Relationships between cognitive pattern recognition and specific mathematical domains in mathematics education

This paper addresses the applications of the science of learning principles to support the teaching and learning of cognitive pattern recognition. The paper first provides a brief introduction to the science of learning and cognitive pattern recognition. Six science of learning principles have been identified to be relevant to the teaching and learning of cognitive pattern recognition and are discussed individually. The paper also offers suggestions on how to integrate the various science of learning principles for teachers to teach cognitive pattern recognition in the classroom. A teaching process model for cognitive pattern recognition is proposed and developed, which incorporates the various science of learning principles to optimize learning and minimize redundancies. Finally, this paper highlights the implications and provides several recommendations for educators to consider when they decided to incorporate the science of learning principles into their curriculum to teach cognitive pattern recognition.

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Theory of Cognitive Pattern Recognition

Pattern Recognition Techniques, Technology and Applications ◽

10.5772/6251 ◽

2008 ◽

Cited By ~ 4

Author(s):

Youguo Pi ◽

Wenzhi Liao ◽

Mingyou Liu ◽

Jianping Lu

Keyword(s):

Pattern Recognition ◽

Cognitive Pattern

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Research and Application on Bionic Pattern Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.341-342.355 ◽

2011 ◽

Vol 341-342 ◽

pp. 355-358

Author(s):

Xiao Jia Wu ◽

Qi Chuan Tian

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Target Recognition ◽

Statistical Pattern Recognition ◽

Human Being ◽

Main Principle ◽

New Model ◽

Statistical Pattern ◽

Cognitive Pattern ◽

Selection Operator

Studies on biology basis from human being or other animals have attracted an ever increasing attention in pattern recognition. This paper describe a new model of pattern recognition principles, witch is based on “matter cognition” instead of “matter classification” in traditional statistical pattern recognition. This new model is better closer to the function of human being, rather than traditional statistical pattern recognition using “optimal separating” as its main principle. So the new model of pattern recognition is called the Bionic（or cognitive） Pattern Recognition. In the support of this theory, the pulse coupled neural network (PCNN), an entirely different neural network from traditional artificial ones, is used for image target recognition. Through the contrast of the image, the linking strength of each pixel can be chosen adaptively. After the processing of PCNN with the adaptive linking strength, new fire mapping images are obtained for each image from sensor. The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image. As a result, the target which was polluted by noise was recognized correctly.

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The Frame of Cognitive Pattern Recognition

2007 Chinese Control Conference ◽

10.1109/chicc.2006.4347565 ◽

2006 ◽

Cited By ~ 1

Author(s):

Pi Youguo ◽

Shu Huailin ◽

Liang Tiancai

Keyword(s):

Pattern Recognition ◽

Cognitive Pattern

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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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