TY  - JOUR
A1  - Elseberg, Jan
A1  - Borrmann, Dorit
A1  - Nüchter, Andreas
T1  - Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms
JF  - Remote Sensing
N2  - Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets.
KW  - mapping
KW  - calibration
KW  - non-rigid registration
KW  - mobile laser scanning
KW  - algorithms
Y1  - 2013
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-130478
VL  - 5
IS  - 11
ER  - 
TY  - JOUR
A1  - Tran-Gia, Johannes
A1  - Wech, Tobias
A1  - Bley, Thorsten
A1  - Köstler, Herbert
T1  - Model-Based Acceleration of Look-Locker T1 Mapping
JF  - PLoS One
N2  - Mapping the longitudinal relaxation time \(T_1\) has widespread applications in clinical MRI as it promises a quantitative comparison of tissue properties across subjects and scanners. Due to the long scan times of conventional methods, however, the use of quantitative MRI in clinical routine is still very limited. In this work, an acceleration of Inversion-Recovery Look-Locker (IR-LL) \(T_1\) mapping is presented. A model-based algorithm is used to iteratively enforce an exponential relaxation model to a highly undersampled radially acquired IR-LL dataset obtained after the application of a single global inversion pulse. Using the proposed technique, a \(T_1\) map of a single slice with 1.6mm in-plane resolution and 4mm slice thickness can be reconstructed from data acquired in only 6s. A time-consuming segmented IR experiment was used as gold standard for \(T_1\) mapping in this work. In the subsequent validation study, the model-based reconstruction of a single-inversion IR-LL dataset exhibited a \(T_1\) difference of less than 2.6% compared to the segmented IR-LL reference in a phantom consisting of vials with \(T_1\) values between 200ms and 3000ms. In vivo, the \(T_1\) difference was smaller than 5.5% in WM and GM of seven healthy volunteers. Additionally, the \(T_1\) values are comparable to standard literature values. Despite the high acceleration, all model-based reconstructions were of a visual quality comparable to fully sampled references. Finally, the reproducibility of the \(T_1\) mapping method was demonstrated in repeated acquisitions. In conclusion, the presented approach represents a promising way for fast and accurate \(T_1\) mapping using radial IR-LL acquisitions without the need of any segmentation.
KW  - algorithms
KW  - cerebrospinal fluid
KW  - NMR relaxation
KW  - data acquisition
KW  - relaxation (physics)
KW  - relaxation time
KW  - central nervous system
KW  - magnetic resonance imaging
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-126436
VL  - 10
IS  - 4
ER  - 
TY  - JOUR
A1  - McIlroy, Benjamin
A1  - Passfield, Louis
A1  - Holmberg, Hans-Christer
A1  - Sperlich, Billy
T1  - Virtual training of endurance cycling – A summary of strengths, weaknesses, opportunities and threats
JF  - Frontiers in Sports and Active Living
N2  - Virtual online training has emerged as one of the top 20 worldwide fitness trends for 2021 and continues to develop rapidly. Although this allows the cycling community to engage in virtual training and competition, critical evaluation of virtual training platforms is limited. Here, we discuss the strengths, weaknesses, opportunities and threats associated with virtual training technology and cycling in an attempt to enhance awareness of such aspects. Strengths include immersive worlds, innovative drafting mechanics, and versatility. Weaknesses include questionable data accuracy, inadequate strength and reliability of power-speed algorithms. Opportunities exist for expanding strategic partnerships with major cycling races, brands, and sponsors and improving user experience with the addition of video capture and “e-coaching.” Threats are present in the form of cheating during competition, and a lack of uptake and acceptance by a broader community.
KW  - algorithms
KW  - cycling
KW  - e-coach
KW  - e-health
KW  - ergometer
KW  - simulation
KW  - virtual training
KW  - SWOT
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-258876
VL  - 3
ER  - 
TY  - JOUR
A1  - Roelofs, Freek
A1  - Blackburn, Lindy
A1  - Lindahl, Greg
A1  - Doeleman, Sheperd S.
A1  - Johnson, Michael D.
A1  - Arras, Philipp
A1  - Chatterjee, Koushik
A1  - Emami, Razieh
A1  - Fromm, Christian
A1  - Fuentes, Antonio
A1  - Knollmüller, Jakob
A1  - Kosogorov, Nikita
A1  - Müller, Hendrik
A1  - Patel, Nimesh
A1  - Raymond, Alexander
A1  - Tiede, Paul
A1  - Traianou, Efthalia
A1  - Vega, Justin
T1  - The ngEHT analysis challenges
JF  - Galaxies
N2  - The next-generation Event Horizon Telescope (ngEHT) will be a significant enhancement of the Event Horizon Telescope (EHT) array, with ∼10 new antennas and instrumental upgrades of existing antennas. The increased uv-coverage, sensitivity, and frequency coverage allow a wide range of new science opportunities to be explored. The ngEHT Analysis Challenges have been launched to inform the development of the ngEHT array design, science objectives, and analysis pathways. For each challenge, synthetic EHT and ngEHT datasets are generated from theoretical source models and released to the challenge participants, who analyze the datasets using image reconstruction and other methods. The submitted analysis results are evaluated with quantitative metrics. In this work, we report on the first two ngEHT Analysis Challenges. These have focused on static and dynamical models of M87* and Sgr A* and shown that high-quality movies of the extended jet structure of M87* and near-horizon hourly timescale variability of Sgr A* can be reconstructed by the reference ngEHT array in realistic observing conditions using current analysis algorithms. We identify areas where there is still room for improvement of these algorithms and analysis strategies. Other science cases and arrays will be explored in future challenges.
KW  - very long baseline interferometry
KW  - black holes
KW  - active galactic nuclei
KW  - radio astronomy
KW  - imaging
KW  - instrument design
KW  - telescopes
KW  - algorithms
KW  - data analysis
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304976
SN  - 2075-4434
VL  - 11
IS  - 1
ER  -