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 _______________________________________________ Discuss-gnuradio mailing list Discuss-gnuradio@gnu.org https://lists.gnu.org/mailman/listinfo/discuss-gnuradio
