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arXiv 2019年

作者： Qi, Hongsheng Mu, Biqiang Petersen, Ian R. Shi, Guodong Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Research School of Electrical Energy and Materials Engineering Australian National University Canberra0200 Australia Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering University of Sydney NSW2008 Australia

In this paper, we study the recursion of measurement outcomes for open quantum networks under sequential measurements. Open quantum networks are networked quantum subsystems (e.g., qubits) with the state evolutions described by a continuous Lindblad master equation. When measurements are performed sequentially along such continuous dynamics, the quantum network states undergo random jumps and the corresponding measurement outcomes can be described by a vector of probabilistic Boolean variables. The induced recursion of the Boolean vectors forms a probabilistic Boolean network. First of all, we show that the state transition of the induced Boolean networks can be explicitly represented through realification of the master equation. Next, when the open quantum dynamics is relaxing in the sense that it possesses a unique equilibrium as a global attractor, structural properties including absorbing states, reducibility, and periodicity for the induced Boolean network are direct consequences of the relaxing property. Particularly, we show that generically, relaxing quantum dynamics leads to irreducible and aperiodic chains for the measurement outcomes. Finally, we show that for quantum consensus networks as a type of non-relaxing open quantum network dynamics, the communication classes of the measurement-induced Boolean networks are encoded in the quantum Laplacian of the underlying interaction graph. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

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Recursive LMMSE Centralized Fusion with Compressed Multi-Radar Measurements

Recursive LMMSE Centralized Fusion with Compressed Multi-Rad...

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International Conference on Control, Automation and Information sciences (ICCAIS)

作者： Qingpeng Zhang Zhansheng Duan Uwe D. Hanebeck Center for Information Engineering Science Research Xi'an Jiaotong University Xi'an Shaanxi China Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9781728123110

ISBN: (纸本)9781728123127

For multi-sensor centralized fusion with linear measurements, simply stacking all measurements up and then applying the Kalman filter at the fusion center can give the optimal estimation performance. This optimal performance is independent of how the measurements from different sensors are stacked up. However, for centralized fusion with multiple radar measurements under the recursive LMMSE filtering framework, the performance really matters as to how to stack the measurements from different radars. In [1], we have shown that centralized fusion with stacked recombined multi-radar measurements outperforms the one with stacked original measurements under the recursive LMMSE filtering framework. In this paper, we further develop a new multi-radar centralized fusion approach by compressing all measurements first and then applying the recursive LMMSE filter with single radar measurements at the fusion center. Numerical examples show that the new centralized fusion with compressed measurements has better estimation accuracy and smaller noncredibility than the ones with stacked measurements.

关键词： Radar measurements Coordinate measuring machines Noise measurement Kalman filters Standards Estimation

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Motion control of nanomanipulation platform based on feedforward compensation inside SEM

Motion control of nanomanipulation platform based on feedfor...

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IEEE International Conference on Nano/Micro Engineered and Molecular systems

作者： Mingyu Wang Lining Sun Zhan Yang Toshio Fukuda Tao Chen Provincial Jiangsu Key Laboratory for Advanced Robotics and Collaborative Innovation Center of Suzhou Nano Science and Technology soochow university Suzhou China Department of Micro-Nano Systems Engineering Nagoya University Japan

With the rapid development and wide application of nanotechnology, nanodevice manufacturing with nanotechnology as its core has become one of the key research areas in nanotechnology. Micro-nano operating system based on scanning electron microscope (SEM) is one of the methods to realize nanodevice manufacturing. SmarAct nanomanipulation platform is an important component of micro-nano operating system, which can realize linear motion in three-dimensional space. However, due to mechanical structure and assembly errors, the SmarAct nanomanipulation platform cannot meet the stable requirements during motion. In this paper, the visual feedback and image processing are used to describe the motion trajectory of SmarAct operator. Based on the motion mechanism of SmarAct operator, the error source is analyzed, at the same time, the motion error is modeled according to the motion trajectory. The feedforward compensation is used to realize the stable linear motion of SmarAct operator, and the interpolation algorithm is used to realize the trajectory motion control of the SmarAct nanomanipulation platform.

关键词： Trajectory Scanning electron microscopy Operating systems Carbon nanotubes Interpolation Real-time systems

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Cooperative Carbon Nanotube Nanomanipulation For Field Effect Transistor

Cooperative Carbon Nanotube Nanomanipulation For Field Effec...

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IEEE International Conference on Nano/Micro Engineered and Molecular systems

作者： Donglei Chen Lining Sun Zhan Yang Toshio Fukuda Tao Chen Provincial Jiangsu Key Laboratory for Advanced Robotics and Collaborative Innovation Center of Suzhou Nano Science and Technology soochow university Suzhou China Department of Micro-Nano Systems Engineering Nagoya University Janpan

In this paper, based on the operating environment of scanning electron microscope (SEM), the cooperative control of carbon nanotubes by controlling multi-operator hand was realized by controlling the robot operator with multiple degrees of freedom. Firstly, the carbon nanotube was picked up from the bulk by the nano manipulations. Then the nanotube was transferred on the surface of the metal electrode of the chip using the two nano manipulations and fixed on by electron beam induced deposition (EBID). In this robotics manipulation system, operation and assembly of carbon nanotube were successfully realized. The voltage-current property of the carbon nanotube was also evaluated in the SEM.

关键词： Carbon nanotubes Electrodes Scanning electron microscopy Metals Robots Probes Electron beams

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Multi-Rate Asynchronous Distributed Filtering Under Randomized Gossip Strategy

Multi-Rate Asynchronous Distributed Filtering Under Randomiz...

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International Conference on Information Fusion

作者： Teng Shao Zhangsheng Duan Uwe D. Hanebeck Center for Information Engineering Science Research Xi'an Jiaotong University Xi'an Shaanxi China Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

In this paper, we consider the extension of synchronous distributed filtering under randomized gossip strategy to multi-rate asynchronous sensor network. To deal with asynchronous measurements due to multi-rate sampling, two processing schemes are proposed, i.e., a batch one and a sequential one. In batch processing, the common fusion period is chosen as the least common multiple of the sampling periods of all sensors. Each measurement of a sensor is propagated to the nearest common fusion time and saved locally. At the common fusion time, multiple propagated measurements of a sensor are compressed to a single measurement and then exchanged with its neighbors. Whereas in sequential processing, the common fusion period is chosen as the greatest common divisor of the sampling periods of all sensors to sequentially process each measurement once it is received. Depending on whether a real measurement is available locally, three exchanging strategies are developed. It is found that unlike in the traditional distributed fusion with a common fusion center, the batch and sequential processing schemes are not equivalent. Pros and cons of these two schemes are analyzed. Numerical experimental results further verify the effectiveness of the proposed schemes.

关键词： Robot sensing systems Estimation Time measurement Extraterrestrial measurements Indexes Kalman filters Noise measurement

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Fusion of metadata and dermoscopic images for melanoma detection: Deep learning and feature importance analysis

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Information Fusion 2025年 124卷

作者： Misbah Ahmad Imran Ahmed Abdellah Chehri Gwangill Jeon Centre for Machine Vision Bristol Robotics Laboratory University of West of England Frenchy Campus Bristol BS16 1QY Bristol UK Department of Animal and Agriculture Hartpury University Hartpury House Gloucester GL19 3BE Gloucestershire UK School of Computing and Information Science Anglia Ruskin University East Road Cambridge CB1 1PT Cambridgeshire UK Department of Mathematics and Computer Science Royal Military College of Canada (RMC) P.O. Box 17000 Station Forces Kingston K7K 7B4 Ontario Canada Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Incheon 22012 Incheon Republic of Korea

In the era of smart healthcare, integrating multimodal data is essential for improving diagnostic accuracy and enabling personalized care. This study presented a deep learning-based multimodal approach for melanoma detection, leveraging both dermoscopic images and clinical metadata to enhance classification performance. The proposed model integrated a multi-layer convolutional neural network (CNN) to extract image features and combined them with structured metadata, including patient age, gender, and lesion location, through feature-level fusion. The fusion process occurred at the final CNN layer, where high-dimensional image feature vectors were concatenated with processed metadata. The metadata was handled separately through a fully connected neural network comprising multiple dense layers. The final fused representation was passed through additional dense layers, culminating in a classification layer that outputted the probability of melanoma presence. The model was trained end-to-end using the SIIM-ISIC dataset, allowing it to learn a joint representation of image and metadata features for optimal classification. Various data augmentation techniques were applied to dermoscopic images to mitigate class imbalance and improve model robustness. Additionally, exploratory data analysis (EDA) and feature importance analysis were conducted to assess the contribution of each metadata feature to the overall classification. Our fusion-based deep learning architecture outperformed single-modality models, boosting classification accuracy. The presented model achieved an accuracy of 94.5% and an overall F1-score of 0.94, validating its effectiveness in melanoma detection. This study aims to highlight the potential of deep learning-based multimodal fusion in enhancing diagnostic precision, offering a scalable and reliable solution for improved melanoma detection in smart healthcare systems.

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Robust Static Output Feedback Stabilization of Continuous-Time Linear systems via Enhanced LMI Conditions

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IFAC-PapersOnLine 2020年第2期53卷 4540-4545页

作者： Hassène Gritli Ali Zemouche Safya Belghith Taous-Meriem Laleg-Kirati Higher Institute of Information and Communication Technologies Université de Carthage 1164 Borj Cedria Tunis Tunisia University of Lorraine CRAN CNRS UMR 7039 54400 Cosnes et Romain France Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis Université de Tunis El Manar BP. 37 Le Belvédère 1002 Tunis Tunisia Mathematical and Computational Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia

This paper addresses the problem of Static Output Feedback (SOF) stabilization for continuous-time linear systems subject to norm-bounded parameter uncertainties. Usually this issue leads to the feasibility of a Bilinear Matrix Inequality (BMI), which is difficult to linearize to get non conservative Linear matrix inequality (LMI) conditions. In this paper, by means of some technical lemmas, we transform the BMI into a new LMI with a line search over two scalar variables. The obtained LMI conditions are less conservative than those existing in the literature. Numerical evaluations are presented to show the superiority of the proposed method.

关键词： Continuous-time Linear systems Robust control Parametric uncertainty Static output feedback Linear matrix inequality (LMI)

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Predictive Inverse Kinematics: optimizing Future Trajectory through Implicit Time Integration and Future Jacobian Estimation

Predictive Inverse Kinematics: optimizing Future Trajectory ...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Ko Ayusawa Wael Suleiman Eiichi Yoshida CNRS-AIST JRL (Joint Robotics Laboratory) UMI13218/RL Intelligent Systems Research Institute National Institute of Advanced Industrial Science and Technology (IS-AIST) Japan Electrical and Computer Engineering Department Faculty of Engineering University of Sherbrooke Canada

This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.

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Front Cover: A Strain-engineered Helical Structure as a Self-adaptive Magnetic Microswimmer (ChemNanoMat 6/2021)

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ChemNanoMat 2021年第6期7卷

作者： Xiaojie Shi Jinrun Liu Ye Kong Dr. Borui Xu Prof. Tianlong Li Prof. Alexander A. Solovev Prof. Yongfeng Mei Department of Materials Science & State Key Laboratory of ASIC and Systems Fudan University 220 Handan Road Shanghai 200433 P. R. China School of Mechatronics Engineering & State Key Laboratory of Robotics and System Harbin Institute of Technology 92 Xi Da Zhi Jie Haerbin 150001 P. R. China

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Hand Tracking Accuracy Enhancement by Data Fusion Using Leap Motion and Myo Armband

Hand Tracking Accuracy Enhancement by Data Fusion Using Leap...

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International Conference on Unmanned systems and Artificial Intelligence

作者： Jingxiang Chen Chao Liu Rongxin Cui Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China LIRMM CNRS-University of Montpellier France School of Marine Engineering Northwestern Polytechnical University Xi'an China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728158594

ISBN: (纸本)9781728158600

In this paper, by using the combination of Leap Motion and Myo armband, two methods for hand tracking and online hand gesture identification are proposed. With the proposed methods, We have improved the measurement accuracy of the palm direction and solved the problem of insufficient accuracy when the palm is at the limit of the measurement range. We use the Kalman filter algorithm and the neural network classification method to process the data measured by Leap Motion and Myo, so that the tracking of the operator's hand gesture is more accurate and robust even when the hand is at positions close to the measurement limit of one single sensor. The methods, which improve the hand tracking accuracy, can be used for robotic control, demonstration or teleoperation. The effectiveness of the proposed methods has been demonstrated through comparative experiments.

关键词： Leap Motion Myo sensor fusion hand tracking hand guesture identification Sensor fusion Hand arm band Data fusion tracking accuracy distance measurement improving precision accuracy of measurement Classification Methods Gestures data integration

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