Memristor-Based Edge Detection Dataset
datasetposted on 30.08.2019 by Daniel Mannion, Tony Kenyon, Adnan Mehonic, Wing Ng
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
This is the data produced for the paper titled: Memristor-Based Edge Detection.
In this study, we investigate the use of silicon dioxde memristors in edge detection. Devices exhibit analogue and volatile behaviours and are connected in potential divider arrangements. Encoding image pixels as spike trains and applying these to the memristors allow us to detect sharp changes in pixel intensity and in turn predict edges within an image.
The dataset contains data from: device characterisation, interpolated simulation models, output images generated in simulation and device variance data.
README files are included throughout the directories to explain the formatting and details of files.