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作者： Joel Reis Pedro Batista Paulo Oliveira Carlos Silvestre Faculty of Science and Technology University of Macau Taipa Macao China Institute for Systems and Robotics LARSyS Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal LAETA - Associated Laboratory for Energy Transports and Aeronautics IDMEC - Institute of Mechanical Engineering Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Instituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (纸本)9781665427883

This paper presents an attitude estimation methodology for robotic platforms equipped with a set of rate gyros that can implicitly measure the Earth’s rotation rate. As is well known, standard strategies for attitude determination rely on explicit body-fixed measurements of at least two inertial reference vectors. In this work, the Earth’s spin is regarded as one of two constant inertial reference vectors. However, because that spin is only implicitly measured, we first implement a Kalman filter to obtain an estimate of it. This filter also yields: unbiased estimates of the second inertial reference vector, which is explicitly measured in the body-fixed frame; and estimates of two biases related to the sensor measurements. The platform’s rotation matrix is then computed using a Kalman-based observer evolving on the manifold $SO(3)$ that is driven by the output of the first filter. Realistic simulation results are presented to showcase the performance of the proposed technique in two different scenarios.

关键词： Manifolds Simulation Sea measurements Estimation Observers Robot sensing systems Gyroscopes

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MCOA: A Multistrategy Collaborative Enhanced Crayfish Optimization Algorithm for Engineering Design and UAV Path Planning

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International Journal of Intelligent Systems 2025年第1期2025卷

作者： Yaning Xiao Hao Cui Ruba Abu Khurma Abdelazim G. Hussien Pedro A. Castillo School of Automation and Intelligent Manufacturing Southern University of Science and Technology Shenzhen 518055 China *** College of Mechanical and Electrical Engineering Northeast Forestry University Harbin 150040 China *** Information Technology Department Al-Huson University College Al-Balqa Applied University Irbid 19117 Jordan bau.edu.jo Department of Computer and Information Science Linköping University Linköping 58183 Sweden liu.se Faculty of Science Fayoum University Faiyum 63514 Egypt fayoum.edu.eg Department of Computer Engineering Automatics and Robotics University of Granada Granada 18071 Spain ugr.es

The crayfish optimization algorithm (COA) is a recent bionic optimization technique that mimics the summer sheltering, foraging, and competitive behaviors of crayfish. Although COA has outperformed some classical metaheuristic (MH) algorithms in preliminary studies, it still manifests the shortcomings of falling into local optimal stagnation, slow convergence speed, and exploration–exploitation imbalance in addressing intractable optimization problems. To alleviate these limitations, this study introduces a novel modified crayfish optimization algorithm with multiple search strategies, abbreviated as MCOA. First, specular reflection learning is implemented in the initial iterations to enrich population diversity and broaden the search scope. Then, the location update equation in the exploration procedure of COA is supplanted by the expanded exploration strategy adopted from Aquila optimizer (AO), endowing the proposed algorithm with a more efficient exploration power. Subsequently, the motion characteristics inherent to Lévy flight are embedded into local exploitation to aid the search agent in converging more efficiently toward the global optimum. Finally, a vertical crossover operator is meticulously designed to prevent trapping in local optima and to balance exploration and exploitation more robustly. The proposed MCOA is compared against twelve advanced optimization algorithms and nine similar improved variants on the IEEE CEC2005, CEC2019, and CEC2022 test sets. The experimental results demonstrate the reliable optimization capability of MCOA, which separately achieves the minimum Friedman average ranking values of 1.1304, 1.7000, and 1.3333 on the three test benchmarks. In most test cases, MCOA can outperform other comparison methods regarding solution accuracy, convergence speed, and stability. The practicality of MCOA has been further corroborated through its application to seven engineering design issues and unmanned aerial vehicle (UAV) path planning tasks

关键词： crayfish optimization algorithm engineering optimization expanded exploration strategy Lévy flight specular reflection learning vertical crossover operator

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Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular robotics

arXiv

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arXiv 2025年

作者： Yao, Tianliang Ban, Madaoji Lu, Bo Pei, Zhiqiang Qi, Peng Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Electrical and Electronic Engineering Faculty of Engineering The University of Hong Kong 999077 Hong Kong Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Jiangsu Suzhou China School of Oriental Pan-Vascular Devices Innovation College University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of Cardiovascular Diseases Medical Innovation Center Shanghai East Hospital School of Medicine Tongji University Shanghai200092 China

Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes. © 2025, CC BY.

关键词： Reinforcement learning

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Electrooculography signal as alternative method to operate wheelchair based on SVM classifier

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AIP Conference Proceedings 2024年第1期2891卷

作者： Muhammad Ilhamdi Rusydi Amimul Ummah Baiqi Muhammad Arief Rahman Adam Jordan Hermawan Nugroho Kojiro Matsushita Syafii Yuli Afmi Ropita Sari Noverika Windasari Agung Wahyu Setiawan Joseph Muguro Minoru Sasaki Electrical Engineering Department Faculty of Engineering Universitas Andalas Padang Indonesia Tech Mahindra Central Jakarta Indonesia Electrical and Electronic Engineering Department University of Nottingham Malaysia Selangor Malaysia Department of Mechanical Engineering Faculty of Engineering Gifu University 1-1 Yanagido Gifu Japan College of Health Science Mercubaktijaya Padang City Indonesia Medical Science Education Medical Faculty Universitas Andalas Padang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Center for Robotics and Biomedical Engineering Department of Electrical & Electronic Engineering Dedan Kimathi University of Technology Private Bag Nyeri 10143 Kenya

People with impairment conditions have some difficulties with daily activities such as controlling a wheelchair. Biosignal is one of the promising methods as an alternative signal to build communication between humans and machines. In this study, an alternative method to control a wheelchair is proposed. A prototype wheelchair is controlled using an EOG signal based on an SVM classifier. Four types of gaze motions: right; left; up and down are clustered using the one versus one rule. The SVM is trained using 356 data and tested with 100 data. The trained SVM has an accuracy of 0.99 for all types of gaze motions. Precision of the up and down gaze motion is 0.96, while the right and left gaze motion has a precision of 1.00. The trained SVM is implemented for real-time control of the wheelchair prototype. Seven participants controlled the wheelchair and moved inside a trajectory from the start to the finish points. The result shows that participants could operate the wheelchair using the EOG signal, even though they had difficulties because of comfort and accuracy issues of the system.

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Deep Learning Techniques for Future Intelligent Cross-Media Retrieval

arXiv

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arXiv 2020年

作者： ur Rehman, Sadaqat Waqas, Muhammad Tu, Shanshan Koubaa, Anis ur Rehman, Obaid Ahmad, Jawad Hanif, Muhammad Han, Zhu Department of Computer Science Namal Institute - an associated college of University of Bradford United Kingdom Faculty of Information Technology Beijing University of Technology Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi23460 Pakistan Robotics and Internet-of-Things research lab Department of Computer Science Prince Sultan University R&D Gai-tech Robotics China CISTER/INESC TEC and ISEP-IPP Porto Portugal Department of EE Sarhad University of Science and IT Pakistan Department of Computer Science Edinburgh Napier University United Kingdom Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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RECENT ADVANCEMENT APPROACH FOR PRECISION AGRICULTURE

RECENT ADVANCEMENT APPROACH FOR PRECISION AGRICULTURE

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作者： Tomislaw, Golębiowski Tadeusz, Juliszewski Pawel, Kielbasa Sylwia, Tomecka-Suchoń Uhl, Tadeusz Faculty of Environmental Engineering Cracow University of Technology ul. Warszawska 24 Cracow31-155 Poland Faculty of Production and Power Engineering University of Agriculture in Krakow ul. Balicka 116 b Cracow30-149 Poland Faculty of Geology Geophysics and Environmental Protection AGH University of Science and Technology al. Mickiewicza 30 Cracow30-059 Poland Department of Robotics and Mechatronics AGH University of Science and Technology ul. Reymonta 9 Cracow30-059 Poland

Precision farming is a technology for the implementation of technical equipment, mainly from area of automation and robotics, coupled with each other (tractors, combine harvesters, machines) for the production biological raw materials. New methods of information technology (IT) integrate tractors, com-bine harvesters, and machines in an technological system, allow to achieve autonomous robotized systems. The development of IT technology in precision agriculture allows achieving high quality of production and reduction of the production costs and laboring input. The paper presents results of researches done by authors on development of agriculture equipment. © 2019, Springer Nature Switzerland AG.

关键词： Soil moisture

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Sensitive Data Transmission System for Biopotential Measurements in Wearable Application

Sensitive Data Transmission System for Biopotential Measurem...

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International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Dominik Grochala Marcin Kajor Michał Grochala Paweł Smoleń Marek Iwaniec Justyna Lemejda Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Kraków Poland sp. z o.o Nordic Semiconductor Poland Kraków Poland Smart Nanotechnologies S.A. Alwernia Poland Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Kraków Poland Department of Prosthodontics Jagiellonian University Medical College Jagiellonian University Kraków Poland

ISBN: (数字)9781728171807

ISBN: (纸本)9781728171814

In this paper we present a wearable biopotential measurement system with expanded data safety feature. The proposed solution combines dry electrodes as a biopotential interface and Bluetooth Low Energy (BLE) transmission which together with a pairing feature based on NFC aim to provide reliable yet safe data transfer. Electrodes were prepared as conductive pads made of composite with Multiwall Carbon Nanotubes (MWCNT) filler. Such a material is convenient for integration with textiles and provides required electrical properties. The main module of the system's electronic part is based on nRF52832 microcontroller with embedded BLE radio supporting Near Field Communication (NFC). The main focus of this paper is the development and testing of safety mechanisms in a short distance communication that can be implemented in medical data transfer.

关键词： sensitive data transfer wearables NFC dry electrodes Wearable negative feedback circuits short-range communication Wheat Sensitive data data transmission Electrodes Transmission systems

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A Survey on Computer Vision based Human Analysis in the COVID-19 Era

arXiv

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arXiv 2022年

作者： Eyiokur, Fevziye Irem Kantarcı, Alperen Erakın, Mustafa Ekrem Damer, Naser Ofli, Ferda Imran, Muhammad Križaj, Janez Salah, Albert Ali Waibel, Alexander Štruc, Vitomir Ekenel, Hazım Kemal Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Engineering Istanbul Technical University Istanbul Turkey Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Qatar Computing Research Institute HBKU Doha Qatar Faculty of Electrical Engineering University of Ljubljana Tržaška cesta 25 Ljubljana1000 Slovenia Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Engineering Boğaziçi University Istanbul Turkey Carnegie Mellon University Pittsburgh United States

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for comp

关键词： COVID-19

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A low voltage, flexible, graphene-based electrothermal heater for wearable electronics and localized heating applications

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Materials Today: Proceedings 2020年 33卷 1840-1844页

作者： Sandra A.N Tembei Amr Hessein Ahmed M.R Fath El-Bab Ahmed A El-Moneim Department of Materials Science and Engineering Egypt-Japan University of Science and Technology New Borg El Arab City Alexandria 21934 Egypt Department of Mathematical and Physical Engineering Faculty of Engineering (Shoubra) Benha University Cairo 11614 Egypt Department of Mechatronics and Robotics Egypt-Japan University of Science and Technology New Borg El Arab City Alexandria 21934 Egypt Graphene Center for Electronic and Energy Applications Egypt Japan University of Science and Technology New Borg El Arab City Alexandria 21934 Egypt Basic and Applied Sciences Institute Egypt-Japan University of Science and Technology New Borg El Arab City Alexandria 21934 Egypt

Recent years have seen a rapid increase in the level of sophistication in modern day devices which has given rise to the demand for better performance in all of their components, one of which is the heating element. Two excellent approaches to this need would be to improve the materials from which these Joule’s heating elements are made and the other to design improved heater geometries for best temperature distribution. In this paper, we discuss a high performance electrothermal heater prepared from laser reduced graphene oxide (LrGO) deposited on Polyethylene Terephthalate (PET) flexible Substrate. The surface morphology and structural properties of the prepared LrGO films were investigated by means of Scanning Electron Microcopy (SEM), X-Ray Diffraction (XRD) and Raman Spectroscopy (RS). Electrothermal (ET) responses of the fabricated electrothermal heaters to different driving DC voltages were studied by Infrared thermal Imagery. An electrothermal heater with a low Sheet resistance of ∼52 O/square was fabricated and it can attained a steady state temperature of up to 135 °C in only 10 s when a low voltage of 9 V was applied. A finite Element model (FEM) was prepared for this heater which agreed well with the experimental results. Power consumption for this heater is as low as 0.389 W/cm 2 , making it a suitable candidate for energy-saving applications such as wearable electronic.

关键词： Flexible electronics Photothermal reduction Graphene oxide Wearable Low voltage Electrothermal heaters

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Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding

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NMR in Biomedicine 2024年第3期37卷 e5063-e5063页

作者： Chen, Shihui Chu, Mei-Lan Liang, Liyuan Liu, Yi-Jui Chen, Nan-Kuei Wang, He Juan, Chun-Jung Chang, Hing-Chiu Department of Biomedical Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Pokfulam Hong Kong Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Biomedical Engineering University of Arizona TucsonAZ United States Brain Imaging and Analysis Center Duke University Medical Center DurhamNC United States Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Ministry of Education Shanghai China Human Phenome Institute Fudan University Shanghai China Department of Medical Imaging China Medical University Hsinchu Hospital Hsinchu Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan Multi-Scale Medical Robotics Center Shatin Hong Kong

Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2. Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time a

关键词： Image reconstruction

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