417 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Envisioning the Future of Cyber Security in Post-Quantum Era: A Survey on PQ Standardization, Applications, Challenges and Opportunities

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    The rise of quantum computers exposes vulnerabilities in current public key cryptographic protocols, necessitating the development of secure post-quantum (PQ) schemes. Hence, we conduct a comprehensive study on various PQ approaches, covering the constructional design, structural vulnerabilities, and offer security assessments, implementation evaluations, and a particular focus on side-channel attacks. We analyze global standardization processes, evaluate their metrics in relation to real-world applications, and primarily focus on standardized PQ schemes, selected additional signature competition candidates, and PQ-secure cutting-edge schemes beyond standardization. Finally, we present visions and potential future directions for a seamless transition to the PQ era

    Various Applications of Methods and Elements of Adaptive Optics

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    This volume is focused on a wide range of topics, including adaptive optic components and tools, wavefront sensing, different control algorithms, astronomy, and propagation through turbulent and turbid media

    Machine Learning-Assisted Method for Efficient and Accurate Antenna Modelling

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    Antenna modelling is an important tool for engineers and researchers in the field of telecommunications, as it allows for the design and optimisation of antennas in different scenarios and for a variety of applications. However, conventional methods of antenna modelling can be computationally expensive and time-consuming, which can limit the exploration of design space and lead to the inaccurate or even failed in antenna design and optimisation. With the rapid development of wireless communication technology, antenna design has attracted extensive attention. As device for transmitting and receiving electromagnetic (EM) signals, antenna has a significant impact on the performance of wireless communication systems. Over the past decade, various new antenna and analysis methods have been proposed. Generally, the modelling and analysis of antenna are carried out in EM simulation software such as Computer Simulation Technology (CST) Microwave Studio, High-Frequency Structure Simulator (HFSS), which can be used to model and simulate various kinds of antennas, and the corresponding performance such as reflection coefficient, gain, radiation pattern and impedance of antenna can be directly obtained through simulation. Unfortunately, modern antenna design is more complicated because of the increasing number of design variables, complex structures, and environmental factors. Parametric sweep is an important function of EM simulation software that allows designers to get the information of an antenna under different conditions, the time cost to run an EM simulation for individual candidate solution varies from seconds to minutes, or even several hours. An antenna with complex structure may require thousands of EM simulation to model, the cost in time and computational resources are impractical and unacceptable for most designers and researchers. To address these challenges, machine learning (ML) methods have been developed and applied to improve the efficiency and accuracy of antenna modelling. These methods involve using ML algorithms to train models on data, which can then be used to predict the performance of antennas for a given set of design variables. This thesis employs and combines different ML-assisted antenna modelling methods to reduce time, cost, and computational intensity in antenna design and accelerate the design process without compromising accuracy. First, quick estimation can be performed using the linear regression (LR) method based on limited data and computational resources to obtain guidance and check the feasibility of an antenna design. Then one of the ANN-based methods can be selected for antenna modelling and optimisation according to the antenna design complexity. These methods can be combined into a systematic antenna design process for modern antenna design. This set of processes can model and optimise antenna for different applications and scenarios with broad ranges of design variables. Compared to EM simulation-based and conventional ML-based antenna design methods. This process can perform accurate antenna modelling using significantly reduced time and computational resources and eliminate unnecessary costs in optimisation, fabrication and testing. In the first part, a concrete embedded antenna is proposed to mitigate the space occupation and aesthetic problems of indoor dense small cell deployment. The LR method is employed to fast estimate the relationship between antenna performance (radiation efficiency, gain, and input impedance) and embedding ambient (embedding depth and concrete dielectric constant) since the EM simulation-based antenna modelling is time-consuming. The complex mutual coupling between the antenna and the concrete leads to a limited amount of simulated data, and LR can model and predict the performance parameters of the antenna with limited data and a few computing resources. LR can also use limited resources to evaluate the feasibility of antenna design before implementation and fabrication, which can reduce unnecessary overhead and identify potential issues in the antenna. The findings of this study are beneficial to antenna designers for indoor communication concrete embedding antenna design and deployment, as well as communication-friendly building materials. In the second part, a heuristic algorithm-enhanced artificial neural network (ANN) is proposed to model concrete embedded antenna. The utilisation of ANN can handle the complex and non-linear relationship between inputs and outputs, and it can also make a prediction on antenna performance when new design points are given. A global optimisation algorithm is used to enhance ANN to eliminate local minima issues, and Bayesian regularisation (BR) is employed to improve the network prediction accuracy at new design points. The network accuracy and efficiency are higher than the conventional back-propagation ANN. The third part proposes a multi-fidelity neural network for antenna modelling and optimisation. Two sources of simulated data are involved and combined to perform antenna modelling with a large amount of cheap and inaccurate models and a small amount of expensive and accurate models. The correlation between two sources of data can be learned adaptively by decomposing the correlation into linear and non-linear components. The feasibility of the approach is validated by three antenna structures, the results show that this method can make prediction for broad ranges of input parameters with satisfactory accuracy; then the surrogate model is directly applied in the optimisation algorithm framework to replace EM simulation to accelerate antenna optimisation procedure

    Proceedings of the 29th International Symposium on Analytical and Environmental Problems

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    A Bibliography of NPS Space Systems Related Student Research, 2013-2022

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    Dudley Knox Library, Naval Postgraduate School.Approved for Public Release; distribution is unlimite

    Measurement uncertainty in machine learning - uncertainty propagation and influence on performance

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    Industry 4.0 is based on the intelligent networking of machines and processes in industry and makes a decisive contribution to increasing competitiveness. For this, reliable measurements of used sensors and sensor systems are essential. Metrology deals with the definition of internationally accepted measurement units and standards. In order to internationally compare measurement results, the Guide to the Expression of Uncertainty in Measurement (GUM) provides the basis for evaluating and interpreting measurement uncertainty. At the same time, measurement uncertainty also provides data quality information, which is important when machine learning is applied in the digitalized factory. However, measurement uncertainty in line with the GUM has been mostly neglected in machine learning or only estimated by cross-validation. Therefore, this dissertation aims to combine measurement uncertainty based on the principles of the GUM and machine learning. For performing machine learning, a data pipeline that fuses raw data from different measurement systems and determines measurement uncertainties from dynamic calibration information is presented. Furthermore, a previously published automated toolbox for machine learning is extended to include uncertainty propagation based on the GUM and its supplements. Using this uncertainty-aware toolbox, the influence of measurement uncertainty on machine learning results is investigated, and approaches to improve these results are discussed.Industrie 4.0 basiert auf der intelligenten Vernetzung von Maschinen und Prozessen und trägt zur Steigerung der Wettbewerbsfähigkeit entscheidend bei. Zuverlässige Messungen der eingesetzten Sensoren und Sensorsysteme sind dabei unerlässlich. Die Metrologie befasst sich mit der Festlegung international anerkannter Maßeinheiten und Standards. Um Messergebnisse international zu vergleichen, stellt der Guide to the Expression of Uncertainty in Measurement (GUM) die Basis zur Bewertung von Messunsicherheit bereit. Gleichzeitig liefert die Messunsicherheit auch Informationen zur Datenqualität, welche wiederum wichtig ist, wenn maschinelles Lernen in der digitalisierten Fabrik zur Anwendung kommt. Bisher wurde die Messunsicherheit im Bereich des maschinellen Lernens jedoch meist vernachlässigt oder nur mittels Kreuzvalidierung geschätzt. Ziel dieser Dissertation ist es daher, Messunsicherheit basierend auf dem GUM und maschinelles Lernen zu vereinen. Zur Durchführung des maschinellen Lernens wird eine Datenpipeline vorgestellt, welche Rohdaten verschiedener Messsysteme fusioniert und Messunsicherheiten aus dynamischen Kalibrierinformationen bestimmt. Des Weiteren wird eine bereits publizierte automatisierte Toolbox für maschinelles Lernen um Unsicherheitsfortpflanzungen nach dem GUM erweitert. Unter Verwendung dieser Toolbox werden der Einfluss der Messunsicherheit auf die Ergebnisse des maschinellen Lernens untersucht und Ansätze zur Verbesserung dieser Ergebnisse aufgezeigt

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    Naval Postgraduate School Academic Catalog - February 2023

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    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum
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