Self Organizing Feature Maps Data Sets

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dc.contributor.authorNair, Manish
dc.contributor.author233
dc.contributor.authorDang, Shuping
dc.contributor.author234
dc.contributor.authorBeach, Mark
dc.date.accessioned2024-06-03T06:46:01Z
dc.date.available2024-06-03T06:46:01Z
dc.date.issued2023-07-25 08:39
dc.description.abstractSelf 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.sponsorshipSecure Wireless Agile Networks (SWAN)
dc.identifier.citationNair, 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.doi10.17862/cranfield.rd.23736597
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21780
dc.publisherCranfield University
dc.relation.supplementshttps://doi.org/10.1109/MCOM.002.2200705'
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectUnsupervised Machine Learning'
dc.subject'RF Fingerprinting'
dc.titleSelf Organizing Feature Maps Data Sets
dc.typeDataset

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