Spreading positive change: societal benefits of meditation. - In: Frontiers in psychiatry, ISSN 1664-0640, Bd. 14 (2023), 1038051, S. 01-08
Mindful Universities Research Group: Reyk Albrecht, Christian Dobel, Nicola Döring, Veronika Engert, Orlando Guntinas Lichius, Jens Haueisen, Philipp Kanske, Mike Sandbothe. - The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1038051/full#supplementary-material
Research over the past decades has revealed a variety of beneficial effects of meditation training. These beneficial effects span the levels of health and well-being, cognition, emotion, and social behavior. Around the same time, sociologists have shown that traits and outcomes on the individual level have the potential to spread in communities over three or more degrees. This means, for example, that changes can spread from one person to the next, and on to yet another person. Here, we propose that meditation-induced changes may likewise spread through the social networks of meditation practitioners. Such spreading may happen by positively influencing others through prosocial actions, improved cognitive functioning, and increased positive affect. Positive affective states and their underlying physiological correlates may also be shared in the literal sense. We argue that the spreading of positive meditation effects could provide the basis for collective responses to some of the urgent challenges we face in our current time and society and call for future meditation research to examine the phenomenon.
https://doi.org/10.3389/fpsyt.2023.1038051
Adaptive velocity estimation for Lagrangian systems using modulating functions. - In: 2023 IEEE International Conference on Mechatronics, (2023), insges. 8 S.
Information about the internal position and velocity of a robotic system is crucial for its control. Especially, under uncertain models, changing dynamic parameters and noisy position measurement signals, an adaptive differentiation is needed combining structural knowledge of the model with adequate filtering of the sensor data. To this end, the Modulating Function Method is applied to the Lagrange formulation of the robotic system to preserve the structure while enabling to incorporate nonlinear terms into the integral transform methodology. Different types of Modulating Functions and the function projection approach are used to develop a simultaneous parameter and state estimation procedure for the general structure of open kinematic chains. The developed algorithm for an adaptive velocity estimation is capable of robustly reconstructing the generalized state and consists of an efficient Finite Impulse Response (FIR) filter type implementation. The resulting architecture is demonstrated on a two-link robot setup.
https://doi.org/10.1109/ICM54990.2023.10101935
Vascular MPI: visualization and tracking of rapidly moving samples. - In: International journal on magnetic particle imaging, ISSN 2365-9033, Bd. 9 (2023), 1, 2303044, S. 1-3
Magnetic Particle Imaging (MPI) is a fast imaging technique for the visualization of the distribution of superparamagnetic iron-oxide nanoparticles (SPIONs) in 3D. For spatial encoding, a field free area is moved rapidly through the field of view (FOV) generating a localized signal. Fast moving samples, e.g., a bolus of SPIONs traveling through the large veins in the human body carried by blood flow with velocities in the order of ˜45 cm/s and higher, cause temporal blurring in MPI measurements using common sequences and reconstruction techniques. This hampers the evaluation of dynamics of rapidly moving samples. In this abstract, initial results of rapidly moving samples in form of SPION boluses visualized within an MPI scanner are shown.
https://doi.org/10.18416/IJMPI.2023.2303044
Point cloud processing for environmental analysis in Autonomous Driving using Deep Learning. - Ilmenau : Universitätsverlag Ilmenau, 2023. - 1 Online-Ressource (xvi, 120, LVI Seiten)
Technische Universität Ilmenau, Dissertation 2023
Eines der Hauptziele führender Automobilhersteller sind autonome Fahrzeuge. Sie benötigen ein sehr präzises System für die Wahrnehmung der Umgebung, dass für jedes denkbare Szenario überall auf der Welt funktioniert. Daher sind verschiedene Arten von Sensoren im Einsatz, sodass neben Kameras u. a. auch Lidar Sensoren ein wichtiger Bestandteil sind. Die Entwicklung auf diesem Gebiet ist für künftige Anwendungen von höchster Bedeutung, da Lidare eine genauere, von der Umgebungsbeleuchtung unabhängige, Tiefendarstellung bieten. Insbesondere Algorithmen und maschinelle Lernansätze wie Deep Learning, die Rohdaten über Lernzprozesse direkt verarbeiten können, sind aufgrund der großen Reichweite und der dreidimensionalen Auflösung der gemessenen Punktwolken sehr wichtig. Somit hat sich ein weites Forschungsfeld mit vielen Herausforderungen und ungelösten Problemen etabliert. Diese Arbeit zielt darauf ab, dieses Defizit zu verringern und effiziente Algorithmen zur 3D-Objekterkennung zu entwickeln. Sie stellt ein tiefes Neuronales Netzwerk mit spezifischen Schichten und einer neuartigen Fehlerfunktion zur sicheren Lokalisierung und Schätzung der Orientierung von Objekten aus Punktwolken bereit. Zunächst wird ein 3D-Detektor entwickelt, der in nur einem Vorwärtsdurchlauf aus einer Punktwolke alle Objekte detektiert. Anschließend wird dieser Detektor durch die Fusion von komplementären semantischen Merkmalen aus Kamerabildern und einem gemeinsamen probabilistischen Tracking verfeinert, um die Detektionen zu stabilisieren und Ausreißer zu filtern. Im letzten Teil wird ein Konzept für den Einsatz in einem bestehenden Testfahrzeug vorgestellt, das sich auf die halbautomatische Generierung eines geeigneten Datensatzes konzentriert. Hierbei wird eine Auswertung auf Daten von Automotive-Lidaren vorgestellt. Als Alternative zur zielgerichteten künstlichen Datengenerierung wird ein weiteres generatives Neuronales Netzwerk untersucht. Experimente mit den erzeugten anwendungsspezifischen- und Benchmark-Datensätzen zeigen, dass sich die vorgestellten Methoden mit dem Stand der Technik messen können und gleichzeitig auf Effizienz für den Einsatz in selbstfahrenden Autos optimiert sind. Darüber hinaus enthalten sie einen umfangreichen Satz an Evaluierungsmetriken und -ergebnissen, die eine solide Grundlage für die zukünftige Forschung bilden.
https://doi.org/10.22032/dbt.55809
Model predictive control of parabolic PDE systems under chance constraints. - In: Mathematics, ISSN 2227-7390, Bd. 11 (2023), 6, 1372, S. 1-23
Model predictive control (MPC) heavily relies on the accuracy of the system model. Nevertheless, process models naturally contain random parameters. To derive a reliable solution, it is necessary to design a stochastic MPC. This work studies the chance constrained MPC of systems described by parabolic partial differential equations (PDEs) with random parameters. Inequality constraints on time- and space-dependent state variables are defined in terms of chance constraints. Using a discretization scheme, the resulting high-dimensional chance constrained optimization problem is solved by our recently developed inner-outer approximation which renders the problem computationally amenable. The proposed MPC scheme automatically generates probability tubes significantly simplifying the derivation of feasible solutions. We demonstrate the viability and versatility of the approach through a case study of tumor hyperthermia cancer treatment control, where the randomness arises from the thermal conductivity coefficient characterizing heat flux in human tissue.
https://doi.org/10.3390/math11061372
TMS and fMRI-based localization of the attention network. - In: Brain stimulation, ISSN 1876-4754, Bd. 16 (2023), 1, S. 291-292
Richtiger Name des 4. Verfassers: Jens Haueisen
https://doi.org/10.1016/j.brs.2023.01.517
Closed-loop robotic TMS motor mapping using an online-optimized sampling scheme. - In: Brain stimulation, ISSN 1876-4754, Bd. 16 (2023), 1, S. 320
https://doi.org/10.1016/j.brs.2023.01.593
Fast incremental reconfiguration of dynamic time-sensitive networks at runtime. - In: Computer networks, Bd. 224 (2023), 109606, S. 1-13
Static configurations in Time-sensitive Networking (TSN) using the Time-aware Shaper allow precise calculations of deterministic, tight bandwidth and latency guarantees for real-time industrial application streams. However, this static configuration makes introducing flexible changes to a TSN system at runtime very hard. Scenarios of adaptive TSN networks envision that the network configuration evolves with time in accordance with anticipated changes, such as the dynamicity of machine formations and machine reconfigurations. In this paper, we propose a notion of flexibility of scheduler configurations along a network path that facilitates introducing changes to TSN network configurations at runtime. Based on this notion, we develop and analyze algorithms to incrementally reconfigure TSN using the Time-Aware Shaper. These reconfigurations include determining the admissibility of new or changed streams that may possess individual deadlines.
https://doi.org/10.1016/j.comnet.2023.109606
Transferability of cathodal tDCS effects from the primary motor to the prefrontal cortex: a multimodal TMS-EEG study. - In: Brain stimulation, ISSN 1876-4754, Bd. 16 (2023), 2, S. 515-539
Neurophysiological effects of transcranial direct current stimulation (tDCS) have been extensively studied over the primary motor cortex (M1). Much less is however known about its effects over non-motor areas, such as the prefrontal cortex (PFC), which is the neuronal foundation for many high-level cognitive functions and involved in neuropsychiatric disorders. In this study, we, therefore, explored the transferability of cathodal tDCS effects over M1 to the PFC. Eighteen healthy human participants (11 males and 8 females) were involved in eight randomized sessions per participant, in which four cathodal tDCS dosages, low, medium, and high, as well as sham stimulation, were applied over the left M1 and left PFC. After-effects of tDCS were evaluated via transcranial magnetic stimulation (TMS)-electroencephalography (EEG), and TMS-elicited motor evoked potentials (MEP), for the outcome parameters TMS-evoked potentials (TEP), TMS-evoked oscillations, and MEP amplitude alterations. TEPs were studied both at the regional and global scalp levels. The results indicate a regional dosage-dependent nonlinear neurophysiological effect of M1 tDCS, which is not one-to-one transferable to PFC tDCS. Low and high dosages of M1 tDCS reduced early positive TEP peaks (P30, P60), and MEP amplitudes, while an enhancement was observed for medium dosage M1 tDCS (P30). In contrast, prefrontal low, medium and high dosage tDCS uniformly reduced the early positive TEP peak amplitudes. Furthermore, for both cortical areas, regional tDCS-induced modulatory effects were not observed for late TEP peaks, nor TMS-evoked oscillations. However, at the global scalp level, widespread effects of tDCS were observed for both, TMS-evoked potentials and oscillations. This study provides the first direct physiological comparison of tDCS effects applied over different brain areas and therefore delivers crucial information for future tDCS applications.
https://doi.org/10.1016/j.brs.2023.02.010
The HAInich: a multidisciplinary vision data-set for a better understanding of the forest ecosystem. - In: Scientific data, ISSN 2052-4463, Bd. 10 (2023), 1, 168, S. 1-11
We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities and paves the way for testing forest environment 3D perception tasks and mission set automation for robotics.
https://doi.org/10.1038/s41597-023-02010-8