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IoT-Based Autonomous Fire Mitigating Vehicle

IoT-Based Autonomous Fire Mitigating Vehicle

引用

Smart and Sustainable Developments in Electrical Engineering (SSDEE), ieee international conference on

作者： Snigdha Kurre Sunkara Nikitha M. G. V. Sai Lakshmi Kritika Bansal School of Electronics Engineering Vellore Institute of Technology-Amaravati Andhra Pradesh India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Fire incidents in hazardous or remote areas require quick response to prevent extensive damage. This paper presents an IoT -based autonomous firefighting robot designed to detect fire hazards and provide real-time location data for emergency responders. The system is built using an Arduino micro controller and integrates GPS technology for precise tracking. This approach provides a cost-effective, efficient solution for early fire detection and response, with potential applications in environments requiring continuous monitoring.

关键词： Electrical engineering Electric potential real-time systems Hazards Sustainable development Robots Monitoring Global Positioning System

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Health Status Assessment Method Based on Entropy-Variable Weight TOPSIS Fuzzy Comprehensive Assessment for Substation UAV

Health Status Assessment Method Based on Entropy-Variable We...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Yucheng Qian Changwei Zhao Weiming Huang Shudong Wang Institute of Electric Power Science State Grid Anhui Electric Power Co. Ltd Hefei China Equipment Management Department State Grid Anhui Electric Power Co. Ltd Hefei China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In order to ensure the long-term stable operation of substation Unmanned Aerial Vehicle (UAV), a UAV health status assessment method based on the entropy weight-variable weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) fuzzy evaluation is proposed in this paper. By selecting operational evaluation parameters, the ideal positive and negative solution vectors of UAV flight parameters are analyzed and calculated, and the real-time health indicators of UAVs are obtained by calculating the distance of UAV real-time operational parameters to the ideal and anti-ideal solution vectors. Considering that the impact of abnormal individual operational parameters on UAV health status may be obscured by health indicators, variable weight theory is introduced to modify the abnormal indicators, making the assessment results more accurate. Health status levels are established, and the obtained health indicators are mapped to different health status levels, achieving the health status assessment of UAVs at substations. Case study results show that the proposed method intuitively provides the overall health status level of UAVs and is easy to implement in engineering.

关键词： Substations Accuracy Computer applications Autonomous aerial vehicles Vectors real-time systems Power electronics Entropy

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A Simulation Pipeline to Facilitate real-World Robotic Reinforcement Learning applications

A Simulation Pipeline to Facilitate Real-World Robotic Reinf...

引用

Annual ieee systems conference

作者： Jefferson Silveira Joshua A. Marshall Sidney N. Givigi Dept. of Electrical and Computer Engineering Queen's University Kingston ON Canada Ingenuity Labs Research Institute Queen's University Kingston ON Canada School of Computing Queen's University Kingston ON Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Reinforcement learning (RL) has gained traction for its success in solving complex tasks for robotic applications. However, its deployment on physical robots remains challenging due to safety risks and the comparatively high costs of training. To avoid these problems, RL agents are often trained on simulators, which introduces a new problem related to the gap between simulation and reality. This paper presents an RL pipeline designed to help reduce the reality gap and facilitate developing and deploying RL policies for real-world robotic systems. The pipeline organizes the RL training process into an initial step for system identification and three training stages: core simulation training, high-fidelity simulation, and real-world deployment, each adding levels of realism to reduce the sim-to-real gap. Each training stage takes an input policy, improves it, and either passes the improved policy to the next stage or loops it back for further improvement. This iterative process continues until the policy achieves the desired performance. The pipeline's effectiveness is shown through a case study with the Boston Dynamics Spot mobile robot used in a surveillance application. The case study presents the steps taken at each pipeline stage to obtain an RL agent to control the robot's position and orientation.

关键词： Training Costs Surveillance Pipelines Reinforcement learning System identification Safety Iterative methods Mobile robots Robots

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Enhanced EEGNet Optimization Using Lightweight Deep Neural Network for Detecting Human Stress Levels from Raw EEG Signals Dataset

Enhanced EEGNet Optimization Using Lightweight Deep Neural N...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Silabdi Hana Raini Hassan Ahmed Rimaz Faizabadi Abdullah Gubbi Mohammed Zakir Bellary V Manjula Department of Computer Science Kulliyyah of Information & Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Department of Computer Science Kulliyyah of Information & Communication Technology International Islamic University Kuala Lumpur Malaysia Centre for Unmanned Technologies International Islamic University Malaysia Kuala Lumpur Malaysia Department of Electronics and Communication Engineering Bearys Institute of Technolgy (VTU) Mangalore India Department of Artificial Intelligence and Machine learning P.A.College of Engineering(VTU) Mangalore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Accurate stress detection is essential in neuroscience and healthcare since human stress levels have a substantial impact on general well-being. Although EEG-based stress detection is a popular non-invasive method, real-time applications are limited by the high processing costs of classic feature extraction techniques. This work removes the requirement for intricate feature extraction by presenting an efficient EEGNet framework for real-time stress prediction utilizing raw EEG signals. The suggested approach improves accuracy and efficiency by utilizing Z-score normalization and adjusting batch size. Compared to traditional CNNs, the lightweight EEGNet architecture's depth-wise and separable convolutions ensure computational efficiency by reducing trainable parameters by a factor of ten. The proposed method outperformed current EEGNet-based techniques in stress identification by 5% to 18%. Furthermore, the improved system efficiently handles raw EEG signals, which is suitable for real-time, resource-constrained scenarios. The results shows the effectiveness of model-centric optimization in deep learning and open the door to the creation of more effective stress detection techniques. This work provides a workable solution for real-time non-invasive stress monitoring, highlighting the significance of lightweight frameworks customized for particular applications.

关键词： Deep learning Accuracy Feature extraction Brain modeling Electroencephalography real-time systems Data models Stress Optimization Monitoring

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YOLOv11-Powered Emergency Vehicle Detection Algorithm Using Tello Quadrotor Drone

YOLOv11-Powered Emergency Vehicle Detection Algorithm Using ...

引用

Robotics and Technologies for Industrial Automation (ROBOTHIA), ieee international conference on

作者： Alicia Loh I-Ling Goh Yu Xiang Kishore Bingi Rosdiazli Ibrahim Department of Electrical and Electronics Engineering Universiti Teknologi PETRONAS Seri Iskandar Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

This study presents a YOLO-powered vehicle detection and tracking system integrated with a Tello quadrotor drone, addressing the limitations of conventional detection systems. Traditional methods often generalize vehicles into a single category, overlooking their specific operational needs and urgency levels. Furthermore, stationary and GPS-based tracking systems face constraints in coverage, environmental adaptability, and signal clarity. Leveraging the agility and real-time capabilities of the Tello drone, combined with the YOLOv11 segmentation model, this research introduces a robust framework for differentiating emergency vehicles such as ambulances and fire trucks. The system employs a carefully constructed dataset of annotated images processed using Roboflow and YOLOv11. The model achieves high accuracy metrics, including mean average precision (mAP) scores above 0.95 for bounding boxes. real-time deployment demonstrates the model's reliability in detecting emergency vehicles under diverse scenarios, with ambulances exhibiting slightly better detection consistency than fire trucks due to their standardized design. Challenges such as variable lighting and occlusions were noted but addressed through data augmentation and hyperparameter optimization. This research highlights the potential of drone-based AI solutions to enhance emergency response, traffic management, and public safety, setting a foundation for future advancements in dynamic, real-world applications.

关键词： YOLO Service robots Vehicle detection Emergency services real-time systems Public security time factors Vehicle dynamics Drones Quadrotors

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A Fully Integrated, Flexible and Tunable Capacitive Strain Sensor Based on Magneto-Dielectric for Human Motion Monitoring

A Fully Integrated, Flexible and Tunable Capacitive Strain S...

引用

ieee international conference on Micro Electro Mechanical systems

作者： Mujeeb Yousuf Pushpapraj Singh Indian Institute of Technology Delhi

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

Flexible strain sensors for monitoring human motion require a wide working range and high sensitivity. Here, we present a mechanically flexible and fully integrated capacitive strain sensor designed for human motion monitoring. The device’s enhanced sensitivity and wide operational range are achieved using a magneto-dielectric layer (MDL) composed of magnetic nanoparticles embedded in polydimethylsiloxane (PDMS). These nanoparticles dynamically realign in response to strain stimuli, significantly amplifying the capacitance change. The sensor exhibits a sensitivity four times greater than comparable devices using PDMS alone as the dielectric layer. Additionally, it demonstrates a wide working range (20000 µm – 6000 µm), high linearity (0.99), and rapid detection time (0.2 s). By bridging the gap between the capacitive sensing, conditioning, processing and data transmission, the presented work integrates flexible sensor with integrated silicon chips on flexible substrate, offering a complete solution for monitoring human motion.

关键词： Sensitivity Magnetic devices Magnetic nanoparticles Capacitive sensors real-time systems Motion detection Sensors Data communication Monitoring Substrates

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Lightweight Low-Light Image Enhancement Model Training and Design Considerations

Lightweight Low-Light Image Enhancement Model Training and D...

引用

Annual ieee systems conference

作者： Hajira Saleem Reza Malekian Hussan Munir Dept. of computer science Malmö University Malmö Sweden Internet of Things and People Research Centre Malmö University Malmö Sweden

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Low-light conditions significantly degrade the performance of RGB cameras, particularly in applications like visual odometry estimation for autonomous vehicles, where feature visibility is critical for accurate navigation. Traditional image enhancement techniques often require manual tuning and struggle in extremely dark environments. However, deep-learning-based methods, though promising, typically demand high computational resources, making them unsuitable for real-time applications. This paper presents top low-light image enhancement (LLIE) models obtained through our experimentation with designing models to improve the visibility of features crucial for odometry tasks while minimizing computational overhead. We explore design and training strategies for real-time low-light enhancement using U-Net, a CNN architecture, alongside other CNN models with residual blocks and attention mechanisms. Through experimentation with two datasets-LOL-v2 and KITTI datasets, we demonstrate that our models offer significant improvements in feature visibility without compromising real-time performance. We also report that training the model on image patches reduces Graphics processing unit (GPU) memory usage and improves image enhancement quality, though training on full images may sometimes be necessary. Compared to a high-performing baseline model, our approach-despite yielding lower image quality is better suited for real-time applications. Some use cases, such as robotic navigation, can benefit from our lightweight model, where high-resolution details are less critical, as the focus is on real-time performance and general feature visibility. Additionally, we find that using U-Net without residual blocks and attention mechanisms results in degraded image quality. Future work will focus on transfer learning our model for odometryspecific image enhancement and integrating it into autonomous localization systems to optimize computational efficiency and enhance performance.

关键词： Training Location awareness Image quality Attention mechanisms Navigation Computational modeling Graphics processing units real-time systems Odometry Image enhancement

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Advancing real-time Object Detection for Autonomous Vehicles with YOLOv11

Advancing Real-Time Object Detection for Autonomous Vehicles...

引用

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

作者： Manish Mahala Manisha Pattanaik Gaurav Pandey ABV-Indian Institute of Information Technology and Management (IIITM) Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The safe and efficient operation of autonomous vehicles relies heavily on robust and real-time object detection systems. This paper presents YOLOv11, the latest advancement in the YOLO (You Only Look Once) series, optimized for detecting critical objects in autonomous driving scenarios, including vehicles, pedestrians, and road signs. YOLOv11 incorporates novel architectural enhancements, such as the C3k2 block for efficient feature extraction and the Cross-Stage Partial with Spatial Attention (C2PSA) module for enhanced spatial attention, enabling state-of-the-art accuracy and real-time performance. Evaluations conducted on the KITTI dataset, augmented with advanced data augmentation and multi-scale training techniques, demonstrate substantial improvements in precision, recall, and mean Average Precision (mAP) compared to its predecessors. Furthermore, the integration of YOLOv11 into autonomous systems leverages sensor fusion to deliver comprehensive perception capabilities, establishing its potential for real-world deployment. This study highlights YOLOv11’s contributions to advancing autonomous vehicle technology and provides valuable insights for developing robust, scalable, and efficient object detection frameworks for future applications.

关键词： YOLO Training Technological innovation Accuracy Computational modeling Roads Sensor fusion real-time systems Usability Autonomous vehicles

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Comprehensive Stride Analysis Through Plantar Pressure

Comprehensive Stride Analysis Through Plantar Pressure

引用

international conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： V Harsha Vardhan B Senthilnaathan Venkatasubramanian K Dept. of ECE Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The use of stride analysis extends to various domains, particularly in studying human plantar pressure. Most importantly, plantar pressure measurement is vital in the management and treatment of several conditions, including diabetes, trauma, stroke, ankle instability, sports injuries, and Parkinson’s disease. This study presents a comprehensive system for stride analysis using a smart insole embedded with piezoelectric sensors, capturing real-time pressure variations across different regions of the foot. The voltage signals generated by piezoelectric sensors in response to foot pressure are collected and then transmitted via Bluetooth to a Serial Bluetooth Terminal application for further processing, calibration, and mapping into meaningful pressure values. This system collects foot pressure changes from four essential regions, which it converts into heat maps to assist with the detection of gait and stride abnormalities. The results show that the developed system successfully measures foot pressure fluctuations as well as displays real-time information on walking patterns and weight distribution patterns. This system demonstrates its potential to support ongoing monitoring applications, which further benefits rehabilitation programs and helps reduce injuries and enhance athletic performance.

关键词： Wireless sensor networks Bluetooth Sensor systems real-time systems Pressure measurement Heat maps Intelligent sensors Injuries Foot Diseases

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ECU s Fingerprint Identification Based on CAN Bus Using 1D-CNN

ECU s Fingerprint Identification Based on CAN Bus Using 1D-C...

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Consumer Technology (ICCT-Pacific), international conference on

作者： Yao Hsu Chang-Ming Wu Jiun-Wei Horng Department of Electronic Engineering Chung Yuan Christian University Taoyuan City Taiwan R.O.C.

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Traditional Electronic Control Units (ECUs) fingerprinting classification methods for Controller Area Network (CAN) systems rely on manually defined features such as maximum voltage and average voltage, requiring a large number of features to effectively distinguish between different ECUs. However, these methods often face challenges in scalability and generalization across diverse environments. This study addresses these issues by adopting a one-dimensional Convolutional Neural Network (1D-CNN) model with automatic feature extraction capabilities, significantly reducing the reliance on manual feature engineering. The model features a short training cycle and fast inference speed, making it highly suitable for real-time applications in resource-constrained environments. Experimental results demonstrate that the model achieves a classification accuracy of 99.98%. Moreover, this approach shows great potential for deployment on embedded platforms and adaptability to dynamic operating conditions, paving the way for future advancements in vehicle security systems.

关键词： Adaptation models Accuracy Computational modeling Scalability Noise Fingerprint recognition Feature extraction real-time systems Controller area networks Voltage control

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