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arXiv 2022年

作者： Jüstel, Dominik Irl, Hedwig Hinterwimmer, Florian Dehner, Christoph Simson, Walter Navab, Nassir Schneider, Gerhard Ntziachristos, Vasilis Institute of Biological and Medical Imaging Helmholtz Zentrum München Neuherberg Germany Institute of Computational Biology Helmholtz Zentrum München Neuherberg Germany School of Medicine Technical University of Munich Germany Department of Anesthesiology and Intensive Care School of Medicine Klinikum Rechts Der Isar Technical University of Munich Munich Germany Department of Orthopaedics and Sport Orthopaedics School of Medicine Klinikum Rechts Der Isar Technical University of Munich Munich Germany Computer Aided Medical Procedures and Augmented Reality Technical University of Munich Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany

Various morphological and functional parameters of peripheral nerves and their vascular supply are indicative of pathological changes due to injury or disease. Based on recent improvements in optoacoustic image quality, we explore the ability of multispectral optoacoustic tomography, in tandem with ultrasound imaging (OPUS), to investigate the vascular environment and morphology of peripheral nerves in vivo in a pilot study on healthy volunteers. We showcase the unique ability of optoacoustic imaging to visualize the vasa nervorum by observing intraneurial vessels in healthy nerves in vivo for the first time. In addition, we demonstrate that the label-free spectral optoacoustic contrast of the perfused connective tissue of peripheral nerves can be linked to the endogenous contrast of haemoglobin and collagen. We introduce metrics to analyze the composition of tissue based on its optoacoustic contrast and show that the high-resolution spectral contrast reveals specific differences between nervous tissue and reference tissue in the nerve's surrounding. We discuss how this showcased extraction of peripheral nerve characteristics using multispectral optoacoustic and ultrasound imaging can offer new insights into the pathophysiology of nerve damage and neuropathies, for example, in the context of diabetes. Copyright © 2022, The Authors. All rights reserved.

关键词： Tissue

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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Cryo-CACTI: Cryogenic-Aware CACTI for Cache Modeling down to 10K in Advanced 7nm FinFETs

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IEEE Transactions on Computers 2025年

作者： Ravipati, Divya Praneetha van Santen, Victor M. Parihar, Shivendra Singh Chauhan, Yogesh Singh Panda, Preeti Ranjan Amrouch, Hussam Indian Institute of Technology Delhi Department of Computer Science and Engineering New Delhi India Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Germany Kanpur Nanolab Department of Electrical Engineering Uttar Pradesh India

Cryogenic circuits are currently employed in fields such as quantum computing, particle detectors, magnetic resonance imaging, and space applications. While cryogenic circuits are being researched, there is limited work on designing cryogenic caches at temperatures below 77K. Moreover, there is no tool to estimate the delay, power, and area of cryogenic caches at advanced technology nodes. Our research focuses on the development of cryogenic caches tailored for the 7nm technology node, operating at 10K. However, a key challenge is the lack of cryogenic measurement data, especially in recent technologies. Consequently, through conducting our own FinFET transistor measurements, we calibrate cryogenic transistor models at 10K. With the 7nm cryogenic transistor data, we model Cryo-CACTI for cryogenic caches (due to cache’s vital role in improving performance and their considerable share in area and power of the processor). Using Cryo-CACTI, our evaluation reveals considerable improvements in the energy efficiency (up to 99%) of cryogenic caches of larger sizes compared to the caches at room temperature (300K). Additionally, we explore alternative cache configurations at circuit-level to optimize cryogenic operation. Furthermore, we use Cryo-CACTI to explore the performance/energy consumption of cryogenic caches while simulating workloads such as SPEC CPU2017 and machine learning via neural networks. © 1968-2012 IEEE.

关键词： Combinatorial circuits

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Point Cloud-based Grasping for Soft Hand Exoskeleton

arXiv

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arXiv 2025年

作者： Hu, Chen Tricomi, Enrica Rho, Eojin Kim, Daekyum Masia, Lorenzo Luo, Shan Gionfrida, Letizia King’s College London LondonWC2R 2LS United Kingdom Heidelberg University Heidelberg69120 Germany School of Computing KAIST Daejeon34141 Korea Republic of School of Mechanical Engineering School of Smart Mobility Korea University Seoul02841 Korea Republic of Munich Institute for Robotics and Machine Intelligence Technical University of Munich Munich80333 Germany

Grasping is a fundamental skill for interacting with and manipulating objects in the environment. However, this ability can be challenging for individuals with hand impairments. Soft hand exoskeletons designed to assist grasping can enhance or restore essential hand functions, yet controlling these soft exoskeletons to support effectively users remains difficult due to the complexity of understanding the environment. This study presents a vision-based predictive control framework that leverages contextual awareness from depth perception to predict the grasping target and determine the next control state for activation. Unlike data-driven approaches that require extensive labelled datasets and struggle with generalizability, our method is grounded in geometric modelling, enabling robust adaptation across diverse grasping scenarios. The Grasping Ability Score (GAS) was used to evaluate performance, with our system achieving a state-of-the-art GAS of 91 ± 2% across 15 objects and healthy participants, demonstrating its effectiveness across different object types. The proposed approach maintained reconstruction success for unseen objects, underscoring its enhanced generalizability compared to learning-based models. Copyright © 2025, The Authors. All rights reserved.

关键词： Exoskeleton (robotics)

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Tracking holistic object representations

arXiv

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arXiv 2019年

作者： Sauer, Axel Aljalbout, Elie Haddadin, Sami Munich School of Robotics and Machine Intelligence Technical University of Munich

Recent advances in visual tracking are based on siamese feature extractors and template matching. For this category of trackers, latest research focuses on better feature embeddings and similarity measures. In this work, we focus on building holistic object representations for tracking. We propose a framework that is designed to be used on top of previous trackers without any need for further training of the siamese network. The framework leverages the idea of obtaining additional object templates during the tracking process. Since the number of stored templates is limited, our method only keeps the most diverse ones. We achieve this by providing a new diversity measure in the space of siamese features. The obtained representation contains information beyond the ground truth object location provided to the system. It is then useful for tracking itself but also for further tasks which require a visual understanding of objects. Strong empirical results on tracking benchmarks indicate that our method can improve the performance and robustness of the underlying trackers while barely reducing their speed. In addition, our method is able to match current state-of-the-art results, while using a simpler and older network architecture and running three times *** Codes 68T45, 62H30, 62H35, 51N99, 68T05, 68T20, 68T40, 68W25, 15B99 Copyright © 2019, The Authors. All rights reserved.

关键词： Template matching

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LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

arXiv

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arXiv 2024年

作者： Dorent, Reuben Khajavi, Roya Idris, Tagwa Ziegler, Erik Somarouthu, Bhanusupriya Jacene, Heather LaCasce, Ann Deissler, Jonathan Ehrhardt, Jan Engelson, Sofija Fischer, Stefan M. Gu, Yun Handels, Heinz Kasai, Satoshi Kondo, Satoshi Maier-Hein, Klaus Schnabel, Julia A. Wang, Guotai Wang, Litingyu Wald, Tassilo Yang, Guang-Zhong Zhang, Hanxiao Zhang, Minghui Pieper, Steve Harris, Gordon Kikinis, Ron Kapur, Tina Brigham and Women’s Hospital Harvard Medical School BostonMA United States Massachusetts General Hospital Harvard Medical School BostonMA United States Yunu Inc. CaryNC United States Isomics Inc CambridgeMA United States Dana-Farber Cancer Institute BostonMA United States Technical University Munich Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Munich Germany Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Institute of Medical Informatics University of Lübeck Lübeck Germany German Research Center for Artificial Intelligence Lübeck Germany Niigata University of Health and Welfare Niigata Japan Muroran Institute of Technology Hokkaido Japan Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China University of Electronic Science and Technology of China Chengdu China Heidelberg Germany University of Heidelberg Heidelberg Germany Shanghai AI laboratory Shanghai China

Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite the variety of weakly-supervised techniques proposed, most have been validated only on private datasets or small publicly available datasets. To address this limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge was organized in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to advance weakly-supervised segmentation methods by providing a new, partially annotated dataset and a robust evaluation framework. A total of 16 teams from 5 countries submitted predictions to the validation leaderboard, and 6 teams from 3 countries participated in the evaluation phase. The results highlighted both the potential and the current limitations of weakly-supervised approaches. On one hand, weakly-supervised approaches obtained relatively good performance with a median Dice score of 61.0%. On the other hand, top-ranked teams, with a median Dice score exceeding 70%, boosted their performance by leveraging smaller but fully annotated datasets to combine weak supervision and full supervision. This highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance. . . © 2024, CC BY.

关键词： Image segmentation

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Energy-based Adaptive Control and Learning for Patient-Aware Rehabilitation

Energy-based Adaptive Control and Learning for Patient-Aware...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Erfan Shahriari Dinmukhamed Zardykhan Alexander Koenig Elisabeth Jensen Sami Haddadin Munich School of Robotics and Machine Intelligence Technical University of Munich Munich Germany Reactive Robotics GmbH Munich Germany

In this paper we propose a novel energy-based control scheme for an assist-as-needed rehabilitation strategy, which both adapts the level of support based on patient participation and allows the patient to deviate from the prescribed motion in favor of his/her safety. We build an energy network model, with which we can monitor the energy flow through the system and prescribe a threshold on stored energy. We also develop an adaptive motion control law that shapes the desired trajectory in order to respect the stored energy threshold. Next, we show how adapting the stored energy threshold can be used to change the level of responsiveness to the patient as well as to prevent excessive energy transfer to the human by the system. A criterion is defined for setting this energy threshold, which can be further used for monitoring the patient active participation and for adapting and learning the appropriate assistance level during rehabilitation. Experimental results based on implementation in MATLAB Simscape® and on the VEMO robotic system demonstrate the feasibility of the suggested approach. The presented control scheme can be applied to any system, including position- and torque-controlled robots, and does not require the use of EMG sensors or precise force measurements.

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Exploring the Spatial Correlation of Shadowing in RF-based Device-Free Localization by Block Sparse Bayesian Learning

Exploring the Spatial Correlation of Shadowing in RF-based D...

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IEEE International Conference on robotics and Biomimetics

作者： Jiaju Tan Xin Zhao Xuemei Guo Guoli Wang School of Mathematics and Computational Science Wuyi University Jiangmen China Tianjin Key Laboratory of Intelligent Robotic (tjKLIR) Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781665405362

Device-Free Localization (DFL) realized by the Radio Frequency (RF) signal sensing, is an emerging technology to obtain the target’s position without needing the target carrying any electronic devices or tags. Due to its through-wall detectability and privacy protection, the RF-based DFL is becoming a promising research topic in the Location-based Services (LBS) applications based on Internet of Things (IoT). Among the RF-based DFL approaches, Radio Tomographic Imaging (RTI) is a low-cost and novel computational imaging method to reconstruct the target-induced shadowing effect in the RF monitored area and then estimate the target’s position. However, the multipath interference in the RF sensing network often induces the reconstruction degradation in the RTI system and leads to the position misestimation in DFL applications. Addressing this problem, considering that the target’s shadowing may occupy a small spatial range and then express some spatial structure, this aricle focuses on the spatial correlation of the target’s shadowing. Then an RTI reconstruction methods based on the Block Sparse Bayesian Learning is proposed to explore the spatial correlation in the sparse target’s shadowing. Moreover, the actual localization experiments are conducted to validate the spatial correlation in shadowing is an important property to improve the RTI reconstruction performance and DFL accuracy.

关键词： Performance evaluation Location awareness Radio frequency Correlation Robot sensing systems Bayes methods Sensors

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Joint Velocity and Acceleration Estimation in Serial Chain Rigid Body and Flexible Joint Manipulators

Joint Velocity and Acceleration Estimation in Serial Chain R...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Seyed Ali Baradaran Birjandi Johannes Kühn Sami Haddadin Chair of Robotics Science and Systems Intelligence Munich School of Robotics and Machine Intelligence Technical University Munich (TUM) Heßstr. 134 Munich GERMANY

This paper deals with the problem of accurately computing and estimating joint velocity and acceleration in robotic manipulators. Generally, it is well known that numerical differentiation of noisy position signals even with significant filtering is no viable solution. This is especially true for computing joint acceleration. Specifically, our solution to this problem fuses joint position measurement with link accelerometers, which are affordable and easy to install. Since the sensor readings are affected by noise, drift and bias, suitable data fusion and filtering methods are proposed for improving the estimation for practical use. Simulation results based on a realistic dynamics model of a 7-DoF robot including various parasitic effects and experimental results with a 7-DoF robot demonstrate the effectiveness of our approach. This method would have multiple use, e.g., in monitoring external joint torques and handle possibly unforeseen collisions. Furthermore, other applications such as load identification and compensation as well as state feedback linearization for flexible joint robots could finally become possible also practical.

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The Role of Robot Payload in the Safety Map Framework

The Role of Robot Payload in the Safety Map Framework

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Mazin Hamad Nico Mansfeld Saeed Abdolshah Sami Haddadin Institute of Robotics and Systems Intelligence Munich School of Robotics and Machine Intelligence Technical University of Munich (TUM) Germany Nico Mansfeld is with the Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany

In practical robotic applications various types of tools are attached for manipulating objects. Besides adding gravitational load to the robot, which results in larger joint torques, such payloads influence the collision safety characteristics through changing surface curvature properties, reflected mass and effective robot speed along a desired motion direction. In this paper, we evaluate the effect a known, unactuated pay-load that is attached to the end-effector has on the intantaneous reflected inertial parameters and maximum task velocity of a robot. The proposed mass update approach relies on the analysis of the kinetic energy matrices, while the velocity maximization is tackled by formulating static optimization problems with different constraints on angular motion of the end-effector. Finally, for analyzing the validity of the introduced approach in the framework of Safety Maps, we discuss simulation results of a PUMA 560 robot that has an exemplary payload attached to its end-effector.

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