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

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

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

Symbolic Meta-Modeling of RIS-Assisted FSO Communication Channels

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TechRxiv

TechRxiv 2023年

作者： Ndjiongue, Alain Richard Ngatched, Telex M.N. Dobre, Octavia A. Haas, Harald Shin, Hyundong The Faculty of Engineering and Applied Science Memorial University of Newfoundland Canada The Department of Electrical and Computer Engineering McMaster University Canada The LiFi Research and Development Center Dpt. Electronic and Electrical Engineering The University of Strathclyde Glasgow United Kingdom The School of Electronics and Information Kyung Hee University 1 Seocheon Kihung Kyungki Yongin446-701 Korea Republic of

Due to their advantages and role in the beyond fifth-generation mobile network, free-space optical (FSO) communication systems continue to gain attention. The line-of-sight (LoS) link in FSO communications is fundamental as it delivers all the power detected by the user. Therefore, its obstruction is detrimental to the transmission system. In the event of an obstruction, the LoS requirements can be met by introducing the reconfigurable intelligent surface (RIS) technology. However, with the implementation of an RIS infrastructure, FSO channels are divided into two sub-channels, creating an inherent diversity end-to-end (e2e) channel model. In this paper, leveraging on the Meijer G-functions, which are symbolic representations of mathematical functions, we derive symbolic models for e2e RIS-assisted FSO systems that are built on the probability density functions (PDFs) of the fading model characterizing the sub-channels. We study some of the most popular models, derive closed-form expressions of PDFs, cumulative distributions, and moment-generating functions that are further exploited to analyze systems’ performance. By comparing scenarios in which the sub-channels are similar and different, we provide a clear understanding of how the overall RIS-assisted FSO channel behaves, along with comparative results based on commonly used models. © 2023, CC BY.

关键词： Probability density function

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

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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

Self-supervised feature learning of 1D convolutional neural networks with contrastive loss for eating detection using an in-ear microphone

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arXiv 2021年

作者： Papapanagiotou, Vasileios Diou, Christos Delopoulos, Anastasios The Multimedia Understanding Group Dpt. of Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Greece Department of Informatics and Telematics Harokopio University of Athens Greece

The importance of automated and objective monitoring of dietary behavior is becoming increasingly accepted. The advancements in sensor technology along with recent achievements in machine-learning-based signal-processing algorithms have enabled the development of dietary monitoring solutions that yield highly accurate results. A common bottleneck for developing and training machine learning algorithms is obtaining labeled data for training supervised algorithms, and in particular ground truth annotations. Manual ground truth annotation is laborious, cumbersome, can sometimes introduce errors, and is sometimes impossible in free-living data collection. As a result, there is a need to decrease the labeled data required for training. Additionally, unlabeled data, gathered in-the-wild from existing wearables (such as Bluetooth earbuds) can be used to train and fine-tune eating-detection models. In this work, we focus on training a feature extractor for audio signals captured by an in-ear microphone for the task of eating detection in a self-supervised way. We base our approach on the SimCLR method for image classification, proposed by Chen et al. from the domain of computer vision. Results are promising as our self-supervised method achieves similar results to supervised training alternatives, and its overall effectiveness is comparable to current state-of-the-art methods. Code is available at https://***/mug-auth/ssl-chewing. Copyright © 2021, The Authors. All rights reserved.

关键词： Microphones

Robust Voltage Control in DC Networks with Uncertain Nonlinear Load Dynamics

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

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

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

A letter on convergence of in-parameter-linear nonlinear neural architectures with gradient learnings

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arXiv 2021年

作者： Bukovsky, Ivo Dohnal, Gejza Benes, Peter M. Ichiji, Kei Homma, Noriyasu The Dpt. of Computer Science Faculty of Science University of South Bohemia Ceske Budejovice Czech Republic The Dpt. of Mechaniccs Biomechanics and Mechatronics Faculty of Mechanical Engineering Czech Technical University in Prague Czech Republic The Dpt. of Technical Mathematics Faculty of Mechanical Engineering Technical University Prague Czech Republic The Tohoku University Graduate School of Medicine 2-1 Seriyo-machi Aoba-ku Sendai980-8575 Japan

This letter summarizes and proves the concept of bounded-input bounded-state (BIBS) stability for weight convergence of a broad family of in-parameter-linear nonlinear neural architectures as it generally applies to a broad family of incremental gradient learning algorithms. A practical BIBS convergence condition results from the derived proofs for every individual learning point or batches for real-time applications. © 2021, CC BY-NC-ND.

关键词： Network architecture

Towards a Blockchain and Opportunistic Edge Driven Metaverse of Everything

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arXiv 2024年

作者： Fraga-Lamas, Paula Lopes, Sérgio Ivan Fernández-Caramés, Tiago M. Dpt. of Computer Engineering Centro de investigación CITIC Faculty of Computer Science Universidade da Coruña A Coruña15071 Spain ADiT-Lab Instituto Politécnico de Viana do Castelo Viana do Castelo4900-348 Portugal CiTin Centro de Interface Tecnológico Industrial Arcos de Valdevez4970-786 Portugal IT—Instituto de Telecomunicações Campus Universitário de Santiago Aveiro3810-193 Portugal

Decentralized Metaverses, built on Web 3.0 and Web 4.0 technologies, have attracted significant attention across various fields. This innovation leverages blockchain, Decentralized Autonomous Organizations (DAOs), Extended Reality (XR) and advanced technologies to create immersive and interconnected digital environments that mirror the real world. This article delves into the Metaverse of Everything (MoE), a platform that fuses the Metaverse concept with the Internet of Everything (IoE), an advanced version of the Internet of Things (IoT) that connects not only physical devices but also people, data and processes within a networked environment. Thus, the MoE integrates generated data and virtual entities, creating an extensive network of interconnected components. This article seeks to advance current MoE, examining decentralization and the application of Opportunistic Edge Computing (OEC) for interactions with surrounding IoT devices and IoE entities. Moreover, it outlines the main challenges to guide researchers and businesses towards building a future cyber-resilient opportunistic MoE. © 2024, CC BY.

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Scientific Contribution of Sharjah-Sat-1 to X-ray Observations 28

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Scientific Contribution of Sharjah-Sat-1 to X-ray Observatio...

28th IAA Symposium on Small Satellite Missions 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Kalemci, Emrah Manousakis, Antonios Fernini, Ilias Al Naimiy, Hamid Bozkurt, Ayhan Aslan, A. Rüstem Altingün, A. Mürteza Veziroǧlu, Kaan Yalçin, Refik Gökalp, Kaya Diba, Milad Yaşar, Ali Oztaban, Efe Karabulut, Boǧaç Oztekin, Onur Farouk, Yousuf Alsabt, Ibrahim AlKaabi, Tarifa Shaikh, Muhammad Mubasshir Madara, Sahith Reddy Faculty of Engineering and Natural Sciences Sabanci University Turkey Dpt. of Applied Physics and Astronomy Sharjah Academy for Astronomy Space Sciences and Technology University of Sharjah United Arab Emirates Space Systems Design and Test Laboratory Istanbul Technical University Turkey Sharjah Academy for Astronomy Space Sciences and Technology University of Sharjah United Arab Emirates ISAE-Supaero University of Toulouse France

Sharjah-Sat-1 is currently being developed as a collaborative research project among the Sharjah Academy for Astronomy, Space Sciences, and Technology (SAASST), University of Sharjah (UoS), Istanbul Technical University (ITU), and Sabanci University (SU). A 3U CubeSat design has been adopted with a dual payload onboard: (i) an improved X-ray Detector (iXRD) and (ii) a system of two optical cameras. The mission's primary scientific targets are the bright, hard X-ray sources in our Galaxy and the solar coronal holes. A complimentary payload, consisting of the two optical cameras, will serve as a low-resolution remote sensing application. This project's main technological aim is to develop a CubeSat, from A to Z, operational in the electromagnetic spectrum's hard X-ray regime. The Sharjah-Sat-1 would be the first CubeSat mission to be developed by the SAASST team and UoS students to not only design, fabricate, test, and launch the CubeSat itself but also building the capacities and expertise necessary for future CubeSat missions as well. The anticipated launch is planned for late Q2-2022. The primary science payload onboard is the iXRD (developed by Sabanci University) with a pixelated CdZnTe-based crystal as the active material and a Tungsten collimator with a field of view of 4.26 degrees. The energy range is from 20 keV to 200 keV with a target spectral resolution of 6 keV at 60 keV. Its primary science goal is to observe the very bright galactic hard X-ray sources, transient and persistent. Black hole candidates and pulsars can emit radiation up to a few 100 keVs, making them potential targets. Besides, solar observations will be conducted to study hard X-ray spectra of flares and coronal holes. Other opportunity targets are transient bright events, such as gamma-ray bursts (GRB) and magnetar bursts. A complete in-orbit background analysis has been conducted. While the detector's effective area is only 6.5 cm2, it is expected to reach a sensitivity of 60 mCrab in a

关键词： X ray detectors