M. Sc. Kristina Dingel

AI for Computationally Intelligent Systems (AI4CIS)

Telephone: +49 561 804-6029
Fax: +49 561 804-6022
Email: kristina.dingel[at]uni-kassel[dot]de

Site: Wilhelmshöher Allee 67
34121 Kassel

Room: WA 67, 2111

Publications

[ 2022 ]
[ 2021 ]
[ 2020 ]
[ 2018 ]

2022[ to top ]

Huhnstock, R., Reginka, M., Sonntag, C., Merkel, M., Dingel, K., Sick, B., Vogel, M., Ehresmann, A.: Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes Scientific Reports. 12, 1–10 (2022). https://doi.org/10.1038/s41598-022-25391-z.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
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 particle–substrate interactions. Thus, the detection of a modified particle–substrate 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 “hopping”-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 particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.
@article{huhnstock2022three, 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 particle–substrate interactions. Thus, the detection of a modified particle–substrate 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 “hopping”-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 particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.}, author = {Huhnstock, Rico and Reginka, Meike and Sonntag, Claudius and Merkel, Maximilian and Dingel, Kristina and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = {1–10}, publisher = {Springer Nature}, title = {Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes}, volume = 12, year = 2022 }
%0 Journal Article %1 huhnstock2022three %A Huhnstock, Rico %A Reginka, Meike %A Sonntag, Claudius %A Merkel, Maximilian %A Dingel, Kristina %A Sick, Bernhard %A Vogel, Michael %A Ehresmann, Arno %D 2022 %I Springer Nature %J Scientific Reports %N 1 %P 1--10 %R 10.1038/s41598-022-25391-z %T Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes %U https://www.nature.com/articles/s41598-022-25391-z %V 12 %X 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 particle–substrate interactions. Thus, the detection of a modified particle–substrate 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 “hopping”-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 particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.
Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses Scientific Reports. 12, 1–14 (2022). https://doi.org/10.1038/s41598-022-21646-x.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
X-ray free-electron lasers (XFELs) as the world’s 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 time–energy 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.
@article{dingel2022artificial, abstract = {X-ray free-electron lasers (XFELs) as the world’s 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 time–energy 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.}, author = {Dingel, Kristina and Otto, Thorsten and Marder, Lutz and Funke, Lars and Held, Arne and Savio, Sara and Hans, Andreas and Hartmann, Gregor and Meier, David and Viefhaus, Jens and Sick, Bernhard and Ehresmann, Arno and Ilchen, Markus and Helml, Wolfram}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = {1–14}, publisher = {Nature Publishing Group}, title = {Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses}, volume = 12, year = 2022 }
%0 Journal Article %1 dingel2022artificial %A Dingel, Kristina %A Otto, Thorsten %A Marder, Lutz %A Funke, Lars %A Held, Arne %A Savio, Sara %A Hans, Andreas %A Hartmann, Gregor %A Meier, David %A Viefhaus, Jens %A Sick, Bernhard %A Ehresmann, Arno %A Ilchen, Markus %A Helml, Wolfram %D 2022 %I Nature Publishing Group %J Scientific Reports %N 1 %P 1--14 %R 10.1038/s41598-022-21646-x %T Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses %U https://www.nature.com/articles/s41598-022-21646-x %V 12 %X X-ray free-electron lasers (XFELs) as the world’s 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 time–energy 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.
Dingel, K.: Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization In: Tomforde, S. en Krupitzer, C. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll 86–98. kassel university press (2022).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.
@incollection{dingel2022actively, abstract = {Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.}, author = {Dingel, Kristina}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {86–98}, publisher = {kassel university press}, title = {Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization}, year = 2022 }
%0 Book Section %1 dingel2022actively %A Dingel, Kristina %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 86--98 %T Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization %X Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.

2021[ to top ]

Reginka, M., Hoang, H., Efendi, Özge, Merkel, M., Huhnstock, R., Holzinger, D., Dingel, K., Sick, B., Bertinetti, D., Herberg, F., Ehresmann, A.: Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration Langmuir. 37, 8498–8507 (2021). https://doi.org/10.1021/acs.langmuir.1c00900.
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@article{reginka2021transport, author = {{Reginka}, Meike and {Hoang}, Hai and {Efendi}, {\"O}zge and {Merkel}, Maximilan and {Huhnstock}, Rico and {Holzinger}, Dennis and {Dingel}, Kristina and {Sick}, Bernhard and {Bertinetti}, Daniela and {Herberg}, Friedrich and {Ehresmann}, Arno}, journal = {Langmuir}, keywords = {itegpub}, number = 28, pages = {8498–8507}, publisher = {ACS}, title = {Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration}, volume = 37, year = 2021 }
%0 Journal Article %1 reginka2021transport %A Reginka, Meike %A Hoang, Hai %A Efendi, Özge %A Merkel, Maximilan %A Huhnstock, Rico %A Holzinger, Dennis %A Dingel, Kristina %A Sick, Bernhard %A Bertinetti, Daniela %A Herberg, Friedrich %A Ehresmann, Arno %D 2021 %I ACS %J Langmuir %N 28 %P 8498--8507 %R 10.1021/acs.langmuir.1c00900 %T Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration %U https://pubs.acs.org/doi/abs/10.1021/acs.langmuir.1c00900 %V 37
Huhnstock, R., Reginka, M., Tomita, A., Merkel, M., Dingel, K., Holzinger, D., Sick, B., Vogel, M., Ehresmann, A.: Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes Scientific Reports. 11, 21794 (2021). https://doi.org/10.1038/s41598-021-01351-x.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability.
@article{huhnstock2021translatory, abstract = {Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability.}, author = {Huhnstock, Rico and Reginka, Meike and Tomita, Andreea and Merkel, Maximilian and Dingel, Kristina and Holzinger, Dennis and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = 21794, publisher = {Nature Publishing Group}, title = {Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes}, volume = 11, year = 2021 }
%0 Journal Article %1 huhnstock2021translatory %A Huhnstock, Rico %A Reginka, Meike %A Tomita, Andreea %A Merkel, Maximilian %A Dingel, Kristina %A Holzinger, Dennis %A Sick, Bernhard %A Vogel, Michael %A Ehresmann, Arno %D 2021 %I Nature Publishing Group %J Scientific Reports %N 1 %P 21794 %R 10.1038/s41598-021-01351-x %T Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes %U https://www.nature.com/articles/s41598-021-01351-x %V 11 %X Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability. %@ 0123456789
Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses arXiv e-prints. arXiv:2108.13979 (2021).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on 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 like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, 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 time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.
@article{dingel2021ai, abstract = {X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on 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 like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, 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 time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.}, author = {{Dingel}, Kristina and {Otto}, Thorsten and {Marder}, Lutz and {Funke}, Lars and {Held}, Arne and {Savio}, Sara and {Hans}, Andreas and {Hartmann}, Gregor and {Meier}, David and {Viefhaus}, Jens and {Sick}, Bernhard and {Ehresmann}, Arno and {Ilchen}, Markus and {Helml}, Wolfram}, journal = {arXiv e-prints}, keywords = {Accelerator}, pages = {arXiv:2108.13979}, title = {{Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses}}, year = 2021 }
%0 Journal Article %1 dingel2021ai %A Dingel, Kristina %A Otto, Thorsten %A Marder, Lutz %A Funke, Lars %A Held, Arne %A Savio, Sara %A Hans, Andreas %A Hartmann, Gregor %A Meier, David %A Viefhaus, Jens %A Sick, Bernhard %A Ehresmann, Arno %A Ilchen, Markus %A Helml, Wolfram %D 2021 %J arXiv e-prints %P arXiv:2108.13979 %T Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses %X X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on 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 like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, 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 time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.
Dingel, K., Liehr, A., Vogel, M., Degener, S., Meier, D., Niendorf, T., Ehresmann, A., Sick, B.: AI - Based On The Fly Design of Experiments in Physics and Engineering In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 150–153. IEEE (2021). https://doi.org/10.1109/ACSOS-C52956.2021.00048.
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- [ EndNote ]
@inproceedings{dingel2021aia, author = {Dingel, Kristina and Liehr, Alexander and Vogel, Michael and Degener, Sebastian and Meier, David and Niendorf, Thomas and Ehresmann, Arno and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {150–153}, publisher = {IEEE}, title = {AI - Based On The Fly Design of Experiments in Physics and Engineering}, year = 2021 }
%0 Conference Paper %1 dingel2021aia %A Dingel, Kristina %A Liehr, Alexander %A Vogel, Michael %A Degener, Sebastian %A Meier, David %A Niendorf, Thomas %A Ehresmann, Arno %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 150--153 %R 10.1109/ACSOS-C52956.2021.00048 %T AI - Based On The Fly Design of Experiments in Physics and Engineering
Dingel, K., Huhnstock, R., Knie, A., Ehresmann, A., Sick, B.: AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems Computer Physics Communications. 262, 107859 (2021). https://doi.org/10.1016/j.cpc.2021.107859.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.
@article{dingel2021adapt, abstract = {Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.}, author = {Dingel, Kristina and Huhnstock, Rico and Knie, Andr{\'{e}} and Ehresmann, Arno and Sick, Bernhard}, journal = {Computer Physics Communications}, keywords = {itegpub}, pages = 107859, publisher = {Elsevier}, title = {{AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems}}, volume = 262, year = 2021 }
%0 Journal Article %1 dingel2021adapt %A Dingel, Kristina %A Huhnstock, Rico %A Knie, André %A Ehresmann, Arno %A Sick, Bernhard %D 2021 %I Elsevier %J Computer Physics Communications %P 107859 %R 10.1016/j.cpc.2021.107859 %T AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems %V 262 %X Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.

2020[ to top ]

Scharei, K., Heidecker, F., Bieshaar, M.: Knowledge Representations in Technical Systems -- A Taxonomy arXiv e-prints. arXiv:2001.04835 (2020).
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@article{scharei2020knowledge, author = {Scharei, Kristina and Heidecker, Florian and Bieshaar, Maarten}, journal = {arXiv e-prints}, keywords = {Robotics}, pages = {arXiv:2001.04835}, title = {{Knowledge Representations in Technical Systems – A Taxonomy}}, year = 2020 }
%0 Journal Article %1 scharei2020knowledge %A Scharei, Kristina %A Heidecker, Florian %A Bieshaar, Maarten %D 2020 %J arXiv e-prints %P arXiv:2001.04835 %T Knowledge Representations in Technical Systems -- A Taxonomy

2018[ to top ]

Scharei, K., Herde, M., Bieshaar, M., Calma, A., Kottke, D., Sick, B.: Automated Active Learning with a Robot Archives of Data Science, Series A (Online First). 5, 16 (2018). https://doi.org/10.5445/KSP/1000087327/16.
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@article{scharei2018automated, author = {Scharei, Kristina and Herde, Marek and Bieshaar, Maarten and Calma, Adrian and Kottke, Daniel and Sick, Bernhard}, journal = {Archives of Data Science, Series A (Online First)}, keywords = {imported}, number = 1, pages = 16, publisher = {KIT}, title = {Automated Active Learning with a Robot}, volume = 5, year = 2018 }
%0 Journal Article %1 scharei2018automated %A Scharei, Kristina %A Herde, Marek %A Bieshaar, Maarten %A Calma, Adrian %A Kottke, Daniel %A Sick, Bernhard %D 2018 %I KIT %J Archives of Data Science, Series A (Online First) %N 1 %P 16 %R 10.5445/KSP/1000087327/16 %T Automated Active Learning with a Robot %V 5