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This paper presents a novel approach that recognizing heart rhythm with the combination of adaptive Hermite decomposition and support vector machines (SVM) classification. The novelty lies in two aspects. In the first aspect, for the goal of feature extraction, the orthogonal transformation based on Hermite basis functions is proposed to characterize the morphological features of ECG data. In the...
This paper deals with automatic modulation classification of communication signals. A new scheme of automatic modulation classification using wavelet analysis and wavelet support vector machine (WSVM) is proposed. Further, a new way of training for wavelet features is carried out to adapt to signals which are non-stable and varied in a wide range of signal-to-noise rates (SNR). Through such training,...
This paper is concerned with the classification and de-noising problem for non-linear signals. It is known that using kernel functions, a non-linear signal can be transformed into a linear signal in a higher dimensional space. In that feature space, a linear algorithm can be applied to a non-linear problem. It is proposed that using the principal components extracted from the feature space, the signal...
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