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Synchronization of Diesel Generator using Micro-Controller

Synchronization of Diesel Generator using Micro-Controller

引用

2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Brintha, N.C. Sai, T. Veera Venkata Sai, B. Niteesh Satya Vikram, K. Induvarshini, Y.K. Nidita, P. Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering Tamilnadu Krishanankoil India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Development of the world is engaged with the power. One of the main resources of power generation is a diesel generator which uses the non-renewable resource diesel. This paper will explain how to save diesel and increase the lifetime of generators by synchronizing the generator. Synchronization is a technique of parallel operation of generators based on the load. According to load, the microcontroller will power-on that many required generators without any manual intervention. The generators will be started based on the least run time by the micro-controller. After a predefined cool time, the micro-controller will again check for the least run time DGs and it selects the least run time DG for powering on. By this, we can increase the durability of the generator and the main aspect is we can save diesel for our future generations. © 2022 IEEE.

关键词： Synchronization

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Performance Improvement of Joint Space Distance Measurement for PIP joints 7

Performance Improvement of Joint Space Distance Measurement ...

引用

7th International Conference on Imaging, Signal Processing and Communications, ICISPC 2023

作者： Suzuki, Moeko Goto, Tomio Nagoya Institute of Technology Dept. of Creative Engineering Program Nagoya Japan Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350325072

In order to analyze progresses of rheumatoid arthritis, we are developing an application to measure the distance between finger joints from X-ray images. In this paper, we focus on second joints, which are known to be prone to arthritis. We have proposed a method of filtering images and detecting edges, which has improved accuracy. Experimental results show that practical application of this system will reduce the burden on physicians and contribute to early detection of rheumatoid arthritis. © 2023 IEEE.

关键词： Diseases

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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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Object Detection Model for Tacking System Implemtation using Deep Learning Technique

Object Detection Model for Tacking System Implemtation using...

引用

Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Navjot Singh Talwandi Payal Thakur Shanu Khare Dept of Computer Science Engineering Chandigarh University Punjab India

Thing discovery and tracking using deep learning has emerged as a cutting-edge technology with applications spanning autonomous vehicles, surveillance systems, robotics, and more. This paper provides an overview of the principles, methodologies, and recent advancements in the field of object detection and tracking using deep learning techniques. We delve into the fundamental concepts of convolutional neural networks (CNNs), region-based approaches, and tracking algorithms that have revolutionized object detection. Furthermore, we explore the challenges and potential applications of this technology, from real-time video analysis to enhancing safety and security. The research in this domain continues to push the boundaries of what is achievable, making thing discovery and following by means of bottomless erudition a key enabler of future smart systems. Object detection and tracking are important tasks in computer vision that have numerous applications in various domains, including surveillance, robotics, and autonomous driving. Deep learning algorithms have shown remarkable performance in these tasks, enabling accurate and efficient detection and tracking of objects in complex environments. This paper presents an overview of object detection and tracking using deep learning, highlighting the key techniques and algorithms used in these tasks. We discuss popular deep learning architectures, such as Faster R-CNN, YOLO, and SSD, and their applications in object detection. We also review deep learning-based tracking algorithms, such as Siamese networks and correlation filters, and their performance in object tracking. Finally, we discuss the challenges and future directions of object detection and tracking using deep learning.

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Stomach Cancer Prediction and Detection using Deep Learning: A Review

Stomach Cancer Prediction and Detection using Deep Learning:...

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Sharandeep Kaur Paurav Goel Nitika Kapoor dept. of Computer Science Engineering Chandigarh University Mohali India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Stomach cancer, sometimes referred to as gastric cancer, is still one of the most common and widespread deadly disease worldwide offers a significant challenge in oncology due to late detection and high mortality rates. The advent of deep learning has revolutionized stomach cancer prediction by leveraging sophisticated models such as convolutional neural net-works (CNNs), multimodal learning and hybrid frameworks that integrate multiple data sources like medical imaging, histological slides, clinical records and genetic profiles and provide better performance with high accuracy. This review comprehensively ex-plores recent advances in deep learning applications for the detection of stomach cancer, highlighting their performance, accuracy, and clinical implications. Comparative analysis demonstrates that different deep learning models, such as ResNet50, DenseNet121, EfficientNetB5, and MobileNetV2, provide impressive results with accuracy up to 99% and higher sensitivity and specificity. It also indicates that the system accuracy needs to be improved to 100 % in order to detect stomach cancer as early as possible. Early detection helps in the stoppage of further spreading of the disease and lowers the death rates.

关键词： Deep learning Stomach Accuracy Reviews Soft sensors Mortality Predictive models Cancer Biomedical imaging Tumors

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Cloud-Based Framework for Data Exchange to Enhance Global Healthcare 19

Cloud-Based Framework for Data Exchange to Enhance Global He...

引用

19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Naz, Ammerha Ali, Muhammad Cheema, Sehrish Munawar Pires, Ivan Miguel Dept. of Computer Science University of Sialkot Punjab Sialkot Pakistan Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries. © 2024 The Authors. Published by Elsevier B.V.

关键词： Electronic health record

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Enhancing Question-Answering with Knowledge Graph Retrieval and Generation using LLMs

Enhancing Question-Answering with Knowledge Graph Retrieval ...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Supraj Gijre Rishi Agrawal Priyash Laddha Gunjan Keswani Dept. Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India School of Computer Science and Engineering Ramdeobaba University Nagpur India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

This paper describes a novel technique to improving Large Language Models (LLMs) for document analysis that employs knowledge graphs and retrieval-augmented generation (RAG). We are working on constructing a chatbot system that can handle and analyze large documents from a variety of fields. Our approach addresses basic LLM issues including context maintenance and hallucination avoidance. The system combines document chunking, vector embedding, and similarity search with graph-based knowledge representation. Users can upload large papers and answer questions accurately. We show that integrating standard information retrieval approaches with graph-based storage and LLM capabilities improves context awareness and response accuracy across a wide range of document genres. This strategy is especially promising for complicated publications such as financial reports.

关键词： Text analysis Quantum computing Large language models Soft sensors Retrieval augmented generation Knowledge graphs Information retrieval Vectors Maintenance Standards

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Securing Satellite Communication: Exploring Cybersecurity Measures in Satellite Networks

Securing Satellite Communication: Exploring Cybersecurity Me...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Shanu Khare Navjot Singh Talwandi Dept of Computer Science Engineering Chandigarh University Punjab India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This paper discusses the importance of cybersecurity measures in satellite networks, highlighting potential vulnerabilities and threats that can compromise communication systems. With increasing dependence on satellite-based services for various applications such as navigation, remote sensing, and broadband connectivity, ensuring their security becomes crucial to prevent unauthorized access, data breaches, and service disruptions. The article examines various existing and emerging countermeasures aimed at enhancing the protection of satellite networks against cyber attacks. These include encryption techniques, authentication mechanisms, network segmentation, intrusion detection systems (IDS), and physical security measures. Furthermore, this study emphasizes the need for international cooperation, regulatory framework development, and continuous research and innovation to address evolving challenges in securing satellite communications.

关键词： Technological innovation Satellites Statistical analysis Intrusion detection Authentication Satellite navigation systems Encryption

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Digital Filter Design Using Soft Computing Technique

Digital Filter Design Using Soft Computing Technique

引用

Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Navjot Singh Talwandi Swati Dept of Computer Science Engineering Chandigarh University Punjab India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Digital filter design plays a crucial role in signal processing applications, aiming to enhance, extract, or suppress specific components of a signal. Soft computing techniques have emerged as effective methods for designing digital filters due to their ability to handle complex, non-linear problems. This paper explores the utilization of soft computing approaches, such as neural networks, fuzzy logic, and evolutionary algorithms, in the design and optimization of digital filters. By leveraging the adaptive and learning capabilities of these techniques, this study aims to achieve efficient and optimized filter designs, catering to various signal processing requirements. The comparative analysis of soft computing-based digital filter designs demonstrates their potential in achieving superior performance metrics compared to conventional methods. Moreover, this paper discusses the advantages, challenges, and future directions in employing soft computing techniques for digital filter design, highlighting their potential for addressing complex signal processing tasks in diverse applications.

关键词： Finite impulse response filters Signal processing algorithms Filtering algorithms Signal processing Linear programming Information filters Robustness

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License plate Detection with object classification for Road Vehicles (LPDOBV)

License plate Detection with object classification for Road ...

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： K. Suresh S Shibi Raj S Vikram Surya S Raghul Dept. of Computer Science and Engineering KPRIET Coimbatore India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Video surveillance plays a significant role in the domain of intelligent transportation systems. Intelligent transportation system aims to utilize advanced technologies to enhance the efficiency and safety of transportation system. Video surveillance has several uses, including identifying the reason behind an accident, locating a particular vehicle, and figuring out the best routes between important places. Object identification and shadow removal are the primary objectives of intelligent transportation systems. Moreover, video surveillance has other difficulties, such as text recognition. This work proposes an inner outer outline profile line (IOOPL) method for recognizing the set of objects boundary layers based on shadow elevation. It also tackles the issue of object shadows in-vehicle image segmentation not being recognized as a component of the item itself. Utilizing the delta learning algorithm, often known as the Widrow-Hoff learning rule, this work suggests a procedure for identifying and classifying cars by removing their shadow counterparts. The system is trained using a variety of vehicle kinds based on their look, colors, and construction types. Furthermore, this work employs an artificial neural networks trained approach using the high-performance delta learning technique, to classify cars and gather data on their journeys. Additionally, it introduces a strategy for license plate identification through edge dilation and text correlation. Recognizing number plates poses a challenging task in the realm of video text recognition.

关键词： Correlation Text recognition Image edge detection Video surveillance Automobiles Object recognition Task analysis

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