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Full-Text Articles in Engineering
Supporting Real-Time Cognitive State Classification On A Mobile Individual, Michael C. Dorneich, Stephen D. Whitlow, Santosh Mathan, Patricia May Ververs, Deniz Erdogmus, Andre Adami, Misha Pavel, Tian Lan
Supporting Real-Time Cognitive State Classification On A Mobile Individual, Michael C. Dorneich, Stephen D. Whitlow, Santosh Mathan, Patricia May Ververs, Deniz Erdogmus, Andre Adami, Misha Pavel, Tian Lan
Michael C. Dorneich
The effectiveness of neurophysiologically triggered adaptive systems hinges on reliable and effective signal processing and cognitive state classification. Although this presents a difficult technical challenge in any context, these concerns are particularly pronounced in a system designed for mobile contexts. This paper describes a neurophysiologically derived cognitive state classification approach designed for ambulatory task contexts. We highlight signal processing and classification components that render the electroencephalogram (EEG) -based cognitive state estimation system robust to noise. Field assessments show classification performance that exceeds 70% for all participants in a context that many have regarded as intractable for cognitive state classification using …
Markov Chain Monte Carlo Defect Identification In Nde Images, Aleksandar Dogandžić, Benhong Zhang
Markov Chain Monte Carlo Defect Identification In Nde Images, Aleksandar Dogandžić, Benhong Zhang
Aleksandar Dogandžić
We derive a hierarchical Bayesian method for identifying elliptically‐shaped regions with elevated signal levels in NDE images. We adopt a simple elliptical parametric model for the shape of the defect region and assume that the defect signals within this region are random following a truncated Gaussian distribution. Our truncated‐Gaussian model ensures that the signals within the defect region are higher than the baseline level corresponding to the noise‐only case. We derive a closed‐form expression for the kernel of the posterior probability distribution of the location, shape, and defect‐signal distribution parameters (model parameters). This result is then used to develop Markov …
Including Effects Of Microstructure And Anisotropy In Theoretical Models Describing Hysteresis Of Ferromagnetic Materials, H. Hauser, Y. Melikhov, David C. Jiles
Including Effects Of Microstructure And Anisotropy In Theoretical Models Describing Hysteresis Of Ferromagnetic Materials, H. Hauser, Y. Melikhov, David C. Jiles
David C. Jiles
Two recent theoreticalhysteresis models (Jiles-Atherton model and energetic model) are examined with respect to their capability to describe the dependence of the magnetization on magnetic field, microstructure, and anisotropy. It is shown that the classical Rayleigh law for the behavior of magnetization at low fields and the Stoner-Wohlfarth theory of domain magnetization rotation in noninteracting magnetic single domain particles can be considered as limiting cases of a more general theoretical treatment of hysteresis in ferromagnetism.