Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "Schlegel, Martin" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Application of a Multiplex PCR with Specific PCR Primers for the Detection of the Genus Paramecium and the Paramecium aurelia Complex
Autorzy:
Haentzsch, Madlen
Bernhard, Detlef
Berendonk, Thomas U.
PRZYBOŚ, Ewa
Schlegel, Martin
Powiązania:
https://bibliotekanauki.pl/articles/763704.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
Tematy:
Multiplex PCR, Paramecium, saprobic level, species specific primers
Opis:
The representatives of the genus Paramecium are well-studied ciliates and can be used in water quality assessment and the determinations of saprobic levels. For these applications, a clear and unambiguous identification of ciliate assemblages is essential, which is typically based on morphological characters requiring a sound taxonomic knowledge and experience in species determination including microscopic identification of both living and stained specimens. Therefore, we developed and applied specific PCR primers for the detection of species belonging to the genus Paramecium and the Paramecium aurelia complex. These primers were successfully tested with different Paramecium species including representatives of the P. aurelia complex as well as closely related species like Frontonia sp. and Tetrahymena sp. in both experimental and environmental samples. These primers can be used in a simultaneous approach achieving fast and reliable results with regard to determination of ciliate community and water assessment.
Źródło:
Acta Protozoologica; 2011, 50, 3
1689-0027
Pojawia się w:
Acta Protozoologica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recognition of sports exercises using inertial sensor technology
Autorzy:
Krutz, Pascal
Rehm, Matthias
Schlegel, Holger
Dix, Martin
Powiązania:
https://bibliotekanauki.pl/articles/30148258.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
human activity recognition
machine learning
neural networks
classifier
Opis:
Supervised learning as a sub-discipline of machine learning enables the recognition of correlations between input variables (features) and associated outputs (classes) and the application of these to previously unknown data sets. In addition to typical areas of application such as speech and image recognition, fields of applications are also being developed in the sports and fitness sector. The purpose of this work is to implement a workflow for the automated recognition of sports exercises in the Matlab® programming environment and to carry out a comparison of different model structures. First, the acquisition of the sensor signals provided in the local network and their processing is implemented. Realised functionalities include the interpolation of lossy time series, the labelling of the activity intervals performed and, in part, the generation of sliding windows with statistical parameters. The preprocessed data are used for the training of classifiers and artificial neural networks (ANN). These are iteratively optimised in their corresponding hyper parameters for the data structure to be learned. The most reliable models are finally trained with an increased data set, validated and compared with regard to the achieved performance. In addition to the usual evaluation metrics such as F1 score and accuracy, the temporal behaviour of the assignments is also displayed graphically, allowing statements to be made about potential causes of incorrect assignments. In this context, especially the transition areas between the classes are detected as erroneous assignments as well as exercises with insufficient or clearly deviating execution. The best overall accuracy achieved with ANN and the increased dataset was 93.7 %.
Źródło:
Applied Computer Science; 2023, 19, 1; 152-163
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies