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Annual IEEE Symposium on computer-Based Medical systems

作者： Enrique Fernández-Morales Carlos L. Sanchez-Bocanegra Rafael Pastor-Vargas José-Juan Pereyra-Rodriguez Juan M. Haut José Alberto Benítez-Andrades Communications and Control Systems Dpt. Computer Science Faculty UNED Madrid Spain Communications and Control Systems Dpt. Computer Science Faculty UNED Malaga Spain Virgen Del Rocio Hospital Universitario Seville Spain School of Tecnology University of Extremadura Cáceres Spain Dept. of Electric Systems and Automatics Engineering Universidad de León León Spain

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.

关键词： Accuracy Melanoma Collective intelligence Image capture Transformers Solids Artificial intelligence

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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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Decision Support Framework for Automating the Optimization of Edge Computing Federations

Decision Support Framework for Automating the Optimization o...

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Antonio F. Rodríguez-Liria Román Cárdenas Patricia Arroba José M. Moya José L. Risco-Martín Gabriel Wainer Laboratorio de Sistemas Integrados (LSI) CCS–Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Dpt. of Systems and Computer Engineering Carleton University Ottawa ON Canada

Modern IoT applications present demanding Quality of Service (QoS) requirements that can no longer rely on a centralized solution to offload compute-intensive tasks. Edge Computing tackles these limitations by extending the Cloud to the edge of the network. However, the distributed and complex nature of Edge infrastructures introduces new technical challenges that must be addressed to develop a successful solution. Modeling, Simulation, and Optimization (M&S&O) tools can aid in the automatic design of modern Edge deployments. Here we present a decision support module that allows practitioners to automatically improve aspects of their Edge infrastructure by applying different optimization algorithms, such as simulated annealing, tabu search, and stochastic hill climbing. Furthermore, the structure of this module allows us to extend the available optimization methods if needed. We illustrate how this tool can help us to improve energy consumption policies of Edge Computing facilities and reduce their operational costs.

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Results From a Large-Scale IoT-based Intervention for Energy Saving and Sustainability Awareness via Behavior Change in 25 K-12 Schools in Europe

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IEEE Internet of Things Journal 2025年

作者： Mylonas, Georgios Amaxilatis, Dimitrios Paganelli, Federica Chatzigiannakis, Ioannis Koulouris, Pavlos Falkouskaya, Yelizaveta Markopoulos, Panos Athena Research Center Industrial Systems Institute Patras26504 Greece Comp. Tech. Institute and Press "Diophantus" 26504 Greece Spark Works Ltd GalwayH91 AHR1 Ireland Univ. of Pisa Dpt. of Computer Science Pisa56127 Italy CNIT Florence50134 Italy Sapienza University of Rome Dpt. of Computer Control and Management Engineering 00185 Italy Ellinogermaniki Agogi Athens Greece Eindhoven University of Technology AI Systems Institute Industrial Design Noord-Brabant Eindhoven5612 AE Netherlands

The unfolding climate crisis has resulted in a rising interest for increasing sustainability awareness and achieving energy savings worldwide. Several interventions within educational environments have been aimed at mobilizing younger audiences towards such goals. However, most of the interventions carried out so far are based on a subset of possible tools (e.g., IoT monitoring, gamification) and offer "all or nothing"approaches, which do not cope well with the uniqueness of schools and the need to adapt interventions to specific contexts (e.g. school size, building, curriculum, teacher involvement). We present a large-scale intervention in 25 school buildings in Europe over the course of two school years. Our intervention was based on the installation of a relatively low-cost IoT infrastructure in schools, that produced real-time energy-related data from the school buildings involved, and the application of a strategy combining, in a flexible and customizable way, educational activities, monitoring tools, gamification, and competition to motivate behavior change and achieve energy savings. Overall, 2983 students and 204 educators were directly involved to a variable degree of participation. Our results indicate a positive overall result in terms of short-term energy savings in most schools involved, with an average reduction of 20-25% in the energy that could be affected by end-users, as well as a 32.9% increase after the intervention in students self-reporting good or very good sustainability awareness. These findings suggest that such interventions can be a valuable step towards educating young students regarding sustainability and towards sustainable schools, if designed and implemented properly. © 2014 IEEE.

关键词： Behavior Change Education for Sustainable Development Energy Literacy Energy Saving Environmental Education Gamification Internet of Things K-12 Sustainability Sustainable Smart Schools

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The DEVStone Metric: Performance Analysis of DEVS Simulation Engines

arXiv

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arXiv 2023年

作者： Cárdenas, Román Henares, Kevin Arroba, Patricia Risco-Martín, José L. Wainer, Gabriel A. Integrated Systems Laboratory Universidad Politécnica de Madrid Madrid Spain Dept. of Systems and Computer Engineering Carleton University OttawaON Canada Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain

The DEVStone benchmark allows us to evaluate the performance of discrete-event simulators based on the DEVS formalism. It provides model sets with different characteristics, enabling the analysis of specific issues of simulation engines. However, this heterogeneity hinders the comparison of the results among studies, as the results obtained on each research work depend on the chosen subset of DEVStone models. We define the DEVStone metric based on the DEVStone synthetic benchmark and provide a mechanism for specifying objective ratings for DEVS-based simulators. This metric corresponds to the average number of times that a simulator can execute a selection of 12 DEVStone models in one minute. The variety of the chosen models ensures we measure different particularities provided by DEVStone. The proposed metric allows us to compare various simulators and to assess the impact of new features on their performance. We use the DEVStone metric to compare some popular DEVS-based simulators. © 2023, CC BY-SA.

关键词： Discrete event simulation

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Multi-Objective Genetic Algorithm for the Joint Optimization of Energy Efficiency and Rule Reduction in Software-Defined Networks 11

Multi-Objective Genetic Algorithm for the Joint Optimization...

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11th International Conference on Network of the Future, NoF 2020

作者： Galan-Jimenez, Jaime Berrocal, Javier Herrera, Juan Luis Polverini, Marco University of Extremadura Dpt. of Computer Systems and Telematics Engineering Spain University of Rome 'Sapienza' Diet Department Rome Italy

ISBN: (纸本)9781728180557

Although the use of Ternary Content-Addressable Memories (TCAMs) in the flow tables of the Software-Defined Network (SDN) switches increases the efficiency of packets matching procedure, drawbacks such as their large power consumption and the limitation on the number of flow rules that can be installed must be taken into account. This paper tackles the joint problem of power consumption and TCAM size limitation in SDN. By exploiting the Rate Adaptation technique and compression methods, a Multi-Objective Genetic Algorithm is proposed to practically solve it. Simulations on a real network topology show that our proposed solution outperforms other state-of-The-Art approaches, both in terms of power saving gains (20% at non-peak TM) and maximum TCAM utilization (5%). © 2020 IEEE.

关键词： Energy efficiency

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Recognition of food-texture attributes using an in-ear microphone

arXiv

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arXiv 2021年

作者： Mars, Monica Diou, Christos Papapanagiotou, Vasileios van den Boer, Janet Delopoulos, Anastasios Multimedia Understanding Group Dpt. Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Greece Department of Informatics and Telematics Harokopio University of Athens Greece Dpt. of Biomedical Signals and Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Division of Human Nutrition and Health Wageningen University Netherlands

Food texture is a complex property;various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness. Copyright © 2021, The Authors. All rights reserved.

关键词： Wearable technology

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Robust Voltage Control in DC Networks with Uncertain Nonlinear Load Dynamics

Robust Voltage Control in DC Networks with Uncertain Nonline...

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IEEE Conference on Decision and Control

作者： Amirreza Silani Giacomo Casadei Michele Cucuzzella Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen AG Groningen The Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Laboratoire Ampere Dpt. EEA of the Ecole Centrale de Lyon Université de Lyon Ecully France Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this letter, we propose a control scheme for regulating the voltage in Direct Current (DC) power networks. In contrast with other works in the literature where the loads are assumed to be constant, we consider uncertain time-varying loads described as the outputs of nonlinear dynamical exosystems with uncertain parameters. Based on the output regulation methodology, the proposed control scheme ensures the robust stability of the overall network and achieves voltage regulation in presence of impedance (Z), current (I), and power (P) loads. The simulation results illustrate excellent performance of the proposed control scheme.

关键词： Robust stability Simulation Regulation Impedance Voltage control

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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

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Fake news detection in Spanish using deep learning techniques

arXiv

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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

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