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Electronic engineering (ICEEM), International Conference on

作者： A. K. Abu-Nab A. F. Abu-Bakr Zain AbuShaeer Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin 32511 Egypt Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow Russia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Department of Basic Science Higher Institute of Engineering and Technology Kafrelsheikh Egypt

This work presents a numerical investigation of acoustic cavitation in Newtonian fluid based on frequency ultrasound. The model of ultrasonic cavitation dynamics is formulated by a modified rayleigh-Plesset equation and an acoustic pressure equation. The exponential B-Spline collection method was employed to obtain the numerical solutions of the given cavitation bubble model, considering the effect of acoustic pressure. The behaviour of the cavitation microbubbles' radius and the internal pressure of acoustic cavitation were examined at different values of frequencies. The obtained results reveal the importance of frequencies in ultrasound, which play a vital role in changing the behaviour of cavitation bubbles.

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Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification

arXiv

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arXiv 2024年

作者： Gheorghiu, Adrian Tăiatu, Iulian-Marius Cercel, Dumitru-Clementin Marin, Iuliana Pop, Florin Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Engineering in Foreign Languages National University of Science and Technology POLITEHNICA Bucharest Romania National Institute for Research and Development in Informatics - ICI Bucharest Romania Academy of Romanian Scientists Bucharest Romania

The detection and classification of diseases in Robusta coffee leaves are essential to ensure that plants are healthy and the crop yield is kept high. However, this job requires extensive botanical knowledge and much wasted time. Therefore, this task and others similar to it have been extensively researched subjects in image classification. Regarding leaf disease classification, most approaches have used the more popular PlantVillage dataset while completely disregarding other datasets, like the Robusta Coffee Leaf (RoCoLe) dataset. As the RoCoLe dataset is imbalanced and does not have many samples, fine-tuning of pre-trained models and multiple augmentation techniques need to be used. The current paper uses the RoCoLe dataset and approaches based on deep learning for classifying coffee leaf diseases from images, incorporating the pix2pix model for segmentation and cycle-generative adversarial network (CycleGAN) for augmentation. Our study demonstrates the effectiveness of Transformer-based models, online augmentations, and CycleGAN augmentation in improving leaf disease classification. While synthetic data has limitations, it complements real data, enhancing model performance. These findings contribute to developing robust techniques for plant disease detection and classification. © 2024, CC BY-NC-SA.

关键词： Image classification

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Using Machine Learning for Industry 5.0 Efficiency Prediction Based on Security and Proposing Models to Enhance Efficiency 11

Using Machine Learning for Industry 5.0 Efficiency Predictio...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Pant, Piyush rajawat, Anand Singh Goyal, S.B. Singh, Deepmala Constantin, Neagu Bogdan raboaca, Maria Simona Verma, Chaman School of Computer Sciences and Engineering Sandip University Nashik India School of Computer Science and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia India Gheorghe Asachi Technical University of Iasi Department of Power Engineering Iasi Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

Machine learning, with its huge untapped potential, is being researched all over the world to develop truly intelligent systems. Its applications are not enclosed in just one domain but in almost everything, from prediction models, recommender systems, and anomaly detection to automation and teaching a computer how to fly a helicopter. In this research, Multivariate Linear regression of supervised machine learning is studied to predict the efficiency of Industry 5.0, however, the efficiency of the model would be dependent on many factors and components such as security protocols and models, Industrial IoT - performance, connectivity, reachability, availability and many more. These factors and components would be categorized as the features of the algorithm which would be assigned weight 'w' and bias 'b'. To improve the efficiency of the model, these components could be changed and updated in order to enhance the overall model. Previous research papers discussed the integration of 'hot' technologies like 5G, Blockchain, AI, and IIoT in the industry 5.0 model, but this research is presented as their future work as it proposes to determine the efficiency of the model based on the features provided so that ultimate and optimal model could be determined. Later it proposes security and IIoT models that could improve the overall Industry 5.0. Quorum blockchain is proposed by the research in order to implement the ultimate security in the Industry 5.0. © 2022 IEEE.

关键词： Efficiency

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Indoor Channel Characterization With Extremely Large Reconfigurable Intelligent Surfaces at 300 GHz

Indoor Channel Characterization With Extremely Large Reconfi...

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European Conference on Antennas and Propagation, EuCAP

作者： Fabio Cardoso Sérgio Matos Luís M. Pessoa George C. Alexandropoulos Department of Information Science and Technology University Institute of Lisbon Portugal Instituto de Telecomunicações Portugal INESC TEC Portugal Faculty of Engineering University of Porto Portugal Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago IL USA

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

The technology of Reconfigurable Intelligent Surfaces (RISs) is lately being considered as a boosting component for various indoor wireless applications, enabling wave propagation control and coverage extension. However, the incorporation of extremely large RISs, as recently being considered for ultrahigh capacity industrial environments at subTHz frequencies, imposes certain challenges for indoor channel characterization. In particular, such RISs contribute additional multipath components and their large sizes with respect to the signal wavelength lead to near-field propagation. To this end, ray tracing approaches become quite cumbersome and need to be rerun for different RIS unit cell designs. In this paper, we present a novel approach for the incorporation of RISs in indoor multipath environments towards their efficient channel characterization. An 100 × 100 RIS design with 2-bit resolution unit cells realizing a fixed anomalous reflection at 300 GHz is presented, whose radar cross section patterns are obtained via full-wave simulations. It is showcased that the RIS behavior can be conveniently approximated by a three-ray model, which can be efficiently incorporated within available ray tracing tools, and that the far-field approximation is valid for even very small distances from the RIS.

关键词： Wireless communication Indoor communication radar cross-sections Europe Reconfigurable intelligent surfaces ray tracing Boosting Antennas and propagation Terahertz communications

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Unveiling the Post-Covid Economic Impact Using NLP Techniques 15

Unveiling the Post-Covid Economic Impact Using NLP Technique...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Barhanpurkar, Kanishk Mandlik, Nikita rajawat, Anand Singh Goyal, S.B. Mihaltan, Traian Candin Verma, Chaman raboaca, Maria Simona Thomas J. Watson College of Engineering and Applied Science Binghamton University United States School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca40033 Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest1053 Hungary Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Ramnicu Valcea Romania

ISBN: (纸本)9798350321388

The research paper presents a novel analysis of textual data based on Natural Language Processing (NLP) techniques to analyse New York Times articles from January 2019 to May 2023. The purpose of this paper is to gain an understanding of the economic impact that follows Covid-19 disease. New York Times (NYT), it was started in 1884 is one of the most prominent newspapers in the world. Additionally, we have used the New York Times Archive API to collect the data for the given timeframe. By analysing sentiment analysis, topic modelling, entity recognition, and keyword extraction, valuable insights can be gathered into market trends, industry shifts, and policy interventions. The authors have created a data science pipeline using Amazon Web Services (AWS), which enables data collection, storage, and visualization. It contributes to a better understanding of the pandemic's short-term and long-term economic effects. The results of this study demonstrate Natural Language Processing techniques' potential as a tool for financial analytics, assisting policymakers, economists, and businesses in formulating recovery strategies. © 2023 IEEE.

关键词： Economic and social effects

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Industrial Speed Control of im Based Model Predictive Controller Using Zeta Converter 1

Industrial Speed Control of im Based Model Predictive Contro...

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1st International Conference on Advances in Computational Science and engineering, ICACSE 2020

作者： Pattanaik, Balachandra Barani Sundaram, B. Mishra, Manish Kumar Thirumoorthy, Dhanabal rastogi, Umang Department of Electronics and Communications Bule Hora University Bule Hora Ethiopia Department of Computer Sciences Bule Hora University Bule Hora Ethiopia Department of Computer Sciences University of Gondar Ethiopia College of Informatics Bule Hora University Bule Hora Ethiopia Department of Computer Science Engineering Meerut Institute of Engineering and Technology Uttar Pradesh Meerut India

For induction, machine-based configuration, the model predictive control scheme, power converter based renewable system is presented in this paper. The PV system is proposed to provide an input source for the model predictive control (MPC) based system. The control method predictive is used based on vector control. According to this, the drive system behaviour of the fault-tolerant capacity is enhanced. The ZETA converter is proposed to improve the system input source, which fed on the PV system. The proposed ZETA converter is used to increase the DC link voltage of the system. The motor's speed and torque can be controlled by the model predictive control method based on vector control. The results of the proposed system are obtained and verified using the MATLAB/Simulink. © Published under licence by IOP Publishing Ltd.

关键词： Model predictive control

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RFID-Based Student Identification Card Attendance Monitoring System 1

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Aderinto, Muqeetat Tolulope Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (数字)9789819919833

ISBN: (纸本)9789819919826

Passive (low-frequency) radio-frequency identification (RFID) technology is being used in an increasing number of contactless systems. This project presents contactless attendance system passive radio-frequency identification (RFID)-based approach. RFID is a technology that uses radio waves to send information from an RFID tag attached to an object to a reader for the purpose of monitoring and identifying the object. This technology is one of the most widely used in the world;almost every organization uses RFID-based tracking, identification, and other functions on some level. A contactless RFID-based attendance monitoring system is a less cost-effective and efficient automated solution. The proposed system focuses on an RFID-based attendance monitoring system that uses RFID technology in connection with a programmable logic circuit (such as an Arduino) to address the problems that traditional paper and contact-based attendance systems experience. Each user (student or lecturer) will be given an RFID card. When the user puts the RFID card near the RFID reader, the RFID reader scans the user's data stored on the card, recording the current time using the real-time clock (RTC) module and the RFID tag number (UID) on the SD card. The Arduino uses the SD card module to communicate with the SD card. The buzzer beeps when a tag is scanned, and the system employs a 16 × 2 LCD module for display purposes. This system will take attendance automatically, and the attendance records will be stored in the database. The SD card serves as backup and can help to generate accurate reports when required. Therefore, the system rectifies human errors, saves time, and gives clear attendance. The system has been implemented using C# programming language. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： radio waves

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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning

Predictive Design for Quality Assessment Employing Cloud Com...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Gaurav Jindal Vidhika Tiwari Riyaz Mahomad Anita Gehlot Mimoh Jindal Dibyhash Bordoloi Department of Master of Computer Applications G L Bajaj College of Technology and Management Gr. Noida Mechanical Engineering Department DPGITM Gurugram School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India National Informatics Centre Delhi India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure.

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Retraction Note: Resnet-based deep learning multilayer fault detection model-based fault diagnosis

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Multimedia Tools and Applications 2024年第38期83卷 86323-86324页

作者： Jaber, Mustafa Musa Ali, Mohammed Hasan Abd, Sura Khalil Jassim, Mustafa Mohammed Alkhayyat, Ahmed Majid, Mohammed Sh. Alkhuwaylidee, Ahmed rashid Alyousif, Shahad Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Najaf Iraq Directorate of Research and development Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq College of Technical Engineering The Islamic University Najaf Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Technical Engineering Mazaya University College Thi Qar Iraq Department of Electrical and Electronic Engineering College of Engineering Gulf University Sanad Kingdom of Bahrain Research Centre University of Mashreq Baghdad Iraq

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Dual Dielectric Metasurface for Simultaneous Sensing and Reconfigurable Reflections

arXiv

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arXiv 2025年

作者： Birari, Mahesh Nagarkoti, Deepak Singh Katsounaros, Anestis Mudireddy, Hruday Kumar Reddy Malik, Jagannath Alexandropoulos, George C. EpsilonR United Kingdom Texas Instruments United States Space Innovation Solutions Greece Indian Institute of Technology Patna India Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago United States

This paper presents a novel dual-functional hybrid Reconfigurable Intelligent Surface (RIS) for simultaneous sensing and reconfigurable reflections. We design a novel hybrid unit cell featuring dual elements, which share the same phase center, to support both intended functionalities, with the antenna being miniaturized via a high dielectric material approach. The hybrid unit cell has a size of one eighth of the wavelength forming the foundation of an innovative metasurface that incorporates a sub-wavelength reflecting array of split-ring unit cells integrated with a load-tuning matrix. In particular, two interleaved sensing arrays of half-wavelength spacing, orthogonal polarization, and quarter-wavelength offset are embedded within the proposed dual-functional RIS, each tasked to sense the channel parameters towards one of the end communication nodes wishing to profit from the surface’s reconfigurable reflections. Our full-wave simulations, indicatively centered around the frequency of 5.5 GHz, showcase the promising performance of both designed hybrid unit cells and reflective split-ring ones. © 2025, CC BY-NC-ND.

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