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IEEE International Conference on Big Data

作者： Francesco Lamonaca Domenico Luca Carnì Dept. of Computer Science Modelling Eletctronics and Systems Engineering University of Calabria Arcavacata di Rende Italy

This paper would provide a comprehensive review of the synergistic integration of measurement science and artificial intelligence (AI) in the realm of smart agriculture. As agriculture undergoes a transformative phase toward precision and data-driven practices, the paper explores the critical role of measurement science in providing accurate data, while AI processes and analyzes this data to facilitate intelligent decision-making in smart agricultural systems.

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Modular Hardware, Open Source Software and Training Material for PLC Training

Modular Hardware, Open Source Software and Training Material...

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Harsha Priyanka Guntaka Rajesh Kushalkar Nirmala Venkat Akshay Chipkar Vishnu Easwaran Kannan Moudgalya Dept. of Systems & Control Engineering IIT Bombay Mumbai India Dept. of Computer Science & Enginering IIT Bombay Mumbai India Dept. of Chemical Engineering IIT Bombay Mumbai India

ISBN: (纸本)9781665490498

A low cost, open source, rugged, modular OpenPLC, based on ATmega16 microcontroller, has been developed for instructional purposes. Modularity permits flexible use of the OpenPLC. The open source software LDmicro, created for ladder logic operations, has been enhanced, and ported to Linux. A total of 27 Spoken Tutorials of about 10 minute duration each, suitable for self learning, are created for OpenPLC training. A pilot workshop is conducted for 16 teachers of polytechnic colleges using the Spoken Tutorials and the simulation capability of LDmicro. The feedback from the workshop is positive, and the method is suitable for a large scale training.

关键词： Training Mechatronics Costs Automation Microcontrollers Conferences Linux

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Optimizing Energy and QoS in VANETs through Approximate Computation on Heterogeneous MPSoC

Optimizing Energy and QoS in VANETs through Approximate Comp...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Ahmed A. J. Al-Hchaimi Abdul Hadi M. Alaidi Yousif Raad Muhsen Mohammed F. Alomari Nasri Bin Sulaiman Mamika Ujianita Romdhini Dept. of Electromechanical Systems Engineering Southern Technical University Basra Iraq Dept. of Computer Science and Information Technology Universiti Tenaga National Kuala Lumpur Malaysia Dept. of Computer Science Universiti Putra Malaysia Kuala Lumpur Selangor Malaysia Dept. of Electrical and Electronic Engineering Universiti Putra Malaysia Kuala Lumpur Malaysia Dept. of Mathematics University of Mataram Mataram Indonesia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

This paper proposes a new algorithm called Dynamic Optimization of Energy and QoS in VANETs (DOEQ-VANET), designed to maximize the energy efficiency and Quality of Service (QoS) of the Vehicular Ad-hoc Networks (VANETs). Through exploiting the heterogeneity of Multiprocessor System-on-Chip (MPSoC) employing an approximate computation, DOEQ-VANET is capable of dynamically adjusting the computational accuracy for non-critical applications which results in energy conservation without the risk of propagating the misinformation. Task assignment and scheduling are continuously dynamically adjusted as they respond to network fluctuation, vehicle dynamics and energy availability. The simulation result and validation proved the power of the proposed algorithm, which would mark a large advancement toward more green and smart vehicular networks within the field of Intelligent Transport systems (ITS). This algorithm is crucial for improving the sustainability and efficiency of future transportation systems.

关键词： Heuristic algorithms Green products Vehicular ad hoc networks Transportation Quality of service Approximation algorithms Prediction algorithms

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Sustainable Cultural Tourism Strategy and Promotion: An ICT Management Platform

Sustainable Cultural Tourism Strategy and Promotion: An ICT ...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Kashyap Raiyani Shabnam Pasandideh Sajib Ahmed Pedro Pereira João Martins Dept. Electrical and Computer Engineering Center of Technology and Systems-UNINOVA NOVA School of Science and Technology NOVA University Lisbon Caparica Portugal

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Cultural Tourism (CT) is a significant element of today’s economy, accounting for around 37% of the total tourist industry and expanding at a pace of over 15% each year. This function and economic impact can benefit some EU and non-EU locations and areas with high cultural, social, and environmental potential. Other synergistic variables such as know-how, Information Communication Technologies (ICTs), gastronomy, identity, local culture, values, intangible legacy, or other characteristics also contribute to this influence. The work presented in this paper is part of the Social Innovation and TEchnologies for Sustainable Growth through Participative Cultural TOURism (TExTOUR) project, which brings together partners from the quintuple social innovation helix (knowledge, business, society, government, and entrepreneurs) to co-design, validate, and scale up policies and strategies that have a positive impact on socioeconomic territorial development based on cultural tourism. TExTOUR collaborates with eight CT Pilots in lesser-known destinations to develop collaborative work methodologies for developing CT strategies for local sites, utilizing ICTs and social innovation tools. The CT-Labs assist stakeholders in putting CT ad hoc strategies and action plans into action, monitoring them, and validating them. As a result, a technological platform (ICT tool) is presented in this paper, with its components outlined.

关键词： Industries Technological innovation Federated learning Government Information and communication technology Cultural differences Global communication Stakeholders Socioeconomics Monitoring

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Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics 46

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Mod...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Siviero, Ilaria Helmhold, Florian Ray, Andreas M. Bibián, Carlos Menegaz, Gloria Murguialday, Ander Ramos Storti, Silvia F. Dept. of Computer Science University of Verona Verona Italy Inst. of Medical Psychology and Behavioral Neurobiology University of Tübingen Tübingen Germany Gtc of Neuroscience Imprs for Cognitive and Systems Neuroscience Tübingen Germany Dept. of Engineering for Innovation Medicine University of Verona Verona Italy Tecnalia Basque Research and Technology Alliance San Sebastian Spain Dept. of Neurology and Stroke University of Tübingen Tübingen Germany Athenea Neuroclinics San Sebastian Spain

ISBN: (纸本)9798350371499

Technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like electromyographic (EMG) and inertial measurement unit (IMU) data are crucial for decoding continuous human actions. Classical modeling methods, such as linear, adaptive, or static filters, lack the capacity to capture complex relationships between surface EMG and kinematics, as well as generalizability across subjects. We propose a deep learning (DL) model for the continuous decoding of hand motion. Custom-made wearable devices acquired EMG-IMU data from 27 healthy subjects performing a wrist flexion/extension task. Two regression models were compared with a hybrid convolutional neural network and a gated recurrent unit. To address inter-subject variability, a leave-one-subject-out cross-validation approach was implemented. The DL model showed a mean R2 increase of 0.18 compared to the polynomial regressor. Our approach enhances wrist kinematics decoding providing a generalized model based on data captured with wearable devices. The findings hold potential for innovative home-based telemedicine solutions in motor rehabilitation. © 2024 IEEE.

关键词： Adaptive filters

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Energy Consumption Model for LoRaWAN via Field Experiments for Smart Agriculture

Energy Consumption Model for LoRaWAN via Field Experiments f...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Satoshi Yamazaki Taiki Ogura Dept. of Control and Computer Engineering National Institute of Technology Numazu College Numazu Japan Dept. of Electrical Electronics and Information Engineering Nagaoka University of Technology Niigata Japan

This paper proposes a scheme to model the energy consumption of LoRaWAN, which is a popular example of low-power wide-area networks (LPWANs), nodes via the results of outdoor field experiments by assuming regional smart agriculture as a use case for internet of things (IoT). Specifically, we derive an experimental approximation formula to estimate the battery lifetime by introducing parameters such as spread factor and payload length. The validity of the proposed scheme is demonstrated by confirming that the results obtained by the approximate formula, the experimental and theoretical results generally agree, regardless of the node state and spread factor. Furthermore, we show that the obtained approximate formula can be used to identify the current consumption value in the sleep state that should be achieved to achieve the desired battery lifetime.

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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An Adaptive CUSUM Approach for Automating Sleep Apnoea Analysis Based on Pulse and Oximetry Data 20

An Adaptive CUSUM Approach for Automating Sleep Apnoea Analy...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yang, Dongjin Bhargava, Eishaan Elphick, Heather Mihaylova, Lyudmila S. University of Sheffield Dept. of Automatic Control and Systems Engineering United Kingdom Sheffield Children's Hospital Department of Paediatric Otolaryngology Sheffield United Kingdom Sheffield Children's Hospital Department of Paediatric Respiratory and Sleep Medicine Sheffield United Kingdom

ISBN: (纸本)9798350320831

Sleep apnoea is a common sleep disorder during human sleep. It is usually diagnosed by a doctor after recording one nights' sleep signals. Patients have to go to the hospital to record sleep signals, which is time-consuming and resource-intensive. The study focused on two signals, pulse data and oximetry data, with the aim of detecting apnoea using a single signal. This paper introduced an anomaly detection approach using the adaptive cumulative sum (ACUSUM) change point detection algorithm to monitor outliers in the signal. In addition, the test results of ACUSUM will be compared with the test results of classical CUSUM. Besides, the threshold selection has been changed from an unchanging constant to a value related to the standard deviation of the selected signal based on a rational subgroup process. The results of the comparison confirm that ACUSUM is better than classical CUSUM in the accuracy of automatic detection. © 2023 IEEE.

关键词： Anomaly detection

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Novel Adaptive Dynamic Event-Triggered Bipartite Consensus Protocols with Intermittent Updating and Interaction

Novel Adaptive Dynamic Event-Triggered Bipartite Consensus P...

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2021 China Automation Congress, CAC 2021

作者： Cheng, Bin Lv, Yuezu Li, Zhongkui Dept. of Control Science and Engineering Tongji University Shanghai China Dept. of Systems Science Southeast University Nanjing China Dept. of Mechanics and Engineering Science Peking University Beijing China

ISBN: (纸本)9781665426473

This paper addresses the distributed bipartite consensus control problem faced with some intricate demands, namely, discrete-time control updating for each agent, intermittent interaction among neighbors, and the fully distributed fashion of the protocol design requiring no global information of the network topology. Given that no existing results meet all the three essential demands, we in this paper establish a unified framework on fully distributed adaptive bipartite consensus control with intermittent updating and interaction. The core is a novel dynamic triggering function decoupled with the network topology, based on which a discretely updated controller for each agent is designed accordingly. Then, adaptive dynamic event-triggered protocols can be modularly designed with the two-component framework. Finally, a numerical example is provided to verify the effectiveness of the presented protocols. © 2021 IEEE

关键词： Network topology

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Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications

Resource Efficient Federated Learning for Deep Anomaly Detec...

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International Conference on Digital Signal Processing (DSP)

作者： Alexandros Gkillas Aris Lalos Athena Research Center Industrial Systems Institute Greece Dept. of Computer Engineering & Informatics University of Patras

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%.

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