A Pattern Recognition Function of Integral Geometry

Recent research on topics related to geometrical probability is reviewed. The survey includes articles on random points, lines, flats, and networks in Euclidean spaces, pattern recognition, random coverage and packing, random search, stereology, and probabilistic aspects of integral geometry.

Download Full-text

Evolution of the C-Type Lectin-Like Receptor Genes of the DECTIN-1 Cluster in the NK Gene Complex

The Scientific World JOURNAL ◽

10.1100/2012/931386 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 16

Author(s):

Susanne Sattler ◽

Hormas Ghadially ◽

Erhard Hofer

Keyword(s):

Pattern Recognition ◽

Sequence Data ◽

Gene Duplications ◽

Gene Complex ◽

Adaptive Immune ◽

Recognition Function ◽

Recent Developments ◽

Evolutionary Emergence ◽

Related Proteins ◽

Nk Gene Complex

Pattern recognition receptors are crucial in initiating and shaping innate and adaptive immune responses and often belong to families of structurally and evolutionarily related proteins. The human C-type lectin-like receptors encoded in the DECTIN-1 cluster within the NK gene complex contain prominent receptors with pattern recognition function, such as DECTIN-1 and LOX-1. All members of this cluster share significant homology and are considered to have arisen from subsequent gene duplications. Recent developments in sequencing and the availability of comprehensive sequence data comprising many species showed that the receptors of the DECTIN-1 cluster are not only homologous to each other but also highly conserved between species. Even inCaenorhabditis elegans, genes displaying homology to the mammalian C-type lectin-like receptors have been detected. In this paper, we conduct a comprehensive phylogenetic survey and give an up-to-date overview of the currently available data on the evolutionary emergence of the DECTIN-1 cluster genes.

Download Full-text

RRAM-based synapse for neuromorphic system with pattern recognition function

2012 International Electron Devices Meeting ◽

10.1109/iedm.2012.6479016 ◽

2012 ◽

Cited By ~ 73

Author(s):

S. Park ◽

H. Kim ◽

M. Choo ◽

J. Noh ◽

A. Sheri ◽

...

Keyword(s):

Pattern Recognition ◽

Recognition Function ◽

Neuromorphic System

Download Full-text

A New Fault Location Method for 10 KV Distribution Lines with Branches

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.986-987.1437 ◽

2014 ◽

Vol 986-987 ◽

pp. 1437-1442

Author(s):

Feng Yan ◽

Shuang Shuang Li

Keyword(s):

Pattern Recognition ◽

Traveling Wave ◽

Fault Location ◽

Single Phase ◽

Distribution Networks ◽

Support Vector ◽

Location Method ◽

Vector Machines ◽

Recognition Function ◽

Simulation Results

By using the advantages of C-type of traveling wave fault location method in distance measuring and the pattern recognition function of support vector machines (SVM),a new fault location method is presented.The first step of the method is determining the fault distance by C-type of traveling wave location method; the second one is locating the fault section by SVM. It also adopts the wavelet to reduce the noise of fault signal,and the accuracy can be more precise when the parameters of SVM are optimized.The simulation results of ATP and MATLAB show that the method can determine the single phase grounding fault point in 10kv distribution networks with branches accurately.

Download Full-text

A third note on recent research in geometrical probability

Advances in Applied Probability ◽

10.2307/1426209 ◽

1974 ◽

Vol 6 (1) ◽

pp. 103-130 ◽

Cited By ~ 15

Author(s):

D. V. Little

Keyword(s):

Pattern Recognition ◽

Integral Geometry ◽

Random Search ◽

Euclidean Spaces ◽

Geometrical Probability

Recent research on topics related to geometrical probability is reviewed. The survey includes articles on random points, lines, flats, and networks in Euclidean spaces, pattern recognition, random coverage and packing, random search, stereology, and probabilistic aspects of integral geometry.

Download Full-text

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.

Download Full-text

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.

Download Full-text