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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： Computerized tomography

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Virtual-dynamics-based reference gait speed generator for limit-cycle-based bipedal gait

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ROBOMECH Journal 2018年第1期5卷 1-17页

作者： Kobayashi, Taisuke Sekiyama, Kosuke Hasegawa, Yasuhisa Aoyama, Tadayoshi Fukuda, Toshio Division of Information Science Nara Institute of Science and Technology 8916-5 Takayama IkomaNara630-0192 Japan Department of Micro-Nano Mechanical Science and Engineering Nagoya University Nagoya Japan Faculty of Science and Engineering Meijo University Nagoya Japan Intelligent Robotics Institute School of Mechatronic Engineering Beijing Institute of Technology Beijing China

This paper addresses a reference gait speed generator for limit-cycle-based bipedal gait of humanoid robots in order to achieve secure and efficient acceleration and deceleration without falling down at respective gait speeds. The reference gait speed generator is hard to be developed by analytical approaches due to the necessity of barren work for identifying the respective basins of attraction. We challenge this issue by designing virtual dynamics among a robot, a virtual leader point, and a goal, and adapting it according to a falling risk of the robot. The virtual dynamics, which has settling and acceleration times as design parameters, gives the reference speeds derived from states of the robot and the leader point to a gait speeds controller. In the dynamics, the robot’s mass is optimized virtually to maximize efficiency while ensuring stability stochastically by using a selection algorithm for locomotion. Even when there were obstacles or an up slope in traveling courses of simulations, the robot achieved the autonomous traveling from the start to the goal securely. Specific resistance was also kept small in comparison with local-stability-based walking. The proposed method makes the limit-cycle-based bipedal gait more practical and contributes toward replacing the major method that ensures stability of every step. © 2018, The Author(s).

关键词： Anthropomorphic robots

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Classification and applications of titanium and its alloys: A review

Journal of Alloys and Compounds Communications

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Journal of Alloys and Compounds Communications 2024年 3卷

作者： Mojtaba Najafizadeh Sahar Yazdi Mansoor Bozorg Mehran. Ghasempour-Mouziraji Morteza Hosseinzadeh Masoumeh Zarrabian Pasqual Cavaliere School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Faculty of Chemical and Materials Engineering Shahrood University of Technology Shahrood *** Iran Department of Innovation Engineering University of Salento Via per Arnesano Lecce 73100 Italy School of Medical Sciences Shahrood Branch Islamic Azad University Shahrood Iran TEMA - Centre for Mechanical Technology and Automation University of Aveiro Portugal LASI Intelligent Systems Associate Laboratory Guimarães Portugal Engineering and Sustainable Technology ManagementIndustry 4.0: Automation Robotics & 3D Manufacturing SRH Hochschule Berlin Germany Department of Engineering Ayatollah Amoli Branch Islamic Azad University Amol Iran

Due to its numerous properties, iron is one of the most useful materials on the planet. The mechanical and physical properties of iron and its alloys are suitable and manageable. These alloys have significant issues like heavyweight, low corrosion resistivity and some other limitations. The researchers and companies for solving problems look out for other alternative materials. Titanium and its alloys, according to the researchers, have a huge variety of engineering applications. Titanium and titanium alloys have high strength and corrosion resistance, and they perform well at high temperatures up to nearly 600 °C. Titanium and its alloys have a density of about 60 % that of steel and its alloys, and about 50 % that of nickel alloys. Even though titanium and its alloys have cost and supply problems, they're utilized in numerous industries, which includes aerospace, automotive, and navy projects. Titanium and its alloys have unique properties, indicating that they are cost-effective components that require special manufacturing techniques. This study aims to explore the potential of titanium and its alloys to replace iron in various engineering applications. Addressing the critical need for materials with superior performance characteristics is essential for advancing technological development.

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Modeling, Simulation and Control of a Pipe Inspection Mobile Robot with an Active Adaptation System

Modeling, Simulation and Control of a Pipe Inspection Mobile...

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作者： Ciszewski, Michal Buratowski, Tomasz Giergiel, Mariusz AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Al. Mickiewicza 30 Kraków30-059 Poland

In this paper, a tracked inspection mobile robot with an active adaptation system is presented. It can be used for visual inspection of various pipelines. Mathematical modeling of pedipulator mechanisms that allow adaptation of the robot to different shapes and sizes of pipes is described with focus on forward and inverse kinematics methods, implemented in MATLAB software. Application of a custom trajectory planning algorithm for the pedipulators is shown with numerical and graphical validation. Co-simulations of the robot motion, prepared in V-REP and MATLAB software are performed for horizontal and rough surfaces and horizontal pipe segments with connections and reducers. A prototype of the robot with a model-based control system is tested and laboratory experiments are compared to the simulation results. Different motion scenarios of the robot are shown with focus on adaptation capabilities of the motion unit to work environment. © 2018

关键词： Mobile robots

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Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

arXiv

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arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

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Noise generated during the passage through the bridge expansion joints in Cracow – Case study 47

Noise generated during the passage through the bridge expans...

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47th International Congress and Exposition on Noise Control Engineering: Impact of Noise Control Engineering, INTER-NOISE 2018

作者： Chojnacki, Bartlomiej Matys, Magdalena Piotrowski, Kamil AGH University of Science and Technology Department of Mechanics and Vibroacoustics Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Kraków30-059 Poland

In the process of assessment of annoyance of traffic or road noise, phenomena which may cause increased annoyance, e.g. impulsive noise are often neglected. In the cities located upon rivers, noise with strong impulsive character is generated during passage through the bridge expansion joints, the special slots in the structures dedicated to reduce excessive deformation caused by temperature changes. Such noise can be hearable far away from the bridge, and thus affect large area in the urban soundscape. The paper deals with the problem of assessment of impulsive noise generated during the passages of vehicles over the expansion joints measured at several bridges in Cracow. Results of experimental studies, values of measurable parameters useful in the identification and evaluation of annoyance was presented, together with an attempt of its impact on the environment. © INTER-NOISE 2018 - 47th International Congress and Exposition on Noise Control Engineering: Impact of Noise Control Engineering. All rights reserved.

关键词： Impulse noise

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Correction to: Deep learning approaches for speech emotion recognition: state of the art and research challenges

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Multimedia Tools and Applications 2021年第16期80卷 23813-23813页

作者： Jahangir, Rashid Teh, Ying Wah Hanif, Faiqa Mujtaba, Ghulam Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer Science COMSATS University Islamabad Vehari Campus Pakistan Center of Excellence for Robotics Artificial Intelligence and Blockchain Department of Computer Science Sukkur IBA University Sukkur Pakistan

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PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery

arXiv

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arXiv 2024年

作者： Das, Adrito Khan, Danyal Z. Psychogyios, Dimitrios Zhang, Yitong Hanrahan, John G. Vasconcelos, Francisco Pang, You Chen, Zhen Wu, Jinlin Zou, Xiaoyang Zheng, Guoyan Qayyum, Abdul Mazher, Moona Razzak, Imran Li, Tianbin Ye, Jin He, Junjun Plotka, Szymon Kaleta, Joanna Yamlahi, Amine Jund, Antoine Godau, Patrick Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Rivoir, Dominik Pérez, Alejandra Rodriguez, Santiago Arbeláez, Pablo Stoyanov, Danail Marcus, Hani J. Bano, Sophia Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department of Neurosurgery National Hospital for Neurology and Neurosurgery London United Kingdom HKISI CAS China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Heart and Lung Institute Faculty of Medicine Imperial College London United Kingdom Centre for Medical Image Computing University College London London United Kingdom University of New South Wales Sydney Australia Shanghai AI Lab Shanghai China Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center University of Amsterdam Amsterdam Netherlands Sano Center for Computational Medicine Krakow Poland Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg a partnership between DKFZ University Hospital Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Muroran Institute of Technology Hokkaido Japan Niigata University of Health and Welfare Niigata Japan Konica Minolta Inc. Osaka Japan National Center for Tumor Diseases Dresden Germany Centre for Tactile Internet TUD Dresden Germany Universidad de los Andes Bogota Colombia DKFZ UKDD TUD Germany

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed;and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery;during live surgery;and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision;and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://***/10.5522/04/26531686. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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Ballistic Object Trajectory and Launch Point Estimation from Radar Measurements using Long-Short Term Memory Networks

Ballistic Object Trajectory and Launch Point Estimation from...

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International Conference on Robot Intelligence technology and Applications (RiTA)

作者： Juhyung Kim Ming Chong Lim Soon-Seo Park Iksoo Kim Han-Lim Choi Ph.D. candidates in the Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Faculty of Aerospace Engineering & KI for Robotics Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

In this paper, we consider the problem of estimating the trajectory of the ballistic object, especially the ballistic missiles, and its launch point based on radar measurements using promising deep learning technique-the Long-Short Term Memory(LSTM) Network. The conventional way of estimating the trajectories are solely based on the dynamic model of a missile to the Filter(KF). the implement Kalman However, since dynamics of missile change over the flight phases(boost, ballistic, reentry phase) and the flight phase of the missile would be difficult to differentiate using only radar observations, here we suggest a model-free method that utilizes the LSTM for the estimation. By implementing the suggested method, we can accurately estimate the trajectories and launch points of ballistic missiles only using the radar measurement without any consideration of the dynamics and parameters of the missile flight system.

关键词： Trajectory Missiles Radar measurements Estimation Acceleration Noise measurement

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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