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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71-80 订阅

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Secure Step: IoT-Enabled Anti-Theft Flooring Mat for Home Security

Secure Step: IoT-Enabled Anti-Theft Flooring Mat for Home Se...

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

作者： B.Gopinath R.Bavithra J.K.Vaishnavi S.Varsha Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

A smart flooring mat-based theft detection system is proposed by integrating piezoelectric sensors and IoT technology to improve home security. Multiple piezoelectric sensor nodes are embedded in the mat prototype model for detecting and measuring pressure changes when it is subjected to force by an intruder. The sensor nodes generate an appropriate electrical signal which is directly proportional to the applied pressure. A NodeMCU microcontroller handles this digital data, generates alarm signals and transmits the same to a cloud platform via Wi-Fi module in real-time. Because of the usage of a cloud platform, it enables remote supervising of changes in pressure conditions and generates appropriate alerts to the authorized person in case of unauthorized access of an intruder. A web application is also used to provide users with suitable graphical visualization of real-time pressure data. In addition to this, the proposed prototype model has a local LCD display unit to display real-time status updates as "STATUS NORMAL" or "THEFT DETECTED" in accordance with the detected normal or abnormal pressure patterns. The security system guarantees compactness and energy competence to use it appropriate for remote or separate arrangements. The proposed system with sensor technology and IoT creates an active and practical solution to detect unauthorized intruders in homes, workplaces, and privileged areas. By the fusion of smart active sensors and cloud usage, the system establishes a practical methodology to improve automated surveillance and protection capabilities.

关键词： Temperature sensors Vibrations Cloud computing Voltage measurement Surveillance sensor systems Real-time systems Security Intelligent sensors Wireless fidelity

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Formation Navigation and Positioning of Quadrotor UAV Based on Multi-Source Information Fusion Improved RRT* Algorithm

Formation Navigation and Positioning of Quadrotor UAV Based ...

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sensors and Information technology (ICSI), International conference on

作者： Qihao Gong Tao Deng School of Aeronautics Chongqing Jiaotong University Chongqing China Chongqing Key Laboratory of Green Aviation Energy and Power Chongqing China The Green Aerotechnics Research Institute of Chongqing Jiaotong University Chongqing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

This paper analyzes the traditional RRT* algorithm. Considering the performance constraints of quadrotor unmanned aerial vehicles (UAVs) and the limitations of the RRT* algorithm itself, the traditional RRT* algorithm is improved. Secondly, for the multi-source information fusion algorithm, a federated Kalman filter is adopted to fuse the data from multiple sensors. To address the limitation of the single reference system in the federated Kalman filter, the path points planned by the improved RRT* algorithm are used as the main reference information input, and combined with the inertial navigation system (INS) of the sub-reference system to form a two-layer reference system, which is then input into the federated Kalman filter. Finally, the effectiveness of the designed algorithm is verified in the simulation software.

关键词： Software algorithms radar Vision sensors sensor fusion Position measurement Autonomous aerial vehicles Software sensor systems Kalman filters Quadrotors

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A Power Amplifier for Electronic Photonic Integrated D-Band radar Applications

A Power Amplifier for Electronic Photonic Integrated D-Band ...

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German Microwave conference

作者： Deniz Tas Kai Scheller Amelie Hagelauer Marco Dietz Robert Weigel Fraunhofer Institute for Electronic Microsystems and Solid State Technologies EMFT Germany Institute for Smart Electronics and Systems Friedrich-Alexander Universität Erlangen-Nürnberg Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

In this work, a 3-stage power amplifier (PA) is implemented on an electronic photonic integrated circuit (EPIC) semiconductor technology. While silicon photonics are being found extensive usage in communication data center applications, there is a lack of research on their performance in wireless communication, radar, and sensing applications. This work describes the development and the evaluation of a power amplifier using IHP SiGe 250 nm BiCMOS EPIC technology. 5.5 dBm saturated output power was measured while the maximum power gain is 24.9 dB at 122.3 GHz and the 3 dB gain bandwidth is between 114 GHz to 134 GHz which is sufficient to cover ISM Band applications. The power amplifier consumes 157 mW and occupies 800 µm × 300 µm core area and the total chip area is 1250 µm × 580 µm.

关键词： Wireless communication Semiconductor device measurement Wireless sensor networks Power measurement Power amplifiers Gain measurement Solids BiCMOS integrated circuits Power generation Gain

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Design and Implementation of a Highly Efficient LIDAR-Based Teleoperated Driving System

Design and Implementation of a Highly Efficient LIDAR-Based ...

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Consumer Communications and Networking conference, CCNC IEEE

作者： Johann Nikolai Hark Julian Pfeifer Jorin Kouril Bernd Schäufele Ilja Radusch Daimler Center for Automotive Information Technology Innovations (DCAITI) Berlin Germany Fraunhofer Institute for Open Communication Technologies (FOKUS) Berlin Germany

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Automated driving has advanced significantly, but challenges remain in scenarios like urban areas and parking garages due to technical and legal constraints. As automated vehicles rise, connected vehicle technologies gain focus. Tele-operated driving serves as a bridge technology for situations where automated driving is limited. This paper presents a novel teleoperated driving system to extend the Operational Design Domain of automated vehicles, such as in underground parking, using Direct Short Range Communication (IEEE 802.11p). The approach emphasizes efficient sensor data transmission from LIDAR, preprocessing raw data to filter point clouds and detect freespace and objects, enhancing responsiveness and reliability.

关键词： Point cloud compression Bridges Laser radar Connected vehicles Law Urban areas Reliability data communication

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Improving Grape Quality in Viticulture with Autonomous Robots Using Deep Learning and sensor Fusion

Improving Grape Quality in Viticulture with Autonomous Robot...

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Advancement in Computation & Computer Technologies (InCACCT), International conference on

作者： T Kumanan K. Nagarathna R. Vinodha S. Balasubramani S. N. Sheela Evangelin Prasad S. Sujatha Department of CSE Dr Mgr Educational and Research Institute Maduravoyal Chennai Tamil Nadu India Department of CSE Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of ECE R.M.D Engineering College Chennai Tamil Nadu India Department of CSE Sri Krishna Engineering College Chennai 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)9798331543891

ISBN: (纸本)9798331543907

The pursuit of enhancing grape quality in viticulture has increased with the emergence of autonomous robots using advanced artificial intelligence (AI) methods. This research introduces an innovative methodology that combines deep learning (DL) and sensor fusion with Region-Based Convolutional Neural Networks (R-CNN) to enhance precision viticulture. Autonomous robots with multispectral and RGB cameras roam vineyards independently, taking high-resolution images and environmental data. The R-CNN algorithm analyses this data to precisely detect grape diseases, evaluate fruit ripeness, and monitor overall vineyard health. The technology employs sensor fusion to integrate visual data with environmental characteristics, enhancing predictions and insights on grape quality. Field investigations indicate substantial improvements in grape output and quality with quick interventions informed by automated evaluations. Findings highlight the capability of integrating robots with DL techniques to transform vineyard management, resulting in sustainable practices and improved production efficiency. The R-CNN-based robotic system demonstrated 95% accuracy in evaluating grape quality, improving efficiency and reducing the need for manual inspections.

关键词： Deep learning visualization Pipelines Production Transforms sensor fusion Robot sensing systems Sustainable development Diseases Autonomous robots

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Machine Learning-Driven Hybrid Adaptive Beamforming for Efficient Interference Mitigation in Dense Wireless sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

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

作者： P. Loganathan A.T. Priyeshkumar K. Kodeeswari K Jose Reena I. Poonguzhali M. Balaji Department of Electronics and Communication Engineering Excel Engineering College(Autonomous) Namakkal Department of Electronics and Communication Engineering Mahendra College of Engineering Salem Department of Electronics and Communication Engineering Kongu Engineering College(Autonomous) Erode Department of Computer Applications B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Vivekananda College of Technology for Women Tiruchenhode

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The Adaptive Beamforming Algorithm (ABA) represents a promising approach to suppress interference in dense wireless sensor networks (WSNs), enhancing signal fidelity and network throughput. In dense WSNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array Signal Processing (AASP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (SINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant SINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WSNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(SINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency.

关键词： Wireless sensor networks Adaptive systems Array signal processing Scalability Prevention and mitigation Interference Throughput Real-time systems Energy efficiency Signal to noise ratio

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Autonomous Passenger Counting And Tracking System

Autonomous Passenger Counting And Tracking System

引用

data Science, Agents & Artificial Intelligence (ICDSAAI), International conference on

作者： Pown M Dhilipan R Ganesh A Vicraman S V Dept. of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

In metropolitan city, public transportation plays a crucial role in day-to-day life of the people. The effective transportation will be ensured by the smart counting system with the help of LIDAR sensor. The VL53L1X (LIDAR) sensor will be employed for counting and recording of passengers. In this case, the sensor will do passenger counting and recording using 940 nm class one laser for monitoring purposes. SIM7600 4G GSM module will be used to enable connectivity and facilitate data transmission, communication as well as internet access for the application and ESP32 Microcontroller plays a major role as main control unit of all the sensors. Neo 6M GPS module will ensure efficient route management, real-time updates and precision in location tracking. Incorporation with Firebase and Flutter flow made it possible to create a mobile application that is user-friendly where one gets green for enough space, yellow for limited space and red when it is fully booked. It will also enhance capacity management as well as planning for the passenger movements. The counting device along with GPS tracking makes this app more convenient among the users due to the additional seat availability along with the live location.

关键词： GSM Laser radar Tracking Microcontrollers Urban areas Aerospace electronics Recording Mobile applications Public transportation Global Positioning System

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IoT-Based Home Automation: Augmented Reality Enabled Plant Watering System

IoT-Based Home Automation: Augmented Reality Enabled Plant W...

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

作者： Shital Pawar Kshitija Dupare Tushar Ganvir Divyanshu Chore Department of Electronics & Telecommunication Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

In recent years, the convergence of technology and daily life has led to significant innovations, among which Augmented Reality (AR) and Virtual Reality (VR) stand out remarkably. AR and VR technologies have transcended their initial applications to permeate various sectors including Home Automation and Internet of Things (IoT) devices. The paper presents the implementation of a Home Automation system, specifically an AR-enabled Plant Watering System using IoT devices, exploring their functionalities and influence in today’s world. The system employs the ESP8266-12E microcontroller for sensor data acquisition, actuator control, and communication with an AR interface developed using Unity Hub and Vuforia Engine. Real-time data from DHT11 and soil moisture sensors are visualized through an AR interface, enabling intuitive user interaction and control of watering actions. The performance evaluation of our system highlights a sensor accuracy of ±2°C for temperature, ±5% for humidity, and ±3% for soil moisture, with an average response time of 2 seconds. The system demonstrated high reliability with 98% uptime and control accuracy of 95% along with the task success rate of 92% with an average task completion time of 15 seconds. These results underscore the effectiveness of the AR-enabled plant watering system in enhancing user experience and engagement in indoor gardening, while offering significant improvements in accuracy, efficiency, and user interaction.

关键词： Technological innovation Home automation Accuracy Plants (biology) Soil moisture data visualization User experience Internet of Things Augmented reality Engines

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Flood Prediction and Adaptive Farming Solutions Using IoT, Machine Learning, and Remote Sensing for Climate-Resilient Agriculture

Flood Prediction and Adaptive Farming Solutions Using IoT, M...

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Intelligent Control, Computing and Communications (IC3), International conference on

作者： Karthiyayini J Alok Jain Kashi Sai Prasad Tamam Ali Abd Ulabbas Abedi Yedida Prasanna Karthikeyan Murugesan Department of Information Science Engineering New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Information Technology GRIET Bachupally Hyderabad Telangana India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Flood is one of the most disastrous natural disasters to the people of agriculture, people and infrastructure. Mitigation of these risks and promotion of climate resilient agriculture depends on accurate flood predictions and progressive farming strategies. This paper presents an integrated model consisting of Internet of Things (IoT) sensors, Machine Learning (ML) and Remote Sensing technologies to provide precise, real time flood prediction and adaptive farming options. Using advanced deep learning algorithms and real time sensor data to predict and risk assess floods in real time, the model leverages real time satellite based Synthetic Aperture radar (SAR). Another function of the model is to offer adaptive farming solutions including optimized irrigation, flood resilient crop selection, and disaster recovery recommendations for regions impacted by the disaster. A hybrid AI technique assimilating the social media data, and blockchain for integrity of data is more effective to make decisions for the local authorities and farmers. This study seeks to fill the gaps in interdisciplinary collaboration, data scarcity and governance and provide a solution for flood management and sustainable agriculture as a whole.

关键词： Adaptation models Accuracy Disasters Predictive models Real-time systems sensors Floods Remote sensing Synthetic aperture radar Farming

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Real-Time Monitoring of Patient Movement and Restraint Usage in healthcare Settings with IoT and Deep Learning Model

Real-Time Monitoring of Patient Movement and Restraint Usage...

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

作者： S. Dhanalakshmi L.N. Jayanthi P. Sangeetha J. Bhuvana Gowri S. Karthikeyan M. Rajmohan Department of Information Technology Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Department of Electronics and Communication Engineering K.S.R. College of Engineering Tiruchengode Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Ensuring patient safety and maximizing care delivery in healthcare settings requires effective monitoring of patient movement and restraint usage. To address this challenge, this paper proposes a real-time monitoring system that combines Internet of Things (IoT) technology with a Deep Learning (DL) Model, specifically Neural Networks. Utilizing an IoT sensor network, the system regularly gathers and sends data on patient movements and instances of regular application of restraint. The primary innovation is applying neural networks to the real-time recognition and classification of movement patterns and restraint events. This methodology allows the system to discriminate between different movement types (e.g., walking, sitting, and lying down) and precisely detect when a restraint is used. Healthcare professionals can obtain important insights into patient behavior and the efficacy of restraint techniques through real-time analysis of these data streams. Intelligent monitoring and analysis of the proposed system will increase the effectiveness of using restraints, thus improving patient care. Healthcare providers can reduce the risks associated with the inappropriate use of restraints and develop a safer environment by predicting patients' needs and responding proactively with timely alerts and actionable insights. It advances healthcare technology for real-time patient monitoring and safety management in healthcare settings.

关键词： Deep learning Patient monitoring Ethics Machine learning algorithms data analysis Visual analytics Neural networks Medical services Real-time systems Internet of Things

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