Introduces and proves stability for a persistent homology-based bi-conditional periodicity score for pairwise time series similarity.
Recurrence plots for the analysis of complex systems
3 Pith papers cite this work. Polarity classification is still indexing.
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Photometric redshift uncertainties bias Anderson-Darling and Gaussian-mixture tests toward relaxed cluster classifications, with Gaussian errors producing ~95% relaxed recovery versus ~5% for unrelaxed clusters.
Personalized deep learning models on multimodal physiological signals from an Empatica E4 sensor achieve 92.68% accuracy for driver state classification in real-world automated driving, compared to 54% for generalized models across four drivers.
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Human Centered Non Intrusive Driver State Modeling Using Personalized Physiological Signals in Real World Automated Driving
Personalized deep learning models on multimodal physiological signals from an Empatica E4 sensor achieve 92.68% accuracy for driver state classification in real-world automated driving, compared to 54% for generalized models across four drivers.