Titlebook: High Performance Computing in Science and Engineering ‘22; Transactions of the Wolfgang E. Nagel,Dietmar H. Kröner,Michael M. Res Conferen

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Badal Mondal,Raza Ullah Khan,Florian Kreuter,Patrick Maue,Sudip Pan,Fabian Pieck,Hendrik Weiske,Ralfred depth specifying information. We present a computational model to evaluate how does self-locomotion experience influences the estimation of distance in infants. The model assigns an estimated distance label to salient objects in the scene, through a . that computes binocular disparities. Emphasi

Trigger-Point

M. Krenz,A. Bocchini,T. Biktagirov,A. Kozub,S. Badalov,S. Neufeld,I. A. Ruiz Alvarado,U. Gerstmann,W the end of May 2021..The 7 full papers presented in the volume were carefully reviewed and selected from 22 submissions. The papers are organized in topical sections on biomedical applications and alternative proposals and its applications..

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high contamination of artifacts, large sample sizes, reproducibility of the procedure, and the number of EEG channels. However, traditional approaches still use manual rejection, which is unsustainable. This article proposes an automatic preprocessing procedure for shared neural representations betw

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Jiasheng Yang,Alexander Stroh,Pourya Forooghidorff distance (WHD) as part of the loss function during the training and validation of a fully convolutional neural network (FCNN) model for detecting ladybird beetle specimens. The FCNN-based detector was trained and validated using a ten-fold cross-validation method on a database composed of 2,63

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Johannes M. F. Peter,Tobias Gibis,Markus J. Klokere variety of tasks related to agriculture, education, mining, healthcare, security, and defense sectors. Therefore, this chapter introduces the design and development of a robotic arm with four degrees of freedom (DoF) intended for implementation in educational environments as an interactive robotic

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Xu Chu,Wenkang Wang,Bernhard Weigandg neural signals. Electroencephalography (EEG)-based BCIs, in combination with Motorized Mini Exercise Bikes (MMEBs), have emerged as promising tools for post-stroke patient rehabilitation. Nevertheless, the EEG signal-to-noise ratio (SNR) remains a challenge, susceptible to interference from physic

Flat-Feet

Johanna Potyka,Jonathan Stober,Jonathan Wurst,Matthias Ibach,Jonas Steigerwald,Bernhard Weigand,Kathating this process would allow large-scale research on biodiversity hotspots of large conspicuous mammals and bird species. This paper explores the use of deep learning species-level object detection and classification models for this task, using two state-of-the-art architectures, YOLOv5 and Faster

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Junaid Ullah,Marco Hillebrand,Maximilian Ehrle,Thorsten Lutz,Wolfgang Heinze,Jochen Wildon that conventional techniques require manual annotations that are difficult to acquire. This study aims to obtain reliable segmentations without requiring large data sets labeled by expert personnel. To address this problem, we use the Barlow Twins approach to pre-train the encoder of a UNet model