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European Signal Processing Conference (EUSIPCO)

作者： Sabri Mustafa Kahya Muhammet Sami Yavuz Eckehard Steinbach Department of Computer Engineering Technical University of Munich School of Computation Information and Technology Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Infineon Technologies AG

Out-of-distribution (OOD) detection recently has drawn attention due to its critical role in the safe deployment of modern neural network architectures in real-world applications. The OOD detectors aim to distinguish samples that lie outside the training distribution in order to avoid the overconfident predictions of machine learning models on OOD data. Existing detectors, which mainly rely on the logit, intermediate feature space, softmax score, or reconstruction loss, manage to produce promising results. However, most of these methods are developed for the image domain. In this study, we propose a novel reconstruction-based OOD detector to operate on the radar domain. Our method exploits an autoencoder (AE) and its latent representation to detect the OOD samples. We propose two scores incorporating the patch-based reconstruction loss and the energy value calculated from the latent representations of each patch. We achieve an AUROC of 90.72% on our dataset collected by using 60 GHz short-range FMCW Radar. The experiments demonstrate that, in terms of AUROC and AUPR, our method outperforms the baseline (AE) and the other state-of-the-art methods. Also, thanks to its model size of 641 kB, our detector is suitable for embedded usage.

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Mcrood: Multi-Class Radar Out-Of-Distribution Detection

Mcrood: Multi-Class Radar Out-Of-Distribution Detection

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sabri Mustafa Kahya Muhammet Sami Yavuz Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Technical University of Munich School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Infineon Technologies AG

Out-of-distribution (OOD) detection has recently received special attention due to its critical role in safely deploying modern deep learning (DL) architectures. This work proposes a reconstruction-based multi-class OOD detector that operates on radar range doppler images (RDIs). The detector aims to classify any moving object other than a person sitting, standing, or walking as OOD. We also provide a simple yet effective pre-processing technique to detect minor human body movements like breathing. The simple idea is called respiration detector (RESPD) and eases the OOD detection, especially for human sitting and standing classes. On our dataset collected by 60GHz short-range FMCW Radar, we achieve AUROCs of 97.45%, 92.13%, and 96.58% for sitting, standing, and walking classes, respectively. We perform extensive experiments and show that our method outperforms state-of-the-art (SOTA) OOD detection methods. Also, our pipeline performs 24 times faster than the second-best method and is very suitable for real-time processing.

关键词： Legged locomotion Pipelines Neural networks Radar detection Detectors Signal processing Radar imaging

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CSM: Contact Sensitivity Maps for Benchmarking Robot Collision Handling systems

CSM: Contact Sensitivity Maps for Benchmarking Robot Collisi...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Robin Jeanne Kirschner João Jantalia Nico Mansfeld Saeed Abdolshah Sami Haddadin Institute for Robotics and Systems Intelligence Munich School of Robotics and Machine Intelligence Technical University of Munich Munich Germany

In physical human-robot interaction (pHRI), robots need to detect and react to intended and unintended contacts in a safe manner. Proprioceptive sensing capabilities and collision detection and identification techniques differ among commercially available robots, which means that also their sensitivity to detect dynamic collisions with the environment or the human co-worker differ. Up to now, there exists no standardized procedure for assessing the contact sensitivity of a robotic system. In this paper, we propose the concept of contact sensitivity maps (CSM), a relationship between the robot's dynamic impact properties and the reliability of its collision handling. The CSM allows the robot user to determine for which robot workspace areas and dynamic collision parameters (mass, velocity) reliable contact detection and reaction can be expected. We propose a standardized benchmarking procedure and test setup for deriving CSMs. Finally, we analyze and compare the experimental results of the Universal Robots UR10e, UR5e, and Franka Emika Panda, where we observe significant differences in contact sensitivity.

关键词： Sensitivity Human-robot interaction Benchmark testing Tools Robot sensing systems Sensors Safety

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Real-Time Semantic Video Communication of General Scenes

Real-Time Semantic Video Communication of General Scenes

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IEEE International Conference on Image Processing

作者： Cem Eteke Alexander Griessel Wolfgang Kellerer Eckehard Steinbach Chair of Media Technology School of Computation Information and Technology Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence School of Computation Information and Technology Technical University of Munich Munich Germany Chair of Communication Networks School of Computation Information and Technology Technical University of Munich Munich Germany

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This paper presents a real-time semantic video communication method for general scenes, combining lossy semantic map coding with motion compensation to achieve reduced bit rates while maintaining perceptual and semantic quality. Our findings show that semantic image synthesis effectively adapts to minute errors resulting from motion estimation, eliminating the need to transmit the residuals. We recommend the Group of Pictures approach as a more efficient alternative. Comparative assessments against HEVC and VVC confirm the method’s effectiveness. This research paves the way for efficient real-time semantic video communication, addressing the demands of data-intensive visual applications.

关键词： Video coding Training Visualization Image coding Motion estimation Semantics Bit rate

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Nonlinearity of optoacoustic signals and a new contrast mechanism for imaging

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光：科学与应用（英文版） 2025年第5期14卷 1420-1432页

作者： Jaber Malekzadeh-Najafabadi Jaya Prakash Daniel Razansky Jorge Ripoll Vipul Gujrati Vasilis Ntziachristos Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Institute for Biomedical Engineering Department of Information Technology and Electrical EngineeringETH ZurichZurichSwitzerland Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Department of Instrumentation and Applied Physics Indian Institute of ScienceBengaluruIndia Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology Faculty of MedicineUniversity of ZurichZurichSwitzerland Institute for Biomedical Engineering Department of Information Technology and Electrical EngineeringETH ZurichZurichSwitzerland Department of Bioengineering and Aerospace Engineering Universidad Carlos Ⅲ de MadridMadridSpain Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Munich Institute of Robotics and Machine Intelligence(MIRMI) Technical University of MunichMunichGermany

Optoacoustic signals behave nonlinearly at light fluences above a few mJ/cm2,which may affect the interpretation and quantification of *** has been proposed that optoacoustic nonlinearity arises from the heat-induced formation of nanobubbles or changes in local thermo-physical ***,such explanations are only valid at much higher fluences than typically used in biomedical optoacoustic imaging(＞20 mJ/cm2)or in the presence of materials with high absorption coefficients such as gold *** propose herein that electromagnetic permittivity changes in response to photon absorption are major source of optoacoustic signal nonlinearity at low *** provide theoretical and experimental evidence that supports this postulation and show that optoacoustic pressure responses due to permittivity changes,which are function of thermally excited third-order nonlinear susceptibility,can explain the nonlinear behavior of the optoacoustic *** different materials exhibit different thermally excited third-order nonlinear susceptibility,this property could function as a new contrast mechanism that can identify the sensitivity of a substance's dielectric constant to photon-induced temperature ***,we propose an imaging method based on nonlinear optoacoustic signals that exploits this newly identified contrast *** findings may have far-reaching implications for improving the accuracy of optoacoustics and utilizing the proposed new contrast mechanism would advance our understanding of cellular and tissue functionality.

关键词： mechanism contrast imaging nonlinearity optoacoustic signals

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Three-Dimensional Vehicle Dynamics State Estimation for High-Speed Race Cars under varying Signal Quality

Three-Dimensional Vehicle Dynamics State Estimation for High...

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

作者： Sven Goblirsch Marcel Weinmann Johannes Betz Department of Mobility Systems Engineering TUM School of Engineering and Design Technical University of Munich Garching Germany Professorship of Autonomous Vehicle Systems TUM School of Engineering and Design Technical University of Munich Garching Germany Munich Institute of Robotics and Machine Intelligence (MIRMI)

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work aims to present a three-dimensional vehicle dynamics state estimation under varying signal quality. Few researchers have investigated the impact of three-dimensional road geometries on the state estimation and, thus, neglect road inclination and banking. Especially considering high velocities and accelerations, the literature does not address these effects. Therefore, we compare two- and three-dimensional state estimation schemes to outline the impact of road geometries. We use an Extended Kalman Filter with a point-mass motion model and extend it by an additional formulation of reference angles. Furthermore, virtual velocity measurements significantly improve the estimation of road angles and the vehicle’s side slip angle. We highlight the importance of steady estimations for vehicle motion control algorithms and demonstrate the challenges of degraded signal quality and Global Navigation Satellite system dropouts. The proposed adaptive covariance facilitates a smooth estimation and enables stable controller behavior. The developed state estimation has been deployed on a high-speed autonomous race car at various racetracks. Our findings indicate that our approach outperforms state-of-the-art vehicle dynamics state estimators and an industry-grade Inertial Navigation system. Further studies are needed to investigate the performance under varying track conditions and on other vehicle types.

关键词： Temperature measurement Geometry Global navigation satellite system Roads Banking Prediction algorithms Velocity measurement Automobiles State estimation Vehicle dynamics

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Lossy Coding for Spatially Adaptive Conditioning in Semantic Image Communication

Lossy Coding for Spatially Adaptive Conditioning in Semantic...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Cem Eteke Alexander Griessel Wolfgang Kellerer Eckehard Steinbach Chair of Media Technology Information and Technology Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence School of Computation Information and Technology Technical University of Munich Munich Germany Chair of Communication Networks Information and Technology Technical University of Munich Munich Germany

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The increasing demand for high-quality, real-time visual communication and the growing user expectations, coupled with limited network resources, necessitate novel approaches to semantic image communication. This paper presents a method to enhance semantic image communication that combines a novel lossy semantic encoding approach with spatially adaptive semantic image synthesis models. By developing a model-agnostic training augmentation strategy, our approach substantially reduces susceptibility to distortion introduced during encoding, effectively eliminating the need for lossless semantic encoding. Comprehensive evaluation across two spatially adaptive conditioning methods and three popular datasets indicates that this approach enhances semantic image communication at very low bit rate regimes.

关键词： Training Adaptation models Image coding Visual communication Image synthesis Image communication Semantics Bit rate Distortion Real-time systems

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QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation

arXiv

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arXiv 2023年

作者： Blanco-Mulero, David Alcan, Gokhan Abu-Dakka, Fares J. Kyrki, Ville School of Electrical Engineering Aalto University Finland Munich Institute of Robotics and Machine Intelligence Technische Universität München München80992 Germany

Pre-defined manipulation primitives are widely used for cloth manipulation. However, cloth properties such as its stiffness or density can highly impact the performance of these primitives. Although existing solutions have tackled the parameterisation of pick and place locations, the effect of factors such as the velocity or trajectory of quasi-static and dynamic manipulation primitives has been neglected. Choosing appropriate values for these parameters is crucial to cope with the range of materials present in house-hold cloth objects. To address this challenge, we introduce the Quasi-Dynamic Parameterisable (QDP) method, which optimises parameters such as the motion velocity in addition to the pick and place positions of quasi-static and dynamic manipulation primitives. In this work, we leverage the framework of Sequential Reinforcement Learning to decouple sequentially the parameters that compose the primitives. To evaluate the effectiveness of the method we focus on the task of cloth unfolding with a robotic arm in simulation and real-world experiments. Our results in simulation show that by deciding the optimal parameters for the primitives the performance can improve by 20% compared to sub-optimal ones. Real-world results demonstrate the advantage of modifying the velocity and height of manipulation primitives for cloths with different mass, stiffness, shape and size. Supplementary material, videos, and code, can be found at https://***/view/qdp-srl. Copyright © 2023, The Authors. All rights reserved.

关键词： Stiffness

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QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation

QDP: Learning to Sequentially Optimise Quasi-Static and Dyna...

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

作者： David Blanco-Mulero Gokhan Alcan Fares J. Abu-Dakka Ville Kyrki School of Electrical Engineering Aalto University Finland Munich Institute of Robotics and Machine Intelligence Technische Universität München München Germany Intelligent Robotics Group EEA Aalto University Espoo Finland

Pre-defined manipulation primitives are widely used for cloth manipulation. However, cloth properties such as its stiffness or density can highly impact the performance of these primitives. Although existing solutions have tackled the parameterisation of pick and place locations, the effect of factors such as the velocity or trajectory of quasi-static and dynamic manipulation primitives has been neglected. Choosing appropriate values for these parameters is crucial to cope with the range of materials present in house-hold cloth objects. To address this challenge, we introduce the Quasi-Dynamic Parameterisable (QDP) method, which optimises parameters such as the motion velocity in addition to the pick and place positions of quasi-static and dynamic manipulation primitives. In this work, we leverage the framework of Sequential Reinforcement Learning to decouple sequentially the parameters that compose the primitives. To evaluate the effectiveness of the method, we focus on the task of cloth unfolding with a robotic arm in simulation and real-world experiments. Our results in simulation show that by deciding the optimal parameters for the primitives the performance can improve by 20% compared to sub-optimal ones. Real-world results demonstrate the advantage of modifying the velocity and height of manipulation primitives for cloths with different mass, stiffness, shape, and size. Supplementary material, videos, and code, can be found at https://***/view/qdp-srl.

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Unveiling the Hidden Impact of Self-Heating on Ferroelectric FinFET and FDSOI based In-Memory Computing

Unveiling the Hidden Impact of Self-Heating on Ferroelectric...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Swetaki Chatterjee Nistha Baruah Swati Deshwal Anirban Kar Om Prakash Shivendra Singh Parihar Yogesh Singh Chauhan Hussam Amrouch Indian Institute of Technology Kanpur India University of Stuttgart Germany NIT Silchar India Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Munich Germany

Ferroelectric Field-Effect Transistors (FeFETs) having the same structure as normal transistors are affected by self-heating. Consequently, there is a reduction in the ON current and a decrease in the sensing margin which increases the probability of error for in-memory computing applications. In this paper, we have investigated for the first time how self-heating impacts the reliability of Fe-FinFET and Fe-FDSOI-based systems. Our analysis unveils that Fe-FinFET devices are more prone to self-heating effects as compared to Fe-FDSOI devices due to channel confinement. Furthermore, we have evaluated how self-heating impacts the performance of Fe-FET-based various in-memory computing systems.

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