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Topological Landau–Zener nanophotonic circuits

Advanced Photonics 2023年第3期5卷 127-134页

作者： Bing-Cong Xu Bi-Ye Xie Li-Hua Xu Ming Deng Weijin Chen Heng Wei Fengliang Dong Jian Wang Cheng-Wei Qiu Shuang Zhang Lin Chen Huazhong University of Science and Technology School of Optical and Electronic InformationWuhan National Laboratory for OptoelectronicsWuhanChina The Chinese University of Hong Kong School of Science and EngineeringShenzhenChina CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology of ChinaNanofabrication LaboratoryCAS Key Laboratory for Nanosystems and Hierarchical FabricationCAS Key Laboratory for Nanophotonic Materials and DevicesBeijingChina National University of Singapore Department of Electrical and Computer EngineeringSingapore University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics EngineeringBeijingChina The University of Hong Kong Department of PhysicsHong KongChina Peking University School of PhysicsState Key Laboratory for Mesoscopic PhysicsBeijingChina

Topological edge states(TESs),arising from topologically nontrivial phases,provide a powerful toolkit for the architecture design of photonic integrated circuits,since they are highly robust and strongly localized at the boundaries of topological *** is highly desirable to be able to control TES transport in photonic *** the coupling between the TESs in a finite-size optical lattice is capable of exchanging light energy between the boundaries of a topological lattice,hence facilitating the flexible control of TES ***,existing strategies have paid little attention to enhancing the coupling effects between the TESs through the finite-size ***,we establish a bridge linking the interaction between the TESs in a finite-size optical lattice using the Landau–Zener model so as to provide an alternative way to modulate/control the transport of topological *** experimentally demonstrate an edge-to-edge topological transport with high efficiency at telecommunication wavelengths in silicon waveguide *** results may power up various potential applications for integrated topological photonics.

关键词： topological edge states finite-size effect Landau-Zener model edge-to-edge topological transport nanophotonic integrated circuits

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Planning of Community Microgrid for Improving the Resilience of System by Using HOMER Grid

Planning of Community Microgrid for Improving the Resilience...

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European Conference on Power Electronics and Applications

作者： Majid Ali Meisam Rajabnia Juan C. Vasquez Josep M. Guerrero Yajuan Guan Abdullah M. Abusorrah Center for Research on Microgrids (CROM) AAU Energy Aalborg University Aalborg Denmark Department of Electrical and Computer Engineering Center of Research Excellence in Renewable Energy and Power Systems Faculty of Engineering K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

Natural Disasters (NDs), such as tsunamis, floods, and earthquakes, may cause damage to the transmission and distribution infrastructures as well as communication systems. In these situations, generators may even be intentionally shut down for security reasons. The result is a major power outage or a blackout, which may disrupt industrial production, disturb commercial and household activities and imperil human lives. Contrary to large-scale power systems, Micro Grids (MGs) may offer a higher resilience to NDs because of their compact sizes, manageable loads, and constrained bounds. Such an MG may include Photo-Voltaic (PV) system, Battery Energy Storage (BES) system, winds turbine, diesel generators, and local electric loads. It operates in grid-connected in normal conditions, and in islanded mode during NDs, ensuring that no disruption in the supply of critical loads happens. The main objective of this article is to study the planning of Community MG (CMG) for hospital loads to ensure high resilience against NDs. Hospital loads in the Northwest Lombok region of Indonesia are considered as the case study. The goal is to achieve three days of uninterruptible power supply during NDs. HOMER Grid carries out the simulation research. According to the results, the planned CMG could provide the necessary hospital load during the intended time period without any interruptions.

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Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

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computer Modeling in engineering & Sciences 2022年第12期133卷 843-872页

作者： Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan Mazen Ebraheem Assiri Department of Electronics&Telecommunication LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Electronics&Telecommunication Vilad GhatDr.Vithalrao Vikhe Patil College of EngineeringAhmednagarMaharashtra414111India Centre for AdvancedModelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydney2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Universiti KebangsaanInstitute of Climate ChangeMalaysiaSelangor43600Malaysia Department of Computer Science LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Center for Applied Artificial Intelligence(SCAAI) LavaleSymbiosis International(Deemed University)Pune412115India

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent ***’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing *** learning models are at the forefront of this ***,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their *** a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep *** situation is substantially different in safety-critical *** lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this ***,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an *** lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment *** is the term introduced in the preceding *** AImodel’s black box nature will become explainable with these *** in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial *** models’explainable natures will help them commercially in diagnosis decisions in the medical *** paper explores the different frameworks for the explainability of AI models in the medical *** available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

关键词： Medical imaging explainability artificial intelligence XAI

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Unveiling the Efficacy of Unani Medicine in Female Disorders Through Machine Learning: Current Challenges and Opportunities 20

Unveiling the Efficacy of Unani Medicine in Female Disorders...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Sultana, Arshiya Akhtar, Faijan Bin Heyat, Md Belal Singh, Priya Parveen, Saba Kumar, Shailendra Sayeed, Eram Akhtar, Soha Singh, Bandana Rahmani, Khaleequr Li, Jianping National Institute of Unani Medicine Ministry of AYUSH Department of Amraze Niswan Wa Ilmul Qabalat Karnataka Bengaluru India School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Engineering Westlake University CenBRAIN Neurotech Center of Excellence Zhejiang Hangzhou China Dreamvcssels Technology UP India College of Electronic and Information Engineering Shenzhen University Guangdong Shenzhen China Integral University Department of Electronics and Communication Engineering Lucknow India Government Co-ed Sarvodaya Vidyalaya Madanpur Khadar New Delhi India Hamdard Public School New Delhi India School of Media and Communication Shenzhen University Shenzhen China National Institute of Unani Medicine Ministry of AYUSH Department of Ilmul Saidla Karnataka Bengaluru India

ISBN: (纸本)9798350318982

In the past decade, the healthcare sector has seen a noteworthy increase in the application of artificial intelligence (AI) technologies across various therapeutic domains. AI-powered technologies have been increasingly used in the context of female diseases within Unani medicine. This study unveils the efficacy of Unani medicine in female diseases through machine learning. This review abides by the preferred reporting items for systematic reviews and meta-analysis extensions for scoping reviews. Studies published after January 1, 2000, were included in 2000 to 2023. A total of 402 full-length articles were searched from the PubMed and Science direct. Two studies from published articles and two from the proposed dataset exclusively focused on the efficacy of Unani medicine, along with one cross-sectional study. This review demonstrates that this research has been conducted using various machine-learning techniques to detect the efficacy of Unani treatment in female disoder. Machine learning was also applied in a cross-sectional study on abnormal vaginal discharge and Mizaj. Machine learning models were used. While research on machine learning models in Unani herbal medicine is in its preliminary stages, the findings from the selected research suggest that ML methods can enhance the services provided by Unani practitioners to their patients. Nevertheless, future advancements should involve the application of diverse ML models across a broad spectrum of Unani treatments. © 2023 IEEE.

关键词： Machine learning

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software Testing Issues Improvement in Quality Assurance 2

Software Testing Issues Improvement in Quality Assurance

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Salahat, Mohammed Said, Raed A. Hamid, Khalid Haseeb, Usama Abdel Maguid Abdel Ghani, Elsaid Abualkishik, Abedallah Iqbal, Muhammad Waseem Inairat, Mohammad University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Canadian University Dubai Faculty of Management Dubai United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Superior University Department of Software Engineering Lahore Pakistan Canadian University Dubai Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates VICE CHANCELLOR Skyline University College Dubai United Arab Emirates

ISBN: (纸本)9798350335644

Quality assurance ensures that the project is carried out following the agreed-upon supplies, principles, and then purposes. The goal of type and its calibration investigation remains to better comprehend the multifaceted course of software analysis. Current software rehearses have some errors such as analysis techniques, user arrogances, and logistic culture. Altogether of these subjects, including such rushed testing, way to quickly times, totally inadequate supporting documents, and so on others must be addressed together. Describe a solution to the significant trouble of product testing in quality managing. We will discuss about the solutions of testing problems in quality management. propose a solution to the glitches mentioned above. © 2023 IEEE.

关键词： Quality control

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Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength

arXiv

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arXiv 2023年

作者： Zheng, Tongya Zhang, Tianli Guan, Qingzheng Huang, Wenjie Feng, Zunlei Song, Mingli Chen, Chun Big Graph Center College of Computer Science Hangzhou City University China College of Computer Science Zhejiang University China College of Software Technology Zhejiang University China Department of Civil Engineering Zhejiang University China

Graph Neural Networks have emerged as an effective machine learning tool for multi-disciplinary tasks such as pharmaceutical molecule classification and chemical reaction prediction, because they can model non-euclidean relationships between different entities. Particle crushing, as a significant field of civil engineering, describes the breakage of granular materials caused by the breakage of particle fragment bonds under the modeling of numerical simulations, which motivates us to characterize the mechanical behaviors of particle crushing through the connectivity of particle fragments with Graph Neural Networks (GNNs). However, there lacks an open-source large-scale particle crushing dataset for research due to the expensive costs of laboratory tests or numerical simulations. Therefore, we firstly generate a dataset with 45,000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing. Secondly, we devise a hybrid framework based on GNNs to predict particle crushing strength in a particle fragment view with the advances of state-of-the-art GNNs. Finally, we compare our hybrid framework against traditional machine learning methods and the plain MLP to verify its effectiveness. The usefulness of different features is further discussed through the gradient attribution explanation w.r.t the predictions. Our data and code are released at https://***/doujiang-zheng/GNN-For-Particle-Crushing. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Robust Cooperative Sparse Representation Solutions for Detecting and Mitigating Spoofing Attacks in Autonomous Vehicles

Robust Cooperative Sparse Representation Solutions for Detec...

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Mediterranean Conference on Control and Automation (MED)

作者： N. Piperigkos A. S. Lalos C. Anagnostopoulos S. Z. N. Zukhraf C. Laoudias M. K. Michael Industrial Systems Institute Athena Research Center Patras Science Park Greece KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

The new era of Industry 4.0 and its key-enabling Internet of Things technologies promises fundamental advances during data collection, processing and analysis from a variety of agents and sensors, for the collective benefit of society. In this regard, connected and autonomous vehicles equipped with integrated perception sensors and communication abilities formulate a cluster or swarm of intelligent nodes capable to transform the transportation sector into a new smart mobility system. However, its feasible operation may be potentially threatened by hijackers whose goal is to cause malfunctioning to critical vehicular sensors, harnessing the perception system of vehicle. Therefore, in this paper we discuss the impact of cyberattacks such as GPS spoofing on autonomous vehicles, and design efficient detection and mitigation centralized schemes which provide location awareness and security monitoring over the whole cluster of vehicles. More specifically, we exploit the cooperation among the interacting vehicles, and develop robust sparse coding solutions based on graph signal processing and Alternating Direction Method of Multipliers. Cooperative based approach is further benefited by a in-vehicle module which provides spoofing detection alerts at the level of individual vehicle. Experimental analysis using the renowned CARLA simulator indicates highly efficient mitigation performance for different rates of compromised vehicles, as well as spoofing detection metrics greater than 94%.

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Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

arXiv

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arXiv 2024年

作者： Absur, Md. Nurul Saha, Sourya Nova, Sifat Nawrin Nasif, Kazi Fahim Ahmad Nasib, Md. Rahat Ul Department of Computer Science City University of New York New York United States Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia United States Samsung Austin Research Center AustinTX United States

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments. © 2024, CC BY-NC-ND.

关键词： Scalability

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Social Media and Stock Market Prediction: A Big Data Approach

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computers, Materials & Continua 2021年第5期67卷 2569-2583页

作者： Mazhar Javed Awan Mohd Shafry Mohd Rahim Haitham Nobanee Ashna Munawar Awais Yasin Azlan Mohd Zain Azlanmz School of Computing Faculty of EngineeringUniversity Teknologi MalaysiaJohorMalaysia Department of Software Engineering University of Management and TechnologyLahorePakistan Collage of Business Abu Dhabi UniversityAbu DhabiUnited Arab Emirates Oxford Center for Islamic Studies The University of OxfordOxfordUK The University of Liverpool Management School The University of LiverpoolLiverpoolUK Department of Computer Engineering National University of TechnologyIslamabadPakistan School of Computing UTM Big Data CentreUniversiti Teknologi MalaysiaJohorMalaysia

Big data is the collection of large datasets from traditional and digital sources to identify trends and *** quantity and variety of computer data are growing exponentially for many *** example,retailers are building vast databases of customer sales *** are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and *** of big data include volume and variety in both structured and unstructured *** this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional *** studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision *** implemented naive Bayes and logistic regression classification *** results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.

关键词： Big data analytics artificial intelligence machine learning stock market social media business analytics

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Wirelessly Enabled Beam Steering MSF Antenna for Biomedical Imaging Applications

Wirelessly Enabled Beam Steering MSF Antenna for Biomedical ...

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URSI International Symposium on Electromagnetic Theory (EMTS)

作者： Asif Bilal Abdul Quddious Amin Darvazehban Sasan Ahdi Rezaeieh Stavros Iezekiel Marco A. Antoniades EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Research Center for Excellence University of Cyprus Nicosia Cyprus Chair of RF and Photonics Engineering TU Dresden Dresden Germany School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia Department of Electrical Computer & Biomedical Engineering Toronto Metropolitan University Toronto ON Canada

ISBN: (数字)9798350399288

ISBN: (纸本)9798350399295

In this work, a wirelessly enabled beam steering metasurface (MSF) antenna for bio-imaging is proposed. The design consists of multiple layers, the top layer consists of an MSF layer, while the bottom layer consists of two layers, one that contains the feeding antennas and the second consists of the feeding network. In the feeding network of the MSF antenna, an RF energy harvesting (EH) system has been incorporated that allows dynamic switching to achieve the desired beam steering required for torso scanning. In the designed EH system, a receiving antenna and rectifying circuit harvests the low power received RF signal at three different frequencies in the 2.4 GHz ISM band. In order to avoid coupling between the EH system and MSF antenna, the former operates in the 2.4 GHz ISM band, while the latter operates in the 900 MHz ISM band. The proposed approach has potential to be used in biomedical imaging applications and to provide an alternative to complex biasing networks required to reconfigure devices.

关键词： Microwave antennas Torso Beam steering Antenna theory Switches Wireless power transfer Antenna feeds Metasurfaces Antennas Biomedical imaging

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