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Miniaturized multi-band stopband filter using circular split ring resonator and null gap separations between all parallel lines

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Progress In Electromagnetics Research C 2020年 106卷 137-150页

作者： Ahmed, Badiaa Ait Aghzout, Otman Naghar, Azzeddin Alejos, Ana V. Dpt. of Computer Science Engineering SIGL-Lab. ENSA University of Abdelmalek Essaadi Tétouan Morocco BSA-Innovation LAB Radio Frequency Systems RFS Lannion France Dpt. of Signal Theory and communications University of Vigo Pontevedra Spain

This paper presents a new multi-band stopband filter loaded by a shorted metamaterial circuit. Firstly, two filters loaded by stubs and open ring resonators (ORRs) are studied and compared. The ORRs allow more effects in terms of miniaturization by a shifting toward low frequencies and rejection bandwidth (57.34%). To improve the filter efficiency, coupled split ring resonators (SRRs) are used. The final filter is characterized by a miniaturized size of 18.840 mm2, wide rejection bandwidth, high selectivity level, and multiple resonances over S, C, X, and Ku bands. L-C equivalent circuit model filter and other characteristics are investigated. A prototype of the filter with coupled SRRs has been fabricated and measured. Good matching among EM-simulation, equivalent circuit modelling, and measured results are achieved. © 2020, Electromagnetics Academy. All rights reserved.

关键词： Circuit simulation

A Generalized Model to Estimate Reaction Time Corresponding to Visual Stimulus Using Single-Trial EEG

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A Generalized Model to Estimate Reaction Time Corresponding ...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mohammad Samin Nur Chowdhury Arindam Dutta Matthew K. Robison Chris Blais Gene Brewer Daniel W. Bliss School of Electrical Computer & Energy Engineering Arizona State University Dpt. of Psychology Arizona State University

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The estimation of the visual stimulus-based reaction time (RT) using subtle and complex information from the brain signals is still a challenge, as the behavioral response during perceptual decision making varies inordinately across trials. Several investigations have tried to formulate the estimation based on electroencephalogram (EEG) signals. However, these studies are subject-specific and limited to regression-based analysis. In this paper, for the first time to our knowledge, a generalized model is introduced to estimate RT using single-trial EEG features for a simple visual reaction task, considering both regression and classification-based approaches. With the regression-based approach, we could predict RT with a root mean square error of 111.2 ms and a correlation coefficient of 0.74. A binary and a 3-class classifier model were trained, based on the magnitude of RT, for the classification approach. Accuracy of 79% and 72% were achieved for the binary and the 3-class classification, respectively. Limiting our study to only high and low RT groups, the model classified the two groups with an accuracy of 95%. Relevant EEG channels were evaluated to localize the part of the brain significantly responsible for RT estimation, followed by the isolation of important features.

关键词： Electroencephalography Feature extraction Brain modeling Forestry Regression tree analysis Time-frequency analysis Visualization

Improving the reliability of network intrusion detection systems through dataset integration

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arXiv 2021年

作者： Magán-Carrión, Roberto Urda, Daniel Diaz-Cano, Ignacio Dorronsoro, Bernabé Dpt. of Signal Theory Telematics & Communications School of Computer Engineering & Telecommunications University of Granada Spain Departamento de Ingeniería Informática Escuela Politécnica Superior Universidad de Burgos Spain Superior Engineering School University of Cadiz Spain

This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML models to work on integrated datasets, empowering the learning process with diverse information from different datasets. Therefore, R-NIDS targets the design of more robust models, that generalize better than traditional approaches. We also propose a new dataset, called UNK21. It is built from three of the most well-known network datasets (UGR’16, USNW-NB15 and NLS-KDD), each one gathered from its own network environment, with different features and classes, by using a data aggregation approach present in R-NIDS. Following R-NIDS, in this work we propose to build two well-known ML models (a linear and a non-linear one) based on the information of three of the most common datasets in the literature for NIDS evaluation, those integrated in UNK21. The results that the proposed methodology offers show how these two ML models trained as a NIDS solution could benefit from this approach, being able to generalize better when training on the newly proposed UNK21 dataset. Furthermore, these results are carefully analyzed with statistical tools that provide high confidence on our conclusions. Copyright © 2021, The Authors. All rights reserved.

关键词： Data integration

An Augmentation Strategy to Mimic Multi-Scanner Variability in MRI

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An Augmentation Strategy to Mimic Multi-Scanner Variability ...

IEEE International Symposium on Biomedical Imaging

作者： Maria Ines Meyer Ezequiel de la Rosa Nuno Barros Roberto Paolella Koen Van Leemput Diana M. Sima Health Technology Dpt Technical University of Denmark Lyngby Denmark icometrix Leuven Belgium Dpt. of Computer Science Technical University of Munich Munich Germany imec Vision Lab University of Antwerp Antwerp Belgium Martinos Center for Biomedical Imaging MGH & Harvard Medical School Boston MA USA

Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data. We propose a novel data augmentation approach with the aim of approximating the variability in terms of intensities and contrasts present in real world clinical data. We use a Gaussian Mixture Model based approach to change tissue intensities individually, producing new contrasts while preserving anatomical information. We train a deep learning model on a single scanner dataset and evaluate it on a multi-center and multi-scanner dataset. The proposed approach improves the generalization capability of the model to other scanners not present in the training data.

关键词： Deep learning Protocols Magnetic resonance imaging Biological system modeling Training data Mixture models Brain modeling

Naive regularizers for low-resource neural machine translation 12

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Naive regularizers for low-resource neural machine translati...

12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019

作者： Beloucif, Meriem Gonzalez, Ana Valeria Bollmann, Marcel Søgaard, Anders Language Technology Group Universität Hamburg Hamburg Germany Dpt. of Computer Science University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9789544520557

Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios. They require large volumes of data and often perform poorly when limited data is available. We show that using naive regularization methods, based on sentence length, punctuation and word frequencies, to penalize translations that are very different from the input sentences, consistently improves the translation quality across multiple low-resource languages. We experiment with 12 language pairs, varying the training data size between 17k to 230k sentence pairs. Our best regularizer achieves an average increase of 1.5 BLEU score and 1.0 TER score across all the language pairs. For example, we achieve a BLEU score of 26.70 on the IWSLT15 English-Vietnamese translation task simply by using relative differences in punctuation as a regularizer. © 2019 Association for Computational Linguistics (ACL). All rights reserved.

关键词： Neural machine translation

On deploying the artificial sport trainer into practice

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arXiv 2021年

作者： Fister, Iztok Iglesias, Andres Deb, Suash Fister, Iztok Galvez, Akemi Fister, Dušan Faculty of Electrical Engineering and Computer Science University of Maribor SI- Maribor2000 Slovenia Dpt. of Applied Mathematics and Computational Sciences University of Cantabria Santander39005 Spain IT & educational Consultant Ranchi Jharkhand India Faculty of Economics and Business University of Maribor MariborSI-2000 Slovenia

—Computational Intelligence methods for automatic generation of sport training plans in individual sport disciplines have achieved a mature phase. In order to confirm their added value, they have been deployed into practice. As a result, several methods have been developed for generating well formulated training plans on computers automatically that, typically, depend on the collection of past sport activities. However, monitoring the realization of the performed training sessions still represents a bottleneck in automating the process of sport training as a whole. The objective of this paper is to present a new low-cost and efficient embedded device for monitoring the realization of sport training sessions that is dedicated to monitor cycling training sessions. We designed and developed a new bike computer, i.e. the AST-Monitor, that can be mounted easily on almost every bicycle. The aforementioned bike computer is based on the Raspberry Pi device that supports different external sensors for capturing the data during the realization of sport training sessions. An adjusted GUI tailored to the needs of athletes is developed, along with the hardware. The proof of concept study, using the AST-Monitor in practice, revealed the potential of the proposed solution for monitoring of realized sport training sessions automatically. The new device also opens the door for the future utilization of Artificial Intelligence in a wide variety of sports. Copyright © 2021, The Authors. All rights reserved.

关键词： Sports

Fake news detection in Spanish using deep learning techniques

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arXiv 2021年

作者： Martínez-Gallego, Kevin Álvarez-Ortiz, Andrés M. Arias-Londoño, Julián D. Intelligent Information Systems Lab Universidad de Antioquia Calle 67 No. 53 - 108 Medellín050010 Colombia Intelligent Information Systems Lab Dpt. of Systems Engineering and Computer Science Universidad de Antioquia Calle 67 No. 53 - 108 Medellín050010 Colombia

This paper addresses the problem of fake news detection in Spanish using Machine Learning techniques. It is fundamentally the same problem tackled for the English language;however, there is not a significant amount of publicly available and adequately labeled fake news in Spanish to effectively train a Machine Learning model, similarly to those proposed for the English language. Therefore, this work explores different training strategies and architectures to establish a baseline for further research in this area. Four datasets were used, two in English and two in Spanish, and four experimental schemes were tested, including a baseline with classical Machine Learning models, trained and validated using a small dataset in Spanish. The remaining schemes include state-of-the-art Deep Learning models trained (or fine-tuned) and validated in English, trained and validated in Spanish, and fitted in English and validated with automatic translated Spanish sentences. The Deep Learning architectures were built on top of different pre-trained Word Embedding representations, including GloVe, ELMo, BERT, and BETO (a BERT version trained on a large corpus in Spanish). According to the results, the best strategy was a combination of a pre-trained BETO model and a Recurrent Neural Network based on LSTM layers, yielding an accuracy of up to 80%;nonetheless, a baseline model using a Random Forest estimator obtained similar outcomes. Additionally, the translation strategy did not yield acceptable results because of the propagation error;there was also observed a significant difference in models performance when trained in English or Spanish, mainly attributable to the number of samples available for each language. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

Enabling Conditions for the Deployment of Integrated Local Energy Communities in Europe

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Enabling Conditions for the Deployment of Integrated Local E...

Blockchain for Renewables Integration (BLORIN), Workshop on

作者： Martina Caliano Amedeo Buonanno Marialaura Di Somma Giorgio Graditi Christina Papadimitriou Chrysanthos Charalambous Przemysław Dopierała Leszek Bronk Dept. of Energy Technologies and Renewable Energy Sources ENEA Portici NA Italy Electrical Engineering dpt. Eindhoven University of Technology (TU/e) Eindhoven The Netherlands Department of Electrical and Computer Engineering University of Cyprus FOSS Research Centre for Sustainable Energy Nicosia Cyprus Department of Control Information and Communications Instytut Energetyki - Instytut Badawczy Gdansk Poland Department of Strategy and System Development Instytut Energetyki - Instytut Badawczy Gdansk Poland

ISBN: (纸本)9781665472500

The main goal of eNeuron H2020 project (Nov 2020-Oct. 2024, ID: 957779) is to develop innovative tools for the optimal design and operation of local energy communities, integrating distributed energy resources and multiple energy carriers at different scales. This paper presents a review study of the enabling conditions for the deployment of integrated local energy communities (ILECs) in Europe, performed within the project. The enabling conditions are addressed by defining the key actors and their interests in the implementation of an energy community at local level and through a detailed mapping of the enabling and emerging energy and information and communication technologies at both household and community levels. Special focus is also on mapping of the demand-side flexibility technologies to understand the benefits of local flexibility and impacts on the larger systems. In such analysis, the key issues for implementation and adaptation of this new energy paradigm are investigated through covering technological, socio-economic, environmental, and regulatory aspects.

关键词： Renewable energy sources Conferences Europe Information and communication technology Distributed power generation Blockchains

Leader-follower synchronization of a network of boundary-controlled parabolic equations with in-domain coupling

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arXiv 2022年

作者： Kabalan, Abbas Ferrante, Francesco Casadei, Giacomo Cristofaro, Andrea Prieur, Christophe Mines Paris PSL University Paris75006 France Department of Engineering University of Perugia Via G. Duranti 67 Perugia06125 Italy Laboratoire Ampere Dpt. EEA The École Centrale de Lyon Université de Lyon Ecully 69134 France Department of Computer Control and Management Engineering Sapienza University of Rome Italy Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

In this paper, we study the leader-synchronization problem for a class of partial differential equations with boundary control and in-domain coupling. We describe the problem in an abstract formulation and we specialize it to a network of parabolic partial differential equations. We consider a setting in which a subset of the followers is connected to the leader through a boundary control, while interconnections among the followers are enforced by distributed in-domain couplings. Sufficient conditions in the form of matrix inequalities for the selection of the control parameters enforcing exponential synchronization are given. Numerical simulations illustrate and corroborate the theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Partial differential equations