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Smart signaling: A Smart internet of Things Assisted Traffic Light Controlling and Monitoring System using Intelligent Sensors 9

Smart Signaling: A Smart Internet of Things Assisted Traffic...

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9th international conference on Science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Saranya, G. Ratheesh, R. Vijayalakshmi, S. Arunsundar, B. Swarna, M. Sri Krishna College of Engineering And Technology Department of Electronics And Communication Engineering Coimbatore Kuniyamuthur India Agni College of Technology Department of Ece Chennai India R.M.K. Engineering College Department of Ece Chennai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India Dr. M.G.R. Educational And Research Institute University Department of Ece Chennai India

ISBN: (纸本)9798350365092

In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a Smart internet of Things (IoT)-assisted Traffic Light Controlling and Monitoring System designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability. © 2024 IEEE.

关键词： internet of things

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A Smart NIDS Design for SDN-based Cloud IoT Network using Optimal Path Selection and RNN 9

A Smart NIDS Design for SDN-based Cloud IoT Network using Op...

引用

9th international conference on signal Processing and Communication, ICSC 2023

作者： Kumar, Narendra Aeron, Anurag Dawar, Ishaan Kumar, Santosh Department of Computer Science and Engineering Amity University Jharkhand Ranchi India School of Computing DIT University Dehradun Dehradun India Computer science and Information Technology Siksha 'O' Anusandhan University Bhubaneshwar India

ISBN: (纸本)9798350383201

The internet of Things (IoT) has greatly increased the possibility for developing intelligent connections and applications in many facets of daily life. Traditional security solutions frequently fail to solve security issues in cloud-based IoT systems, proving ineffective and insufficient. Software Defined Networking (SDN), which effectively detects and monitors network security vulnerabilities thanks to its programmable characteristics, presents a viable solution to these problems. In order to strengthen computer systems and fend off threats to network security, machine learning (ML) techniques have recently been included into SDN-Network Intrusion Detection systems (NIDS). Deep learning (DL), one of these cutting-edge ML techniques, has become more popular in the SDN environment. For SDN-based cloud IoT networks, this paper design an efficient path selection system in this study utilizing DL approaches. The strategy comprises employing a Recurrent Neural Network (RNN) to identify threats and a selection mechanism to pick the best route for data delivery. This study performed tests on two datasets, KDD and UNSWNB15, and the results show that the suggested RNN outperformed existing DL methods by achieving an amazing accuracy ranging from 91% to 95% on both datasets. © 2023 IEEE.

关键词： internet of things

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An External Denoising Framework for Magnetic Resonance Imaging: Leveraging Anatomical Similarities Across Subjects with Fast Searches 8

An External Denoising Framework for Magnetic Resonance Imagi...

引用

8th international conference on signal and image Processing, ICSIP 2023

作者： Mei, Lifeng Liu, Sixing Tang, Chenhui Cai, Jiaqia Wang, Jingli Liu, Yilong Wu, Ed X. Lyu, Mengye Shenzhen Technology University College of Health Science and Environmental Engineering Shenzhen China Shenzhen Technology University College of Big Data and Internet Shenzhen China Guangzhou China The University of Hong Kong Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350397932

External denoising, also known as reference-based denoising, utilizes information from clean reference images, yielding more robust results than internal denoising, especially in situations with high noise levels. The effectiveness of reference-based denoising relies on the similarity between reference and noisy images. In this paper, we introduce a novel external denoising framework for magnetic resonance imaging (MRI) that takes advantage of the inherent anatomical structure similarities across subjects. Our framework employs noise-resistant neural networks to extract deep features, which facilitate searches for high-quality reference images from a large external database. This versatile framework is compatible with all reference-based denoising algorithms, obviating the need for acquiring additional reference images from the subject under examination. © 2023 IEEE.

关键词： Magnetic resonance imaging

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A Cloud-Edge Integrated Water Extraction Using Superpixel Segmentation 18th

A Cloud-Edge Integrated Water Extraction Using Superpixel Se...

引用

18th international conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zhang, Qingmin Chen, Chen Zhou, Yang Wang, Zhiyi Jin, Fan Lu, Haitao Wan, Shaohua School of Communication Engineering Xidian University Xi’an China Xidian Hangzhou Institute of Technology Hangzhou311231 China The State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China Ningbo Joynext Technology Corporation Ningbo361000 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

ISBN: (纸本)9783031714665

With the development of the Industrial internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Advanced Wheelchair Rehabilitation: Surface EMG-augmented 9-DoF System for Upper and Lower Limb 46

Advanced Wheelchair Rehabilitation: Surface EMG-augmented 9-...

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46th Annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Jagan, P. Sasirekha, G.V.K. Rao, Madhav Bapat, Jyotsna Das, Debabrata Ciet International Institute of Information Technology Bangalore India

ISBN: (纸本)9798350371499

The rehabilitation is a most important phase in recovery of motor movements in the body. Using advanced robotic systems in the field of rehabilitation brings effective therapy in recuperation treatment. In this paper we propose a novel Electromyography (EMG) signal augmented, upper and lower limb integrated, internet of Things (IoT) enabled, wheel chair based exoskeleton system called XoRehab. The proposed system is designed for patients affected with hemiplegic, paraplegia and tetraplegia. Existing systems as per literature survey are, for either for upper or lower limbs, with limited degrees of freedom. A system with integrated upper and lower limb mechanisms can help in reducing the patients' movement for treatment from one system to another. The addition of EMG signals helps assess the recovery status. Additionally, providing remote access using IoT framework enables home-based rehabilitation. The system prototype has been successfully developed and experiments for various muscle movements have been conducted. The photograph of the developed system and the plots of the muscle movements captured by EMG signals, have been included as results. © 2024 IEEE.

关键词： Electromyography

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Improved Performance of Photovoltaic Panels by image Processing 6th

Improved Performance of Photovoltaic Panels by Image Process...

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6th international conference on Big Data Cloud and internet of Things, BDIoT 2022

作者： Didi, Zaidan El Azami, Ikram Faculty of Sciences Ibn Tofail Kénitra University Kénitra Morocco

ISBN: (纸本)9783031283864

In this paper, we have based on image processing with the internet of things technology to detect the position of the sun, seek its contour, and determine its center, we will propose with the smallest details an innovative method that allows us to better benefit and make the most of solar energy, this study focuses on improving the accuracy of solar spectrum tracking with respect to cloud cover problems. Our study is based on a Binary image processing algorithm to analyze the images acquired by a camera. We have integrated into this realization a webcam to capture the images of the sky at each time interval, and two servo motors to ensure the vertical and horizontal rotation of a joint of two axes, finally a Raspberry Pi with an open-source operating system, We exploited raspberry pi with the use of the Python programming language and the OpenCV library to perform image processing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Solar energy

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international conference on Applied Soft Computing and Communication Networks, ACN 2023

International Conference on Applied Soft Computing and Commu...

引用

international conference on Applied Soft Computing and Communication Networks, ACN 2023

ISBN: (纸本)9789819720033

The proceedings contain 40 papers. The special focus in this conference is on Applied Soft Computing and Communication Networks. The topics include: Intelligent Holo-Assistant Avatar with Lip-Syncing;historical Analysis of Financial Fraud and Its Future;a Study on Colour-Emotion Association for Happiness Among the Indian Youth Using Artificial Intelligence;comparative Analysis of Chicken Swarm Optimization and IbI Logics Algorithm for Multiobjective Optimization in k-Coverage and m-Connectivity Problem;Machine Learning-based Detection of Attacks and Anomalies in Industrial internet of Things (IIoT) Networks;privacy in Data Handling in Agile Development Environments;feature Enriched Framework for Rumor Detection Using Tweets;Enhancing KYC Verification: A Secure and Efficient Approach Utilizing Blockchain technology;a Smartphone Data Steganography Framework based on K-mean image Selection and Random Embedding in Three image Channels;Comparative Analysis of LSTM and GRU for Uplink Data Rate Prediction in 5G Networks;scheduling in Time-Sensitive Networks Using Deep Reinforcement Learning;power Control for Collaborative Sensors in internet of Things Environments Using K-means Approach;Optimizing the EDFA Gain for Optical WDM Network Using Simulation Method;RFID-based Smart Trolley with Mobile Application;SAKM-ITS: Secure Authentication and Key Management Protocol Concerning Intelligent Transportation systems;addressing Single Point of Failure in Group Communication of Constrained Environments;An RFID-based Authentication Protocol for Smart Healthcare Applications;on Credit Card Fraud Detection Using Machine Learning Techniques;refactoring Web Applications Using Functional Programming Principles to Improve Security;private Contact Tracing on Trajectory Data;innovating Threat Detection: Behavioral Rule Generators for Malware Families;stratifying Malware Clusters: A Solution Mapping Paradigm;a Multimodal Deep Learning Approach for High-Resolution Land Surface Temperature Es

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Tor encrypted traffic recognition and classification technology based on flow information graph and deep learning

Tor encrypted traffic recognition and classification technol...

引用

2024 international conference on image Processing and Artificial Intelligence, ICIPAl 2024

作者： Zhang, Yu Jia, Zhenhong Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei Lu, Changwu School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

ISBN: (纸本)9781510681514

internet traffic analysis is the core approach to network management and security. In the rapidly changing environment of encrypted traffic, traditional plaintext-based analysis methods have become obsolete. Although there are currently some methods for analysing encrypted traffic, they overlook the inherent logic and hierarchy of different encrypted traffic analysis requirements and lack research into the essential characteristics of encrypted *** article proposes a framework FM-ENet based on Graph Neural Network and Deep learning for Tor encrypted traffic classification to meet the practical needs of network management and security monitoring.A Flow Information Graph has been designed on the basis of Graph Neural Network, which can effectively alleviate the problem of concept *** the area of deep neural networks, we have developed an end-to-end multi-level spatio-temporal feature fusion enhanced network module ST-FENet, which has the advantage of automatically learning the non-linear relationship between input and output data without the need for manual feature extraction,this structure can complement each other in classification performance and solve the problem of low classifier efficiency. We compared FM-ENet to current popular methods using the public ISCX-Tor dataset. FM-ENet can achieve higher performance while saving costs, with a 9% improvement in accuracy compared to FlowPrint and a 4.4%, 4.5% and 4.4% improvement in PR, RC and F1 indicators compared to TSCRNN. © 2024 SPIE.

关键词： Network security

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image Recognition and Parameter Analysis based on Joint Mixup and ResNet 5th

Image Recognition and Parameter Analysis Based on Joint Mixu...

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5th international conference on 3D Imaging Technologies—Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： She, Xingqian College of Computer Science and Technology Taiyuan University of Technology Taiyuan China

ISBN: (纸本)9789819751808

This research paper centers on the evaluation of convolutional neural networks (CNNs) in the realm of image classification tasks, specifically employing the CIFAR-10 dataset to assess their effectiveness. To further enhance the performance of image classification, the authors introduce a combination of the Residual Network (ResNet) model and the Mixup technique, aiming to analyze the influence of Mixup training on the predictive capabilities of the model. In more detail, the paper conducts a comparative analysis between models trained with and without Mixup, specifically examining their performance on the test set. The aim is to investigate the influence of Mixup on the trained models and their capacity to achieve precise image classification. The experimental results reveal that employing Mixup during training yields a noteworthy improvement of nearly 1% in prediction accuracy on the test set compared to models trained without Mixup. The findings of this study provide valuable insights into the potential of Mixup as an effective technique for improving the performance of CNNs in image classification tasks. The combination of Mixup with the ResNet model offers a promising avenue for future research and practical applications in the field of computer vision. By leveraging the benefits of Mixup, researchers and practitioners can further advance the accuracy and robustness of CNN-based image classification systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Real-Time Defect Detection Monitoring System For Extrusion-based 3D Food Printing Using internet of Things 3

Real-Time Defect Detection Monitoring System For Extrusion-b...

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3rd international conference on Smart Cities, Automation and Intelligent Computing systems, ICON-SONICS 2023

作者： Mawardi, Cholid Buono, Agus Priandana, Karlisa Herianto Institut Pertanian Bogor Department of Computer Science Bogor Indonesia Universitas Gadjah Mada Department of Mechanical and Industrial Engineering Yogyakarta Indonesia

ISBN: (纸本)9781509062805

Quality control remains a significant challenge in 3D food printing, in which defects during the printing process will lead to resource and time wastage. This work proposes a novel approach integrating cameras, image processing, and supervised machine learning to autonomously assess the quality of 3D food printed items. At crucial stages of the printing process, images of partially manufactured parts are captured and analyzed based on its geometry. Utilizing smartphones and the internet of Things for efficient monitoring, the designed system aims to minimize material wastage, a prevalent concern in the creative food industry. The monitoring tool employs a 3D printer embedded with Raspberry Pi and a primary camera, with image processing carried out by the Obico plugin on Raspberry Pi to issue commands for real-time performance monitoring. The implementation of this technology not only addresses early defect detection but also enhancing food design and minimizing environmental impact, presenting promising prospects for the future of the food industry. © 2023 IEEE.

关键词： internet of things

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