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International Conference on Manipulation, Automation and robotics at Small Scales (MARSS)

作者： Yuzhu Liu Ruimin Li Jinyu Qiu Zuqi Wang Minghui Li Xin Zhao Qili Zhao National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Tianjin Key Laboratory of Intelligent Robotic (tikLIR) Institute of Robotics and Automatic Information System(IRAIS). Nankai University. Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

The inside-out patch clamp technique is widely used in brain science and neuroscience research because of its ability to detect the extremely weak currents flowing through a single ion channel. In the inside-out patch clamp operations, a micrometer-scale dimensions pipette is used to aspirate part of the cell membrane, creating a giga omega-scale seal (usually termed as gigaseal) between the cell membrane and the inner wall of the micropipette to suppress the environmental electromagnetic noise. The aspirated cell membrane is then gently pulled away from the cell to form a vesicle, which is subsequently exposed to low-calcium liquid or air to induce its rupture, forming an inside-out seal for signal detection on a single ion channel. Current manual operations of inside-out patch clamp are complex and require a high level of operator expertise. Moreover, existing robotic inside-out patch clamp technologies are usually applicable to suspension cells, limiting the widespread application of this technique. This paper proposed a robotic inside-out recording patch clamp technique based on vesicle rupture detection to address these limitations. Firstly, impedance models were established to detect the state of the vesicle before and after the rupture. Further, the detection of vesicle rupture was performed based on transmembrane current responses under step inputs. Based on the above work, a robotic inside-out recording patch clamp method based on vesicle rupture detection was established in this paper. Experimental results demonstrated the effectiveness of the proposed method in achieving successful inside-out recording patch clamp mode on adherent HEK-293 cells with a success rate of 60 %, laying the foundation for subsequent studies on single ion channel functionality.

关键词： Vesicles Neuroscience Liquids Limiting Seals Ions Recording

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ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding

ASI-Seg: Audio-Driven Surgical Instrument Segmentation with ...

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

作者： Zhen Chen Zongming Zhang Wenwu Guo Xingjian Luo Long Bai Jinlin Wu Hongliang Ren Hongbin Liu Centre for Artificial Intelligence and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China Department of Electronic Engineering Chinese University of Hong Kong China State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Surgical instrument segmentation is crucial in surgical scene understanding, thereby facilitating surgical safety. Existing algorithms directly detected all instruments of predefined categories in the input image, lacking the capability to segment specific instruments according to the surgeon’s intention. During different stages of surgery, surgeons exhibit varying preferences and focus toward different surgical instruments. Therefore, an instrument segmentation algorithm that adheres to the surgeon’s intention can minimize distractions from irrelevant instruments and assist surgeons to a great extent. The recent Segment Anything Model (SAM) reveals the capability to segment objects following prompts, but the manual annotations for prompts are impractical during the surgery. To address these limitations in operating rooms, we propose an audio-driven surgical instrument segmentation framework, named ASI-Seg, to accurately segment the required surgical instruments by parsing the audio commands of surgeons. Specifically, we propose an intention-oriented multimodal fusion to interpret the segmentation intention from audio commands and retrieve relevant instrument details to facilitate segmentation. Moreover, to guide our ASI-Seg segment of the required surgical instruments, we devise a contrastive learning prompt encoder to effectively distinguish the required instruments from the irrelevant ones. Therefore, our ASI-Seg promotes the workflow in the operating rooms, thereby providing targeted support and reducing the cognitive load on surgeons. Extensive experiments are performed to validate the ASI-Seg framework, which reveals remarkable advantages over classical state-of-the-art and medical SAMs in both semantic segmentation and intention-oriented segmentation. The source code is available at https://***/Zonmgin-Zhang/ASI-Seg.

关键词： Instruments Semantic segmentation Source coding Surgery Contrastive learning Manuals Cognitive load Safety Intelligent robots Biomedical imaging

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Decomposition of 3D Joint Angle by Kinematic Modeling of Free Flight Pigeon Forelimb

Decomposition of 3D Joint Angle by Kinematic Modeling of Fre...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Yishi Shen Shi Zhang Weimin Huang Chengrui Shang Yiming Sun Qing Shi Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China Intelligent Robotics Institute School of mechatronic Engineering Beijing Institute of Technology Beijing China Key Laboratory of Animal Ecology and Conservation Biology Institute of Zoology Chinese Academy of Sciences Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Birds utilize complex coupled motions in their forelimbs to maintain exceptional flight performance. Understanding these dynamics is essential for designing biomimetic unmanned aerial vehicles (UAVs). Typically, the forelimb skeleton of birds is modeled as a spatial six-bar linkage mechanism through the analysis of physical specimens. However, the detailed description of skeletal transformations in birds during free flight remains uncharted. To address this gap, we propose an open-chain kinematic model to simulate the skeletal motion variations of pigeons (Columba livia) in free flight. Kinematic data of pigeon wings were collected during free flight, and the skeletal model was fitted to the collected data using global optimization. This allowed us to derive the joint motion angles of the skeleton during the take-off, leveling flight, and landing phases. This study elucidates the mechanisms of bird wing deformation, providing valuable insights and guidance for developing bio-inspired flying robots.

关键词： Wrist Three-dimensional displays Biomimetics Biological system modeling Birds Skeleton Data models Trajectory Vehicle dynamics Optimization

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AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

arXiv

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arXiv 2023年

作者： Benz, Thomas Ottaviano, Alessandro Balas, Robert Garofalo, Angelo Restuccia, Francesco Biondi, Alessandro Benini, Luca Integrated Systems Laboratory ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Computer Science and Engineering UC San Diego San DiegoCA United States Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa Italy

The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufacturing processes have led to the transition from federated to integrated critical real-time embedded systems (CRTESs). This leads to higher integration challenges of conventional timing predictability techniques due to access contention on shared resources, which can be resolved by providing system-level observability and controllability in hardware. We focus on the interconnect as a shared resource and propose AXI-REALM, a lightweight, modular, and technology-independent real-time extension to industry-standard AXI4 interconnects, available open-source. AXI-REALM uses a credit-based mechanism to distribute and control the bandwidth in a multi-subordinate system on periodic time windows, proactively prevents denial of service from malicious actors in the system, and tracks each manager’s access and interference statistics for optimal budget and period selection. We provide detailed performance and implementation cost assessment in a 12nm node and an end-to-end functional case study implementing AXI-REALM into an open-source Linux-capable RISC-V SoC. In a system with a general-purpose core and a hardware accelerator’s DMA engine causing interference on the interconnect, AXI-REALM achieves fair bandwidth distribution among managers, allowing the core to recover 68.2 % of its performance compared to the case without contention. Moreover, near-ideal performance (above 95 %) can be achieved by distributing the available bandwidth in favor of the core, improving the worst-case memory access latency from 264 to below eight cycles. Our approach minimizes buffering compared to other solutions and introduces only 2.45 % area overhead compared to the original SoC. Copyright © 2023, The Authors. All rights reserved.

关键词： Embedded systems

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Hybrid Deep Learning with Attention Mechanism based Health State Intelligent Diagnosis of Lithium-Ion Batteries

Hybrid Deep Learning with Attention Mechanism based Health S...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Walid Mchara Mohamed Abdellatif Khalfa Lazhar Manai Laboratory of Robotics Informatics and Complex Systems (RISC) National Engineering School of Tunis (ENIT) University of El Manar Le Belvédère Tunis Higher Institute of Information and Communication Technologies Hammam Chatt Tunisia CRETn Borj-Cedria Photovoltaic Laboratory (LPV) RISC Le Belvédère ENIT University of El Manar Tunis Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

With the increasing utilization of lithium-ion batteries in electric vehicles, substantial research has been undertaken to ensure the secure and dependable performance of the Battery Management System (BMS). The precise estimation of State of Health (SOH), a pivotal function of the BMS, is critical for the safe operation of electric vehicles. Addressing this challenge, we present a time series model tailored for predicting the State of Health (SOH) of Li-ion batteries. Our approach combines a CNN with a Bidirectional Long Short-Term Memory (BI-LSTM) architecture, enhanced by an attention mechanism. Experimental results demonstrate that this enhanced LSTM approach notably enhances prediction accuracy. The results from this study offer a promising solution for enhancing the reliability and performance of electric vehicle battery systems, ultimately contributing to the widespread adoption and sustainability of electric transportation.

关键词： Lithium-ion batteries Deep learning Accuracy Time series analysis Transportation Predictive models Prediction algorithms

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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SLAM-Based Joint Calibration of Differential RSS Sensor Array and Source Localization

SLAM-Based Joint Calibration of Differential RSS Sensor Arra...

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Annual Conference of Industrial Electronics Society

作者： Linya Fu Xu Qiao Shoudong Huang Guoqiang Mao Zhiyun Lin Youfu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern Uni. of Sci. and Tech. Shenzhen China Robotics Institute Uni. of Technology Sydney Sydney Australia State Key Laboratory of Integrated Services Networks Xidian University China Department of Mechanical Engineering City University of Hong Kong China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern Uni. of Sci. and Tech. Shenzhen China

Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing methods are impractical in that they assume to know signal source positions or certain parameters (i.e., path loss exponent), and try to estimate the others. In this paper, we adopt graph simultaneous localization and mapping (SLAM) as a general framework for jointly estimating the source positions and parameters of the DRSS sensor array. Our contributions are twofold. On the one hand, by using a Fisher information matrix approach, we conduct a systematic observability analysis of the corresponding SLAM setup for the calibration problem. On the other hand, we propose an effective procedure to select the initial value which is fed to Levenberg-Marquardt iterations for further improving optimization accuracy and convergence. Extensive simulation and hardware experiments show that the proposed method renders high-quality calibration results. All the codes and data are publicly available at https://***/SUSTech2022/DRSS-sensor-array-calibration.

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ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environmental Microorganism Image Object Detection

ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environm...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Yang, Hechen Yang, Jinzhu Jiang, Tao Zhao, Xin Zhang, Jinghua Zhao, Peng Chen, Ao Nie, Qianqing Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Institute of Robotics and Intelligent Systems Chengdu China Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network. © 2023 IEEE.

关键词： Attention Convolutional Neural Network Environmental Miroorganisms Feature Fusion Object Detection

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Investigation of the Compass Biped Walker's Passive Dynamics and Trajectory Tracking Method for Chaos Control

Investigation of the Compass Biped Walker's Passive Dynamics...

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International Conference on Advanced systems and Electric Technologies (IC_ASET)

作者： Essia Added Hassène Gritli Safya Belghith Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia

The aim of this research is to manage the precise and rhythmic movements of a bipedal robot with a compass configuration as it undergoes a passive dynamic walk. To approximate the walking pattern of a biped robot, we implement an impulsive hybrid nonlinear system. To start with, we showcase the fact that a passive walk by a bipedal compass robot can exhibit unexpected behaviors like bifurcations and chaos with the aid of bifurcation diagrams. In order to suppress such complex nonlinear tendencies, we resort to two methods for regulating the bipedal compass-type robot and achieving a one-periodic gait. The control approach used in this study is based on the passive dynamic walking of the biped robot, and the objective is to follow the trajectory of the period-1 passive movement during the swing phase.

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Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

arXiv

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arXiv 2024年

作者： Ou, Ni Chen, Zhuo Luo, Shan The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China The Robot Perception Lab. Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are available at the project website https://***/Marker-Markerless-Transition/ Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

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