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Social Distance and Face Mask Detection using vision-based sensors and IP cameras 3

Social Distance and Face Mask Detection using vision-based s...

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Vineela, A. Devi, G. Lavanya Deekshit, G. Vinay GMR Institute of Technology Department of Computer Science and Engineering Rajam India Department of Computer Science and System Engineering Vishakhapatnam India

ISBN: (纸本)9781665494007

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

关键词： computer vision

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Home Automation Using Wi-Fi: ESP32-Based System for Remote control and Environmental Monitoring 3

Home Automation Using Wi-Fi: ESP32-Based System for Remote C...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Abdul Lateef Haroon, P.S. Shafiulla, Mohammed Naveed, Shaik Mohammed Ahmed, Suhail Nawaz, Shaik Mohammed Kumar, Utkarsh Ballari Institute Of Technology & Management Department Of Electronics And Communication Engineering Ballari India Ballari Institute Of Technology And Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Artificial Intelligence And Machine Learning Ballari India

ISBN: (纸本)9798350318609

This project introduces a comprehensive home automation system leveraging Wi-Fi connectivity and ESP32 micro controllers, aimed at enhancing modern living through seamless automation and user-centric design. The background emphasizes the fusion of IoT technology with home automation, highlighting Wi-Fi's significance in enabling remote monitoring and control of household devices. Methodologically, the project integrates relay modules and DHT11 sensors, orchestrated through a user-friendly smartphone application. Results demonstrate successful implementation and testing, showcasing functionality, reliability, and user-friendliness. Real-time environmental monitoring and remote appliance control fulfill the objectives of enhancing home comfort, convenience, and energy efficiency. The conclusion underscores the project's contribution to advancing smart home technologies, offering users a scalable and customizable solution. Future research opportunities include enhancing system capabilities and addressing security and privacy concerns. © 2024 IEEE.

关键词： Microcontrollers

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Effective Garbage Monitoring System Using GSM Module 3

Effective Garbage Monitoring System Using GSM Module

引用

3rd IEEE international conference on Intelligent Techniques in control, Optimization and Signal Processing, INCOS 2024

作者： Kumar, P Sathish Sree, V Bindhu Ramanjaneyulu, N. Nagurvali, Shaik Rohith, V. Teja, R. Sai Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhnagar India

ISBN: (纸本)9798350361186

Inefficient garbage collection not only leads to overflowing bins and unpleasant but also poses significant environmental and health risks. This paper proposes an effective garbage monitoring system utilizing a GSM module for real-time fill-level detection and automated alerts. The system employs ultrasonic sensors to measure garbage level within the bin, triggering an alert (SMS or data transmission) to designated authorities via the GSM module when a predefined threshold is reached. This enables optimized collection schedules, reducing unnecessary trips, fuel consumption, and associated emissions. Additionally, the system can be integrated with GPS for location tracking, facilitating targeted waste management in geographically dispersed areas. The proposed system offers a cost-effective and scalable solution for enhancing waste management efficiency, promoting environmental sustainability, and improving public health. © 2024 IEEE.

关键词： Global system for mobile communications

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Incentive Mechanism Of Distributed Electric Energy Transaction Based On Blockchain 3

Incentive Mechanism Of Distributed Electric Energy Transacti...

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Wang, Zhiyu Zhang, Jing Changchun University of Science and Technology College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665459112

The centralized electric energy transaction has deficiencies of large network overhead and high transaction cost, however, users trading electric energy in the distributed mode characterized by inactivity. Simultaneously, information disclosure easily occurs under complex transaction environment. Therefore, a distributed multi-party electric energy transaction incentive mechanism based on blockchain is proposed in this paper. According to the supply and demand relationship for electric energy, the transaction pricing model among multiple parties is established based on the auction theory, and the genetic algorithm is adopted to maximize the social welfare of participants in the distributed electric energy transaction. Meanwhile, the anonymous electric energy transaction is realized based on the blockchain, which can ensure the security of user transaction information to the greatest extent. The simulation results proved that the multi-party transaction incentive mechanism proposed in this paper can protect users' privacy and maximize social welfare of the energy transaction system. © 2022 IEEE.

关键词： Blockchain

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A-Eye: computer vision and Deep learning based Smart-Glasses with Edge computing for Visually Impaired 3

A-Eye: Computer vision and Deep learning based Smart-Glasses...

引用

3rd international conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Rajaraman, Sanjay Shivapriya, P.S. Easwari Engineering College Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350320749

India, where a third of the world's blind people reside, has about 12 million blind people, especially in comparison to a total of 39 million worldwide, according to the National Programme for control of Blindness (NPCB). Visually challenged people, including children and elderly people with low vision, still have trouble performing everyday tasks, even in this world of rapidly developing technologies. The existing solutions provide a straight forward solution using Artificial Intelligence technologies. To take this technological solution to the next level we propose a solution with all the AI features seamlessly integrated together and a dedicated mobile application gives full control and independency to their life. The A-Eye (smart glasses) system that is being proposed consists of a wearable smart glass solution, a mobile application, and a cloud-based data management platform. This project shows how Mobile Edge Computing (MEC) can be used to process data on the mobile phone other than to process the camera's video stream in the cloud or locally at the system level. The project's use of Mobile Edge Computing (MEC) expands the scope of its ability to add and update an endless number of features without being constrained by hardware. Therefore, the suggested smart glass system can minimize latency and be highly scalable while using heavy and effective models to effectively mitigate the problem and assist visually challenged individuals. © 2023 IEEE.

关键词： Internet of things

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Vehicle Analysis System Based on DeepSORT and YOLOv5 3

Vehicle Analysis System Based on DeepSORT and YOLOv5

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Guo, Fuheng Xu, Yi Shandong University of Technology School of Computer Science and Technology Zibo China

ISBN: (纸本)9781665459112

Nowadays, the constantly expanding coverage of expressways leads to an astronomical volume of daily high-speed traffic. In order to address the poor real-time performance of traditional traffic statistics and low accuracy in detection of densely distributed small targets for original algorithms, this paper proposes a vehicle analysis system that is based on DeepSORT and YOLOv5. More specifically, YOLOv5 is adopted to detect the target, Kalman filter is employed for prediction and update, and the Hungarian algorithm is applied for trajectory matching in the cascade matching, so as to realize the multi-target tracking of vehicles. Besides, inverse perspective mapping is performed to obtain the actual distance traveled by a vehicle per unit time, and to calculate the speed of it. The experimental results show that the vehicle analysis system based on DeepSORT and YOLOv5 has an average accuracy of 96% for vehicle targets, a 98.36% accuracy of vehicle speed measurement, and a lower model loss of than 2% on average, which meets the needs of real-time analysis. © 2022 IEEE.

关键词： Vehicles

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Unleashing Vanilla vision Transformer with Masked Image Modeling for Object Detection

Unleashing Vanilla Vision Transformer with Masked Image Mode...

引用

IEEE/CVF international conference on computer vision (ICCV)

作者： Fang, Yuxin Yang, Shusheng Wang, Shijie Ge, Yixiao Shan, Ying Wang, Xinggang Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China Tencent AI Lab Shenzhen Peoples R China Tencent PCG ARC Lab Shenzhen Peoples R China

ISBN: (纸本)9798350307184

We present an approach to efficiently and effectively adapt a masked image modeling (MIM) pre-trained vanilla vision Transformer (ViT) for object detection, which is based on our two novel observations: (i) A MIM pre-trained vanilla ViT encoder can work surprisingly well in the challenging object-level recognition scenario even with randomly sampled partial observations, e.g., only 25% similar to 50% of the input embeddings. (ii) In order to construct multi-scale representations for object detection from single-scale ViT, a randomly initialized compact convolutional stem supplants the pre-trained patchify stem, and its intermediate features can naturally serve as the higher resolution inputs of a feature pyramid network without further upsampling or other manipulations. While the pre-trained ViT is only regarded as the 3rd-stage of our detector's backbone instead of the whole feature extractor. This naturally results in a ConvNet-ViT hybrid architecture. The proposed detector, named MIMDET, enables a MIM pre-trained vanilla ViT to outperform leading hierarchical architectures such as Swin Transformer, MViTv2 and ConvNeXt on COCO object detection & instance segmentation, and achieves better results compared with the previous best adapted vanilla ViT detector using a more modest fine-tuning recipe while converging 2.8x faster. Code and pre-trained models are available at https://***/hustvl/MIMDet.

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Head-mounted Display System Based on Metaverse VR Technology 3

Head-mounted Display System Based on Metaverse VR Technology

引用

3rd international conference on computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Sun, Qingwen School of Computer Dalian Neusoft University of Information Dalian China

ISBN: (纸本)9781510671720

As the main carrier of Metaverse VR technology, the head-mounted display system plays a vital role in leading this technological trend. This paper implements a head-mounted display system based on Metaverse VR technology. It mainly applies the high-resolution LCD screen, microcontroller, and embedded programming technology. The system that collects data from gyroscopes and directional sensors in the head-mounted display terminal to realize the angle of view and scene switching on a developed Android system. Through the handheld terminal based on STM32 microcontroller, the system realizes human-system interaction. It isolates people's vision and hearing from the outside world and guides the user to a sense of being in a virtual environment. Then, the image generated by the small display in the helmet is magnified by the Fresnel lens, and a three-dimensional sense is generated in the mind by obtaining this differential information from the human eyes through the different images of the left and right eyes. © 2023 SPIE.

关键词： Virtual reality

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Design and Implementation of Five Stages Piplined RISC Processor on FPGA 3

Design and Implementation of Five Stages Piplined RISC Proce...

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3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic control and computer Engineering, MI-STA 2023

作者： Ben Yousuf, Amani Najeeb Khaled Alamen, Dania Eljhani, Mohamed Muftah University of Tripoli Department of Computer Engineering Tripoli Libya

ISBN: (纸本)9798350319897

This research focuses on designing and implementing a processor with a five-stage pipeline for educational purposes. The proposed processor can execute five 16-bit instructions simultaneously and is designed and simulated using Verilog HDL. It is implemented on a Cyclone IV FPGA on the DE2i-150 design board. The processor is suitable for applications that require high processing speed and low power consumption, including mobile computers, consumer electronics, security systems, and more. This RISC-based processor has simple instructions that consume low power and execute quickly. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Which performs better, linear scanning or the KNN searching algorithm based on 2D grids? 3

Which performs better, linear scanning or the KNN searching ...

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3rd international conference on Robotics, Automation and Intelligent control, ICRAIC 2023

作者： Ji, Tong Lin, Yifeng Yang, Yuer Liu, Hui Jinan University College of Information Science and Technology Guangzhou China Jinan University College of Cyber Security Guangzhou China The University of Hong Kong Department of Computer Science Hong Kong Hong Kong University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China

ISBN: (纸本)9798350360813

The K-nearest neighbor (KNN) algorithm is widely used in navigation, such as traffic management, driverless vehicles, and logistics planning. While it offers powerful instance-based learning, its performance can be influenced by data distribution and distance metric choice, and scalability can be an issue with large datasets. In this paper, we compare KNN with linear scanning in 2D grid-based search algorithms. KNN excels in accuracy, multidimensional feature processing, flexibility, and stepwise optimization, making it a strong choice for efficient and accurate 2D grid data search. Especially when the dimension of the grid is 200 ∗ 200 and the grade is 1, the performance of KNN is 99.70% better than linear scanning. However, we acknowledge that KNN's performance hinges on distance metric selection, and different metrics may perform differently in various applications. KNN is also sensitive to noise and outliers, which highlights the need for future enhancements and optimizations. © 2023 IEEE.

关键词： Large datasets

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