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https://doi.org/10.21256/zhaw-30070| Publication type: | Conference paper |
| Type of review: | Peer review (abstract) |
| Title: | A spiking neural network for classifying NIR spectra of fruits |
| Authors: | Wróbel, Anna Sandamirskaya, Yulia Ott, Thomas |
| et. al: | No |
| DOI: | 10.21256/zhaw-30070 |
| Conference details: | International Symposium on Nonlinear Theory and Its Applications (NOLTA), Catania, Italy, 26-29 September 2023 |
| Issue Date: | Sep-2023 |
| Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Language: | English |
| Subjects: | DYNAP-SE; Neuromorphic computing |
| Subject (DDC): | 006: Special computer methods |
| Abstract: | 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 |
| Fulltext version: | Submitted version |
| License (according to publishing contract): | CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Appears in collections: | Publikationen Life Sciences und Facility Management |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Wrobel-etal_Spiking-neural-network-for-classifying-NIR-spectra-in-fruits.pdf | 1.83 MB | Adobe PDF |  View/Open |
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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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