Self Organizing Feature Maps Data Sets
| dc.contributor.author | Nair, Manish | |
| dc.contributor.author | Dang, Shuping | |
| dc.contributor.author | Beach, Mark | |
| dc.date.accessioned | 2024-06-03T06:46:01Z | |
| dc.date.available | 2024-06-03T06:46:01Z | |
| dc.date.issued | 2023-07-25 08:39 | |
| dc.description.abstract | Self Organzing Feature Maps [SOFM] Data Sets for LoRa transmitters, generated by the Batch SOFM Competitive Learning algorihtm. In the algorithm, initially, a Kohonen layer of artificial neurons (of dimensions 10x10) trains upon the data set of raw LoRa I/Qs through randomly initialized set of weights. The original ANN then 'self-organize' or cluster into a batch of six 'offspring' ANNs at every epoch. Except in the 1st epoch, the algorithm trains on the set of offspring ANNs and not the raw I/Qs. By the 200th epoch, the extent of cluster is sufficient to produce distinct SOFM patterns corresponding specifically to a particular LoRa I/Q in the raw I/Qs. The raw LoRa I/Q data, comprising of samples from six sources [5 from LoRa modules and 1 from an ARB], collected from a customized RF penetration test-bed, are also provided. | |
| dc.description.sponsorship | Secure Wireless Agile Networks (SWAN) | |
| dc.identifier.citation | Nair, Manish; Dang, Shuping; Beach, Mark (2023). Self Organizing Feature Maps Data Sets. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.23736597 | |
| dc.identifier.doi | 10.17862/cranfield.rd.23736597 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/21780 | |
| dc.publisher | Cranfield University | |
| dc.relation.supplements | https://doi.org/10.1109/MCOM.002.2200705 | |
| dc.rights | CC BY 4.0 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Unsupervised Machine Learning | |
| dc.subject | RF Fingerprinting | |
| dc.title | Self Organizing Feature Maps Data Sets | |
| dc.type | Dataset |
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