Browsing by Subject "Fingerprinting"

Now showing 1 - 2 of 2

Open Access
CDs have fingerprints too
(Springer, Berlin, Heidelberg, 2009) Hammouri G.; Dana, Aykutlu; Sunar, B.
We introduce a new technique for extracting unique fingerprints from identical CDs. The proposed technique takes advantage of manufacturing variability found in the length of the CD lands and pits. Although the variability measured is on the order of 20 nm, the technique does not require the use of microscopes or any advanced equipment. Instead, we show that the electrical signal produced by the photodetector inside the CD reader is sufficient to measure the desired variability. We investigate the new technique by analyzing data collected from 100 identical CDs and show how to extract a unique fingerprint for each CD. Furthermore, we introduce a technique for utilizing fuzzy extractors over the Lee metric without much change to the standard code offset construction. Finally, we identify specific parameters and a code construction to realize the proposed fuzzy extractor and convert the derived fingerprints into 128-bit cryptographic keys. © 2009 Springer.
Open Access
Indoor localization with transfer learning
(IEEE, 2022-08-29) Korkmaz, İlter Onat; Özateş, Tuna; Koç, Enes; Aydın, Ege; Kor, Ege; Dilek, Doğaç; Güngen, Murat Alp; Köse, İdil Gökalp; Akman, Çağlar
Indoor positioning methods aim to estimate positions of transmitters where the GPS signals are unavailable. These systems usually employ algorithms explicitly trained for a single location such as fingerprinting method. For that reason, they can only be used in a particular location. This restriction prevents the use of the fingerprint method in tasks such as search and rescue operations where there is no prior knowledge of the place. A fingerprinting system using a trained algorithm with data collected from many places can work in multiple places. This paper proposes an indoor positioning system that uses the parameters of a pre-trained neural network trained with the data obtained from finite difference time domain simulations with transfer learning without collecting large amounts of data. The initial parameters for the model to be trained with the received signal strength (RSS) data collected from real places are used as be the parameters of the artificial neural network trained with the aforementioned simulation data. Performance results of the trained model are comparable to the results of the works in which fingerprinting method is employed in a single environment.