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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Diaz-Fortuny, Javier Benkhelifa, Mahdi Grill, Alexander Bury, Erik Degraeve, Robin Kaczer, Ben Amrouch, Hussam Imec Kapeldreef 75 Leuven3001 Belgium Technical University of Munich Munich Germany Tum School of Computation Information and Technology Munich Germany Ai Processor Design Munich Germany Munich Institute of Robotics and Machine Intelligence Munich Germany

ISBN: (纸本)9798331504779

The increasing interest in cryogenic circuits is driven by their transformative potential across high-performance computing, medical devices, space exploration, and quantum technologies. Operating transistors at cryogenic temperatures, such as 77 K and below, yields substantial improvements, including increased ON current, reduced OFF current, and enhanced sub-threshold slope, paving the way for significant gains in efficiency. While recent studies have explored device-level reliability at cryogenic temperatures, circuit-level reliability-particularly under bias temperature instability (BTI) and hot carrier degradation (HCD)-remains underexamined, leaving critical aging mechanisms at these temperatures poorly understood. To bridge this gap, we designed and fabricated a fully customized chip in a commercial 28 nm technology. The chip integrates diverse ring oscillator (RO) circuits for precise characterization of BTI and HCD aging effects, enabling evaluation of their impact on performance at cryogenic temperatures as low as 4 K. This work ensures that as cryogenic CMOS technologies drive innovation in critical applications, their reliability is comprehensively understood and advanced. © 2025 IEEE.

关键词： CMOS integrated circuits

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Adapting Learned Image Codecs To Screen Content Via Adjustable Transformations

Adapting Learned Image Codecs To Screen Content Via Adjustab...

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IEEE International Conference on Image Processing

作者： H. Burak Dogaroglu A. Burakhan Koyuncu Atanas Boev Elena Alshina Eckehard Steinbach School of Computation Information and Technology TU Munich Huawei Munich Research Center Munich Institute of Robotics and Machine Intelligence

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

As learned image codecs (LICs) become more prevalent, their low coding efficiency for out-of-distribution data becomes a bottleneck for some applications. To improve the performance of LICs for screen content (SC) images without breaking backwards compatibility, we propose to introduce parameterized and invertible linear transformations into the coding pipeline without changing the underlying baseline codec’s operation flow. We design two neural networks to act as prefilters and postfilters in our setup to increase the coding efficiency and help with the recovery from coding artifacts. Our end-to-end trained solution achieves up to $10 \%$ bitrate savings on SC compression compared to the baseline LICs while introducing only $1 \%$ extra parameters.

关键词： Codecs Image coding Costs Microscopy Neural networks Bit rate Pipelines

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Towards Flexible Biolaboratory Automation: Container Taxonomy-Based, 3D-Printed Gripper Fingers*

Towards Flexible Biolaboratory Automation: Container Taxonom...

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

作者： Henning Zwirnmann Dennis Knobbe Utku Culha Sami Haddadin Chair of Robotics and Systems Intelligence School of Computation Information and Technology Technical University of Munich Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany

Automation in the life science research laboratory is a paradigm that has gained increasing relevance in recent years. Current robotic solutions often have a limited scope, which reduces their acceptance and prevents the realization of complex workflows. The transport and manipulation of laboratory supplies with a robot is a particular case where this limitation manifests. In this paper, we deduce a taxonomy of biolaboratory liquid containers that clarifies the need for a flexible grasping solution. Using the taxonomy as a guideline, we design fingers for a parallel robotic gripper which are developed with a monolithic dual-extrusion 3D print that integrates rigid and soft materials to optimize gripping properties. We design fine-tuned fingertips that provide stable grasps of the containers in question. A simple actuation system and a low weight are maintained by adopting a passive compliant mechanism. The ability to resist chemicals and high temperatures and the integration with a tool exchange system render the fingers usable for daily laboratory use and complex workflows. We present the task suitability of the fingers in experiments that show the wide range of vessels that can be handled as well as their tolerance against displacements and their grasp stability.

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A Force-Sensitive Exoskeleton for Teleoperation: An Application in Elderly Care robotics

A Force-Sensitive Exoskeleton for Teleoperation: An Applicat...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Alexander Toedtheide Xiao Chen Hamid Sadeghian Abdeldjallil Naceri Sami Haddadin Chair of Robotics and Systems Intelligence Munich School of Robotics and Machine Intelligence Technical University of Munich Munich Germany Faculty of Engineering University of Isfahan Isfahan Iran

With the increasing demand for new healthcare solutions and technologies, such as those resulting from the COVID-19 crisis, and the growing elderly population, exoskeletons for teleoperation are a promising solution for many future medical applications. In this context, we propose two force- sensitive upper-limb exoskeletons for teleoperation, that are characterized by: i) torque-controlled robotic actuators, ii) rigid-body model compensations, and iii) a lightweight design achieved through the use of Bowden cable transmissions and remotely placed actuators. Specifically, we present a semi-active upper-limb exoskeleton for which we demonstrate human- device interaction control and bilateral teleoperation with force- feedback, evaluated via simulation, in the lab and over the Internet. We also introduce a design for a future fully-active upper-limb exoskeleton with two contact force/torque sensors, for a dual-arm device, which features a novel 3-degrees-of- freedom exoskeleton shoulder design and a contact wrench mitigation controller, as demonstrated through simulation. With this work, we propose the essential technical steps towards a novel teleoperation system for elderly care.

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Biomechanical Test Setup for the Investigation of Forehead Suture Techniques

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Current Directions in Biomedical Engineering 2023年第1期9卷 532-535页

作者： Micheler, Carina M. Lang, Jan J. Wilhelm, Nikolas J. Schaack, Victor G. Eisenhart-Rothe, Rüdiger Von Burgkart, Rainer H. H. Bäumlisberger, Anja Wachtel, Nikolaus Department of Orthopaedics and Sports Orthopaedics University Hospital Rechts der Isar School of Medicine Technical University of Munich Munich Germany Institute for Machine Tools and Industrial Management School of Engineering and Design Technical University of Munich Garching near Munich Germany Non-destructive Testing School of Engineering and Design Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence Department of Electrical and Computer Engineering Technical University of Munich Munich Germany Division of Hand Plastic and Aesthetic Surgery University Hospital LMU Munich Munich Germany

Wound healing can be delayed if the biomechanical stability of the wound closure is inadequate. Therefore, it is necessary to investigate different suturing techniques for their biomechanical stability. In this study, suturing techniques suitable for the forehead area were investigated. For this application, a special test setup was developed to simulate the curvature of the forehead and the corresponding physiological configuration. The average forehead curvature is 62.24 ± 4.11 mm in radius. To simulate this curvature, the skin specimens are subjected to tensile stress over the spherical surface using a standard uniaxial testing machine. For the evaluation, an automated evaluation tool for MATLAB was also developed. Three different suturing techniques (Straight, Lazy-S, Zigzag) were investigated and tested for their biomechanical stability. Of the three suturing techniques, the Zigzag suture proved to be the most stable with the highest stiffness of 44.23 ± 8.18 % and the highest final failure of 32.60 ± 4.95 % (relative to the control sample without incision). The study has shown that the test setup can be used to investigate different forehead suture techniques. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： MATLAB

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Snapshot: Towards Application-Centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments

Snapshot: Towards Application-Centered Models for Pedestrian...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2025

作者： Uhlemann, Nico Zhou, Yipeng Mohr, Tobias Simeon Lienkamp, Markus Technical University of Munich Germany School of Engineering & Design Institute of Automotive Technology Munich Institute of Robotics and Machine Intelligence Germany

ISBN: (纸本)9798331536626

This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they often revolve around pedestrian datasets excluding traffic-related information, or resemble architectures that are either not real-time capable or robust. To address these limitations, we first introduce a dedicated benchmark based on Argoverse 2, specifically targeting pedestrians in traffic environments. Following this, we present Snapshot, a modular, feed-forward neural network that outperforms the current state of the art, reducing the Average Displacement Error (ADE) by 8.8 % while utilizing significantly less information. Despite its agent-centric encoding scheme, Snapshot demonstrates scalability, real-time performance, and robustness to varying motion histories. Moreover, by integrating Snapshot into a modular autonomous driving software stack, we showcase its real-world applicability.11https://***/TUMFTM/Snapshot © 2025 IEEE.

关键词： Feedforward neural networks

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Scenario Understanding of Traffic Scenes Through Large Visual Language Models

Scenario Understanding of Traffic Scenes Through Large Visua...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2025

作者： Rivera, Esteban Lubberstedt, Jannik Uhlemann, Nico Lienkamp, Markus Technical University of Munich Germany School of Engineering & Design Institute of Automotive Technology Munich Institute of Robotics and Machine Intelligence Germany

ISBN: (纸本)9798331536626

Deep learning models for autonomous driving, encompassing perception, planning, and control, depend on vast datasets to achieve their high performance. However, their generalization often suffers due to domain-specific data distributions, making an effective scene-based categorization of samples necessary to improve their reliability across diverse domains. Manual captioning, though valuable, is both laborintensive and time-consuming, creating a bottle-neck in the data annotation process. Large Visual Language Models (LVLMs) present a compelling solution byautomating image analysis and categorization through contextual queries, often without requiring retraining for new categories. In this study, we evaluate the capabilities of LVLMs, including GPT-4 and LLaVA, to understand and classify urban traffic scenes on both an inhouse dataset and the BDD100K. We propose a scalable captioning pipeline that integrates state-of-the-art models, enabling a flexible deployment on new datasets. Our analysis, combining quantitative metrics with qualitative insights, demonstrates the effectiveness of LVLMs to understand urban traffic scenarios and highlights their potential as an efficient tool for data-driven advancements in autonomous driving. © 2025 IEEE.

关键词： Deep learning

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Scenario Understanding of Traffic Scenes Through Large Visual Language Models

arXiv

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arXiv 2025年

作者： Rivera, Esteban Lübberstedt, Jannik Uhlemann, Nico Lienkamp, Markus Technical University of Munich Germany School of Engineering & Design Institute of Automotive Technology Munich Institute of Robotics and Machine Intelligence Germany

Deep learning models for autonomous driving, encompassing perception, planning, and control, depend on vast datasets to achieve their high performance. However, their generalization often suffers due to domain-specific data distributions, making an effective scene-based categorization of samples necessary to improve their reliability across diverse domains. Manual captioning, though valuable, is both labor-intensive and time-consuming, creating a bottleneck in the data annotation process. Large Visual Language Models (LVLMs) present a compelling solution by automating image analysis and categorization through contextual queries, often without requiring retraining for new categories. In this study, we evaluate the capabilities of LVLMs, including GPT-4 and LLaVA, to understand and classify urban traffic scenes on both an in-house dataset and the BDD100K. We propose a scalable captioning pipeline that integrates state-of-the-art models, enabling a flexible deployment on new datasets. Our analysis, combining quantitative metrics with qualitative insights, demonstrates the effectiveness of LVLMs to understand urban traffic scenarios and highlights their potential as an efficient tool for data-driven advancements in autonomous driving. © 2025, CC BY.

关键词： Visual languages

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RadarCNN: Learning-Based Indoor Object Classification from IQ Imaging Radar Data

RadarCNN: Learning-Based Indoor Object Classification from I...

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IEEE National Conference on Radar

作者： Stefan Hägele Fabian Seguel Driton Salihu Marsil Zakour Eckehard Steinbach Chair of Media Technology Technical University of Munich School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

Radar sensors operating in the mmWave frequency range face challenges when used as indoor perception and imaging devices, primarily due to noise and multipath signal distortions. These distortions often impair the sensors' ability to accurately perceive and image the indoor environment. Nevertheless, this sensor offers distinct advantages over camera and LiDAR sensors. This encompasses the estimation of object reflectivity, known as radar cross-section (RCS), and the ability to penetrate through objects that are thin or have low reflectivity. This results in a ‘through-the-wall’ sensing capability. Due to the aforementioned disadvantages, most research in the field of imaging radar tends to exclude indoor areas. We introduce a machine learning-based mmWave MIMO FMCW imaging radar object classifier designed to identify small, hand-sized objects in indoor settings, utilizing only radar IQ samples as input. This system achieves 97–99% accuracy on our test set and maintains approximately 50% accuracy even under challenging conditions, such as increased background noise and occlusion of sample objects, without the need for adjusting training or pre-processing. This demonstrates the robustness of our approach and offers insights into what needs to be improved in the future to achieve generalization and very high accuracy even in the presence of significant indoor perturbations.

关键词： Reflectivity Image sensors Training Radar cross-sections Radar Radar imaging Distortion

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Robust Independent Component Analysis based EMG decomposition – a comparison study

Robust Independent Component Analysis based EMG decompositio...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ioannis Xygonakis Melissa Zavaglia Sami Haddadin Technical University of Munich Germany TUM School of Computation Information and Technology Chair of Robotics and Systems Intelligence (RSI) Munich Institute of Robotics and Machine Intelligence (MIRMI)

High density surface Electromyography (HD-sEMG) provides a high fidelity measurement of the myoelectric activity that can be leveraged by EMG decomposition methods to estimate the motor neuron discharges. Independent Component Analysis (ICA) methods are used as basis for many EMG decomposition algorithms, for the estimation of motor unit action potential signals. Accurate source separation is a non-trivial task in EMG decomposition. While FastICA is widely used for this purpose, other methods with attractive characteristics, such as RobustICA, remain relatively unexplored. The purpose of the current work is to compare three different ICA-based EMG decomposition methods (FastICA, RobustICA and RobustICA LCH ) in terms of decomposition accuracy and computation time. The evaluation was performed on simulated data using a decomposition algorithm inspired by previous studies. Our results demonstrate that RobustICA outperforms the other methods in terms of number of correctly identified motor units, high decomposition accuracy, and low computation time, across different muscle contraction levels.

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