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Detector-Free Fiber-Based Bidirectional Symmetric Communication Scheme Based on Compound States of Two Mutually Coupled Discrete-Mode Lasers

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Physical Review applied 2024年第1期21卷 014049-014049页

作者： Andreas Herdt Markus Weidmann Adonis Bogris Richard Phelan Wolfgang Elsäßer Institute of Applied Physics Technische Universität Darmstadt Schlossgartenstraße 7 64289 Darmstadt Germany Department of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo 12243 Athens Greece Eblana Photonics Ltd 3 West Pier Business Campus Dun Laoghaire Co. Dublin A96 A621 Ireland Also with: School of Physics Trinity College Dublin Dublin 2 Ireland Associated with Istituto di elettronica e di ingegneria dell’informazione e delle telecomunicazioni (IEIIT) del Consiglio Nazionale delle Richerche (CNR) Politecnico di Torino Italy

Most modern high-data-rate optical communication systems require at each end a light source to transmit the data and a detector to receive the data. In order to realize a communication scheme where the transmitter unites with the receiver, we take advantage of a coupled injection-locked semiconduct. From the theoretical description of two mutually coupled diode lasers using a set of six nonlinear differential-delay equations we outline the stable compound cavity solutions of this system and investigate their dependencies in terms of locking bandwidth, coupling strength and coupling phase. From these modeling results we then design a remarkably simple communication system without any additional detectors that exploits compound laser states of different frequency detunings of the coupled laser system as information carriers with simultaneous upstream information of the first laser and downstream information of the second laser. The data recognition is accomplished by evaluating the compound state utilizing the change in the terminal voltage of both lasers (laser-as-detector principle) with respect to their solitary case (i.e., the terminal voltage of the uncoupled lasers), yielding the message via the agreed decoding scheme. The functionality of the principle had been demonstrated by a table-top experiment with good error-free transmission and potential to improve the transmission speed of this proof-of-principle demonstration. Our concept reduces the complexity compared to conventional transmission systems, thus providing the potential to significantly reduce the costs of the overall transmission setup an,d in particular, transferring the concept to other challenging wavelengths and other semiconductor lasers. Since the optical signal then no longer needs to be detected by an optical detection unit, the amount of optical components required for ordinary data transmission is reduced.

关键词： Coupled oscillators Laser dynamics Optical bistability Photonics Synchronization Optical fibers Optical sources & detectors Semiconductor lasers

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FAULT DIAGNOSIS IN NEW WIND TURBINES USING KNOWLEDGE FROM EXISTING TURBINES BY GENERATIVE DOMAIN ADAPTATION

arXiv

引用

arXiv 2025年

作者： Jonas, Stefan Meyer, Angela School of Engineering and Computer Science Bern University of Applied Sciences Biel Switzerland Faculty of Informatics Università della Svizzera Italiana Lugano Switzerland Department of Geoscience and Remote Sensing Delft University of Technology Delft Netherlands

Intelligent condition monitoring of wind turbines is essential for reducing downtimes. Machine learning models trained on wind turbine operation data are commonly used to detect anomalies and, eventually, operation faults. However, data-driven normal behavior models (NBMs) require a substantial amount of training data, as NBMs trained with scarce data may result in unreliable fault diagnosis. To overcome this limitation, we present a novel generative deep learning approach to make SCADA samples from one wind turbine lacking training data resemble SCADA data from wind turbines with representative training data. Through CycleGAN-based domain mapping, our method enables the application of an NBM trained on an existing wind turbine to one with severely limited data. We demonstrate our approach on field data mapping SCADA samples across 7 substantially different WTs. Our findings show significantly improved fault diagnosis in wind turbines with scarce data. Our method achieves the most similar anomaly scores to an NBM trained with abundant data, outperforming NBMs trained on scarce training data with improvements of +10.3% in F1-score when 1 month of training data is available and +16.8% when 2 weeks are available. The domain mapping approach outperforms conventional fine-tuning at all considered degrees of data scarcity, ranging from 1 to 8 weeks of training data. The proposed technique enables earlier and more reliable fault diagnosis in newly installed wind farms, demonstrating a novel and promising research direction to improve anomaly detection when faced with training data scarcity. (Image Present) © 2025, CC BY-NC-ND.

关键词： Wind turbines

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Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

arXiv

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arXiv 2023年

作者： Bousbiat, Hafsa Bousselidj, Roumaysa Himeur, Yassine Amira, Abbes Bensaali, Faycal Fadli, Fodil Mansoor, Wathiq Elmenreich, Wilfried University of Applied Sciences Institut für Informatic Wiener Neustadt2700 Austria University of Skikda Department of Computer Science Mezghich Skikda21000 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates University of Sharjah College of Computing and Informatics Sharjah United Arab Emirates De Montfort University Leicester Institute of Artificial Intelligence United Kingdom Qatar University Department of Electrical Engineering Doha2713 Qatar Qatar University College of Engineering Department of Architecture and Urban Planning Doha2713 Qatar University of Klagenfurt Smart Grids Research Group Klagenfurt9020 Austria

Consumer’s privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented. © 2023, CC BY-NC-SA.

关键词： Internet of Energy

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A computational framework for Carreau nanomaterial induced by stretchy regime considering Brownian diffusion, viscous dissipation, ferromagnetism and thermophoresis

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Partial Differential Equations in applied Mathematics 2025年 13卷

作者： Hussain, Iftikhar Khan, Waqar Azeem Tabrez, Muhammad Waqas, M. Ibrahim, Talib K. Saidani, Taoufik Kausar, M.S. Zamri, Nurnadiah Abdullaeva, Barno Garalleh, Hakim AL Department of Mathematics Mohi-ud-Din Islamic University Nerian Sharif Jammu and Kashmir Azad 12010 Pakistan School of Mathematics and Statistics Beijing Institute of Technology Beijing 100081 China NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut 22200 Malaysia Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin (Kampus Gong Badak) Terengganu Kuala Terengganu 21300 Malaysia Department of Mathematics and Information Technologies Vice-Rector for Scientific Affairs Tashkent State Pedagogical University Tashkent Uzbekistan Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia

In recent decade, the research in nanotechnology brought the idea of nanofluids which are highly conducting materials. Some important applications of nanofluids are justified in pharmaceutical industry, coolants in vehicles engine, preparation process of various medicines for treatment of fatal diseases like cancer, aircraft industry, latest heat exchangers etc. The ferro particles are special class of nanomaterial which attract variety of interest from the researchers. The objective of current research is to perform the investigation for heat and mass transfer subject to Carreau nanofluid to observe the enhancement of heat transmission phenomena. The induction of magnetic dipole has been observed for thermal transport phenomenon. The flow is causing by stretched surface. A simplified system is obtained by utilizing the dimensionless variables. Physical interpretation of results have been presented. Result showed that temperature profile in presence of ferrofluid fluid boosts up with escalation in thermophoresis parameter, while opposite behavior is noted for power law index. The concentration of ferrofluid showing declining behavior for Schmidt numbers and rises for thermophoresis parameter. © 2024

关键词： Carreau ferrofluid Heat and mass transfer Magnetic dipole Numerical solution

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Identifying bias in name matching tasks 22

Identifying bias in name matching tasks

引用

22nd International Conference on Extending Database Technology, EDBT 2019

作者： Karakasidis, Alexandros Pitoura, Evaggelia Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Computer Science and Engineering University of Ioannina Ioannina Greece

ISBN: (纸本)9783893180813

One of the applications of string similarity measures regards identifying the same entity within one or more corpora of data, also known as the problem of entity resolution. While a lot of methods have been introduced for assessing the similarity of two strings, there has not been yet a study to examine whether these methods are appropriate, in terms of effectiveness and fairness when applied to names of specific groups of individuals. In this paper, we provide extensive experimental results over a number of popular string measures which indicate that string comparison methods fall short when applied to specific groups, a fact leading to algorithmic bias against these groups. We also share some thoughts and guidelines on the way we envision, database practitioners should address such cases. © 2019 Copyright held by the owner/author(s).

关键词： Database systems

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Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

Research Square

引用

Research Square 2021年

作者： Gothai, E. Moorthy, Usha Sathishkumar, V.E. Alnuaim, Abeer Ali Hatamleh, Wesam Atef Sweidan, Dirar Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu Erode638060 India School of Computer Science and Engineering REVA University Banglore India Department of Information and Communication Engineering Sunchon National University Suncheon Korea Republic of Department of Computer Science and Engineering College of Applied Studies and Community Services King Saud University Saudi Arabia Department of Computer Science College of Computer and Information Sciences King Saud University Saudi Arabia School of Informatics University of Skövde Högskolevägen 1 Skövde 541 28 Sweden

With the evolution of Internet standards and advancements in various Internet and mobile technologies, especially since web 4.0, more and more web and mobile applications emerge such as e-commerce, social networks, online gaming applications and Internet of Things based applications. Due to the deployment and concurrent access of these applications on the Internet and mobile devices, the amount of data and the kind of data generated increases exponentially and the new era of Big Data has come into existence. Presently available data structures and data analyzing algorithms are not capable to handle such Big Data. Hence, there is a need for scalable, flexible, parallel and intelligent data analyzing algorithms to handle and analyze the complex massive data. In this article, we have proposed a novel distributed supervised machine learning algorithm based on the MapReduce programming model and Distance Weighted k-Nearest Neighbor algorithm called MR-DWkNN to process and analyze the Big Data in the Hadoop cluster environment. The proposed distributed algorithm is based on supervised learning performs both regression tasks as well as classification tasks on large-volume of Big Data applications. Three performance metrics, such as Root Mean Squared Error (RMSE), Determination coefficient (R2) for regression task, and Accuracy for classification tasks are utilized for the performance measure of the proposed MR-DWkNN algorithm. The extensive experimental results shows that there is an average increase of 3% to 4.5% prediction and classification performances as compared to standard distributed k-NN algorithm and a considerable decrease of Root Mean Squared Error (RMSE) with good parallelism characteristics of scalability and speedup thus, proves its effectiveness in Big Data predictive and classification applications. © 2021, CC BY.

关键词： Scalability

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Automating the Practice of Science – Opportunities, Challenges, and Implications

arXiv

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications

arXiv

引用

arXiv 2024年

作者： Nguyen, Cong T. Saputra, Yuris Mulya Van Huynh, Nguyen Nguyen, Tan N. Hoang, Dinh Thai Nguyen, Diep N. Pham, Van-Quan Voznak, Miroslav Chatzinotas, Symeon Tran, Dinh-Hieu Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh City70000 Viet Nam Faculty of Information Technology Duy Tan University Da Nang50000 Viet Nam Internet Engineering Technology Department of Electrical Engineering and Informatics Vocational College Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Nokia Bell Labs Murray Hill NJ07974 United States Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava708 00 Czech Republic University of Luxembourg Esch-sur-Alzette4365 Luxembourg Nokia France

Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era. © 2024, CC BY.

关键词： Radio access networks

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Detection of Violence in Cartoon Videos Using Visual Features

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Procedia computer Science 2021年 192卷 4962-4971页

作者： Tahira Khalil Javed Iqbal Bangash Abdul Waheed Khan Saima Anwar Lashari Abdullah Khan Dzati Athiar Ramli Department of Computing Abasyn University Peshawar Pakistan nstitute of Computer Science and IT (ICS/IT) The Agriculture University Peshawar Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Haripur Pakistan College of Computing and Informatics Saudi Electronic University Riyadh KSA Intelligent Biometrics Group (IBG) School of Electrical and Electronic Engineering USM Engineering Campus University Sains Malaysia 14300 Nibong Tebal Pulau Pinang

Technology play an important role in today era. Tremendous growth has been observed in the field of communication and technology. Same is noteworthy in case of video and multimedia. An enormous amount of visual clips containing all types of scenes can easily be accessed both online and offline. In the use of internet has increase the trend of watching cartoons as a source of entertainment among children. There is a tendency that cartoon video has scenes that make some people uncomfortable because of the contents of the video that may be against their feelings and acceptability levels. There could be a number of objectionable elements in cartoon videos. This work presents a framework based on knowledgebase, with visual features which is divided into two categories i.e. character and object database. That deal with the detection of violent contents in cartoon videos which can have negative impact on child personality. A prototype of this system is developed and implemented in Mat lab. The experimental results show that the proposed system successfully detects the violent scenes in cartoon videos.

关键词： Low-Level Features Object Extraction Violence Detection Cartoon Videos

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