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A novel swarm intelligence algorithm and its application in solving wireless sensor networks coverage problems

Journal of Networks 2012年第12期7卷 2037-2043页

作者： Mo, Yuanbin Liu, Jizhong Wang, Baolei Jonathan Wu, Q.M. College of Mathematics and Computer Science Guangxi University for Nationalities Nanning 530006 China School of Mechanical and Electrical Engineering Institute of Robotics of Nanchang University Nanchang University Nanchang 330031 China Department of Electrical and Computer Engineering Computer Vision and Sensing Systems Laboratory University of Windsor Windsor N9B3P4 Canada

Wireless sensor networks (WSNs) have attracted a great deal of research due to their wide-range of potential applications. Sensor deployment and coverage problems are their important issues. This article briefly introduces the principle of swarm intelligence (SI). A novel SI algorithm based on information sharing of Particle Swarm Optimization (PSO) and diversity maintenance mechanism of Artificial Immune System (AIS) is designed and the model of coverage problems is given. Its applications in solving different deterministic and random coverage problems are given. The algorithm used in obtaining maximum coverage probability with given number of sensor nodes and minimum number of sensor nodes with required coverage probability of WSNs deterministic coverage, and determining the selected sensor nodes with coverage probability and connectivity requirement of WSNs random coverage, are analyzed in detail. The simulation results show the algorithm is practical. The applications of SI on K- coverage and connectivity problems in the future are also projected in the article. © 2012 ACADEMY PUBLISHER.

关键词： Swarm intelligence

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Autonomous I-AUV docking for fixed-base manipulation 19

Autonomous I-AUV docking for fixed-base manipulation

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19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Palomeras, Narcís Ridao, Pere Ribas, David Vallicrosa, Guillem Computer Vision and Robotics Research Institute Scientific and Technological Park of the University of Girona CIRS Building C/Pic de Peguera Girona CataloniaCO 17071 Spain

ISBN: (纸本)9783902823625

While commercially available autonomous underwater vehicles (AUVs) are routinely used in survey missions, a new set of applications exist demanding intervention capabilities. This is the case, for instance, of the maintenance of permanent underwater observatories or submerged oil wells. These tasks, currently undertaken by remotely operated vehicles (ROVs), can be automated using intervention AUVs (I-AUVs) reducing their complexity and costs. The TRITON spanish funded project proposes the use of light I-AUV for autonomous intervention tasks, such as valve turning or connector pluging/unpluging, in adapted sub-sea infrastructures. To this aim, this paper presents the design and implementation of an I-AUV-friendly sub-sea docking panel, as well as the vision-based autonomous docking procedure for the Girona 500 lightweight I-AUV. The panel implements a funnel-based docking method for passive accommodation. It also includes a T valve and a custom designed hot stab connector. Once docked, the I-AUV and the panel become rigid and basic fixed-base manipulation strategies can be used for manipulation. © IFAC.

关键词： Autonomous underwater vehicles

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Preliminary Study on Slack-Free Model Predictive Control of Twisted String-Based Antagonistic Joints

Preliminary Study on Slack-Free Model Predictive Control of ...

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Nonlinearity, Information and robotics, (NIR) International Conference

作者： Oleg Balakhnov Simeon Nedelchev Igor Gaponov Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187648

In this work, we propose a model predictive controller (MPC) for simultaneous position and tension tracking in twisted string actuator-based antagonistic joints. The proposed controller takes into account unidirectional nature of the cables and minimize trajectory tracking error as well as control effort. The approach has been evaluated numerically in a series of simulations that demonstrated its feasibility and highlighted its advantage over the unconstrained controller, with position tracking accuracy improved by a factor of 10. The proposed MPC can be generalized and serve as the starting point for antagonistic control in multiple-degree-of-freedom systems based on twisted string actuators.

关键词： Actuators Trajectory tracking Predictive models Control systems Numerical models Robots Predictive control

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IMU-based Parameter Identification and Position Estimation in Twisted String Actuators

IMU-based Parameter Identification and Position Estimation i...

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Simeon Nedelchev Daniil Kirsanov Igor Gaponov Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

This study proposes a technique to estimate the output state of twisted string actuators (TSAs) based on payload's acceleration measurements. We outline differential kinematics relationships of the actuator, re-formulate these into a nonlinear parameter identification problem and then apply linearization techniques to efficiently solve it as a quadratic program. Using accurate estimates of string parameters obtained with the proposed method, we can predict TSA position with sub-millimeter accuracy via conventional kinematic relationships. In addition, the proposed method supports accurate estimation under varying operating conditions, unpredictable perturbations, and poorly-excited trajectories. This technique can be employed to improve the accuracy of trajectory tracking when the use of direct position measurements is challenging, with the list of potential applications including flexible and soft robots, long-span cable robots, multi-DOF joints and others.

关键词： Actuators Parameter estimation Trajectory tracking Estimation Kinematics Soft robotics Trajectory

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Graph Neural Networks for modelling breast biomechanical compression

arXiv

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arXiv 2024年

作者： Awwad, Hadeel García, Eloy Martí, Robert Computer Vision and Robotics Institute University of Girona Girona Spain

Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise alignment and improved analysis. Although Finite Element Analysis (FEA) is reliable for approximating soft tissue deformation, it struggles with balancing accuracy and computational efficiency. Recent studies have used data-driven models trained on FEA results to speed up tissue deformation predictions. We propose to explore Physics-based Graph Neural Networks (PhysGNN) for breast compression simulation. PhysGNN has been used for data-driven modelling in other domains, and this work presents the first investigation of their potential in predicting breast deformation during mammographic compression. Unlike conventional data-driven models, PhysGNN, which incorporates mesh structural information and enables inductive learning on unstructured grids, is well-suited for capturing complex breast tissue geometries. Trained on deformations from incremental FEA simulations, PhysGNN's performance is evaluated by comparing predicted nodal displacements with those from finite element (FE) simulations. This deep learning (DL) framework shows promise for accurate, rapid breast deformation approximations, offering enhanced computational efficiency for real-world scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Tissue

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DCGANs for realistic breast mass augmentation in X-Ray mammography

arXiv

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arXiv 2019年

作者： Alyafi, Basel Diaz, Oliver Marti, Robert Computer Vision and Robotics Institute University of Girona Sabadell Spain

Early detection of breast cancer has a major contribution to curability, and using mammographic images, this can be achieved non-invasively. Supervised deep learning, the dominant CADe tool currently, has played a great role in object detection in computer vision, but it suffers from a limiting property: the need of a large amount of labelled data. This becomes stricter when it comes to medical datasets which require high-cost and time-consuming annotations. Furthermore, medical datasets are usually imbalanced, a condition that often hinders classifiers performance. The aim of this paper is to learn the distribution of the minority class to synthesise new samples in order to improve lesion detection in mammography. Deep Convolutional Generative Adversarial Networks (DCGANs) can efficiently generate breast masses. They are trained on increasing-size subsets of one mammographic dataset and used to generate diverse and realistic breast masses. The effect of including the generated images and/or applying horizontal and vertical flipping is tested in an environment where a 1:10 imbalanced dataset of masses and normal tissue patches is classified by a fully-convolutional network. A maximum of ∼ 0.09 improvement of F1 score is reported by using DCGANs along with flipping augmentation over using the original images. We show that DCGANs can be used for synthesising photo-realistic breast mass patches with a considerable diversity. It is demonstrated that appending synthetic images in this environment, along with flipping, outperforms the traditional augmentation method of flipping solely, offering faster improvements as a function of the training set *** Codes 68U10 (Primary) 68U20 (Secondary) Copyright © 2019, The Authors. All rights reserved.

关键词： Mammography

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Comparative Analysis of Linear and Exact Control Laws in Zonotope-Based MPC

Comparative Analysis of Linear and Exact Control Laws in Zon...

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School on Dynamics of Complex Networks and their Application in Intellectual robotics (DCNAIR)

作者： Ali Jnadi Sergei Savin Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (数字)9798350390834

ISBN: (纸本)9798350390841

Zonotopes are a special type of symmetric polytopes that have been an emerging control design tool, being used for such tasks as state estimation and explicit model predictive control. Zonotopes are used to represent sets of control actions and states, encountered by trajectories of the system, originating from a set of initial conditions. However, an inherent limitation of zonotope geometry (their symmetries, for example) limits the range of control design scenarios where zonotopes can be used as an adequate set representation, while the use of linear control laws can prevent change in the desired behavior of the closed-loop system. The first issue can be remedied by the use of constrained zonotopes, which can represent arbitrary polytopes. In this paper we present an analysis of the second problem, demonstrating the effects of linear control law and exact, convex programmingbased control law. We demonstrate the efficacy of the use of the lifting-up method for constrained zonotopes as pertains to control design.

关键词： Geometry Control design Complex networks Trajectory Dynamic programming Quadratic programming Closed loop systems State estimation Predictive control

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Survival prediction using ensemble tumor segmentation and transfer learning

arXiv

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arXiv 2018年

作者： Cabezas, Mariano Valverde, Sergi González-Villà, Sandra Clérigues, Albert Salem, Mostafa Kushibar, Kaisar Bernal, Jose Oliver, Arnau Lladó, Xavier Research Institute of Computer Vision and Robotics University of Girona Spain

Segmenting tumors and their subregions is a challenging task as demonstrated by the annual BraTS challenge. Moreover, predicting the survival of the patient using mainly imaging features, while being a desirable outcome to evaluate the treatment of the patient, it is also a difficult task. In this paper, we present a cascaded pipeline to segment the tumor and its subregions and then we use these results and other clinical features together with image features coming from a pretrained VGG-16 network to predict the survival of the patient. Preliminary results with the training and validation dataset show a promising start in terms of segmentation, while the prediction values could be improved with further testing on the feature extraction part of the network. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Bioinspired Musculoskeletal Model-based Soft Wrist Exoskeleton for Stroke Rehabilitation

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Journal of Bionic Engineering 2020年第6期17卷 1163-1174页

作者： Ning Li Tie Yang Yang Yang Peng Yu Xiujuan Xue Xingang Zhao Guoli Song Imad HElhajj Wenxue Wang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Itelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Bejing 100049China Rehabilitation Center for the Disabled Shenyang 110015China Vision and Robotics Lab Department of Electrical and Computer EngineeringAmerican University of BeirutBeirut 11072020Lebanon Emerging Technologies Instinute Department of Industrial&Manufacturing Systems EngineeringUniversity of Hong KongPokfulamHong Kong 999077China

Exoskeleton robots have demonstrated the potential to rehabilitate stroke ***,poor human-machine physiological coupling causes unexpected damage to human of muscles and ***,inferior humanoid kinematics control would restrict human natural *** to deal with these problems results in bottlenecks and hinders its *** this paper,the simplified muscle model and muscle-liked kinematics model were proposed,based on which a soft wrist exoskeleton was established to realize natural human ***,we simplified the redundant muscular system related to the wrist joint from ten muscles to four,so as to realize the human-robot physiological ***,according to the above human-like musculoskeletal model,the humanoid distributed kinematics control was established to achieve the two DOFs coupling kinematics of the *** results show that the wearer of an exoskeleton could reduce muscle activation and joint force by 43.3%and 35.6%,***,the humanoid motion trajectories similarity of the robot reached 91.5%.Stroke patients could recover 90.3%of natural motion ability to satisfy for most daily *** work provides a fundamental understanding on human-machine physiological coupling and humanoid kinematics control of the exoskeleton robots for reducing the post-stroke complications.

关键词： stroke rehabilitation simplified muscle model soft wrist exoskeleton robot bionic kinematics model

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Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors

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computers in Biology and Medicine 2024年 179卷 108811-108811页

作者： Clèrigues, Albert Valverde, Sergi Oliver, Arnau Lladó, Xavier Institute of Computer Vision and Robotics University of Girona Spain Tensor Medical Girona Spain

Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer's disease or multiple sclerosis. However, its use in individualized assessments is currently discouraged due to a series of technical and biological issues. In this work, we present a deep learning pipeline for segmentation-based brain atrophy quantification that improves upon the automated labels of the reference method from which it learns. This goal is achieved through tissue similarity regularization that exploits the a priori knowledge that scans from the same subject made within a short interval must have similar tissue volumes. To train the presented pipeline, we use unlabeled pairs of T1-weighted MRI scans having a tissue similarity prior, and generate the target brain tissue segmentations in a fully automated manner using the fsl_anat pipeline implemented in the FMRIB Software Library (FSL). Tissue similarity regularization is enforced during training through a weighted loss term that penalizes tissue volume differences between short-interval scan pairs from the same subject. In inference, the pipeline performs end-to-end skull stripping and brain tissue segmentation from a single T1-weighted MRI scan in its native space, i.e., without performing image interpolation. For longitudinal evaluation, each image is independently segmented first, and then measures of change are computed. We evaluate the presented pipeline in two different MRI datasets, MIRIAD and ADNI1, which have longitudinal and short-interval imaging from healthy controls (HC) and Alzheimer's disease (AD) subjects. In short-interval scan pairs, tissue similarity regularization reduces the quantification error and improves the consistency of measured tissue volumes. In the longitudinal case, the proposed pipeline shows reduced variability of atrophy measures and higher effect sizes of differences in annualized rates bet

关键词： Pipelines

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