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International Conference on Networking and Advanced systems (ICNAS)

作者： Sabrina Sakraoui Makhlouf Derdour Ahmed Ahmim Department of Computer Science Networks and Systems Laboratory Badji Mokhtar Annaba University Annaba Algeria Department of Computer Science Networks and Systems Laboratory Oum El Bouaghi University Oum El Bouaghi Algeria Dpt. of Mathematics and Computer Science Networks and Systems Laboratory Mohamed-Cherif Messaadia University Souk Ahras Algeria

As the development of wireless networks advances towards the deployment of 6G technology, ensuring robust security measures becomes crucial. In this paper, we propose 6G-SECUREIDS, a novel intrusion detection system designed specifically for 6G wireless networks. The system leverages machine learning techniques and blockchain technology to detect and mitigate security threats, safeguard data privacy, and ensure efficient and optimized training. Our experimental results demonstrate the effectiveness and reliability of the system, with the clients achieving high accuracy scores across different model architectures. The promising results highlight the potential of 6G-SECUREIDS as a valuable security solution for future 6G networks, contributing to the overall protection and integrity of the wireless communication ecosystem.

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Ageing does not affect the dynamic balance margins of gait

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Gait & Posture 2021年 90卷 248-249页

作者： Sloot, L. Millard, M. Mombaur, K. Hallemans, A. van Criekinge, T. Heidelberg University Institute of Computer Engineering ZITI Heidelberg Germany University of Waterloo Dpt of Systems Design Engineering & Dpt of Mechanical and Mechatronics Engineering Waterloo Canada University of Antwerp Dpt of Rehabilitation Sciences and Physiotherapy- Faculty of Medicine and Health Science Antwerp Belgium KU Leuven Dpt of Rehabilitation Sciences and Physiotherapy Brugge Belgium

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Modiàed Kirchhoff diffraction of pulsed EM waves radiated from a slot-excited Fabry-Pérot resonator antenna 12

Modiàed Kirchhoff diffraction of pulsed EM waves radiated f...

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12th European Conference on Antennas and Propagation, EuCAP 2018

作者： Stumpf, Martin Vandenbosch, Guy A.E. Lager, Ioan E. SIX Research Centre Brno University of Technology Technická 3082/12 Brno616 00 Czech Republic Electrical Engineering Dpt. TELEMIC Katholieke Universiteit Leuven Kasteelpark Arenberg 10 bus 2444 LeuvenB-3001 Belgium Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

ISBN: (纸本)9781785617911

Diffraction of pulsed electromagnetic (EM) waves in a slot excited Fabry Perót type resonator antenna is studied analytically in the modiÀed Kirchhoff approximation. Closed-form space-time expressions for the radiated EM Àelds are found using the Cagniard-DeHoop technique. Illustrative numerical examples are given. © 2018 Institution of engineering and *** Rights Reserved.

关键词： Diffraction

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OPTIMAL LOGIC SYNTHESIS OF COMBINATIONAL CIRCUITS USING K-LUT FPGA

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IFAC Proceedings Volumes 2007年第18期40卷 661-666页

作者： Ion I. Bucur Alexandru E. şuşu University Politehnica of Bucharest Computer Science and Engineering Dpt. bucurb@cs.pub.ro Swiss Federal Institute of Technology Lausanne (EPFL) IC/STI Department alex.susu@epfl.ch

Determining if a given arbitrary, wide function can be implemented by a programmable logic block, is unfortunately, in general, a very difficult problem, called the Boolean matching problem. We introduce a novel implemented algorithm able to map for performance combinational networks using k -LUT based FPGAs. We consider in this paper delay optimum and area optimal k-LUT FPGA mapping algorithms and compare them against a newly developed and recently improved algorithm.

关键词： k-LUT based FPGA algorithms for delay optimal mapping k-feasible cones

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Morphological analysis of 316l laser powder bed fusion melt-pool via the enriched analytical solution method

Morphological analysis of 316l laser powder bed fusion melt-...

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ASME 2020 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2020

作者： Michopoulos, J.G. Steuben, J.C. Birnbaum, A.J. Iliopoulos, A.P. Aroh, J. Rollett, A.D. Gould, B. Computational Multiphysics Systems Laboratory Center of Materials Physics and Technology U.S. Naval Research Laboratory WashingtonDC20375 United States Dpt. of Materials Science and Engineering Carnegie Mellon University PittsburghPA15213 United States Applied Materials Division Argonne National Laboratory ArgonneIL60439 United States

ISBN: (纸本)9780791883983

The recent development of the Enriched Analytical Solution Method (EASM) for evaluating the spatio-temporal distribution of the temperature fields generated during the Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM) processes is provides an opportunity to study the sensitivity of the morphological parameters characterizing the associated melt-pools as a function of process parameters. The present work exercises the EASM for the case of a single-path trace over a 316L base plate under LPBF heat deposition conditions. To assist in the evaluation of solidification parameters, the spatial derivatives of the EASM are also derived. A process parameter subspace spanned by the scan velocity and the laser power is considered and the EASM is utilized for deriving a number of geometrical morphological characteristics of the melt pool as well as the quantities controlling the evolution of the solidification front. Finally, comparisons with initial experimental results obtained by in-situ high speed synchrotron X-ray imaging, capturing the spatio-temporal evolution of the melt pool profile are also presented. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Solidification

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BAYESIAN LOGISTIC REGRESSION WITH SPARSE GENERAL REPRESENTATION PRIOR FOR MULTISPECTRAL IMAGE CLASSIFICATION

BAYESIAN LOGISTIC REGRESSION WITH SPARSE GENERAL REPRESENTAT...

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IEEE International Conference on Image Processing

作者： Juan G. Serra Pablo Ruiz Rafael Molina Aggelos K. Katsaggelos Dpto. de Ciencia de la Computacion e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science Northwestern University

ISBN: (纸本)9781467399623

In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm is validated on both synthetic and real experiments and compared with other state-of-the-art methods, such as Support Vector Machine and Gaussian Processes, demonstrating its improved performance.

关键词： Image Classification Bayes Methods Inference Algorithms Image classification Bayes methods Inference algorithms Variational Inference MULTISPECTRAL Taxonomy Gaussian processes regression function images Support Vector Network

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Thermal Properties of Refractory SWCNT-Based Nanocomposites Investigated by Automated Approach

Thermal Properties of Refractory SWCNT-Based Nanocomposites ...

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International Conference on Modern Problems of Radio engineering, Telecommunications and computer science

作者： Illia Zhydenko Halyna Klym Ivan Karbovnyk Yuriy Kostiv Ivanna Vasylchyshyn Dmytro Chalyy Department of fire tactics and emergency rescue work Lviv State University of Life Safety Lviv Ukraine Specialized Computer Systems Dpt. Lviv Polytechnic National University Lviv Ukraine Radiophysics and Computer Technology Dpt. Ivan Franko National University of Lviv Lviv Ukraine Information Technology Security Dpt. Lviv Polytechnic National University Lviv Ukraine Department of Theoretical and General Electrical Engineering Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

Thermal properties of refractory nanocomposites with embedded single-walled carbon nanotubes were investigated using proposed automated approach. In this approach, differences in nanocomposites with varying parameters, observed under temperature differentials and thermal loads, are highlighted. These findings are crucial for their integration into sensor systems and for applications requiring enhanced fire resistance. The results indicate that nanocomposites with a nanotube dispersion time of 2 hours exhibit a peak temperature difference of 55 “C, while those with a 1-hour dispersion time show a maximum difference of 50 °C. A comparative analysis of heat transfer dynamics suggests that extending the dispersion time by 1 hour increases fire resistance by 15%.

关键词： Temperature sensors Temperature distribution Nanocomposites Thermal resistance Sensor systems Telecommunications Thermal loading Dispersion Heat transfer Resistance heating

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Maximin Fairness-profit Tradeoff in Project Budget Allocation

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Procedia computer science 2016年 100卷 313-320页

作者： Maurizio Naldi Gaia Nicosia Andrea Pacifici Ulrich Pferschy Dpt. of Computer Science and Civil Engineering University of Rome Tor Vergata Rome Italy Dipartimento di Ingegneria Universita’ Roma Tre Rome Italy Department of Statistics and Operations Research University of Graz Graz Austria

Companies typically select those projects that maximize their profit as the primary criterion, within the limited budget at their disposal. This criterion may lead to some company departments getting an exceedingly large share of the overall budget and induce a negative perception of unfairness among the less favourite ones. We investigate how profit optimization can be sought after while achieving the desired level of fairness at the same time. Adopting a maximin approach to fairness and using an Integer Linear Programming solver, we show that a linear trade-off is possible, since fairness and profit exhibit a nearly perfect linear anticorrelation. Fairness can be improved by even a relatively small reduction of profit, especially in large companies (i.e., managing a large number of projects).

关键词： Budget allocation Multi-project management Project Portfolio Fairness

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Variational Gaussian process for missing label crowdsourcing classification problems

Variational Gaussian process for missing label crowdsourcing...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Pablo Ruiz Emre Besler Rafael Molina Aggelos K. Katsaggelos Dpto. de Ciencias de la Computación e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science Northwestern University

ISBN: (纸本)9781509007479

In this paper we address the crowdsourcing problem, where a classifier must be trained without knowing the real labels. For each sample, labels (which may not be the same) are provided by different annotators (usually with different degrees of expertise). The problem is formulated using Bayesian modeling, and considers scenarios where each annotator may label a subset of the training set samples only. Although Bayesian approaches have been previously proposed in the literature, we introduce Variational Bayes inference to develop an iterative algorithm where all latent variables are automatically estimated. In the experimental section the proposed model is evaluated and compared with other state-of-the-art methods on two real datasets.

关键词： Crowdsourcing Bayes methods Reliability Probabilistic logic Training Gaussian processes Noise measurement

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LEARNING FILTERS IN GAUSSIAN PROCESS CLASSIFICATION PROBLEMS

LEARNING FILTERS IN GAUSSIAN PROCESS CLASSIFICATION PROBLEMS

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IEEE International Conference on Image Processing

作者： Pablo Ruiz Javier Mateos Rafael Molina Aggelos K. Katsaggelos Dpto. de Ciencia de la Computacion e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science. Northwestern University

ISBN: (纸本)9781479957521

Many real classification tasks are oriented to sequence (neighbor) labeling, that is, assigning a label to every sample of a signal while taking into account the sequentiality (or neighborhood) of the samples. This is normally approached by first filtering the data and then performing classification. In consequence, both processes are optimized separately, with no guarantee of global optimality. In this work we utilize Bayesian modeling and inference to jointly learn a classifier and estimate an optimal filterbank. Variational Bayesian inference is used to approximate the posterior distributions of all unknowns, resulting in an iterative procedure to estimate the classifier parameters and the filterbank coefficients. In the experimental section we show, using synthetic and real data, that the proposed method compares favorably with other classification/filtering approaches, without the need of parameter tuning.

关键词： Gaussian Process classification Filter estimation Analysis representation

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