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Thus, time-frequency representations are crucial for the analysis and classification of these signals.
A guideline for the analysis and classification of nonlinear ill-posed problems to detect practical identifiability problems is given.
We propose the analysis, design and implementation of an intelligent system for the analysis and classification of the low-resolution optical spectra of supergiant, giant and dwarf stars, with luminosity levels I, III and V, respectively.
With the aim of validating the described approach, a dataset for the analysis and classification of physical activity was obtained from PAMAP2, as mentioned above.
Advances in high-throughput sequencing technologies, combined with directed studies of venom producing cells [ 36- 39], have resulted in a data deluge which requires dedicated tools for the analysis and classification of conopeptide sequences.
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However, our applications focused on the analysis and classification of signals for spectrum monitoring.
The analysis and classification of EEG signals can lead to better diagnostic techniques for brain-related disorders.
The proposed method may be applied for analysis and classification of other non-stationary biomedical signals.
For the analysis the following classifications of clusters were used.
The LccED comes with a set of tools for phylogenetic analysis and classification.
The traditional receiver operating characteristic analysis was extended for single-signal detection and classification of multiple signals [6].
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