7 research outputs found
Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis
Splitting of sequential data, such as videos and time series, is an essential
step in various data analysis tasks, including object tracking and anomaly
detection. However, splitting sequential data presents a variety of challenges
that can impact the accuracy and reliability of subsequent analyses. This
concept article examines the challenges associated with splitting sequential
data, including data acquisition, data representation, split ratio selection,
setting up quality criteria, and choosing suitable selection strategies. We
explore these challenges through two real-world examples: motor test benches
and particle tracking in liquids
Domain Imaging in Periodic Submicron Wide Nanostructures by Digital Drift Correction in Kerr Microscopy
Gefördert im Rahmen des Projekts DEA
Domain Imaging in Periodic Submicron Wide Nanostructures by Digital Drift Correction in Kerr Microscopy
Magnetoâoptical Kerr microscopy is a powerful method for imaging magnetic domains. Even though domain imaging below the diffractive resolution limit is possible, such investigations are getting increasingly complex with decreasing structure size due to the decreasing Kerr contrast. As magnetic domain images free of topographical artifacts are obtained by subtracting a reference image from the actual image, the corresponding challenges are additionally increased by unavoidable sample motion in the time interval between acquiring the two images. Softwareâbased drift corrections typically rely on a unique structure in the image's region of interest (ROI), recognized automatically or selected manually by the user. By digital image shifting, the ROI positions in the actual and reference images are aligned, and the sample motion is compensated. For magnetic domain imaging in periodically arranged microâ or nanoâobjects, unique topographical features are not given, making the drift correction by ROIs difficult, often even impossible. Herein, a novel softwareâbased approach is presented for drift corrections to image domains with features close/below the optical resolution limit and for investigating periodically arranged microâ or nanoâobjects without utilizing ROIs. Highâcontrast images are obtained, enabling the characterization of periodically arranged 1D, 2D, and 3D magnetic objects with lateral dimensions below 100ânm
Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes
Abstract The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particleâsubstrate interactions. Thus, the detection of a modified particleâsubstrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a âhoppingâ-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particleâsubstrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy
Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes
Gefördert durch den Publikationsfonds der UniversitÀt Kasse
Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses
X-ray free-electron lasers (XFELs) as the worldâs brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact timeâenergy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines