Extended method for Statistical Signal Characterization using moments and cumulants: Application to recognition of pattern alterations in pulse-like waveforms employing Artificial Neural Networks
We propose a statistical procedure to characterize and extract features from a waveform that can be applied as a pre-processing signal stage in a pattern recognition task using Artificial Neural Networks. Such a procedure is based on measuring a 30-parameters set of moments and cumulants from the wa...
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| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
23-12-2022
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | We propose a statistical procedure to characterize and extract features from
a waveform that can be applied as a pre-processing signal stage in a pattern
recognition task using Artificial Neural Networks. Such a procedure is based on
measuring a 30-parameters set of moments and cumulants from the waveform, its
derivative, and its integral. The technique is presented as an extension of the
Statistical Signal Characterization method existing in the literature.
As a testing methodology, we used the procedure to distinguish a pulse-like
signal from different versions of itself with frequency spectrum alterations or
deformations. The recognition task was performed by single feed-forward
back-propagation networks trained for the case Sinc-, Gaussian-, and
Chirp-pulse waveform. Because of the success obtained in these examples, we can
conclude that the proposed extended statistical signal characterization method
is an effective tool for pattern-recognition applications. In particular, we
can use it as a fast pre-processing stage in embedded systems with limited
memory or computational capability. |
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| DOI: | 10.48550/arxiv.2212.14783 |