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Prospects for the use of wireless reading of prosthetic control signals and methods for generating commands

IOP Conference Series: Materials Science and engineering 2021年第1期1019卷

作者： D Malakhov V Zhmud V Avrmachuk D Gryaznov J Nosek L Dimitrov Department of Automation Novosibirsk State Technical University Novosibirsk Russia Department of Complex Information Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Department of Mechatronics and Computer Engineering (MTI) Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Liberec Czech Republic Vice rector Technical University of Sofia Sofia Bulgaria

The paper provides a rationale for the use of low-power wireless communication to create non-invasive bionic limb prostheses. The need for such a connection is dictated, on the one hand, by the expediency of using sensors located in various parts of the body, often very remote from each other, on the other hand, by the desire to free the disabled person's body from the wires that would envelop it like a spider web. For each of these positions, arguments are given.

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LGG-33. NETWORK ANALYSIS INTEGRATING MICRORNAS AND CLINICAL DATA TO DETERMINE PROGNOSTIC OUTCOME IN PEDIATRIC LOW-GRADE GLIOMA PATIENTS

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Neuro-Oncology 2024年第Supplement_4期26卷 0–0页

作者： Giuseppina Catanzaro Federica Conte Zein Mersini Besharat Federica D’Antonio Zaira Spinello Andrea Carai Luca Massimi Marco Gessi Dominique Figarella-Branger Paola Paci Franco Locatelli Angela Mastronuzzi Elisabetta Ferretti Department of Life Health and Health Professions Sciences Link Campus University Rome Italy Department of Experimental Medicine Sapienza University of Rome Rome Italy Institute for Systems Analysis and Computer Science ‘‘A. Ruberti’’ (IASI) National Research Council (CNR) Rome Italy Department of Oncology/Hematology Gene and Cell Therapy and Hemopoietic Transplant Bambino Gesù Children’s Hospital IRCCS Rome Italy Department of Neuroscience and Neurorehabilitation Bambino Gesù Children’s Hospital IRCCS Rome Italy Pediatric Neurosurgery Policlinico Universitario A. Gemelli Catholic University Sacro Cuore Rome Italy Department of Women Children and Public Health Sciences Policlinico Universitario A. Gemelli Catholic University Sacro Cuore Rome Italy Service d’Anatomie Pathologique Et de Neuropathologie Hôpital de La Timone Institut de Neurophysiopathologie Aix-Marseille Université AP-HM CNRS Marseille France Department of Computer Control and Management Engineering Sapienza University Rome Italy

BACKGROUND Low-grade pediatric gliomas (pLGGs) are the most common pediatric brain tumors. Surgical resection, when feasible, represents the standard of care. If complete resection cannot be achieved, disease progression occurs in about 30% of the cases, and these patients receive adjuvant therapy. The identification of predictive biomarkers to predict tumors that will progress after partial resection, requiring systemic treatment (Chemotherapy or target therapy), could support clinicians in the decision‐making strategy. MicroRNAs have been related to tumorigenesis and tumor maintenance and can be used as important biomarkers. This study aims to develop a predictive model able to determine prognostic outcomes and to better stratify pLGG patients according to disease progression.

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Achieving data utility-privacy tradeoff in internet of medical things: A machine learning approach

arXiv

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arXiv 2019年

作者： Guan, Zhitao Lv, Zefang Du, Xiaojiang Wu, Longfei Guizani, Mohsen School of Control and Computer Engineering North China Electric Power University China School of Mathematics and Physics North China Electric Power University China Department of Computer and Information Science Temple University PhiladelphiaPA United States Department of Mathematics and Computer Science Fayetteville State University FayettevilleNC United States Electrical and Computer Engineering Department University of Idaho MoscowID United States

The emergence and rapid development of the Internet of Medical Things (IoMT), an application of the Internet of Things into the medical and healthcare systems, have brought many changes and challenges to modern medical and healthcare systems. Particularly, machine learning technology can be used to process the data involved in IoMT for medical analysis and disease diagnosis. However, in this process, the disclosure of personal privacy information must receive considerable attentions especially for sensitive medical data. Cluster analysis is an important technique for medical analysis and disease diagnosis. To enable privacy-preserving cluster analysis in IoMT, this paper proposed an Efficient Differentially Private Data Clustering scheme (EDPDCS) based on MapReduce framework. In EDPDCS, we optimize the allocation of privacy budgets and the selection of initial centroids to improve the accuracy of differentially private Kmeans clustering algorithm. Specifically, the number of iterations of the K-means algorithm is set to a fixed value according to the total privacy budget and the minimal privacy budget of each iteration. In addition, an improved initial centroids selection method is proposed to increase the accuracy and efficiency of the clustering algorithm. Finally, we prove that the proposed EDPDCS can improve the accuracy of the differentially private k-means algorithm by comparing the Normalized Intra-Cluster Variance (NICV) produced by our algorithm on two datasets with two other algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： MapReduce

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Fault-tolerant Individual Pitch control using Adaptive Sliding Mode Observer

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IFAC-PapersOnLine 2018年第24期51卷 1127-1132页

作者： Liu, Yanhua Patton, Ron J. Lan, Jianglin School of Engineering and Computer Science University of Hull Cottingham Road HullHUG 7RX United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Portobello Street SheffieldS1 3JD United Kingdom

Due to the increasing size of wind turbines, the unbalanced loads caused by the uneven spatial distribution of wind speed and turbulence are becoming larger and larger. As has been proved, individual pitch control (IPC) can mitigate the blade asymmetric loads greatly in region 3. On the other hand, the pitch actuator faults can affect the pitching performance with slow dynamics, resulting in generator power instability and even deteriorating the unbalanced loads of blades. However, the effects of unbalanced blade loads deterioration caused by pitch actuator faults have not been taken into account by the traditional IPC design. In the present paper, a fault-tolerant control (FTC) strategy using adaptive sliding mode estimation is combined with a traditional IPC system based on two different control methods (Proportional-Integral and H∞ loop-shaping). It maintains the nominal pitch performance and removes the negative effects of pitch actuator faults on generator power and unbalanced blade loads perfectly. The effectiveness of the proposed strategy is verified on the 5MW NREL wind turbine system. © 2018

关键词： Adaptive control systems Actuators Deterioration Fault tolerance Sliding mode control Two term control systems Wind Wind turbines Actuator fault Adaptive sliding mode Adaptive sliding mode observer Fault tolerant control Individual pitch control Load reduction Proportional integral Sliding mode observers

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Preface

Lecture Notes in Control and Information Sciences

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Lecture Notes in control and Information Sciences 2021年 485卷 v-viii页

作者： Faulwasser, Timm Müller, Matthias A. Worthmann, Karl Department of Electrical Engineering and Information Technology Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Faculty of Electrical Engineering and Computer Science Institute of Automatic Control Leibniz University Hannover Hannover Germany Faculty of Mathematics and Natural Sciences Institute for Mathematics Optimization-based Control Technische Universität Ilmenau Ilmenau Germany

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computer vision based object grasping 6DoF robotic arm using picamera 4

Computer vision based object grasping 6DoF robotic arm using...

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4th International Conference on control, Automation and Robotics, ICCAR 2018

作者： Kumar, Vishal Wang, Qiang Minghua, Wang Rizwan, Syed Shaikh, S.M. Liu, Xuan Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9781538663387

This article presents a project to design a robust robotic arm which can perform multifunctional tasks. The controller of the manipulator is based on Arduino mega 2560 Microcontroller. The aim of the project is to focus all axes of manipulator to lift, carry and unload the objects at a desired location. This requires a precise drive motion control that incorporate electric motors as a drive system. Further experiments are done to implement a camera based 3D vision system integrated with a computer vision algorithm to recognize object deformation and spatial coordination to control the deviation from the original training. The 3D visualization systems are able to detect the objects as well as their distance from the End-effecter and transmit the signals to the drive system. The vision system requires a separate computing hardware capable of processing complex vision algorithms. We utilize Raspberry pi microcontroller for processing the vision data, separately making the vision system capable of recognizing the specified object as per program commands. © 2018 IEEE.

关键词： Robotic arms

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Extended Kalman Filtering with Low-Rank Tensor Networks for MIMO Volterra System Identification

Extended Kalman Filtering with Low-Rank Tensor Networks for ...

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IEEE Annual Conference on Decision and control

作者： Kim Batselier Ching-Yun Ko Ngai Wong Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This article reformulates the multiple-input-multiple-output Volterra system identification problem as an extended Kalman filtering problem. This reformulation has two advantages. First, it results in a simplification of the solution compared to the Tensor Network Kalman filter as no tensor filtering equations are required anymore. The second advantage is that the reformulation allows to model correlations between the parameters of different multiple-input-single-output Volterra systems, which can lead to better accuracy. The curse of dimensionality in the exponentially large parameter vector and covariance matrix is lifted through the use of low-rank tensor networks. The computational complexity of our tensor network implementation is compared to the conventional implementation and numerical experiments demonstrate the effectiveness of the proposed method.

关键词： Tensile stress Mathematical model Covariance matrices Kalman filters MIMO communication Noise measurement MISO communication

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Media and non-media WebRTC communication between a terrestrial station and a drone: the case of a flying IoT system to monitor parking

Media and non-media WebRTC communication between a terrestri...

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Agnieszka Chodorek Robert R. Chodorek Krzysztof Wajda Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology Kielce Poland Department of Telecommunications The AGH University of Science and Technology Kraków Poland

In this paper we propose a flying IoT system designed to monitor parking lot. The system is composed of a flying mobile station and a terrestrial station. The mobile station consists of visual monitoring camera associated with air quality monitoring sensors, mounted on a Turnigy SK450 quad copter controlled with Pixhawk autopilot hardware. Data gathered from the monitoring system are transmitted to an IoT broker running on a Raspberry Pi 3+ microcontroller, also mounted on a drone, that aggregates data into a common flow and retransmits them to the terrestrial station. The transmission system was build according to WebRTC architecture. In order to assure separation of the production network (for transmission of application data) and the management network (for management and control purposes), two different transmission channels are used: one for the WebRTC transmission and the other for controlling the drone. The paper includes a description of the system and the results of field trials carried out on the parking lot of the AGH University of technology.

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Predictive generalized graph fourier transform for attribute compression of dynamic point clouds

arXiv

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arXiv 2019年

作者： Xu, Yiqun Wang, Shanshe Zhang, Xinfeng Wang, Shiqi Ma, Siwei Guo, Zongming Gao, Wen Institute of Computing Technology University of Chinese Academy of Sciences National Engineering Laboratory for Video Technology Peking University Institute of Computer Science and Technology Peking University National Engineering Laboratory for Video Technology Peking University School of Computer and Control Engineering University of the Chinese Academy of Sciences Department of Computer Science City University of Hong Kong

As 3D scanning devices and depth sensors advance, dynamic point clouds have attracted increasing attention as a format for 3D objects in motion, with applications in various fields such as tele-presence, navigation for autonomous driving and heritage reconstruction. Nevertheless, the tremendous amount of data in dynamic point clouds significantly burden transmission and storage. We thus propose a complete compression framework for attributes of 3D dynamic point clouds, focusing on optimal inter-coding. Firstly, we derive the optimal inter-prediction and predictive transform coding assuming the Gaussian Markov Random Field model for attributes of dynamic point clouds, where the optimal predictive transform proves to be the Generalized Graph Fourier Transform (GGFT). Secondly, we propose refined motion estimation via efficient registration prior to inter-prediction, which searches the temporal correspondence between adjacent frames of point clouds. Finally, we construct a complete framework based on the optimal inter-coding and our previously proposed intra-coding, where we determine the optimal coding mode from rate-distortion optimization with the proposed offline-trained λ-Q model. Experimental results show that we achieve 13:4% bitrate reduction on average and up to 25:8% over the state-of-the-art Region-adaptive Hierarchical Transform method. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion estimation

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Randomised benchmarking for characterizing and forecasting correlated processes

arXiv

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arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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