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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ravi, Kiran Chand Kannan, K.S. Porselvi, R. Banumathi, S. Manikandan, R. Sultan Saleem, A. MLR Institute of Technology Department of ECE Hyderabad India Kalasalingam Academy of Research and Education Department of CSE Tamil Nadu Krishnankoil India Tagore Engineering College Department of Electronics and Communication Engineering India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering India Panimalar Engineering College Department of Electronics & Communication Engineering Chennai India Dhanalakshmi College of Engineering Department of CSE Chennai India

ISBN: (纸本)9798350354171

The significance of the saying 'a picture is worth a thousand words' is evident in our daily experiences, showcasing the profound impact of images. This paper delves into the contemporary and emerging applications of imaging in our everyday lives, offering a broad spectrum for further exploration. With the advent of digital image processing, there has been a transformative shift in various technical domains, opening avenues for extensive research. There exists considerable potential for exploration and advancement within the realm of image processing, as highlighted in this paper. It encapsulates the foundational stages of digital image processing while emphasizing the diverse areas where research endeavors can enhance the quality of human life. © 2024 IEEE.

关键词： Image segmentation

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Accelerating Mobile Edge Generation (MEG) by Constrained Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1854-1869页

作者： Xu, Xiaoxia Liu, Yuanwei Mu, Xidong Xing, Hong Nallanathan, Arumugam Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong School of Electronics Electrical Engineering and Computer Science BelfastBT3 9DT United Kingdom The Hong Kong University of Science and Technology IoT Thrust Guangzhou Guangzhou511453 China Hong Kong University of Science and Technology Department of ECE Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

A novel mobile edge generation (MEG) framework is proposed for low-latency image generation on mobile devices. Exploiting a latent diffusion model (LDM) distributed across the edge server (ES) and the user equipment (UE), only low-dimension features need to be transmitted for creating artificial intelligence generative content (AIGC). Two novel modules, namely dynamic diffusion and feature merging, are conceived to compress the diffusion model and transmitted features, respectively. By jointly optimizing compression rates of denoising steps and feature merging, the image quality maximization problem is formulated subject to latency and energy consumption constraints. To address this problem in dynamic channel conditions, a low-complexity compression protocol is developed. First, a backbone LDM architecture is learned by offline distillation to support various compression options. Then, compression rates are predicted in online environment specific to channel and task features. To solve the resultant constrained Markov Decision Process (MDP), a constrained variational policy optimization (CVPO) based MEG algorithm, MEG-CVPO, is further developed to learn constraint-guaranteed optimization. Numerical results demonstrate that: 1) The proposed framework improves image distortions while reducing over 40% latency compared to conventional generation schemes. 2) MEG-CVPO stringently guarantee constraints and realizes a flexible trade-off between generation qualities and overheads. © 2015 IEEE.

关键词： Markov processes

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Automated Early Diagnosis of Lung Tumor Based on Deep Learning Algorithms 1

Automated Early Diagnosis of Lung Tumor Based on Deep Learni...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Maheswari, M. Jothi, B. Devi, P. Gayathri Chitradevi, B. Pradeep, Kumar S SRM Institute of Science and Technology Department of Computational Intelligence Chennai India Panimalar Engineering College Department of ECE Chennai India Department of Computer Science Engineering Perambalur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350323795

The main goals of this study are to identify the early stages of lung cancer and investigate the degree of accuracy of various machine learning algorithms. A thorough review of the literature revealed that certain classifiers have low accuracy while others have higher accuracy but are challenging to approach nearer to 100%. Low accuracy and high implementation costs as a result of inappropriate DICOM image handling Many various types of images are utilised in medical image processing, however CT scans are typically chosen since they have less noise. Deep learning has been shown to be the most effective technique for processing medical images, identifying and classifying lung nodules, extracting features, and predicting the stage of lung cancer. This system's initial stage employed image processing methods to separate lung sections. Using K Means, the segmentation is carried out. Segmented images are used to extract the features, which are then classified using a variety of machine learning algorithms. The accuracy, sensitivity, and specificity of the proposed techniques, as well as their classification time, are used to assess their performance. © 2023 IEEE.

关键词： computerized tomography

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Nano Functionalized Antenna Based IoT Enabled Devices for Health Care Applications 1

Nano Functionalized Antenna Based IoT Enabled Devices for He...

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1st International Conference on electronics, Communication and Signal Processing, ICECSP 2024

作者： Janish Blessy, J. Jaison, B. Daniel Naresh Kumar, M. Velammal Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Surappet India R.M.K. Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of ECE Tamil Nadu Chennai India

ISBN: (纸本)9798350364590

Continuous glucose monitoring (CGM) is crucial in diabetes management, necessitating the development of advanced sensing technologies. This study proposes a novel approach utilizing nano functionalized antenna-based IoT-enabled devices for CGM applications. The proposed method harnesses the unique properties of graphene, including high electrical conductivity, large surface area, and biocompatibility, to enhance glucose sensing capabilities. Evaluation of graphene-based materials involves assessing their electrical conductivity, surface area, and biocompatibility using techniques such as four-point probe measurements, BET analysis, and biocompatibility assays. A comparison with existing CGM models highlights the advantages of the proposed method, including high sensitivity, biocompatibility, miniaturization, and integration with IoT platforms. Numerical ratings on various parameters provide a quantitative assessment of the proposed method's performance relative to existing models. Overall, the proposed nano functionalized antenna-based IoT-enabled devices offer a promising solution for accurate and reliable CGM, with potential implications for improving diabetes management and patient outcomes. © 2024 IEEE.

关键词： Glucose sensors

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Automated 3D Urban Landscapes Visualization Using Open Data Sources on the Example of the City of Zagreb

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KN - Journal of Cartography and Geographic Information 2022年第2期72卷 139-152页

作者： Komadina, Adrian Mihajlović, Željka Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb 10000 Croatia

Geographical location visualization is important to create virtual environments when performing simulations. This study uses publicly available data sources to create three-dimensional (3D) views of geographical locations, focusing on the city of Zagreb as a case study, and presents an automated solution that integrates data from four different sources to achieve this. Publicly available data sources (OpenStreetMap, Google Maps Elevation API, MapBox Static Images API) were used, and GIS Zrinjevac which is unique to Zagreb. Based on these sources, individual 3D objects were generated and displayed virtually in the form of an interactive 3D map. The proposed solution attempts to balance the differing levels of detail achieved using other techniques. The solution was implemented in the form of an application created using the Unity game engine. The results were analyzed to evaluate the solution’s validity and the created application’s performance. The analysis showed that the total execution time dependence is quadratically polynomial to the amount of retrieved data, and linear to the number of height map points. Examining the application’s update rate showed that the buildings and individual models used for polygon and point data had the greatest impact. Finally, possible improvements, alternative approaches, and advantages and disadvantages of the proposed solution are compared with other techniques used in this research area. © 2022, Deutsche Gesellschaft für Kartographie e.V.

关键词： Cartographic data Data visualization GIS Zrinjevac Google maps elevation API OpenStreetMap Virtual environment

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computer Vision Assisted Deep Learning Enabled Gas Pipeline Leak Detection Framework 5

Computer Vision Assisted Deep Learning Enabled Gas Pipeline ...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Nuthalapati, Suri Babu Arun, M. Prajitha, C. Rinesh, S. Abubeker, K.M. Cloudera Santa Clara CA United States Department of Mechanical Engineering Saveetha School of Engineering Thandalam India Karpagam Academy of Higher Education Department of Ece Centre for Interdisciplinary Research Coimbatore India V.S.B College of Engineering Technical Campus Department of Computer Science and Engineering Coimbatore India Department of Electronics and Communication Engineering Kerala India

ISBN: (纸本)9798331504403

Safe and economical infrastructure facilitates gas and oil transportation via pipeline networks. Leaks in natural gas pipelines, caused by ageing and other reasons, are common yet difficult to identify. Nevertheless, the most prevalent issue that impacts pipeline operation is leakage failure. To a large extent, the flow properties of the particular pipeline determine the accuracy with which a leaking location may be located. One helpful method for analyzing the many assumptions used to explain the fluid flow process and parameters is numerical modelling based on Artificial Intelligence (AI). Hence, this r4search suggests the Deep Learningassisted Gas Pipeline Leakage Detection System (DLGPLDS) using infrared cameras to accurately detect the leak location. A system of infrared cameras is suggested for use in gas plants, transportation, and manufacturing to track the undetectable leaks of methane gas in real-time. This research recommends using a histogram equalization procedure to remove the background noise effectively. The leaking of invisible gas may be successfully identified by this knowledge-based infrared camera system. This novel approach uses CNNs to categorize infrared camera images into groups based on whether or not they contain natural gas leaks. The research findings demonstrate that the suggested DLGPLDS model increases the gas leakage prediction ratio by 98.7%, classification accuracy ratio by {9 7. 4%}, PSNR ratio by 96.5%, and error rate by 10.3% compared to other state-of-art models. © 2024 IEEE.

关键词： Natural gas pipelines

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Revolutionizing UAV: Experimental Evaluation of IoT-Enabled Unmanned Aerial Vehicle-Based Agricultural Field Monitoring Using Remote Sensing Strategy

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 411-425页

作者： Narayanappa, Gireesh Babu Chandanadur Abbas, Syed Hauider Annamalai, Lavanya Meenakshi, Ramakrishnan Singh, Mangal Yadav, Tumikipalli Nagaraju Kumar, Aarthi Ramesh Department of CSE/ISE BMS Institute of Technology and Management Bengaluru India Department of Computer Science & amp Engineering Integral University Lucknow India Department of ECE Dr. MGR Educational and Research Institute Chennai India Department of CSE Chennai Institute of Technology Chennai India Department of Electronics and Telecommunication Engg Symbiosis International (Deemed University) Symbiosis Institute of Technology Pune India Department of Computer Science and Engineering Koneru Lakshmaih Education Foundation AP Vaddeswaram India Department of ECE R.M.D Engineering College R. S. M Nagar Gummidipoondi India

Agricultural monitoring has evolved significantly with the advent of unmanned aerial vehicles (UAVs), which offer innovative solutions for precision farming. Traditional agricultural practices often rely on manual inspection and limited ground-based sensors, which can be time-consuming and prone to inaccuracies. This paper explores the integration of UAV and ground-based IoT sensor data through advanced data fusion techniques to enhance data reliability and precision. Our hybrid machine learning model (HMLM), combining random forest and support vector machine algorithms, is employed for accurate crop health assessment, disease detection, and yield prediction. The implementation of UAV-based agricultural monitoring systems leverages the unique capabilities of quadcopters and fixed-wing UAVs. Quadcopters are utilized for detailed field surveys and spot checks, offering high-resolution imaging for precise inspections, while fixed-wing UAVs are deployed for large-scale monitoring, providing extensive coverage and long-flight endurance. Equipped with advanced IoT devices and sensors, including multispectral and hyperspectral cameras, GPS modules, and real-time communication systems, these UAVs enable comprehensive data collection and analysis. Our hybrid machine learning model (HMLM) improves classification accuracy and predictive power, achieving an impressive accuracy of 98.74%, leading to precise crop management and higher yields. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Agricultural monitoring Artificial intelligence (AI) Deep learning GPS modules Hybrid machine learning model (HMLM) Internet of things (IoT) Precision agriculture Sustainable farming Unmanned aerial vehicles (UAVs)

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A Deep Learning Approach for Automated Skin Disease Classification and Diagnosis 2

A Deep Learning Approach for Automated Skin Disease Classifi...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Suresh, Helina Rajini Amutha, M. Tamizharasi, A. Jaya Krishna, A.P. Lalitha, S. Nithya, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sona College of Technology Department of ECE Tamil Nadu Salem India R.M.D. Engineering College Department of Computer Science and Engineering Kavarapettai Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamilnadu Salem India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India

ISBN: (纸本)9798350317558

Everyone is ware that a wide range of problems affect skin structure, function, and appearance. Dermatological issues or dermatoses are common names used to represent the skin related illness. Early detection of skill llness is possible by employing Artificial Intellengence. A wide range of artificial intelligence techniques have been devised to promptly detect and diagnose skin problems. In this article we are proposing Mobilenetv2 and LSTM algorithms to identify skin disorders at an early stage. The combination of MobileNetV2 and LSTM (Long Short-Term Memory) for skin disease detection involves leveraging a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) with LSTM units for sequence modeling, this architecture aims to capture both spatial and temporal dependencies in skin image data, making it well-suited for skin disease detection tasks. The method shown in this study can identify five distinct diseases with ease, and with accuracy rates of 99.2%. © 2024 IEEE.

关键词： Image segmentation

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Signal Processing Techniques for Enhanced Wireless Network Performance 2

Signal Processing Techniques for Enhanced Wireless Network P...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Nirmala, G. Haripriya, M.P. Shaik, Mohammed Rizwan Vijayalakshmi, S. Martis, Jason Elroy Babu, M. Dinesh Department of Computer Science and Engineering R.M.D. Engineering College Chennai India Karnataka Bengaluru India Department of Electrical & Electronics Engineering College of Engineering& Computer Science Jazan University Jazan Saudi Arabia Department of ECE R.M.K. Engineering College Chennai India Department of ISE NMAM Institute of Technology Nitte Deemed to be University Karnataka Mangaluru India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9798331541217

With increasing complexity and data rates of wireless communication networks, an effective signal processing technique is significantly required. Memoryless predistortion techniques undoubtedly help to minimise nonlinear distortions in RF power amplifiers. This is so because they guarantee high amplifier output. Methodology: This paper presents a new multichannel wireless signal processing technique to give wireless communication systems memoryless predistortion. The predistortion approach is efficiently applied over numerous channels using wireless signal processing resources. This method lets run in real time with minimum unnecessary hardware overhead. Findings: The experiments reveal that the method based on wireless signal processing is an effective approach for correcting for nonlinear distortions developing in RF power amplifiers. © 2024 IEEE.

关键词： Nonlinear distortion

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An improved intrusion detection systems for Social security using AI-based technique 15

An improved intrusion detection systems for Social security ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Niranjana, S. Kumar, K. Suresh Malathy, K. Chandrakala, K. Abid, Y. Rajalakshmi Institute of Technology Department of Ai & Ds Chennai India Panimalar Engineering College Department of Mba Chennai India S.A. Engineering College Department of Master of Computer Applications Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Saveetha University Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798350370249

- the need for reliable intrusion detection structures (IDS) has resulted in increasing essentials in state-of-the-art virtual panoramas, especially regarding defensive, touchy, non-public statistics, such as social safety numbers. Conventional IDS techniques have obstacles, which include excessive false alarm costs and low detection accuracy. Thus, an advanced IDS system for social protection using AIprimarily based strategies is proposed. The machine uses artificial intelligence (AI) algorithms, including gadget learning and deep learning, to investigate network visitors and discover potential intrusions. By constantly studying historical information, the gadget can adapt to new threats and improve its detection talents. The gadget employs feature choice and measurement reduction strategies to reduce computational burden and enhance detection accuracy. It additionally includes anomaly detection methods to discover ordinary behaviors and stumble on state-of-the-art assaults that conventional rule-primarily based structures might also leave out. The proposed gadget has undergone rigorous checking out and achieved superior overall performance compared to standard IDS techniques. © 2024 IEEE.

关键词： Accuracy Algorithms Intrusions Panoramas Techniques

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