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ESP32-CAM-based Wireless Human Detection System with PIR sensor and Instant Alerts via GSM and Blynk App

ESP32-CAM-based Wireless Human Detection System with PIR Sen...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Rajya Lakshmi Avalareddy Bindhu Priya Makala Pushpavathi Tokala Prudhvi Maradani Venkata Ratna Prabha K Manoj Kumar D Dept. of ECE V.R. Siddhartha Engineering College Deemed to be University Vijayawada India Dept. of ECE SRM Institute of Science and Technology Ramapuram Tamilnadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Security and observation are fundamental in different applications, counting private security, mechanical checking, and smart city administration. this paper presents a cost-effective, remote human location framework that coordinating a Inactive Infrared (PIR) sensor, ESP32-CAM, Worldwide Framework for Portable Communications (GSM) module, and the Blynk stage for real-time checking. the framework identifies human development utilizing the PIR sensor, captures pictures through the ESP32-CAM, and sends moment alarms through GSM and Blynk, empowering clients to get notices on their versatile gadgets. By dispensing with the require for complex wiring, the framework improves effectiveness and ease of arrangement. Outlined to be power-efficient, versatile, and user-friendly, it guarantees dependable security observing. Test comes about show tall precision in human location and quick caution transmission, making it reasonable for different security applications. A comparative investigation with existing frameworks illustrates made strides in discovering unwavering quality and decreased reaction timecite1. this inquire about contributes to the progression of farther reconnaissance arrangements and lays the establishment for future upgrades, such as AI fueled acknowledgment and cloud-based capacity for improved security administration. .

关键词： GSM Wiring Wireless sensor networks Surveillance Streaming media Real-time systems Security Internet of things Communication system security Streams

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Application and Research of Multi-Pose Face Recognition Method Based on Intelligent Classroom

Application and Research of Multi-Pose Face Recognition Meth...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Xuliang Yang Rodolfo C. Raga Dongguan City University Dongguan Guangdong China National University Manila Philippines

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

this study aims to improve the accuracy and reliability of multi-pose face recognition in intelligent classroom. In educational scenarios, facial recognition faces challenges such as pose change, illumination difference and partial occlusion. therefore, this paper proposes a method that combines the improved RetinaFace model with the generation of adversarial network (GAN). Firstly, MobileNet v3 and attention mechanism are used to optimize the RetinaFace model to improve the face detection accuracy. Secondly, GAN is used for positive processing of face images with different poses to ensure that the generated images are realistic and retain identity features. the experimental results show that the proposed method performs well under a variety of complex conditions, especially under extreme attitude changes, and maintains a high recognition rate. the research provides an efficient and robust face recognition solution for smart classroom and promotes the development of intelligent education.

关键词： Accuracy Attention mechanisms Face recognition Reconstruction algorithms Generative adversarial networks Transformers Robustness Optimization Monitoring Principal component analysis

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Sensing of Surface Texture using Smart Glove for Post Stroke Rehabilitation

Sensing of Surface Texture using Smart Glove for Post Stroke...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： S Priya P Sherin Heera Rj Hemalatha Department of Biomedical Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Post-stroke patients need to be rehabilitated in order to gain partly or fully some of the motor facilities that seem to have ceased to function effectively. According to the research, post-stroke deficits in the human ability to palpate and comprehend surface features is the major difficulty a stroke survivor may experience; often they cause harm or accidentally destroy objects due to an incorrect manner of holding them. this research offers a new strategy with the aid of wearable haptic interface in the form of a smart glove that employs an ESP32 microcontroller and force sensors to identify surface texture. Using pattern recognition, we can effectively distinguish between a rough and a smooth surface to adapt to the pressure required to hold any object. this innovation apart from improving safety also gives the patient feedback in the process of rehabilitation. the use of machine learning in particular proves to be more preferable when it comes to texture recognition preventing shortcomings observed in other senor-based approaches.

关键词： Wireless sensor networks Machine learning Stroke (medical condition) Motors Surface roughness Surface texture Rough surfaces Haptic interfaces Force sensors Surface treatment

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A Collaborative Change Detection Network Based on Feature Pyramids

A Collaborative Change Detection Network Based on Feature Py...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Gang Wu Luyao Liu Yigao Liu Zenghui Zhang Li Cong Chengbin Huang Ning Lv State Grid Jilin Province Electric Power Company Changchun China Engineering Technology Research Institute Xidian University Xi'an China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

the application of high-resolution satellite images in deep learning-based change detection methods has become increasingly popular. However, the down-sampling and cropping strategies deployed to fit the GPU memory constraint often result in incomplete semantic information and loss of fine details. this paper proposes a collaborative network based on feature pyramids, which enables networks to learn local and global features from cropped and down-sampled image patches. A feature-sharing mechanism is also introduced to fuse global features and local features. We evaluated the network on the LEVIR and S2Looking datasets by comparing it with some representative change detection networks. Comparative experiments show that the proposed network performs better in multi-scale change detection, improving precision by 2.69% on the LEVIR-CD dataset, and improving precision and recall by 6.83% and 2.68% on the S2Looking dataset, respectively.

关键词： Fuses Federated learning Image edge detection Semantics Memory management Graphics processing units Feature extraction Power electronics Satellite images Remote sensing

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Evaluating HLS4ML For Converting a CNN Model Into High Level Synthesis

Evaluating HLS4ML For Converting a CNN Model Into High Level...

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Advanced Research in Computing (ICARC), international conference on

作者： Noura Jariri Mohammed Chaman Rania Majdoubi Abdelkader Hadjoudja Electronic Systems Information Processing Mechanics and Energy Laboratory Faculty of Science Ibn Tofail University Kenitra Morocco

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Deep learning models, although high-performing, often require hardware acceleration to be effectively deployed on Field Programmable Gate Arrays (FPGAs). this paper investigates the use of the hls4ml framework to convert a Convolutional Neural Network (CNN) model into High Level Synthesis (HLS). hls4ml is a Python tool that translates neural networks into FPGA-compatible firmware for high-performance inference with reduced latency and power usage. It employs High-Level Synthesis (HLS) techniques to translate high-level programming languages like C++ or Python into hardware description languages, such as VHDL or Verilog. We evaluate the model’s performance in terms of accuracy and demonstrate the effectiveness of converting a deep learning model, specifically a CNN, for hardware implementation. the results highlight the potential of hls4ml facilitating embedded artificial intelligence applications, offering an efficient and scalable solution for deploying deep learning models on resource-constrained platforms.

关键词： Deep learning Accuracy Embedded systems VHDL Predictive models Software High level synthesis Convolutional neural networks Field programmable gate arrays Python

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Human Activity Recognition Using STFT and Attention-Enhanced 2D-CNN on Smartphone and Wearable sensors

Human Activity Recognition Using STFT and Attention-Enhanced...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Peichen Jin Chuanzhi Zang College of Artificial Intelligence Shenyang University of Technology Shenyang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Traditional Human Activity Recognition (HAR) approaches often rely on handcrafted features and incomplete feature extraction, limiting their effectiveness. To address these challenges, we propose a 2D Convolutional Neural Network (2D-CNN) model with an attention mechanism, incorporating Short-Time Fourier Transform (STFT) to convert raw time-series sensor data into time-frequency representations, enabling richer spatiotemporal feature extraction. the 2D-CNN is designed to capture hierarchical patterns, while the Triplet Attention mechanism learns cross-dimensional dependencies. the proposed framework is evaluated on three widely used HAR datasets, WISDM, UCI-HAR, and PAMAP2, achieving classification accuracies of 96.65%, 96.3%, and 97.32%. Experimental results demonstrate that our method outperforms existing deep learning models, delivering improved recognition accuracy and robustness across diverse activities.

关键词： Time-frequency analysis Accuracy Attention mechanisms Fourier transforms Computational modeling Feature extraction Spatiotemporal phenomena Human activity recognition Convolutional neural networks Wearable sensors

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AI-IoT Solution for Railway Train Collision Prevention on Identical Tracks and Animal Detection

AI-IoT Solution for Railway Train Collision Prevention on Id...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Bhuvanesh K Chandru S Hariharan S Poorna Kumar D Department of ECE KS Rangasamy College of Technology Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

It is an IoT-based system that detects and prevents animal incidents Utilizing cutting-edge sensors and continuous monitoring on railway tracks to minimize the risk of train accidents involving animals has been a notable issue. the proposed system integrates various sensors such as LIDAR, infrared (IR), ultrasonic, and vibration sensors with an ESP32 microcontroller to detect animals and obstacles on the tracks. the system constantly observes the track and transmits the data to a mobile application (Blynk) and a web interface for real-time monitoring. the LIDAR sensor measures the distance to objects on the track, while the IR sensor detects the heat signature of the animals. the ultrasonic sensor detects obstacles in close range, and the vibration sensor detects vibrations due to trains, disturbances, etc. When an anomaly, such as an animal, is detected, the system immediately alerts the operator via the Blynk app and provides a notification on the web interface. the ESP32 CAM captures an image of the detected object for visual confirmation. the ESP32 microcontroller facilitates real-time processing and decision making, while the Blynk app provides an intuitive interface for notifications and sensor data visualization. the Web interface allows operators to monitor multiple track sections, access live sensor data, and remotely manage the system. It will save the animals on the tracks and improve railway safety by providing efficient real-time warnings and allowing immediate intervention. Using low-cost IoT components, the system provides a scalable and practical solution to reduce animal-related incidents, contributing to safer and more efficient rail transport.

关键词： Vibrations Laser radar Animals Microcontrollers sensor systems Real-time systems Rail transportation Acoustics sensors Accidents

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A Smart IoT-Enabled Wildfire Monitoring and Early Detection System using Convolutional Neural networks and sensor Data Analysis

A Smart IoT-Enabled Wildfire Monitoring and Early Detection ...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： U.V. Anbazhagu A. Alice Blessie R. Santhana Krishnan G. Yamini J. Relin Francis Raj P. Sundaravadivel Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of English Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Wildfires pose a significant threat to ecosystems, biodiversity, and human settlements, with climate change and deforestation exacerbating the frequency and intensity. Conventional forest fire detection methods, including satellite-based monitoring and ground surveillance, suffer from limitations such as delayed detection, high false alarm rates, and dependency on manual intervention. Existing sensor-based detection systems often lack integration with intelligent decision-making frameworks, leading to inefficient fire prevention strategies. the absence of real-time processing and predictive capabilities further reduces the effectiveness of current methodologies in mitigating wildfire risks. this work presents an IoT-based deep learning approach utilizing Convolutional Neural networks (CNNs) for real-time forest fire detection and prevention. Data is collected from satellite images, drone feeds, and IoT sensors, including temperature, humidity, gas concentration, and infrared readings. Advanced data preprocessing techniques, including image augmentation, normalization, and feature extraction, enhance the robustness of the model. A CNN-based classification model is implemented to analyze fire patterns and assess risk levels. the system is trained using sensor data and image datasets, ensuring high detection accuracy with minimal false positives. the key features of this methodology include real-time wildfire detection, automated risk assessment, and deployment on IoT edge devices for immediate decision-making. the integration of sensor-driven insights with deep learning improves early detection capabilities while reducing reliance on manual surveillance. Efficient data handling and cloud-based alert mechanisms enable proactive measures against fire outbreaks. the proposed system enhances wildfire monitoring by ensuring fast response times, improved accuracy, and scalable deployment in fire-prone regions.

关键词： Deep learning Wildfires Accuracy Prevention and mitigation Surveillance Decision making Manuals Real-time systems Satellite images Time factors Climate change Fires Detection algorithms

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Application of Deep Learning in Natural Language processing Algorithm for User Service

Application of Deep Learning in Natural Language Processing ...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Zongbao Sun Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Natural language processing (NLP) is becoming more and more widely used in the field of user services. this paper discusses the innovative application of NLP algorithm based on deep learning in improving user service quality, and focuses on several key algorithms and their optimization methods. Firstly, by introducing pre-trained language models such as BERT and GPT-3, the efficient understanding of user services and the generation of responses are realized, which greatly improves the accuracy and fluency of the dialogue system. Secondly, for the intent recognition task in the user service, a multi-head attention mechanism based on Transformer architecture is proposed, which enhances the ability of the model to capture long-distance dependencies and enables the system to analyze the user's intent more accurately. In addition, in order to solve the challenge of context understanding in multi-turn dialogue, we designed a memory-enhanced recurrent neural network (RNN) with a fusion memory network, which can maintain and update the dialogue state in multiple rounds of interaction, so as to provide a coherent service experience. Finally, this paper discusses the challenges and solutions in the process of model deployment, real-time inference efficiency and data privacy protection, and looks forward to the application prospects of supervised learning and adaptive transfer learning.

关键词： Deep learning Adaptation models Accuracy Recurrent neural networks Computational modeling Transfer learning Transformers Natural language processing Data models Unsupervised learning

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IoT and AI for Efficient Forest Fire Detection, Monitoring, and Risk Prediction With Real-Time Alerts

IoT and AI for Efficient Forest Fire Detection, Monitoring, ...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： M. Janardhan Bhupalam Likhitha Oruganti Purandeswari Poteti Rani Golla Harshini Department of Artificial Intelligence G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Wildfires pose severe threats to ecosystems, wildlife, and human settlements, necessitating efficient early detection and monitoring systems. this research introduces a real-time wildfire detection and risk assessment system powered by IoT and AI. the proposed system continuously monitors environmental parameters such as temperature, humidity, and gas concentrations using IoT-based sensors for early fire detection. AI-driven models analyze both historical and real-time sensor data to identify high-risk areas, improving predictive accuracy. A cloud-integrated architecture facilitates data collection, processing, and transmission between sensor nodes, cloud servers, and a mobile alert system. Fire response teams can leverage the system’s real-time fire alerts, GPS-based tracking, and predictive analytics via a smartphone application, enhancing emergency response coordination. Extensive testing demonstrates high accuracy, efficiency, and scalability, significantly reducing detection times while improving resource allocation. Designed for cost efficiency and adaptability, this framework presents a proactive strategy for wildfire prevention, disaster mitigation, and environmental protection.

关键词： Temperature sensors Cloud computing Wildfires Prevention and mitigation Scalability Forestry Real-time systems sensor systems sensors Predictive analytics

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