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Ultra-Wideband Pattern Reconfigurable Antenna for Base station Applications 5

Ultra-Wideband Pattern Reconfigurable Antenna for Base stati...

引用

5th IEEE India Council International Subsections Conference, INDISCON 2024

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

ISBN: (纸本)9798350376753

The proposed work aims at 5G base station antenna communication applications. The Reconfigurable Antenna is built with an idea of parasitic loaded elements in antenna. The proposed design has a zig-zag S-shape structure with a driven Monopole antenna located along the central axis and a parasitic structure enfolding around the monopole. Reconfiguration is accomplished by adjusting the PIN diodes bias voltages. The 2.44-9.46 GHz frequency range i.e., the Ultra-Wide-Band frequency spectrum is used at which this reconfigurable antenna is designed to operate. This paper has three radiation pattern which are obtained by two PIN diodes. Beam-steering capability in the elevation plane is achieved i.e., θ (0°-180°). The outcome shows the design's potential and its wide range concurrent applications for base station antennas. © 2024 IEEE.

关键词： Monopole antennas

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TRUSTCACHE: Trust-based Content Caching in Information-Centric Networks

TRUSTCACHE: Trust-based Content Caching in Information-Centr...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Tsikteris, Sean Rahman, Aisha B. Siraj, Md Sadman Charatsaris, Panagiotis Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE's operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation. © 2024 IEEE.

关键词： Reinforcement learning

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Evaluating 10Gbps Wireless Optical communication Using NRZ Modulation: Analyzing BER, Signal Quality, and system Performance 7

Evaluating 10Gbps Wireless Optical Communication Using NRZ M...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Khant, Shailesh Makwana, Mayur Patel, Atul Faculty of Computer Science and Applications Charusat Changa Anand India Dept. Electronics and Communication Engineering Charusat Changa Anand India

ISBN: (纸本)9798350350067

Free space optical (FSO) communication is a fast and wireless data transfer technique that serves as a viable alternative to traditional fiber-optic systems. This study illustrates how FSO can effectively address the growing demand for rapid data transmission and suggests its potential applicability in various scenarios. FSO can be employed to establish connections between buildings within a corporate campus without the need for physical wiring, or it can provide high-speed internet access in locations where installing cables is challenging. We used Optsim software to assess critical parameters such as Bit Error Rate (BER), eye diagrams, power, and signal quality. The results offer valuable insights into enhancing the efficiency and reliability of FSO systems. FSO can find applications in cellular networks to interconnect network equipment, as well as in high-definition video surveillance. These findings highlight the areas in which FSO can advance communication technology and provide valuable insights for future developments. © 2024 IEEE.

关键词： Bit error rate

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HSV Model based Skin Color Segmentation using Uncomplicated Threshold and Logical AND Operation 9

HSV Model based Skin Color Segmentation using Uncomplicated ...

引用

9th International Conference on Advanced Computing and communication systems, ICACCS 2023

作者： Ganesan, P. Sathish, B.S. Leo Joseph, L.M.I. Sajiv, G. Murugesan, R. Akilandeswari, A. Gomathi, S. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Sri Venkateshwara College of Engineering Dept. of Computer Science Engineering AI Bengaluru India SR University Dept. of Electronics and Communication Engineering Telangana Warangal India SIMATS Saveetha School of Engineering Chennai India Narshima Reddy Engineering College Dept. of Electronics and Communication Engineering Secunderabad India

ISBN: (纸本)9798350397376

In human, skin tone is significantly varying from one extend (darkest) to other (lightest) due to the difference in the amount of pigmentation (melanin). Even though skin color detection and segmentation is the challenging and complex task which finds applications in face recognition, human tracking, video conferencing, content based image retrieval. The variation in the pigmentation is the consequence of the exposure to the amount of solar radiation and heredity. The segmentation is the clustering process wherein the whole image is clustered into small groups according to the color or texture characteristics. The attainment of image analysis is exclusively based on the upshot of the segmentation. The proposed segmentation procedure is provocative due to the attributes like illumination, the characteristics of the image capturing device, ethnicity and individual characteristics. In skin color segmentation, pixels clustered as the skinny or non-skin pixels. The proposed method utilized HSV color model as a tool for the skin tone detection and segmentation. The outcome of the proposed system is weighed against with RGB color model. Experimental results clearly illustrated that the proposed method has a very good efficiency to segment skin color pixels. © 2023 IEEE.

关键词： RGB color model

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Applying LSTM for River's Water Level Forecasting as Potential Flood Indication: A Case Study in Greece 15

Applying LSTM for River's Water Level Forecasting as Potenti...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Tziolas, Theodoros Papageorgiou, Elpiniki Papageorgiou, Konstantinos Zardas, Georgios Lekidis, Alexios Panagiotis, Papazoglou University of Thessaly Dept. of Energy Systems Gaiopolis Campus Larisa41500 Greece University of Thessaly Dept. of Electrical and Computer Engineering Volos Greece National and Kapodistrian University of Athens Dept. of Architecture and Computer and Communication Simulation Systems Chalkida Greece

ISBN: (纸本)9798350368833

Floods can have significant life-threatening and economic impacts on human societies. Flood forecasting with data-driven methods is a crucial aspect of disaster risk management, providing valuable insights into potential flood events and allowing for timely mitigation measures. This work illustrates the application of a Long Short-Term Memory (LSTM) neural network for short-term forecasting of a river's water level. The water treatment data used for training the LSTM models were gathered from Sperchios river, located in central Greece. Historical time-series data in a half-hour interval of the river's water level, water velocity, and area precipitation were utilized for the development of the proposed scheme. The developed model is deployed in the backend of a web application for half-hour ahead predictions of the river's water level that are visualized in the frontend. The post-analysis of the deployed forecasting model indicates that it consistently delivers forecasts with minimal error and demonstrates high accuracy in recent flood events. © 2024 IEEE.

关键词： Rivers

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BanijyaPrayukti: E-Commerce Recommendation Engine for Sustainable Products 7

BanijyaPrayukti: E-Commerce Recommendation Engine for Sustai...

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7th IET Smart Cities Symposium, SCS 2023

作者： Pradhan, Aditya Ghosh, Amartya Mondal, Rintu Saxena, Kumar Abhi Dutta, P.K. Mitra, Anirban Dept. of Computer Science and Engineering Amity School of Engineering and Technology Kolkata Kolkata India Dept. of Electronics and Communication Engineering Amity School of Engineering and Technology Kolkata Kolkata India

ISBN: (纸本)9781839539831

The rapid growth of e-commerce has revolutionized the way businesses operate and interact with customers. E- commerce has revolutionized the way businesses operate, making it easier than ever to reach customers and sell products. However, with this convenience comes a challenge: how can we promote sustainable products and encourage consumers to make eco- friendly choices? The primary objective of this project is to pro- mote sustainability by utilizing artificial intelligence techniques in recommending eco-friendly alternatives. Our goal is to provide users with recommendations on sustainable products based on their shopping history, behavior patterns, and demographic information. The system continuously learns from user interactions, enabling it to provide increasingly accurate and personalized recommendations over time. © The Institution of engineering & Technology 2023.

关键词： Electronic commerce

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Developing and Implementing An Effective Big Data Classification system 2

Developing and Implementing An Effective Big Data Classifica...

引用

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

作者： Managuli, Manjunath Pujar, Sagar Managuli, Sangamesh C Krishnamurthy, K.T. Nandalike, Rajesh Kunchur, Pavan Kls Gogte Institute of Technology Dept. of Electronics & Communication Belagavi India Kls Gogte Institute of Technology Dept. of Computer Science Belagavi India Pes University Dept. of Mechanical Engineering Bangalore India B M S College of Engineering Dept. of Electronics & Instrumentation Bangalore India Nitte Meenakshi Institute of Technology Dept. of Electronics & Communication Bangalore India

ISBN: (纸本)9798350317558

Relevant data Technology advancements have led to a massive growth in classifications. Deep learning systems, however, need help handling such a massive volume of data. This work aims to use the Evaluation of different classification algorithms for big data, Comparison of accuracy, precision, recall, and F1 score among various algorithms, and Identification of algorithms suitable for handling large-scale datasets and conventional deep learning to analyze a large amount of data. Classifiers allowed researchers to examine how various random sampling strategies affected the model's performance. Two methods of feature selection were employed in the tests. The Bayesian Network likewise employs a single deep learning classifier. By lowering the dimensionality of the data, this study seeks to maximize model performance. A recording dataset larger than 60 GB was made less dimensional to conduct the trials. The findings demonstrate that, when employing both sampled and non-sample data, overall performance varies between 78% accuracy and 82% accuracy, a minor variation in performance while utilizing both sampled and non-sample (all the data) datasets. The random subset approach and the Bayesian network classifier were employed in the trials, and the overall findings show that the variety experiment had the best performance out of all the combinations of experiments. Test Cradle Experimentation: Variation length, delay variation, and packet loss are used to assess the predicted scheme's efficiency. © 2024 IEEE.

关键词： Bayesian Network deep learning feature classification Feature selection

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Study of Fiber Optic Sensor for Various Biomedical Applications

Study of Fiber Optic Sensor for Various Biomedical Applicati...

引用

2023 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2023

作者： D'Sa, Joyline Germine Ullal, Poojith Kumar Srajana, Srajana Maroli, Sushanth Shetty, Nikhil Sahyadri College of Engineering & Management Dept. of Computer Science and Engineering Adyar Mangalore India Sahyadri College of Engineering & Management Dept. of Electronics and Communication Engineering Adyar Mangalore India

ISBN: (纸本)9798350340129

Fiber optic sensors have been widely used in various biomedical applications due to their high sensitivity, reliability, and non-invasiveness. In this paper, we propose a novel approach to designing an optical sensor for detecting impurities in a mixture. The proposed sensor utilizes a flexible polymer layer and an optical fiber cable to detect variations in the power of the adulterated mixture. The polymer layer is made of PDMS, which swells or shrinks in response to the level of adulteration present in the mixture. As a result, the intensity of the output spectra changes, which can be used to detect impurities. The sensor's results will be compared with those obtained from the pure mixture to evaluate its accuracy. In conclusion, the proposed sensor has several advantages, such as low cost, easy to handle, ease of fabrication, and high sensitivity, making it a promising candidate for detecting impurities in various mixtures. © 2023 IEEE.

关键词： Medical applications

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WikiTND24: A Chinese Text Normalization Database 27

WikiTND24: A Chinese Text Normalization Database

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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Li, Wu-Hao Chiang, Chen-Yu College of Electrical Engineering and Computer Science National Taipei University Taiwan Dept. of Communication Engineering National Taipei University New Taipei City Taiwan

ISBN: (纸本)9798331506032

Labeling large datasets for constructing a text normalization system is cumbersome and time-consuming. Although some self-supervised learning models can reduce the amount of training data needed, labeling sufficient data still requires significant labor and time. To address this issue, we used a self-developed rule-based text normalization system to nor-malize Chinese Wikipedia texts, generating a large amount of training data to develop a non-standard word recognition model. Besides verifying the performance of our model with the FlatTN database test set, we also demonstrated that it can help detect errors in the rule-based system and accelerate the development of a data-driven or machine-learning-based text normalization system. Additionally, we are making this database, WikiTND24, publicly available for everyone to use. © 2024 IEEE.

关键词： Self-supervised learning

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Art of Rare Pattern Discovery Techniques in Data Mining

Art of Rare Pattern Discovery Techniques in Data Mining

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2023 IEEE Global Conference on Information Technologies and communications, GCITC 2023

作者： Mahendra, M. Adarsha, S.P. Saraswat, Anant Nagaraju, D. Sanskrithi School of Engineering Dept. of Computer Science and Engineering Andhra Pradesh Puttaparthi India Sanskrithi School of Engineering Dept. of Electronics and Communication Engineering Andhra Pradesh Puttaparthi India

ISBN: (纸本)9798350308167

In the current era, data analysis has become an integral part of decision-making processes. Data is collected from various sources, such as cloud warehouses, for decision-making and subjected to different classifications and data mining algorithms. One of the most crucial tasks in data mining is rare item set mining, which employs different association-based techniques and threshold values to uncover hidden data in the form of patterns. The research paper provides a comprehensive overview of the latest rare pattern mining techniques and their applications, which can be highly beneficial in achieving efficient and effective decision-making processes. © 2023 IEEE.

关键词： Association Rules Data Mining Frequent Item-sets Rare Item-sets Rare pattern Threshold

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