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IEEE/ICACT20230149 Slide.13        [Big Slide]	Oral Presentation
	In this slide I showed how the reconstruction-based novelty detection method works: The idea is quite simple. An autoencoder can reconstruct the signals well with what it is trained with and for unseen or novel signals we will obtain higher reconstruction error. We used this reconstruction error parameter to differentiate known and novel signals. Here I showed an example. As you can see, the known IQ signals are reconstructed very well whereas, for the unseen signals, we obtained high reconstruction errors. This concept is used for novelty detection. For novelty clustering, we use the DBSCAN method. And as parameters, we used the reconstruction error and bandwidth and dwell time, where the bandwidth and dwell time parameters were extracted by YOLO.

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