IEEE and ISSPA
Faculty of Computing, Health and Science
School of Engineering and Mathematics
This paper compares two types of classifiers applied to bandlimited FSK4 and FSK8 signals. The first classifier employs the decision-theoretic approach and the second classifier is a neural network structure. Key features are extracted using a zero crossing sampler. A novel decision tree is proposed and optimum threshold values are found for the decision theoretic approach. For the neural network, the optimum structure is found to be the smallest structure to give 100% overall success rate. The performance of the both classifiers has been evaluated by simulating bandlimited FSK4 and FSK8 signals corrupted by Gaussian noise. It is shown that the neural network outperforms the decision-theoretic approach particularly for SNR
This is an Author's Accepted Manuscript of: Ramakonar, V. S., Habibi, D. , & Bouzerdoum, A. (2001). Classification of bandlimited fsk4 and fsk8 signals. Proceedings of 6th Internatrional Symposium on Signal Processing and its Applications. (pp. 398 - 401 vol.2 ). Malaysia. IEEE and ISSPA. Available here
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