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WRC Symposium on Advanced robotics and Automation (WRC SARA)

作者： Peng Ba Pengyi Wang Hongde Wu Qian Yang Yongqiang Feng Junchen Wang Yida David Hu Changsheng Li Wenyong Liu Shaolong Kuang Bai-Quan Su Medical Robotics Laboratory School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Plastic Surgery Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Hair direction is an important external feature of hair, and recognising hair direction is a prerequisite for processing hair. In this paper, a new algorithm is proposed and systematically verified experimentally for the problem of recognising hair direction. The main goal of this paper is to develop an algorithm that can identify hair direction in a complex image environment. A curve segment analysis method based on image skeletonisation is adopted, which is based on skeleton extraction, intersection identification, curve segmentation and direction prediction. In addition, this paper combines the technique of non-maximal value suppression and PCA analysis to improve the accuracy and stability of the estimation. In the experimental design, this paper chooses a representative image dataset to verify the effectiveness of this paper's algorithm. The experimental process includes the steps of image preprocessing, skeletonisation processing, intersection detection and merging, and direction prediction. The experimental results show that the method in this paper can accurately and effectively identify the hair direction. The main contribution of this paper is to propose a new hair direction recognition method and experimentally verify its effectiveness and accuracy in complex backgrounds.

关键词： Hair Image segmentation Accuracy Three-dimensional displays Merging Prediction algorithms Stability analysis

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Grand Challenges in the Verification of Autonomous systems

arXiv

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arXiv 2024年

作者： Leahy, Kevin Asgari, Hamid Dennis, Louise A. Feather, Martin S. Fisher, Michael Ibanez-Guzman, Javier Logan, Brian Olszewska, Joanna I. Redfield, Signe Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Complex and Autonomous Systems Research Group Thales UK Research Reading United Kingdom Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Department of Computer Science University of Manchester Manchester United Kingdom Renault Guyancourt France Department of Computing Science University of Aberdeen Aberdeen United Kingdom Department of Information and Computing Sciences Utrecht University Utrecht Netherlands School of Computing and Engineering University of the West of Scotland Glasgow United Kingdom U.S. Naval Research Laboratory WashingtonDC United States

Autonomous systems use independent decisionmaking with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE robotics and Automation Society Technical Committee for Verification of Autonomous systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them. © 2024, CC BY.

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Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition

arXiv

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arXiv 2024年

作者： Bian, Shirui Wang, Zezhou Leng, Siyang Lin, Wei Shi, Jifan School of Mathematical Sciences Fudan University Shanghai200433 China Shanghai Center for Mathematical Sciences Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Fudan University Shanghai200032 China

Early-warning signals of delicate design are always used to predict critical transitions in complex systems, which makes it possible to render the systems far away from the catastrophic state by introducing timely interventions. Traditional signals including the dynamical network biomarker (DNB), based on statistical properties such as variance and autocorrelation of nodal dynamics, overlook directional interactions and thus have limitations in capturing underlying mechanisms and simultaneously sustaining robustness against noise perturbations. This paper therefore introduces a framework of causal network markers (CNMs) by incorporating causality indicators, which reflect the directional influence between variables. Actually, to detect and identify the tipping points ahead of critical transition, two markers are designed: CNM-GC for linear causality and CNM-TE for non-linear causality, as well as a functional representation of different causality indicators and a clustering technique to verify the system’s dominant group. Through demonstrations using benchmark models and real-world datasets of epileptic seizure, the framework of CNMs shows higher predictive power and accuracy than the traditional DNB indicator. It is believed that, due to the versatility and scalability, the CNMs are suitable for comprehensively evaluating the systems. The most possible direction for application includes the identification of tipping points in clinical disease. Copyright © 2024, The Authors. All rights reserved.

关键词： Autocorrelation

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Frank Kulwa Oluwarotimi Williams Samuel Mojisola Grace Asogbon Tolulope Tofunmi Oyemakinde Olumide Olayinka Obe Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Computer Science Federal University of Technology Akure Nigeria

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.

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Holographic MIMO Communications with Arbitrary Surface Placements: Near-Field LoS Channel Model and Capacity Limit

arXiv

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arXiv 2023年

作者： Gong, Tierui Wei, Li Huang, Chongwen Yang, Zhijia He, Jiguang Debbah, Mérouane Yuen, Chau School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates KU 6G Research Center Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits. Empowered by the nearly continuous, large and energy-efficient surfaces with powerful electromagnetic (EM) wave control capabilities, H-MIMO opens up the opportunity for signal processing in a more fundamental EM-domain, paving the way for realizing holographic imaging level communications in supporting the extremely high spectral efficiency and energy efficiency in future networks. In this article, we propose a generalized EM-domain near-field channel modeling and study its capacity limit of point-to-point H-MIMO systems that equips arbitrarily placed surfaces in a line-of-sight (LoS) environment. Two effective and computational-efficient channel models are established from their integral counterpart, where one is with a sophisticated formula but showcases more accurate, and another is concise with a slight precision sacrifice. Furthermore, we unveil the capacity limit using our channel model, and derive a tight upper bound based upon an elaborately built analytical framework. Our result reveals that the capacity limit grows logarithmically with the product of transmit element area, receive element area, and the combined effects of 1/d2mn, 1/d4mn, and 1/d6mn over all transmit and receive antenna elements, where dmn indicates the distance between each transmit element n and receive element m. Particularly, 1/d6mn dominates in the near-field region whereas 1/d2mn dominates in the far-field region. Numerical evaluations validate the effectiveness of our channel models, and showcase the slight disparity between the upper bound and the exact capacity, which is beneficial for predicting practical system performance. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images

arXiv

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arXiv 2024年

作者： Geng, Yimeng Meng, Gaofeng Chen, Mingcong Cao, Guanglin Zhao, Mingyang Zhao, Jianbo Liu, Hongbin State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HK Institute of Science & Innovation Chinese Academy of Sciences Hong Kong City University of Hong Kong Hong Kong School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom

Accurate identification of arteries and veins in ultrasound images is crucial for vascular examinations and interventions in robotics-assisted surgeries. However, current methods for ultrasound vessel segmentation face challenges in distinguishing between arteries and veins due to their morphological similarities. To address this challenge, this study introduces a novel force sensing guided segmentation approach to enhance artery-vein segmentation accuracy by leveraging their distinct deformability. Our proposed method utilizes force magnitude to identify key frames with the most significant vascular deformation in a sequence of ultrasound images. These key frames are then integrated with the current frame through attention mechanisms, with weights assigned in accordance with force magnitude. Our proposed force sensing guided framework can be seamlessly integrated into various segmentation networks and achieves significant performance improvements in multiple U-shaped networks such as U-Net, Swin-unet and Transunet. Furthermore, we contribute the first multimodal ultrasound artery-vein segmentation dataset, Mus-V, which encompasses both force and image data simultaneously. The dataset comprises 3114 ultrasound images of carotid and femoral vessels extracted from 105 videos, with corresponding force data recorded by the force sensor mounted on the US probe. Our code and dataset will be publicly available. © 2024, CC BY-NC-SA.

关键词： Ultrasonic imaging

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EFaR 2023: Efficient Face Recognition Competition

arXiv

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

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Myoelectric Prosthetic Hand with Adjustable Tightening Socket for Varied Residual Limb Shapes*

Myoelectric Prosthetic Hand with Adjustable Tightening Socke...

引用

IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Yuki Inoue Yuki Kuroda Yoshiko Yabuki Yusuke Yamanoi Xu Yong Xiaobei Jing Hiroshi Yokoi Department of Mechanical and Intelligent Engineering The University of Electro-Communications Chofu Tokyo JAPAN OMRON SINIC X Corporation Bunkyo-ku Tokyo JAPAN department of Electrical Engineering Faculty of Engineering Tokyo University of Science Katushika-ku Tokyo JAPAN CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) and the SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen CHINA Department of Mechanical and Intelligent Engineering Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Chofu Tokyo JAPAN Mu-BORG K.K. Chofu JAPAN

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

The prosthetic socket serves as an interface between the body and the machine. If the socket is not well-fitted to the residual limb, pain can occur. The shape of the residual limb changes over time as surgical scars stabilize or the patient’s body weight fluctuates. A socket with a cutout on the side can be tightened from the outside to adjust its circumference and accommodate these changes. However, when the circumference of the rigid socket is adjusted to fit, soft skin can become trapped due to friction between the skin and the socket. This phenomenon causes pain due to pinching. Consequently, this mechanism cannot be used for myoelectric prosthetic hands, where direct contact between the socket and the skin is necessary. In this study, we developed a socket with a variable circumference that suppresses skin pinching. We applied it to participants with different residual limb shapes. A Box and Block Test (BBT) was performed using two types of sockets. The first used a conventional socket designed to fit their residual limb shape. The second used the proposed socket, which was designed to fit the residual limb’s shape of one out of two participants and adapted to the residual limb’s changing shape. The results of the BBT showed not much difference in the number of blocks moved between the conventional socket and the proposed socket. Therefore, the performance of the proposed socket was comparable to that of conventional sockets and could be applied to different residual limbs. In other words, the proposed socket was shown to adapt to changes in the residual limb without causing pain. Sockets with the proposed functionality could be used in a variety of bio-interfaces, allowing for flexible connections between the body and machines.

关键词： Limbs Shape Pain Sockets Human-machine systems Friction Surgery Skin Interviews Prosthetic hand

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Mechanical Control of Cell Proliferation Increases Resistance to Chemotherapeutic Agents

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Physical Review Letters 2020年第12期125卷 128103-128103页

作者： Ilaria Francesca Rizzuti Pietro Mascheroni Silvia Arcucci Zacchari Ben-Mériem Audrey Prunet Catherine Barentin Charlotte Rivière Hélène Delanoë-Ayari Haralampos Hatzikirou Julie Guillermet-Guibert Morgan Delarue CNRS UPR8001 LAAS-CNRS 7 Avenue du Colonel Roche F-31400 Toulouse France Laboratory of Nanotechnology for Precision Medicine Fondazione Istituto Italiano di Tecnologia Via Morego 30 16163 Genoa Italy Computer Science and Technology Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Via All’Opera Pia 13 16145 Genoa Italy Department of Systems Immunology and Braunschweig Integrated Center of Systems Biology (BRICS) Helmholtz Centre for Infection Research Rebenring 56 38106 Braunschweig Germany INSERM U1037 CRCT Universite Paul Sabatier F-31037 Toulouse France Laboratoire d’Excellence TouCAN F-31037 Toulouse France Univ Lyon Univ Claude Bernard Lyon 1 CNRS Institut Lumière Matière F-69622 Villeurbanne France

While many cellular mechanisms leading to chemotherapeutic resistance have been identified, there is an increasing realization that tumor-stroma interactions also play an important role. In particular, mechanical alterations are inherent to solid cancer progression and profoundly impact cell physiology. Here, we explore the influence of compressive stress on the efficacy of chemotherapeutics in pancreatic cancer spheroids. We find that increased compressive stress leads to decreased drug efficacy. Theoretical modeling and experiments suggest that mechanical stress decreases cell proliferation which in turn reduces the efficacy of chemotherapeutics that target proliferating cells. Our work highlights a mechanical form of drug resistance and suggests new strategies for therapy.

关键词： Biomechanics Cancer Cell growth Cell mechanics Cell processes & properties Tumors

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Correction to: Fabrication of high-strength polyoxymethylene fibers by gel spinning

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Journal of Materials science 2018年第18期53卷 13316-13316页

作者： Xudong Fang Tom Wyatt Jing Shi Donggang Yao School of Mechanical Engineering Xi’an Jiaotong University Xi’an China School of Materials Science and Engineering Georgia Institute of Technology Atlanta USA State Key Laboratory for Manufacturing Systems Engineering Xi’an China State Key Laboratory of Robotics and System (HIT) Harbin China

In the original article, the name of author Donggang Yao was misspelled. It is correct here.

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