Autonomic Nervous Pattern Recognition of Students' Learning States in Real Classroom Situation

The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.

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Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014484x ◽

1986 ◽

Vol 44 ◽

pp. 694-695

Author(s):

G.Y. Fan ◽

J.M. Cowley

Keyword(s):

Image Processing ◽

Pattern Recognition ◽

Computer Software ◽

Processing System ◽

Light Level ◽

Host Computer ◽

Low Light ◽

Image Processing System ◽

Recent Developments ◽

On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

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Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100150800 ◽

1993 ◽

Vol 51 ◽

pp. 994-995

Author(s):

L. Fei ◽

P. Fraundorf

Keyword(s):

Pattern Recognition ◽

Perfect Crystal ◽

Unit Cell ◽

Ion Milling ◽

Beam Radiation ◽

Major Interest ◽

Unit Cells ◽

Mapping Process ◽

Increased Sensitivity ◽

Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

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