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https://doi.org/10.21256/zhaw-30070| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Abstract) |
| Titel: | A spiking neural network for classifying NIR spectra of fruits |
| Autor/-in: | Wróbel, Anna Sandamirskaya, Yulia Ott, Thomas |
| et. al: | No |
| DOI: | 10.21256/zhaw-30070 |
| Angaben zur Konferenz: | International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023 |
| Erscheinungsdatum: | Sep-2023 |
| Verlag / Hrsg. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Sprache: | Englisch |
| Schlagwörter: | DYNAP-SE; Neuromorphic computing |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Near-Infrared (NIR) Spectroscopy is widely applied in agriculture and food industry for the determination of fruit ripeness, the content in soluble solids, pH and acidity. In this study, we report on the develeopment of a novel neuromorphic classifier based on Spiking Neural Networks (SNNs) to classify NIR spectra of fruit species. Neuromorphic computing holds the potential for a low-power real-time recognition system based on NIR spectroscopy signals that could be used not only in food industry, but also in pharmaceutical and medical applications. For benchmarking, we compare the performance of the classifier to the performance of non-spiking Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Furthermore, we show how our SNN-based algorithm can be implemented in the mixedsignal analog-digital neuromorphic device DYNAP-SE. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30070 |
| Volltext Version: | Eingereichte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY-NC-ND 4.0: Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
| Departement: | Life Sciences und Facility Management |
| Organisationseinheit: | Institut für Computational Life Sciences (ICLS) |
| Enthalten in den Sammlungen: | Publikationen Life Sciences und Facility Management |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2023_Wrobel-etal_Spiking-neural-network-for-classifying-NIR-spectra-in-fruits.pdf | 1.83 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Wróbel, A., Sandamirskaya, Y., & Ott, T. (2023, September). A spiking neural network for classifying NIR spectra of fruits. International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023. https://doi.org/10.21256/zhaw-30070
Wróbel, A., Sandamirskaya, Y. and Ott, T. (2023) ‘A spiking neural network for classifying NIR spectra of fruits’, in International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-30070.
A. Wróbel, Y. Sandamirskaya, and T. Ott, “A spiking neural network for classifying NIR spectra of fruits,” in International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023, Sep. 2023. doi: 10.21256/zhaw-30070.
WRÓBEL, Anna, Yulia SANDAMIRSKAYA und Thomas OTT, 2023. A spiking neural network for classifying NIR spectra of fruits. In: International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. September 2023
Wróbel, Anna, Yulia Sandamirskaya, and Thomas Ott. 2023. “A Spiking Neural Network for Classifying NIR Spectra of Fruits.” Conference paper. In International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-30070.
Wróbel, Anna, et al. “A Spiking Neural Network for Classifying NIR Spectra of Fruits.” International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2023, https://doi.org/10.21256/zhaw-30070.
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