SmartPIV: flow velocity estimates by smartphones for education and field studies. - In: Experiments in fluids, ISSN 1432-1114, Bd. 62 (2021), 8, 172, S. 1-13
In this paper, a smartphone application is presented that was developed to lower the barrier to introduce particle image velocimetry (PIV) in lab courses. The first benefit is that a PIV system using smartphones and a continuous wave (cw-) laser is much cheaper than a conventional system and thus much more affordable for universities. The second benefit is that the design of the menus follows that of modern camera apps, which are intuitively used. Thus, the system is much less complex and costly than typical systems, and our experience showed that students have much less reservations to work with the system and to try different parameters. Last but not least the app can be applied in the field. The relative uncertainty was shown to be less than 8%, which is reasonable for quick velocity estimates. An analysis of the computational time necessary for the data evaluation showed that with the current implementation the app is capable of providing smooth live display vector fields of the flow. This might further increase the use of modern measurement techniques in industry and education.
https://doi.org/10.1007/s00348-021-03262-z
Reactive auto-completion of modeling activities. - In: IEEE transactions on software engineering, ISSN 1939-3520, Bd. 47 (2021), 7, S. 1431-1451
Assisting and automating software engineering tasks is a state-of-the-art way to support stakeholders of development projects. A common assistance function of IDEs is the auto-completion of source code. Assistance functions, such as auto-completion, are almost entirely missing in modeling tools though auto-completion in general gains continuously more importance in software development. We analyze a user’s performed editing operations in order to anticipate modeling activities and to recommend appropriate auto-completions for them. Editing operations are captured as events and modeling activities are defined as complex event patterns, facilitating the matching by complex-event-processing. The approach provides adapted auto-completions reactively upon each editing operation of the user. We implemented the RapMOD prototype as add-in for the modeling tool Sparx Enterprise Architect™ . A controlled user experiment with 37 participants performing modeling tasks demonstrated the approach's potential to reduce modeling effort significantly. Users having auto-completions available for a modeling scenario performed the task 27 percent faster, needed to perform 56 percent less actions, and perceived the task 29 percent less difficult.
https://doi.org/10.1109/TSE.2019.2924886
When bears get machine support: applying machine learning models to scalable DataFrames with Grizzly. - In: Datenbanksysteme für Business, Technologie und Web (BTW 2021), (2021), S. 195-214
The popular Python Pandas framework provides an easy-to-use DataFrame API that enables a broad range of users to analyze their data. However, Pandas faces severe scalability issues in terms of runtime and memory consumption, limiting the usability of the framework. In this paper we present Grizzly, a replacement for Python Pandas. Instead of bringing data to the operators like Pandas, Grizzly ships program complexity to database systems by transpiling the DataFrame API to SQL code. Additionally, Grizzly offers user-friendly support for combining different data sources, user-defined functions, and applying Machine Learning models directly inside the database system. Our evaluation shows that Grizzly significantly outperforms Pandas as well as state-of-the-art frameworks for distributed Python processing in several use cases.
Übertragungsverhalten bildgebender Systeme in der Medizin
Unicopy Campus Edition. - Ilmenau : Unicopy Ilmenau, 2021. - 156 Seiten. - (Ilmenauer Editionen) ISBN 978-3-942646-07-9
The effect of experimental and modeling parameters on combined EEG/MEG source analysis and transcranial electric stimulation optimization of somatosensory and epilepsy activity. - Ilmenau : Universitätsbibliothek, 2021. - 1 Online-Ressource (x, 126 Seiten)
Technische Universität Ilmenau, Dissertation 2021
Neue experimentelle und modellierende Parameter werden eingeführt, um die Auswirkungen auf die kombinierte Elektroenzephalographie (EEG) und Magnetenzephalographie (MEG) zu untersuchen - EMEG-Quellenanalyse und Optimierung der transkraniellen elektrischen Stimulation (TES) von somatosensorisch evozierter und epileptischer Aktivität. Es werden simultane Daten gemessen, einschließlich somatosensorisch evozierter Potentiale (SEP) und Felder (SEF), die durch verschiedene Stimulationstypen für gruppenbasierte Sensitivitätsuntersuchungen und spontane EEG- und MEG-Messungen für die präoperative Epilepsiediagnose hervorgerufen werden. Bei der Lösung des Vorwärtsproblems der Quellenanalyse werden individualisierte Finite-Elemente-Kopfvolumenleitermodelle konstruiert. Zu diesem Zweck wird ein quasi-automatisches Bildverarbeitungsverfahren eingeführt, das T1-gewichtete und T2-gewichtete MRTs kombiniert. Zur realistischen Modellierung der leitfähigen Eigenschaften des Gehirns wird die Diffusionstensor-Bildgebung verwendet. Die Leitfähigkeit des Schädels wird aufgrund ihrer hohen Variabilität zwischen den Probanden und ihres Einflusses auf EEG- und EMEG-Quellenrekonstruktionen individuell kalibriert. Es wird auch dargestellt, wie unterschiedliche Stimulationsarten, Kopfmodelle und Messmodalitäten (EEG, MEG oder EMEG) die Quellenrekonstruktion der SEP/SEF-Antwort bei 20 ms nach dem Stimulus (P20/N20) und das Targeting bei der mehrkanaligen TES-Optimierung beeinflussen. Die Inter-Subjekt-Variabilität der Schädel-Leitfähigkeit und -Dicke über das Alter wird nicht-invasiv untersucht. Schließlich wird die EMEG-Quellenanalyse mit realistischen Kopfmodellen, die Schädelgratlöcher beinhalten, für die präoperative Diagnose eines medikamentenresistenten Epilepsiepatienten evaluiert. Die optimierte TES wird als Alternative zur Operation zur Unterdrückung epileptischer Anfälle untersucht. Die Ergebnisse zeigen, dass das MEG die P20/N20-Lokalisation stabilisiert und das EEG zur Bestimmung der Quellenorientierung beiträgt. Die Komplementarität beider Modalitäten im EMEG kann auf der Basis von detaillierten und individualisierten Kopfmodellen ausgenutzt werden. Anschließend wird berichtet, dass optimierte TES-Elektrodenmontagen von der P20/N20-Orientierungskomponente beeinflusst werden. Für die Kopfmodellierung wird dargestellt, dass die Variabilität der Leitfähigkeit und der Dicke des Schädels zwischen den Probanden groß ist und bei der Quellenanalyse und TES berücksichtigt werden sollte. In dieser Hinsicht sind das Alter der Probanden und die Schädeldicke signifikant mit der Leitfähigkeit des Schädels verbunden. Bei der präoperativen Epilepsiebeurteilung weist die EMEG-Quellenanalyse mit kalibrierten und anisotropen Kopfmodellen auf eine fokale kortikale Dysplasie (FCD) zu Beginn der epileptischen Spike-Spitze hin. Vereinfachte Kopfmodelle, die Verwendung einer einzelnen Modalität oder Zeitpunkte in der Nähe des Spike-Peaks verursachen nicht zu vernachlässigende Einflüsse auf die Bestimmung der FCD. Schließlich spiegeln Änderungen an der Kopfmodellierung erhebliche Einflüsse auf die optimierte TES und den Fluss der injizierten Gleichströme zur FCD wider.
https://nbn-resolving.org/urn:nbn:de:gbv:ilm1-2021000093
Updatable materialization of approximate constraints. - In: 2021 IEEE 37th International Conference on Data Engineering, (2021), S. 1991-1996
Modern big data applications integrate data from various sources. As a result, these datasets may not satisfy perfect constraints, leading to sparse schema information and non-optimal query performance. The existing approach of PatchIndexes enable the definition of approximate constraints and improve query performance by exploiting the materialized constraint information. As real world data warehouse workloads are often not limited to read-only queries, we enhance the PatchIndex structure towards an update-conscious design in this paper. Therefore, we present a sharded bitmap as the underlying data structure which offers efficient update operations, and describe approaches to maintain approximate constraints under updates, avoiding index recomputations and full table scans. In our evaluation, we prove that PatchIndexes provide more lightweight update support than traditional materialization approaches.
https://doi.org/10.1109/ICDE51399.2021.00189
The Flora Incognita app - interactive plant species identification. - In: Methods in ecology and evolution, ISSN 2041-210X, Bd. 12 (2021), 7, S. 1335-1342
Being able to identify plant species is an important factor for understanding biodiversity and its change due to natural and anthropogenic drivers. We discuss the freely available Flora Incognita app for Android, iOS and Harmony OS devices that allows users to interactively identify plant species and capture their observations. Specifically developed deep learning algorithms, trained on an extensive repository of plant observations, classify plant images with yet unprecedented accuracy. By using this technology in a context-adaptive and interactive identification process, users are now able to reliably identify plants regardless of their botanical knowledge level. Users benefit from an intuitive interface and supplementary educational materials. The captured observations in combination with their metadata provide a rich resource for researching, monitoring and understanding plant diversity. Mobile applications such as Flora Incognita stimulate the successful interplay of citizen science, conservation and education.
https://doi.org/10.1111/2041-210X.13611
Reachability problems on reliable and lossy queue automata. - In: Theory of computing systems, ISSN 1433-0490, Bd. 65 (2021), 8, S. 1211-1242
We study the reachability problem for queue automata and lossy queue automata. Concretely, we consider the set of queue contents which are forwards resp. backwards reachable from a given set of queue contents. Here, we prove the preservation of regularity if the queue automaton loops through some special sets of transformation sequences. This is a generalization of the results by Boigelot et al. and Abdulla et al. regarding queue automata looping through a single sequence of transformations. We also prove that our construction is possible in polynomial time.
https://doi.org/10.1007/s00224-021-10031-2
Defocus particle tracking : a comparison of methods based on model functions, cross-correlation, and neural networks. - In: Measurement science and technology, ISSN 1361-6501, Bd. 32 (2021), 9, 094011, insges. 14 S.
Defocus particle tracking (DPT) has gained increasing importance for its use to determine particle trajectories in all three dimensions with a single-camera system, as typical for a standard microscope, the workhorse of todays ongoing biomedical revolution. DPT methods derive the depth coordinates of particle images from the different defocusing patterns that they show when observed in a volume much larger than the respective depth of field. Therefore it has become common for state-of-the-art methods to apply image recognition techniques. Two of the most commonly and widely used DPT approaches are the application of (astigmatism) particle image model functions (MF methods) and the normalized cross-correlations between measured particle images and reference templates (CC methods). Though still young in the field, the use of neural networks (NN methods) is expected to play a significant role in future and more complex defocus tracking applications. To assess the different strengths of such defocus tracking approaches, we present in this work a general and objective assessment of their performances when applied to synthetic and experimental images of different degrees of astigmatism, noise levels, and particle image overlapping. We show that MF methods work very well in low-concentration cases, while CC methods are more robust and provide better performance in cases of larger particle concentration and thus stronger particle image overlap. The tested NN methods generally showed the lowest performance, however, in comparison to the MF and CC methods, they are yet in an early stage and have still great potential to develop within the field of DPT.
https://doi.org/10.1088/1361-6501/abfef6
Von Daten zu Künstlicher Intelligenz - Datenmanagement als Basis für erfolgreiche KI-Anwendungen. - In: Digitale Welt, ISSN 2569-1996, Bd. 5 (2021), 3, S. 75-79
https://doi.org/10.1007/s42354-021-0383-z