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12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Tsinganos, Panagiotis Cornelis, Jan Cornelis, Bruno Jansen, Bart Skodras, Athanassios University of Patras Dept. of Electrical and Computer Engineering Patras Greece Vrije Universiteit Brussel Dept. of Electronics and Informatics Brussels Belgium Imec Leuven3001 Belgium

ISBN: (纸本)9781665400329

The latest advancements in the field of deep learning and biomedical engineering have allowed for the development of myoelectric interfaces based on deep neural networks. A longstanding problem of these interfaces is that the models cannot easily be applied to new users due to the high variability and stochastic nature of the electromyography signals. Further training a new model for every new subject requires the collection of large volumes of data. Therefore, this work proposes a transfer learning (TL) scheme which allows reusing the knowledge of a pre-existing model for a new user. Firstly, a convolutional neural network (CNN) is trained on an initial dataset using the data of multiple subjects. Then, the weights of this model are fine-tuned for a new target subject. The approach is evaluated on the Ninapro datasets DB2 and DB7. The experimentation included three different CNN models and eight preprocessing alternatives. The results showed that the success of the TL method depends on how the data are preprocessed. Specifically, the biggest accuracy improvement (+5.14%) is achieved when only the first 20% of the signal duration is used. © 2021 IEEE.

关键词： Convolutional neural networks

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Action Recognition with Spatiotemporal Analysis and Support Vector Machine for Elderly Monitoring System 23

Action Recognition with Spatiotemporal Analysis and Support ...

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23rd International Electronics Symposium, IES 2021

作者： Awal, Mahaputra Ilham Iksan, Luqmanul Hakim Fhamy, Rizky Zull Basuki, Dwi Kurnia Sukaridhoto, Sritrusta Wada, Kazuyoshi Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Electronic Engineering Surabaya Indonesia Tokyo Metropolitan University Faculty of Systems Design Tokyo Japan

ISBN: (纸本)9781665443463

Nowadays, action recognition is widely used in various aspects of life, one of which is for monitoring purposes. One of the challenges in this problem is the process of detecting and recognizing the actions of the elderly. In this research, an integrated system is proposed to detect and monitor the actions of the elderly based on computer Vision. This system using joint skeleton coordinate data as a system input. The system can detect actions taken by the elderly based on the dataset we created. There are seven actions: standing, sitting, walking, falling, drinking, sleeping, and eating. This system uses the OpenPose library to detect skeletons from the elderly. Then for feature extraction using spatiotemporal to extract the joint position data of the elderly every 15 frames. For classification using the Support Vector Machine in this system. Then, the recognized action data will be sent by the system to the backend of the platform. The test shows that the system can detect seven actions of the elderly with an accuracy value of 87.81% on the training model used. © 2021 IEEE.

关键词： Support vector machines

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Horizontal scaling method for a hyperconverged network

Horizontal scaling method for a hyperconverged network

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2021 International Conference on Information and Digital Technologies, IDT 2021

作者： Kovalenko, Andriy Kuchuk, Heorhii Kuchuk, Nina Kostolny, Jozef Kharkiv National University of Radio Electronics Faculty of Computer Engineering and Control Kharkiv Ukraine National Technical University KhPI Faculty of Computer and Information Technologies Kharkiv Ukraine Faculty of Management Science and Informatics Univeraity of Zilina Zilina Slovakia

ISBN: (纸本)9781665436922

Today cloud platforms are gradually being replaced by hyperconverged. With a hyperconverged infrastructure, the servers, networks, storage and computing power are combined. This is done through specific software. The main advantage of hyperconverged network is to reduce operating costs. However, system performance can be reduced due to the standardization of hardware modules and centralization of control, and, consequently, QoS deteriorates. At the same time, companies that operate computer systems are attracted by the ability to quick horizontal scaling and lower operating costs. Hyperconverged networks often use addition of a new hardware modules to meet QoS requirements using a simple horizontal scaling implementation. The purpose of the paper is to develop such a horizontal scaling method, which allows decreasing transaction processing time while total cost of data transmission does not exceed the specified values. The method also allows determining the optimal characteristics of additional hardware. © 2021 IEEE.

关键词： Resource allocation

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Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 827-838页

作者： Kui, Xiaoyan Ji, Zexin Zou, Beiji Li, Yang Dai, Yulan Chen, Liming Vera, Pierre Ruan, Su Central South University School of Computer Science and Engineering Changsha 410083 China Henri Becquerel Cancer Center Department of Nuclear Medicine Rouen France Hunan University of Chinese Medicine School of Informatics Changsha 410208 China Ecole Centrale de Lyon Department of Mathematics and Computer Science Ecully 69130 France University of Rouen-Normandy AIMS - QuantIF UR 4108 Rouen F-76000 France

High-resolution medical images can provide more detailed information for better diagnosis. Conventional medical image super-resolution relies on a single task which first performs the extraction of the features and then upscaling based on the features. The features extracted may not be complete for super-resolution. Recent multi-task learning, including reconstruction and super-resolution, is a good solution to obtain additional relevant information. The interaction between the two tasks is often insufficient, which still leads to incomplete and less relevant deep features. To address above limitations, we propose an iterative collaboration network (ICONet) to improve communications between tasks by progressively incorporating reconstruction prior to the super-resolution learning procedure in an iterative collaboration way. It consists of a reconstruction branch, a super-resolution branch, and a SR-Rec fusion module. The reconstruction branch generates the artifact-free image as prior, which is followed by a super-resolution branch for prior knowledge-guided super-resolution. Unlike the widely-used convolutional neural networks for extracting local features and Transformers with quadratic computational complexity for modeling long-range dependencies, we develop a new residual spatial-channel feature learning (RSCFL) module of two branches to efficiently establish feature relationships in spatial and channel dimensions. Moreover, the designed SR-Rec fusion module fuses the reconstruction prior and super-resolution features with each other in an adaptive manner. Our ICONet is built with multi-stage models to iteratively upscale the low-resolution images using steps of 2x and simultaneously interact between two branches in multi-stage supervisions. Quantitative and qualitative experimental results on the benchmarking dataset show that our ICONet outperforms most state-of-the-art approaches. © 2015 IEEE.

关键词： medical imaging multi-task learning reconstruction Super-resolution

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Real-time Vital Signs Monitoring Based on COTS WiFi Devices

Real-time Vital Signs Monitoring Based on COTS WiFi Devices

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Gu, Yu Zhang, Xiang Yan, Huan Liu, Zhi Ji, Yusheng Hefei University of Technology School of Computer and Information Hefei China The University of Electro-Communications Dept. of Computer and Network Engineering Tokyo Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo Japan

ISBN: (纸本)9781665401265

Real-time vital signs (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Current solutions are mostly based on wearable sensors or cameras, the former affects the quality of sleep, while the latter is not conducive to privacy protection, and the cost of these methods is usually expensive. In this paper, we propose Wital, a real-time vital signs monitoring system based on the low-cost and widespread COTS WiFi device. Most of the existing WiFi-based vital signs monitoring solutions utilize the line of sight (LOS) WiFi signals to achieve powerful performance. However, in our daily environments, NLOS sensing is more common. In this article, we first model the relationship between the energy ratio of LOS/NLOS signals and the ability to monitor vital signs based on the Ricean-K theory and theoretically prove that blocking LOS signals in NLOS sensing is more beneficial. We have also established a real-time vital signs monitoring system to verify our method, and the experimental results prove the effectiveness of our method. © 2021 IEEE.

关键词： Wi-Fi

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Review of battery management systems

Review of battery management systems

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2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2021

作者： Darwish, Mohamed Ioannou, Stelios Janbey, Alan Amreiz, Hassan Marouchos, Christos C. Brunel University Department of Electronic and Electrical Engineering London United Kingdom University of Central Electrical and Electronic Engineering Lancashire Pyla Larnaca Cyprus London College UCK Division of Engineering London United Kingdom Cyprus University of Technology Electrical Engineering Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9781665412629

In this work the authors investigate the different parts and functions offered by Battery Management Systems (BMS) specifically designed for secondary/rechargeable lithium batteries. Compared to other chemistries, lithium batteries offer high energy density and cell voltage, which makes them the most attractive choice for electronic devices including EV and RES. However, lithium technology is vulnerable and highly susceptible to catastrophic failures which result in fire. Hence, the use of electronic safety designs is a must. BMS are responsible for the monitoring of the battery state, ensuring operation within safe limits. BMS offer multiple functionalities with the state of charge (SoC) estimation being the most challenging hence the most studied by engineers. All estimation methodologies and algorithms have pros and cons, which best suits the application that is developed for. This research concludes that according to designers, the optimum BMS provides battery packs with the needed protection, good functioning conditions and accurate prediction for the battery's state including charge and life. Finally, this research presents and validates an SoC algorithm based on the reformulated Peukert's equation which is also valid for variable load and multi-pulsing scenarios with an accuracy exceeding 95%. © 2021 IEEE.

关键词： Battery management systems

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LearnedFTL: A Learning-based Page-level FTL for Reducing Double Reads in Flash-based SSDs

arXiv

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arXiv 2023年

作者： Wang, Shengzhe Lin, Zihang Wu, Suzhen Jiang, Hong Zhang, Jie Mao, Bo Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Fujian Xiamen China Computer Science and Engineering Department University of Texas at Arlington ArlingtonTX United States School of Computer Science Peking University Beijing China

We present LearnedFTL, a new on-demand pagelevel flash translation layer (FTL) design, which employs learned indexes to improve the address translation efficiency of flashbased SSDs. The first of its kind, it reduces the number of double reads induced by address translation in random read accesses. LearnedFTL proposes three key techniques: an in-place-update linear model to build learned indexes efficiently, a virtual PPN representation to obtain contiguous PPNs for sorted LPNs, and a group-based allocation and model training via GC/rewrite strategy to reduce the training overhead. By tightly integrating the aforementioned key techniques, LearnedFTL considerably speeds up address translation while reducing the number of flash read accesses caused by the address translation. Our extensive experiments on a FEMU-based prototype show that LearnedFTL can reduce up to 55.5% address translation-induced double reads. As a result, LearnedFTL reduces the P99 tail latency by 2.9× ∼ 12.2× with an average of 5.5× and 8.2× compared to the state-of-the-art TPFTL and LeaFTL schemes, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Flash-based SSDs

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Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Mondal, Abhishek Mishra, Deepak Alexandropoulos, George C. Al-Nahari, Azzam Jantti, Riku National Institute of Technology Calicut Department of Electronics and Communication Engineering Kerala 673601 India University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia National and Kapodistrian University of Athens Department of Informatics and Telecommunications Athens15784 Greece University of Illinois Department of Electrical and Computer Engineering ChicagoIL60601 United States Aalto University Department of Information and Communications Engineering Espoo02150 Finland Ibb University Department of Electrical Engineering Ibb70270 Yemen

Effective solutions for intelligent data collection in terrestrial cellular networks are crucial, especially in the context of Internet of Things (IoT) applications. The limited spectrum and coverage area of terrestrial base stations (BSs) pose challenges in meeting the escalating data rate demands of network users. Unmanned aerial vehicles (UAVs), known for their high agility, mobility, and flexibility, present an alternative means to offload data traffic from terrestrial BSs, serving as additional access points. In this paper, we aim to efficiently maximize the utilization of multiple UAVs for data traffic offloading from terrestrial BSs. Specifically, the focus is on maximizing user association with UAVs by jointly optimizing UAV trajectories and users' association indicators under quality-of-service (QoS) constraints. Since the formulated practical UAV control problem is non-convex and combinatorial, this study leverages the multi-agent reinforcement learning (MARL) framework. Employing MARL, each UAV acts as an independent agent, aiming to maintain inter-UAV cooperative behaviour. The proposed approach utilizes the finite-state Markov decision process (MDP) to account for UAVs' velocity constraints and the relationship between their trajectories and state space. A low-complexity distributed state-action-reward-state-action (SARSA) algorithm is presented to determine UAVs' optimal sequential decision-making policies over training episodes. The extensive simulation results validate the proposed analysis and offer valuable insights into the optimal UAV trajectories. The derived trajectories demonstrate superior average UAV association performance compared to benchmark techniques, such as Q-learning and particle swarm optimization. © 1965-2011 IEEE.

关键词： Markov processes

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A note on the complexity of S4.2

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Journal of Applied Non-Classical Logics 2021年第2期31卷 108-129页

作者： Chalki, Aggeliki Koutras, Costas D. Zikos, Yorgos Computation Reasoning Laboratory (CoReLab) School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Informatics and Telecommunications University of Peloponnese Tripolis Greece Graduate Programme in Algorithms Logic Discrete Mathematics (ALMA) Department of Informatics Telecommunications National and Kapodistrian University of Athens Athens Greece

(Formula presented.) is the modal logic of directed partial pre-orders and/or the modal logic of reflexive and transitive relational frames with a final cluster. It holds a distinguished position in philosophical logic, where it has been advocated as the ‘correct’ logic of knowledge;it has also found interesting applications in the temporal logic of relativistic spacetime and the metamathematics of forcing in set theory. The satisfiability problem for (Formula presented.) is (Formula presented.) -complete: this is a result established in an AiML 2004 paper of Shapirovsky [(2004). On PSPACE-decidability in transitive modal logic. In R. A. Schmidt, I. Pratt-Hartmann, M. Reynolds, & H. Wansing (Eds.), Advances in modal logic 5 (pp. 269–287). King's College Publications] where the complexity classification of (Formula presented.) emerges as a consequence of a very general method for constructing (Formula presented.) decision procedures for transitive modal logics. We provide here a ‘classical’ proof in the standard Halpern-Moses style of Halpern and Moses [(1992). A guide to completeness and complexity for modal logics of knowledge and belief. Artificial Intelligence, 54(2), 319–379]. With little extra work, it is shown that the (Formula presented.) -completeness result extends to (Formula presented.), the multimodal version of (Formula presented.). We prove that the (Formula presented.) -completeness characterisation of monomodal (Formula presented.) persists even if we restrict ourselves to fragments with bounded modal depth, but the problem is (Formula presented.) -complete when it is restricted to formulae with modal depth at most one. The complexity of (Formula presented.) satisfiability for the fragment of the language with a finite number of propositional variables (but unbounded modal depth) remains (Formula presented.) -hard. For a finite language and bounded modal depth, (Formula presented.) -satisfiability can be checked in linear time. © 2021 Informa UK Limi

关键词： complexity of multimodal logics Modal logic S4.2 logic tableaux proof procedures

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Simulation of An Intelligent Detection System Virus SARS-CoV-2 in Enclosed Spaces 16

Simulation of An Intelligent Detection System Virus SARS-CoV...

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16th International Conference on Electronics computer and Computation, ICECCO 2021

作者： Moldabayeva, Zhanar S. Schmidt, Peter Seitbattalov, Zhexen Y. Amankeldinov, Tanzharyk A. L.N. Gumilyov Eurasian National University Department of Computer and Software Engineering Nur-Sultan Kazakhstan University of Economics in Bratislava Department of Applied Informatics Bratislava Slovakia

ISBN: (纸本)9781665409452

During the COVID-19 pandemic, methods to determine the presence of coronavirus are very relevant. In this article, a new method has been developed to detect the presence of the SARS-CoV-2 virus in the air. The article analyzes in detail the signs indicating favorable conditions for the spread of the virus. The influence of changes in temperature, humidity, the presence of particles in the air, the level of CO2 and the ventilation of the room on the probability of infection with the virus is *** on the studied parameters, a model was built. It allows to detect the probability of the threat of the spread of the virus. The model was implemented as a fuzzy controller in the Matlab environment. The studied parameters were taken as input parameters for constructing the model. The output parameter was the detector value indicating the presence or absence of a virus in the ***, the built system allows detecting situations in which coronavirus infection is most likely. The system makes a conclusion about the degree of risk of the spread of the virus and, in case of danger, informs about the need for disinfection actions. The use of this system will reduce the cost of processing enclosed spaces, since it will not be carried out according to schedule, but in the case of a system signal. © 2021 IEEE.

关键词： COVID-19

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