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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

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STAR-RIS-NOMA Assisted Power Constrained Covert Communication Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Lei, Yuxin Huang, Gaojian Li, Xingwang Tian, Zhifa Mumtaz, Shahid Asif, Muhammad Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Silesian University of Technology Department of Applied Informatics Gliwice Poland Department of Engineering Nottingham Trent University United Kingdom Jiangsu University School of Computer Science and Communication Engineering Zhenjiang China

In this paper, a covert communication system assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in non-orthogonal multiple access (NOMA) networks is proposed. When the maximum channel power gain from the transmitter in the main network to the users in adjacent networks is smaller than the threshold, the transmitter sends the NOMA signals to the users with the assistance of STAR-RIS. Moreover, channel estimation errors and imperfect successive interference cancellation (SIC) are considered in the system analysis. To characterize the covert performance, the detection error probability (DEP) and the average minimum detection error probability (AMDEP) are first derived. Then, the closed-form solutions of the outage probability (OP) for the users are obtained. Finally, the maximum effective covert rate (ECR) subject to the covertness constraint and non-interruption constraint is studied. The numerical simulation results show that: 1) the detection performance of Willie (warden) deteriorates as the channel estimation errors increase;2) when the transmitter power exceeds the threshold, communication in the main network is interrupted;3) under a certain transmit power, the maximum ECR increases with the increasing of the number of STAR-RIS reflection elements. © 2025 IEEE.

关键词： Channel estimation

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Supporting the Pedagogical Assessment Process in Educational Institutions with Social Background Data Analysis and WTCAi System Integration

Supporting the Pedagogical Assessment Process in Educational...

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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Éva Karl György Molnár József Cserkó Beáta Orosz Eniko Nagy Várkerti Primary School Várpalota Hungary Széchenyi University Doctoral School of Multidisciplinary Engineering Sciences (MMTDI) Győr Hungary Kandó Kálmán Faculty of Electrical Engineering Budapest Óbuda University Hungary János Faculty of Humanities Education and Social Sciences Széchenyi University Apáczai Csere Győr Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Óbuda University Doctoral School of Innovation Management Budapest Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Primary educational institutions aim to prepare children for further education and successful integration into the workforce. To consistently achieve this, they must meet the demands and expectations imposed by the constantly changing economic and social environments. Research shows that applying a constructive pedagogical approach is the most successful path, best fitting the expectations above. An essential and integral part of the educational process is the pedagogical supervision and evaluation procedure, aimed to assess performance and improve and support the learning process. A sad feature of the current system is that it needs to support individual learning paths sufficiently. Additionally, communication issues arising from the generational gap between teachers and students jeopardize the validity of the assessment tools. Educational institutions' advancement and continuous development can only be realized if the pedagogical supervision and evaluation system progresses and evolves to meet new challenges. Our research aims to develop such a method. We are working on creating a complex evaluation method supported by artificial intelligence (WTCAi - When The Child Ask with AI), which suggests appropriate items for assessment and evaluation to subject teachers. To achieve our main objective, we have planned to collect additional social background data from our students. Through analyzing these data, we hope to uncover numerous correlations that significantly influence the obtained data and results, thereby steering the development of the supervision-evaluation system in the most appropriate direction.

关键词： Economics Data analysis Correlation Fitting Education System integration Artificial intelligence informatics Machine intelligence

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An adaptive Mealy machine model for monitoring crop status

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Journal of Integrative Agriculture 2017年第2期16卷 252-265页

作者： Berk Ustundag Agricultural and Environmental Informatics Application & Research Center/Faculty of Informatics and Computer Engineering Istanbul Technical University Istanbul 34469 Turkey

Variation in phenological stage is the major nonlinearity in monitoring, modeling and various estimations of agricultural systems. Indices are used as a common means of evaluating agricultural monitoring data from remote sensing and terrestrial observation systems, and many of these indices have linear characteristics. The analysis of and relationships between indices are dependent on the type of plant, but they are also highly variable with respect to its phenologicat stage. For this reason, variations in the phenologica! stage affect the performance of spatiotemporal crop status monitoring. We hereby propose an adaptive event-triggered model for monitoring crop status based on remote sensing data and terrestrial observations. In the proposed model, the estimation of phenological stage is a part of predicting crop status, and spatially distributed remote sensing parameters and temporal terrestrial monitoring data are used together as inputs in a state space system model. The temporal data are segmented with respect to the phenological stage-oriented timing of the spatial data, so instead of a generalized discrete state space model, we used logical states combined with analog inputs and adaptive trigger functions, as in the case of a Mealy machine model. This provides the necessary nonlinearity for the state transi- tions. The results showed that observation parameters have considerably greater significance in crop status monitoring with respect to conventional agricultural data fusion techniques.

关键词： plant phenology monitoring yield prediction finite automata Mealy machine remote sensing

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Blockchain Hybrid-model Scheme for Scalable Cross-domain Authorisation

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Blockchain: Research and Applications 2025年

作者： Munkenyi Mukhandi Jorge Granjal João P. Vilela CISUC Dep. of Informatics Engineering University of Coimbra Portugal CISUC Dep. of Informatics Engineering & CRACS / INESCTEC & Dep. of Computer Science Faculty of Sciences University of Porto Portugal

We introduce a novel blockchain-based inter-ledger authorization scheme, a unique solution designed to support delegated authorization management in cross-domain cloud environments. This innovative approach enables client applications, acting on behalf of a user, to request access to specific resources owned by a different organization. It benefits cross-domain multi-cloud applications, such as large-scale collaboration projects involving multiple cloud service providers. Multi-cloud presents authorization challenges because of complex access control operations in multiple distinct domains. OAuth2 is the state-of-the-art choice for delegated authorization. However, it falls short in handling such complex interactions with multiple authorization servers and resource servers, and this is our motivation. Compared to OAuth2, our approach overcomes the limitations of a centralized model and achieves coordinated distinct delegations among multiple entities without a third-party trusted component. Authorization servers are blockchain participants and designated access token issuers, ensuring minimum client data collection and promoting client privacy. Multiple smart contracts are deployed in our multi-ledger blockchain environment to guarantee the provenance of the authorization operations and facilitate scalable cross-domain authorization. Our ‘hybrid inter-ledger model’, combines security techniques such as blockchain-based device authentication, hashed-time locks (HTCLs), and service discovery. The blockchain-based device authentication ensures secure and trusted interactions, HTCLs enable time-bound transactions, and service discovery simplifies finding and connecting to the required cloud services. Our experimental evaluation provides insights into the scalability of our approach, which achieves decentralized authorization in a multi-cloud environment with acceptable latencies compared to related works and shows stable throughput against concurrent authorization reque

关键词： OAuth2 multi-cloud decentralised coordinated authorisation multi-ledgers blockchain security smart contracts

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IoT-Driven Facial Expression Recognition for Personalized Healthcare in Industry 5.0

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IEEE Internet of Things Journal 2025年

作者： Ali, Shehzad Sajjad, Muhammad Lee, Ik Hyun Cheikh, Faouzi Alaya Ribigan, Athena Cristina Pedulla, Ludovico Papagiannakis, Nikolaos Hijji, Mohammad Muhammad, Khan School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of Department of Computer Science Peshawar Pakistan IKLAB Inc Faculty of Mechatronics Engineering Tech University of Korea Korea Republic of Norway Carol Davila University Emergency Hospital Bucharest University of Medicine and Pharmacy Bucharest Romania Italian Multiple Sclerosis Foundation Italy National and Kapodistrian University of Athens Department of Neurology Eginitio Hospital Medical School Greece University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia

Facial emotion recognition (FER) plays a critical role in understanding human behavior, especially for individuals suffering from neurological disorders (ND) like Parkinson's Disease (PD), Multiple Sclerosis (MS), and stroke. Early and accurate detection of emotions is crucial for both diagnosis and continuous monitoring. However, traditional methods often fall short in providing non-invasive, real-time solutions and lack the clinical expertise necessary to identify the specific emotion types associated with each ND category. In response, this research conducted under the ALAMEDA consortium presents an IoT-based FER AI Toolkit designed to enhance early diagnosis and treatment for brain diseases. The toolkit is in line with the consortium's clinical guidelines and provides a personalized, patient-focused solution that supports the goals of Industry 5.0 in healthcare. In line with Industry 5.0 principles, the FER AI Toolkit uses edge devices to collect real-time facial data while deep learning models running on cloud servers process this data. The recognized emotions are uploaded to the Semantic Knowledge Graph (SemKG) server. This allows healthcare professionals to make informed decisions based on real-time data. Additionally, the toolkit integrates seamlessly with key components of the ALAMEDA, including the Identity Authentication Manager (IAM) for secure access and the ALAMEDA Innovation Hub (AIH) for efficient resource management. By offering continuous and personalized healthcare insights, the FER AI Toolkit helps bridge the gap between diagnosis and patient well-being, ultimately advancing healthcare systems. Training materials and video demonstrations are available here for further learning. © 2014 IEEE.

关键词： Neurodegenerative diseases

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Interval Partitioning for Packet Classification in OpenFlow vSwitch

IEEE Networking Letters

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IEEE Networking Letters 2020年第3期2卷 128-131页

作者： Akkoc, Mertkan Canberk, Berk Department of Computer Engineering Faculty of Computer and Informatics Istanbul Technical University İstanbul34469 Turkey

Packet classification is a key factor for choosing proper action for incoming packet and has to be done fast and effectively, especially in OpenFlow. But OpenFlow vSwitch technology doesn't always allow to use some fast hardware technology for packet classification like TCAM (Ternary Content Addressable Memory). Instead of TCAM, decision tree methods are preferred solutions for fast classification in OpenFlow vSwitch in the literature. But most of these methods can cause the rule replication problem. As a result, while the duration of packet classification decreases, rule update duration increases. There are also rule partitioning methods in the literature to solve this problem, but the running time of these methods mostly depends on the number of rule fields. Also, some of these solutions don't overcome the rule replication problem. At that point, the main question of this letter is that how can we make the rule partitioning fast by both preventing the rule replication and allowing fast packet classification and rule update in OpenFlow vSwitch? To solve the rule partitioning problem, we propose a greedy based interval partitioning strategy. Here, the running time of the partitioning algorithm only depends on the rule number. After partitioning, we propose to use HyperCuts to construct decision trees for fast packet classification and rule update. According to performance evaluation results, we do the rule partitioning and rule updates faster than the PartitonSort method with the percentage of 88, 15, respectively. Also, we classify packet faster than the TupleMerge method with the percentage of 40 for online and 50 for offline scenarios. © 2019 IEEE.

关键词： Decision trees

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Dynamic Bayesian network models for self-management of chronic diseases: Rheumatoid arthritis case-study

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computers in Biology and Medicine 2025年 189卷 109909-109909页

作者： Fahmi, Ali MacBrayne, Amy Humby, Frances Curzon, Paul Marsh, William Centre for Health Informatics School of Health Sciences Faculty of Biology Medicine and Health The University of Manchester Manchester United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom North Middlesex University Hospital National Health Service Trust London United Kingdom Guy's and St Thomas' National Health Service Foundation Trust London United Kingdom

Dynamic Bayesian Networks (DBNs) are temporal probabilistic graphical models with a set of random variables and dependencies between them. DBNs have a meaningful structure and can model the continuity of events in discrete time-slices. In this study, we aimed to show how to build DBN models for self-management of chronic diseases using multiple sources of evidence. Chronic diseases need a life-long treatment. People with chronic diseases are commonly provided fixed-interval clinic visits, but they can suffer from sudden increases of disease activity. We proposed an approach to build DBN models for self-management of chronic diseases in order to advise on treatment decisions. We used Rheumatoid Arthritis (RA) as a case-study, and employed rheumatology experts’ knowledge, clinical data, clinical guidelines, and established literature to identify the variables, their states, dependencies between the variables, and parameters of the model. Due to the unavailability of the ideal data (i.e., large data with enough frequency), we adopted two approaches to make inferences for initial evaluation of the model: manipulation of the clinical data to increase their frequency and creating dummy patient scenarios. The initial evaluation indicated promising results for treatment decisions. The proposed approach used multiple sources of evidence to build DBN models for self-management of chronic diseases. The resulting DBN for RA case-study had a clinically meaningful structure, although it needed to be further evaluated and calibrated. Resulting DBN model has the potential to be used as a decision-support tool to help patients and clinicians better manage RA. © 2025 The Authors

关键词： Bayesian networks

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Optimizing novel thermal energy storage systems: Enhancing melting efficiency with tubes, stands, and advanced machine learning techniques

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Journal of Energy Storage 2025年 124卷

作者： Almadhor, Ahmad Basem, Ali Singh, Pradeep Kumar Othman, Nashwan Adnan Samad, Sarminah Alhomayani, Fahad M. Abduvalieva, Dilsora Ali, H. Elhosiny Akram, Muhammad Afzal, Abdul Rahman Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Department of Mechanical Engineering Institute of Engineering & Technology GLA University U.P. Mathura281406 India Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Management College of Business Administration Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia College of Computers and Information Technology Taif University Saudi Arabia Applied College Taif University Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent100070 Uzbekistan Physics Department Faculty of Science King Khalid University P.O. Box 9004 Abha Saudi Arabia Faculty of Design Informatics and Business University of Abertay Scotland Dundee United Kingdom Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia

The transition to renewable energy is crucial to meet growing global energy demands while minimizing environmental impact. Solar energy, a leading renewable source, faces limitations due to its intermittent nature, making energy storage systems essential for continuous supply. Thermal energy storage systems (TESSs) provide a compelling solution, especially by utilizing latent heat storage with phase change materials (PCMs), which efficiently store large amounts of energy. However, PCM-based systems suffer from low thermal conductivity and weak heat transfer performance. Numerous techniques to amend the thermal conductivity of PCMs, whether applied individually or together, show promise for improving the efficiency of TESSs. However, challenges like increased viscosity, decreased latent heat capacity, high costs, and complex manufacturing often limit their practical use. Thus, it is crucial to investigate more straightforward and more cost-effective solutions, like incorporating extended surfaces into the PCM container. In this study, a novel approach is taken to ameliorate the heat transfer surface area in TESSs by incorporating tubes and stands inside the PCM container rather than relying on conventional fins. A new compact TESS design was introduced, featuring fixed tubes supported by aluminum-made stands. Two artificial neural networks (ANN) models were trained with input parameters, including the length of the vertical stands, horizontal stands, and tilted stands, to anticipate the half-melting time and complete melting time of the PCM. In the end, two optimal designs of O-TESS1 and O-TESS2 were proposed by a single-objective optimization, focusing on minimizing the half and complete melting times of the PCM, respectively. A multi-objective optimization was also performed to balance the mentioned objectives. Using the generated data, a Pareto front and TOPSIS-selected designs were developed to visualize the trade-offs between these competing objectives. It took

关键词： Solar heating

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Developing a wireless stereoscopic optical palpation probe towards cost effective intraoperative breast cancer detection 12

Developing a wireless stereoscopic optical palpation probe t...

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Optical Elastography and Tissue Biomechanics XII 2025

作者： Jones, Rhys Fang, Qi Bharakhda, Ankit Sanderson, Rowan W. Zilkens, Renate Saunders, Christobel M. Kennedy, Brendan F. BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre Nedlands Centre for Medical Research The University of Western Australia CrawleyWA6009 Australia Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia CrawleyWA6009 Australia Medical School The University of Western Australia CrawleyWA6009 Australia Department of Surgery Melbourne Medical School The University of Melbourne ParkvilleVIC3010 Australia The Royal Melbourne Hospital 300 Grattan Street ParkvilleVIC3050 Australia Australian Research Council Centre for Personalised Therapeutics Technologies Australia Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń ul. Grudziądzka 5 Toruń87-100 Poland

ISBN: (纸本)9781510683907

Optical coherence tomography (OCT)-based elastography techniques, such as optical palpation, have shown promise for breast tumor margin assessment. However, the high cost and bulk of OCT systems may limit their clinical feasibility. Here, we present a wireless optical palpation probe utilizing stereoscopic cameras, offering a cost-effective and portable alternative. This stereoscopic optical palpation (SOP) probe measures the deformation of a silicone layer embedded with phosphorescent microparticles. Under UV illumination, these particles emit visible light, allowing the cameras to capture 2D strain images of the layer when compressed against the tissue. These strain images are then converted into stress maps based on the pre-characterized mechanical properties of the silicone layer. The probe is constructed with a 3D-printed case, housing the cameras, a Wi-Fi transceiver, and a rechargeable battery. Real-time video feeds are transmitted to a laptop for stress image processing. We demonstrated the SOP probe on silicone inclusion phantoms, identifying the location of embedded stiff silicone inclusions based on elevated stress measurements caused by increased mechanical contrast. The SOP probe offers a compact, cost-effective solution for real-time breast tumor margin assessment on excised specimens and also holds potential for in vivo detection of residual tumor in the surgical cavity. The low cost, portability, and accessibility of the probe make it suitable for widespread application, including in resource-limited settings. Future work will focus on enhancing measurement accuracy and estimating tissue elasticity to further improve clinical utility. © 2025 SPIE.

关键词： Stress measurement

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