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Number Plate Detection Using Drone Surveillance 9

Number Plate Detection Using Drone Surveillance

引用

9th IEEE Uttar Pradesh Section International Conference on electrical, Electronics and computer engineering, UPCON 2022

作者： Jain, Swati Patel, Shaili Mehta, Avi Verma, Jai Prakash Dept. of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

Drones, or unmanned aerial vehicles, have a wide range of uses in a variety of sectors. Surveillance, photography, surveying physically difficult locations, and traffic patrols are some of the applications. License plate detection and identification by utilizing UAV s based video streaming is one such scenario on which we have engaged. Recognizing license plates in drone photographs is a difficult task since the images may be distorted, blurred, or contain background noise such as other cars, banners, or people. The automatic license plate detecting system is a well-established system in which the majority of research has been conducted. These approaches, on the other hand, concentrate on photographs taken from the front. The suggested study is limited to a subset of drone photos. We examined multiple state-of-the-art methods like Wpodnet and yolov5 for detecting license plates in subsets of drone images in this paper. © 2022 IEEE.

关键词： Drones

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Attention-Based CRNN Models for Identification of Respiratory Diseases from Lung Sounds 14

Attention-Based CRNN Models for Identification of Respirator...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sanjana, J. Naik, Poornanand Purushottam Padukudru, Mahesh A. Koolagudi, Shashidhar G. Rajan, Jeny National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal India J.S.S. Medical College Jssaher Dept. of Respiratory Medicine Mysore India

ISBN: (纸本)9798350335095

Respiratory diseases are a major global health concern, with millions of people suffering from disorders such as asthma, bronchitis, chronic obstructive pulmonary disease (COPD), and pneumonia. In recent years, machine learning and other forms of Artificial Intelligence have proven to be useful resources for resolving issues in the medical field. In this study, we examine the diagnostic utility of Convolutional Recurrent Neural Network (CRNN) models for identifying respiratory diseases using digitally recorded lung sounds. We developed two deep learning models to diagnose and classify lung diseases: a binary classification to classify COPD and non-COPD, and a multi-class classification model to classify five lung disorders (COPD, URTI-upper respiratory tract infection, Pneumonia, Bronchiectasis and Bronchiolitis) and healthy conditions. The ICBHI 2017 challenge dataset [1] was used to develop the models. The accuracy of the binary and multiclass classification models was 98.6% and 97.6%, respectively, with ICBHI Scores of 0.9866 and 0.9723. © 2023 IEEE.

关键词： Pulmonary diseases

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Time Series Pattern Matching Technique for Low-Energy Arrhythmia Classification in Implantable Cardiac Devices

Time Series Pattern Matching Technique for Low-Energy Arrhyt...

引用

Computing and Machine Intelligence (ICMI), International Conference on

作者： Prianka Sengupta Eugene B. John Dept. of Electrical and Computer Engineering Texas A&M University College Satation TX Dept. of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Implantable cardiac devices (ICDs) are often used as an effective treatment for arrhythmia. Although these devices have access to a live Electrocardiogram (ECG) stream, currently they do not offer on-device classification of arrhythmia due to the limited computing capability and severe power constraints. In this paper we propose a low-energy computing method for extracting shape-based features from ECG in combination with machine learning techniques for classifying nine different cases of arrhythmia. This is achieved by using a Localized Longest Common Subsequence (LLCS) algorithm which has low computational requirements that allows on-device execution. The proposed method strongly focuses on maintaining minimal energy and computational footprint, in line with the operating constraints of implantable devices. To demonstrate the energy efficiency and low computation load of the proposed method we implement the classification pipeline on a low-power RISC microcontroller and compare its performance with existing classification techniques. The classification accuracy and energy of the proposed method is compared with state-of-the art research in arrhythmia classification.

关键词： Performance evaluation Accuracy Arrhythmia Time series analysis Pipelines Signal processing algorithms Implants

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Controlling Age of Information in a Dual-Server System with Transmission Freezing and Preemption

Controlling Age of Information in a Dual-Server System with ...

引用

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Nail Akar Sennur Ulukus Electrical and Electronics Engineering Dept. Bilkent University Ankara Turkey Dept. of Electrical and Computer Engineering University of Maryland College Park MD USA

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

We study age of information (AoI) in a status update system for the generate-at-will (GAW) scenario, consisting of a single information source, dual heterogeneous servers, and a monitor. For this system, stochastic hybrid systems (SHS) were used to obtain the mean AoI for the work-conserving zero wait (ZW) policy with out-of-order packet discarding at the monitor. In this paper, we propose a non-work-conserving freeze/preempt (F/P) policy for which the sampling and transmission process is frozen for an Erlang-distributed duration upon each new transmission, and out-of-order (obsolete) packets are preempted at the source, rather than being discarded at the monitor upon reception. We use the absorbing Markov chain (AMC) method to obtain the exact distributions of AoI and also the peak AoI (PAoI) processes, for the F/P policy. Numerical results are presented for the validation of the proposed analytical model and a comparative evaluation of ZW and F/P policies.

关键词： Out of order Wireless communication Analytical models Conferences Stochastic processes Signal processing Information age Servers Monitoring

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FIPSO Algorithm Based Energy and Cost Optimization In Microgrid

FIPSO Algorithm Based Energy and Cost Optimization In Microg...

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International Conference on computer and Information technology (ICCIT)

作者： Md. Alamgir Hossain Md. Shahjahan Dept. Electrical and Electronic Engineering Khulna University of Engineering & Technology Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In this paper, the modified form of well known heuristic PSO search algorithm known as fully informed PSO (FIPSO) algorithm is proposed to optimize the energy and cost to reduce the frequent fluctuation of energy sources output in a microgrid. The conventional PSO suffers from the premature convergence that limit the energy optimization and cost reduction. The effectiveness of the suggested FIPSO algorithm is contrasted with the conventional PSO and it is noticed that the suggested algorithm outperforms the existing method. Several computer simulations have been performed using Matlab environment to investigate the performance of the energy sources with and without optimization algorithm.

关键词： Costs Fluctuations Heuristic algorithms Optimization methods Microgrids Cost function Mathematical models Particle swarm optimization MATLAB Information technology

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Optimal Sizing of Two Interactive Microgrids by Implementation of PSO Algorithm

Optimal Sizing of Two Interactive Microgrids by Implementati...

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International Conference on Control, Instrumentation, and Automation

作者： Mohammad Reza Dodani Azam Salari Mahdi Zeinali Mousa Marzband Dept. Electrical Engineering Sahand University of Technology Tabriz Iran Dept. Electrical Engineering Islamic Azad University Lahijan Iran

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

According to the conditions of the present era and the importance of electricity in our world, it is felt more moving towards applying of new and technological methods to produce this energy in cleaner and more economical way and investing in this sector. Distributed Generations as one of the main solutions for this aim have been considered by energy policymakers in countries. Distributed generations that restructure the power system and reduces centralization in power generation are usually used in a new structure called a microgrid. The microgrid is one of the main components in the smart grid subset, which is a grid with new telecommunication technologies, measurement and energy production. A very important issue in microgrids is the optimization of investment required to create microgrids and costs over the life of the project by considering the required elements and infrastructures. In this dissertation, by considering two interacting microgrids that are adjacent to each other, the issue of optimizing the size of their components using particle swarm optimization (PSO) algorithm and using an intelligent multi agent system in each microgrid has been investigated. The simulation results of both interactive microgrids, which are the optimal size of their components and the optimal cost of creating each microgrid, have been obtained and examined using MATLAB software.

关键词： Costs Hydrogen storage Software algorithms Hydrogen Fuel cells Microgrids Production Control systems Distributed power generation Optimization

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A Comprehensive Review on Advancements in Additive Manufacturing for Active Parts of electrical Machines

A Comprehensive Review on Advancements in Additive Manufactu...

引用

electrical Machines and Drives (ICEMD), International Conference on

作者： Aryan Khazaei Mohsen Arabkhazaeli Hamidreza Dalirnateri Ebrahim Afjei Dept. of Electrical Engineering Shahid Beheshti University Tehran Iran Dept. of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

This article reviews additive manufacturing (AM) methods and their applications in producing the active components of electrical machines, such as cores, windings, and permanent magnets. The use of AM has expanded across various industries, including aerospace, automotive, and robotics, due to advantages like enabling complex geometries, reducing weight, and enhancing design flexibility. Although AM presents challenges, such as high equipment and material costs, recent advancements, especially in the field of electrical machines, have made it an appealing option. Additionally, this paper provides a comprehensive overview of the latest developments in using AM to enhance the design and performance of active components in electrical machines.

关键词： Industries Reviews Service robots Magnetic cores Windings Production Three-dimensional printing Thermal management Permanent magnets Manufacturing

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SYNERGY OF ROBOTICS AND IOT MONITORING IN INDUSTRIES USING DEEP RESNETS

SYNERGY OF ROBOTICS AND IOT MONITORING IN INDUSTRIES USING D...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gobinath Kumarasamy, M. Kongu Kumar, M. Anbarasu, L. Padmavathi, N. Kumar, S. Sathish Ramya, P. Dept. of Computer Science and Engineering Chettinad College of Engineering and Technology Puliyur Karur Tamilnadu India College of Engineering Tamilnadu Karur India Engineering College Tamilnadu Perundurai India Department of Electronics and Communication Engineering Chettinad College of Engineering and Technology Puliyur Karur Tamilnadu India Department of EEE Erode Sengunthar Engineering College Tamilnadu Perundurai India General Engineering- Electrical RMD Engineering College Chennai India Department of EEE M. Kumarasamy College of Engineering Karur Tamilnadu India Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350305708

This paper explores the synergistic integration of robotics and Internet of Things (IoT) monitoring within industrial settings, employing Deep Residual Networks (Deep ResNets). The convergence of robotics and IoT has the potential to revolutionize various industries by enabling efficient data collection, real-time analysis, and informed decision-making. Deep ResNets, known for their exceptional feature extraction capabilities, further enhance the fusion of these technologies. In this study, we delve into the collaborative framework of robotics and IoT, elucidating how Deep ResNets bolster the process. We discuss the benefits of this integration, including optimized resource allocation, predictive maintenance, and adaptable production systems. Through empirical analysis, we demonstrate the efficacy of Deep ResNets in handling complex data from integrated robotics and IoT sources. Ultimately, this research sheds light on the transformative impact of amalgamating robotics, IoT monitoring, and Deep ResNets, paving the way for intelligent and responsive industries. © 2023 IEEE.

关键词： Robotics

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ML Assisted Web Application Firewall

ML Assisted Web Application Firewall

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Pranav Kalariya Manthan Jethva Yasser Alginahi Electrical and Computer Engineering Dept. University of Windsor Windsor Canada Computer Science Dept. Adrian College MI USA

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept.at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests.

关键词： Adaptation models Machine learning algorithms Firewalls (computing) Machine learning SQL injection Feature extraction Data models

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Enhancing Diabetes Detection: A Weighted Averaging Approach for Combined Model Accuracy 9

Enhancing Diabetes Detection: A Weighted Averaging Approach ...

引用

9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Sami, Areesha Naseer, Anum Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Dept. of Computer Science Pakistan University of Central Punjab Faculty of Information Technology Department of Computer Science Lahore Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan University Lahore Information Technology Pakistan

ISBN: (纸本)9798350394474

Diabetes, marked by prolonged high blood sugar levels, poses a significant global health challenge. Precise early prediction is vital but faces hurdles due to limited data and complexities like outliers. Uncontrolled diabetes heightens severe complications, driving research into advanced machine and deep learning techniques. Healthcare professionals recognize the crucial role of Machine Learning Algorithms (MLA) in enhancing disease identification. This study integrates deep and machine learning models through techniques like K-means, CNN, NB, and SVM. Combining their predictions using weighted averaging yields a 79.5% accuracy, improving diabetes prediction globally. © 2024 IEEE.

关键词： Support vector machines

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