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Integrated Circuits, communication, and Computing Systems (ICIC3S), International Conference on

作者： Shalini Bhickta Naveen Jaglan Amit Kumar Department of Electronics and Communication Engineering Jaypee University of Information Technology Waknaghat Solan India Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat Solan India

ISBN: (数字)9798350364088

ISBN: (纸本)9798350364095

In modern wireless tele- communication, the key element for better and higher performance of the communication system is an Antenna. An antenna which removes all complex issues of weather, scattering and effects of multiple paths is in demand for current wireless system issues. The Antenna with Reconfiguration capabilities in current scenario solves these issues. The popularity of Reconfiguration in antennas has seen a significant growth due to its versatile capabilities. Therefore, reconfiguration in the structure of an Antenna without changing the physical dimensions and saving space realize the goal of high speed, gain, channel capacity and signal-to-Noise Ratios (SNR). In this paper a brief knowledge of various methods of designing a reconfiguration antenna is reviewed. The different switching techniques used for the activating the reconfiguration mechanism is discussed. The advancement of reconfiguration in multi-input multi-output (MIMO) systems is also studied.

关键词： Wireless communication Integrated circuits Reviews Design methodology Scattering Switches Antennas

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Modified Whale Optimization Algorithm for Task Scheduling in Cloud Computing 2

Modified Whale Optimization Algorithm for Task Scheduling in...

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

作者： Girirajan, B. Nijaguna, G.S. Pramodhini, R. Adnan, Myasar Mundher Shivakanth, Gandla SR University Department of ECE Telangana Warangal India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Yelahanka Department of Electronics and Communication Engineering Bengaluru India The Islamic University Najaf Iraq Koneru Laksmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350317558

In recent years, an efficiency of task scheduling is evolved as a major challenge in cloud platforms. Especially, identifying the optimal resources for input tasks is the major challenges faced by the task scheduler. So, in this research, a Modified Whale Optimization Algorithm (MWOA) is proposed to improve the behaviour in task scheduling by applying the parameters such as resource allocation and load balancing. Capacity criteria based MWOA algorithm determines the effective Virtual Machine (VM) for execution of tasks in queue. An effectiveness of proposed WOA algorithm is analysed by the utilization of performance measures such as memory storage, execution time, cost and makespan. The attained results from the proposed WOA algorithm are outperformed in various predictable optimization algorithms such as Storm, Spark, Flink and Kafka. The results of proposed WOA algorithms demonstrates the better performances in minimum execution time of 612ms, and memory storage of 309Kb on Kafka platform. © 2024 IEEE.

关键词： Load Balancing Modified Whale Optimization Algorithm Optimal resources Resource Allocation Task Scheduling

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Cloud-Enabled Regression Model for Enhanced Patient Authentication Systems

Cloud-Enabled Regression Model for Enhanced Patient Authenti...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Sindhuja, K. Thanalakshmi, S. Jai Ganesh, B. Muthumarilakshmi, S. Ganeshbabu, T.R. Sujatha, S. J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Gojan School of Business and Technology Department of Management Studies Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Protecting private health information and facilitating easy access to healthcare services depends on reliable patient authentication. Improving patient authentication systems via cloud computing and Artificial Intelligence (AI) approaches is the main topic of this paper. It uses linear regression (LR) models to provide a scalable solution that uses big datasets to enhance authentication efficiency and accuracy by using cloud-based platforms. LR examines several authentication elements to predict and validate patient identities, such as user behavior patterns and biometric data. The proposed solution aims to reduce authentication failures and mitigate possible security concerns while preserving a user-friendly experience. With better performance in terms of authentication accuracy and system responsiveness, experimental findings reveal that the cloud enabled strategy is beneficial. This system provides valuable information for future research on strong patient authentication systems and shows how cloud computing and AI may work together to improve healthcare security. © 2025 IEEE.

关键词： Sensitive data

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A Mobile Visitor Management System Using a QR Code and PIN for Access Control 2nd

A Mobile Visitor Management System Using a QR Code and PIN f...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Awotunde, Joseph Bamidele Adeniyi, Abidemi Emmanuel Imoize, Agbotiname Lucky Mejdoub, Youssef Abdualazizu, Zakariyya Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science Bowen University Osun State Iwo Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

Visitor management is a common activity that happens every day all around the world. Accepting visitors at home or in the office, going to the bank, or visiting a private residential area;some sort of visitor management goes on in all those activities to ensure access control and security. Insecurity is a big challenge that comes with receiving visitors and that is why visitor management is important. Traditionally, visitor management is handled physically by recording visitors’ information into a log book and asking visitors to contact their hosts before gaining access to a secured environment but this process is time-consuming and the visitor’s record is exposed to exploitation. Automated Visitor Management Systems (VMS) overcome the shortcomings of the traditional method of visitor management. VMS automates the process of recording visitors’ information, keeping a log of all visits, and ensuring some sort of access control or visitor authentication just in case of an imposter or if anything ever goes wrong. This study aims to design and model a flexible Mobile Visitor Management system that can be used to record visitors’ information and authenticate visitors for access control purposes. The study was achieved using Figma software to design and prototype the system and then using React Native and SQLite for the development of the system. The system has proven to be more convenient than the common traditional way of recording visitors’ information into a physical log book. The system protects its users from loss of information, waste of time, and insecurity. The flexible and inexpensive nature of the system makes it adaptable to almost any organization or property owner, this gives the general public a great opportunity to try out Visitor Management Technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Authentication

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Artificial Neural Network Based Selection Method for Antenna Parameters in Microwave Application

Artificial Neural Network Based Selection Method for Antenna...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Prabha, R. Senthil, G.A. Priyadharshini, J. Subashini, V. Rajesh Kanna, R. Divya, B. Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India Agni College of Technology Department of Information Technology Chennai India Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Chennai India Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394580

The usage of machine learning in the field of microwave engineering has increased. This project proposes a design optimized model in order to achieve parameter optimization of 2 × 2 square patch antenna using Artificial Neural Network (ANN) Technique. An artificial neural network was trained to get the optimum value of the antenna parameters in less time. The input of the model is length, width of the square patch and range of antenna parameter value, the output is S-parameters, VSWR. This was made a perfect choice for communication systems engineers due to its low weight, low cost. The formulations are used to calculate the neural network cost function. The proposed neural network can tune and get the best value for the parameter. Then with the help of a machine learning algorithm, geometrical parameters of the patch antenna were designed and optimized. The substrate called fr4 is used. Hence a 2 × 2 square patch antenna element was optimized, designed, and developed. © 2023 IEEE.

关键词： Antenna Artificial Neural Network (ANN) Deep Neural Network Rectified Linear Unit (ReLU) S-Parameters

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Breast Cancer Detection and Classification from Mammogram Images Using Improved Convolutional Neural Network Model 2nd

Breast Cancer Detection and Classification from Mammogram Im...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Olanloye, Odunayo Dauda Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Mejdoub, Youssef Department of Computer Science Bowen University Osun Iwo Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Lagos Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

A prominent application in healthcare is the classification and detection of breast cancer using convolutional neural networks (CNNs). It entails creating a system that can precisely pinpoint malignant spots or gauge the tendency of breast cancer through the examination of diagnostic pictures like mammograms. This study focuses on the use of Convolutional Neural Networks for the detection and classification of breast cancer. The proposed model analyzes mammogram images to distinguish cancerous and non-cancerous instances automatically. Preparing the dataset developing the CNNs model’s architecture, training the model, assessing its performance, and producing predictions based on hypothetical data are all the steps in the process. The CNN model learns to extract relevant features and patterns from a large dataset, increasing classification accuracy. Tests show that the CNN model is effective at correctly identifying breast cancer. This method will help medical professionals diagnose patients quickly and correctly, improving patient outcomes in the fight against breast cancer. The prediction can be improved using post-processing techniques and the trained model can be used in practical applications. The model accuracy was 0.98 and the model loss was 0.14. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Dual-Band Reflectarray Antennas Using Integrated Resonant and Non-Resonant Natures of Metallic Waveguide Elements at Millimeter Wave Frequencies

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Radio Science 2023年第1期58卷 1-13页

作者： Chou, H.-T. Wang, N.-N. Fang, M. Wang, L.-Q. Akkaraekthalin, P. Torrungrueng, D. Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Research Center of Innovation Digital and Electromagnetic Technology Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut's University of Technology North Bangkok Bangkok Thailand

Pure metallic reflectarray antennas have good power efficiency and low fabrication cost, limited to single-band operation in the past. A dual-band reflectarray antenna is developed for two directional beams by two feeds. The reflecting elements are metal waveguides and have novel properties of both resonant and non-resonant modes at the two frequency bands. The low and high-frequency bands can be easily separated by the cutoff frequency of waveguide modes. The phase-changing mechanisms via ray optics and fundamental waveguide modes, respectively, provide simple formulations for the elemental structure design of directional beams. Radiation characteristics of the linear and circular polarizations are cross-examined with a good performance by full-wave simulations using HFSS, FEKO, and CST at 28 and 60 GHz bands for 5G front-haul network applications. In particular, we have used HFSS to simulate the reflecting elements' basic characteristics of phase variation, FEKO to simulate the linear polarization antenna case, and CST to simulate the circular polarization antenna case, respectively. © 2023. American Geophysical Union. All Rights Reserved.

关键词： Cutoff frequency

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Enhancing Perceptual Quality of Images using Deep Residual U-Net and PatchGAN Discriminator

Enhancing Perceptual Quality of Images using Deep Residual U...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Satvik Raghav Sathwik Narkedimilli Prasanth Ayitapu Raja Karthikeya Lalitha Sreeram Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science Engineering Indian Institute of Information Technology (IIIT) Dharwad India

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

This paper presents a novel approach for perceptually aware image enhancement utilizing a deep residual U-Net generator combined with a Patch-based Generative Adversarial Network (PatchGAN) discriminator. Our method incorporates a perceptual loss function based on a pre-trained VGG16 network, ensuring that enhanced images exhibit improved color sharpness, clarity, and retention of critical high-level features. Experiments show improved structural similarity, with SSIM of 0.9270 and FSIM of 0.9998. These results indicate both perceptual and structural enhancement, while the model efficiently generates high-quality images with minimal training. Our method consistently outperforms conventional approaches in visual appeal and computational efficiency, making it relevant for real-world applications such as radiology, self-driving vehicles, and low-light photography. Our implementation is open-sourced and available here: https://***/bp8zsl.

关键词： Training Visualization Technological innovation Smart cities Service robots Computational modeling Surveillance Image enhancement Biomedical imaging Sports

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Land Cover Segmentation for Agricultural Monitoring Using Synthetic Aperture Radar (SAR) Imagery with Convolutional Neural Networks and Attention-Based Transformer Models

Land Cover Segmentation for Agricultural Monitoring Using Sy...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Prakash E. P K.G. Sathiya S. Manoranjitham G. Sekar S Munaf R. K. Santhia Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Coimbatore Department of Electronics and Communication Engineering Arunai Engineering College Tiruvannamalai Department of Information Technology Agni college of Technology Chennai Department of Electronics and Communication Engineering VSB College Of Engineering Technical Campus Coimbatore Department of Electronics and Communication Engineering Sri Ramakrishna Institute of Technology Coimbatore Department of Computer Science and Engineering Pondicherry University

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Automated classification of land cover types is one of the most crucial aspects within many remote sensing applications, including the monitoring of agricultural land and territories. This work proposes a novel approach for division of land covers using a hybrid of CNNs and Attention-based Transformer networks. CNNs are adopted to extract the spatial features from different levels of the SAR image, while Transformer Model Arch applies self-attention functions to explore the long-range dependency and contextual relation of the entire image. The model learning process is performed end-to-end from multi-temporal SAR data to differentiate different types of land covers, including crops, vegetation, water, and urban surfaces. In this case, we apply the proposed approach to different agricultural areas to show that it outperforms traditional methods, such as thresholding and other machine learning approaches. The results highlight the viability of this mixed form to solve the noise and changeable nature of SAR imagery, in terms of enhancing the use of the agricultural monitoring application.

关键词： Image segmentation Accuracy Computational modeling Noise Land surface Transformers Feature extraction Radar polarimetry Convolutional neural networks Monitoring

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Energy Efficient Routing Scheme for Performance Enhancement of MANET Through Dominating Set

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第4期17卷 89-100页

作者： Sangeetha, M. Erna, G. Kannan, S. Rajes Salameh, Anas A. Kishore, Kakarla Hari Department of Data Science and Business Systems SRMIST KattankulathurChennai India Ongole India Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile Ad-Hoc network is a distributed wireless network that is self-organized and self-maintained and it doesn’t require a fixed framework or central administration. Wireless nodes in a mobile ad hoc network are transient and have a short lifespan. They combine to form a self-configured, infrastructure-free network where routing is a significant difficulty. The wireless nodes in the network must synchronize in order to decide connectivity and routing. Ad hoc network synchronization includes activities including node organization, neighborhood detection, and routing. The predetermined framework resulted in an increase in network control overhead. If a node can link to every other node in the network, it is referred to as a dominating node, and a set of dominating nodes make up the dominating set. The main purpose of this article is to lessen routing overhead in the wireless network with the dominating set based routing (DSBR). In order to discover routes, shorten reroute establishment times, and increase packet delivery rates, this research presents an energy-efficient routing algorithm (EERA) that considerably increases the network performance. The effectiveness of this technique is demonstrated by the simulation analysis of the outcome. © 2023,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Mobile ad hoc networks

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