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Elevating Gas Safety Standards: ESP32-based Detection Systems with Blynk Integration

Elevating Gas Safety Standards: ESP32-based Detection System...

引用

2024 ieee Region 10 symposium, TENSYMP 2024

作者： Sarkar, Rupak Chowdhury, Md. Ashiqul Haider Ahmed, Sheikh Iftekhar Hasib, Abdullah All Tanzim, Md Wazih Yeaser Mudassir, Razi Islam, Md. Jawad Ul Shahnaz, Celia Bangladesh University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350364866

In this paper we introduces a novel approach to elevating gas safety standards through an ESP32-based detection system integrated with the Blynk IoT platform. The proposed system is designed to meet the growing need for reliable and adaptable gas leak detection across various environments, including residential and commercial settings. By connecting individual detection units to a centralized hub, the system offers real-time monitoring, instant notifications, and automatic safety responses, such as triggering alarms and cutting off the gas supply. The integration of the Blynk platform enhances user interaction, providing a customizable mobile app and server interface that can be tailored to specific user preferences. This flexibility not only improves the user experience but also ensures the system's scalability, allowing it to be easily deployed and managed in diverse settings. The findings highlight the system's effectiveness in detecting gas leaks and mitigating potential hazards promptly. The ability to monitor gas levels remotely and respond quickly to leaks represents a significant advancement in gas safety technology. By empowering users with the tools to monitor, analyze, and respond to gas-related risks, this system offers a promising solution for enhancing safety and peace of mind in various applications. © 2024 ieee.

关键词： Gas hazards

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AttYOLO: A model for object detection

AttYOLO: A model for object detection

引用

Computer applications and Information technology (ISCAIT), International symposium on

作者： Quan Long College of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

As a fundamental task in computer vision, object detection serves as critical infrastructure for advanced image understanding by providing structured localization and classification data. Addressing the limitation of existing lightweight models in feature representation within complex scenarios, this study proposes an enhanced architecture based on YOLOv7-tiny. The integration of Efficient Channel Attention (ECA) modules establishes cross-channel interaction mechanisms, which significantly improves discriminative feature capture while preserving real-time inference capabilities. Experimental results demonstrate a $1.1 \%$ absolute improvement in mAP metrics on coco dataset, offering a novel technical solution for high-precision real-time detection on embedded systems.

关键词： Measurement Location awareness Computer vision embedded systems Computational modeling Object detection Computer architecture real-time systems Critical infrastructure Information technology

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Autonomous Number Plate Detection System for Vehicle Identification and Tracking 2

Autonomous Number Plate Detection System for Vehicle Identif...

引用

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Nareddy, Neha Sri Polneni, Abhinaya Baithi, Rekha Kunchamwar, Pratyusha Suvarna, Sunil Kumar Battu, Durgabhavani Budda, Naveena Agarwal, Vikas Munimanda, Premchander AGVs Group Hyderabad India Malla Reddy Engineering College for Women Department of Information Technology Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta United States School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (纸本)9798350366570

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy. © 2024 ieee.

关键词： Deep neural networks

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Personal Identification Based on Joint Motions with Error Correction Using Particle Filter

Personal Identification Based on Joint Motions with Error Co...

引用

2024 International symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Aoyagi, Kosuke Yuda, Yuki Ohkoba, Masakazu Adachi, Miho Miyamoto, Ryusuke Department of Computer Science School of Science and Technology Meiji University Kawasaki Japan Department of Computer Science Graduate School of Science and Technology Meiji University Kawasaki Japan

ISBN: (纸本)9798350389210

Gait analysis is useful for personal identification in public spaces. Since the emergence of deep learning that can accurately estimate the joint locations of a human in an image, gait analysis has become feasible in practical applications that have only a visible light camera. This paper proposes a novel method for personal identification based on joint motions that can improve utility of gait analysis. Specifically, the proposed method adopts a particle filter to correct the misclassification of left and right legs using the time-series input of their locations. Experimental results using datasets comprising real-world images showed that the proposed error correction improved the accuracy of personal classification from 84.19% to 85.94% when joint locations were estimated using ViTPose. © 2024 ieee.

关键词： Error correction

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Design and Implementation of an Improved Watchdog timer for Memory applications

Design and Implementation of an Improved Watchdog Timer for ...

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2023 ieee Global Conference on Information Technologies and Communications, GCITC 2023

作者： Devi, V. Rama Sreedhar, Jadapalli Cvr College of Engineering Ece Department Hyderabad India Vignana Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9798350308167

A watchdog timer is a type of electrical timer that detects and recovers from computer errors. In embedded systems and other computer-controlled equipment where individuals are unable to immediately access the equipment or react to faults in a timely way, watchdog timers are often employed. Limiting the amount of CPU time accessible to untrusted programmes in a sandbox can assist avoid denial-of-service attacks. The computer automatically refreshes the watchdog timer during normal operation avoid the timeout. The timeout signal is used to stop a programme from running indicate whether or not a corrective action should be taken. The architecture and design of an improved adjustable windowed watchdog timer for memory applications are described. The watchdog has several defect detection algorithms built in, this makes it more durable. In terms of capabilities and operations, it may be used to monitor any real-time processor-based system. architecture may be modified to a variety of situations may be readily tailored to different applications while keeping the overall system cost low, and owing to the processor reliance the recommended watchdog has less time limitations than the present watchdog. The simulation results are used to investigate the effectiveness of the proposed watchdog timer in detecting and responding to errors. Verilog is used to code the design. Xilinx Vivado 20.1 was used to implement the design. By injecting flaws into the programme while the processor is running, the design is evaluated in real-time hardware. © 2023 ieee.

关键词： Field Programmable Gate Array Frame Window Memory applications Service Window Windowed Watchdog timer

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Opinion Formation Forecasts in Social Networks: A Graph Convolutional Neural Network Approach 27

Opinion Formation Forecasts in Social Networks: A Graph Conv...

引用

27th ieee/ACM International symposium on Distributed Simulation and real time applications, DS-RT 2023

作者： Ou, Lizhen Yao, Yiping Tang, Wenjie Yuan, Haozhe Chen, Li-Li College of Systems Engineering National University of Defense Technology Changsha China National Innovation Institute of Defense Artificial Intelligence Research Center Beijing China

ISBN: (纸本)9798350337846

Opinion formation in social networks has a significant impact on the society. Consequently, accurate opinion formation forecasts that can be effectively controlled and handled are particularly desirable. Current opinion prediction is primarily based on two methods: a. modeling and simulation and b. machine learning. Although modeling and simulation approaches have the advantage of incorporating a priori knowledge, such as topological information about social networks, existing studies suggest that the predictive accuracy of such models may be lower than expected. Neural network-based methods demonstrate improved performance;however, incorporating a priori knowledge in these models is difficult. To address these limitations, in this study, we develop a graph convolutional neural network-based opinion evolution prediction method, called GCNN. The method follows the opinion dynamics in the model training process to directly involve the topology of the network in opinion prediction. This mechanism shrinks the search space of the neural network parameters and improves the accuracy rate by exploiting the network topology as a priori knowledge. To validate our method, we perform experimental validation on nine synthetic datasets and one real dataset. The results reveal that our method outperforms benchmark models. In addition, our method exhibits superior accuracy rates compared to other candidate models, even when trained on only one-sixth of the training data typically used by other models. © 2023 ieee.

关键词： Forecasting

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Quantization and Deployment Study of Classification Models for embedded Platforms 28

Quantization and Deployment Study of Classification Models f...

引用

28th International Conference on Automation and Computing, ICAC 2023

作者： Huang, Zihan Jin, Jin Zhang, Chaolong Xu, Zhijie Xu, Yuanping Kong, Chao Wen, Qin Tang, Dan School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing & Engineering University of Hudderseld Queensgate Hudderseld United Kingdom College of Artificial Intelligence Neijiang Normal University

ISBN: (纸本)9798350335859

Deep learning models find extensive applications across various domains. However, their large number of parameters, high storage requirements, and computational overhead pose challenges for deploying these models on resource-constrained embedded devices. This study focuses on addressing this issue by exploring techniques to optimize and deploy lightweight models on embedded devices. The approach involves optimization and adjustment of the model, followed by model conversion, quantization, and quantization calibration, aimed at reducing model size and improving inference speed. Notably, improvements are made to the quantization calibration algorithm to mitigate accuracy loss caused by model quantization. The experimental results demonstrate that light quantization significantly reduces model size, facilitating storage on embedded devices. Although there is a slight reduction in accuracy, the inference speed is substantially improved, enabling real-time human face recognition in video scenarios. © 2023 ieee.

关键词： Calibration

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'Smart Air purification: Enhancing Indoor Air quality using Arduino Uno and ThingSpeak Integration'

'Smart Air purification: Enhancing Indoor Air quality using ...

引用

2024 International Conference on Smart Systems for applications in Electrical Sciences, ICSSES 2024

作者： Sridhar, H.S. Nuthana, V. Rakesh, K.B. Siddaganga Institute of Technology Dept. Of Electrical & Electronics Engineering Karnataka Tumakuru India Embedded & Software engineer Abeyaantrix Softlab OPC Pvt. Ltd Karnataka Davanagere India

ISBN: (纸本)9798350364040

The Indoor air quality (IAQ) has become a growing concern due to the significant impact it has on human health and well-being. Traditional air purifiers offer solutions, but their efficiency and effectiveness often remain limited due to manual operation and lack of real-time monitoring capabilities. To address this issue, this paper proposes the development of Smart Air purifier system, integrating Arduino microcontroller technology with the ThingSpeak platform for enhanced IAQ monitoring and control. The proposed system employees Arduino as the central processing unit, with sensors for detecting various air pollutants and harmful gases such as CO, LPG etc., and temperature-humidity levels. These sensors continuously collect the data on IAQ parameters, which are then processed by the Arduino and transmitted to the ThingSpeak cloud platform using Wi-Fi connectivity. © 2024 ieee.

关键词： Air cleaners

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Performance Evaluation of YOLOv3, YOLOv4 and YOLOv5 for real-time Human Detection 2

Performance Evaluation of YOLOv3, YOLOv4 and YOLOv5 for Real...

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2nd International Conference on Paradigm Shifts in Communications embedded Systems, Machine Learning and Signal Processing, PCEMS 2023

作者： Heda, Lokesh Sahare, Parul Indian Institute of Information Technology Department of Electronics and Communication Engineering Nagpur India Shri Ramdeobaba College of Engineering and Management Department of Electronics Engineering Nagpur440013 India

ISBN: (纸本)9798350310719

The main concern of human detection using computer vision is to correctly identify people in an image and video. Human detection has been a topic of intensive study over the last decade. YOLO being single stage algorithms happen to offer better speed than two stage algorithms hence making them a better choice for real time object detection. This strategy has the benefit of offering a comprehensive study of contemporary human detection techniques as well as a manual for selecting the best ones for actual applications. In addition, real-time human detection and occlusion issues are also looked at. In this paper, experimentation is done on real time image to verify the performance of different models of YOLO family i.e YOLOv3, YOLOv4 and YOLOv5. The experiment shows that YOLOv5 is best performer in terms of mAP with precision of 0.84 while YOLO v3 is the fastest but with a slightly less precision of 0.71. The mAP of the three algorithms were 0.86, 0.89 and 0.91 respectively. © 2023 ieee.

关键词： Object detection

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Lodestar: An Integrated embedded real-time Control Engine

Lodestar: An Integrated Embedded Real-Time Control Engine

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2023 ieee Conference on Control technology and applications, CCTA 2023

作者： El-Kebir, Hamza Bentsman, Joseph Ornik, Melkior University of Illinois Urbana-Champaign Dept. of Aerospace Engr. UrbanaIL61801 United States The University of Illinois Urbana-Champaign Dept. of Mech. Sci. and Engr. UrbanaIL61801 United States The University of Illinois Urbana-Champaign Coordinated Sci. Lab. Dept. of Aerospace Engr. UrbanaIL61801 United States

ISBN: (纸本)9798350335446

In this work we present Lodestar, an integrated engine for rapid real-time control system development. Using a functional block diagram paradigm, Lodestar allows for complex multi-disciplinary control software design, while automatically resolving execution order, circular data-dependencies, and networking. In particular, Lodestar presents a unified set of control, signal processing, and computer vision routines to users, which may be interfaced with external hardware and software packages using interoperable user-defined wrappers. Lodestar allows for user-defined block diagrams to be directly executed, or for them to be translated to overhead-free source code for integration in other programs. We demonstrate how our framework departs from approaches used in state-of-the-art simulation frameworks to enable real-time performance, and compare its capabilities to existing solutions in the realm of control software, emphasizing the convenience of using Lodestar in low-level control system design and implementation. To demonstrate the utility of Lodestar in real-time control systems design, we have applied Lodestar to implement a real-time torque-based controller for a robotic arm. To compare the algorithm design approach in Lodestar to a classical ground-up approach, we have developed a novel autofocus algorithm for use in thermography-based localization and parameter estimation in electrosurgery and other areas of robot-assisted surgery. We use this example to illustrate that Lodestar considerably eases the design process. We also show how Lodestar can seamlessly interface with existing simulation and networking framework in a number of simulation examples. © 2023 ieee.

关键词： Engines

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