Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Michael Arens"'
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 4, Pp 966-989 (2021)
Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized as being non-transparent and their predictions n
Externí odkaz:
https://doaj.org/article/db49392b33284f79b3f5ee87b0fffa83
Publikováno v:
ISPRS Open Journal of Photogrammetry and Remote Sensing, Vol 5, Iss , Pp 100019- (2022)
Automated change detection based on urban mobile laser scanning data is the foundation for a whole range of applications such as building model updates, map generation for autonomous driving and natural disaster assessment. The challenge with mobile
Externí odkaz:
https://doaj.org/article/a6201ce6d2a34cae94c99d35823f7fd2
Publikováno v:
IEEE Access, Vol 9, Pp 77693-77704 (2021)
Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuiti
Externí odkaz:
https://doaj.org/article/c5315c88d29b4c96970f197e3776b9bd
Autor:
Juri Sidorenko, Volker Schatz, Dimitri Bulatov, Norbert Scherer-Negenborn, Michael Arens, Urs Hugentobler
Publikováno v:
IEEE Access, Vol 8, Pp 65726-65733 (2020)
Self-calibration of time-of-arrival positioning systems is made difficult by the non-linearity of the relevant set of equations. This work applies dimension lifting to this problem. The objective function is extended by an additional dimension to all
Externí odkaz:
https://doaj.org/article/aec68a0440614e60b2d2edaa1236355d
Autor:
Stéphane Vujasinović, Stefan Becker, Timo Breuer, Sebastian Bullinger, Norbert Scherer-Negenborn, Michael Arens
Publikováno v:
Applied Sciences, Vol 10, Iss 21, p 7622 (2020)
Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D
Externí odkaz:
https://doaj.org/article/af9051e12ef541f5b336d2ce882a4765
Autor:
Juri Sidorenko, Volker Schatz, Dimitri Bulatov, Norbert Scherer-Negenborn, Michael Arens, Urs Hugentobler
Publikováno v:
Sensors, Vol 20, Iss 7, p 2079 (2020)
The time-difference-of-arrival (TDOA) self-calibration is an important topic for many applications, such as indoor navigation. One of the most common methods is to perform nonlinear optimization. Unfortunately, optimization often gets stuck in a loca
Externí odkaz:
https://doaj.org/article/341360b1884a4dcbbd4d86507bb9c08a
Autor:
Francisco Molina Martel, Juri Sidorenko, Christoph Bodensteiner, Michael Arens, Urs Hugentobler
Publikováno v:
Sensors, Vol 19, Iss 20, p 4366 (2019)
In this work we introduce a relative localization method that estimates the coordinate frame transformation between two devices based on distance measurements. We present a linear algorithm that calculates the relative pose in 2D or 3D with four degr
Externí odkaz:
https://doaj.org/article/e6b6e0b71e374600ac2d0365e3ff9da9
Publikováno v:
Sensors, Vol 19, Iss 13, p 2942 (2019)
The position accuracy based on Decawave Ultra-Wideband (UWB) is affected mainly by three factors: hardware delays, clock drift, and signal power. This article discusses the last two factors. The general approach to clock drift correction uses the pha
Externí odkaz:
https://doaj.org/article/8783bd9bc5d74789b48adc1e1b2b3ff3
Autor:
Jörg Haeberle, Karsten Henkel, Hassan Gargouri, Franziska Naumann, Bernd Gruska, Michael Arens, Massimo Tallarida, Dieter Schmeißer
Publikováno v:
Beilstein Journal of Nanotechnology, Vol 4, Iss 1, Pp 732-742 (2013)
We report on results on the preparation of thin (
Externí odkaz:
https://doaj.org/article/085f5ffa5f374f9282fd7c970ba4a0d6
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 48, Pp 966-989 (2021)
Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized to be non-transparent and their predictions not
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af9015fdc6dc6127609a4b33eaaecd22
https://www.mdpi.com/2504-4990/3/4/48
https://www.mdpi.com/2504-4990/3/4/48