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arXiv 2023年

作者： Ramesh, Sanat Dall’Alba, Diego Gonzalez, Cristians Yu, Tong Mascagni, Pietro Mutter, Didier Marescaux, Jacques Fiorini, Paolo Padoy, Nicolas Altair Robotics Lab Department of Computer Science University of Verona Verona Italy ICube University of Strasbourg CNRS IHU Strasbourg France University Hospital of Strasbourg IHU Strasbourg France IRCAD France Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy

Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of current vision-based activity recognition methods relies heavily on a high volume of manually annotated data. This data is difficult and time-consuming to generate and requires domain-specific knowledge. In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos. We introduce a step-phase dependency loss to exploit the weak supervision signal. We then employ a Single-Stage Temporal Convolutional Network (SS-TCN) with a ResNet-50 backbone, trained in an end-to-end fashion from weakly annotated videos, for temporal activity segmentation and recognition. We extensively evaluate and show the effectiveness of the proposed method on a large video dataset consisting of 40 laparoscopic gastric bypass procedures and the public benchmark CATARACTS containing 50 cataract surgeries. © 2023, CC BY-SA.

关键词： Convolution

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Curating corpora with classifiers: A case study of clean energy sentiment online

arXiv

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arXiv 2023年

作者： Arnold, Michael V. Dodds, Peter Sheridan Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries. © 2023, CC BY.

关键词： Tweets

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Design of photoacoustic modular helmet for neonatatal neurocritical care

Design of photoacoustic modular helmet for neonatatal neuroc...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Ananya Tandri Keshuai Xu May W. Chen Emad Boctor Jeeun Kang Dept. Biomedical Engineering Johns Hopkins University Baltimore MD USA Dept. Computer Science Johns Hopkins University Baltimore MD USA Dept. Pediatrics Johns Hopkins University Baltimore MD USA Lab. for Computational Sensing & Robotics Johns Hopkins University Baltimore MD USA Dept. Anesthesiology and Critical Care Medicine Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Perinatal arterial ischemic stroke (PAIS) is a form of focal vascular neurological injury that occurs during or shortly after birth. We recently proposed a modular PA helmet (PAH) system, in which multiple modules constitutes a flexible helmet to accommodate any neonatal head shape and size to provide volumetric monitoring of neonatal brain oxygen saturation. Here, optical tracking of the PAH configuration enables spatial image reconstruction. Given the large freedom in PAH configuration, it is critical to understand the implications of various design parameters in imaging quality. In this paper, we present our simulation-aided design approach using acoustic and optical simulation tools. Our design criteria are to optimize 1) signal contrast and 2) spatial resolution with different number of modules and their mean distances, and is further challenged with modular position error inevitable in modular position recognition. Results of the simulation established an acceptable error threshold for optical tracking and informed module prototype configuration and shape. The prototype based on the optimal design specifications is also demonstrated.

关键词： Pediatrics Head Shape Prototypes Optical saturation Optical variables control Optical imaging Adaptive optics Acoustics Safety

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Qudit Dynamical Decoupling on a Superconducting Quantum Processor

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Physical Review Letters 2025年第5期134卷 050601-050601页

作者： Vinay Tripathi Noah Goss Arian Vezvaee Long B. Nguyen Irfan Siddiqi Daniel A. Lidar Department of Physics and Astronomy University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science and Technology University of Southern California Los Angeles California 90089 USA Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Math and Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA Department of Chemistry University of Southern California Los Angeles California 90089 USA

Multilevel qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decoherence than qubits due to increased loss channels, noise sensitivity, and crosstalk. To address these challenges, we develop protocols for dynamical decoupling (DD) of qudit systems based on the Heisenberg-Weyl group. We implement and experimentally verify these DD protocols on a superconducting transmon processor that supports qudit operation based on qutrits (d=3) and ququarts (d=4). Specifically, we demonstrate single-qudit DD sequences to decouple qutrits and ququarts from system-bath-induced decoherence. We also introduce two-qudit DD sequences designed to suppress the detrimental cross-Kerr couplings between coupled qudits. This allows us to demonstrate a significant improvement in the fidelity of time-evolved qutrit Bell states. Our results highlight the utility of leveraging DD to enable scalable qudit-based quantum computing.

关键词： Qubits

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Electrochemical impedance behavior of implantable neural interface following the change of equivalent circuit element 12

Electrochemical impedance behavior of implantable neural int...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Zhang, Jing Liu, Lusheng Wang, Aiping Wang, Junkongshuai Fang, Tao Niu, Lan Bin, Jianxiong Wang, Pengchao Mu, Wei Jia, Jie Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The simulation of two basic equivalent circuit models of the implantable neural interface is described in this paper. The simulated equivalent circuits are created based on the physicochemical mechanism of charge transfer on the surface of the implantable neural interface. Each parameter of the equivalent circuit model was varied to investigate the regular pattern of individual element affecting the performance of the implantable neural interface. In this paper, one circuit model is based on the charge transfer process, and another circuit model is based on the mixed charge transfer and diffusion process. The equivalent circuits are split into two parts. The neural interface of the implantable neural interface device is simulated in one part, and the rest of that is simulated in another part. The electrochemical impedance data is acquired firstly by simulating the equivalent circuit with Z-view software. Then, the simulated data was analyzed to investigate the change tendency of a single parameter, providing a theoretical method for developing customized implantable neural interface with specific electrochemical impedance. © 2022 IEEE.

关键词： Equivalent circuits

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Experimental Validation of Sensitivity-Aware Trajectory Planning for a Quadrotor UAV Under Parametric Uncertainty

Experimental Validation of Sensitivity-Aware Trajectory Plan...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Ali Srour Salvatore Marcellini Tommaso Belvedere Marco Cognetti Antonio Franchi Paolo Robuffo Giordano CNRS Univ Rennes Inria IRISA Rennes Cedex France Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy LAAS-CNRS Université de Toulouse CNRS UPS Toulouse France LAAS-CNRS Robotics and Mechatronics Department Electrical Engineering Mathematics Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

In this work, we provide an experimental vali-dation of the recent concepts of closed-loop state and input sensitivity in the context of robust flight control for a quadrotor (UAV) equipped with the popular PX4 1 1 https://***/ controller. Our objective is to experimentally assess how the optimization of the reference trajectory w.r.t. these sensitivity metrics can improve the closed-loop system performance against model uncertainties commonly affecting the quadrotor systems. To accomplish this, we present a series of experiments designed to validate our optimization approach on two distinct trajectories, with the primary aim of assessing its precision in guiding the quadrotor through the center of a window at relatively high speeds. This approach provides some interesting insights for increasing the closed-loop robustness of the robot state and inputs against physical parametric uncertainties that may degrade the system's performance.

关键词： Uncertainty Sensitivity Target tracking Trajectory planning Perturbation methods Autonomous aerial vehicles Robustness

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Planning Under Uncertainties with Closed-Loop Sensitivity: Recent Results and Perspectives 16th

Planning Under Uncertainties with Closed-Loop Sensitivity: ...

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16th European robotics Forum, ERF 2025

作者： Belvedere, Tommaso Afifi, Amr Wasiela, Simon Pupa, Andrea CNRS Université de Rennes Inria IRISA Campus de Beaulieu Rennes France Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands LAAS-CNRS Université de Toulouse CNRS UPS Toulouse France Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Modena Italy

ISBN: (纸本)9783031894701

This paper presents a comprehensive summary of recent advancements in motion planning under parametric uncertainties, focusing on the application of closed-loop state sensitivity. This concept provides a framework for quantifying how deviations in model parameters affect the behavior of a system in closed-loop, facilitating the generation of robust trajectories. Various methods have been proposed to improve the resilience of robotic systems to model inaccuracies. However, these approaches often face challenges such as computational complexity and limitations in real-time applications. This paper synthesizes key results from several recent works, highlighting the development of techniques that optimize trajectory robustness while reducing computational overhead. Additionally, we outline the practical applications of these methods, discussing their validation through simulations and experiments on robotic systems subject to non-negligible uncertainties in their models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robots

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Multi-Exposure Image Fusion Using Edge-Aware Network

Multi-Exposure Image Fusion Using Edge-Aware Network

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International Conference on Emerging Technologies, ICET

作者： Ghazala Aslam Muhammad Imran Bushra Haq Anayat Ullah Ersin Elbasi Department of Computer Science BUITEMS Quetta Pakistan Department of Electrical Engineering FICT BUITEMS Quetta Pakistan Control Automotive and Robotics Lab NCRA Rawalpindi Pakistan Department of Computer Science FICT BUITEMS Quetta Pakistan Department of Electronic Engineering FICT BUITEMS Quetta Pakistan College of Engineering & Technology American University of Middle East Egaila Kuwait

ISBN: (纸本)9781665459938

The natural dynamic range is much broader than the dynamic range a digital camera can capture. As a result, the images captured by ordinary cameras suffer from visibility issues; some images look overly bright, while others look overly dark. Therefore, in the research article, we propose a solution for image fusion, where multiple images can be combined to produce a single comprehensive image with better perceptibly. To achieve that, we designed a feature extraction block, which can extract features of an image at different levels and combines them to get more refined and detailed feature maps. In addition to that, we also designed a custom layer to extract edges, and those edges later are combined with the feature maps to give a fused image. We evaluated and compared our proposed architecture with some of the latest architecture, designed for the same reason, in terms of Image Structure Similarity and Human Perception-Based metrics. Experimental results corroborate that the proposed scheme outperforms the contemporary techniques for image fusion.

关键词： Image edge detection Lighting Deep architecture Dynamic range Feature extraction Digital cameras Image fusion

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Exploring Humanoid Robot Face Preference Using Brain Functional Connectivity and Graph Neural Network 12

Exploring Humanoid Robot Face Preference Using Brain Functio...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Huashan Hospital Fudan University Department of Rehabilitation Medicine Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Graph neural networks (GNN) have been applied in EEG signal analysis. However, it is not clear how to describe the connection relationships between electrodes, and a reasonable representation of the adjacency matrix cannot be neglected for the study of GNN based on EEG signals. In this paper, we use brain functional connectivity to analyze the face preferences of a humanoid robot and explore the feasibility of using the matrix obtained based on brain functional connectivity measurements as a GNN connectivity matrix. The results show that it is feasible and an average accuracy of 73.47% is achieved. In addition, the results of the brain functional connectivity analysis also show that there is a difference between preferences and non-preferences. © 2022 IEEE.

关键词： Anthropomorphic robots

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