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Comparative analysis of FSO, OFC and diffused channel links in photonics using artificial intelligence based S-band, C-band and L-band of the attenuation metrics

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Optical and Quantum electronics 2022年第7期54卷 1-20页

作者： Karpagarajesh, G. Santhana Krishnan, R. Harold Robinson, Y. Vimal, S. Thamizharasan, S. Subbulakshmi, P. Kaliappan, M. Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Tirunelveli India School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of ECE Sri Manakula Vinayagar Engineering College Puducherry Madagadipet 605107 India School of computer science and Engineering VIT Chennai India

Optical communication system is preferred over microwave and radio frequency communication system because of license free operation. Simulative analysis of 10gbps Optical Communication using different optical bands of L-Band S-Band, E-Band and C-Band under different attenuation condition has been performed in this paper. The Free Space Optics system has been analyzed with transmission distance of 1 km using Optical Wireless Communication channel and the effect of variation in attenuation value in the scenario of different weather conditions (clear, haze, rain and fog) were also examined. The Optical Fiber Communication is also examined under conditions of attenuations due to physical imperfections. The cross-layer Artificial Intelligence Framework is implemented to perform the prediction of different optical communication. The diffused channel is studied by varying the parameters of transmitter half angle and the distance of separation between the transmitter and the receiver. The attenuation effects of the links are also taken into consideration. Bit Error Rate and Quality factor performance of the different optical links are carried over at different wavelengths;thus the best link for the particular wavelength and the distance is identified. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Attenuation C-band FSO L-band Mach–Zehnder modulator OFC Quality factor S-band

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Enhanced Optimization-based Node Localization Scheme for WSN 8

Enhanced Optimization-based Node Localization Scheme for WSN

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8th International Conference on Communication and electronics Systems, ICCES 2023

作者： Senthil Murugan, M. Ravindranath, P.V. Jayareka, K.S. Sundareswaran, T. Singh, Charanjeet Subbaiyan, Arunmurugan St.Joseph's Institute of Technology Omr Electrical and Electronics Chennai119 India School of Computer Studies Coimbatore Sulur641402 India Sona College of Technology Department of Computer Science and Engineering Salem India Rmk College of Engineering and Technology Physics Chennai India Deenbandhu Chhotu Ram University of Science and Technology Electronics and Communication Department Murthal India Periyar Maniammai Institute of Science and Technology Ece Tamilnadu Thanjavur India

ISBN: (纸本)9798350396638

The location of a sensor node is crucial for several uses of WSN, including environmental sensing, search and rescue, geographical routing and tracking, and so on. The accuracy with which individual sensor terminals in a wireless sensor system can be located has a substantial bearing on the network's overall effectiveness. Using information about the locations of anchor nodes gathered from a variety of measures to pinpoint the unknown target nodes' placements is known as localization. This is a problem classed as NP-hard, which means it cannot be solved using classical deterministic methods. To overcome this difficulty in wireless sensor networks, presented an enhanced version of a swarm intelligence technique called the whale optimization method. This implementation, using a quasi-reflected-based learning method, fixes the problems with the original whale optimization approach. In order to ensure that the proposed metaheuristic performs as well as existing state-of-the-art metaheuristics, it is evaluated using the same network architecture and experimental settings. Proposed method use a Gaussian-modified RSSI to achieve a more accurate reading of the range and a new whale optimization algorithm to optimize the positioning of the nodes to boost the positioning accuracy, both of which are designed to compensate for the shortcomings of the positioning algorithm of both Received signal strength indicator (RSSI) ranging model. Based on the results of 20 separate benchmark function tests, the upgraded whale algorithm outperforms the whale optimization method and other swarm intelligence systems. The suggested location algorithm provides more precise placement than the original RSSI method. It is mentioned that the cluster intelligence algorithm has significant benefits over the currently implemented RSSI in positioning WSN nodes, and the improved algorithm that is described in this work has even more benefits compared to various cluster intelligence methods in tends to

关键词： Swarm intelligence

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Hardware Implementation of SP-N and FN for Lightweight Algorithms in IoT Devices

Hardware Implementation of SP-N and FN for Lightweight Algor...

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International Conference on Unmanned Vehicle Systems-Oman (UVS)

作者： Ali Abdelli Wajih El Hadj Youssef Lazhar Khriji Mohsen Machhout Faculty of Sciences of Monastir Laboratory of Electronics and Microelectronics (LEME) Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Sultanate of Oman

In this paper, we delve into the realm of lightweight cryptography algorithms, specifically focusing on the Light Encryption Device (LED) block cipher with its Substitution-permutation network (SP-N) structure and the Simeck algorithm employing the Feistel Network (FN) structure. Our research carries substantial significance as we endeavor to introduce innovative hardware architectures for each of these algorithms, subsequently subjecting them to rigorous performance analysis within resource-constrained contexts, utilizing the Pynq Z2 platform. Our approach entails the meticulous implementation of these hardware designs in Very high speed integrated circuit Hardware Description Language (VHDL), accompanied by exhaustive assessments encompassing critical parameters such as occupied surface area, operating frequency, throughput, and energy consumption. Through our in-depth analysis, we not only shed light on the efficiency of the LED block cipher and the Simeck algorithm but also emphasize their suitability for resource-limited environments, drawing distinctions and comparisons based on their respective SP-N and FN structures. The outcomes of our study offer valuable insights that go beyond theoretical considerations. They provide practical guidance for informed decision-making when contemplating the implementation of these algorithms in real-world scenarios, taking into account the nuances and performance characteristics associated with the SP-N and FN structures. Our work serves as a pivotal contribution to the field, underlining the importance of tailored hardware designs for lightweight cryptographic algorithms in resource-constrained settings.

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Design of Arrow Shaped Microstrip Patch Antenna for X and Ku- Band Applications 1

Design of Arrow Shaped Microstrip Patch Antenna for X and Ku...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Yuvaraj, D. Nageswara Rao, M.V. Shanmuga priya, P. Naveneetha krishanan, S. Department of Computer Science Cihan University Kurdistan region Duhok Iraq Dept. of ECE GMR Institute of Technology RajamSrikakulam Andhra Pradesh India Department of Computer Science and Engineering M.I.E.T Engineering College Trichy India Department of Electronics and Communication Engineering SACS MAVMM Engineering College Madurai India

ISBN: (纸本)9781607685395

A small size printed patch antenna to cover applications of X and Ku band is suggested in this paper. The suggested antenna consists of an arrow shaped patch on front view of FR 4 substrate and a partial ground on the other side. Initial design started with a rectangular patch of dimensions 10.7 mm x 7 mm. Then it is modified into arrow shaped patch in order to improve the reflection coefficient at lower and higher *** measurement of the antenna is 12 x 15 x 1.6 mm3. The antenna parameters are optimized using FEM based HFSS software. The operating range of frequencies include from 6.75 GHz to 18.96 GHz. The proposed antenna displays a good omnidirectional radiation pattern and maximum gain of 7.41 dBi at 17 GHz. Simulated S11 parameters, VSWR, Gain and current distributions are offered in this paper and S11 simulated parameters are in good agreement with measured parameters. © The Electrochemical Society

关键词： Arrow shaped Ku band Printedmicrostrip antenna X band

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Deep Learning-based Identification and Classification of Rice Plant Diseases for Precision Agriculture 2

Deep Learning-based Identification and Classification of Ric...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Venkatesh, N. Sarkar, Swagata Sunil, A. Priya, T. Guru Aarthi, R. Devi, K. Mekala School of CS&AI SR University Warangal India Artificial intelligence and Data Science Sri Sairam Engineering College Sai Leo NagarWest Tambaram Chennai44 India Computer science and engineering Rajarajeswari College of Engineering Ramohallicross Kumbalgod Mysore road Karnataka Bangalore560074 India Electronics and Communication Engineering Periyar Maniammai Institute of Science and Technology PMIST VALLAM India Department of ECE R.M.D Engineering College Chennai India ECE Department S.A. Engineering college Chennai India

ISBN: (纸本)9798350340235

Because they reduce both the quality and yield of crops, plant diseases seriously threaten our food supply's reliability. Plant diseases often result in a significant reduction or complete absence of grain yield in various agricultural contexts. Classifying rice plant diseases and insects pose a significant challenge due to the intricate structure of their composition and the striking resemblance 5bserved among various disease types and insect species. Diseases and insects in crops must be discovered and categorized as soon as possible, ideally before they may spread. There are a number of methods for spotting plant diseases, but deep learning is now the gold standard. The authors provide a novel deep-learning approach for detecting diseases in rice fields using photographs. Convolutional Neural Networks with attention mechanism (CNNAM) can be used to detect diseases in rice plants. Due to its reliability in picture identification and classification, CNNs have been widely employed by researchers for plant disease identification. The identification of rice plant diseases, however, has only been the subject of a small number of studies. This research illuminates the present state of rice plant disease and the methods of disease detection based on Deep Learning. Deep learning models excel about more conventional machine learning methods. With the suggestedmodel, authors were able to categorize rice photos with a 99.8 percent accuracy by learning latent patterns in the raw images. © 2023 IEEE.

关键词： Agriculture Attention mechanism Convolutional Neural Networks (CNN) Deep learning Rice plane Disease

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Smart Modular Few-Shot Learning Framework Changes Classification of Signal Modulation

Smart Modular Few-Shot Learning Framework Changes Classifica...

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Spoorthi, T. Tabassum, Husna Devika, S.V. Rao, K. B. V. Brahma Ramachandra, A.C. Gst Gitam University Bengaluru Campus Dept. of Cse Bengaluru India Hkbk College of Engineering Dept. of Cse Bengaluru India Hyderabad Institute of Technology and Management Department of Ece Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

The purpose of this research is to categorise signal modulation using few-shot learning. The first approach is an intelligent, modular, few-shot learning framework. This system's two components are IQF and 1D-SFP, and it also has a task-driven network design. The 1D-SFP module is responsible for obtaining high-level characteristics from the raw signal, while the IQF module is in charge of retrieving low-level information. The number of 1D-SFP modules required for processing a large amount of data is calculated by studying the correlation in the hidden layer. We evaluate the suggested method using a publicly available dataset and show that it beats standard strategies in terms of accuracy. It can also learn from a small number of instances and is adaptable enough to handle innovative modulation schemes. This enables it to be used in wireless networks for data transmission and reception. The second approach uses a graph convolutional network (GCN) to calculate how many 1D-SFP modules should be integrated into a neural network architecture for a particular job. On benchmark datasets, the presented frameworks outperformed a range of state-of-the-art, conventional methodologies. Both approaches perform preprocessing, representation learning, and classification using their own distinct algorithmic frameworks. The presented strategies have the potential to increase signal modulation classification precision and efficiency in cognitive radios and other kinds of wireless communication. These gains are possible because of the potential of these techniques. © 2023 IEEE.

关键词： Learning systems

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User-Level Emotion Detection Framework for Social Network Data

User-Level Emotion Detection Framework for Social Network Da...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Saranya. M Ravi Shankar Saxena P Anandan Dineshkumar S R. Dharani Arun Sekar Rajasekaran Department of Electronics and Instrumentation Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics & Communication Engineering GMR Institute of Technology Rajam AP India Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam India Department of ECE Government College of Technology Coimbatore India Department of IT Panimalar Engineering College Chennai India Department of ECE SR University Warangal India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Social media interactions are inherently dynamic, with emotions fluctuating over time based on various events and user engagements. Traditional methods often overlook the temporal aspect of these interactions, limiting the depth of emotional analysis and the insights that can be derived. In this paper, we propose a novel temporal graph framework for detecting and analyzing emotions within social media networks. By leveraging the temporal nature of social media interactions, our framework incorporates both dynamic graph structures and sentiment analysis to track emotional trends over time. Proposed method demonstrate the effectiveness of our approach through empirical experiments on real-world social media data, revealing significant patterns and trends in user emotions. Our results highlight the potential of temporal graph models to enhance emotion detection and provide actionable insights for various applications, including sentiment-driven marketing and crisis management. The framework achieved an accuracy of 89% in detecting user emotions, a significant improvement over traditional static models, which averaged around 76%. Additionally, the temporal model identified emotional trends with a precision of 85% and a recall of 83%, compared to 70% precision and 68% recall achieved by baseline methods. These results indicate that the temporal graph framework not only tracks emotional trends more accurately over time but also provides deeper insights into the evolution of user sentiments within social networks.

关键词： Emotion recognition Sentiment analysis Privacy Accuracy Machine learning algorithms Social networking (online) Market research Depression Prediction algorithms Optimization

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Optimizing Controller Tuning using Bio-Inspired Algorithms for Robot Path Tracking 3

Optimizing Controller Tuning using Bio-Inspired Algorithms f...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Rani, Taneti Lilly Deepa, S. Joon, Rakesh Kumar Jayaseelan, P. Soudagar, Manzoore Elahi M. Dhanraj, Joshuva Arockia Jawaharlal Nehru Technological University College of Engineering Kakinada University College of Engineering in JNTU Department of Civil Engineering Andhra Pradesh Kakinada533003 India RMD Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavarapettai601206 India Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana Kablana124104 India Achariya College of Engineering Technology Department of Mechanical Engineering Puducherry605110 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9798350396157

In the field of industrial and commercial robotics, there has been a recent boom in research towards the navigation of wheel-based mobile robots. There are numerous aspects of this complex problem that need fixing, including issues with sensory data gathering and processing, trajectory planning, and controlling motion. In the field of motion control, creating reliable trajectory controllers is the highest priority. This study improves the Proportional-Integral-Derivative (PID) controller using Ant Colony Optimization (ACO) and Cat Swarm Optimization (CSO) to get the best possible trajectory tracking. The kinematics of a two-wheeled mobile robot serve as the foundation for its mathematical model. The user-specified reference trajectory is given to the robot. The conventional PID, ACO-PID, and CSO-PID controller's trajectory tracking capacity are evaluated using the metrics. The performance metrics such as the Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE) are taken. According to the simulation results, the CSO-PID controller beats the conventional PID and ACO-PID controllers in terms of error metrics. This discovery highlights the effectiveness of using CSO-PID to improve the trajectory-Tracking performance of mobile robots. © 2024 IEEE.

关键词： Three term control systems

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Integration of SAP and Intelligent Robotic Process Automation

Integration of SAP and Intelligent Robotic Process Automatio...

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2023 IEEE International Conference on Next Generation electronics, NEleX 2023

作者： Yendluri, Dileep Kumar Tatikonda, Reshmi Thatikonda, Ramya Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science WilliamsburgMO United States University of the Central Missouri Department of Computer Information Systems WarrensburgMO United States University of the Cumberlands Department of Information Technology WilliamsburgMO United States Jawaharlal Technological University Department of ECE Hyderbad India Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350319088

This article explores how implementing IRPA in enterprises could have a revolutionary effect. Businesses looking to maximize efficiency can find a partnership between IRPA and SAP systems to be a wise way to achieve their goals. This study investigates how several company operations including payroll processing, human administration, financial and accounting processes, and logistics are impacted by integration. This talk explores the benefits, challenges, and best practices for integrating IRPA with SAP, offering specific instances from real-world projects that highlight the effectiveness of this relationship. The future scope of the IRPA-SAP integration looks at enhanced analytics, hyper-automation, cutting-edge cognitive features, broader industry applications, and the growth of human-robot cooperation. A thorough analysis highlights tactics that businesses may use to use SAP's and IRPA's combined power to accelerate digital transformation and achieve peak performance. © 2023 IEEE.

关键词： Integration

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Exploring InterferenceAware Spectrum Allocation in 6G Cellular Networks using dynamic resource Sharing Algorithm 23

Exploring InterferenceAware Spectrum Allocation in 6G Cellul...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Logeshwaran Jaganathan S Dhanasekaran Praveen Kantha Amit Garg Department of ECE Sri Eshwar College of Engineering India Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Computer Science & Engineering School of Engineering and Technology Chitkara University Himachal Pradesh India Department of Computer Science and Engineering Manipal University Jaipur India

ISBN: (纸本)9798400709418

This paper looks at interference-conscious spectrum allocation in 6G cell networks. A novel dynamic resource sharing a set of rules is proposed, aiming to efficaciously use available stay spectral resources and minimize the interference not unusual to more than one technique or customer. The proposed set of rules consists of sub-algorithms: the first segment is a channel selection set of rules, which selects the most excellent channels for each licensee based totally on their signal-to-interference-plus-noise ratio (SINR) and interference degrees. The second segment is an optimization set of rules, which promotes the most valuable spectrum to get admission to and aid allocation in line with the interference necessities of the specific consumer or network. The proposed rules embrace the access strategy mentioned in 3GPP spec 5G-NR, wherein a fast spectrum allocation and aid-sharing principles throughout multiple licensees are used to maximize spectrum usage. Outcomes suggest that the algorithm can attain powerful spectrum utilization even by imparting high levels of interference mitigation. The proposed gadget gives a promising technique to enhance 6G spectrum allocation. It is predicted to offer an attractive answer for operators searching to deploy a dynamic, interference-resistant communications carrier.

关键词： AI-based Optimization Q-Learning Algorithms Resource Scheduling Spectrum Utilization

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