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IEEE/ICACT20260241 Slide.10        [Big Slide]	Oral Presentation
	We built our dataset in MATLAB using 4-QAM signals in AWGN noise, from -19 to -11 dB SNR. Each observation is transformed with FFT, and we extract energy and standard deviation features from the real and imaginary parts. This provides us a 5-dimensional feature vector, labeled 0 for noise-only and 1 for signal present, which is ready for machine learning training and testing.

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