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This paper proposes a modulation classification method based on Stacked Denoising Sparse Autoencoder (SDAE). This method can extract modulation features automatically, and classify input signals based on the features it extracts. The scenarios of rapid classification and high accuracy classification are considered. In the rapid classification scenario, a long symbols sequence is not attainable for...
In this paper, we propose a novel modulation classification method based on deep network as well as higher-order cumulants. The proposed algorithm uses the higher-order cumulants as the features, and thus achieves impressive noise suppression. We use Stacked Denoising Sparse Autoencoder as a classifier for single-carrier modulation classification. This classifier can classify different modulated signals...
In order to realize improving signal detection probability using interference canceler and multi-system detection techniques in spectrum sharing environment in cognitive radio of satellite and terrestrial system, it is required to classify the digital modulation type in an environment without handshaking between the transmitter and receiver. The previous methods can hardly classify modulation type...
Surface electromyography (SEMG) systems are able to effectively sense muscle activity, irrespective of any apparent body motion, in a highly convenient and non-intrusive manner. These advantages make SEMG based systems highly attractive for use as a human computer interface. Despite such advantages, there are still a significant amount of challenges that should be resolved before such systems can...
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