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检索条件"任意字段=Conference on Radar Sensor Technology and Data Visualization"

共 4468 条记录，以下是51-60 订阅

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Frozen Food Supply Chain Monitoring with IoT

Frozen Food Supply Chain Monitoring with IoT

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International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Mohd Sallehin Mohd Kassim Muhammad Haziq bin Rozman Faridah Yahya Advanced Telecommunication Technology Cluster International Industrial and Institutional Partnership Department Computer Engineering Technology Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Bachelor of Information Technology (Hons) Internet of Thing Computer Engineering Technology Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Applied Statistics and Data Science Clusters (ASAD) Computer Engineering Technology Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This project aimed to implement a prototype capable of real-time monitoring and control of various parameters in information management setting with Internet of Things (IoT). Research methodology involved designing and implementing the system prototype to resemble a supply container or transportation truck with installed sensors controlled by a microcontroller. Main components used temperature sensors (AHT20), RFID reader for access control, GPS module for location tracking, and a web/mobile dashboard for remote monitoring. Additionally, a robust database system was developed to store sensor readings and transaction history, ensuring data integrity and facilitating seamless communication between components for real-time data transmission. Key findings indicate that the system effectively monitors temperature, manages access control through RFID authentication, tracks real-time location via GPS, and provides a user-friendly dashboard interface for data visualization.

关键词： Temperature sensors Access control Temperature measurement Supply chain management Supply chains Prototypes data visualization Real-time systems Temperature control sensors

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A Laser SLAM Localization Method Fusing Multi-sensor Velocity Observations

A Laser SLAM Localization Method Fusing Multi-Sensor Velocit...

引用

sensors and Information technology (ICSI), International conference on

作者： Zehao Chen School of automation and intelligence Beijing Beijing Jiaotong University Beijing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Simultaneous Localization and Mapping (SLAM) is widely used in the field of autonomous robot navigation. Laser SLAM can leverage high-precision environmental geometric information obtained from LiDAR to achieve relatively accurate pose estimation in static and structured environments. To address the decline in accuracy and robustness of the laser odometry system in dynamic environments, under sensor observation constraints, and in cases of feature loss, a scalable multi-sensor fusion system is proposed. Based on the Kalman filter framework, this system integrates external sensor observations such as IMU, GNSS, and Camera with laser odometry localization information, enhancing the system's localization accuracy, stability, and motion estimation smoothness. The fusion method allows for the flexible addition or removal of sensors with poor observations according to actual needs, thereby adapting to different application scenarios. Experimental data from campus environments demonstrate that the proposed multi-sensor fusion localization method exhibits higher localization accuracy and stability compared to a single laser odometry system.

关键词： Location awareness Simultaneous localization and mapping Accuracy Laser radar Laser stability Cameras Robustness Odometry Kalman filters Laser fusion

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

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

引用

Visual Analytics and data visualization (ICVADV), International conference on

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Temperature sensors Temperature measurement Pathogens Hospitals Atmospheric modeling Surveillance Real-time systems data models sensor systems Internet of Things

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Study and Efficiency Analysis of data Frames Unification Process for a Pair of Wireless sensor Networks Protocols ZigBee and WirelessHART

Study and Efficiency Analysis of Data Frames Unification Pro...

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Farah J. Yousef Department of Information Systems Moscow State University of Technology “STANKIN” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents an efficiency and performance analysis of the data frame unification process for two wireless sensor network (WSN) protocols, ZigBee and WirelessHART, at the data link layer. A software emulator was developed to enable the unification and interoperability of multiple data frames for both standards, based on a proposed theoretical solution to address interoperability issues in WSN standards. The emulator was implemented using Python within the Visual Studio Code environment. Key performance indicators, such as frame loss rate and total frame processing time, were evaluated under various scenarios and parameters. The results demonstrate that increasing the packet intensity and the number of nodes leads to a proportional increase in both frame processing time and frame loss rate. These findings provide valuable insights into the scalability and efficiency of the proposed solution for WSN interoperability.

关键词： Wireless sensor networks visualization Protocols Codes Scalability Zigbee Software Interoperability Standards Python

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SurroundSense: Event-Driven User Personalization with Ambient Context Using Ultra-Wideband radar

SurroundSense: Event-Driven User Personalization with Ambien...

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Consumer Communications and Networking conference, CCNC IEEE

作者： Prajwal Ranjan Jamsheed Manja Ppallan Yellappa Damam Sakshi Badiger Madhan Raj K Raghav Mangla Rajip Thakur Chiho Kim Connectivity R&D Samsung R&D Institute India-Bangalore (SRI-B) India Connectivity R&D Group Samsung Electronics Suwon South Korea

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

In this paper, we propose SurroundSense, a novel event-driven framework that generates contextual information based on the user's surroundings using the Impulse Radio Ultra-Wideband (IR-UWB) radar integrated into smartphones. By leveraging the channel impulse response (CIR) data obtained from the IR-UWB radar, SurroundSense constructs a detailed contextual understanding of the user's environment, allowing for tailored recommendations based on the surrounding conditions. The system processes the CIR data and combines it with acoustic sensing information, specifically noise levels and ambient light information to generate the surrounding information. Further-more, utilizing the IMU sensors enables an expanded field-of-view (FOV), providing a comprehensive $360^o$ environmental awareness. This enhanced surrounding information enhances the accuracy and pertinence of personalized recommendations. Our approach showcases the potential of incorporating IR-UWB technology into personalization algorithms, ultimately improving user experiences and facilitating adaptive, context-aware applications.

关键词： Training Power demand Accuracy radar sensor fusion sensors Channel impulse response Smart phones Ultra wideband technology Testing

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Drone-Based Aerial Surveillance and Real-Time Hazardous Gas Leakage Detection System with Power BI Dashboard Integration for Enhanced Environmental Safety

Drone-Based Aerial Surveillance and Real-Time Hazardous Gas ...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： Krithick Balaji Ramesh Saran S Department of Computing Technologies SRM Institute of Science & Technology Chennai India Department of (AIML) SRM Institute of Science & Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This paper focuses on an embedded system for air quality control and hazardous gas detection using multiple sensors to trigger real-time danger alerts. The system employs and integrates gas sensors MQ-2, MQ-3, and MQ-9, temperature, and humidity sensors for assessing many combustible gases including methane, carbon monoxide alcohol smoke and even fire. These sensors send signals to an embedded microcontroller which controls air and environmental quality for the circumferential range. Apart from the gas sensors, a fire sensor has been integrated within the system, improving its efficiency in ascertaining cases of fire outbreak in the presence of gas leakages. In addition to this, Power BI works with the system for sensor monitoring and data visualization through the sensors being connected online. The dashboard shows the most important parameters such as the level of gas leakage and the type of gas involved, the temperature of the surroundings, and the humidity. Power BI supports how to visualize what is going on at that time to monitor environmental conditions, how bad is the gas leakage, and make quick decisions. The dashboard also helps in looking out for risks by showing past trends of data and carrying out market predictions. The integration of multi-sensor systems with a drone is intensified to outstretch the boundaries even further where remote monitoring of industrial sites, areas that have been hit by disasters, and even of cities, is possible. The drone equipped with the embedded system provides the real time gas leak detection temperature, humidity monitoring and fire threat assessment in environments where the human presence is quite dangerous. This system is also easy, cheap and provides an admirable solution for environmental monitoring on a day to day basis for the sake of public safety, reduction in the response rates to dangerous situations and the encouragement of smart city designs. The research in this study focused on both stationary and mobile w

关键词： Temperature sensors Gases Embedded systems data visualization Humidity Real-time systems sensor systems sensors Monitoring Gas detectors

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Band Prompting Aided SAR and Multi-Spectral data Fusion Framework for Local Climate Zone Classification

Band Prompting Aided SAR and Multi-Spectral Data Fusion Fram...

引用

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

作者： Haiyan Lan Shujun Li Mingjie Xie Xuanjia Zhao Hongning Liu Pengming Feng Dongli Xu Guangjun He Jian Guan College of Computer Science and Technology Harbin Engineering University Harbin China School of Astronautics Beihang University Beijing China School of Software Dalian University of Technology Dalian China State Key Laboratory of Space-Ground Integrated Information Technology Beijing China Independent Researcher

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Local climate zone (LCZ) classification is of great value for understanding the complex interactions between urban development and local climate. Recent studies have increasingly focused on the fusion of synthetic aperture radar (SAR) and multi-spectral data to improve LCZ classification performance. However, it remains challenging due to the distinct physical properties of these two types of data and the absence of effective fusion guidance. In this paper, a novel band prompting aided data fusion framework is proposed for LCZ classification, namely BP-LCZ, which utilizes textual prompts associated with band groups to guide the model in learning the physical attributes of different bands and semantics of various categories inherent in SAR and multi-spectral data to augment the fused feature, thus enhancing LCZ classification performance. Specifically, a band group prompting (BGP) strategy is introduced to align the visual representation effectively at the level of band groups, which also facilitates a more adequate extraction of semantic information of different bands with textual information. In addition, a multivariate supervised matrix (MSM) based training strategy is proposed to alleviate the problem of positive and negative sample confusion by completing the supervised information. The experimental results demonstrate the effectiveness and superiority of the proposed data fusion framework.

关键词： Training visualization Semantics data integration Signal processing Robustness data mining Speech processing Synthetic aperture radar Meteorology

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An Autonomous Charging System for A Two Wheeled Movable Robot

An Autonomous Charging System for A Two Wheeled Movable Robo...

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sensors and Information technology (ICSI), International conference on

作者： Jiamei Wang Guangliang Liu Quancheng Lin Lin Zhu Yali Wang Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Robot and Manufacturing Automation Technology Jinan China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

This paper designs an autonomous charging system for a two-wheeled mobile robot. The system utilizes LiDAR to identify a charging station with a specific structure, collects and processes data to determine the target point, and employs a feedback control law to achieve smooth motion of the robot. Ultimately, the robot precisely docks with the charging station and total the charging job.

关键词： Smart agriculture Laser radar Robot kinematics Process control Charging stations Robot sensing systems sensor systems Safety Mobile robots Intelligent sensors

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Optimization of Target Detection and Tracking Algorithm Combined with Multimodal Photoelectric sensors

Optimization of Target Detection and Tracking Algorithm Comb...

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Intelligent Systems and Computational Networks (ICISCN), International conference on

作者： Yiwei Liu Yanshuo Wu Wenbin Xie School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In complex environments, the accuracy and robustness of single-mode optoelectronic sensors in target detection and tracking are often limited. This article proposes an algorithm optimization method that combines multimodal optoelectronic sensors. Firstly, integrate data from multiple sensors such as visible light, infrared, and radar to obtain more comprehensive target information. Secondly, a target detection model based on YOLO (You Only Look Once) was designed. Then, the improved Kalman filter algorithm is used for target tracking. Finally, optimize the collaborative processing of multisource information through data fusion strategies. The results indicate that the research system exhibits good adaptability, verifying the effectiveness of multimodal fusion and algorithm optimization. Specifically, based on visible light sensors, the detection accuracy was improved by 10.1% after adopting multimodal fusion methods; Compared with the traditional Kalman filter, the improved Kalman filter reduces tracking error by 51.7%; When dealing with complex scenarios, the accuracy and recall of the system reach 92.6% and 90.8%, respectively.

关键词： YOLO Target tracking Accuracy radar sensor fusion radar tracking Robustness Real-time systems sensors Kalman filters

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Earthquake Prediction by Generating and Detecting Seismic Wave

Earthquake Prediction by Generating and Detecting Seismic Wa...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Angel Joy K M Mysa Anlin P Paul Joyson Johnson Binet Rose Siji Joseph Department of Electronics and Communication Engineering Sahrdaya College of Engineering and Technology Kodakara Kerala India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This project aims to develop a real-time earthquake detection and classification system using a combination of hardware, machine learning, cloud computing, and mobile application development. The system utilizes an MPU6050 accelerometer and vibration sensor to continuously monitor seismic activity, with data transmission handled by an ESP8266 WiFi module to store readings in Firebase for further analysis. The collected data is labeled under various conditions, namely normal, vibration, and earthquake, to train a Random Forest classifier that could distinguish between the seismic states effectively. The model is then used to classify the real-time inputs from the sensor, thus correctly detecting the events of earthquakes. To make the system more user-friendly, a Flutter-based mobile application is developed for real-time visualization of sensor data and state classification. The system sends out instant voice notifications and phone vibrations whenever it detects an earthquake-level vibration. The model was tested using different levels of vibration by simulating various seismic conditions, and the results proved that it was reliable in the detection of earthquakes. While the current implementation performs well, future enhancements could include integrating additional sensors, refining the classification model, and optimizing real-time data processing for increased accuracy. This project presents a scalable and practical earthquake detection system, leveraging sensor technology, machine learning, and mobile applications to provide an efficient early warning mechanism for earthquake-prone regions.

关键词： Vibrations Accelerometers Cloud computing Earthquakes Refining Real-time systems data models Mobile applications Reliability data communication

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