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From: | sreeraj r |
Subject: | Re: [Discuss-gnuradio] GSOC '16: Signal Intelligence (gr-sigint) |
Date: | Tue, 8 Mar 2016 16:19:41 +0100 |
Hi everyone,
I'm very interested in working on the Signal Intelligence (gr-sigint)
project for the Google Summer of Code.
I'm currently a PhD student at Lancaster University, UK, studying attack detection
in a privacy preserving manner.
I achieved an MSc in Bristol, UK, making use of machine learning techniques to detect viruses - http://www.lancaster.ac.uk/pg/richarc2/dissertation.pdf.
As mentioned in the idea suggested by Mr Rajendran "Another approach is to use available waterfall images and run some image comparison algorithms",
I am curious if I could make use of such machine learning techniques to achieve this.
I am also especially interested in how the performance of such classifiers could be measured through conducting real-world experiments,
with 2 SDRs (one for transmission and one for reception) at a range of increasing distances, potentially making use of
techniques such as Receiver Operating Characteristic (ROC) curves and the Area Under Curve (AUC) as a metric for quantifying
the performance of a classifier.
I'm currently reading more about algorithms to detect cyclostationary features along with a survey on Automatic Modulation Recognition.
I'm also looking at existing GNU Radio modules such as gr-specest.
If anyone could point me at further reading material or suggestions for the proposal, that would be great!
Kind Regards
Christopher Richardson
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