the reconstructed parallel robot pose, and the interaction force and its pose using the proposed estimation method, indicates the effectiveness of the proposed interaction method with embodied mechanical intelligence for 6-PSS parallel robot.

关键词： Neural networks

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Wireless-Sensing-Based Human-Vehicle Classification Method via Deep Learning: Analysis and Implementation

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IEEE INTERNET OF THINGS JOURNAL 2024年第9期11卷 16009-16020页

作者： Lou, Liangliang Song, Mingxin Chen, Xinquan Zhao, Xiaoming Zhang, Shiqing Zhan, Ming Taizhou Univ Inst Intelligent Informat Proc Taizhou 318000 Zhejiang Peoples R China Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Zhejiang Peoples R China

Wireless-sensing-based human-vehicle classification (WHVC) offer cost-effective advantages and enhance the detection efficiency of traffic parameters in intelligent transportation systems (ITSs). Existing WHVC methods primarily utilize channel state information (CSI) or received signal strength (RSS) features extracted from the surrounding wireless signals. Although CSI data provides more detailed and accurate channel information compared to RSS data, extracting and processing CSI is more challenging than RSS. Moreover, for applications that do not require fine-grained human-vehicle classification, such as intelligent street lighting systems, RSS-based WHVC has the advantages of easy implementation and low cost. Therefore, investigating the performance of CSI- and RSS-based WHVC methods in different application scenarios could provide valuable insights for the WHVC domain. To address this issue, this article proposes a deep-learning-based WHVC method, which employs deep learning as a tool to evaluate the performance of RSS and CSI methods in various classification tasks. Specifically, this article collects CSI and RSS data for seven different classification tasks in real traffic road scenarios and evaluates these tasks using a convolutional neural network (CNN)-based deep learning model designed in this article. Experimental results demonstrate that for road user categories less than four, RSS-based WHVC achieves higher accuracy than CSI-based WHVC. However, as the number of categories increases, CSI-based WHVC exhibits superior accuracy compared to RSS-based WHVC. Additionally, the developed data set is publicly available at https://***/TZ-mx/mixed-dataset.

关键词： Wireless sensor networks Wireless communication Roads Feature extraction Wireless fidelity sensors Transceivers Channel state information (CSI) deep learning human-vehicle classification (HVC) received signal strength (RSS) wireless sensing

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Active Sensing for Multiuser Beam Tracking With Reconfigurable intelligent Surface

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 540-554页

作者： Han, Han Jiang, Tao Yu, Wei Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Marvell Semicond Canada Inc Toronto ON M5H 1J9 Canada Qualcomm Technol Inc Santa Clara CA 95051 USA

This paper studies a beam tracking problem in which an access point (AP), in collaboration with a reconfigurable intelligent surface (RIS), dynamically adjusts its downlink beamformers and the reflection pattern at the RIS in order to maintain reliable communications with multiple mobile user equipments (UEs). Specifically, the mobile UEs send uplink pilots to the AP periodically during the channel sensing intervals, the AP then adaptively configures the beamformers and the RIS reflection coefficients for subsequent data transmission based on the received pilots. This is an active sensing problem, because channel sensing involves configuring the RIS coefficients during the pilot stage and the optimal sensing strategy should exploit the trajectory of channel state information (CSI) from previously received pilots. Analytical solution to such an active sensing problem is very challenging. In this paper, we propose a deep learning framework utilizing a recurrent neural network (RNN) to automatically summarize the time-varying CSI obtained from the periodically received pilots into state vectors. These state vectors are then mapped to the AP beamformers and RIS reflection coefficients for subsequent downlink data transmissions, as well as the RIS reflection coefficients for the next round of uplink channel sensing. The mappings from the state vectors to the downlink beamformers and the RIS reflection coefficients for both channel sensing and downlink data transmission are performed using graph neural networks (GNNs) to account for the interference among the UEs. Simulations demonstrate significant and interpretable performance improvement of the proposed approach over the existing data-driven methods with nonadaptive channel sensing schemes.

关键词： sensors Reconfigurable intelligent surfaces Reflection coefficient Vectors Downlink Correlation Data communication Adaptation models Reflection Long short term memory Active sensing beam tracking deep learning reconfigurable intelligent surface graph neural network recurrent neural network long short-term memory (LSTM)

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Recent advancements in Wireless sensor networks for Air Pollution Monitoring: A Survey Report of 2021-2022 1

Recent advancements in Wireless Sensor Networks for Air Poll...

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1st IEEE International conference on Advances in Electronics, Communication, Computing and intelligent information Systems, ICAECIS 2023

作者： Posina, Pranavee Chellamani, Ganesh Kumar Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Ece Chennai India

ISBN: (纸本)9798350348057

Air pollution has become a major global concern, as it affects the health and well-being of millions of people worldwide. One of the major advancements in Wireless sensor networks (WSN) for air pollution monitoring is the integration of cost effective, low-power sensors with wireless communication technologies. This has led to the development of low-cost, portable and easy-to-deploy air quality monitoring systems that can be deployed in remote areas to monitor air quality. The paper surveys the recent advancements in air pollution monitoring using wireless sensor networks. WSN is comprised of a network of wireless sensors deployed in the environment to collect data of various air pollutants, such as CO-Carbon monoxide, O3-Ozone, SO2-Sulfur dioxide, NO2-Nitrogen dioxide and PM-Particulate matter. In addition, the paper explores the usage of WSN for air pollution monitoring. © 2023 IEEE.

关键词： Microcontrollers

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Combined navigation system for substation inspection robot based on DGPS/DR multi-sensor fusion 2

Combined navigation system for substation inspection robot b...

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2023 2nd International conference on Energy and Electrical Power Systems, ICEEPS 2023

作者： Yan, Yu Yu, Xi Liao, Jianbin Liu, Liangyu Gong, Jie Yang, Yingwei State Grid Hunan Electric Power Company Extra High Voltage Substation Company Hunan Province Changsha410004 China Substn. Intelligent Operation and Inspection Laboratory of State Grid Hunan Electric Power Co. Ltd. Hunan Province Changsha410004 China School of Power and Mechanical Engineering Wuhan University Hubei Province Wuhan430072 China

To realize the routine inspection work of the capacitor tower inspection operation robot on the capacitor tower in the substation, and to guarantee the accuracy and safety of the robot's movement in the substation, a multi-sensor combination navigation system and the corresponding Kalman fusion filter are designed. Combined with the overall structure of the substation and the working environment of the inspection robot, the navigation and positioning requirements of the robot are analyzed and compared, and the multi-sensor combination positioning technology using a combination of DGPS, attitude sensors and velocity sensors is determined. The Kalman fusion filter based on the constant velocity model is used to fuse the positioning information of multiple sensors, and more accurate and reliable navigation information is obtained compared with the single-sensor positioning system. According to the working environment of the substation, an outdoor planar robot mobile navigation experiment is carried out to collect the positioning information of multiple sensors and carry out data fusion processing to verify the accuracy and robustness of multi-sensor combination navigation and positioning. © Published under licence by IOP Publishing Ltd.

关键词： Navigation systems

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Real Time Tracking of the Position of intelligent Logistics Cold Chain Transportation Vehicles Based on Wireless sensor networks 7th

Real Time Tracking of the Position of Intelligent Logistics ...

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7th EAI International conference on Advanced Hybrid information processing, ADHIP 2023

作者： Zhou, Dong’e Bao, Xunyan Guangzhou Huashang Vocational College Guangzhou511300 China Zhejiang Changzheng Vocational and Technical College Hangzhou310012 China

ISBN: (纸本)9783031505515

In order to more effectively track intelligent logistics cold chain transportation vehicles in real-time, This article proposes a real-time tracking algorithm for the position of intelligent logistics cold chain transportation vehicles based on wireless sensor networks. In the first stage, the received RSSI value of the anchor node is directly used for coarse positioning. In the second stage, the coarse positioning value is used as the initial solution optimization iteration of the wireless sensor network. In addition, this article also conducts research on the prediction and tracking of location nodes for smart logistics cold chain transportation vehicles, and discusses real-time tracking algorithms for EKF and wireless sensor networks. In view of the fact that EKF algorithm needs to abandon the information of higher order items of the system when approaching the nonlinear, which has caused error accumulation to some extent, the real-time tracking algorithm of wireless sensor network uses UT transformation and deterministic sampling strategy to linearly map the nonlinear system, which retains the information of the system to the greatest extent, and realizes effective prediction and real-time tracking of the nonlinear system. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Tracking (position)

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Clustering in Wireless sensor networks Using Fuzzy Logic for Clusterhead Election: Review 1

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8th International conference on information System Design and intelligent Applications, ISDIA 2024

作者： Harrouz, Fatima Omari, Mohammed Kaddi, Mohammed LDDI Laboratory Mathematics and Computer Science Department University of Adrar Adrar Algeria Computer Science and Engineering Department School of Engineering American University of Ras Al Khaimah Ras Al-Khaimah United Arab Emirates

ISBN: (数字)9789819786664

ISBN: (纸本)9789819786657

Wireless sensor networks refer to a collection of sensors strategically deployed in specific areas for purposes such as environmental monitoring, surveillance, military applications, and various other uses. These sensor nodes are responsible for detecting information and transmitting it to a central base station. The sensors are generally battery operated and cannot be rechargeable. Since monitoring and data transmission processes consume a significant amount of energy, achieving energy efficiency is a primary concern. Therefore, clustering has become the most popular approach to improving energy efficiency. This approach consists of dividing the network into clusters, each characterized by a cluster head sensor and several member sensors. The cluster head assumes the responsibility of collecting data from member sensors within its cluster, aggregating this data and then transmitting it to the central base station. The central aspect of this clustering approach lies in the selection of the optimal cluster head, as it significantly influences the performance and lifetime of the network. To address this challenge, various protocols have been introduced using fuzzy logic, which has become one of the most widely used methodologies in this context due to its ability to handle uncertain and ambiguous inputs, closely mimicking human reasoning processes. This survey paper aims to provide a comprehensive overview of clustering techniques that use fuzzy logic for selection of cluster heads, using a range of inputs such as residual energy levels, distance to node receiver, network density, and other relevant criteria. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： sensor nodes

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EO and radar fusion for fine-grained target classification with a strong few-shot learning baseline 33

EO and radar fusion for fine-grained target classification w...

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conference on Signal processing, sensor/information Fusion, and Target Recognition XXXIII

作者： Ballan, Luca Melo, Jorge G. O. van den Broek, Sebastiaan P. Baan, Jan Heslinga, Friso G. Huizinga, Wyke Dijk, Judith Dilo, Arta TNO Intelligent Imaging Oude Waalsdorperweg 63 NL-2597AK The Hague Netherlands

ISBN: (纸本)9781510674332;9781510674325

Combining data from multiple sensors to improve the overall robustness and reliability of a classification system has become crucial in many applications, from military surveillance and decision support, to autonomous driving, robotics, and medical imaging. This so-called sensor fusion is especially interesting for fine-grained target classification, in which very specific sub-categories (e.g. ship types) need to be distinguished, a task that can be challenging with data from a single modality. Typical modalities are electro-optical (EO) image sensors, that can provide rich visual details of an object of interest, and radar, that can yield additional spatial information. Defined by the approach used to combine data from these sensors, several fusion techniques exist. For example, late fusion can merge class probabilities outputted by separate processing pipelines dedicated to each of the individual sensor data. In particular, deep learning (DL) has been widely leveraged for EO image analysis, but typically requires a lot of data to adapt to the nuances of a fine-grained classification task. Recent advances in DL on foundation models have shown a high potential when dealing with in-domain data scarcity, especially in combination with few-shot learning. This paper presents a framework to effectively combine EO and radar sensor data, and shows how this method outperforms stand-alone single sensor methods for fine- grained target classification. We adopt a strong few-shot image classification baseline based on foundation models, which robustly handles the lack of in-domain data and exploits rich visual features. In addition, we investigate a weighted and a Bayesian fusion approach to combine target class probabilities outputted by the image classification model and radar kinematic features. Experiments with data acquired in a measurement campaign at the port of Rotterdam show that our fusion method improves on the classification performance of individual modalities.

关键词： deep learning sensor fusion image classification few-shot learning foundation models EO and radar decision support

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Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation 32

Informed Graph Learning By Domain Knowledge Injection and Sm...

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32nd European Signal processing conference (EUSIPCO)

作者： Niresi, Keivan Faghih Kuhn, Lucas Frusque, Gaetan Fink, Olga Ecole Polytech Fed Lausanne Intelligent Maintenance & Operat Syst IMOS Lab Lausanne Switzerland

ISBN: (纸本)9789464593617;9798331519773

Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informative patterns and structures across various domains. However, acquiring the underlying graphs for specific applications remains a challenging task. While graph inference based on smooth graph signal representation has become one of the state-of-the-art methods, these approaches usually overlook the unique properties of networks, which are generally derived from domain-specific knowledge. Overlooking this information could make the approaches less interpretable and less effective overall. In this study, we propose a new graph inference method that leverages available domain knowledge. The proposed methodology is evaluated on the task of denoising and imputing missing sensor data, utilizing graph signal reconstruction techniques. The results demonstrate that incorporating domain knowledge into the graph inference process can improve graph signal reconstruction in district heating networks. Our code is available at ***/Keiv4n/IGL.

关键词： Graph learning graph signal processing graph signal reconstruction smooth representation domain knowledge

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