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1

Optical Pattern Recognition  

NASA Astrophysics Data System (ADS)

Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

Yu, Francis T. S.; Jutamulia, Suganda

2008-10-01

2

Correlation, functional analysis and optical pattern recognition  

SciTech Connect

Correlation integrals have played a central role in optical pattern recognition. The success of correlation, however, has been limited. What is needed is a mathematical operation more complex than correlation. Suitably complex operations are the functionals defined on the Hilbert space of Lebesgue square integrable functions. Correlation is a linear functional of a parameter. In this paper, we develop a representation of functionals in terms of inner products or equivalently correlation functions. We also discuss the role of functionals in neutral networks. Having established a broad relation of correlation to pattern recognition, we discuss the computation of correlation functions using acousto-optics.

Dickey, F.M.; Lee, M.L.; Stalker, K.T.

1994-03-01

3

Image processing for new optical pattern recognition encoders  

Microsoft Academic Search

An all new type of absolute, optical encoder with ultra-high sensitivity has been developed at NASA's Goddard Space Flight Center. These position measuring encoders are unconventional in that they rely on computational pattern recognition of high speed, electronic images, made of a moving, backlit scale which carries absolute position information of either linear or rotary format. The pattern recognition algorithms

Douglas B. Leviton

2000-01-01

4

Image processing for new optical pattern recognition encoders  

NASA Astrophysics Data System (ADS)

An all new type of absolute, optical encoder with ultra-high sensitivity has been developed at NASA's Goddard Space Flight Center. These position measuring encoders are unconventional in that they rely on computational pattern recognition of high speed, electronic images, made of a moving, backlit scale which carries absolute position information of either linear or rotary format. The pattern recognition algorithms combine edge detection, threshold level sensing, spatial compression, and centroiding along with fault recovery through scale image defect detection. Details of the encoder scale patterns and their design rules and the image processing algorithm which gives these encoders their unique and unparalleled performance characteristics are discussed.

Leviton, Douglas B.

2000-11-01

5

Optical Processing of Speckle Images with Bacteriorhodopsin for Pattern Recognition  

NASA Technical Reports Server (NTRS)

Logarithmic processing of images with multiplicative noise characteristics can be utilized to transform the image into one with an additive noise distribution. This simplifies subsequent image processing steps for applications such as image restoration or correlation for pattern recognition. One particularly common form of multiplicative noise is speckle, for which the logarithmic operation not only produces additive noise, but also makes it of constant variance (signal-independent). We examine the optical transmission properties of some bacteriorhodopsin films here and find them well suited to implement such a pointwise logarithmic transformation optically in a parallel fashion. We present experimental results of the optical conversion of speckle images into transformed images with additive, signal-independent noise statistics using the real-time photochromic properties of bacteriorhodopsin. We provide an example of improved correlation performance in terms of correlation peak signal-to-noise for such a transformed speckle image.

Downie, John D.; Tucker, Deanne (Technical Monitor)

1994-01-01

6

Automated target recognition and tracking using an optical pattern recognition neural network  

NASA Technical Reports Server (NTRS)

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Chao, Tien-Hsin

1991-01-01

7

Pattern Recognition in Optical Remote Sensing Data Processing  

NASA Astrophysics Data System (ADS)

Computational procedures of the land surface biophysical parameters retrieval imply that modeling techniques are available of the outgoing radiation description together with monitoring techniques of remote sensing data processing using registered radiances between the related optical sensors and the land surface objects called “patterns”. Pattern recognition techniques are a valuable approach to the processing of remote sensing data for images of the land surface - atmosphere system. Many simplified codes of the direct and inverse problems of atmospheric optics are considered applicable for the imagery processing of low and middle spatial resolution. Unless the authors are not interested in the accuracy of the final information products, they utilize these standard procedures. The emerging necessity of processing data of high spectral and spatial resolution given by imaging spectrometers puts forward the newly defined pattern recognition techniques. The proposed tools of using different types of classifiers combined with the parameter retrieval procedures for the forested environment are maintained to have much wider applications as compared with the image features and object shapes extraction, which relates to photometry and geometry in pixel-level reflectance representation of the forested land cover. The pixel fraction and reflectance of “end-members” (sunlit forest canopy, sunlit background and shaded background for a particular view and solar illumination angle) are only a part in the listed techniques. It is assumed that each pixel views collections of the individual forest trees and the pixel-level reflectance can thus be computed as a linear mixture of sunlit tree tops, sunlit background (or understory) and shadows. Instead of these photometry and geometry constraints, the improved models are developed of the functional description of outgoing spectral radiation, in which such parameters of the forest canopy like the vegetation biomass density for particular forest species and age are embedded. This permits us to calculate the relationships between the registered radiances and the biomass densities (the direct problem of atmospheric optics). The next stage is to find solutions of this problem as cross-sections of the related curves in the multi-dimensional space given by the parameters of these models (the inverse problem). The typical solutions may not be mathematically unique and the computational procedure is undertaken to their regularization by finding minima of the functional called “the energy for the particular class of forests”. The relevant optimization procedures serve to identify the likelihood between any registered set of data and the theoretical distributions as well as to regularize the solution by employing the derivative functions characterizing the neighborhood of the pixels for the related classes. As a result, we have elaborated a rigorous approach to optimize spectral channels based on searching their most informative sets by combining the channels and finding correlations between them. A successive addition method is used with the calculation of the total probability error. The step up method consists in fixing the level of the probability error that is not improved by further adding the channels in the calculation scheme of the pattern recognition. The best distinguishable classes are recognized at the first stage of this procedure. The analytical technique called “cross-validation” is used at its second stage. This procedure is in removing some data before the classifier training begins employing, for instance, the known “leaving-out-one” strategy. This strategy serves to explain the accuracy category additionally to the standard confusion matrix between the modeling approach and the available ground-based observations, once the employed validation map may not be perfect or needs renewal. Such cross-validation carried out for ensembles of airborne data from the imaging spectrometer produced in Russia enables to conclude that the forest classes on a test area are separ

Kozoderov, Vladimir; Kondranin, Timofei; Dmitriev, Egor; Kamentsev, Vladimir

8

Pattern recognition technique  

NASA Technical Reports Server (NTRS)

Technique operates regardless of pattern rotation, translation or magnification and successfully detects out-of-register patterns. It improves accuracy and reduces cost of various optical character recognition devices and page readers and provides data input to computer.

Hong, J. P.

1971-01-01

9

Recent advances and applications of NASA's new ultrahigh-sensitivity absolute optical pattern recognition encoders  

Microsoft Academic Search

NASA's new optical encoders use pattern recognition for images of encoder scales to encode both rotary and linear, absolute, mechanical position with ultra-high sensitivity. These encoders have advanced beyond prototype stage and are now being used in a variety of demanding applications both in the laboratory and in optical ground support equipment for space flight instrumentation. Rotary versions of these

Douglas B. Leviton; Mario S. Garza

2000-01-01

10

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal  

NASA Astrophysics Data System (ADS)

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

11

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988  

NASA Technical Reports Server (NTRS)

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Juday, Richard D. (editor)

1988-01-01

12

A Novel Optical/digital Processing System for Pattern Recognition  

NASA Technical Reports Server (NTRS)

This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network.

Boone, Bradley G.; Shukla, Oodaye B.

1993-01-01

13

Muscle Sensor Model Using Small Scale Optical Device for Pattern Recognitions  

PubMed Central

A new sensor system for measuring contraction and relaxation of muscles by using a PANDA ring resonator is proposed. The small scale optical device is designed and configured to perform the coupling effects between the changes in optical device phase shift and human facial muscle movement, which can be used to form the relationship between optical phase shift and muscle movement. By using the Optiwave and MATLAB programs, the results obtained have shown that the measurement of the contraction and relaxation of muscles can be obtained after the muscle movements, in which the unique pattern of individual muscle movement from facial expression can be established. The obtained simulation results, that is, interference signal patterns, can be used to form the various pattern recognitions, which are useful for the human machine interface and the human computer interface application and discussed in detail. PMID:24222730

Tamee, Kreangsak; Chaiwong, Khomyuth; Yothapakdee, Kriengsak; Yupapin, Preecha P.

2013-01-01

14

Vibration pattern recognition and classification in OTDR based distributed optical-fiber vibration sensing system  

NASA Astrophysics Data System (ADS)

In this paper we propose and demonstrate the scheme of vibration pattern recognition and classification in the OTDR based distributed optical-fiber vibration sensing system. We set up the engineering system with signal processing PC for perimeter security in some high-tech park in Nanjing. Three types of disturbing actions, including climbing up and kicking at the wall by a person, and watering on the sensing optical fiber cable same as the rain falling on, are implemented. By using level crossing rate (LCR), we can obtain their individual pattern features, so that the eigenvalue database for three disturbing actions can be built in the system. By comparing three types of vibrations, the differences among these can be given out. The results show three vibration patterns can be recognized and classified effectively.

Zhu, Hui; Pan, Chao; Sun, Xiaohan

2014-03-01

15

Recent advances and applications of NASA's new ultrahigh-sensitivity absolute optical pattern recognition encoders  

NASA Astrophysics Data System (ADS)

NASA's new optical encoders use pattern recognition for images of encoder scales to encode both rotary and linear, absolute, mechanical position with ultra-high sensitivity. These encoders have advanced beyond prototype stage and are now being used in a variety of demanding applications both in the laboratory and in optical ground support equipment for space flight instrumentation. Rotary versions of these new pattern recognition encoders have sensitivity down to 0.01 arcseconds while linear models have demonstrated sensitivity of 10 nm (0.01 micrometer) with higher sensitivities achievable in both formats. The means for encoding is a radical departure from that for conventional optical encoders and offers advantages of absolute operation, very low cost, compact form, considerable immunity to scale-damage-induced dropouts of position information, an order of magnitude or more higher sensitivity over commercially available encoders, demonstrated applicability in cryostatic and vacuum environments, and suitability for space flight. Operational details of the encoder are given. Representative sensitivity performance is presented along with several examples of uses to date. Planned future development is also discussed.

Leviton, Douglas B.; Garza, Mario S.

2000-10-01

16

Design of coupled mace filters for optical pattern recognition using practical spatial light modulators  

NASA Technical Reports Server (NTRS)

Spatial light modulators (SLMs) are being used in correlation-based optical pattern recognition systems to implement the Fourier domain filters. Currently available SLMs have certain limitations with respect to the realizability of these filters. Therefore, it is necessary to incorporate the SLM constraints in the design of the filters. The design of a SLM-constrained minimum average correlation energy (SLM-MACE) filter using the simulated annealing-based optimization technique was investigated. The SLM-MACE filter was synthesized for three different types of constraints. The performance of the filter was evaluated in terms of its recognition (discrimination) capabilities using computer simulations. The correlation plane characteristics of the SLM-MACE filter were found to be reasonably good. The SLM-MACE filter yielded far better results than the analytical MACE filter implemented on practical SLMs using the constrained magnitude technique. Further, the filter performance was evaluated in the presence of noise in the input test images. This work demonstrated the need to include the SLM constraints in the filter design. Finally, a method is suggested to reduce the computation time required for the synthesis of the SLM-MACE filter.

Rajan, P. K.; Khan, Ajmal

1993-01-01

17

Pattern recognition in bioinformatics.  

PubMed

Pattern recognition is concerned with the development of systems that learn to solve a given problem using a set of example instances, each represented by a number of features. These problems include clustering, the grouping of similar instances; classification, the task of assigning a discrete label to a given instance; and dimensionality reduction, combining or selecting features to arrive at a more useful representation. The use of statistical pattern recognition algorithms in bioinformatics is pervasive. Classification and clustering are often applied to high-throughput measurement data arising from microarray, mass spectrometry and next-generation sequencing experiments for selecting markers, predicting phenotype and grouping objects or genes. Less explicitly, classification is at the core of a wide range of tools such as predictors of genes, protein function, functional or genetic interactions, etc., and used extensively in systems biology. A course on pattern recognition (or machine learning) should therefore be at the core of any bioinformatics education program. In this review, we discuss the main elements of a pattern recognition course, based on material developed for courses taught at the BSc, MSc and PhD levels to an audience of bioinformaticians, computer scientists and life scientists. We pay attention to common problems and pitfalls encountered in applications and in interpretation of the results obtained. PMID:23559637

de Ridder, Dick; de Ridder, Jeroen; Reinders, Marcel J T

2013-09-01

18

Anthropomorphic pattern formation and recognition systems  

NASA Astrophysics Data System (ADS)

In first part the Optical Pattern Formation system, i.e. the non-traditional Super-Scanning Locator, is shortly described. The goal of the second part--creation the Optical Pattern Recognition system working alike the Natural one, where receiving information, before reach the natural computer (brain), is pre-processed by the 5 organs of sense, mostly by analogous methods.

Ginzburg, Vera M.

2000-03-01

19

Degraded character recognition based on gradient pattern  

NASA Astrophysics Data System (ADS)

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

2010-02-01

20

Pattern Recognition in Photoacoustic Dataset  

NASA Astrophysics Data System (ADS)

In photoacoustic imaging, optical absorption properties of matter are imaged by detecting the ultrasound that is produced when the material is illuminated by a laser. For medical imaging, photoacoustics is a useful tool since matter in the human body has different optical absorption properties. In this study, pattern recognition systems are used to study a set of medical images for tumor identification and extraction—to detect the specific area in which the tumor is present. The objective is to incorporate this information into real-time image acquisition systems to improve medical diagnosis. Preliminary results obtained by studying the image dataset demonstrated the interchangeability of the proposed system. A system of automatic classification was constructed, using a set of images with and without cancerous tumors to evaluate the proposed method. The training set used was manually labeled, and the test set was never seen by the training set. The results helped us determine the feasibility of the proposed system.

Guzmán-Cabrera, R.; Guzmán-Sepúlveda, J. R.; Torres-Cisneros, M.; May-Arrioja, D. A.; Ruiz-Pinales, J.; Ibarra-Manzano, O. G.; Avińa-Cervantes, G.

2013-09-01

21

Optical Character Recognition.  

ERIC Educational Resources Information Center

This paper describes computer-based optical character recognition (OCR) systems, focusing on their components (the computer, the scanner, the OCR, and the output device); how the systems work; and features to consider in selecting a system. A list of 26 questions to ask to evaluate systems for potential purchase is included. (JDD)

Converso, L.; Hocek, S.

1990-01-01

22

Certifiable optical character recognition  

Microsoft Academic Search

A general-purpose approach for enhancing the accuracy of optical character recognition is described. By taking the view that the printed page is a data transmission channel, the authors raise the possibility of error detecting\\/correcting codes designed specifically for the OCR process. They present experimental results that demonstrate the feasibility of fully automated, 100% accurate OCR for computer typeset documents

Daniel P. Lopresti; Jonathan S. Sandberg

1993-01-01

23

Pattern and form recognition of statistically distributed defects on functional optical surfaces  

NASA Astrophysics Data System (ADS)

The inspection of the surface quality of optical components is an essential characterization method for high power laser applications. We report about two different mapping methods based on the measurement of Total Scattering (TS) and phase contrast microscopy. The mappings are used for the determination of the defect density distribution of optical flat surfaces. The mathematical procedure relating data points to a defect area and to the form of objects will be illustrated in details. The involved differential operators and the optimized sub routines adapted to a large number of defects will be underlined. For the decision about the form of the objects, a parameter set including the "fill factor", "edge ratio" and the "polar distance" will be discussed in respect to their versatility range for the basic forms. The calculated distribution will be expressed in terms of affine probability compared to the basic forms. The extracted size and form distribution function of the defects will be presented for selected high reflective and anti-reflective coating samples.

Kadkhoda, P.; Chubak, P.; Lassahn, M.; Ristau, D.

2013-04-01

24

ARTICLE IN PRESS Pattern Recognition ( )  

E-print Network

ARTICLE IN PRESS Pattern Recognition ( ) ­ www.elsevier.com/locate/patcog The method of N-grams on the comparison of vocabularies of N-grams. In contrast to the regular N-grams approach, the proposed N-grams method is based on calculation of imperfect occurrences of N-grams in a text up to a number of mismatched

Kirzhner Valery

25

Anthropomorphic pattern recognition systems  

NASA Astrophysics Data System (ADS)

Periodic defocusing of the crystalline lens leads to generalization of an image projected on the retina. In the process, the fine structure of the image is eliminated while informative fragments (IF), that is, brighter spots with abrupt contour change, are emphasized. The shape and arrangement of IFs may be utilized by a visual system in order to trigger saccades and form receptive areas. Results of theoretical and experimental optics studies imitating physiological processes are described. These results may be of interest to physiologists and can be used to develop anthropomorphic technical systems. A brief comparative analysis of other anthropomorphic systems proposed by A. P. Ginsburg, T. Podijo, D. I. Tomsit and others is given. A specific model of anthropomorphic robot representing a holographic correlator processing image by defocusing and by applying a set of spatial filters. These filters are constructed using a set of elementary images formed from two images, a straight stripe and a round spot, recognized by all living beings. Such a robot can be used for drawing letters and numerals from their written images; for classification of many similar images; for processing aerial photographs to determine boundaries between woods, various crops, etc. Input image defocusing is shown to be also useful for narrowing bandwidth in TV systems; for automatic loading of optical information after removal of noise.

Ginzburg, Vera M.

1997-03-01

26

Stem and Calyx Recognition on `Jonagold' Apples by Pattern Recognition  

E-print Network

Stem and Calyx Recognition on `Jonagold' Apples by Pattern Recognition D. Unay TCTS Labs., Facult of `Jonagold' apples by pattern recognition is proposed. The method starts with background removal and object- ment relative to the ones introduced in the literature. Key words: apple, stem, calyx, machine vision

Dupont, Stéphane

27

Perception & Pattern Recognition Classic Model of Perception  

E-print Network

the clustering of letters in the reaction time task to the similarities of the features Groupings by RT #121 Perception & Pattern Recognition Classic Model of Perception Pattern Recognition · Process Perception Geons · Can help explain recognition of degraded objects Degraded Objects · Disrupt Geon

Coulson, Seana

28

Pattern Recognition Receptors and Autophagy  

PubMed Central

The immune system senses exogenous threats or endogenous stress through specialized machinery known as pattern recognition receptors (PRRs). These receptors recognize conserved molecular structures and initiate downstream signaling pathways to control immune responses. Although various immunologic pathways mediated by PRRs have been described, recent studies have demonstrated a link between PRRs and autophagy. Autophagy is a specialized biological process involved in maintaining homeostasis through the degradation of long-lived cellular proteins and organelles. In addition to this fundamental function, autophagy plays important roles in various immunologic processes. In this review, we focus on the reciprocal influences of PRRs and autophagy in modulating innate immune responses. PMID:25009542

Oh, Ji Eun; Lee, Heung Kyu

2014-01-01

29

Silicon coupled-resonator optical-waveguide-based biosensors using light-scattering pattern recognition with pixelized mode-field-intensity distributions  

NASA Astrophysics Data System (ADS)

Chip-scale, optical microcavity-based biosensors typically employ an ultra-high-quality microcavity and require a precision wavelength-tunable laser for exciting the cavity resonance. For point-of-care applications, however, such a system based on measurements in the spectral domain is prone to equipment noise and not portable. An alternative microcavity-based biosensor that enables a high sensitivity in an equipment-noise-tolerant and potentially portable system is desirable. Here, we demonstrate the proof-of-concept of such a biosensor using a coupled-resonator optical-waveguide (CROW) on a silicon-on-insulator chip. The sensing scheme is based on measurements in the spatial domain, and only requires exciting the CROW at a fixed wavelength and imaging the out-of-plane elastic light-scattering intensity patterns of the CROW. Based on correlating the light-scattering intensity pattern at a probe wavelength with the light-scattering intensity patterns at the CROW eigenstates, we devise a pattern-recognition algorithm that enables the extraction of a refractive index change, ?n, applied upon the CROW upper-cladding from a calibrated set of correlation coefficients. Our experiments using an 8-microring CROW covered by NaCl solutions of different concentrations reveal a ?n of ~1.5 × 10-4 refractive index unit (RIU) and a sensitivity of ~752 RIU-1, with a noise-equivalent detection limit of ~6 × 10-6 RIU.

Wang, Jiawei; Yao, Zhanshi; Lei, Ting; Poon, Andrew W.

2014-12-01

30

Silicon coupled-resonator optical-waveguide-based biosensors using light-scattering pattern recognition with pixelized mode-field-intensity distributions.  

PubMed

Chip-scale, optical microcavity-based biosensors typically employ an ultra-high-quality microcavity and require a precision wavelength-tunable laser for exciting the cavity resonance. For point-of-care applications, however, such a system based on measurements in the spectral domain is prone to equipment noise and not portable. An alternative microcavity-based biosensor that enables a high sensitivity in an equipment-noise-tolerant and potentially portable system is desirable. Here, we demonstrate the proof-of-concept of such a biosensor using a coupled-resonator optical-waveguide (CROW) on a silicon-on-insulator chip. The sensing scheme is based on measurements in the spatial domain, and only requires exciting the CROW at a fixed wavelength and imaging the out-of-plane elastic light-scattering intensity patterns of the CROW. Based on correlating the light-scattering intensity pattern at a probe wavelength with the light-scattering intensity patterns at the CROW eigenstates, we devise a pattern-recognition algorithm that enables the extraction of a refractive index change, ?n, applied upon the CROW upper-cladding from a calibrated set of correlation coefficients. Our experiments using an 8-microring CROW covered by NaCl solutions of different concentrations reveal a ?n of ~1.5 × 10(-4) refractive index unit (RIU) and a sensitivity of ~752?RIU(-1), with a noise-equivalent detection limit of ~6 × 10(-6)?RIU. PMID:25519726

Wang, Jiawei; Yao, Zhanshi; Lei, Ting; Poon, Andrew W

2014-01-01

31

Silicon coupled-resonator optical-waveguide-based biosensors using light-scattering pattern recognition with pixelized mode-field-intensity distributions  

PubMed Central

Chip-scale, optical microcavity-based biosensors typically employ an ultra-high-quality microcavity and require a precision wavelength-tunable laser for exciting the cavity resonance. For point-of-care applications, however, such a system based on measurements in the spectral domain is prone to equipment noise and not portable. An alternative microcavity-based biosensor that enables a high sensitivity in an equipment-noise-tolerant and potentially portable system is desirable. Here, we demonstrate the proof-of-concept of such a biosensor using a coupled-resonator optical-waveguide (CROW) on a silicon-on-insulator chip. The sensing scheme is based on measurements in the spatial domain, and only requires exciting the CROW at a fixed wavelength and imaging the out-of-plane elastic light-scattering intensity patterns of the CROW. Based on correlating the light-scattering intensity pattern at a probe wavelength with the light-scattering intensity patterns at the CROW eigenstates, we devise a pattern-recognition algorithm that enables the extraction of a refractive index change, ?n, applied upon the CROW upper-cladding from a calibrated set of correlation coefficients. Our experiments using an 8-microring CROW covered by NaCl solutions of different concentrations reveal a ?n of ~1.5 × 10?4 refractive index unit (RIU) and a sensitivity of ~752?RIU?1, with a noise-equivalent detection limit of ~6 × 10?6?RIU. PMID:25519726

Wang, Jiawei; Yao, Zhanshi; Lei, Ting; Poon, Andrew W.

2014-01-01

32

Pattern Recognition in Remote Sensing Development of new pattern recognition techniques for the  

E-print Network

Editorial Pattern Recognition in Remote Sensing Development of new pattern recognition techniques to be developed to detect and recognize them. Furthermore, a charac- teristic peculiar to remote sensing a popular research topic for several decades. Consequently, the pattern recognition and remote sensing

Aksoy, Selim

33

Chapter 25 Information Granulation and Pattern Recognition  

Microsoft Academic Search

Summary. We discuss information granulation applications in pattern recognition. The chap- ter consists of two parts. In the first part, we present applications of rough set methods for feature selection in pattern recognition. We emphasize the role of different forms of reducts that are the basic constructs of the rough set approach in feature selection. In the overview of methods

Andrzej Skowron; Roman W. Swiniarski

34

Fuzzy Logic-Based Audio Pattern Recognition  

NASA Astrophysics Data System (ADS)

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Malcangi, M.

2008-11-01

35

Pattern recognition, inner products and correlation filters  

SciTech Connect

In this paper, we review correlation filters as an approach to pattern recognition with a special emphasis on the consequences of normalizing the correlation to achieve intensity invariance. Intensity invariance is effected using the Cauchy-Schwarz inequality to normalize the correlation integral. We discuss the implications of this criterion for the application of correlation filters to the pattern recognition problem. It is shown that normalized phase-only and synthetic discriminate functions do not provide the recognition/discrimination obtained with the classical matched filter. 34 refs., 5 figs.

Dickey, F.M.; Romero, L.A.

1991-01-01

36

Large-memory real-time multichannel multiplexed pattern recognition  

NASA Technical Reports Server (NTRS)

The principle and experimental design of a real-time multichannel multiplexed optical pattern recognition system via use of a 25-focus dichromated gelatin holographic lens (hololens) are described. Each of the 25 foci of the hololens may have a storage and matched filtering capability approaching that of a single-lens correlator. If the space-bandwidth product of an input image is limited, as is true in most practical cases, the 25-focus hololens system has 25 times the capability of a single lens. Experimental results have shown that the interfilter noise is not serious. The system has already demonstrated the storage and recognition of over 70 matched filters - which is a larger capacity than any optical pattern recognition system reported to date.

Gregory, D. A.; Liu, H. K.

1984-01-01

37

Layered neural nets for pattern recognition  

Microsoft Academic Search

A pattern recognition concept involving first an `invariance net' and second a `trainable classifier' is proposed. The invariance net can be trained or designed to produce a set of outputs that are insensitive to translation, rotation, scale change, perspective change, etc., of the retinal input pattern. The outputs of the invariance net are scrambled, however. When these outputs are fed

BERNARD WIDROW; RODNEY G. WINTER; ROBERT A. BAXTER

1988-01-01

38

ARTICLE IN PRESS Pattern Recognition ( ) --  

E-print Network

Recognition journal homepage: www.elsevier.com/locate/pr Synthetic handwritten CAPTCHAs Achint Oommen Thomas Received in revised form 30 November 2008 Accepted 19 December 2008 Keywords: HIP CAPTCHA Handwriting generation CAPTCHAs (completely automated public Turing test to tell computers and humans apart

Govindaraju, Venu

39

Experiences in Pattern Recognition for Machine Olfaction  

NASA Astrophysics Data System (ADS)

Pattern recognition is essential for translating complex olfactory sensor responses into simple outputs that are relevant to users. Many approaches to pattern recognition have been applied in this field, including multivariate statistics (e.g. discriminant analysis), artificial neural networks (ANNs) and support vector machines (SVMs). Reviewing our experience of using these techniques with many different sensor systems reveals some useful insights. Most importantly, it is clear beyond any doubt that the quantity and selection of samples used to train and test a pattern recognition system are by far the most important factors in ensuring it performs as accurately and reliably as possible. Here we present evidence for this assertion and make suggestions for best practice based on these findings.

Bessant, C.

2011-09-01

40

Associative Pattern Recognition In Analog VLSI Circuits  

NASA Technical Reports Server (NTRS)

Winner-take-all circuit selects best-match stored pattern. Prototype cascadable very-large-scale integrated (VLSI) circuit chips built and tested to demonstrate concept of electronic associative pattern recognition. Based on low-power, sub-threshold analog complementary oxide/semiconductor (CMOS) VLSI circuitry, each chip can store 128 sets (vectors) of 16 analog values (vector components), vectors representing known patterns as diverse as spectra, histograms, graphs, or brightnesses of pixels in images. Chips exploit parallel nature of vector quantization architecture to implement highly parallel processing in relatively simple computational cells. Through collective action, cells classify input pattern in fraction of microsecond while consuming power of few microwatts.

Tawel, Raoul

1995-01-01

41

Rulegraphs for graph matching in pattern recognition  

Microsoft Academic Search

In Pattern Recognition, the Graph Matching problem involves thematching of a sample graph with the subgraph of a larger model graph wherevertices and edges correspond to pattern parts and their relations. In this paper,we present Rulegraphs, a new method that combines the Graph Matchingapproach with Rule-Based approaches from Machine Learning. They reducethe cardinality of the (NP-Complete) Graph Matching problem by

Adrian R. Pearce; Terry M. Caelli; Walter F. Bischof

1994-01-01

42

Artificial neural networks for pattern recognition  

Microsoft Academic Search

This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition.\\u000a ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models\\u000a are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between\\u000a pattern and data

B Yegnanarayana

1994-01-01

43

Conformal Predictions in Multimedia Pattern Recognition  

ERIC Educational Resources Information Center

The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…

Nallure Balasubramanian, Vineeth

2010-01-01

44

Face Recognition with Local Binary Patterns  

Microsoft Academic Search

{tahonen,hadid,mkp}@ee.oulu.fi, http:\\/\\/www.ee.oulu.fi\\/mvg\\/ Abstract. In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face im- ages. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a

Timo Ahonen; Abdenour Hadid; Matti Pietikäinen

2004-01-01

45

Computerized pattern recognition applied to battery testing  

Microsoft Academic Search

The primary goal of this work has been to develop non-destructive testing methods as a screening procedure for batteries to predict lifetime and identify probably failure mechanisms. A secondary goal has been to develop criteria for predicting imminent failure from battery performance data. We believe that these goals can be met by the application of computerized pattern recognition to the

S. P. Perone

1980-01-01

46

QUANTITATIVE PATTERN RECOGNITION USING NONLINEAR MODELBASED ANALYSIS  

E-print Network

QUANTITATIVE PATTERN RECOGNITION USING NONLINEAR MODEL­BASED ANALYSIS A Dissertation Presented and management aspects of a major research pro- gram. I also appreciate his sensitivity and consideration of my in the imaging, robotics, and intelligent systems lab have assisted in various stages of this research and I

Abidi, Mongi A.

47

Fast Star Pattern Recognition Using Planar Triangles  

E-print Network

Fast Star Pattern Recognition Using Planar Triangles Craig L. Cole John L. Crassidis Abstract A new method for star identification based on using planar triangles is developed and compared to a standard angle method approach. The angle method creates angles between stars within the field of view of a star

Crassidis, John L.

48

Fast Star Pattern Recognition Using Spherical Triangles  

E-print Network

Fast Star Pattern Recognition Using Spherical Triangles Craig L. Cole Orbital Sciences Corporation-4400 A current method by which star trackers identify stars is to match the angles between stars within its field of view to angles stored in a catalog. If an angle can be matched to one pair of stars, the attitude

Crassidis, John L.

49

ARTICLE IN PRESS Pattern Recognition ( ) --  

E-print Network

measurement, over many months or years, of the flux of radiation (optical light or radio waves) from a very signals that originate from the same source but travel along different paths to the observer, is a real.1016/j.patcog.2009.07.016 times in the different images, according to the travel time along the various

Yao, Xin

50

Gender Recognition from Faces Using Bandlet and Local Binary Patterns  

E-print Network

Gender Recognition from Faces Using Bandlet and Local Binary Patterns Faten A. Alomar, Ghulam-- In this paper, multi-scale bandlet and local binary pattern (LBP) based method for gender recognition from faces recognition; face images; FERET; bandlet I. INTRODUCTION The current research on face recognition involves

Bebis, George

51

Pattern recognition monitoring of PEM fuel cell  

DOEpatents

The CO-concentration in the H.sub.2 feed stream to a PEM fuel cell stack is monitored by measuring current and voltage behavior patterns from an auxiliary cell attached to the end of the stack. The auxiliary cell is connected to the same oxygen and hydrogen feed manifolds that supply the stack, and discharges through a constant load. Pattern recognition software compares the current and voltage patterns from the auxiliary cell to current and voltage signature determined from a reference cell similar to the auxiliary cell and operated under controlled conditions over a wide range of CO-concentrations in the H.sub.2 fuel stream.

Meltser, Mark Alexander (Pittsford, NY)

1999-01-01

52

Pattern recognition monitoring of PEM fuel cell  

DOEpatents

The CO-concentration in the H{sub 2} feed stream to a PEM fuel cell stack is monitored by measuring current and voltage behavior patterns from an auxiliary cell attached to the end of the stack. The auxiliary cell is connected to the same oxygen and hydrogen feed manifolds that supply the stack, and discharges through a constant load. Pattern recognition software compares the current and voltage patterns from the auxiliary cell to current and voltage signature determined from a reference cell similar to the auxiliary cell and operated under controlled conditions over a wide range of CO-concentrations in the H{sub 2} fuel stream. 4 figs.

Meltser, M.A.

1999-08-31

53

Applications of chaotic neurodynamics in pattern recognition  

NASA Astrophysics Data System (ADS)

Network algorithms and architectures for pattern recognition derived from neural models of the olfactory system are reviewed. These span a range from highly abstract to physiologically detailed, and employ the kind of dynamical complexity observed in olfactory cortex, ranging from oscillation to chaos. A simple architecture and algorithm for analytically guaranteed associative memory storage of analog patterns, continuous sequences, and chaotic attractors in the same network is described. A matrix inversion determines network weights, given prototype patterns to be stored. There are N units of capacity in an N node network with 3N2 weights. It costs one unit per static attractor, two per Fourier component of each sequence, and three to four per chaotic attractor. There are no spurious attractors, and for sequences there is a Liapunov function in a special coordinate system which governs the approach of transient states to stored trajectories. Unsupervised or supervised incremental learning algorithms for pattern classification, such as competitive learning or bootstrap Widrow-Hoff can easily be implemented. The architecture can be ''folded'' into a recurrent network with higher order weights that can be used as a model of cortex that stores oscillatory and chaotic attractors by a Hebb rule. Network performance is demonstrated by application to the problem of real-time handwritten digit recognition. An effective system with on-line learning has been written by Eeckman and Baird for the Macintosh. It utilizes static, oscillatory, and/or chaotic attractors of two kinds--Lorenze attractors, or attractors resulting from chaotically interacting oscillatory modes. The successful application to an industrial pattern recognition problem of a network architecture of considerable physiological and dynamical complexity, developed by Freeman and Yao, is described. The data sets of the problem come in three classes of difficulty, and performance of the biological network is favorably compared with that of several other network and statistical pattern recognition methods.

Baird, Bill; Freeman, Walter J.; Eeckman, Frank H.; Yao, Yong

1991-08-01

54

Pattern Recognition 32 (1999) 753--771 Silhouette recognition using high-resolution pursuit  

E-print Network

Pattern Recognition 32 (1999) 753--771 Silhouette recognition using high-resolution pursuit Seema introduces a simple new approach to object recognition from silhouettes. This new approach utilizes features-based algorithm in the presence of occlusion and localized silhouette variations. 1999 Pattern Recognition Society

Willsky, Alan S.

55

Artificial Immune Systems: A Novel Paradigm to Pattern Recognition  

E-print Network

Artificial Immune Systems: A Novel Paradigm to Pattern Recognition L. N. de Castro and J. Timmis to perform pattern recognition, named Artificial Immune Systems (AIS). AIS take inspiration from the immune. The basic immune theories used to explain how the immune system perform pattern recognition are described

Kent, University of

56

All-optical multibit address recognition at 20 Gb/s based on TOAD  

NASA Astrophysics Data System (ADS)

All-optical multibit address recognition at 20 Gb/s is demonstrated based on a special AND logic of terahertz optical asymmetric demultiplexer (TOAD). The semiconductor optical amplifier (SOA) used in the TOAD is biased at transparency status to accelerate the gain recovery. This is the highest bit rate that multibit address recognition is demonstrated with SOA-based interferometer. The experimental results show low pattern dependency. With this method, address recognition can be performed without separating address and payload beforehand.

Yan, Yumei; Wu, Jian; Lin, Jintong

2005-04-01

57

Pattern recognition receptors in antifungal immunity.  

PubMed

Receptors of the innate immune system are the first line of defence against infection, being able to recognise and initiate an inflammatory response to invading microorganisms. The Toll-like (TLR), NOD-like (NLR), RIG-I-like (RLR) and C-type lectin-like receptors (CLR) are four receptor families that contribute to the recognition of a vast range of species, including fungi. Many of these pattern recognition receptors (PRRs) are able to initiate innate immunity and polarise adaptive responses upon the recognition of fungal cell wall components and other conserved molecular patterns, including fungal nucleic acids. These receptors induce effective mechanisms of fungal clearance in normal hosts, but medical interventions, immunosuppression or genetic predisposition can lead to susceptibility to fungal infections. In this review, we highlight the importance of PRRs in fungal infection, specifically CLRs, which are the major PRR involved. We will describe specific PRRs in detail, the importance of receptor collaboration in fungal recognition and clearance, and describe how genetic aberrations in PRRs can contribute to disease pathology. PMID:25420452

Plato, Anthony; Hardison, Sarah E; Brown, Gordon D

2014-11-25

58

Discussion of problems in pattern recognition  

Microsoft Academic Search

Various problems encountered in pattern recognition were examined by an eight-man panel during the Thursday afternoon session. The panel included O. G. Selfridge, M.I.T. Lincoln Laboratory, Session Chairman; R. A. Kirsch, National Bureau of Standards; M. Minsky, Massachusetts Institute of Technology; U. Neisser, Brandeis University; and the authors of the four preceding papers --- R. J. Evey, I.B.M. Corporation, \\

W. W. Bledsoe; J. S. Bomba; I. Browning; R. J. Evey; R. A. Kirsch; R. L. Mattson; M. Minsky; U. Neisser; O. G. Selfridge

1959-01-01

59

Developing Signal-Pattern-Recognition Programs  

NASA Technical Reports Server (NTRS)

Pattern Interpretation and Recognition Application Toolkit Environment (PIRATE) is a block-oriented software system that aids the development of application programs that analyze signals in real time in order to recognize signal patterns that are indicative of conditions or events of interest. PIRATE was originally intended for use in writing application programs to recognize patterns in space-shuttle telemetry signals received at Johnson Space Center's Mission Control Center: application programs were sought to (1) monitor electric currents on shuttle ac power busses to recognize activations of specific power-consuming devices, (2) monitor various pressures and infer the states of affected systems by applying a Kalman filter to the pressure signals, (3) determine fuel-leak rates from sensor data, (4) detect faults in gyroscopes through analysis of system measurements in the frequency domain, and (5) determine drift rates in inertial measurement units by regressing measurements against time. PIRATE can also be used to develop signal-pattern-recognition software for different purposes -- for example, to monitor and control manufacturing processes.

Shelton, Robert O.; Hammen, David

2006-01-01

60

Pattern recognition and control in manipulation  

NASA Technical Reports Server (NTRS)

A new approach to the use of sensors in manipulator or robot control is discussed. The concept addresses the problem of contact or near-contact type of recognition of three-dimensional forms of objects by proprioceptive and/or exteroceptive sensors integrated with the terminal device. This recognition of object shapes both enhances and simplifies the automation of object handling. Several examples have been worked out for the 'Belgrade hand' and for a parallel jaw terminal device, both equipped with proprioceptive (position) and exteroceptive (proximity) sensors. The control applications are discussed in the framework of a multilevel man-machine system control. The control applications create interesting new issues which, in turn, invite novel theoretical considerations. An important issue is the problem of stability in control when the control is referenced to patterns.

Bejczy, A. K.; Tomovic, R.

1976-01-01

61

Pigment Melanin: Pattern for Iris Recognition  

E-print Network

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly s...

Hosseini, Mahdi S; Soltanian-Zadeh, Hamid

2009-01-01

62

Statistical pattern recognition algorithms for autofluorescence imaging  

NASA Astrophysics Data System (ADS)

In cancer diagnostics the most important problems are the early identification and estimation of the tumor growth and spread in order to determine the area to be operated. The aim of the work was to design of statistical algorithms helping doctors to objectively estimate pathologically changed areas and to assess the disease advancement. In the research, algorithms for classifying endoscopic autofluorescence images of larynx and intestine were used. The results show that the statistical pattern recognition offers new possibilities for endoscopic diagnostics and can be of a tremendous help in assessing the area of the pathological changes.

Kulas, Zbigniew; Bere?-Pawlik, El?bieta; Wierzbicki, Jaros?aw

2009-02-01

63

Interpretation techniques. [image enhancement and pattern recognition  

NASA Technical Reports Server (NTRS)

The image enhancement and geometric correction and registration techniques developed and/or demonstrated on ERTS data are relatively mature and greatly enhance the utility of the data for a large variety of users. Pattern recognition was improved by the use of signature extension, feature extension, and other classification techniques. Many of these techniques need to be developed and generalized to become operationally useful. Advancements in the mass precision processing of ERTS were demonstrated, providing the hope for future earth resources data to be provided in a more readily usable state. Also in evidence is an increasing and healthy interaction between the techniques developers and the user/applications investigators.

Dragg, J. L.

1974-01-01

64

Pattern recognition of shape-encoded hydrogel biosensor arrays  

NASA Astrophysics Data System (ADS)

A pattern-recognition and encoding system has been developed for a biochip platform using shaped hydrogel sensors batch produced via photolithography. Each sensor shape is fashioned with a unique pattern of dots that makes it identifiable to a pattern recognition system. By linking the sensor's function to its shape, ``random'' arrays can be created (i.e., arrays that do not require sensors to be located at specific positions). Random arraying can be quickly and cost-effectively achieved via self-assembly methods. Pattern-recognition software was written to perform automated recognition of micrographs exhibiting fluorescing sensors. As a test of the recognition process, an array of shape-encoded DNA sensors was fabricated using lithography. Fluorescent micrographs were taken of a DNA-sensing experiment, and then processed with the pattern-recognition software. The results show that this process is quite viable with 98% recognition accuracy of the nondefective sensors in both images.

Meiring, Jason E.; Grayson, Scott M.

2009-03-01

65

A biologically inspired model for pattern recognition*  

PubMed Central

In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent. PMID:20104646

Gonzalez, Eduardo; Liljenström, Hans; Ruiz, Yusely; Li, Guang

2010-01-01

66

Adaptation in statistical pattern recognition using tangent vectors.  

PubMed

We integrate the tangent method into a statistical framework for classification analytically and practically. The resulting consistent framework for adaptation allows us to efficiently estimate the tangent vectors representing the variability. The framework improves classification results on two real-world pattern recognition tasks from the domains handwritten character recognition and automatic speech recognition. PMID:15376902

Keysers, Daniel; Macherey, Wolfgang; Ney, Hermann; Dahmen, Jörg

2004-02-01

67

Comparison of computer-based and optical face recognition paradigms  

NASA Astrophysics Data System (ADS)

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB(c) software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.

Alorf, Abdulaziz A.

68

Guideline for Optical Character Recognition Forms.  

ERIC Educational Resources Information Center

This publication provides materials relating to the design, preparation, acquisition, inspection, and application of Optical Character Recognition (OCR) forms in data entry systems. Since the materials are advisory and tutorial in nature, this publication has been issued as a guideline rather than as a standard in the Federal Information…

National Bureau of Standards (DOC), Washington, DC.

69

Searching for pulsars using image pattern recognition  

E-print Network

In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surv eys using image pattern recognition with deep neural nets---the PICS(Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interferences by looking for patterns from candidate. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of up to thousands pixel of image data. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its $\\sim$9000 neurons. Different from other pulsar selection programs which use pre-designed patterns, the PICS AI teaches itself the salient features of different pulsars from a set of human-labeled candidates through machine learning. The deep neural networks in this AI system grant it superior ability in recognizing various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated wi...

Zhu, W W; Madsen, E C; Tan, M; Stairs, I H; Brazier, A; Lazarus, P; Lynch, R; Scholz, P; Stovall, K; Random, S M; Banaszak, S; Biwer, C M; Cohen, S; Dartez, L P; Flanigan, J; Lunsford, G; Matinez, J G; Mata, A; Rohr, M; Walker, A; Allen, B; Bhat, N D R; Bogdanov, S; Camilo, F; Chatterjee, S; Cordes, J M; Crawford, F; Deneva, J S; Desvignes, G; Ferdman, R D; Hessels, J W T; Jenet, F A; Kaplan, D; Kaspi, V M; Knispel, B; Lee, K J; van Leeuwen, J; Lyne, A G; McLaughlin, M A; Spitler, L G

2014-01-01

70

Optical associative memory for high-order correlation patterns  

Microsoft Academic Search

An input pattern weighting method is proposed for increasing the efficiency of the recognition of high-correlated patterns. Numerical simulation results are given. An optical implementation of the high-order associative memory model using an inner product scheme with the weighting method is suggested.

Boris S. Kiselev; Nikolai Iu. Kulakov; Andrei L. Mikaelian; Vladimir A. Shkitin

1992-01-01

71

Pattern recognition: A basis for remote sensing data analysis  

NASA Technical Reports Server (NTRS)

The theoretical basis for the pattern-recognition-oriented algorithms used in the multispectral data analysis software system is discussed. A model of a general pattern recognition system is presented. The receptor or sensor is usually a multispectral scanner. For each ground resolution element the receptor produces n numbers or measurements corresponding to the n channels of the scanner.

Swain, P. H.

1973-01-01

72

SPARSE REPRESENTATION FOR COMPUTER VISION AND PATTERN RECOGNITION  

E-print Network

SPARSE REPRESENTATION FOR COMPUTER VISION AND PATTERN RECOGNITION By John Wright Yi Ma Julien OF IEEE, MARCH 2009 1 Sparse Representation For Computer Vision and Pattern Recognition John Wright to see significant impact in computer vision, often on non-traditional applications where the goal

73

Image pattern recognition supporting interactive analysis and graphical visualization  

NASA Technical Reports Server (NTRS)

Image Pattern Recognition attempts to infer properties of the world from image data. Such capabilities are crucial for making measurements from satellite or telescope images related to Earth and space science problems. Such measurements can be the required product itself, or the measurements can be used as input to a computer graphics system for visualization purposes. At present, the field of image pattern recognition lacks a unified scientific structure for developing and evaluating image pattern recognition applications. The overall goal of this project is to begin developing such a structure. This report summarizes results of a 3-year research effort in image pattern recognition addressing the following three principal aims: (1) to create a software foundation for the research and identify image pattern recognition problems in Earth and space science; (2) to develop image measurement operations based on Artificial Visual Systems; and (3) to develop multiscale image descriptions for use in interactive image analysis.

Coggins, James M.

1992-01-01

74

Stem and calyx recognition on `Jonagold' apples by pattern recognition D. Unay *, B. Gosselin  

E-print Network

Stem and calyx recognition on `Jonagold' apples by pattern recognition D. Unay *, B. Gosselin TCTS Abstract In this paper, a novel method to recognize stem or calyx regions of `Jonagold' apples by pattern to the ones introduced in the literature. Ă? 2005 Elsevier Ltd. All rights reserved. Keywords: Apple; Stem

Ă?nay, Devrim

75

Low-Cost Optical Character Recognition System  

NASA Astrophysics Data System (ADS)

Optical Character Recognition (OCR) equipment previously has been complex, massive, and very expensive, hence really practical only for large credit-card operations, insurance companies, and postal applications. However, because of the rapid advance in large scale integrated (LSI) circuit technology and the need for a low cost OCR device to improve business data entry operations, designing and engineering such a low cost OCR system is feasible. The described system is capable of reading directly from a human readable information source, thus eliminating manual keying of data for conversion to a computer processable form. The optical front-end for data acquisition, image conversion and correlation, and recognition processing subsystems using LSI circuits and high speed microprocessors, the algorithms of feature analysis and contextual editing, as well as output and control operational considerations are all essential segments of the described system.

Cheng, Charles C. K.

1980-02-01

76

Pattern-recognition receptors in human eosinophils  

PubMed Central

The pattern-recognition receptor (PRR) family includes Toll-like receptors (TLRs), nucleotide-binding oligomerization domain (NOD) -like receptors (NLRs), RIG-I-like receptors (RLRs), C-type lectin receptors (CLRs) and the receptor for advanced glycation end products (RAGE). They recognize various microbial signatures or host-derived danger signals and trigger an immune response. Eosinophils are multifunctional leucocytes involved in the pathogenesis of several inflammatory processes, including parasitic helminth infection, allergic diseases, tissue injury and tumour immunity. Human eosinophils express several PRRs, including TLR1–5, TLR7, TLR9, NOD1, NOD2, Dectin-1 and RAGE. Receptor stimulation induces survival, oxidative burst, activation of the adhesion system and release of cytokines (interleukin-1?, interleukin-6, tumour necrosis factor-? and granulocyte–macrophage colony-stimulating factor), chemokines (interleukin-8 and growth-related oncogene-?) and cytotoxic granule proteins (eosinophil cationic protein, eosinophil-derived neurotoxin, eosinophil peroxidase and major basic protein). It is also evident that eosinophils play an immunomodulatory role by interacting with surrounding cells. The presence of a broad range of PRRs in eosinophils indicates that they are not only involved in defence against parasitic helminths, but also against bacteria, viruses and fungi. From a clinical perspective, eosinophilic PRRs seem to be involved in both allergic and malignant diseases by causing exacerbations and affecting tumour growth, respectively. PMID:22242941

Kvarnhammar, Anne Mĺnsson; Cardell, Lars Olaf

2012-01-01

77

Pattern fusion in feature recognition neural networks for handwritten character recognition.  

PubMed

B. Hussain and M.R. Kabuka (1994) proposed a feature recognition neural network to reduce the network size of neocognitron. However, a distinct subnet is created for every training pattern. Therefore, a big network is obtained when the number of training patterns is large. Furthermore, recognition rate can be hurt due to the failure of combining features from similar training patterns. We propose an improvement by incorporating the idea of fuzzy ARTMAP in the feature recognition neural network. Training patterns are allowed to be merged, based on the measure of similarity among features, resulting in a subnet being shared by similar patterns. Because of the fusion of training patterns, network size is reduced and recognition rate is increased. PMID:18255980

Lee, S J; Tsai, H L

1998-01-01

78

Pattern recognition issues on anisotropic smoothed particle hydrodynamics  

NASA Astrophysics Data System (ADS)

This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.

Pereira Marinho, Eraldo

2014-03-01

79

Searching for Pulsars Using Image Pattern Recognition  

NASA Astrophysics Data System (ADS)

In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ~9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The performance of this system can be improved over time as more training data are accumulated. This AI system has been integrated into the PALFA survey pipeline and has discovered six new pulsars to date.

Zhu, W. W.; Berndsen, A.; Madsen, E. C.; Tan, M.; Stairs, I. H.; Brazier, A.; Lazarus, P.; Lynch, R.; Scholz, P.; Stovall, K.; Ransom, S. M.; Banaszak, S.; Biwer, C. M.; Cohen, S.; Dartez, L. P.; Flanigan, J.; Lunsford, G.; Martinez, J. G.; Mata, A.; Rohr, M.; Walker, A.; Allen, B.; Bhat, N. D. R.; Bogdanov, S.; Camilo, F.; Chatterjee, S.; Cordes, J. M.; Crawford, F.; Deneva, J. S.; Desvignes, G.; Ferdman, R. D.; Freire, P. C. C.; Hessels, J. W. T.; Jenet, F. A.; Kaplan, D. L.; Kaspi, V. M.; Knispel, B.; Lee, K. J.; van Leeuwen, J.; Lyne, A. G.; McLaughlin, M. A.; Siemens, X.; Spitler, L. G.; Venkataraman, A.

2014-02-01

80

Face recognition system and method using face pattern words and face pattern bytes  

DOEpatents

The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

Zheng, Yufeng

2014-12-23

81

Pattern Recognition as Rule-Guided Inductive Inference  

Microsoft Academic Search

The determination of pattern recognition rules is viewed as a problem of inductive inference, guided by generalization rules, which control the generalization process, and problem knowledge rules, which represent the underlying semantics relevant to the recognition problem under consideration. The paper formulates the theoretical framework and a method for inferring general and optimal (according to certain criteria) descriptions of object

Ryszard S. Michalski

1980-01-01

82

Human-Computer Interaction for Complex Pattern Recognition Problems  

E-print Network

Human-Computer Interaction for Complex Pattern Recognition Problems Jie Zou and George Nagy of human-computer interaction that alleviate the complexity of visual recognition by partitioning. They require, however, a domain-specific visible model that makes sense to both human and computer. 1

Salama, Khaled

83

Visual Pattern Recognition in Drosophila Is Invariant for  

E-print Network

Visual Pattern Recognition in Drosophila Is Invariant for Retinal Position Shiming Tang,1 of their visual field where they had originally seen them. Tethered flies (Drosophila melanogaster) in a flight simulator can rec- ognize visual patterns. Because their eyes are fixed in space and patterns can

Field, David

84

IR spectrometer using spectral pattern recognition: a feasibility study  

NASA Astrophysics Data System (ADS)

The application of a neural network for processing of the output signal of an infrared (IR) emission mode spectrometer is investigated in this paper. A set of spectral patterns representative of 16 different compounds has been simulated using normal distribution line profiles. These data have then been combined with atmospheric transmittance and path radiance. The network, following training, has been presented with a test set consisting of perturbed versions of the spectra. Perturbations analyzed were line width, peak height, and center variations. The last effect is due to slit image curvature caused by a finite length slit in our hypothetical spectrometer. The purpose of the atmospheric and optical analysis was to insure a realistic estimate of phenomena expected in a field application. The network was found to recognize the input patterns correctly over a broad range of perturbation parameters. We propose that once a satisfactory set of connection weights is established, these should be transferred to a parallel processor (electronic or optical). The network considered in this paper proved capable of generalization under all but the most extreme conditions. Such performance allows by passing of intermediate signal processing for spectral analysis. Consequently, this sort of a system would form a fast and accurate spectral recognition instrument capable of operation under unpredictable conditions.

Descour, Michael R.; Dereniak, Eustace L.

1993-07-01

85

PATTERN RECOGNITION STUDIES OF COMPLEX CHROMATOGRAPHIC DATA SETS  

EPA Science Inventory

Chromatographic fingerprinting of complex biological samples is an active research area with a large and growing literature. Multivariate statistical and pattern recognition techniques can be effective methods for the analysis of such complex data. However, the classification of ...

86

Visual cluster analysis and pattern recognition template and methods  

DOEpatents

A method of clustering using a novel template to define a region of influence is disclosed. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques. 30 figs.

Osbourn, G.C.; Martinez, R.F.

1999-05-04

87

Proceedings of the NASA/MPRIA Workshop: Pattern Recognition  

NASA Technical Reports Server (NTRS)

Outlines of talks presented at the workshop conducted at Texas A & M University on February 3 and 4, 1983 are presented. Emphasis was given to the application of Mathematics to image processing and pattern recognition.

Guseman, L. F., Jr.

1983-01-01

88

Pattern Recognition Using Artificial Neural Network: A Review  

NASA Astrophysics Data System (ADS)

Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, artificial neural network techniques theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

Kim, Tai-Hoon

89

Albedo Pattern Recognition and Time-Series Analyses in Malaysia  

NASA Astrophysics Data System (ADS)

Pattern recognition and time-series analyses will enable one to evaluate and generate predictions of specific phenomena. The albedo pattern and time-series analyses are very much useful especially in relation to climate condition monitoring. This study is conducted to seek for Malaysia albedo pattern changes. The pattern recognition and changes will be useful for variety of environmental and climate monitoring researches such as carbon budgeting and aerosol mapping. The 10 years (2000-2009) MODIS satellite images were used for the analyses and interpretation. These images were being processed using ERDAS Imagine remote sensing software, ArcGIS 9.3, the 6S code for atmospherical calibration and several MODIS tools (MRT, HDF2GIS, Albedo tools). There are several methods for time-series analyses were explored, this paper demonstrates trends and seasonal time-series analyses using converted HDF format MODIS MCD43A3 albedo land product. The results revealed significance changes of albedo percentages over the past 10 years and the pattern with regards to Malaysia's nebulosity index (NI) and aerosol optical depth (AOD). There is noticeable trend can be identified with regards to its maximum and minimum value of the albedo. The rise and fall of the line graph show a similar trend with regards to its daily observation. The different can be identified in term of the value or percentage of rises and falls of albedo. Thus, it can be concludes that the temporal behavior of land surface albedo in Malaysia have a uniform behaviours and effects with regards to the local monsoons. However, although the average albedo shows linear trend with nebulosity index, the pattern changes of albedo with respects to the nebulosity index indicates that there are external factors that implicates the albedo values, as the sky conditions and its diffusion plotted does not have uniform trend over the years, especially when the trend of 5 years interval is examined, 2000 shows high negative linear trend relationship (R2 = 0.8017), while in 2005 the R2 is 0.4428 of positive linear trend relationship and in 2009 its negative relationship has remarkably change when the R2 is 0.9663 according to the second order polynomial trend line.

Salleh, S. A.; Abd Latif, Z.; Mohd, W. M. N. Wan; Chan, A.

2012-07-01

90

A dynamical pattern recognition model of ? activity in auditory cortex.  

PubMed

This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75-150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain. PMID:22327049

Zavaglia, M; Canolty, R T; Schofield, T M; Leff, A P; Ursino, M; Knight, R T; Penny, W D

2012-04-01

91

Adaptive optics to enhance target recognition  

NASA Astrophysics Data System (ADS)

Target recognition can be enhanced by reducing image degradation due to atmospheric turbulence. This is accomplished by an adaptive optic system. We discuss the forms of degradation when a target is viewed through the atmosphere1: scintillation from ground targets on a hot day in visible or infrared light; beam spreading and wavering around in time; atmospheric turbulence caused by motion of the target or by weather. In the case of targets we can use a beacon laser that reflects back from the target into a wavefront detector to measure the effects of turbulence on propagation to and from the target before imaging.1 A deformable mirror then corrects the wavefront shape of the transmitted, reflected or scattered data for enhanced imaging. Further, recognition of targets is enhanced by performing accurate distance measurements to localized parts of the target using lidar. Distance is obtained by sending a short pulse to the target and measuring the time for the pulse to return. There is inadequate time to scan the complete field of view so that the beam must be steered to regions of interest such as extremities of the image during image recognition. Distance is particularly valuable to recognize fine features in range along the target or when segmentation is required to separate a target from background or from other targets. We discuss the issues involved.

McAulay, Alastair D.

2012-06-01

92

Syntactic pattern recognition for HRR signatures  

NASA Astrophysics Data System (ADS)

A classifier based on a syntactic approach is developed for High range resolution (HRR) radar target recognition. An attribute grammar is used to represent the structure of an HRR signature and an error-correcting parsing mechanism is implemented to extract peaks in the HRR profile and suppress the extraneous spikes. In the training phase, an error correcting grammatical inference technique is employed for structural inference of HRR signatures using a positive sample set. Recognition is done using a minimum distance classifier where Levenshtein error measure is used as the distance metric. The error-correcting parsing procedure for peak extraction is used to perform both inference and recognition. Experiments performed using public release MSTAR database indicate that this approach has sufficient discrimination power to perform target detection in HRR signatures.

Bhatnagar, Raj K.; Williams, Robert L.; Tennety, Vijay

2000-08-01

93

Postprocessing for character recognition using pattern features and linguistic information  

NASA Astrophysics Data System (ADS)

We propose a new method of post-processing for character recognition using pattern features and linguistic information. This method corrects errors in the recognition of handwritten Japanese sentences containing Kanji characters. This post-process method is characterized by having two types of character recognition. Improving the accuracy of the character recognition rate of Japanese characters is made difficult by the large number of characters, and the existence of characters with similar patterns. Therefore, it is not practical for a character recognition system to recognize all characters in detail. First, this post-processing method generates a candidate character table by recognizing the simplest features of characters. Then, it selects words corresponding to the character from the candidate character table by referring to a word and grammar dictionary before selecting suitable words. If the correct character is included in the candidate character table, this process can correct an error, however, if the character is not included, it cannot correct an error. Therefore, if this method can presume a character does not exist in a candidate character table by using linguistic information (word and grammar dictionary). It then can verify a presumed character by character recognition using complex features. When this method is applied to an online character recognition system, the accuracy of character recognition improves 93.5% to 94.7%. This proved to be the case when it was used for the editorials of a Japanese newspaper (Asahi Shinbun).

Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

1993-04-01

94

A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation  

Technology Transfer Automated Retrieval System (TEKTRAN)

Perception of pathogen-associated molecular patterns (PAMPs) by surface-localised pattern-recognition receptors (PRRs) is a key component of plant innate immunity. Most known plant PRRs are receptor kinases and initiation of PAMP-triggered immunity (PTI) signalling requires phosphorylation of the PR...

95

Structural pattern recognition using genetic algorithms with specialized operators  

Microsoft Academic Search

This paper presents a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. The objective of our work is to improve the GA-based ARG matching procedures leading to a faster convergence rate and better quality mapping between a scene ARG and a set of given model ARGs. In

K. G. Khoo; Ponnuthurai N. Suganthan

2003-01-01

96

Optical music recognition system which learns  

NASA Astrophysics Data System (ADS)

This paper describes an optical music recognition system composed of a database and three interdependent processes: a recognizer, an editor, and a learner. Given a scanned image of a musical score, the recognizer locates, separates, and classifies symbols into musically meaningful categories. This classification is based on the k-nearest neighbor method using a subset of the database that contains features of symbols classified in previous recognition sessions. Output of the recognizer is corrected by a musically trained human operator using a music notation editor. The editor provides both visual and high-quality audio feedback of the output. Editorial corrections made by the operator are passed to the learner which then adds the newly acquired data to the database. The learner's main task, however, involves selecting a subset of the database and reweighing the importance of the features to improve accuracy and speed for subsequent sessions. Good preliminary results have been obtained with everything from professionally engraved scores to hand-written manuscripts.

Fujinaga, Ichiro

1993-01-01

97

Mathematical Pattern Recognition Spring Semester 2011  

E-print Network

Page 3 Course Texts Required texts: R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern Classification", Second Edition (Wiley-Interscience, John Wiley and Sons, Inc., New York, 2001) David G. Stork and Elad

Southern California, University of

98

Analog parallel processor hardware for high speed pattern recognition  

NASA Technical Reports Server (NTRS)

A VLSI-based analog processor for fully parallel, associative, high-speed pattern matching is reported. The processor consists of two main components: an analog memory matrix for storage of a library of patterns, and a winner-take-all (WTA) circuit for selection of the stored pattern that best matches an input pattern. An inner product is generated between the input vector and each of the stored memories. The resulting values are applied to a WTA network for determination of the closest match. Patterns with up to 22 percent overlap are successfully classified with a WTA settling time of less than 10 microsec. Applications such as star pattern recognition and mineral classification with bounded overlap patterns have been successfully demonstrated. This architecture has a potential for an overall pattern matching speed in excess of 10 exp 9 bits per second for a large memory.

Daud, T.; Tawel, R.; Langenbacher, H.; Eberhardt, S. P.; Thakoor, A. P.

1990-01-01

99

Pattern Recognition Approach to Neuropathy and Neuronopathy  

PubMed Central

Synopsis Neuropathic disorders encompass those that affect the neuron’s cell body or neuronopathies, those affecting the peripheral process, or peripheral neuropathies. The peripheral neuropathies can be broadly subdivided into the myelinopathies and axonopathies. These conditions can be hereditary or acquired. Each of these disorders has distinct clinical features that enable neurologists to recognize the various patterns of presentation. Once a particular pattern is established, further laboratory studies can be performed to confirm the clinical impression. PMID:23642713

Barohn, Richard J; Amato, Anthony A.

2014-01-01

100

Auditory orientation in crickets: Pattern recognition controls reactive steering  

NASA Astrophysics Data System (ADS)

Many groups of insects are specialists in exploiting sensory cues to locate food resources or conspecifics. To achieve orientation, bees and ants analyze the polarization pattern of the sky, male moths orient along the females' odor plume, and cicadas, grasshoppers, and crickets use acoustic signals to locate singing conspecifics. In comparison with olfactory and visual orientation, where learning is involved, auditory processing underlying orientation in insects appears to be more hardwired and genetically determined. In each of these examples, however, orientation requires a recognition process identifying the crucial sensory pattern to interact with a localization process directing the animal's locomotor activity. Here, we characterize this interaction. Using a sensitive trackball system, we show that, during cricket auditory behavior, the recognition process that is tuned toward the species-specific song pattern controls the amplitude of auditory evoked steering responses. Females perform small reactive steering movements toward any sound patterns. Hearing the male's calling song increases the gain of auditory steering within 2-5 s, and the animals even steer toward nonattractive sound patterns inserted into the speciesspecific pattern. This gain control mechanism in the auditory-to-motor pathway allows crickets to pursue species-specific sound patterns temporarily corrupted by environmental factors and may reflect the organization of recognition and localization networks in insects. localization | phonotaxis

Poulet, James F. A.; Hedwig, Berthold

2005-10-01

101

Control of antiviral immunity by pattern recognition and the microbiome  

PubMed Central

Summary Human skin and mucosal surfaces are in constant contact with resident and invasive microbes. Recognition of microbial products by receptors of the innate immune system triggers rapid innate defense and transduces signals necessary for initiating and maintaining the adaptive immune responses. Microbial sensing by innate pattern recognition receptors is not restricted to pathogens. Rather, proper development, function, and maintenance of innate and adaptive immunity rely on continuous recognition of products derived from the microorganisms indigenous to the internal and external surfaces of mammalian host. Tonic immune activation by the resident microbiota governs host susceptibility to intestinal and extra-intestinal infections including those caused by viruses. This review highlights recent developments in innate viral recognition leading to adaptive immunity, and discusses potential link between viruses, microbiota and the host immune system. Further, we discuss the possible roles of microbiome in chronic viral infection and pathogenesis of autoimmune disease, and speculate on the benefit for probiotic therapies against such diseases. PMID:22168422

Pang, Iris K.; Iwasaki, Akiko

2013-01-01

102

Real time Face Recognition using Local Ternary Patterns with Collaborative Representation based  

E-print Network

Real time Face Recognition using Local Ternary Patterns with Collaborative Representation based classification. This combination enhances the efficiency of face recognition under different illumination and noisy conditions. Our method achieves high recognition rates on challenging face databases and can run

Zell, Andreas

103

Breaking Visual CAPTCHAs with Naive Pattern Recognition Algorithms  

Microsoft Academic Search

Visual CAPTCHAs have been widely used across the Internet to defend against undesirable or malicious bot programs. In this paper, we document how we have broken most such visual schemes provided at Captchaservice.org, a publicly available web service for CAPTCHA generation. These schemes were effectively resistant to attacks conducted using a high-quality Optical Character Recognition program, but were broken with

Jeff Yan; Ahmad Salah El Ahmad

2007-01-01

104

Detecting Compounded Anomalous SNMP Situations Using Cooperative Unsupervised Pattern Recognition  

Microsoft Academic Search

\\u000a This research employs unsupervised pattern recognition to approach the thorny issue of detecting anomalous network behavior.\\u000a It applies a connectionist model to identify user behavior patterns and successfully demonstrates that such models respond\\u000a well to the demands and dynamic features of the problem. It illustrates the effectiveness of neural networks in the field\\u000a of Intrusion Detection (ID) by exploiting their

Emilio Corchado; Álvaro Herrero; José Manuel Sáiz

2005-01-01

105

Hypothesis Support Mechanism for Mid-Level Visual Pattern Recognition  

NASA Technical Reports Server (NTRS)

A method of mid-level pattern recognition provides for a pose invariant Hough Transform by parametrizing pairs of points in a pattern with respect to at least two reference points, thereby providing a parameter table that is scale- or rotation-invariant. A corresponding inverse transform may be applied to test hypothesized matches in an image and a distance transform utilized to quantify the level of match.

Amador, Jose J (Inventor)

2007-01-01

106

International Journal of Pattern Recognition and Artificial Intelligence  

E-print Network

International Journal of Pattern Recognition and Artificial Intelligence Vol. 18, No. 7 (2004) 1339 DISTANCE STATISTICS MEENAKSHI K. KALERA Department of Computer Science and Engineering, SUNY at Buffalo studied for several years now. Forensic document examiners, e.g. Robertson, 1991, Russell, et al., 1992

107

Driving Pattern Recognition for Control of Hybrid Electric Trucks  

E-print Network

Driving Pattern Recognition for Control of Hybrid Electric Trucks CHAN-CHIAO LIN1 , SOONIL JEON2 was initiated, aiming to duplicate the success of the hybrid powertrain on passenger cars to light and heavy economy improvement demonstrated by several prototype hybrid passenger cars, produced under

Peng, Huei

108

Artificial immune pattern recognition for structure damage classification  

Microsoft Academic Search

Damage detection in structures is one of the research topics that have received growing interest in research communities. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage classification problem. This paper presents an Artificial Immune Pattern Recognition (AIPR) approach for the damage classification in structures. An

Bo Chen; Chuanzhi Zang

2009-01-01

109

Categorical Representation and Recognition of Oscillatory Motion Patterns  

Microsoft Academic Search

Many communicative behaviors in the animal kingdom consist of performing and recognizing specialized patterns of oscillatory motion. Here we present an approach to the representation and recognition of these oscillatory motions based on the categorical organization of a simple sinusoidal model having very specific and limited parameter values. This characterization is used to specify the types and layout of computation

James W. Davis; Whitman Richards; Aaron F. Bobick

2000-01-01

110

Statistical pattern recognition with neural networks: benchmarking studies  

Microsoft Academic Search

Three basic types of neural-like networks (backpropagation network, Boltzmann machine, and learning vector quantization), were applied to two representative artificial statistical pattern recognition tasks, each with varying dimensionality. The performance of each network's approach to solving the tasks was evaluated and compared, both to the performance of the other two networks and to the theoretical limit. The learning vector quantization

Teuvo Kohonen; Gyorgy Barna; Ronald Chrisley

1988-01-01

111

SPEECH RECOGNITION WITH LOCALIZED TIME-FREQUENCY PATTERN DETECTORS  

E-print Network

SPEECH RECOGNITION WITH LOCALIZED TIME-FREQUENCY PATTERN DETECTORS Ken Schutte, James Glass MIT Computer Science and Arti cial Intelligence Laboratory 32 Vassar St., Cambridge, MA 02139, USA {kschutte,glass fea- tures, and more explicit modeling of temporal dynamics. Traditional ASR systems rely on frame

112

Applications of Support Vector Machines for Pattern Recognition: A Survey  

Microsoft Academic Search

In this paper, we present a comprehensive survey on applica- tions of Support Vector Machines (SVMs) for pattern recognition. Since SVMs show good generalization performance on many real-life data and the approach is properly motivated theoretically, it has been applied to wide range of applications. This paper describes a brief introduction of SVMs and summarizes its numerous applications.

Hyeran Byun; Seong-whan Lee

2002-01-01

113

Artificial Neural Networks Lab 1 Introduction to Pattern Recognition  

E-print Network

Artificial Neural Networks ­ Lab 1 Introduction to Pattern Recognition Purpose To implement (using work together in a pair, then both students must be present and able to demonstrate the software_range.m face.m letter.m pic.mat show.m show_all_faces.m show_all_letters.m startlab1.m Note: Read each task

Duckett, Tom

114

Pattern Recognition in Gamma-Gamma Coincidence Data sets  

Microsoft Academic Search

Considerable amounts of tedious labor are required to manually scan high-resolution 1D slices of two dimensional ?-? coincident matrices for relevant and exciting structures. This is particularly true when the interesting structures are of weak intensity. We are working on automated search methods for the detection of rotational band structures in the full 2D space using pattern recognition techniques. For

D. R. Manatt; F. L. Barnes; J. A. Becker; J. V. Candy; E. A. Henry; M. J. Brinkman

1991-01-01

115

Pattern recognition applied to earthquake epicenters in California  

Microsoft Academic Search

A pattern recognition procedure is explained which uses geological data and the earthquake history of a region, in this case California, and learns how to separate earthquake epicenters from other places. Sites of future earthquake epicenters are predicted as well as places where epicenters will not occur. The problem is formulated in several ways and control experiments are devised and

I. M. Gelfand; Sh. A. Guberman; V. I. Keilis-Borok; L. Knopoff; E. Ya. Ranzman; I. M. Rotwain; A. M. Sadovsky

1976-01-01

116

Pattern Recognition of Human Grasping Operations Based on EEG  

Microsoft Academic Search

The pattern recognition of the complicated grasping operation based on electroencephalography (simply named as EEG) is very helpful on realtime control of the robotic hand. In the paper, a new spectral feature analysis method based on Band Pass Filter (simply named as BPF) and Power Spectral Analysis (simply named as PSA) is presented for discriminating the complicated grasping operations. By

Xiao Dong Zhang; Hyouk Ryeol Choi

2006-01-01

117

Infrared target simulation environment for pattern recognition applications  

Microsoft Academic Search

The generation of complete databases of IR data is extremely useful for training human observers and testing automatic pattern recognition algorithms. Field data may be used for realism, but require expensive and time-consuming procedures. IR scene simulation methods have emerged as a more economical and efficient alternative for the generation of IR databases. A novel approach to IR target simulation

Andreas E. Savakis; Nicholas George

1994-01-01

118

Pattern Recognition Receptors in Innate Immunity, Host Defense, and Immunopathology  

ERIC Educational Resources Information Center

Infection by pathogenic microbes initiates a set of complex interactions between the pathogen and the host mediated by pattern recognition receptors. Innate immune responses play direct roles in host defense during the early stages of infection, and they also exert a profound influence on the generation of the adaptive immune responses that ensue.…

Suresh, Rahul; Mosser, David M.

2013-01-01

119

Computerized pattern recognition applied to battery testing. Final report  

Microsoft Academic Search

The primary goal of this work has been to develop non-destructive testing methods as a screening procedure for batteries to predict lifetime and identify probably failure mechanisms. A secondary goal has been to develop criteria for predicting imminent failure from battery performance data. We believe that these goals can be met by the application of computerized pattern recognition to the

Perone

1980-01-01

120

[Chemical pattern recognition of traditional Chinese medicine kudingcha (II)].  

PubMed

In this paper, the HPLC data from 78 samples of Kudingcha were treated with back propagation algorithm of artifical neural network pattern recognition, and the computer-aided classification of Ilex cornuta Lindl., Ilex latifolia Thunb. and Ligustrum lucidum Ait. was accomplished. This paper provides a scientific, advanced and feasible method for identification of traditional Chinese medicine. PMID:12567945

Su, W; Wu, Z; He, X; Chen, J

1998-04-01

121

http://www.cubs.buffalo.edu Pattern Recognition  

E-print Network

http://www.cubs.buffalo.edu Pattern Recognition Classification cost Biometric system errors ROC curve #12;http://www.cubs.buffalo.edu Bayesian classification · Bayes classification rule: classify x samples of class 1 2 - the cost of misclassifying samples of class 2 #12;http://www.cubs.buffalo.edu Total

Govindaraju, Venu

122

Pattern storage and recognition using ferrofluids  

NASA Astrophysics Data System (ADS)

An implementation of an associative memory based on a ferromagnetic nanocolloid is proposed. The design contains inductive input and output units for training the ferrofluid as well as sensors incorporated into the output units for performing recall. Using Monte Carlo simulations of the system we demonstrate the possibility of creating nanoparticle configurations that can serve to associate input/output pattern pairs.

Ban, Shuai; Korenivski, V.

2006-04-01

123

Pattern recognition for identification of lysozyme droplet solution chemistry.  

PubMed

Pattern formation during evaporation of a colloidal sessile droplet is a phenomenon relevant to a wide variety of scientific disciplines. The patterns remaining on the substrate are indicative of the transport mechanisms and phase transitions occurring during evaporation and may reflect the solution chemistry of the fluid [1-18]. Pattern formation during evaporation of droplets of biofluids has also been examined and these complex patterns may reflect the health of the patient [23-31]. Automatic detection of variations in the fluid composition based on these deposit patterns could lead to rapid screening for diagnostic or quality control purposes. In this study, a pattern recognition algorithm is presented to differentiate between deposits containing various solution compositions. The deposits studied are from droplets of simplified, model biological fluids of aqueous lysozyme and NaCl solutions. For the solution concentrations examined here, the deposit patterns are dependent upon the initial solution composition. Deposit images are represented by extracting features using the Gabor wavelet, similar to the method used for iris recognition. Two popular pattern recognition algorithms are used to classify the deposits. The k-means clustering algorithm is used to test if incremental changes in solution concentration result in reproducible and statistically interpretable variations in the deposit patterns. The k-nearest neighbor algorithm is also used to classify the deposit images by solution concentration based on a set of training images for each class. Here, we demonstrate that the deposit patterns may act as a "fingerprint" for identification of solution chemistry. The results of this study are very promising, with classification accuracies of 90-97.5%. PMID:24342799

Gorr, Heather Meloy; Xiong, Ziye; Barnard, John A

2014-03-01

124

AN OPTICAL CHARACTER RECOGNITION RESEARCH AND DEMONSTRATION PROJECT.  

ERIC Educational Resources Information Center

RESEARCH AND DEVELOPMENT OF PROTOTYPE LIBRARY SYSTEMS WHICH UTILIZE OPTICAL CHARACTER RECOGNITION INPUT HAS CENTERED AROUND OPTICAL PAGE READERS AND DOCUMENT READERS. THE STATE-OF-THE-ART OF BOTH THESE OPTICAL SCANNERS IS SUCH THAT BOTH ARE ACCEPTABLE FOR LIBRARY INPUT PREPARATION. A DEMONSTRATION PROJECT UTILIZING THE TWO TYPES OF READERS, SINCE…

1968

125

An Evaluation of PC-Based Optical Character Recognition Systems.  

ERIC Educational Resources Information Center

The review examines six personal computer-based optical character recognition (OCR) systems designed for use by blind and visually impaired people. Considered are OCR components and terms, documentation, scanning and reading, command structure, conversion, unique features, accuracy of recognition, scanning time, speed, and cost. (DB)

Schreier, E. M.; Uslan, M. M.

1991-01-01

126

3D face database for human pattern recognition  

NASA Astrophysics Data System (ADS)

Face recognition is an essential work to ensure human safety. It is also an important task in biomedical engineering. 2D image is not enough for precision face recognition. 3D face data includes more exact information, such as the precision size of eyes, mouth, etc. 3D face database is an important part in human pattern recognition. There is a lot of method to get 3D data, such as 3D laser scan system, 3D phase measurement, shape from shading, shape from motion, etc. This paper will introduce a non-orbit, non-contact, non-laser 3D measurement system. The main idea is from shape from stereo technique. Two cameras are used in different angle. A sequence of light will project on the face. Human face, human head, human tooth, human body can all be measured by the system. The visualization data of each person can form to a large 3D face database, which can be used in human recognition. The 3D data can provide a vivid copy of a face, so the recognition exactness can be reached to 100%. Although the 3D data is larger than 2D image, it can be used in the occasion where only few people include, such as the recognition of a family, a small company, etc.

Song, LiMei; Lu, Lu

2008-10-01

127

The recognition of biomaterials: pattern recognition of medical polymers and their adsorbed biomolecules.  

PubMed

All biomedical materials are recognized as foreign entities by the host immune system despite the substantial range of different materials that have been developed by material scientists and engineers. Hydrophobic biomaterials, hydrogels, biomaterials with low protein binding surfaces, and those that readily adsorb a protein layer all seem to incite similar host responses in vivo that may differ in magnitude, but ultimately result in encapsulation by fibrotic tissue. The recognition of medical materials by the host is explained by the very intricate pattern recognition system made up of integrins, toll-like receptors, scavenger receptors, and other surface proteins that enable leukocytes to perceive almost any foreign body. In this review, we describe the various pattern recognition receptors and processes that occur on biomedical material surfaces that permit detection of a range of materials within the host. PMID:23613455

Love, Ryan J; Jones, Kim S

2013-09-01

128

A new concept of vertically integrated pattern recognition associative memory  

SciTech Connect

Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing fast pattern recognition for a track trigger, requiring about three orders of magnitude more associative memory patterns than what was used in the original CDF SVT. Scaling of current technologies is unlikely to satisfy the scientific needs of the future, and investments in transformational new technologies need to be made. In this paper, we will discuss a new concept of using the emerging 3D vertical integration technology to significantly advance the state-of-the-art for fast pattern recognition within and outside HEP. A generic R and D proposal based on this new concept, with a few institutions involved, has recently been submitted to DOE with the goal to design and perform the ASIC engineering necessary to realize a prototype device. The progress of this R and D project will be reported in the future. Here we will only focus on the concept of this new approach.

Liu, Ted; Hoff, Jim; Deptuch, Grzegorz; Yarema, Ray; /Fermilab; ,

2011-11-01

129

A Voting-Based Sequential Pattern Recognition Method  

PubMed Central

We propose a novel method for recognizing sequential patterns such as motion trajectory of biological objects (i.e., cells, organelle, protein molecules, etc.), human behavior motion, and meteorological data. In the proposed method, a local classifier is prepared for every point (or timing or frame) and then the whole pattern is recognized by majority voting of the recognition results of the local classifiers. The voting strategy has a strong benefit that even if an input pattern has a very large deviation from a prototype locally at several points, they do not severely influence the recognition result; they are treated just as several incorrect votes and thus will be neglected successfully through the majority voting. For regularizing the recognition result, we introduce partial-dependency to local classifiers. An important point is that this dependency is introduced to not only local classifiers at neighboring point pairs but also to those at distant point pairs. Although, the dependency makes the problem non-Markovian (i.e., higher-order Markovian), it can still be solved efficiently by using a graph cut algorithm with polynomial-order computations. The experimental results revealed that the proposed method can achieve better recognition accuracy while utilizing the above characteristics of the proposed method. PMID:24155915

Ogawara, Koichi; Fukutomi, Masahiro; Uchida, Seiichi; Feng, Yaokai

2013-01-01

130

Sophisticated Temporal Pattern Recognition in Retinal Ganglion Cells  

PubMed Central

Pattern recognition is one of the most important tasks of the visual system, and uncovering the neural mechanisms underlying recognition phenomena has been a focus of researchers for decades. Surprisingly, at the earliest stages of vision, the retina is capable of highly sophisticated temporal pattern recognition. We stimulated the retina of tiger salamander (Ambystoma tigrinum) with periodic dark flash sequences and found that retinal ganglion cells had a wide variety of different responses to a periodic flash sequence with many firing when a flash was omitted. The timing of the omitted stimulus response (OSR) depended on the period, with individual cells tracking the stimulus period down to increments of 5 ms. When flashes occurred earlier than expected, cells updated their expectation of the next flash time by as much as 50 ms. When flashes occurred later than expected, cells fired an OSR and reset their temporal expectation to the average time interval between flashes. Using pharmacology to investigate the retinal circuitry involved, we found that inhibitory transmission from amacrine cells was not required, but on bipolar cells were required. The results suggest a mechanism in which the intrinsic resonance of on bipolars leads to the OSR in ganglion cells. We discuss the implications of retinal pattern recognition on the neural code of the retina and visual processing in general. PMID:18272878

Schwartz, Greg; Berry, Michael J.

2010-01-01

131

IEEE Conf. on Computer Vision and Pattern Recognition, 1998. To appear. Illumination Cones for Recognition  

E-print Network

IEEE Conf. on Computer Vision and Pattern Recognition, 1998. To appear. Illumination Cones Center for Computational Vision and Control Department of Electrical Engineering Yale University New in an ob- ject's appearance. While there has been a great deal of literature in computer vision detailing

Jaffe, Jules

132

Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms  

NASA Astrophysics Data System (ADS)

The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.

Nazimov, Alexey I.; Pavlov, Alexey N.; Hramov, Alexander E.; Grubov, Vadim V.; Koronovskii, Alexey A.; Sitnikova, Evgenija Y.

2013-02-01

133

Reducing false positives in molecular pattern recognition.  

PubMed

In the search for new cancer subtypes by gene expression profiling, it is essential to avoid misclassifying samples of unknown subtypes as known ones. In this paper, we evaluated the false positive error rates of several classification algorithms through a 'null test' by presenting classifiers a large collection of independent samples that do not belong to any of the tumor types in the training dataset. The benchmark dataset is available at www2.genome.rcast.u-tokyo.ac.jp/pm/. We found that k-nearest neighbor (KNN) and support vector machine (SVM) have very high false positive error rates when fewer genes (<100) are used in prediction. The error rate can be partially reduced by including more genes. On the other hand, prototype matching (PM) method has a much lower false positive error rate. Such robustness can be achieved without loss of sensitivity by introducing suitable measures of prediction confidence. We also proposed a cluster-and-select technique to select genes for classification. The nonparametric Kruskal-Wallis H test is employed to select genes differentially expressed in multiple tumor types. To reduce the redundancy, we then divided these genes into clusters with similar expression patterns and selected a given number of genes from each cluster. The reliability of the new algorithm is tested on three public datasets. PMID:15706518

Ge, Xijin; Tsutsumi, Shuichi; Aburatani, Hiroyuki; Iwata, Shuichi

2003-01-01

134

Structures of pattern recognition receptors reveal molecular mechanisms of autoinhibition, ligand recognition and oligomerization  

PubMed Central

Pattern recognition receptors (PRRs) are essential sentinels for pathogens or tissue damage and integral components of the innate immune system. Recent structural studies have provided unprecedented insights into the molecular mechanisms of ligand recognition and signal transduction by several PRR families at distinct subcellular compartments. Here we highlight some of the recent discoveries and summarize the common themes that are emerging from these exciting studies. Better mechanistic understanding of the structure and function of the PRRs will improve future prospects of therapeutic targeting of these important innate immune receptors. PMID:24419035

Xiao, T. Sam

2013-01-01

135

Emerging Principles Governing Signal Transduction by Pattern-Recognition Receptors.  

PubMed

The problem of recognizing and disposing of non-self-organisms, whether for nutrients or defense, predates the evolution of multicellularity. Accordingly, the function of the innate immune system is often intimately associated with fundamental aspects of cell biology. Here, we review our current understanding of the links between cell biology and pattern-recognition receptors of the innate immune system. We highlight the importance of receptor localization for the detection of microbes and for the initiation of antimicrobial signaling pathways. We discuss examples that illustrate how pattern-recognition receptors influence, and are influenced by, the general membrane trafficking machinery of mammalian cells. In the future, cell biological analysis likely will rival pure genetic analysis as a tool to uncover fundamental principles that govern host-microbe interactions. PMID:25395297

Kagan, Jonathan C; Barton, Gregory M

2014-11-13

136

Pattern recognition in field crickets: concepts and neural evidence.  

PubMed

Since decades the acoustic communication behavior of crickets is in the focus of neurobiology aiming to analyze the neural basis of male singing and female phonotactic behavior. For temporal pattern recognition several different concepts have been proposed to elucidate the possible neural mechanisms underlying the tuning of phonotaxis in females. These concepts encompass either some form of a feature detecting mechanism using cross-correlation processing, temporal filter properties of brain neurons or an autocorrelation processing based on a delay-line and coincidence detection mechanism. Current data based on intracellular recordings of auditory brain neurons indicate a sequential processing by excitation and inhibition in a local auditory network within the protocerebrum. The response properties of the brain neurons point towards the concept of an autocorrelation-like mechanism underlying female pattern recognition in which delay-lines by long lasting inhibition may be involved. PMID:25348550

Kostarakos, Konstantinos; Hedwig, Berthold

2015-01-01

137

A pattern recognition system for JPEG steganography detection  

NASA Astrophysics Data System (ADS)

This paper builds up a pattern recognition system to detect anomalies in JPEG images, especially steganographic content. The system consists of feature generation, feature ranking and selection, feature extraction, and pattern classification. These processes tend to capture image characteristics, reduce the problem dimensionality, eliminate the noise inferences between features, and further improve classification accuracies on clean and steganography JPEG images. Based on the discussion and analysis of six popular JPEG steganography methods, the entire recognition system results in higher classification accuracies between clean and steganography classes compared to merely using individual feature subset for JPEG steganography detection. The strength of feature combination and preprocessing has been integrated even when a small amount of information is embedded. The work demonstrated in this paper is extensible and can be improved by integrating various new and current techniques.

Chen, C. L. Philip; Chen, Mei-Ching; Agaian, Sos; Zhou, Yicong; Roy, Anuradha; Rodriguez, Benjamin M.

2012-10-01

138

Learning pattern recognition and decision making in the insect brain  

NASA Astrophysics Data System (ADS)

We revise the current model of learning pattern recognition in the Mushroom Bodies of the insects using current experimental knowledge about the location of learning, olfactory coding and connectivity. We show that it is possible to have an efficient pattern recognition device based on the architecture of the Mushroom Bodies, sparse code, mutual inhibition and Hebbian leaning only in the connections from the Kenyon cells to the output neurons. We also show that despite the conventional wisdom that believes that artificial neural networks are the bioinspired model of the brain, the Mushroom Bodies actually resemble very closely Support Vector Machines (SVMs). The derived SVM learning rules are situated in the Mushroom Bodies, are nearly identical to standard Hebbian rules, and require inhibition in the output. A very particular prediction of the model is that random elimination of the Kenyon cells in the Mushroom Bodies do not impair the ability to recognize odorants previously learned.

Huerta, R.

2013-01-01

139

Pattern recognition used to investigate multivariate data in analytical chemistry  

SciTech Connect

Pattern recognition and allied multivariate methods provide an approach to the interpretation of the multivariate data often encountered in analytical chemistry. Widely used methods include mapping and display, discriminant development, clustering, and modeling. Each has been applied to a variety of chemical problems, and examples are given. The results of two recent studies are shown, a classification of subjects as normal or cystic fibrosis heterozygotes and simulation of chemical shifts of carbon-13 nuclear magnetic resonance spectra by linear model equations.

Jurs, P.C.

1986-06-06

140

Pattern recognition receptors: doubling up for the innate immune response.  

PubMed

Antigen presenting cells (macrophages and dendritic cells) express pattern recognition molecules that are thought to recognize foreign ligands during early phases of the immune response. The best known of these are probably the Toll-like receptors, but a number of other receptors are also involved. Several of these recognize endogenous as well as exogenous ligands, suggesting that they play a dual role in normal tissue function and host defense. PMID:12507420

Gordon, Siamon

2002-12-27

141

Neural networks and pattern recognition in human-computer interaction  

Microsoft Academic Search

This paper reports on the activities of the workshop held on Sunday 28th April at the CHI'91 conference. Participants were there to discuss different ideas, methods and approaches to using pattern recognition in human-computer interaction.The workshop aimed to bring together researchers using novel methodologies, such as neural networks, in HCI applications, as well as practitioners using alternative or more traditional

Janet Finlay; Russell Beale

1993-01-01

142

Face Description with Local Binary Patterns: Application to Face Recognition  

Microsoft Academic Search

Abstract This paper presents a novel and efficient facial image,repres entation based on local binary pattern (LBP) texture features. The face image,is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced,feature vector to be used as a face descriptor. The performance,of the proposed method,is assessed in the face recognition problem,under different challenges.

Timo Ahonen; Abdenour Hadid; Matti Pietikäinen

2006-01-01

143

Musical instrument identification: A pattern-recognition approach  

Microsoft Academic Search

A statistical pattern-recognition technique was applied to the classification of musical instrument tones within a taxonomic hierarchy. Perceptually salient acoustic features— related to the physical properties of source excitation and resonance structure—were measured from the output of an auditory model (the log-lag correlogram) for 1023 isolated tones over the full pitch ranges of 15 orchestral instruments. The data set included

Keith D. Martin; Youngmoo E. Kim

1998-01-01

144

[Chemical pattern recognition of traditional Chinese medicine kudingcha (I)].  

PubMed

In this paper, the non-linear mapping method of pattern recognition was adopted to classify 78 samples of traditional Chinese medicine Kudingcha, with macro and trace elements as classified characteristic features. Ilex cornuta Lindl., Ilex latifolia Thunb. and Ligustrum lucidum Ait. were identified accurately. The results agree with those from pharmacognosy. This paper provides a new method for identification of traditional Chinese medicine. PMID:12567936

Su, W; Wu, Z; Chen, J; He, X; Li, J

1998-03-01

145

Some results on contractive mappings as related to pattern recognition  

NASA Technical Reports Server (NTRS)

Several of the techniques used in pattern recognition are reformulated as the problem of determining fixed points of a function. If x sub 0 is a fixed point of f and if f is contractive at x sub 0, then, for any y belonging to a sufficiently small neighborhood of x sub 0 the orbit of y will converge to x sub 0. Several general results regarding contractive mappings are developed with emphasis on functions.

Brown, D. R.; Malley, M. J.

1975-01-01

146

Pattern recognition of earthquake prone area in North China  

E-print Network

zoning of North China, provides a pattern scheme of lineaments, which has been considered in this paper. The pattern recognition method successfully divided those intersections into class-D or class-N where strong earthquakes may or may not occur.... Basic Theory B. The Algorithm CORA-3 III FORI IAL ltIORPHOSTRUCTURAL ZONING . A. Basic Principles B. The Scheme of the Zoning of North China IV EARTHQUAKES IN NORTH CHINA A. The Problem B. The Objects C. Results D. Control Experiments V...

Gu, Ji-Min

1989-01-01

147

Advanced optical correlation and digital methods for pattern matching—50th anniversary of Vander Lugt matched filter  

NASA Astrophysics Data System (ADS)

On the verge of the 50th anniversary of Vander Lugt’s formulation for pattern matching based on matched filtering and optical correlation, we acknowledge the very intense research activity developed in the field of correlation-based pattern recognition during this period of time. The paper reviews some domains that appeared as emerging fields in the last years of the 20th century and have been developed later on in the 21st century. Such is the case of three-dimensional (3D) object recognition, biometric pattern matching, optical security and hybrid optical-digital processors. 3D object recognition is a challenging case of multidimensional image recognition because of its implications in the recognition of real-world objects independent of their perspective. Biometric recognition is essentially pattern recognition for which the personal identification is based on the authentication of a specific physiological characteristic possessed by the subject (e.g. fingerprint, face, iris, retina, and multifactor combinations). Biometric recognition often appears combined with encryption-decryption processes to secure information. The optical implementations of correlation-based pattern recognition processes still rely on the 4f-correlator, the joint transform correlator, or some of their variants. But the many applications developed in the field have been pushing the systems for a continuous improvement of their architectures and algorithms, thus leading towards merged optical-digital solutions.

Millán, María S.

2012-10-01

148

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition  

PubMed Central

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

149

Innate sensing of viruses by pattern recognition receptors in birds.  

PubMed

Similar to mammals, several viral-sensing pattern recognition receptors (PRR) have been identified in birds including Toll-like receptors (TLR) and retinoic acid-inducible gene I (RIG-I)-like receptors (RLR). Avian TLR are slightly different from their mammalian counterparts, including the pseudogene TLR8, the absence of TLR9, and the presence of TLR1La, TLR1Lb, TLR15, and TLR21. Avian TLR3 and TLR7 are involved in RNA virus recognition, especially highly pathogenic avian influenza virus (HPAIV), while TLR15 and TLR21 are potential sensors that recognize both RNA viruses and bacteria. However, the agonist of TLR15 is still unknown. Interestingly, chickens, unlike ducks, geese and finches, lack RIG-I, however they do express melanoma differentiation-associated gene 5 (MDA5) which functionally compensates for the absence of RIG-I. Duck RIG-I is the cytosolic recognition element for HPAIV recognition, while chicken cells sense HPAIV through MDA5. However, the contributions of MDA5 and RIG-I to IFN-? induction upon HPAIV infection is different, and this may contribute to the chicken's susceptibility to highly pathogenic influenza. It is noteworthy that the interactions between avian DNA viruses and PRR have not yet been reported. Furthermore, the role for avian Nod-like receptors (NLR) in viral immunity is largely unknown. In this review, recent advances in the field of viral recognition by different types of PRR in birds are summarized. In particular, the tissue and cellular distribution of avian PRR, the recognition and activation of PRR by viruses, and the subsequent expression of innate antiviral genes such as type I IFN and proinflammatory cytokines are discussed. PMID:24016341

Chen, Shun; Cheng, Anchun; Wang, Mingshu

2013-01-01

150

Phenotypic analysis of bacterial colonies using laser light scatter and pattern-recognition techniques  

NASA Astrophysics Data System (ADS)

The formation of bacterial colonies and biofilms requires coordinated gene expression, regulated cell differentiation, autoaggregation, and intercellular communication. Therefore colonies of bacteria have been recognized as multicellular organisms or "superorganisms." It has consequently been postulated that the phenotype of colonies formed by microorganisms can be automatically recognized and classified using optical systems capable of collecting information related to cellular pattern formation and morphology of colonies. Recently we have reported a first practical implementation of such a system, capable of noninvasive, label-free classification and recognition of pathogenic Listeria species. The design employed computer-vision and pattern-recognition techniques to classify scatter patterns produced by bacterial colonies irradiated with laser light. Herein we report our efforts to extend this system to other genera of bacteria such as Salmonella, Vibrio, Staphylococcus, and E. coli. Application of orthogonal moments, as well as texture descriptors for image feature extraction, provides high robustness in the presence of noise. An improved pattern classification scheme based on an SVM algorithm provides better results than the previously employed neural network system. Low error rates determined by cross-validation, reproducibility of the measurements, and overall robustness of the recognition system prove that the proposed technology can be implemented in automated devices for bacterial detection.

Rajwa, Bartek; Bayraktar, Bulent; Banada, Padmapriya P.; Huff, Karleigh; Bae, Euiwon; Hirleman, E. Daniel; Bhunia, Arun K.; Robinson, J. Paul

2008-02-01

151

Recognition of Human Oncogenic Viruses by Host Pattern-Recognition Receptors  

PubMed Central

Human oncogenic viruses include Epstein–Barr virus, hepatitis B virus, hepatitis C virus, human papilloma virus, human T-cell lymphotropic virus, Kaposi’s associated sarcoma virus, and Merkel cell polyomavirus. It would be expected that during virus–host interaction, the immune system would recognize these pathogens and eliminate them. However, through evolution, these viruses have developed a number of strategies to avoid such an outcome and successfully establish chronic infections. The persistent nature of the infection caused by these viruses is associated with their oncogenic potential. In this article, we will review the latest information on the interaction between oncogenic viruses and the innate immune system of the host. In particular, we will summarize the available knowledge on the recognition by host pattern-recognition receptors of pathogen-associated molecular patterns present in the incoming viral particle or generated during the virus’ life cycle. We will also review the data on the recognition of cell-derived danger associated molecular patterns generated during the virus infection that may impact the outcome of the host–pathogen interaction and the development cancer. PMID:25101093

Di Paolo, Nelson C.

2014-01-01

152

Posterosuperior rotator cuff tears: classification, pattern recognition, and treatment.  

PubMed

The posterosuperior rotator cuff, composed of the supraspinatus and infraspinatus tendons, is the most common site for full-thickness rotator cuff tears and represents a significant source of shoulder disability worldwide. Recognition of and classification of full-thickness tear patterns are essential in order to optimize surgical treatment and to improve prognosis. Until recently, tear patterns have been described using one- or two-dimensional classification systems. Three-dimensional pattern recognition is critical to achieving the most successful outcome possible. For more complex patterns, a combination of side-to-side stitching, margin convergence, and interval slide techniques may be needed to achieve a tension-free tendon-bone repair. Biomechanical and anatomic evidence supports the use of linked double-row repairs for most full-thickness tears. Although double-row repairs seem to result in improved structural outcomes, clinical evidence has not shown differences in outcomes scores between single-row and double-row repairs. Single-row repair may be performed in partial-thickness, small full-thickness, or very massive, immobile tears, whereas double-row repair may be performed in most other cases. PMID:25063750

Millett, Peter J; Warth, Ryan J

2014-08-01

153

Robust non-parametric probabilistic image processing for face recognition and pattern recognition  

NASA Astrophysics Data System (ADS)

Face Recognition has been a pattern recognition application of great interest. Many mathematical models have been used for face recognition and among them probabilistic methods However, up to now probabilistic methods rely heavily on the number of training data and do not fully exploit the 2-dimensional information of the images, both the training and the testing sets. In this paper's method a new 2-D robust probabilistic method of transforming the principal components of the initial image data, allowing support vector machines to efficiently capture the inference between images. This new algorithm encodes every image with the help of Robust Kernel non Parametric Estimation and in the second stage uses Support Vector Machines to classify this encoded information. Results exhibit that Non Parametric Estimation of the Probability Function of the image highlights the unique characteristics of each person making it easier for classifiers to group those instances and efficiently perform the classification of the images and thus leading to better results compared to up to date methods for face recognition.

Pavlidou, Meropi; Zioutas, George

2014-04-01

154

[Research on noninvasive risk evaluation of diabetes mellitus based on neural network pattern recognition].  

PubMed

Advanced glycation end products (AGEs) are highly associated with hyperglycemia in human skin tissue, and they also have the autofluorescence characteristic. A self-developed optical noninvasive detection device was used to measure the autofluorescence in human skin tissue, and then a neural network pattern recognition model was used to assess the risk of diabetes mellitus of the subject under survey. After the fluorescence spectra were acquired and processed with principal component analysis, four of the leading principal components were chosen to represent a whole spectrum. The established neural network pattern recognition model has 4 input nodes, 6 hidden nodes and 1 output node. A dataset consisting of 487 cases collected in Anhui Provincial Hospital was used to train the model. Seventy percent cases were used as the training set, 15% as the validation set and 15% as the test set. The model can output subject's risk of diabetes mellitus, or a dichotomous judgment. Receiver operating characteristic curve can be drawn with the area under curve of 0. 81, with standard error of 0. 02. When using 0. 5 as the threshold between diabetes mellitus and non-diabetes mellitus, the sensitivity and specificity of this model is 72. 4% and 77. 6% respectively, and the overall accuracy is 74. 9%. The method using human skin autofluorescence spectrum combined with neural network pattern recognition model is proposed for the first time, and the results show that this method has a better screening effect compared with currently used fasting plasma glucose and HbAlc. PMID:25095432

Li, Fei; Wang, Yi-Kun; Zhu, Ling; Zhang, Yuan-Zhi; Ji, Min; Zhang, Long; Liu, Yong; Wang, An

2014-05-01

155

Collocation and Pattern Recognition Effects on System Failure Remediation  

NASA Technical Reports Server (NTRS)

Previous research found that operators prefer to have status, alerts, and controls located on the same screen. Unfortunately, that research was done with displays that were not designed specifically for collocation. In this experiment, twelve subjects evaluated two displays specifically designed for collocating system information against a baseline that consisted of dial status displays, a separate alert area, and a controls panel. These displays differed in the amount of collocation, pattern matching, and parameter movement compared to display size. During the data runs, subjects kept a randomly moving target centered on a display using a left-handed joystick and they scanned system displays to find a problem in order to correct it using the provided checklist. Results indicate that large parameter movement aided detection and then pattern recognition is needed for diagnosis but the collocated displays centralized all the information subjects needed, which reduced workload. Therefore, the collocated display with large parameter movement may be an acceptable display after familiarization because of the possible pattern recognition developed with training and its use.

Trujillo, Anna C.; Press, Hayes N.

2007-01-01

156

Spectral pattern recognition in under-sampled functions  

SciTech Connect

Fourier optics and an optical bench model are used to construct an ensemble of candidate functions representing variational patterns in an undersampled two dimensional function g(x,y). The known sample function s(x,y) is the product of g(x,y) and a set of unit impulses on the sample point pattern p(x,y) which, from the optical point of view, is an aperture imposing strict mathematical limits on what the sample can tell g(x,y). The laws of optics enforce much needed - and often lacking - conceptual discipline in reconstructing candidate variational patterns in g(x,y). The Fourier transform (FT) of s(x,y) is the convolution of the FT's of g(x,y) and p(x,y). If the convolution shows aliasing or confounding of frequencies undersampling is surely present and all reconstructions are indeterminate. Then information from outside s(x,y) is required and it is easily expressed in frequency terms so that the principles of optical filtering and image reconstruction can be applied. In the application described and pictured the FT of s(x,y) was filtered to eliminate unlikely or uninteresting high frequency amplitude maxima. A menu of the 100 strongest remaining terms was taken as indicating the principle variations patterns in g(x,y). Subsets of 10 terms from the menu were chosen using stepwise regression. By so restricting the subset size both the variance and the span of their inverse transforms were made consistent with those of the data. The amplitudes of the patterns being overdetermined, it was possible to estimate the phases also. The inverse transforms of 9 patterns so selected are regarded as ensembles of reconstructions, that is as stochastic process models, from which estimates of the mean and other moments can be calculated.

Shurtz, R.F.

1988-08-01

157

Optical character recognition of handwritten Arabic using hidden Markov models  

NASA Astrophysics Data System (ADS)

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.

2011-04-01

158

Optical-digital-neural network system for aided target recognition  

NASA Astrophysics Data System (ADS)

Many military systems have a critical need for aided target recognition, or cuing. This includes several systems with wide field-of-view search missions such as the UAV, EFOG-M, and Comanche. This report discusses one new approach: a multiple region of interest processor based on diffraction pattern sampling and digital neural network processing. In this concept an optical system segments the image into multiple, rectangular regions of interest and in parallel converts each ROI, be it visible, IR, or radar, to a spatial frequency power spectrum and samples that spectrum for 64 features. A neural network learns to correlate those features with target classes or identifications. A digital system uses the network weights to recognize unknown targets. The research discussed in this report using a single ROI processor showed a very high level of performance. Out of 1024 trials with models of five targets of F- 14, F-18, HIND, SCUD, and M1 tanks, there were 1023 correct classifications and 1 incorrect classification. Out of 1514 trials with those images plus 490 real clutter scenes, there were 1514 correct decisions between target or no-target. Of the 1024 target detections, there were 1023 correct classifications. Out of 60 trials with low resolution IR images of real scenes, there were 60 correct decisions between target and no-target. Of the 40 target detections, there were 40 correct classifications.

Farr, Keith B.; Hartman, Richard L.

1995-07-01

159

Recognition of partly occluded patterns: a neural network model.  

PubMed

Human beings are often able to read a letter or word partly occluded by contaminating ink stains. However, if the stains are completely erased and the occluded areas of the letter are changed to white, we usually have difficulty in reading the letter. In this article I propose a hypothesis explaining why a pattern is easier to recognize when it is occluded by visible objects than by invisible opaque objects. A neural network model is constructed based on this hypothesis. The visual system extracts various visual features from the input pattern and then attempts to recognize it. If the occluding objects are not visible, the visual system will have difficulty in distinguishing which features are relevant to the original pattern and which are newly generated by the occlusion. If the occluding objects are visible, however, the visual system can easily discriminate between relevant and irrelevant features and recognize the occluded pattern correctly. The proposed model is an extended version of the neocognitron model. The activity of the feature-extracting cells whose receptive fields cover the occluding objects is suppressed in an early stage of the hierarchical network. Since the irrelevant features generated by the occlusion are thus eliminated, the model can recognize occluded patterns correctly, provided the occlusion is not so large as to prevent recognition even by human beings. PMID:11324336

Fukushima, K

2001-04-01

160

Memristor-MOS analog correlator for pattern recognition system.  

PubMed

Emergence of new materials having significant improved properties continues to influence the formulation of novel architectures and as such new developments pave the way for innovative circuits and systems such as those required in visual imaging and recognition systems. In this paper we introduce a novel approach for the design of an analog comparator suitable for pattern matching using two Memristors as part of both the stored image data as well as that of the input signal. Our proposed comparator based on Memristor-CMOS fabrication process generates a signal indicating similarity/dissimilarity between two pattern data derived from image sensor and the corresponding Memristor-based template memory. For convenience, we also present an overview of a simplified Memristor model and hence provide simulation results for comparison with that of a conventional analog CMOS comparator. PMID:23858860

Han, Ca-Ram; Lee, Sang-Jin; Oh, Kwang-Seok; Cho, Kyoungrok

2013-05-01

161

A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation.  

PubMed

Innate immunity relies on the perception of pathogen-associated molecular patterns (PAMPs) by pattern-recognition receptors (PRRs) located on the host cell's surface. Many plant PRRs are kinases. Here, we report that the Arabidopsis receptor kinase EF-TU RECEPTOR (EFR), which perceives the elf18 peptide derived from bacterial elongation factor Tu, is activated upon ligand binding by phosphorylation on its tyrosine residues. Phosphorylation of a single tyrosine residue, Y836, is required for activation of EFR and downstream immunity to the phytopathogenic bacterium Pseudomonas syringae. A tyrosine phosphatase, HopAO1, secreted by P. syringae, reduces EFR phosphorylation and prevents subsequent immune responses. Thus, host and pathogen compete to take control of PRR tyrosine phosphorylation used to initiate antibacterial immunity. PMID:24625928

Macho, Alberto P; Schwessinger, Benjamin; Ntoukakis, Vardis; Brutus, Alexandre; Segonzac, Cécile; Roy, Sonali; Kadota, Yasuhiro; Oh, Man-Ho; Sklenar, Jan; Derbyshire, Paul; Lozano-Durán, Rosa; Malinovsky, Frederikke Gro; Monaghan, Jacqueline; Menke, Frank L; Huber, Steven C; He, Sheng Yang; Zipfel, Cyril

2014-03-28

162

Snap-drift ADaptive FUnction Neural Network (SADFUNN) for Optical and Pen-Based Handwritten Digit Recognition  

Microsoft Academic Search

An ADaptive Function Neural Network (ADFUNN) is combined with the on-line snap-drift learning method in this paper to solve an Optical Recognition of Handwritten Digits problem and a Pen-Based Recognition of Handwritten Digits problem. Snap- Drift (1) employs the complementary concepts of minimalist learning (snap) and drift (towards the input patterns) learning, and is a fast unsupervised method suitable for

Miao Kang; Dominic Palmer-Brown

163

Inductive class representation and its central role in pattern recognition  

SciTech Connect

The definition of inductive learning (IL) based on the new concept of inductive class representation (ICR) is given. The ICR, in addition to the ability to recognize a noise-corrupted object from the class, must also provide the means to generate every element in the resulting approximation of the class, i.e., the emphasis is on the generative capability of the ICR. Thus, the IL problem absorbs the main difficulties associated with a satisfactory formulation of the pattern recognition problem. This formulation of the IL problem appeared gradually as a result of the development of a fundamentally new formal model of IL--evolving transformation system (ETS) model. The model with striking clarity suggests that IL is the basic process which produces all the necessary {open_quotes}structures{close_quotes} for the recognition process, which is built directly on top of it. Based on the training set, the IL process, constructs optimal discriminatory (symbolic) weighted {open_quotes}features{close_quotes} which induce the corresponding optimal (symbolic) distance measure. The distance measure is a generalization of the weighted Levenshtein, or edit, distance defined on strings over a finite alphabet. The ETS model has emerged as a result of an attempt to unify two basic, but inadequate, approaches to pattern recognition: the classical vector space based and the syntactic approaches. ETS also elucidates with remarkable clarity the nature of the interrelationships between the corresponding symbolic and numeric mechanisms, in which the symbolic mechanisms play a more fundamental part. The model, in fact, suggests the first formal definition of the symbolic mathematical structure and also suggests a fundamentally different, more satisfactory, way of introducing the concept of fuzziness. The importance of the ICR concept to semiotics and semantics should become apparent as soon as one fully realizes that it represents the class and specifies the semantics of the class.

Goldfarb, L. [Univ. of New Brunswick, Fredericton, New Brunswick (Canada)

1996-12-31

164

Pattern recognition at the Fermilab collider and Superconducting Supercollider.  

PubMed Central

In a colliding beam accelerator such as Fermilab or the Superconducting Supercollider (SSC) protons, or antiprotons, collide at a rate between 10(5) (Fermilab) and 10(8) (SSC) collisions per second. In real time experimentalists have to select those events which are candidates for exploring the limit of known phenomena at a much lower rate, 1-100 per second, for recording on permanent media. The rate of events from new physics sources is expected to be much lower, as low as a few per year. This is a severe problem in pattern recognition: with an input data stream of up to 10(15) potential bits per second in its images, we have to pick out those images that are potentially interesting in real time at a discrimination level of 1 part in 10(6), with a known efficiency. I will describe the overall filtering strategies and the custom hardware to do this event selection (a.k.a. pattern recognition). Images Fig. 1 PMID:11607432

Frisch, H J

1993-01-01

165

Pattern recognition of satellite cloud imagery for improved weather prediction  

NASA Technical Reports Server (NTRS)

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products.

Gautier, Catherine; Somerville, Richard C. J.; Volfson, Leonid B.

1986-01-01

166

Teach Your Computer to Read: Scanners and Optical Character Recognition.  

ERIC Educational Resources Information Center

Desktop scanners can be used with a software technology called optical character recognition (OCR) to convert the text on virtually any paper document into an electronic form. OCR offers educators new flexibility in incorporating text into tests, lesson plans, and other materials. (MLF)

Marsden, Jim

1993-01-01

167

Nonlinear dynamics of pattern formation and pattern recognition in the rabbit olfactory bulb  

NASA Astrophysics Data System (ADS)

A mathematical model of the process of pattern recognition in the first olfactory sensory cortex of the rabbit is presented. It explains the formation and alteration of spatial patterns in neural activity observed experimentally during classical Pavlovian conditioning. On each inspiration of the animal, a surge of receptor input enters the olfactory bulb. EEG activity recorded at the surface of the bulb undergoes a transition from a low amplitude background state of temporal disorder to coherent oscillation. There is a distinctive spatial pattern of rms amplitude in this oscillation which changes reliably to a second pattern during each successful recognition by the animal of a conditioned stimulus odor. When a new odor is paired as conditioned stimulus, these patterns are replaced by new patterns that stabilize as the animal adapts to the new environment. I will argue that a unification of the theories of pattern formation and associative memory is required to account for these observations. This is achieved in a model of the bulb as a discrete excitable medium with spatially inhomogeneous coupling expressed by a connection matrix. The theory of multiple Hopf bifurcations is employed to find coupled equations for the amplitudes of competing unstable oscillatory modes. These may be created in the system by proper coupling and selectively evoked by specific classes of inputs. This allows a view of limit cycle attractors as “stored” fixed points of a gradient vector field and thereby recovers the more familiar dynamical systems picture of associative memory.

Baird, Bill

1986-10-01

168

Pattern-Recognition System for Approaching a Known Target  

NASA Technical Reports Server (NTRS)

A closed-loop pattern-recognition system is designed to provide guidance for maneuvering a small exploratory robotic vehicle (rover) on Mars to return to a landed spacecraft to deliver soil and rock samples that the spacecraft would subsequently bring back to Earth. The system could be adapted to terrestrial use in guiding mobile robots to approach known structures that humans could not approach safely, for such purposes as reconnaissance in military or law-enforcement applications, terrestrial scientific exploration, and removal of explosive or other hazardous items. The system has been demonstrated in experiments in which the Field Integrated Design and Operations (FIDO) rover (a prototype Mars rover equipped with a video camera for guidance) is made to return to a mockup of Mars-lander spacecraft. The FIDO rover camera autonomously acquires an image of the lander from a distance of 125 m in an outdoor environment. Then under guidance by an algorithm that performs fusion of multiple line and texture features in digitized images acquired by the camera, the rover traverses the intervening terrain, using features derived from images of the lander truss structure. Then by use of precise pattern matching for determining the position and orientation of the rover relative to the lander, the rover aligns itself with the bottom of ramps extending from the lander, in preparation for climbing the ramps to deliver samples to the lander. The most innovative aspect of the system is a set of pattern-recognition algorithms that govern a three-phase visual-guidance sequence for approaching the lander. During the first phase, a multifeature fusion algorithm integrates the outputs of a horizontal-line-detection algorithm and a wavelet-transform-based visual-area-of-interest algorithm for detecting the lander from a significant distance. The horizontal-line-detection algorithm is used to determine candidate lander locations based on detection of a horizontal deck that is part of the lander.

Huntsberger, Terrance; Cheng, Yang

2008-01-01

169

Automatic target recognition using a feature-based optical neural network  

NASA Technical Reports Server (NTRS)

An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

Chao, Tien-Hsin

1992-01-01

170

Optical implementation of a feature-based neural network with application to automatic target recognition  

NASA Technical Reports Server (NTRS)

An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

Chao, Tien-Hsin; Stoner, William W.

1993-01-01

171

A novel thermal face recognition approach using face pattern words  

NASA Astrophysics Data System (ADS)

A reliable thermal face recognition system can enhance the national security applications such as prevention against terrorism, surveillance, monitoring and tracking, especially at nighttime. The system can be applied at airports, customs or high-alert facilities (e.g., nuclear power plant) for 24 hours a day. In this paper, we propose a novel face recognition approach utilizing thermal (long wave infrared) face images that can automatically identify a subject at both daytime and nighttime. With a properly acquired thermal image (as a query image) in monitoring zone, the following processes will be employed: normalization and denoising, face detection, face alignment, face masking, Gabor wavelet transform, face pattern words (FPWs) creation, face identification by similarity measure (Hamming distance). If eyeglasses are present on a subject's face, an eyeglasses mask will be automatically extracted from the querying face image, and then masked with all comparing FPWs (no more transforms). A high identification rate (97.44% with Top-1 match) has been achieved upon our preliminary face dataset (of 39 subjects) from the proposed approach regardless operating time and glasses-wearing condition.e

Zheng, Yufeng

2010-04-01

172

Pattern recognition by wavelet transforms using macro fibre composites transducers  

NASA Astrophysics Data System (ADS)

This paper presents a novel pattern recognition approach for a non-destructive test based on macro fibre composite transducers applied in pipes. A fault detection and diagnosis (FDD) method is employed to extract relevant information from ultrasound signals by wavelet decomposition technique. The wavelet transform is a powerful tool that reveals particular characteristics as trends or breakdown points. The FDD developed for the case study provides information about the temperatures on the surfaces of the pipe, leading to monitor faults associated with cracks, leaks or corrosion. This issue may not be noticeable when temperatures are not subject to sudden changes, but it can cause structural problems in the medium and long-term. Furthermore, the case study is completed by a statistical method based on the coefficient of determination. The main purpose will be to predict future behaviours in order to set alarm levels as a part of a structural health monitoring system.

Ruiz de la Hermosa González-Carrato, Raúl; García Márquez, Fausto Pedro; Dimlaye, Vichaar; Ruiz-Hernández, Diego

2014-10-01

173

Data-driven parallel architecture for syntactic pattern recognition  

NASA Astrophysics Data System (ADS)

Syntax analysis is the primary operation of a Syntactic Pattern Recognition (SPR) system. A real time SPR system would require efficient architectural supports for syntax analysis. The process of syntax analysis and the execution of a logic program are closely related. In this paper we propose a data-driven parallel architecture for syntax analysis based on the principle of parallel execution of logic programs. The proposed architecture is hybrid in the sense that its functional units unlike those itt traditional fine-grain datafiow model are coarse-grain macro operators capable of performing unification operations. The scheme for compiling the datafiow graphs eliminates the necessity of any operand matching unit in the data-driven architecture. All memory requests are tagged with register identification (similar to IBM 360/91) to provide an efficient hardware support for context switching. The experimental results indicate the proposed architecture is promising.

Tseng, Chien-Chao; Hwang, Shu-Yuen

1991-02-01

174

Pattern recognition in gamma-gamma coincidence data sets  

SciTech Connect

Considerable amounts of tedious labor are required to manually scan high-resolution 1D slices of two dimensional {gamma}-{gamma} coincident matrices for relevant and exciting structures. This is particularly true when the interesting structures are of weak intensity. We are working on automated search methods for the detection of rotational band structures in the full 2D space using pattern recognition techniques. For nominal sized data sets (1024{times}1024), however, these techniques only become computationally feasible through the use of Fourier Transform methods. Furthermore the presentation of data matrices as images rather than series of 1D spectra has been shown to be useful. In this paper we will present the data manipulation techniques we have developed.

Manatt, D.R.; Barnes, F.L.; Becker, J.A.; Candy, J.V.; Henry, E.A. [Lawrence Livermore National Lab., CA (United States); Brinkman, M.J. [Rutgers--the State Univ., New Brunswick, NJ (United States)

1990-10-01

175

Pattern Recognition in Gamma-Gamma Coincidence Data sets  

NASA Astrophysics Data System (ADS)

Considerable amounts of tedious labor are required to manually scan high-resolution 1D slices of two dimensional ?-? coincident matrices for relevant and exciting structures. This is particularly true when the interesting structures are of weak intensity. We are working on automated search methods for the detection of rotational band structures in the full 2D space using pattern recognition techniques. For nominal sized data sets (1024×1024), however, these techniques only become computationally feasible through the use of Fourier Transform methods. Furthermore the presentation of data matrices as images rather than series of 1D spectra has been shown to be useful. In this paper we will present the data manipulation techniques we have developed.

Manatt, D. R.; Barnes, F. L.; Becker, J. A.; Candy, J. V.; Henry, E. A.; Brinkman, M. J.

1991-10-01

176

Using Decision Trees for Comparing Pattern Recognition Feature Sets  

SciTech Connect

Determination of the best set of features has been acknowledged as one of the most difficult tasks in the pattern recognition process. In this report significance tests on the sort-ordered, sample-size normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. Alternative functional forms for feature sets are also examined. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The method is applied to a problem for which a significant portion of the training set cannot be classified unambiguously.

Proctor, D D

2005-08-18

177

Beyond pattern recognition: NOD-like receptors in dendritic cells  

PubMed Central

Innate instruction of adaptive immunity was proposed more than 20 years ago as a mechanism by which long-lived lymphocyte responses are targeted to appropriate antigens. At the time Charles Janeway proposed this theory, most of the innate immune receptors were unknown and the pivotal role of the dendritic cell in instructing T cell priming was debated. There is now overwhelming evidence that the innate and adaptive branches of the immune system must interact to generate immunity. Much of this work has focused on families of innate immune receptors called pattern recognition receptors (PRRs) on dendritic cells, which translate these inflammatory triggers into productive T cell responses. Nevertheless, we are only beginning to understand how these defense molecules shape the generation of immunity. We review the varied roles of one class of PRRs, the NOD-like receptors (NLRs), in immune responses and propose a new model in which adaptive immunity requires coordinated PRR activation within the dendritic cell. PMID:23352728

Krishnaswamy, Jayendra Kumar; Chu, Thach; Eisenbarth, Stephanie C.

2013-01-01

178

Pattern Recognition Letters 15 (1994) 261-271 North-Holland  

E-print Network

23 August 1993 Abstract Pal, S .K., Genetic algorithms for optimal image enhancement, Pattern enhancement, genetic algorithms, ambiguity measures . 1. Introduction Genetic algorithms (GAs) [2Pattern Recognition Letters 15 (1994) 261-271 North-Holland PATREC 1172 Genetic algorithms

Pal, Sankar Kumar

179

Pattern recognition algorithm reveals how birds evolve individual egg pattern signatures.  

PubMed

Pattern-based identity signatures are commonplace in the animal kingdom, but how they are recognized is poorly understood. Here we develop a computer vision tool for analysing visual patterns, NATUREPATTERNMATCH, which breaks new ground by mimicking visual and cognitive processes known to be involved in recognition tasks. We apply this tool to a long-standing question about the evolution of recognizable signatures. The common cuckoo (Cuculus canorus) is a notorious cheat that sneaks its mimetic eggs into nests of other species. Can host birds fight back against cuckoo forgery by evolving highly recognizable signatures? Using NATUREPATTERNMATCH, we show that hosts subjected to the best cuckoo mimicry have evolved the most recognizable egg pattern signatures. Theory predicts that effective pattern signatures should be simultaneously replicable, distinctive and complex. However, our results reveal that recognizable signatures need not incorporate all three of these features. Moreover, different hosts have evolved effective signatures in diverse ways. PMID:24939367

Stoddard, Mary Caswell; Kilner, Rebecca M; Town, Christopher

2014-01-01

180

Pattern recognition applied to mineral characterization of Brazilian coffees and sugar-cane spiritsB  

E-print Network

Pattern recognition applied to mineral characterization of Brazilian coffees and sugar-cane spirits Aluminium, Ca, Cu, Fe, K, Mg, Mn, Na, Pb, S, Se, Si, Sn, Sr, and Zn were determined in coffee and sugar. Keywords: Coffee; Sugar-cane spirit; Chemometrics; Pattern recognition 1. Introduction Globalization has

Ferreira, Márcia M. C.

181

The logical combinatorial approach to pattern recognition, an overview through selected works  

Microsoft Academic Search

The so-called logical combinatorial approach to Pattern Recognition is presented, and works (mainly in Spanish and Russian) that are not ordinarily available, are exposed to the Western reader. The use of this approach for supervised and unsupervised pattern recognition, and for feature selection is reviewed. Also, an unified notation describing the original contributions is presented, thus rendering this important area

José Francisco Mart??nez-Trinidad; Adolfo Guzmán-arenas

2001-01-01

182

Workshop on Standards for Image Pattern Recognition. Computer Seience & Technology Series.  

ERIC Educational Resources Information Center

Automatic image pattern recognition techniques have been successfully applied to improving productivity and quality in both manufacturing and service applications. Automatic Image Pattern Recognition Algorithms are often developed and tested using unique data bases for each specific application. Quantitative comparison of different approaches and…

Evans, John M. , Ed.; And Others

183

The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network  

Microsoft Academic Search

The Adaptive Resonance Theory (ART) architectures discussed here are neural networks that self-organize stable recognition codes in real time in response to arbitrary sequences of input patterns. Within such an ART architecture, the process of adaptive pattern recognition is a special case of the more general cognitive process of hypothesis discovery, testing, search, classification, and learning. This property opens up

Gail A. Carpenter; Stephen Grossberg

1988-01-01

184

Spectral classification using pattern-recognition techniques. II. Application to curium energy levels  

Microsoft Academic Search

Curium energy levels have been classified according to configuration using pattern-recognition techniques. Four features: energy level, Lande g, J, and isotope shift: have been used to describe each level. Forty levels have been assigned with high probability based on consistent results obtained by various pattern recognition techniques. This represents an increase of 9% for even levels and 19% for odd

K. L. Peterson; D. L. Anderson; M. L. Parsons

1978-01-01

185

Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  

E-print Network

Using Genetic Algorithms to Explore Pattern Recognition in the Immune System DRAFT July 28, 1993 an immune system model based on binary strings. The purpose of the model is to study the pattern recognition processes and learning that take place at both the individual and species levels in the immune system

Forrest, Stephanie

186

Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  

Microsoft Academic Search

This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern recognition processes and lear ning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern recognition

Stephanie Forrest; Robert E. Smith; Brenda Javornik; Alan S. Perelson

1993-01-01

187

Zenith angle-based method for pattern recognition of landform elements using feature vector matching  

Microsoft Academic Search

Pattern recognition of landform elements provides fundamental information for landscape research such as landscape evaluation and hazard prediction. Totally different from the existing methods where surface geometrical forms are commonly described by local curvatures, this paper uses zenith angle as a basis for pattern recognition in topography. One property of zenith angle is a regional morphmetrical variable, which has potential

Yanlan Wu; Takashi Oguchi; Yongqiong Liu; Hai Hu; Chuanyong Yang

2009-01-01

188

Pattern recognition in geochemical hydrocarbon exploration: a fuzzy approach  

SciTech Connect

For the Swedish Deep Gas Project some 240 soil samples were collected and analyzed for trace metals and ..delta.. C. The data were determined to not be sufficient as anomalous patterns obtained were merely reflecting underlying crystalline or Paleozoic bedrock. Any possible patterns related to a deep-seated gas source were completely swamped; in addition glacial transport also presented a problem in interpretation. Therefore, the ARIADNE method was applied to the data set. ARIADNE is a pattern recognition system designed for use in a variety of exploration applications, ranging from geochemical regional surveys to detailed geophysical well logging. The system's core is a fuzzy classifier that can work both on differences in location and dispersion in variable space, either combined or separately. For unsupervised classification, a preprocessor, called NARCISSOS, is used, which, by using fuzzy principal components analysis, extracts a robust background and an appropriate number of anomalous populations. Mean vectors and covariance matrices of all populations are submitted to the ARIADNE classifier. By taking advantage of different patterns emerging by using mean vectors or variance-covariance matrices when classifying in the variable space, the relative influence of transport (e.g., glacial transport) can be estimated and probable source areas also can be established. When ARIADNE was applied to the Deep Gas Project data, two anomalous populations emerged. One was strongly tied, both geographically and chemically, to the Paleozoic ring structure circumscribing the target area, and the background reflected general chemical features of granitic bedrocks inside and outside of that structure. The second anomaly, however, was not related to any bedrock composition, but rather to structural phenomena in the bedrock.

Granath, G.

1988-08-01

189

Fast Optical Character Recognition through Glyph Hashing for Document Conversion  

Microsoft Academic Search

This paper proposes a glyph hashing approach to optical character recognition with applications in document conversion. The viability and efficiency of the approach is tested through its implementation in a print driver on 68,987 PDF documents containing 1.15 billion characters. Results indicate that a hash table with (a) 3.2 million hashes is sufficient to represent all characters from these documents,

Kumar Chellapilla; Patrice Simard; Radoslav Nickolov

2005-01-01

190

PRoNTo: pattern recognition for neuroimaging toolbox.  

PubMed

In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univariate techniques, they lack an established and accessible software framework. The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models. The "Pattern Recognition for Neuroimaging Toolbox" (PRoNTo) is open-source, cross-platform, MATLAB-based and SPM compatible, therefore being suitable for both cognitive and clinical neuroscience research. In addition, it is designed to facilitate novel contributions from developers, aiming to improve the interaction between the neuroimaging and machine learning communities. Here, we introduce PRoNTo by presenting examples of possible research questions that can be addressed with the machine learning framework implemented in PRoNTo, and cannot be easily investigated with mass univariate statistical analysis. PMID:23417655

Schrouff, J; Rosa, M J; Rondina, J M; Marquand, A F; Chu, C; Ashburner, J; Phillips, C; Richiardi, J; Mourăo-Miranda, J

2013-07-01

191

Development of biological movement recognition by interaction between active basis model and fuzzy optical flow division.  

PubMed

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach. PMID:24883361

Yousefi, Bardia; Loo, Chu Kiong

2014-01-01

192

Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division  

PubMed Central

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach. PMID:24883361

Loo, Chu Kiong

2014-01-01

193

Hybrid pixel-object pattern recognition in remote sensing  

NASA Astrophysics Data System (ADS)

This research proposes a hybrid pixel-object framework: in which information from both pixels and objects, resulting from image segmentation, is utilized for pattern recognition in remote sensing. This framework was described and exemplified in two pattern recognition problems in remote sensing---land cover classification and road extraction---which compose two parts of this dissertation. In the first part, a competitive pixel-object approach based on Bayesian neural network for land cover classification was developed. In this approach, primary features from pixels and derived features from objects compete with each other through the posterior probability of one feature vector belonging to a particular category generated in the prediction stage of Bayesian neural network. This approach attempts to solve the problem of spectral confusion caused by reflectance similarity of some land cover types, and reduce the unreliability of object feature information produced by over or under image segmentation through a competitive mechanism. The proposed approach obtains higher classification accuracy than pixel based and hybrid pixel-object classification without competition. In pixel based classification, the Bayesian neural network proves to be superior to traditional Gaussian maximum likelihood classifier and back-propagation neural networks. In the second part of this dissertation, a unique approach for road extraction utilizing pixel spectral information for classification and image segmentation-derived object features was developed. In this approach, road extraction was performed in two steps. In the first step, support vector machine (SVM) was employed to classify the image into two groups of categories: a road group and a non-road group. For this classification, support vector machine achieved higher accuracy than Gaussian maximum likelihood. In the second step, the road group image was segmented into geometrically homogeneous objects using a region growing technique based on a similarity criterion, with higher weighting on shape factors over spectral criteria. A simple thresholding on the shape index and density features derived from these objects was performed to extract road features, which were further processed by thinning and vectorization to obtain road centerlines. The experiment shows the proposed approach works well with images comprised by both rural and urban area features.

Song, Mingjun

194

A new phase pattern recognition tool applied to field line resonances  

NASA Astrophysics Data System (ADS)

The detection and characterization of geomagnetic pulsations (standing Alfven waves on magnetospheric field lines, as produced by the field-line resonance (FLR) process) using ground magnetic field data has been based for decades on the interpretation of the longitudinal and latitudinal distributions of pulsation amplitudes and phases. By adopting this approach only clear and single FLRs can be correctly analyzed. Magnetometer array data, however, contain much more phase information due to the coherency of the ground observed FLR wave structures across the array of stations, which remains undisclosed if phase pattern recognition of beamforming techniques are not used. We present theory and applications of such a new phase pattern recognition tool, the Field-Line Resonance Detector (FLRD), which is an adaptation of the wave telescope technique, previously used in seismology and multi-spacecraft analysis. Unlike the traditional methods the FLRD is able to detect and fully characterize multiple superposed or hidden FLR structures, of which the tool allows for an automated detection. We show results of its application in a statistical analysis of one year (2002) of ground magnetometer data from the Canadian magnetometer array CANOPUS (now known as CARISMA, www.carisma.ca) and a comparison of FLRD results with other ground-based data from optical and radar instruments. The remarkable adaptability of the tool to other datasets and phase structures shall also be discussed.

Plaschke, F.; Glassmeier, K.-H.; Milan, S. E.; Mann, I. R.; Motschmann, U.; Rae, I. J.

2009-04-01

195

Magnetic resonance imaging pattern recognition in hypomyelinating disorders  

PubMed Central

Hypomyelination is observed in the context of a growing number of genetic disorders that share clinical characteristics. The aim of this study was to determine the possible role of magnetic resonance imaging pattern recognition in distinguishing different hypomyelinating disorders, which would facilitate the diagnostic process. Only patients with hypomyelination of known cause were included in this retrospective study. A total of 112 patients with Pelizaeus–Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus–Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis, Salla disease and fucosidosis were included. The brain scans were rated using a standard scoring list; the raters were blinded to the diagnoses. Grouping of the patients was based on cluster analysis. Ten clusters of patients with similar magnetic resonance imaging abnormalities were identified. The most important discriminating items were early cerebellar atrophy, homogeneity of the white matter signal on T2-weighted images, abnormal signal intensity of the basal ganglia, signal abnormalities in the pons and additional T2 lesions in the deep white matter. Eight clusters each represented mainly a single disorder (i.e. Pelizaeus–Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, infantile GM1 and GM2 gangliosidosis, Pelizaeus–Merzbacher-like disease and fucosidosis); only two clusters contained multiple diseases. Pelizaeus–Merzbacher-like disease was divided between two clusters and Salla disease did not cluster at all. This study shows that it is possible to separate patients with hypomyelination disorders of known cause in clusters based on magnetic resonance imaging abnormalities alone. In most cases of Pelizaeus–Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus–Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis and fucosidosis, the imaging pattern gives clues for the diagnosis. PMID:20881161

Steenweg, Marjan E.; Vanderver, Adeline; Blaser, Susan; Bizzi, Alberto; de Koning, Tom J.; Mancini, Grazia M. S.; van Wieringen, Wessel N.; Barkhof, Frederik; Wolf, Nicole I.

2010-01-01

196

Pattern recognition and PID procedure with the ALICE-HMPID  

NASA Astrophysics Data System (ADS)

The ALICE apparatus is dedicated to the study of pp, p-Pb and Pb-Pb collisions provided by LHC. ALICE has unique particle identification (PID) capabilities among the LHC experiments exploiting different PID techniques, i.e., energy loss, time-of-flight measurements, Cherenkov and transition radiation detection, calorimetry and topological ID. The ALICE-HMPID is devoted to the identification of charged hadrons. It consists of seven identical RICH counters, with liquid C6F14 as Cherenkov radiator (n?1.299 at ?ph=175 nm). Photons and charged particles detection is performed by a proportional chamber, coupled with a pad segmented CsI coated photo-cathode. In pp and p-Pb events HMPID provides 3 sigmas separation for pions and kaons up to pT = 3 GeV / c and for protons up to pT = 5 GeV / c. PID is performed by means of photon emission angle measurement, a challenging task in the high multiplicity environment of the most central Pb-Pb collisions. A dedicated algorithm has been implemented to evaluate the Cherenkov angle starting from the bi-dimensional ring pattern on the photons detector, it is based on the Hough Transform Method (HTM) to separate signal from background. In this way HMPID is able to contribute to inclusive hadrons spectra measurement as well as to measurements where high purity PID is required, by means of statistical or track-by-track PID. The pattern recognition, the results from angular resolution studies and the PID strategy with HMPID are presented.

Volpe, Giacomo

2014-12-01

197

A pyramidal neural network for visual pattern recognition.  

PubMed

In this paper, we propose a new neural architecture for classification of visual patterns that is motivated by the two concepts of image pyramids and local receptive fields. The new architecture, called pyramidal neural network (PyraNet), has a hierarchical structure with two types of processing layers: Pyramidal layers and one-dimensional (1-D) layers. In the new network, nonlinear two-dimensional (2-D) neurons are trained to perform both image feature extraction and dimensionality reduction. We present and analyze five training methods for PyraNet [gradient descent (GD), gradient descent with momentum, resilient back-propagation (RPROP), Polak-Ribiere conjugate gradient (CG), and Levenberg-Marquadrt (LM)] and two choices of error functions [mean-square-error (mse) and cross-entropy (CE)]. In this paper, we apply PyraNet to determine gender from a facial image, and compare its performance on the standard facial recognition technology (FERET) database with three classifiers: The convolutional neural network (NN), the k-nearest neighbor (k-NN), and the support vector machine (SVM). PMID:17385623

Phung, Son Lam; Bouzerdoum, Abdesselam

2007-03-01

198

Imbalanced learning for pattern recognition: an empirical study  

NASA Astrophysics Data System (ADS)

The imbalanced learning problem (learning from imbalanced data) presents a significant new challenge to the pattern recognition and machine learning society because in most instances real-world data is imbalanced. When considering military applications, the imbalanced learning problem becomes much more critical because such skewed distributions normally carry the most interesting and critical information. This critical information is necessary to support the decision-making process in battlefield scenarios, such as anomaly or intrusion detection. The fundamental issue with imbalanced learning is the ability of imbalanced data to compromise the performance of standard learning algorithms, which assume balanced class distributions or equal misclassification penalty costs. Therefore, when presented with complex imbalanced data sets these algorithms may not be able to properly represent the distributive characteristics of the data. In this paper we present an empirical study of several popular imbalanced learning algorithms on an army relevant data set. Specifically we will conduct various experiments with SMOTE (Synthetic Minority Over-Sampling Technique), ADASYN (Adaptive Synthetic Sampling), SMOTEBoost (Synthetic Minority Over-Sampling in Boosting), and AdaCost (Misclassification Cost-Sensitive Boosting method) schemes. Detailed experimental settings and simulation results are presented in this work, and a brief discussion of future research opportunities/challenges is also presented.

He, Haibo; Chen, Sheng; Man, Hong; Desai, Sachi; Quoraishee, Shafik

2010-10-01

199

Role of pattern recognition receptors in flavivirus infections.  

PubMed

The flaviviral encephalitis has now become a major health concern in global scale. The efficient detection of viral infection and induction of the innate antiviral response by host's innate immune system are crucial to determine the outcome of infection. The intracellular pattern recognition receptors TLRs, RLRs, NLRs and CLRs play a central role in detection and initiation of robust antiviral response against flaviviral infection. Both cytoplasmic RLRs, RIG-I and MDA5 have been shown to be implicated in sensing flaviviral genomic RNA. Similarly among TLRs mainly TLR3 and TLR7 are known to respond in flaviviral infections as they are known to sense dsRNA and ssRNA moiety as their natural cognate ligand. Several studies have also shown the roles of NLRs and CLRs in mounting an innate antiviral response against flavivirus but, it is yet to be completely understood. Until now only few reports have implicated NLRs and CLRs in induction of antiviral and proinflammatory state following flaviviral infection. The current review therefore aims to comprehensively analyze past as well as current understanding on the role of PRRs in flaviviral infections. PMID:24657789

Nazmi, Arshed; Dutta, Kallol; Hazra, Bibhabasu; Basu, Anirban

2014-06-24

200

Optical Fourier diffractometry applied to degraded bone structure recognition  

NASA Astrophysics Data System (ADS)

Image processing and recognition methods are useful in many fields. This paper presents the hybrid optical and digital method applied to recognition of pathological changes in bones involved by metabolic bone diseases. The trabecular bone structure, registered by x ray on the photographic film, is analyzed in the new type of computer controlled diffractometer. The set of image parameters, extracted from diffractogram, is evaluated by statistical analysis. The synthetic image descriptors in discriminant space, constructed on the base of 3 training groups of images (control, osteoporosis, and osteomalacia groups) by discriminant analysis, allow us to recognize bone samples with degraded bone structure and to recognize the disease. About 89% of the images were classified correctly. This method after optimization process will be verified in medical investigations.

Galas, Jacek; Godwod, Krzysztof; Szawdyn, Jacek; Sawicki, Andrzej

1993-09-01

201

Gastric cancer differentiation using Fourier transform near-infrared spectroscopy with unsupervised pattern recognition  

NASA Astrophysics Data System (ADS)

The manuscript has investigated the application of near-infrared (NIR) spectroscopy for differentiation gastric cancer. The 90 spectra from cancerous and normal tissues were collected from a total of 30 surgical specimens using Fourier transform near-infrared spectroscopy (FT-NIR) equipped with a fiber-optic probe. Major spectral differences were observed in the CH-stretching second overtone (9000-7000 cm-1), CH-stretching first overtone (6000-5200 cm-1), and CH-stretching combination (4500-4000 cm-1) regions. By use of unsupervised pattern recognition, such as principal component analysis (PCA) and cluster analysis (CA), all spectra were classified into cancerous and normal tissue groups with accuracy up to 81.1%. The sensitivity and specificity was 100% and 68.2%, respectively. These present results indicate that CH-stretching first, combination band and second overtone regions can serve as diagnostic markers for gastric cancer.

Yi, Wei-song; Cui, Dian-sheng; Li, Zhi; Wu, Lan-lan; Shen, Ai-guo; Hu, Ji-ming

2013-01-01

202

The role of binocular disparity in rapid scene and pattern recognition  

PubMed Central

We investigated the contribution of binocular disparity to the rapid recognition of scenes and simpler spatial patterns using a paradigm combining backward masked stimulus presentation and short-term match-to-sample recognition. First, we showed that binocular disparity did not contribute significantly to the recognition of briefly presented natural and artificial scenes, even when the availability of monocular cues was reduced. Subsequently, using dense random dot stereograms as stimuli, we showed that observers were in principle able to extract spatial patterns defined only by disparity under brief, masked presentations. Comparing our results with the predictions from a cue-summation model, we showed that combining disparity with luminance did not per se disrupt the processing of disparity. Our results suggest that the rapid recognition of scenes is mediated mostly by a monocular comparison of the images, although we can rely on stereo in fast pattern recognition. PMID:23755357

Valsecchi, Matteo; Caziot, Baptiste; Backus, Benjamin T.; Gegenfurtner, Karl R.

2013-01-01

203

Multichannel integrated optical filters for spectral signature recognition  

NASA Astrophysics Data System (ADS)

Recent advances in the development of two-dimensional holographic Bragg reflectors in planar lightwave circuits have demonstrated the feasibility of highly customizable multi-wavelength filters based on photonics nanostructures programmable to recognize spectra with up to 2000 spectral lines. Such filters rival or exceed performance of free space gratings, thin film filters and Bragg gratings and may be monolithically integrated with detectors in III-V active materials or as passive devices in silica. This new technological platform holds a great promise of being next-generation optical engine for spectral signature recognition in the field of remote sensing, biological, chemical and defense applications.

Iazikov, Dmitri; Greiner, Christoph M.; Mossberg, Thomas W.

2004-12-01

204

Optical correlator based target detection, recognition, classification, and tracking.  

PubMed

A dedicated automatic target recognition and tracking optical correlator (OC) system using advanced processing technology has been developed. Rapidly cycling data-cubes with size, shape, and orientation are employed with software algorithms to isolate correlation peaks and enable tracking of targets in maritime environments with future track prediction. The method has been found superior to employing maximum average correlation height filters for which the correlation peak intensity drops off in proportion to the number of training images. The physical dimensions of the OC system may be reduced to as small as 2 in. × 2 in. × 3 in. (51 mm × 51 mm × 76 mm) by modifying and minimizing the OC components. PMID:22858935

Manzur, Tariq; Zeller, John; Serati, Steve

2012-07-20

205

Applications of pattern recognition techniques to online fault detection  

SciTech Connect

A common problem to operators of complex industrial systems is the early detection of incipient degradation of sensors and components in order to avoid unplanned outages, to orderly plan for anticipated maintenance activities and to assure continued safe operation. In such systems, there usually are a large number of sensors (upwards of several thousand is not uncommon) serving many functions, ranging from input to control systems, monitoring of safety parameters and component performance limits, system environmental conditions, etc. Although sensors deemed to measure important process conditions are generally alarmed, the alarm set points usually are just high-low limits and the operator`s response to such alarms is based on written procedures and his or her experience and training. In many systems this approach has been successful, but in situations where the cost of a forced outage is high an improved method is needed. In such cases it is desirable, if not necessary, to detect disturbances in either sensors or the process prior to any actual failure that could either shut down the process or challenge any safety system that is present. Recent advances in various artificial intelligence techniques have provided the opportunity to perform such functions of early detection and diagnosis. In this paper, the experience gained through the application of several pattern-recognition techniques to the on-line monitoring and incipient disturbance detection of several coolant pumps and numerous sensors at the Experimental Breeder Reactor-II (EBR-II) which is located at the Idaho National Engineering Laboratory is presented.

Singer, R.M.; Gross, K.C. [Argonne National Lab., IL (United States); King, R.W. [Argonne National Lab., Idaho Falls, ID (United States)

1993-11-01

206

Proceedings of the NASA Symposium on Mathematical Pattern Recognition and Image Analysis  

NASA Technical Reports Server (NTRS)

The application of mathematical and statistical analyses techniques to imagery obtained by remote sensors is described by Principal Investigators. Scene-to-map registration, geometric rectification, and image matching are among the pattern recognition aspects discussed.

Guseman, L. F., Jr.

1983-01-01

207

COMPARISON OF SIMCA PATTERN RECOGNITION & LIBRARY SEARCH IDENTIFICATION OF HAZARDOUS COMPOUNDS FROM MASS SPECTRA  

EPA Science Inventory

SIMCA pattern recognition methods have been applied to mass spectral data from a target list of hazardous chemicals. cheme has been proposed for classification and identification of five classes of compounds including aromatics, chlorocarbons, bromocarbons, hydrocarbons, and poly...

208

COMPARISON OF SIMCA PATTERN RECOGNITION AND LIBRARY SEARCH IDENTIFICATION OF HAZARDOUS COMPOUNDS FROM MASS SPECTRA  

EPA Science Inventory

SIMCA pattern recognition methods have been applied to mass spectral data from a target list of hazardous chemicals. cheme has been proposed for classification and identification of five classes of compounds including aromatics, chlorocarbons, bromocarbons, hydrocarbons, and poly...

209

Hand posture recognition using jointly optical flow and dimensionality reduction  

NASA Astrophysics Data System (ADS)

Hand posture recognition is generally addressed by using either YC b C r (luminance and chrominance components) or HSV (hue, saturation, value) mappings which assume that a hand can be distinguished from the background from some colorfulness and luminance properties. This can hardly be used when a dark hand, or a hand of any color, is under study. In addition, existing recognition processes rely on descriptors or geometric shapes which can be reliable; this comes at the expense of an increased computational complexity. To cope with these drawbacks, this paper proposes a four-step method recognition technique consisting of (i) a pyramidal optical flow for the detection of large movements and hence determine the region of interest containing the expected hand, (ii) a preprocessing step to compute the hand contour while ensuring geometric and illumination invariance, (iii) an image scanning method providing a signature which characterizes non-star-shaped contours with a one-pixel precision, and (iv) a posture classification method where a sphericity criterion preselects a set of candidate postures, principal component analysis reduces the dimensionality of the data, and Mahalanobis distance is used as a criterion to identify the hand posture in any test image. The proposed technique has been assessed in terms of its performances including the computational complexity using both visual and statistical results.

Boughnim, Nabil; Marot, Julien; Fossati, Caroline; Bourennane, Salah

2013-12-01

210

A dynamical pattern recognition model of gamma activity in auditory cortex  

PubMed Central

This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75–150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain. PMID:22327049

Zavaglia, M.; Canolty, R.T.; Schofield, T.M.; Leff, A.P.; Ursino, M.; Knight, R.T.; Penny, W.D.

2012-01-01

211

Fast traffic sign recognition with a rotation invariant binary pattern based feature.  

PubMed

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed. PMID:25608217

Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

2015-01-01

212

Investigation of time series representations and similarity measures for structural damage pattern recognition.  

PubMed

This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate. PMID:24191136

Liu, Wenjia; Chen, Bo; Swartz, R Andrew

2013-01-01

213

Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition  

PubMed Central

This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate. PMID:24191136

Swartz, R. Andrew

2013-01-01

214

Artificial Intelligent Based Human Motion Pattern Recognition and Prediction for the Surface Electromyographic Signals  

Microsoft Academic Search

In this research, the artificial intelligent method based human motion pattern recognition for surface electromyographic (EMG) signal is proposed. As the EMG signal is a measurement of anatomical and physiological characteristic of the given muscle, the macroscopical movement patterns of the human body can be classified and recognized. By using the technology of wavelet packet transformation, the high-frequency noises can

Xu Guo; Hu Yu; Gao Zhen; Liu Yuliang; Zhang Yong; Zhang Ying

2009-01-01

215

Application of pattern recognition techniques to the identification of aerospace acoustic sources  

NASA Technical Reports Server (NTRS)

A pattern recognition system was developed that successfully recognizes simulated spectra of five different types of transportation noise sources. The system generates hyperplanes during a training stage to separate the classes and correctly classify unknown patterns in classification mode. A feature selector in the system reduces a large number of features to a smaller optimal set, maximizing performance and minimizing computation.

Fuller, Chris R.; Obrien, Walter F.; Cabell, Randolph H.

1988-01-01

216

Pattern recognition in neural networks with competing dynamics: coexistence of fixed-point and cyclic attractors.  

PubMed

We study the properties of the dynamical phase transition occurring in neural network models in which a competition between associative memory and sequential pattern recognition exists. This competition occurs through a weighted mixture of the symmetric and asymmetric parts of the synaptic matrix. Through a generating functional formalism, we determine the structure of the parameter space at non-zero temperature and near saturation (i.e., when the number of stored patterns scales with the size of the network), identifying the regions of high and weak pattern correlations, the spin-glass solutions, and the order-disorder transition between these regions. This analysis reveals that, when associative memory is dominant, smooth transitions appear between high correlated regions and spurious states. In contrast when sequential pattern recognition is stronger than associative memory, the transitions are always discontinuous. Additionally, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of the same set of patterns, there is a discontinuous transition between associative memory and sequential pattern recognition. In contrast, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of independent sets of patterns, the network is able to perform both associative memory and sequential pattern recognition for a wide range of parameter values. PMID:22900014

Herrera-Aguilar, José L; Larralde, Hernán; Aldana, Maximino

2012-01-01

217

Pattern Recognition in Neural Networks with Competing Dynamics: Coexistence of Fixed-Point and Cyclic Attractors  

PubMed Central

We study the properties of the dynamical phase transition occurring in neural network models in which a competition between associative memory and sequential pattern recognition exists. This competition occurs through a weighted mixture of the symmetric and asymmetric parts of the synaptic matrix. Through a generating functional formalism, we determine the structure of the parameter space at non-zero temperature and near saturation (i.e., when the number of stored patterns scales with the size of the network), identifying the regions of high and weak pattern correlations, the spin-glass solutions, and the order-disorder transition between these regions. This analysis reveals that, when associative memory is dominant, smooth transitions appear between high correlated regions and spurious states. In contrast when sequential pattern recognition is stronger than associative memory, the transitions are always discontinuous. Additionally, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of the same set of patterns, there is a discontinuous transition between associative memory and sequential pattern recognition. In contrast, when the symmetric and asymmetric parts of the synaptic matrix are defined in terms of independent sets of patterns, the network is able to perform both associative memory and sequential pattern recognition for a wide range of parameter values. PMID:22900014

Herrera-Aguilar, José L.; Larralde, Hernán; Aldana, Maximino

2012-01-01

218

IMAGINATION IN THE NATURAL SCIENCES: PATTERN RECOGNITION, TRANSFORMATION, AND EXPRESSION  

Microsoft Academic Search

This paper discusses a simple conceptual framework that can help foster imaginative learning in the natural sciences. As a visual tool, it aids perception of relationships in the learning process. The framework involves the transformation of pattern from encountered forms to personally and culturally meaningful forms, i.e. from natural pattern (sensed in nature), through ideal pattern (a poietic cognitive schema),

CHERYL BARTLETT; MURDENA MARSHALL

2006-01-01

219

Contribution of Flagellin Pattern Recognition to Intestinal Inflammation during Salmonella enterica Serotype Typhimurium Infection?  

PubMed Central

Salmonella enterica serotype Typhimurium causes acute inflammatory diarrhea in humans. Flagella contribute to intestinal inflammation, but the mechanism remains unclear since most mutations abrogating pattern recognition of flagellin also prevent motility and reduce bacterial invasion. To determine the contribution of flagellin pattern recognition to the generation of innate immune responses, we compared in two animal models a nonmotile, but flagellin-expressing and -secreting serotype Typhimurium strain (flgK mutant) to a nonmotile, non-flagellin-expressing strain (flgK fliC fljB mutant). In vitro, caspase-1 can be activated by cytosolic delivery of flagellin, resulting in release of the interferon gamma inducing factor interleukin-18 (IL-18). Experiments with streptomycin-pretreated caspase-1-deficient mice suggested that induction of gamma interferon expression in the murine cecum early (12 h) after serotype Typhimurium infection was caspase-1 dependent but independent of flagellin pattern recognition. In addition, mRNA levels of the CXC chemokines macrophage inflammatory protein 2 and keratinocyte-derived chemokine were markedly increased early after serotype Typhimurium infection of streptomycin-pretreated wild-type mice regardless of flagellin expression. In contrast, in bovine ligated ileal loops, flagellin pattern recognition contributed to increased mRNA levels of macrophage inflammatory protein 3? and more fluid accumulation at 2 h after infection. Collectively, our data suggest that pattern recognition of flagellin contributes to early innate host responses in the bovine ileal mucosa but not in the murine cecal mucosa. PMID:19237529

Winter, Sebastian E.; Thiennimitr, Parameth; Nuccio, Sean-Paul; Haneda, Takeshi; Winter, Maria G.; Wilson, R. Paul; Russell, Joseph M.; Henry, Thomas; Tran, Quynh T.; Lawhon, Sara D.; Gomez, Gabriel; Bevins, Charles L.; Rüssmann, Holger; Monack, Denise M.; Adams, L. Garry; Bäumler, Andreas J.

2009-01-01

220

Stage-Specific Sampling by Pattern Recognition Receptors during Candida albicans Phagocytosis  

PubMed Central

Candida albicans is a medically important pathogen, and recognition by innate immune cells is critical for its clearance. Although a number of pattern recognition receptors have been shown to be involved in recognition and phagocytosis of this fungus, the relative role of these receptors has not been formally examined. In this paper, we have investigated the contribution of the mannose receptor, Dectin-1, and complement receptor 3; and we have demonstrated that Dectin-1 is the main non-opsonic receptor involved in fungal uptake. However, both Dectin-1 and complement receptor 3 were found to accumulate at the site of uptake, while mannose receptor accumulated on C. albicans phagosomes at later stages. These results suggest a potential role for MR in phagosome sampling; and, accordingly, MR deficiency led to a reduction in TNF-? and MCP-1 production in response to C. albicans uptake. Our data suggest that pattern recognition receptors sample the fungal phagosome in a sequential fashion. PMID:19043561

Heinsbroek, Sigrid E. M.; Taylor, Philip R.; Martinez, Fernando O.; Martinez-Pomares, Luisa; Brown, Gordon D.; Gordon, Siamon

2008-01-01

221

Automatic micropropagation of plants--the vision-system: graph rewriting as pattern recognition  

NASA Astrophysics Data System (ADS)

The automation of plant-micropropagation is necessary to produce high amounts of biomass. Plants have to be dissected on particular cutting-points. A vision-system is needed for the recognition of the cutting-points on the plants. With this background, this contribution is directed to the underlying formalism to determine cutting-points on abstract-plant models. We show the usefulness of pattern recognition by graph-rewriting along with some examples in this context.

Schwanke, Joerg; Megnet, Roland; Jensch, Peter F.

1993-03-01

222

An Efficient and Robust Singular Value Method for Star Pattern Recognition and Attitude Determination  

NASA Technical Reports Server (NTRS)

A new star pattern recognition method is developed using singular value decomposition of a measured unit column vector matrix in a measurement frame and the corresponding cataloged vector matrix in a reference frame. It is shown that singular values and right singular vectors are invariant with respect to coordinate transformation and robust under uncertainty. One advantage of singular value comparison is that a pairing process for individual measured and cataloged stars is not necessary, and the attitude estimation and pattern recognition process are not separated. An associated method for mission catalog design is introduced and simulation results are presented.

Juang, Jer-Nan; Kim, Hye-Young; Junkins, John L.

2003-01-01

223

A bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets.  

PubMed

A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations. PMID:16013091

Orre, Roland; Bate, Andrew; Norén, G Niklas; Swahn, Erik; Arnborg, Stefan; Edwards, I Ralph

2005-06-01

224

Innate Pattern Recognition and Categorization in a Jumping Spider  

PubMed Central

The East African jumping spider Evarcha culicivora feeds indirectly on vertebrate blood by preferentially preying upon blood-fed Anopheles mosquitoes, the vectors of human malaria1, using the distinct resting posture and engorged abdomen characteristic of these specific prey as key elements for their recognition. To understand perceptual categorization of objects by these spiders, we investigated their predatory behavior toward different digital stimuli - abstract ‘stick figure’ representations of Anopheles constructed solely by known key identification elements, disarranged versions of these, as well as non-prey items and detailed images of alternative prey. We hypothesized that the abstract images representing Anopheles would be perceived as potential prey, and would be preferred to those of non-preferred prey. Spiders perceived the abstract stick figures of Anopheles specifically as their preferred prey, attacking them significantly more often than non-preferred prey, even when the comprising elements of the Anopheles stick figures were disarranged and disconnected from each other. However, if the relative angles between the elements of the disconnected stick figures of Anopheles were altered, the otherwise identical set of elements was no longer perceived as prey. These data show that E. culicivora is capable of making discriminations based on abstract concepts, such as the hypothetical angle formed by discontinuous elements. It is this inter-element angle rather than resting posture that is important for correct identification of Anopheles. Our results provide a glimpse of the underlying processes of object recognition in animals with minute brains, and suggest that these spiders use a local processing approach for object recognition, rather than a holistic or global approach. This study provides an excellent basis for a comparative analysis on feature extraction and detection by animals as diverse as bees and mammals. PMID:24893306

Dolev, Yinnon; Nelson, Ximena J.

2014-01-01

225

Finger Vein Recognition Using Local Line Binary Pattern  

PubMed Central

In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP). PMID:22247670

Rosdi, Bakhtiar Affendi; Shing, Chai Wuh; Suandi, Shahrel Azmin

2011-01-01

226

Development of an Adaptively Controlled Telescope with Star-Pattern Recognition Pointing  

NASA Astrophysics Data System (ADS)

This paper describes the development of a 32-cm f/5 Newtonian telescope intended for use by amateur astronomers in producing scientifically useful observations through high-accuracy computer control. The telescope is designed to achieve a 10-arcsecond pointing accuracy through the use of a star-pattern recognition algorithm. This star-pattern recognition pointing algorithm allows pointing errors such as tube flexure and mount misalignment to be intuitively identified and corrected without the need for calibrating positional encoders. This star-pattern recognition algorithm is based on comparing the shapes of visible patterns of six stars in any given field of view to a pre-compiled catalog of star-patterns that is generated by a software package called Star Field Simulator. A second-generation algorithm is presented in this paper that features an empirical image appearance prediction system, which adds photometric measurements to the star-pattern recognition. This allows the effects of unresolvable clusters of stars, and the presence of non-stellar objects to be included in the star-pattern recognition process through the prediction of an object's pixel brightness and point spread function. Testing with pointing camera images has shown that star appearance on a CCD can be predicted with high accuracy. The telescope hardware features a unique fiberglass and metal composite construction technique for precision component placement. An innovative placement of the autoguiding camera at the Newtonian prime focus through an on-axis tracking platform is also featured. The telescope is controlled with real-time software, on a laptop computer, using modified Firewire video cameras to provide pointing and tracking data. To test the accuracy of the control algorithms and simulate the effects of errors from environmental and mechanical sources, a software application was written. Results from this and other tests have shown that this telescope can operate within the preset pointing and tracking objectives necessary for scientific utilization.

Sick, J. N.

2003-12-01

227

Cross-kingdom patterns of alternative splicing and splice recognition  

PubMed Central

Background Variations in transcript splicing can reveal how eukaryotes recognize intronic splice sites. Retained introns (RIs) commonly appear when the intron definition (ID) mechanism of splice site recognition inconsistently identifies intron-exon boundaries, and cassette exons (CEs) are often caused by variable recognition of splice junctions by the exon definition (ED) mechanism. We have performed a comprehensive survey of alternative splicing across 42 eukaryotes to gain insight into how spliceosomal introns are recognized. Results All eukaryotes we studied exhibit RIs, which appear more frequently than previously thought. CEs are also present in all kingdoms and most of the organisms in our analysis. We observe that the ratio of CEs to RIs varies substantially among kingdoms, while the ratio of competing 3' acceptor and competing 5' donor sites remains nearly constant. In addition, we find the ratio of CEs to RIs in each organism correlates with the length of its introns. In all 14 fungi we examined, as well as in most of the 9 protists, RIs far outnumber CEs. This differs from the trend seen in 13 multicellular animals, where CEs occur much more frequently than RIs. The six plants we analyzed exhibit intermediate proportions of CEs and RIs. Conclusion Our results suggest that most extant eukaryotes are capable of recognizing splice sites via both ID and ED, although ED is most common in multicellular animals and ID predominates in fungi and most protists. PMID:18321378

McGuire, Abigail M; Pearson, Matthew D; Neafsey, Daniel E; Galagan, James E

2008-01-01

228

Artificial Neural Network Circuit for Spectral Pattern Recognition  

E-print Network

Artificial Neural Networks (ANNs) are a massively parallel network of a large number of interconnected neurons similar to the structure of biological neurons in the human brain. ANNs find applications in a large number of fields, from pattern...

Rasheed, Farah

2013-09-04

229

Grain recognition using local binary patterns variants as texture descriptors  

NASA Astrophysics Data System (ADS)

This paper focuses on the use of imaged-based machine learning techniques for identifing grain. In particular we compare several texture descriptors based on Local Binary Patterns(LBP),and we report new experiments using a set of novel texture descriptors based on the combination of the Elongated Quinary Pattern (EQP), the Elongated Ternary Pattern (ELTP) and the Elongated Binary Patterns(ELBP).These three variants of the standard LBP are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. The resulting extracted features are then used for training a machine-learning classifier(support vector machine). Our results show that a local approach based on the EQP feature extractor, which can express both local and holistic features of the grain image, produces a reliable system for identifing grain.

Huang, Meizhi; Yin, Wenqing; Qian, Yan

2011-02-01

230

Design of an Optical Character Recognition System for Camera-based Handheld Devices  

Microsoft Academic Search

This paper presents a complete Optical Character Recognition (OCR) system for camera captured image\\/graphics embedded textual documents for handheld devices. At first, text regions are extracted and skew corrected. Then, these regions are binarized and segmented into lines and characters. Characters are passed into the recognition module. Experimenting with a set of 100 business card images, captured by cell phone

Ayatullah Faruk Mollah; Nabamita Majumder; Subhadip Basu; Mita Nasipuri

2011-01-01

231

Low-Budget, Cost-Effective OCR: Optical Character Recognition for MS-DOS Micros.  

ERIC Educational Resources Information Center

Discusses optical character recognition (OCR) for use with MS-DOS microcomputers. Cost effectiveness is considered, three types of software approaches to character recognition are explained, hardware and operation requirements are described, possible library applications are discussed, future OCR developments are suggested, and a list of OCR…

Perez, Ernest

1990-01-01

232

Laser Opto-Electronic Correlator for Robotic Vision Automated Pattern Recognition  

NASA Technical Reports Server (NTRS)

A compact laser opto-electronic correlator for pattern recognition has been designed, fabricated, and tested. Specifically it is a translation sensitivity adjustable compact optical correlator (TSACOC) utilizing convergent laser beams for the holographic filter. Its properties and performance, including the location of the correlation peak and the effects of lateral and longitudinal displacements for both filters and input images, are systematically analyzed based on the nonparaxial approximation for the reference beam. The theoretical analyses have been verified in experiments. In applying the TSACOC to important practical problems including fingerprint identification, we have found that the tolerance of the system to the input lateral displacement can be conveniently increased by changing a geometric factor of the system. The system can be compactly packaged using the miniature laser diode sources and can be used in space by the National Aeronautics and Space Administration (NASA) and ground commercial applications which include robotic vision, and industrial inspection of automated quality control operations. The personnel of Standard International will work closely with the Jet Propulsion Laboratory (JPL) to transfer the technology to the commercial market. Prototype systems will be fabricated to test the market and perfect the product. Large production will follow after successful results are achieved.

Marzwell, Neville

1995-01-01

233

Optical music recognition on the International Music Score Library Project  

NASA Astrophysics Data System (ADS)

A system is presented for optical recognition of music scores. The system processes a document page in three main phases. First it performs a hierarchical decomposition of the page, identifying systems, staves and measures. The second phase, which forms the heart of the system, interprets each measure found in the previous phase as a collection of non-overlapping symbols including both primitive symbols (clefs, rests, etc.) with fixed templates, and composite symbols (chords, beamed groups, etc.) constructed through grammatical composition of primitives (note heads, ledger lines, beams, etc.). This phase proceeds by first building separate top-down recognizers for the symbols of interest. Then, it resolves the inevitable overlap between the recognized symbols by exploring the possible assignment of overlapping regions, seeking globally optimal and grammatically consistent explanations. The third phase interprets the recognized symbols in terms of pitch and rhythm, focusing on the main challenge of rhythm. We present results that compare our system to the leading commercial OMR system using MIDI ground truth for piano music.

Raphael, Christopher; Jin, Rong

2013-12-01

234

Wavelet GRI-MINACE filter for rotation-invariant pattern recognition  

Microsoft Academic Search

A rotation-invariant optical correlation filter using wavelet transform to produce easily detectable correlation peaks in the presence of noise and to provide better intraclass recognition is proposed. The proposed filter is designed by using the energy spectra of the wavelet transformed reference image and random noise. Because the energy spectrum of the wavelet transformed reference image is higher than that

Ha Woon Lee; Soo J. Kim; Jeong W. Kim; Yang H. Doh

1996-01-01

235

The ART of adaptive pattern recognition by a self-organizing neural network  

SciTech Connect

The Adaptive Resonance Theory (ART) architectures discussed here are neural networks that self-organize stable recognition codes in real time in response to arbitrary sequences of input patterns. Within such an ART architecture, the process of adaptive pattern recognition is a special case of the more general cognitive process of hypothesis discovery, testing, search, classification, and learning. This property opens up the possibility of applying ART systems to more general problems of adaptively processing large abstract information sources and databases. This article outlines the main computational properties of these ART architectures, while comparing and contrasting these properties with those of alternative learning and recognition systems. Technical details are described in greater detail elsewhere, and several books collect articles in which the theory was developed through the analysis and prediction of interdisciplinary data about the brain and behavior.

Carpenter, G.A.; Grossberg, S.

1988-03-01

236

Study on the classification algorithm of degree of arteriosclerosis based on fuzzy pattern recognition  

NASA Astrophysics Data System (ADS)

Pulse wave of human body contains large amount of physiological and pathological information, so the degree of arteriosclerosis classification algorithm is study based on fuzzy pattern recognition in this paper. Taking the human's pulse wave as the research object, we can extract the characteristic of time and frequency domain of pulse signal, and select the parameters with a better clustering effect for arteriosclerosis identification. Moreover, the validity of characteristic parameters is verified by fuzzy ISODATA clustering method (FISOCM). Finally, fuzzy pattern recognition system can quantitatively distinguish the degree of arteriosclerosis with patients. By testing the 50 samples in the built pulse database, the experimental result shows that the algorithm is practical and achieves a good classification recognition result.

Ding, Li; Zhou, Runjing; Liu, Guiying

2010-08-01

237

Reservoir Simulation and Modeling Based on Pattern Recognition Shahab D. Mohaghegh, Intelligent Solutions, Inc. & West Virginia University  

E-print Network

SPE 143179 Reservoir Simulation and Modeling Based on Pattern Recognition Shahab D. Mohaghegh of reservoir models that are developed based on the pattern recognition technologies collectively known class of reservoir simulation and modeling tools break new ground in modeling fluid flow through porous

Mohaghegh, Shahab

238

Foundations for a syntatic pattern recognition system for genomic DNA sequences  

SciTech Connect

The goal of the proposed work is the creation of a software system that will perform sophisticated pattern recognition and related functions at a level of abstraction and with expressive power beyond current general-purpose pattern-matching systems for biological sequences; and with a more uniform language, environment, and graphical user interface, and with greater flexibility, extensibility, embeddability, and ability to incorporate other algorithms, than current special-purpose analytic software.

Searles, D.B.

1993-03-01

239

Polysomnographic pattern recognition for automated classification of sleep-waking states in infants  

Microsoft Academic Search

A robust, automated pattern recognition system for polysomnography data targeted to the sleep-waking state and stage identification\\u000a is presented. Five patterns were searched for: slow-delta and theta wave predominance in the background electro-encephalogram\\u000a (EEG) activity; presence of sleep spindles in the EEG; presence of rapid eye movements in an electro-oculogram; and presence\\u000a of muscle tone in an electromyogram. The performance

P. A. Estévez; C. M. Held; C. A. Holzmann; C. A. Perez; J. P. Pérez; J. Heiss; M. Garrido; P. Peirano

2002-01-01

240

Genetic Granular Cognitive Fuzzy Neural Networks and Human Brains for Pattern Recognition  

Microsoft Academic Search

With ever-improving information technologies and high performance computational power, recent techniques in granular computing,\\u000a soft computing and cognitive science have allowed an increasing understanding of normal and abnormal brain functions, especially\\u000a in the research of human’s pattern recognition by means of computational intelligence. It is well understood that normal brains\\u000a have high intelligence to recognize different geometrical patterns, but a

Cui Lin; Jun Li; Natasha Barrett; Yan-qing Zhang; David A. Washburn

2006-01-01

241

PATTERN RECOGNITION ANALYSIS OF A SET OF MUTAGENIC ALIPHATIC N-NITROSAMINES  

EPA Science Inventory

A set of 21 mutagenic aliphatic N-nitrosamines were subjected to a pattern recognition analysis using ADAPT software. Four descriptors based on molecular connectivity, geometry and sigma charge on nitrogen were capable of achieving a 100% classification using the linear learning ...

242

An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods  

Microsoft Academic Search

Classification methods from statistical pattern recognition, neural nets, and machine learning were applied to four real-world data sets. Each of these data sets has been previously analyzed and reported in the statistical, medical, or machine learning literature. The data sets are characterized by statisucal uncertainty; there is no completely accurate solution to these problems. Training and testing or resampling techniques

Sholom M. Weiss; Ioannis Kapouleas

1989-01-01

243

PYROLYSIS-MASS SPECTROMETRY/PATTERN RECOGNITION ON A WELL-CHARACTERIZED SUITE OF HUMIC SAMPLES  

EPA Science Inventory

A suite of well-characterized humic and fulvic acids of freshwater, soil and plant origin was subjected to pyrolysis-mass spectrometry and the resulting data were analyzed by pattern recognition and factor analysis. A factor analysis plot of the data shows that the humic acids an...

244

An automatic rule base generation method for fuzzy pattern recognition with multiphased clustering  

Microsoft Academic Search

Presents an approach for the automatic generation of fuzzy rule bases for pattern recognition from a given sample data. The general idea of the approach is to use and enhance the fuzzy c-means clustering algorithm. The rule base is generated through a modified iterative feature clustering method. A following cross-checking is used to separate the generated rules. Although the rule

Franjo Ivancic; Ashutosh Malaviya; Liliane Peters

1998-01-01

245

APPLICATION OF SIMCA (SOFT INDEPENDENT MODELING OF CLASS ANALOGY) PATTERN RECOGNITION TO AIR POLLUTANT ANALYTICAL DATA  

EPA Science Inventory

The SIMCA 3B computer program is a modular, graphics oriented pattern recognition package which can be run on a microcomputer with limited memory, e.g. an Osborne 1 with 64K memory. Principal component analysis is used to classify data with this program. The SIMCA program was use...

246

Sensors and Actuators B 125 (2007) 489497 A pattern recognition method for electronic noses  

E-print Network

Sensors and Actuators B 125 (2007) 489­497 A pattern recognition method for electronic noses based from a chemical sensor array of electronic noses, which makes the system more bionics. This paper-two-dimensional feature vectors of a sensor array consisting of eight sensors, in which four features were extracted from

Freeman, Walter J.

247

Z .Pattern Recognition Letters 18 1997 12531259 Image compression and encryption using tree structures 1  

E-print Network

a high compression ratio. With a mobile sys- tem, compression and encryption must be handled by limited .� is efficient in terms of time and space ; � performs image compression, and � performs dataZ .Pattern Recognition Letters 18 1997 1253­1259 Image compression and encryption using tree

Cheng, Howard

248

Structural pattern recognition of Carotid pulse waves using a general waveform parsing system  

Microsoft Academic Search

A general waveform parsing system with application to structural pattern recognition of carotid pulse waves is described. The carotid arterial pulse wave is of medical importance because of variation in its structure induced by arterial aging and cardiovascular disease. The syntax-driven waveform analysis system has been applied with good results to these pulse waves to detect and measure structural variations.

George C. Stockman; Laveen N. Kanal; M. C. Kyle

1976-01-01

249

PATTERN RECOGNITION/EXPERT SYSTEM FOR IDENTIFICATION OF TOXIC COMPOUNDS FROM LOW RESOLUTION MASS SPECTRA  

EPA Science Inventory

An empirical rule-based pattern recognition/expert system for classifying, estimating molecular weights and identifying low resolution mass spectra of toxic and other organic compounds has been developed and evaluated. he system was designed to accommodate low concentration spect...

250

Can superconductivity be predicted with the aid of pattern recognition techniques ?  

E-print Network

97 Can superconductivity be predicted with the aid of pattern recognition techniques ? F. W to superconductivity. Learning machines were constructed using those elements of the periodic system whose super of the order of 90 %. The predictive power of these machines concerning the superconducting behaviour

Boyer, Edmond

251

Pattern Recognition 38 (2005) 24372448 www.elsevier.com/locate/patcog  

E-print Network

of data processing. Among the three levels of information fusion (pixel level, feature level, and decision of handwritten character and face recognition [1­3]. Although the research of the feature level fusion starts of the feature level fusion is obvious. Dif- ferent feature vectors extracted from the same pattern always

252

Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts  

ERIC Educational Resources Information Center

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-01-01

253

Proc. Computer Vision & Pattern Recognition, Vol. I, pp. 536-543 (2004). 1 Clear Underwater Vision  

E-print Network

[2, 7, 13, 36], archaeology [15] and mapping [37]. Moreover, underwater photography [34] is becomingProc. Computer Vision & Pattern Recognition, Vol. I, pp. 536-543 (2004). 1 Clear Underwater Vision Haifa 32000, ISRAEL yoav@ee.technion.ac.il , karpeln@tx.technion.ac.il Abstract Underwater imaging

Schechner, Yoav Yosef

254

Forecast of Wind speed and power of wind generator based on pattern recognition  

Microsoft Academic Search

Forecast of wind speed is very important for making out dispatch scheme of power system and operation in higher reliability, according to the forming mechanism of wind, its influencing factors and its inherent variation rule, one of pattern recognition called as adaptive neuron-fuzzy inference system, abbreviated as ANFIS is used in wind speed forecast. The hybrid algorithm is used to

Hui Zhou; Mei Huang; Xinfhua Wu

2009-01-01

255

LARGE SCALE EVALUATION OF A PATTERN RECOGNITION/EXPERT SYSTEM FOR MASS SPECTRAL MOLECULAR WEIGHT ESTIMATION  

EPA Science Inventory

A fast, personal-computer based method of estimating molecular weights of organic compounds from low resolution mass I spectra has been thoroughly evaluated. he method is based on a rule-based pattern,recognition/expert system approach which uses empirical linear corrections whic...

256

Designing Clinical Examples To Promote Pattern Recognition: Nursing Education-Based Research and Practical Applications.  

ERIC Educational Resources Information Center

Sophomore nursing students (n=162) examined scenarios depicting typical and atypical signs of heart attack. Examples were structured to include essential and nonessential symptoms, enabling pattern recognition and improved performance. The method provides a way to prepare students to anticipate and recognize life-threatening situations. (Contains…

Welk, Dorette Sugg

2002-01-01

257

Artificial neural networks and statistical pattern recognition improve MOSFET gas sensor array calibration  

Microsoft Academic Search

It is noted that the poor selectivity of many gas sensors is disadvantageous when individual gases are studied in gas mixtures or when odors are identified. It has been shown that pattern recognition methods are very promising when gases or odors are identified by means of gas sensor arrays. The quality of predictive models, based on partial least square (PLS),

H. Sundgren; F. Winquist; I. Lundstrom

1991-01-01

258

Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks  

E-print Network

inspired approach within the field of Artificial Intelligence and Machine Learning. By developing abstractDynamic Pattern Recognition in Sport by Means of Artificial Neural Networks JĂĽrgen Perl, Peter to store these data but to transform them into useful information. Artificial Neural Networks turn out

Perl, JĂĽrgen

259

Pattern Recognition in Time Series Jessica Lin Sheri Williamson Kirk Borne David DeBarr  

E-print Network

1 Chapter 1 Pattern Recognition in Time Series Jessica Lin Sheri Williamson Kirk Borne David DeBarr George Mason University Microsoft Corporation jessica@cs.gmu.edu swillif@gmu.edu kborne@gmu.edu dave), transient events (e.g., gamma-ray bursts (GRB), flare stars, novae, su

Lin, Jessica

260

Generation of Polynomial Discriminant Functions for Pattern Recognition  

Microsoft Academic Search

A practical method of determining weights for crossproduct and power terms in the variable inputs to an adaptive threshold element used for statistical pattern classification is derived. The objective is to make it possible to realize general nonlinear decision surfaces, in contrast with the linear (hyperplanar) decision surfaces that can be realized by a threshold element using only first-order terms

Donald F. Specht

1967-01-01

261

Design of an Optical Character Recognition System for Camera-based Handheld Devices  

E-print Network

This paper presents a complete Optical Character Recognition (OCR) system for camera captured image/graphics embedded textual documents for handheld devices. At first, text regions are extracted and skew corrected. Then, these regions are binarized and segmented into lines and characters. Characters are passed into the recognition module. Experimenting with a set of 100 business card images, captured by cell phone camera, we have achieved a maximum recognition accuracy of 92.74%. Compared to Tesseract, an open source desktop-based powerful OCR engine, present recognition accuracy is worth contributing. Moreover, the developed technique is computationally efficient and consumes low memory so as to be applicable on handheld devices.

Mollah, Ayatullah Faruk; Basu, Subhadip; Nasipuri, Mita

2011-01-01

262

Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing  

PubMed Central

There is currently no “gold standard” marker of cognitive performance impairment resulting from sleep loss. We utilized pattern recognition algorithms to determine which features of data collected under controlled laboratory conditions could most reliably identify cognitive performance impairment in response to sleep loss using data from only one testing session, such as would occur in the “real world” or field conditions. A training set for testing the pattern recognition algorithms was developed using objective Psychomotor Vigilance Task (PVT) and subjective Karolinska Sleepiness Scale (KSS) data collected from laboratory studies during which subjects were sleep deprived for 26 – 52 hours. The algorithm was then tested in data from both laboratory and field experiments. The pattern recognition algorithm was able to identify performance impairment with a single testing session in individuals studied under laboratory conditions using PVT, KSS, length of time awake and time of day information with sensitivity and specificity as high as 82%. When this algorithm was tested on data collected under real-world conditions from individuals whose data were not in the training set, accuracy of predictions for individuals categorized with low performance impairment were as high as 98%. Predictions for medium and severe performance impairment were less accurate. We conclude that pattern recognition algorithms may be a promising method for identifying performance impairment in individuals using only current information about the individual’s behavior. Single testing features (e.g., number of PVT lapses) with high correlation with performance impairment in the laboratory setting may not be the best indicators of performance impairment under real-world conditions. Pattern recognition algorithms should be further tested for their ability to be used in conjunction with other assessments of sleepiness in real-world conditions to quantify performance impairment in response to sleep loss. PMID:22959616

St. Hilaire, Melissa A.; Sullivan, Jason P.; Anderson, Clare; Cohen, Daniel A.; Barger, Laura K.; Lockley, Steven W.; Klerman, Elizabeth B.

2012-01-01

263

Femtosecond near-field optical spectroscopy of implantation patterned semiconductors  

E-print Network

Femtosecond near-field optical spectroscopy of implantation patterned semiconductors B. A. Nechay carriers. © 1999 American Institute of Physics. S0003-6951 99 02901-0 Ultrafast optical spectroscopy has-field scanning optical microscope, using a diffraction-limited pump and near-field probe configuration, which

Keller, Ursula

264

Oxidized LDL: Diversity, Patterns of Recognition, and Pathophysiology  

PubMed Central

Abstract Oxidative modification of LDL is known to elicit an array of pro-atherogenic responses, but it is generally underappreciated that oxidized LDL (OxLDL) exists in multiple forms, characterized by different degrees of oxidation and different mixtures of bioactive components. The variable effects of OxLDL reported in the literature can be attributed in large part to the heterogeneous nature of the preparations employed. In this review, we first describe the various subclasses and molecular composition of OxLDL, including the variety of minimally modified LDL preparations. We then describe multiple receptors that recognize various species of OxLDL and discuss the mechanisms responsible for the recognition by specific receptors. Furthermore, we discuss the contentious issues such as the nature of OxLDL in vivo and the physiological oxidizing agents, whether oxidation of LDL is a prerequisite for atherogenesis, whether OxLDL is the major source of lipids in foam cells, whether in some cases it actually induces cholesterol depletion, and finally the Janus-like nature of OxLDL in having both pro- and anti-inflammatory effects. Lastly, we extend our review to discuss the role of LDL oxidation in diseases other than atherosclerosis, including diabetes mellitus, and several autoimmune diseases, such as lupus erythematosus, anti-phospholipid syndrome, and rheumatoid arthritis. Antioxid. Redox Signal. 13, 39–75. PMID:19888833

Volkov, Suncica; Subbaiah, Papasani V.

2010-01-01

265

Hybrid pattern recognition system capable of self-modification  

SciTech Connect

Systems capable of recognizing and learning two-dimensional patterns can be used in imaging systems and robotic perception systems. The symbolic and neuromorphic methods for pattern processing problems of this type are complementary in character. We present a hybrid system that utilizes components of symbolic and neuromorphic type; we employ two hybrid components that simultaneously operate up on the same data to produce hypotheses about the data. To resolve the potential conflicts in these hypotheses, we propose a method that learns a combination rule based on a set of examples. We employ the method of empirical risk minimization and does not require knowledge about the error probability distributions of the modules. We are building a prototype system to recognize control panels using a vision system.

Glover, C.W.; Oblow, E.M. [Oak Ridge National Lab., TN (United States); Rao, N.S.V. [Old Dominion Univ., Norfolk, VA (United States)

1993-06-01

266

Hybrid pattern recognition system capable of self-modification  

SciTech Connect

Systems capable of recognizing and learning two-dimensional patterns can be used in imaging systems and robotic perception systems. The symbolic and neuromorphic methods for pattern processing problems of this type are complementary in character. We present a hybrid system that utilizes components of symbolic and neuromorphic type; we employ two hybrid components that simultaneously operate up on the same data to produce hypotheses about the data. To resolve the potential conflicts in these hypotheses, we propose a method that learns a combination rule based on a set of examples. We employ the method of empirical risk minimization and does not require knowledge about the error probability distributions of the modules. We are building a prototype system to recognize control panels using a vision system.

Glover, C.W.; Oblow, E.M. (Oak Ridge National Lab., TN (United States)); Rao, N.S.V. (Old Dominion Univ., Norfolk, VA (United States))

1993-01-01

267

[Pattern recognition of surface electromyography signal based on multi-scale fuzzy entropy].  

PubMed

Action surface electromyography (SEMG) signals can be acquired from human skin surface. Its pattern recognition plays a very important role in practical applications such as human prosthesis and human-computer interface systems. For the purpose of increasing the recognition accuracy, we proposed a new recognition method combining fuzzy entropy (FuzzyEn) with multi-scale analysis. Considering the nonlinear and non-stationary characteristics of the SEMG, a multi-scale fuzzy entropy (MSFuzzyEn) feature was introduced and applied to the pattern recognition of six type action SEMG signals of the forearm. Firstly, multi-scale decomposition was applied to original signal using wavelet decomposition. Then MSFuzzyEn of the decomposed signals were calculated and inputted to support vector machine (SVM) for classification as feature vectors. The mean recognition accuracy reached 97%, which was 3% greater than that when FuzzyEn of original signal is applied to the classification of SEMG signals. The results have proved that the MSFuzzyEn is effective and precise in the classification of action SEMG signals. PMID:23469553

Zou, Xiaoyang; Lei, Min

2012-12-01

268

Research on Gesture Definition and Electrode Placement in Pattern Recognition of Hand Gesture Action SEMG  

NASA Astrophysics Data System (ADS)

The goal of this study is to explore the effects of electrode place-ment on the hand gesture pattern recognition performance. We have conducted experiments with surface EMG sensors using two detecting electrode channels. In total 25 different hand gestures and 10 different electrode positions for measuring muscle activities have been evaluated. Based on the experimental results, dependencies between surface EMG signal detection positions and hand gesture recognition performance have been analyzed and summarized as suggestions how to define hand gestures and select suitable electrode positions for a myoelectric control system. This work provides useful insight for the development of a medical rehabilitation system based on EMG technique.

Zhang, Xu; Chen, Xiang; Zhao, Zhang-Yan; Tu, You-Qiang; Yang, Ji-Hai; Lantz, Vuokko; Wang, Kong-Qiao

269

A smart pattern recognition system for the automatic identification of aerospace acoustic sources  

NASA Technical Reports Server (NTRS)

An intelligent air-noise recognition system is described that uses pattern recognition techniques to distinguish noise signatures of five different types of acoustic sources, including jet planes, propeller planes, a helicopter, train, and wind turbine. Information for classification is calculated using the power spectral density and autocorrelation taken from the output of a single microphone. Using this system, as many as 90 percent of test recordings were correctly identified, indicating that the linear discriminant functions developed can be used for aerospace source identification.

Cabell, R. H.; Fuller, C. R.

1989-01-01

270

Adsorption and Pattern Recognition of Polymers at Complex Surfaces with Attractive Stripelike Motifs  

NASA Astrophysics Data System (ADS)

We construct the complete structural phase diagram of polymer adsorption at substrates with attractive stripelike patterns in the parameter space spanned by the adsorption affinity of the stripes and temperature. Results were obtained by extensive generalized-ensemble Monte Carlo simulations of a generic model for the hybrid organic-inorganic system. By comparing with adhesion properties at homogeneous substrates, we find substantial differences in the formation of adsorbed polymer structures if translational invariance at the surface is broken by a regular pattern. Beside a more specific understanding of polymer adsorption processes, our results are potentially relevant for the design of macromolecular pattern recognition devices such as sensors.

Möddel, Monika; Janke, Wolfhard; Bachmann, Michael

2014-04-01

271

Application of Hybrid Pattern Recognition for Discriminating Paddy Seeds of Different Storage Periods Based on Vis\\/NIRS  

Microsoft Academic Search

Hybrid pattern recognition was put forward to discriminate paddy seeds of four different storage periods based on visible\\/near\\u000a infrared reflectance spectroscopy (Vis\\/NIRS). The hybrid pattern recognition included extracting feature and building classifier.\\u000a A total of 210 samples of paddy seeds, which belonged to four classes, were used for collecting Vis\\/NIR spectra (325-1075\\u000a nm) using a field spectroradiometer. The hybrid pattern

Li Xiaoli; Cao Fang; He Yong

2007-01-01

272

New Class Of Features For Pattern Recognition And Image Analysis  

NASA Astrophysics Data System (ADS)

A class of features, called "edge features," has been developed and applied to several problems of practical interest in image processing. These features are derived from a vector-valued function of the image called the "edge spectrum-" The edge spectrum at coordinate (x.,y) of the image describes the distribution of edge directions near (x,y). Several applications of edge features are discussed. One is considered in some detail. This application is to identify friendly aircraft descending for landing on an aircraft carrier. Identification is achieved by measuring wingspan - a good discriminant between the A6, A7, E2C and F.14. aircraft. For this purpose an edge feature was designed for locating the wing tips in the image. Wingspan was converted to physical dimension using range information and the known parameters of the optical system.

Choate, W. Clay

1985-07-01

273

Aircraft Rivets Defect Recognition Method Based on Magneto-optical Images  

Microsoft Academic Search

With magneto-optical images for the aircraft rivets, a new automated recognition algorithm of inspecting the existence and the direction of cracks based on fuzzy support vector machine (FSVM) is presented. The binary image of rivet is obtained by preprocessing the magneto-optic image, the star radial vector method is used to acquire the feature of rivet edge, and the approximate center

Bo Li; Xiangfeng Wang; Hongping Yang; Zhenliu Zhou

2010-01-01

274

Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'03) 1063-6919/03 $17.00 2003 IEEE  

E-print Network

Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition on Computer Vision and Pattern Recognition (CVPR'03) 1063-6919/03 $17.00 © 2003 IEEE #12;Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'03) 1063

Vese, Luminita A.

275

[Application of two FTIR pattern recognition methods to the Zanthoxylum nitidum geographical origins determination].  

PubMed

In the present work, the authors explored a rapid method of the Zanthoxylum nitidum geographical origins determination. Based on Fourier transform infrared spectroscopy (FTIR) technology, the band of 1 800-400 cm(-1) which is the IR fingerprint of Zanthoxylum nitidum, the Fisher ratio and the soft independent modeling of class analogies (SIMCA) were used to build a classification model. Respectively, four kinds of Zanthoxylum nitidum in the Guangxi region were detected by the model, and the model was verified by calculating their recognition rate and rejection rate. The results show that the authors can accurately extract the overall information of Chinese herbal medicines by using the FTIR, also established a pattern recognition model to predict unknown samples, and obtained satisfactory recognition rate and rejection rate, indicating that the model has stronger ability of identification. The detection on real time was carried out rapidly with the Fisher model, suggesting that the model has more practical value. PMID:22250538

Mao, Xiao-Li; Zheng, Juan-Mei; Li, Zi-Da; Lei, Xin-Chao; Huang, Shu-Shi; Liu, Hua-Gang

2011-10-01

276

Research on distortion invariant in optical correlation detection and recognition technology  

NASA Astrophysics Data System (ADS)

It is difficult to dectect and recognize moving target for optical correlator owing to the image information containing various rotation and scale variation, though optical processing owns the advantage of high-speed, large capacity and real-time. An efficient approach was proposed to impove correlation peak and enhance recognition performance of target based on Fourier-Mellin transform, by which optical correlator is not sensitive to the distortion of image information, and it will alleviate greatly the complexity of image recognition. We have also constructed an detection and recognition system based on optical joint correlator, the computer simulations and experimental results show that the proposed method has got sharp corrlation peak and improved the detection performance.

Shi, Xiao-wei; Qian, Yi-xian; Hong, Xue-ting; Li, Deng-hui

2014-11-01

277

Visual Scanning Patterns and Executive Function in Relation to Facial Emotion Recognition in Aging  

PubMed Central

Objective The ability to perceive facial emotion varies with age. Relative to younger adults (YA), older adults (OA) are less accurate at identifying fear, anger, and sadness, and more accurate at identifying disgust. Because different emotions are conveyed by different parts of the face, changes in visual scanning patterns may account for age-related variability. We investigated the relation between scanning patterns and recognition of facial emotions. Additionally, as frontal-lobe changes with age may affect scanning patterns and emotion recognition, we examined correlations between scanning parameters and performance on executive function tests. Methods We recorded eye movements from 16 OA (mean age 68.9) and 16 YA (mean age 19.2) while they categorized facial expressions and non-face control images (landscapes), and administered standard tests of executive function. Results OA were less accurate than YA at identifying fear (p<.05, r=.44) and more accurate at identifying disgust (p<.05, r=.39). OA fixated less than YA on the top half of the face for disgust, fearful, happy, neutral, and sad faces (p’s<.05, r’s?.38), whereas there was no group difference for landscapes. For OA, executive function was correlated with recognition of sad expressions and with scanning patterns for fearful, sad, and surprised expressions. Conclusion We report significant age-related differences in visual scanning that are specific to faces. The observed relation between scanning patterns and executive function supports the hypothesis that frontal-lobe changes with age may underlie some changes in emotion recognition. PMID:22616800

Circelli, Karishma S.; Clark, Uraina S.; Cronin-Golomb, Alice

2012-01-01

278

Real-time optical multiple object recognition and tracking system and method  

NASA Technical Reports Server (NTRS)

System for optically recognizing and tracking a plurality of objects within a field of vision. Laser (46) produces a coherent beam (48). Beam splitter (24) splits the beam into object (26) and reference (28) beams. Beam expanders (50) and collimators (52) transform the beams (26, 28) into coherent collimated light beams (26', 28'). A two-dimensional SLM (54), disposed in the object beam (26'), modulates the object beam with optical information as a function of signals from a first camera (16) which develops X and Y signals reflecting the contents of its field of vision. A hololens (38), positioned in the object beam (26') subsequent to the modulator (54), focuses the object beam at a plurality of focal points (42). A planar transparency-forming film (32), disposed with the focal points on an exposable surface, forms a multiple position interference filter (62) upon exposure of the surface and development processing of the film (32). A reflector (53) directing the reference beam (28') onto the film (32), exposes the surface, with images focused by the hololens (38), to form interference patterns on the surface. There is apparatus (16', 64) for sensing and indicating light passage through respective ones of the positions of the filter (62), whereby recognition of objects corresponding to respective ones of the positions of the filter (62) is affected. For tracking, apparatus (64) focuses light passing through the filter (62) onto a matrix of CCD's in a second camera (16') to form a two-dimensional display of the recognized objects.

Chao, Tien-Hsin (Inventor); Liu, Hua-Kuang (Inventor)

1990-01-01

279

Wavelet CHF-SDF filter for distortion-invariant pattern recognition  

Microsoft Academic Search

An optical WCHF-SDF (wavelet circular harmonic function synthetic discriminant function) filter is proposed. The proposed WCHF-SDF filter is synthesized by 2nd order harmonics of the four different wavelet transformed patterns by Haar wavelet function. This filter has full rotation and limited scale invariant properties. The scale invariant range can be increased by using more training patterns.

Seung Hee Lee; Jeong Woo Kim; Ha Woon Lee; Duck Soo Noh; Soo Joong Kim

1996-01-01

280

Bacterial and fungal pattern recognition receptors in homologous innate signaling pathways of insects and mammals  

PubMed Central

In response to bacterial and fungal infections in insects and mammals, distinct families of innate immune pattern recognition receptors (PRRs) initiate highly complex intracellular signaling cascades. Those cascades induce a variety of immune functions that restrain the spread of microbes in the host. Insect and mammalian innate immune receptors include molecules that recognize conserved microbial molecular patterns. Innate immune recognition leads to the recruitment of adaptor molecules forming multi-protein complexes that include kinases, transcription factors, and other regulatory molecules. Innate immune signaling cascades induce the expression of genes encoding antimicrobial peptides and other key factors that mount and regulate the immune response against microbial challenge. In this review, we summarize our current understanding of the bacterial and fungal PRRs for homologous innate signaling pathways of insects and mammals in an effort to provide a framework for future studies.

Stokes, Bethany A.; Yadav, Shruti; Shokal, Upasana; Smith, L. C.; Eleftherianos, Ioannis

2015-01-01

281

Problems Associated with Statistical Pattern Recognition of Acoustic Emission Signals in a Compact Tension Fatigue Specimen  

NASA Technical Reports Server (NTRS)

Acoustic emission (AE) data were acquired during fatigue testing of an aluminum 2024-T4 compact tension specimen using a commercially available AE system. AE signals from crack extension were identified and separated from noise spikes, signals that reflected from the specimen edges, and signals that saturated the instrumentation. A commercially available software package was used to train a statistical pattern recognition system to classify the signals. The software trained a network to recognize signals with a 91-percent accuracy when compared with the researcher's interpretation of the data. Reasons for the discrepancies are examined and it is postulated that additional preprocessing of the AE data to focus on the extensional wave mode and eliminate other effects before training the pattern recognition system will result in increased accuracy.

Hinton, Yolanda L.

1999-01-01

282

Spectral pattern recognition of controlled substances in street samples using artificial neural network system  

NASA Astrophysics Data System (ADS)

The NarTest fluorescent technique is aimed at the detection of analyte of interest in street samples by recognition of its specific spectral patterns in 3-dimentional Spectral Fluorescent Signatures (SFS) measured with NTX2000 analyzer without chromatographic or other separation of controlled substances from a mixture with cutting agents. The illicit drugs have their own characteristic SFS features which can be used for detection and identification of narcotics, however typical street sample consists of a mixture with cutting agents: adulterants and diluents. Many of them interfere the spectral shape of SFS. The expert system based on Artificial Neural Networks (ANNs) has been developed and applied for such pattern recognition in SFS of street samples of illicit drugs.

Poryvkina, Larisa; Aleksejev, Valeri; Babichenko, Sergey M.; Ivkina, Tatjana

2011-04-01

283

The Cytosolic Pattern Recognition Receptor NOD1 Induces Inflammatory Interleukin8 during Chlamydia trachomatis Infection  

Microsoft Academic Search

Inflammation is a hallmark of chlamydial infections, but how inflammatory cytokines are induced is not well understood. Pattern recognition receptors (PRR) of the host innate immune system recognize pathogen molecules and activate intracellular signaling pathways that modulate immune responses. The role of PRR such as Toll-like receptors (TLR) and nucleotide-binding oligomerization domain (NOD) proteins in the endogenous interleukin-8 (IL-8) response

Kerry R. Buchholz; Richard S. Stephens

2008-01-01

284

Application of deconvolution based pattern recognition algorithm for identification of rings in spectra from RICH detectors  

NASA Astrophysics Data System (ADS)

This paper proposes a new pattern recognition algorithm that is applied to determine rings in two-dimensional spectra from RICH detectors. It defines a two-dimensional boosted Gold deconvolution algorithm. This paper also thoroughly analyzes and studies the influence of input parameters for different kinds of data. By choosing suitable input parameters for deconvolution one can obtain an efficient tool for identifying rings in two-dimensional spectra. Illustrative examples prove in favor of the proposed algorithm.

Morhá?, Miroslav; Hlavá?, Stanislav; Veselský, Martin; Matoušek, Vladislav

2010-09-01

285

Development of a Pattern Recognition Methodology for Determining Operationally Optimal Heat Balance Instrumentation Calibration Schedules  

SciTech Connect

The goal of the project is to enable plant operators to detect with high sensitivity and reliability the onset of decalibration drifts in all of the instrumentation used as input to the reactor heat balance calculations. To achieve this objective, the collaborators developed and implemented at DBNPS an extension of the Multivariate State Estimation Technique (MSET) pattern recognition methodology pioneered by ANAL. The extension was implemented during the second phase of the project and fully achieved the project goal.

Kurt Beran; John Christenson; Dragos Nica; Kenny Gross

2002-12-15

286

The estimation of the gradient of a density function, with applications in pattern recognition  

Microsoft Academic Search

Nonparametric density gradient estimation using a generalized kernel approach is investigated. Conditions on the kernel functions are derived to guarantee asymptotic unbiasedness, consistency, and uniform consistency of the estimates. The results are generalized to obtain a simple mcan-shift estimate that can be extended in ak-nearest-neighbor approach. Applications of gradient estimation to pattern recognition are presented using clustering and intrinsic dimensionality

KEINOSUKE FUKUNAGA; LARRY D. HOSTETLER

1975-01-01

287

Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features  

Microsoft Academic Search

For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-recognition process of hydrogen-bonded and geometrical features extracted from x-ray coordinates. Cooperative secondary structure is recognized as repeats

Wolfgang Kabsch; Christian Sander

1983-01-01

288

Applications of matrix derivatives to optimization problems in statistical pattern recognition  

NASA Technical Reports Server (NTRS)

A necessary condition for a real valued Frechet differentiable function of a vector variable have an extremum at a vector x sub 0 is that the Frechet derivative vanishes at x sub 0. A relationship between Frechet differentials and matrix derivatives was established that obtains a necessary condition on the matrix derivative at an extrema. These results are applied to various scalar functions of matrix variables which occur in statistical pattern recognition.

Morrell, J. S.

1975-01-01

289

EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study  

PubMed Central

Background Several studies investigating the use of electromyographic (EMG) signals in robot-based stroke neuro-rehabilitation to enhance functional recovery. Here we explored whether a classical EMG-based patterns recognition approach could be employed to predict patients’ intentions while attempting to generate goal-directed movements in the horizontal plane. Methods Nine right-handed healthy subjects and seven right-handed stroke survivors performed reaching movements in the horizontal plane. EMG signals were recorded and used to identify the intended motion direction of the subjects. To this aim, a standard pattern recognition algorithm (i.e., Support Vector Machine, SVM) was used. Different tests were carried out to understand the role of the inter- and intra-subjects’ variability in affecting classifier accuracy. Abnormal muscular spatial patterns generating misclassification were evaluated by means of an assessment index calculated from the results achieved with the PCA, i.e., the so-called Coefficient of Expressiveness (CoE). Results Processing the EMG signals of the healthy subjects, in most of the cases we were able to build a static functional map of the EMG activation patterns for point-to-point reaching movements on the horizontal plane. On the contrary, when processing the EMG signals of the pathological subjects a good classification was not possible. In particular, patients’ aimed movement direction was not predictable with sufficient accuracy either when using the general map extracted from data of normal subjects and when tuning the classifier on the EMG signals recorded from each patient. Conclusions The experimental findings herein reported show that the use of EMG patterns recognition approach might not be practical to decode movement intention in subjects with neurological injury such as stroke. Rather than estimate motion from EMGs, future scenarios should encourage the utilization of these signals to detect and interpret the normal and abnormal muscle patterns and provide feedback on their correct recruitment. PMID:23855907

2013-01-01

290

Developement of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)  

SciTech Connect

Many next-generation physics experiments will be characterized by the collection of large quantities of data, taken in rapid succession, from which scientists will have to unravel the underlying physical processes. In most cases, large backgrounds will overwhelm the physics signal. Since the quantity of data that can be stored for later analysis is limited, real-time event selection is imperative to retain the interesting events while rejecting the background. Scaling of current technologies is unlikely to satisfy the scientific needs of future projects, so investments in transformational new technologies need to be made. For example, future particle physics experiments looking for rare processes will have to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare processes. In this proposal, we intend to develop hardware-based technology that significantly advances the state-of-the-art for fast pattern recognition within and outside HEP using the 3D vertical integration technology that has emerged recently in industry. The ultimate physics reach of the LHC experiments will crucially depend on the tracking trigger's ability to help discriminate between interesting rare events and the background. Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing pattern recognition for a track trigger, requiring about three orders of magnitude more associative memory patterns than what was used in the original CDF SVT. Significant improvement in the architecture of associative memory structures is needed to run fast pattern recognition algorithms of this scale. We are proposing the development of 3D integrated circuit technology as a way to implement new associative memory structures for fast pattern recognition applications. Adding a 'third' dimension to the signal processing chain, as compared to the two-dimensional nature of printed circuit boards, Field Programmable Gate Arrays (FPGAs), etc., opens up the possibility for new architectures that could dramatically enhance pattern recognition capability. We are currently performing preliminary design work to demonstrate the feasibility of this approach. In this proposal, we seek to develop the design and perform the ASIC engineering necessary to realize a prototype device. While our focus here is on the Energy Frontier (e.g. the LHC), the approach may have applications in experiments in the Intensity Frontier and the Cosmic Frontier as well as other scientific and medical projects. In fact, the technique that we are proposing is very generic and could have wide applications far beyond track trigger, both within and outside HEP.

Deputch, G.; Hoff, J.; Lipton, R.; Liu, T.; Olsen, J.; Ramberg, E.; Wu, Jin-Yuan; Yarema, R.; /Fermilab; Shochet, M.; Tang, F.; /Chicago U.; Demarteau, M.; /Argonne /INFN, Padova

2011-04-13

291

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques  

PubMed Central

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species. PMID:22235330

Lee, Sean; Nitin, Mantri

2012-01-01

292

Magneto-optic pattern generators for electrophotographic printers  

NASA Astrophysics Data System (ADS)

The magneto-optic light switching technique and its feasibility for electronic reproduction of text or graphics by electrophotography were studied. An optical line-pattern generator to be used as electro-optic input device was constructed. Based on magneto-optic switching arrays, the best matching of spectral features, light output power other essential parameters with the electrophotographic process was investigated. The results of recording experiments with an optimized design of light source, imaging optics and light switching array used to expose organic photoconductors in a special electrophotographic test apparatus show the feasibility of the technique at medium recording speed.

Hill, B.; Schmidt, K. P.

1982-01-01

293

Recognition of micro-scale deformation structures in glacial sediments pattern perception, observer bias and the influence of experience  

E-print Network

Recognition of micro-scale deformation structures in glacial sediments ­ pattern perception Leighton, I. D., Hiemstra, J. F. & Weidemann, C. T. 2013 (April): Recognition of micro-scale deformation, however, many of these apparently massive diamictons show micro- scale features that are indicative

Weidemann, Christoph

294

Direct Nano-Patterning With Nano-Optic Devices  

E-print Network

In this study nano-patterning was carried out using two different nano-optic devices namely- the NSOM and Fresnel zone plate. In the first study, NSOM was used to generate nano-patterns on selected semiconducting (Si and Ge) and metallic (Cr, Cu...

Meenashi Sundaram, Vijay

2011-08-08

295

Application of syntactic methods of pattern recognition for data mining and knowledge discovery in medicine  

NASA Astrophysics Data System (ADS)

This paper presents and discusses possibilities of application of selected algorithms belonging to the group of syntactic methods of patten recognition used to analyze and extract features of shapes and to diagnose morphological lesions seen on selected medical images. This method is particularly useful for specialist morphological analysis of shapes of selected organs of abdominal cavity conducted to diagnose disease symptoms occurring in the main pancreatic ducts, upper segments of ureters and renal pelvis. Analysis of the correct morphology of these organs is possible with the application of the sequential and tree method belonging to the group of syntactic methods of pattern recognition. The objective of this analysis is to support early diagnosis of disease lesions, mainly characteristic for carcinoma and pancreatitis, based on examinations of ERCP images and a diagnosis of morphological lesions in ureters as well as renal pelvis based on an analysis of urograms. In the analysis of ERCP images the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis, while in the case of kidney radiogram analysis the aim is to diagnose local irregularities of ureter lumen and to examine the morphology of renal pelvis and renal calyxes. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of description of features of shapes and context-free sequential attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing of width diagrams of the examined structures. Additionally, in order to support the analysis of the correct structure of renal pelvis a method using the tree grammar for syntactic pattern recognition to define its correct morphological shapes has been presented.

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-04-01

296

Towards Unified Analysis of EEG and fMRI - A Comparison of Classifiers for Single-trial Pattern Recognition  

Microsoft Academic Search

Pattern recognition methods, which recently have shown promising potential in the analysis of neurophysio- logical data, are typically model-free and can thus be applied in the analysis of any type of signal. This study demonstrates the feasibility of, after suitable pre-processing steps, applying identical state-of-the-art pattern recognition method to single-trial classification of brain state data acquired with the fundamentally different

Simon Bergstrand; Malin Björnsdotter Ĺberg; Timo Niiniskorpi; Johan Wessberg

2009-01-01

297

Thermal imaging for face recognition in optical security systems  

NASA Astrophysics Data System (ADS)

Face recognition belongs to the most fundamental human perceptual capabilities. Today, the technology is encapsulated in commercial software that runs on any low cost personal computer and is capable to detect the human faces anywhere in a scene and rapidly identifying them by matching the face against its database of known faces.

Kobel, Joanna; Holowacz, Iwona; Podbielska, Halina

2001-08-01

298

Pattern Recognition  

NSDL National Science Digital Library

CSC 475. Topics in Computer Science (3) Prerequisite: Senior standing and permission of instructor. Topics of current interest in computer science not covered in existing courses. May be repeated under a different subtitle

Tagliarini, Gene

2005-04-20

299

Application of the PSO-SVM model for recognition of control chart patterns.  

PubMed

Control chart patterns are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Accurate recognition of control chart patterns is essential for efficient system monitoring to maintain high-quality products. This paper introduces a novel hybrid intelligent system that includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM)-based classifier is proposed. For the optimization module, a particle swarm optimization algorithm is proposed to improve the generalization performance of the recognizer. In this module, it the SVM classifier design is optimized by searching for the best value of the parameters that tune its discriminant function (kernel parameter selection) and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer. PMID:20663504

Ranaee, Vahid; Ebrahimzadeh, Ata; Ghaderi, Reza

2010-10-01

300

Determination of temperature and pressure patterns associated to sea ice fields in Antarctica, with modern tools of pattern recognition  

NASA Astrophysics Data System (ADS)

Monthly sea ice anomalies derived from passive microwave satellite data for Antarctica spanning the period 1979-2010 are classified into 16 different patterns (6 for summer and autumn and 10 for winter and spring). Each of these patterns has an atmospheric temperature and pressure structure associated with it (i.e., a specific mode of climate variability). These results were obtained using principal component analysis (PCA) in T-Mode. Here we attempt to identify the sea ice pattern for 2011 without using the passive microwave data, and instead using what can be inferred from the temperature and pressure fields associated with the patterns. We approach this issue with a multilayer Perceptron (neuronal network) with supervised learning and a back-propagation algorithm. The Perceptron is the most common Artificial Neural Network topology dedicated to image pattern recognition. It was implemented through the use of temperature and pressure anomalies field images that were associated with a group of sea ice anomaly patterns. The variables analyzed included only composites of surface air temperature and pressure to simplify the density of input data and avoid a non-converging solution (monthly input data for the period 1979-2010). The results of this analysis can be used to identify the sea ice patterns without the need of a new PCA analysis of the sea ice data. The anticipated outcome of this study is to obtain an accurate prediction of sea ice and perform a forecast. The findings may be vital to a more accurate planning of future missions to coastal Antarctic bases.

Orquera, F. J.; Torchio, M. A.; Barreira, S.

2012-12-01

301

Spontaneous Formation and Switching of Optical Patterns in Semiconductor Microcavities  

NASA Astrophysics Data System (ADS)

We study spontaneous pattern formation and symmetry breaking in broad area and pre-patterned (spatially modulated) semiconductor microcavities under lasing conditions. In broad area VCSELs, we observe the spontaneous formation of regular arrays consisting of charge "±1" optical vortices. The formation of these patterns stems from transverse mode locking of almost wavelength degenerated Gauss-Laguerre (GL) modes. The observed patterns in Gain modulated broad area VCSELs and their dynamical behavior depends dramatically on the modulation strength. In ring shaped VCSELs lasers we observe necklace-like pattern formation and switching as a function of the injection current. The formation of the patterns and, in particular, their switching is shown to stem from stability loss of the lasing pattern to perturbations of more complex pattern which, in turn, is stable under similar pumping conditions. Having the advantage of a strong, saturating nonlinear response with an inherent loss compensation mechanism, such lasers are potentially the best microlabortories for studying nonlinear phenomena and for the generation and employment of complex optical fields. Applications can be found in optical data storage, information distribution and processing, laser cooling and more.

Scheuer, Jacob; Orenstein, Meir

302

[A leukocyte pattern recognition based on feature fusion in multi-color space].  

PubMed

To solve the ineffective problem of leukocytes classification based on multi-feature fusion in a single color space, we proposed an automatic leukocyte pattern recognition by means of feature fusion with color histogram and texture granular in multi-color space. The interactive performance of three color spaces (RGB, HSV and Lab), two features (color histogram and texture granular) and four similarity measured distance metrics (normalized intersection, Euclidean distance, chi2-metric distance and Mahalanobis distance) were discussed. The optimized classification modes of high precision, extensive universality and low cost to different leukocyte types were obtained respectively, and then the recognition system of tree-integration of the optimized modes was established. The experimental results proved that the performance of the fusion classification was improved by 12.3% at least. PMID:24459942

Hao, Liangwang; Hong, Wenxue

2013-10-01

303

Comparative study of the CCF-like pattern recognition protein in different Lumbricid species.  

PubMed

Coelomic fluid of the Lumbricid Eisenia fetida contains a 42-kDa pattern recognition protein named coelomic cytolytic factor (CCF) that binds microbial cell wall components and triggers the activation of the prophenoloxidase cascade, an important invertebrate defense pathway. Here we report on the sequence characterization of CCF-like molecules of other Lumbricids: Aporrectodea caliginosa, Aporrectodea icterica, Aporrectodea longa, Aporrectodea rosea, Dendrobaena veneta, Lumbricus rubellus and Lumbricus terrestris, and show that CCF from E. fetida has a broader saccharide-binding specificity, being the only one recognizing N,N'-diacetylchitobiose. We suggest that the broad recognition repertoire of E. fetida CCF reflects a particular microbial environment this species lives in. PMID:16386303

Silerová, Marcela; Procházková, Petra; Josková, Radka; Josens, Guy; Beschin, Alain; De Baetselier, Patrick; Bilej, Martin

2006-01-01

304

Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization  

NASA Astrophysics Data System (ADS)

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

Diaz-Ramirez, Victor H.; Cuevas, Andres; Kober, Vitaly; Trujillo, Leonardo; Awwal, Abdul

2015-03-01

305

Pattern recognition analysis in complex molecule synthesis and the preparation of iso-Diels–Alder motifs  

PubMed Central

The identification of synthesizable substructural domains within more complex structural targets is of significant value in designing a workable plan of synthesis. We term this process “pattern recognition analysis” (PRA). In this paper we continued to build on the theme of PRA as a potential resource in retrosynthetic blueprints to reach highly challenging targets. The paper operates at two levels. First, there is provided a clear sense of definitions of categories by which patterns are related to hypothetical reaction types. Although the required reaction type may for the moment not exist, we believe that this method of analysis is likely to promote innovation that identifies unmet needs and opportunities to advance the cause of complex target synthesis. In addition, we describe reductions to practice in expanding the menu of achievable patterns. It is likely that the future value of PRA will be associated with its utility in leading the way to new and exploitable chemical innovation. PMID:23784777

Peng, Feng; Grote, Robin E.; Wilson, Rebecca M.; Danishefsky, Samuel J.

2013-01-01

306

An Update on PYRIN Domain-Containing Pattern Recognition Receptors: From Immunity to Pathology  

PubMed Central

Cytosolic pattern recognition receptors (PRRs) sense a wide range of endogenous danger-associated molecular patterns as well as exogenous pathogen-associated molecular patterns. In particular, Nod-like receptors containing a pyrin domain (PYD), called NLRPs, and AIM2-like receptors (ALRs) have been shown to play a critical role in host defense by facilitating clearance of pathogens and maintaining a healthy gut microflora. NLRPs and ALRs both encode a PYD, which is crucial for relaying signals that result in an efficient innate immune response through activation of several key innate immune signaling pathways. However, mutations in these PRRs have been linked to the development of auto-inflammatory and autoimmune diseases. In addition, they have been implicated in metabolic diseases. In this review, we summarize the function of PYD-containing NLRPs and ALRs and address their contribution to innate immunity, host defense, and immune-linked diseases. PMID:24367371

Ratsimandresy, Rojo A.; Dorfleutner, Andrea; Stehlik, Christian

2013-01-01

307

Pattern recognition receptors--molecular orchestrators of inflammation in inflammatory bowel disease.  

PubMed

Pattern recognition receptors (PRRs) are a family of germline encoded receptors responsible for the detection of "pathogen associated molecular patterns" (PAMPs) or host derived "damage associated molecular patterns" (DAMPs) which induce innate immune signalling to generate a pro-inflammatory profile within the host. Four main classes of PRRs are recognised, Toll-like receptors (TLRs), NOD-like receptors (NLRs), RIG-like receptors (RLRs) and C-type lectin receptors (CLRs). Abnormal activation of PRRs has been implicated in various autoimmune and inflammatory conditions including rheumatoid arthritis and asthma. Recent growing evidence has implicated these PRRs as contributory elements to the pathogenesis of inflammatory bowel disease (IBD) and colitis-associated cancer (CAC). Here, the current literature which implicates PRRs in IBD and CAC is comprehensively reviewed. PMID:23102645

Walsh, David; McCarthy, Joanna; O'Driscoll, Caitriona; Melgar, Silvia

2013-04-01

308

Hybrid system of optics and computer for 3-D object recognition  

NASA Astrophysics Data System (ADS)

In this paper, a hybrid system of optics and computer for 3D object recognition is presented. The system consists of a Twyman-Green interferometer, a He-Ne laser, a computer, a TV camera, and an image processor. The structured light produced by a Twyman-Green interferometer is split in and illuminates objects in two directions at the same time. Moire contour is formed on the surface of object. In order to delete unwanted patterns in moire contour, we don't utilize the moire contour on the surface of object. We place a TV camera in the middle of the angle between two illuminating directions and take two groups of deformed fringes on the surface of objects. Two groups of deformed fringes are processed using the digital image processing system controlled and operated by XOR logic in the computer, moire fringes are then extracted from the complicated environment. 3D coordinates of points of the object are obtained after moire fringe is followed, and points belonging to the same fringe are given the same altitude. The object is described by its projected drawings in three coordinate planes. The projected drawings in three coordinate planes of the known objects are stored in the library of judgment. The object can be recognized by inquiring the library of judgment.

Li, Qun Z.; Miao, Peng C.; He, Anzhi

1992-03-01

309

Illumination invariant recognition and 3D reconstruction of faces using desktop optics.  

PubMed

We propose illumination invariant face recognition and 3D face reconstruction using desktop optics. The computer screen is used as a programmable extended light source to illuminate the face from different directions and acquire images. Features are extracted from these images and projected to multiple linear subspaces in an effort to preserve unique features rather than the most varying ones. Experiments were performed using our database of 4347 images (106 subjects), the extended Yale B and CMU-PIE databases and better results were achieved compared to the existing state-of-the-art. We also propose an efficient algorithm for reconstructing the 3D face models from three images under arbitrary illumination. The subspace coefficients of training faces are used as input patterns to train multiple Support Vector Machines (SVM) where the output labels are the subspace parameters of ground truth 3D face models. Support Vector Regression is used to learn multiple functions that map the input coefficients to the parameters of the 3D face. During testing, three images of an unknown/novel face under arbitrary illumination are used to estimate its 3D model. Quantitative results are presented using our database of 106 subjects and qualitative results are presented on the Yale B database. PMID:21503057

Mian, Ajmal

2011-04-11

310

A local-sky star recognition algorithm based on rapid triangle pattern index for ICCD images  

NASA Astrophysics Data System (ADS)

Local-sky star recognition algorithm is a process of recognizing the extracted stars in image by making use of the prior rough attitude of star sensor in celestial sphere. In order to improve the detection and response performance of star sensor working in dynamic condition, ICCD is applied to imaging stars. However, image taken by ICCD has more non-Gaussian noise and the energy of imaging star is unstable. So a local-sky star recognition algorithm using spatial triangular relationship as matching features is supposed to deal with the difficulties. In the first place, an index array is designed according to Guide Triangles, which is applied to construct Guide Triangle Index List. In the second place, a general directing range of star sensor boresight is calculated according to FOV of star sensor and the output of inertial guidance system, and then, the candidate Guide Triangles set in above region is obtained rapidly. In the third place, construct image triangle patterns by applying position and energy of the extracted stars in the image, and then match the image triangle patterns with the above candidate Guide Triangles set for two stages, until N(N>=2) groups of successfully matched triangles pairs with smallest matching deviations sum are obtained. At the last, the recognized Guide Stars have to be matched posterior referring to the principle of simulated sky image, and the recognition results of image stars are all obtained. The proposed algorithm has compact Guide Database structure, rapid local-sky guide triangles obtaining, and good recognition correction percentage, even it has worse star location precision and more false stars. The simulation tests are performed to validate the relative efficiency and adaptation of the algorithm.

Zhang, Wei; Qi, Sheng-xiang; Zhang, Rui; Yang, Lili; Sun, Ji-fu; Song, Li-quan; Tian, Jin-wen

2013-09-01

311

Bifurcation analysis of oscillating network model of pattern recognition in the rabbit olfactory bulb  

NASA Astrophysics Data System (ADS)

A neural network model describing pattern recognition in the rabbit olfactory bulb is analysed to explain the changes in neural activity observed experimentally during classical Pavlovian conditioning. EEG activity recorded from an 8×8 arry of 64 electrodes directly on the surface on the bulb shows distinct spatial patterns of oscillation that correspond to the animal's recognition of different conditioned odors and change with conditioning to new odors. The model may be considered a variant of Hopfield's model of continuous analog neural dynamics. Excitatory and inhibitory cell types in the bulb and the anatomical architecture of their connection requires a nonsymmetric coupling matrix. As the mean input level rises during each breath of the animal, the system bifurcates from homogenous equilibrium to a spatially patterned oscillation. The theory of multiple Hopf bifurcations is employed to find coupled equations for the amplitudes of these unstable oscillatory modes independent of frequency. This allows a view of stored periodic attractors as fixed points of a gradient vector field and thereby recovers the more familiar dynamical systems picture of associative memory.

Baird, Bill

1986-08-01

312

Hotspot detection using image pattern recognition based on higher-order local auto-correlation  

NASA Astrophysics Data System (ADS)

Below 40nm design node, systematic variation due to lithography must be taken into consideration during the early stage of design. So far, litho-aware design using lithography simulation models has been widely applied to assure that designs are printed on silicon without any error. However, the lithography simulation approach is very time consuming, and under time-to-market pressure, repetitive redesign by this approach may result in the missing of the market window. This paper proposes a fast hotspot detection support method by flexible and intelligent vision system image pattern recognition based on Higher-Order Local Autocorrelation. Our method learns the geometrical properties of the given design data without any defects as normal patterns, and automatically detects the design patterns with hotspots from the test data as abnormal patterns. The Higher-Order Local Autocorrelation method can extract features from the graphic image of design pattern, and computational cost of the extraction is constant regardless of the number of design pattern polygons. This approach can reduce turnaround time (TAT) dramatically only on 1CPU, compared with the conventional simulation-based approach, and by distributed processing, this has proven to deliver linear scalability with each additional CPU.

Maeda, Shimon; Matsunawa, Tetsuaki; Ogawa, Ryuji; Ichikawa, Hirotaka; Takahata, Kazuhiro; Miyairi, Masahiro; Kotani, Toshiya; Nojima, Shigeki; Tanaka, Satoshi; Nakagawa, Kei; Saito, Tamaki; Mimotogi, Shoji; Inoue, Soichi; Nosato, Hirokazu; Sakanashi, Hidenori; Kobayashi, Takumi; Murakawa, Masahiro; Higuchi, Tetsuya; Takahashi, Eiichi; Otsu, Nobuyuki

2011-04-01

313

Team activity analysis and recognition based on Kinect depth map and optical imagery techniques  

NASA Astrophysics Data System (ADS)

Kinect cameras produce low-cost depth map video streams applicable for conventional surveillance systems. However, commonly applied image processing techniques are not directly applicable for depth map video processing. Kinect depth map images contain range measurement of objects at expense of having spatial features of objects suppressed. For example, typical objects' attributes such as textures, color tones, intensity, and other characteristic attributes cannot be fully realized by processing depth map imagery. In this paper, we demonstrate application of Kinect depth map and optical imagery for characterization of indoor and outdoor group activities. A Casual-Events State Inference (CESI) technique is proposed for spatiotemporal recognition and reasoning of group activities. CESI uses an ontological scheme for representation of casual distinctiveness of a priori known group activities. By tracking and serializing distinctive atomic group activities, CESI allows discovery of more complex group activities. A Modified Sequential Hidden Markov Model (MS-HMM) is implemented for trail analysis of atomic events representing correlated group activities. CESI reasons about five levels of group activities including: Merging, Planning, Cooperation, Coordination, and Dispersion. In this paper, we present results of capability of CESI approach for characterization of group activities taking place both in indoor and outdoor. Based on spatiotemporal pattern matching of atomic activities representing a known group activities, the CESI is able to discriminate suspicious group activity from normal activities. This paper also presents technical details of imagery techniques implemented for detection, tracking, and characterization of atomic events based on Kinect depth map and optical imagery data sets. Various experimental scenarios in indoors and outdoors (e.g. loading and unloading of objects, human-vehicle interactions etc.,) are carried to demonstrate effectiveness and efficiency of the proposed model for characterization of distinctive group activities.

Elangovan, Vinayak; Bandaru, Vinod K.; Shirkhodaie, Amir

2012-06-01

314

Morphological Characterization of Mycobacterium tuberculosis in a MODS Culture for an Automatic Diagnostics through Pattern Recognition  

PubMed Central

Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts. The Microscopic Observation Drug Susceptibility (MODS) assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB) to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7–10 days with a low cost. An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS. We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity) and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii). The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity. This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment. PMID:24358227

Alva, Alicia; Aquino, Fredy; Gilman, Robert H.; Olivares, Carlos; Requena, David; Gutiérrez, Andrés H.; Caviedes, Luz; Coronel, Jorge; Larson, Sandra; Sheen, Patricia; Moore, David A. J.; Zimic, Mirko

2013-01-01

315

Pattern recognition techniques for horizontal and vertically upward multiphase flow measurement  

NASA Astrophysics Data System (ADS)

The oil and gas industry need for high performing and low cost multiphase meters is ever more justified given the rapid depletion of conventional oil reserves that has led oil companies to develop smaller and marginal fields and reservoirs in remote locations and deep offshore, thereby placing great demands for compact and more cost effective solutions of on-line continuous multiphase flow measurement for well testing, production monitoring, production optimisation, process control and automation. The pattern recognition approach for clamp-on multiphase measurement employed in this study provides one means for meeting this need. High speed caesium-137 radioisotope-based densitometers were installed vertically at the top of a 50.8mm and 101.6mm riser as well as horizontally at the riser base in the Cranfield University multiphase flow test facility. A comprehensive experimental campaign comprising flow conditions typical of operating conditions found in the Petroleum Industry was conducted. The application of a single gamma densitometer unit, in conjunction with pattern recognition techniques to determine both the phase volume fractions and velocities to yield the individual phase flow rates of horizontal and vertically upward multiphase flows was investigated. The pattern recognition systems were trained to map the temporal fluctuations in the multiphase mixture density with the individual phase flow rates using statistical features extracted from the gamma counts signals as their inputs. Initial results yielded individual phase flow rate predictions to within ±5% relative error for the two phase airwater flows and ±10% for three phase air-oil-water flows data.

Arubi, Tesi I. M.; Yeung, Hoi

2012-03-01

316

New nonuniform sampling image representation method and its application in knowledge-based active pattern recognition  

NASA Astrophysics Data System (ADS)

The research of image representation method based on nonuniform sampling and the development of the foveated sensors are active research fields in recent years. We propose in this paper a nonuniform sampling image representation method based on an improved log-polar transform and apply it into the knowledge-based active pattern recognition. The novelty of our method lies in three aspects. First, compared with other nonuniform representation methods, our method provides a flexible structure between the pure nonuniform sampling and the classical uniform sampling representation and imitates the focus characteristic of human vision. The size of the areas of interest which is uniformly sampled with the highest resolution can be adjusted arbitrarily according to the knowledge of vision task and objects. Second, we proposed a knowledge-based method to decide `where to look next' based on the fovea-periphery structure. By introducing the concept of knowledge grain, knowledge of objects is organized hierarchically, from coarse to fine. We use fine grain knowledge to do the accurate pattern recognition in fovea area and use coarse grain knowledge to locate the fixation point candidates in periphery. Third, we give a general paradigm for knowledge-based active pattern recognition. Nonuniform sampling transform is imposed on the input image to obtain the fovea-periphery structure first. Then different grain of knowledge is used to solve the problems of `what it is' and `where it is` in fovea and periphery. The above procedure is repeated until no more fixation points can be found or the goal of vision task has already been reached. Experimental result in this paper demonstrates our idea to be a valid one.

Long, Fuhui; Zheng, Nanning; Jiang, Jiande

1998-07-01

317

Immunopathological Roles of Cytokines, Chemokines, Signaling Molecules, and Pattern-Recognition Receptors in Systemic Lupus Erythematosus  

PubMed Central

Systemic lupus erythematosus (SLE) is an autoimmune disease with unknown etiology affecting more than one million individuals each year. It is characterized by B- and T-cell hyperactivity and by defects in the clearance of apoptotic cells and immune complexes. Understanding the complex process involved and the interaction between various cytokines, chemokines, signaling molecules, and pattern-recognition receptors (PRRs) in the immune pathways will provide valuable information on the development of novel therapeutic targets for treating SLE. In this paper, we review the immunopathological roles of novel cytokines, chemokines, signaling molecules, PRRs, and their interactions in immunoregulatory networks and suggest how their disturbances may implicate pathological conditions in SLE. PMID:22312407

Yu, Shui-Lian; Kuan, Woon-Pang; Wong, Chun-Kwok; Li, Edmund K.; Tam, Lai-Shan

2012-01-01

318

Immunopathological roles of cytokines, chemokines, signaling molecules, and pattern-recognition receptors in systemic lupus erythematosus.  

PubMed

Systemic lupus erythematosus (SLE) is an autoimmune disease with unknown etiology affecting more than one million individuals each year. It is characterized by B- and T-cell hyperactivity and by defects in the clearance of apoptotic cells and immune complexes. Understanding the complex process involved and the interaction between various cytokines, chemokines, signaling molecules, and pattern-recognition receptors (PRRs) in the immune pathways will provide valuable information on the development of novel therapeutic targets for treating SLE. In this paper, we review the immunopathological roles of novel cytokines, chemokines, signaling molecules, PRRs, and their interactions in immunoregulatory networks and suggest how their disturbances may implicate pathological conditions in SLE. PMID:22312407

Yu, Shui-Lian; Kuan, Woon-Pang; Wong, Chun-Kwok; Li, Edmund K; Tam, Lai-Shan

2012-01-01

319

A concurrent track evolution algorithm for pattern recognition in the HERA-B main tracking system  

NASA Astrophysics Data System (ADS)

A strategy for pattern recognition in the main tracking system of a forward B spectrometer like HERA-B or LHC-B is presented. Intrinsically a local method, it combines the virtues of track following procedures with the necessary ability to optimize between many available paths in a high occupancy environment. A hit-locating procedure suitable for a multiplanar detector geometry has been developed. The performance of the method is tested on HERA-B Monte Carlo events with full detector simulation and a realistic spectrometer geometry.

Mankel, Rainer

1997-02-01

320

Trends in Correlation-Based Pattern Recognition and Tracking in Forward-Looking Infrared Imagery  

PubMed Central

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences. PMID:25061840

Alam, Mohammad S.; Bhuiyan, Sharif M. A.

2014-01-01

321

Microprocessor-based single board computer for high energy physics event pattern recognition  

SciTech Connect

A single board MC 68000 based computer has been assembled and bench marked against the CDC 7600 running portions of the pattern recognition code used at the MPS. This computer has a floating coprocessor to achieve throughputs equivalent to several percent that of the 7600. A major part of this work was the construction of a FORTRAN compiler including assembler, linker and library. The intention of this work is to assemble a large number of these single board computers in a parallel FASTBUS environment to act as an on-line and off-line filter for the raw data from MPS II and ISABELLE experiments.

Bernstein, H.; Gould, J.J.; Imossi, R.; Kopp, J.K.; Love, W.A.; Ozaki, S.; Platner, E.D.; Kramer, M.A.

1981-01-01

322

Unsupervised pattern recognition in continuous seismic wavefield records using Self-Organizing Maps  

NASA Astrophysics Data System (ADS)

Modern acquisition of seismic data on receiver networks worldwide produces an increasing amount of continuous wavefield recordings. In addition to manual data inspection, seismogram interpretation requires therefore new processing utilities for event detection, signal classification and data visualization. The use of machine learning techniques automatises decision processes and reveals the statistical properties of data. This approach is becoming more and more important and valuable for large and complex seismic records. Unsupervised learning allows the recognition of wavefield patterns, such as short-term transients and long-term variations, with a minimum of domain knowledge. This study applies an unsupervised pattern recognition approach for the discovery, imaging and interpretation of temporal patterns in seismic array recordings. For this purpose, the data is parameterized by feature vectors, which combine different real-valued wavefield attributes for short time windows. Standard seismic analysis tools are used as feature generation methods, such as frequency-wavenumber, polarization and spectral analysis. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure. The application to continuous recordings of seismic signals from an active volcano (Mount Merapi, Java, Indonesia) shows that volcano-tectonic and rockfall events can be detected and distinguished by clustering the feature vectors. Similar results are obtained in terms of correctly classifying events compared to a previously implemented supervised classification system. Furthermore, patterns in the background wavefield, that is the 24-hr cycle due to human activity, are intuitively visualized by means of the SOM representation. Finally, we apply our technique to an ambient seismic vibration record, which has been acquired for local site characterization. Disturbing wavefield patterns are identified which affect the quality of Love wave dispersion curve estimates. Particularly at night, when the overall energy of the wavefield is reduced due to the 24-hr cycle, the common assumption of stationary planar surface waves can be violated.

Köhler, Andreas; Ohrnberger, Matthias; Scherbaum, Frank

2010-09-01

323

Absolute position measurement using optical detection of coded patterns  

Microsoft Academic Search

Discusses absolute, one- and two-dimensional position measurement using optical detection of coded patterns. A one-dimensional position transducer is described in which both absolute and incremental information are optically encoded on a single track. This concept is extended to a two-dimensional position encoder and both small-scale applications in instrumentation and large-scale applications in the navigation of guided vehicles are described. The

J. T. M. Stevenson; J. R. Jordan

1988-01-01

324

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position  

Microsoft Academic Search

A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by “learning without a teacher”, and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname “neocognitron”. After completion of self-organization, the network

Kunihiko Fukushima

1980-01-01

325

Probability-Based Pattern Recognition and Statistical Framework for Randomization: Modeling Tandem Mass Spectrum/Peptide Sequence False Match Frequencies  

Technology Transfer Automated Retrieval System (TEKTRAN)

Estimating and controlling the frequency of false matches between a peptide tandem mass spectrum and candidate peptide sequences is an issue pervading proteomics research. To solve this problem, we designed an unsupervised pattern recognition algorithm for detecting patterns with various lengths fr...

326

The Spatial Vision Tree: A Generic Pattern Recognition Engine- Scientific Foundations, Design Principles, and Preliminary Tree Design  

NASA Technical Reports Server (NTRS)

New foundational ideas are used to define a novel approach to generic visual pattern recognition. These ideas proceed from the starting point of the intrinsic equivalence of noise reduction and pattern recognition when noise reduction is taken to its theoretical limit of explicit matched filtering. This led us to think of the logical extension of sparse coding using basis function transforms for both de-noising and pattern recognition to the full pattern specificity of a lexicon of matched filter pattern templates. A key hypothesis is that such a lexicon can be constructed and is, in fact, a generic visual alphabet of spatial vision. Hence it provides a tractable solution for the design of a generic pattern recognition engine. Here we present the key scientific ideas, the basic design principles which emerge from these ideas, and a preliminary design of the Spatial Vision Tree (SVT). The latter is based upon a cryptographic approach whereby we measure a large aggregate estimate of the frequency of occurrence (FOO) for each pattern. These distributions are employed together with Hamming distance criteria to design a two-tier tree. Then using information theory, these same FOO distributions are used to define a precise method for pattern representation. Finally the experimental performance of the preliminary SVT on computer generated test images and complex natural images is assessed.

Rahman, Zia-ur; Jobson, Daniel J.; Woodell, Glenn A.

2010-01-01

327

Polarization based optical sectioning of multilayer cell patterns  

Microsoft Academic Search

In this paper we present a polarization based technique for optical sectioning and imaging of multi-layer cell patterns separated by a weakly diffused media. Multi-layer cell pattern is important to study because this type of structure is often used for heterogeneous three dimensional cell culture and bio-chips applications, where information at different depths would be crucial. Functioning of this type

Sharad Gupta; Jong Chul Ye; David Jaeyun Cho

2006-01-01

328

Laser illuminator and optical system for disk patterning  

DOEpatents

Magnetic recording media are textured over areas designated for contact in order to minimize friction with data transducing heads. In fabricating a hard disk, an aluminum nickel-phosphorous substrate is polished to a specular finish. A mechanical means is then used to roughen an annular area intended to be the head contact band. An optical and mechanical system allows thousands of spots to be generated with each laser pulse, allowing the textured pattern to be rapidly generated with a low repetition rate laser and an uncomplicated mechanical system. The system uses a low power laser, a beam expander, a specially designed phase plate, a prism to deflect the beam, a lens to transmit the diffraction pattern to the far field, a mechanical means to rotate the pattern and a trigger system to fire the laser when sections of the pattern are precisely aligned. The system generates an annular segment of the desired pattern with which the total pattern is generated by rotating the optical system about its optic axis, sensing the rotational position and firing the laser as the annular segment rotates into the next appropriate position. This marking system can be integrated into a disk sputtering system for manufacturing magnetic disks, allowing for a very streamlined manufacturing process.

Hackel, Lloyd A. (Livermore, CA); Dane, C. Brent (Livermore, CA); Dixit, Shamasundar N. (Livermore, CA); Everett, Mathew (Pleasanton, CA); Honig, John (Livermore, CA)

2000-01-01

329

Development of an optical character recognition pipeline for handwritten form fields from an electronic health record  

PubMed Central

Background Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms. Methods We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms. Observations The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%. Discussion While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline. PMID:21890871

Peissig, Peggy L; McCarty, Catherine A; Starren, Justin

2011-01-01

330

A Method of Neighbor Classes Based SVM Classification for Optical Printed Chinese Character Recognition  

PubMed Central

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR. PMID:23536777

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

331

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.  

PubMed

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR. PMID:23536777

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

332

Probabilistic neural network with homogeneity testing in recognition of discrete patterns set.  

PubMed

The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n-grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%-7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. PMID:23811385

Savchenko, A V

2013-10-01

333

Optical Imaging of Flow Pattern and Phantom  

NASA Technical Reports Server (NTRS)

Time-resolved optical imaging technique has been used to image the spatial distribution of small droplets and jet sprays in a highly scattering environment. The snake and ballistic components of the transmitted pulse are less scattered, and contain direct information about the sample to facilitate image formation as opposed to the diffusive components which are due to multiple collisions as a light pulse propagates through a scattering medium. In a time-gated imaging scheme, these early-arriving, image-bearing components of the incident pulse are selected by opening a gate for an ultrashort period of time and a shadowgram image is detected. Using a single shot cooled CCD camera system, the formation of water droplets is monitored as a function of time. Picosecond time-gated image of drop in scattering cells, spray droplets as a function of let speed and gas pressure, and model calcification samples consisted of calcium carbonate particles of irregular shapes ranging in size from 0. 1 to 1.5 mm affixed to a microscope slide have been measured. Formation produced by an impinging jet will be further monitored using a CCD with 1 kHz framing illuminated with pulsed light. The desired image resolution of the fuel droplets is on the 20 pm scale using early light through a highly scattering medium. A 10(exp -6)m displacement from a jet spray with a flow speed of 100 m/sec introduced by the ns grating pulse used in the imaging is negligible. Early ballistic/snake light imaging offers nondestructive and noninvasive method to observe the spatial distribution of hidden objects inside a highly scattering environment for space, biomedical, and materials applications. In this paper, the techniques we will present are time-resolved K-F transillumination imaging and time-gated scattered light imaging. With a large dynamic range and high resolution, time-gated early light imaging has the potential for improving rocket/aircraft design by determining jets shape and particle sizes. Refinements to these techniques may enable drop size measurements in the highly scattering, optically dense region of multi-element rocket injectors. These types of measurements should greatly enhance the design of stable, and higher performing rocket engines.

Galland, Pierre A.; Liang, X.; Wang, L.; Ho, P. P.; Alfano, R. R.; Breisacher, K.

1999-01-01

334

NKp44 and Natural Cytotoxicity Receptors as Damage-Associated Molecular Pattern Recognition Receptors  

PubMed Central

Natural killer (NK) cells are a key constituent of the innate immune system, protecting against bacteria, virally infected cells, and cancer. Recognition and protective function against such cells are dictated by activating and inhibitory receptors on the surface of the NK cell, which bind to specific ligands on the surface of target cells. Among the activating receptors is a small class of specialized receptors termed the natural cytotoxicity receptors (NCRs) comprised of NKp30, NKp46, and NKp44. The NCRs are key receptors in the recognition and termination of virally infected and tumor cells. Since their discovery over 10?years ago, ligands corresponding to the NCRs have largely remained elusive. Recent identification of the cellular ligands for NKp44 and NKp30 as exosomal proliferating cell nuclear antigen (PCNA) and HLA-B-associated transcript 3 (BAT3), respectively, implicate that NCRs may function as receptors for damage-associated molecular pattern (DAMP) molecules. In this review, we focus on NKp44, which surprisingly recognizes two distinct ligands resulting in either activation or inhibition of NK cell effector responses in response to tumor cells. The inhibitory function of NKp44 requires further study as it may play a pivotal role in placentation in addition to being exploited by tumors as a mechanism to escape NK cell killing. Finally, we suggest that the NCRs are a class of pattern recognition receptors, which recognize signals of genomic instability and cellular stress via interaction with the c-terminus of DAMP molecules localized to the surface of target cells by various co-ligands.

Horton, Nathan C.; Mathew, Porunelloor A.

2015-01-01

335

Species recognition through wing interference patterns (WIPs) in Achrysocharoides Girault (Hymenoptera, Eulophidae) including two new species  

PubMed Central

Abstract Wing interference patterns (WIPs) are shown to be an important tool for species recognition in the genus Achrysocharoides Girault (Hymenoptera: Eulophidae). This is demonstrated by combining information from two previously published papers, comprising two cases of cryptic species, and by new material including the description of two new species, Achrysocharoides maieri and Achrysocharoides serotinae from North America. The cryptic species were initially separated through their distinct male WIPs. Subsequent analyses of the external morphology uncovered additional morphological differences supporting the original findings through WIPs, and biological data further strengthened the identity of these species. The new species described here also differ in their WIPs but the WIPs are similar in both sexes. Thus they provide a strong link between male and female and demonstrate that WIPs can also be useful for species recognition when the sexes are otherwise difficult to associate. Both new species are from Connecticut, USA, and were reared from Phyllonorycter propinquinella (Braun) (Lepidoptera: Gracillariidae) on black cherry (Prunus serotina); Achrysocharoides maieri has also been reared from Ph. nr crataegella on pin cherry (Prunus pensylvanica). To facilitate the identification of the new species they are included in a previously published key to North American species of Achrysocharoides. As a supplement to colourful WIPs we also demonstrate that grey scale images of uncoated wings from scanning electron microscopy can be used for visualization of the thickness distribution pattern in wing membranes. PMID:22287914

Shevtsova, Ekaterina; Hansson, Christer

2011-01-01

336

Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation  

PubMed Central

Born in the early nineteen nineties, evidence-based medicine (EBM) is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized. PMID:24185841

Fernández-Llatas, Carlos; Meneu, Teresa; Traver, Vicente; Benedi, José-Miguel

2013-01-01

337

SAW arrays using dendrimers and pattern recognition to detect volatile organics  

SciTech Connect

chemical sensor arrays eliminate the need to develop a high-selectivity material for every analyte. The application of pattern recognition to the simultaneous responses of different microsensors enables the identification and quantification of multiple analytes with a small array. Maximum materials diversity is the surest means to create an effective array for many analytes, but using a single material family simplifies coating development. Here the authors report the successful combination of an array of six dendrimer films with mass-sensitive SAW (surface acoustic wave) sensors to correctly identify 18 organic analytes over wide concentration ranges, with 99.5% accuracy. The set of materials for the array is selected and the results evaluated using Sandia`s Visual-Empirical Region of Influence (VERI) pattern recognition (PR) technique. The authors evaluated eight dendrimer films and one self-assembled monolayer (SAM) as potential SAW array coatings. The 18 organic analytes they examined were: cyclohexane, n-hexane, i-octane, kerosene, benzene, toluene, chlorobenzene, carbon tetrachloride, trichloroethylene, methanol, n-propanol, pinacolyl alcohol, acetone, methyl isobutyl ketone, dimethylmethylphosphate, diisopropylmethylphosphonate, tributylphosphate, and water.

Ricco, A.J.; Osbourn, G.C.; Bartholomew, J.W.; Martinez, R.F. [Sandia National Labs., Albuquerque, NM (United States); Crooks, R.M.; Garcia, M.E.; Peez, R. [Texas A and M Univ., College Station, TX (United States). Dept. of Chemistry; Spindler, R. [Michigan Molecular Inst., Midland, MI (United States); Kaiser, M.E. [Dendritech, Inc., Midland, MI (United States)

1998-08-01

338

Evaluation of the fractal dimension as a pattern recognition feature using neural networks  

NASA Astrophysics Data System (ADS)

In the past fractal dimension has often been computed using a stochastic approach based on a random walk process, which has been found to be very time consuming. More recently, mathematical morphology has been used to compute the fractal dimension in a more timely fashion. This paper describes how the fractal dimension computed using mathematical morphology can be used in the texture analysis of ultrasonic imagery. The discriminatory ability of the fractal dimension as a pattern recognition feature is evaluated and compared to more traditional parameters. This analysis includes comparisons with statistical features in which each parameter is treated as an independent variable and in which interactions between those variables are evaluated. Pattern recognition techniques include Stepwise Discriminant Analysis, Linear Discriminant Analysis, and Nearest Neighbor Analysis in addition to Backpropagation Neural Network Classifiers. Our results identify the fractal dimension as one of the most important parameters for distinguishing between normal and abnormal livers. In this study, consisting of 186 images, a significant statistical difference was found for both the mean and standard deviation of the fractal dimension between the normal and abnormal groups using parametric and nonparametric statistical techniques.

DaPonte, John S.; Parikh, Jo Ann; Decker, James; Vitale, Joseph N.

1993-09-01

339

Pattern recognition algorithms for density estimation of asphalt pavement during compaction: a simulation study  

NASA Astrophysics Data System (ADS)

This paper presents the application of artificial neural network (ANN) based pattern recognition to extract the density information of asphalt pavement from simulated ground penetrating radar (GPR) signals. This study is part of research efforts into the application of GPR to monitor asphalt pavement density during compaction. The main challenge is to eliminate the effect of roller-sprayed water on GPR signals during compaction and to extract density information accurately. A calibration of the excitation function was conducted to provide an accurate match between the simulated signal and the real signal. A modified electromagnetic mixing model was then used to calculate the dielectric constant of asphalt mixture with water. A large database of GPR responses was generated from pavement models having different air void contents and various surface moisture contents using finite-difference time-domain simulation. Feature extraction was performed to extract density-related features from the simulated GPR responses. Air void contents were divided into five classes representing different compaction statuses. An ANN-based pattern recognition system was trained using the extracted features as inputs and air void content classes as target outputs. Accuracy of the system was tested using test data set. Classification of air void contents using the developed algorithm is found to be highly accurate, which indicates effectiveness of this method to predict asphalt concrete density.

Shangguan, Pengcheng; Al-Qadi, Imad L.; Lahouar, Samer

2014-08-01

340

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques  

NASA Astrophysics Data System (ADS)

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

Palacios-Morillo, Ana; Alcázar, Ángela.; de Pablos, Fernando; Jurado, José Marcos

2013-02-01

341

Health monitoring of 90° bolted joints using fuzzy pattern recognition of ultrasonic signals  

NASA Astrophysics Data System (ADS)

Bolted joints are important parts for aerospace structures. However, there is a significant risk associated with assembling bolted joints due to potential human error during the assembly process. Such errors are expensive to find and correct if exposed during environmental testing, yet checking the integrity of individual fasteners after assembly would be a time consuming task. Recent advances in structural health monitoring (SHM) can provide techniques to not only automate this process but also make it reliable. This integrity monitoring requires damage features to be related to physical conditions representing the structural integrity of bolted joints. In this paper an SHM technique using ultrasonic signals and fuzzy pattern recognition to monitor the integrity of 90° bolted joints in aerospace structures is described. The proposed technique is based on normalized fast Fourier transform (NFFT) of transmitted signals and fuzzy pattern recognition. Moreover, experimental observations of a case study on an aluminum 90° bolted joint are presented. We demonstrate the ability of the proposed method to efficiently monitor and indicate bolted joint integrity.

Jalalpour, M.; El-Osery, A. I.; Austin, E. M.; Reda Taha, M. M.

2014-01-01

342

Species recognition through wing interference patterns (WIPs) in Achrysocharoides Girault (Hymenoptera, Eulophidae) including two new species.  

PubMed

Wing interference patterns (WIPs) are shown to be an important tool for species recognition in the genus Achrysocharoides Girault (Hymenoptera: Eulophidae). This is demonstrated by combining information from two previously published papers, comprising two cases of cryptic species, and by new material including the description of two new species, Achrysocharoides maieri and Achrysocharoides serotinae from North America. The cryptic species were initially separated through their distinct male WIPs. Subsequent analyses of the external morphology uncovered additional morphological differences supporting the original findings through WIPs, and biological data further strengthened the identity of these species. The new species described here also differ in their WIPs but the WIPs are similar in both sexes. Thus they provide a strong link between male and female and demonstrate that WIPs can also be useful for species recognition when the sexes are otherwise difficult to associate. Both new species are from Connecticut, USA, and were reared from Phyllonorycter propinquinella (Braun) (Lepidoptera: Gracillariidae) on black cherry (Prunus serotina); Achrysocharoides maieri has also been reared from Ph. nr crataegella on pin cherry (Prunus pensylvanica). To facilitate the identification of the new species they are included in a previously published key to North American species of Achrysocharoides. As a supplement to colourful WIPs we also demonstrate that grey scale images of uncoated wings from scanning electron microscopy can be used for visualization of the thickness distribution pattern in wing membranes. PMID:22287914

Shevtsova, Ekaterina; Hansson, Christer

2011-01-01

343

Acoustic sleepiness detection: framework and validation of a speech-adapted pattern recognition approach.  

PubMed

This article describes a general framework for detecting sleepiness states on the basis of prosody, articulation, and speech-quality-related speech characteristics. The advantages of this automatic real-time approach are that obtaining speech data is nonobstrusive and is free from sensor application and calibration efforts. Different types of acoustic features derived from speech, speaker, and emotion recognition were employed (frame-level-based speech features). Combing these features with high-level contour descriptors, which capture the temporal information of frame-level descriptor contours, results in 45,088 features per speech sample. In general, the measurement process follows the speech-adapted steps of pattern recognition: (1) recording speech, (2) preprocessing, (3) feature computation (using perceptual and signal-processing-related features such as, e.g., fundamental frequency, intensity, pause patterns, formants, and cepstral coefficients), (4) dimensionality reduction, (5) classification, and (6) evaluation. After a correlation-filter-based feature subset selection employed on the feature space in order to find most relevant features, different classification models were trained. The best model-namely, the support-vector machine-achieved 86.1% classification accuracy in predicting sleepiness in a sleep deprivation study (two-class problem, N=12; 01.00-08.00 a.m.). PMID:19587194

Krajewski, Jarek; Batliner, Anton; Golz, Martin

2009-08-01

344

Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes  

NASA Astrophysics Data System (ADS)

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

2009-08-01

345

Activity Recognition using Optical Sensors on Mobile Phones  

Microsoft Academic Search

Each mobile phone with a built-in CMOS sensor can inherently be seen as sophisticated optical sensor being able to analyze its environment in terms of visual events and its own mobility. Due to mass production their price decreases steadily, although their processing capacity increases. Mobile phones are usually attached to people, who are driven by mobility. We define activities arising

Michael Wittke; Uwe Jänen; Aret Duraslan; Emre Cakar; Monika Steinberg; Jürgen Brehm

2009-01-01

346

Patterning via Optical Saturable Transitions - Fabrication and Characterization.  

PubMed

This protocol describes the fabrication and characterization of nanostructures using a novel nanolithographic technique called Patterning via Optical Saturable Transitions (POST). In this technique the chemical properties of organic photochromic molecules that undergo single-photon reactions are exploited, enabling rapid top-down nanopatterning over large areas at low light intensities, thereby, allowing for the circumvention of the far-field diffraction barrier.(4) Simple, cost-effective, high throughput and resolution alternatives to nanopatterning are being explored, such as, two-photon polymerization(5,6), beam pen lithography (BPL)(7), scanning electron beam lithography (SEBL), and focused ion beam (FIB) patterning. However, multi-photon approaches require high light intensities, which limit their potential for high throughput and offer low image contrast. Although, electron and ion beam lithographic processes offer increased resolution, the serial nature of the process is limited to slow writing speeds, which also prevents patterning of features over large areas. Beam-pen lithography is an approach towards parallel near-field optical lithography. However, the gap between the source of the beam and the surface of the photoresist needs to be controlled extremely precisely for good pattern uniformity and this is very challenging to accomplish for large arrays of beams. Patterning via Optical Saturable Transitions (POST) is an alternative optical nanopatterning technique for patterning sub-wavelength features(1-3). Since this technique uses single photons instead of electrons, it is extremely fast and does not require high light intensities(1-3), opening the door to massive parallelization. PMID:25548880

Cantu, Precious; Andrew, Trisha L; Menon, Rajesh

2014-01-01

347

Recognition of ovulatory/anovulatory cycle pattern in adolescents by mucus self-detection.  

PubMed

The high frequency of anovulation during adolescence raises the question of whether a method for the recognition of ovulation by self-detection of cervical mucus patterns is useful in teenagers. We performed a secondary analysis of 1049 completely recorded cycles of 235 teen women 15-17 years of age with a gynecologic age from less than 1 to 7 years. These subjects had learned to monitor their fertility patterns using the Billings Ovulation Method. The cycles were analyzed by the length of the mucus and luteal phases and recorded "peak symptom." Ovulatory cycles were grouped by the length of the luteal phase, short (4-8 days) and average (8-18 days), and plotted against gynecologic age. The frequency of anovulatory cycles was comparable to Vollman's age-stratified monophasic cycle groups. Ovulatory patterns were found at gynecologic ages of 1 year, 49%; 2 years, 60%; 3 years, 72%; 4 years, 61%; 5 years, 86%; and 6 years, 71%. The study proved that teen-age women can distinguish patterns of ovulation and anovulation by self-detection of cervical mucus. PMID:2925476

Klaus, H; Martin, J L

1989-03-01

348

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia.  

PubMed

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 ?m (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations. PMID:23831918

Syed Abdul Mutalib, Sharifah Norsukhairin; Juahir, Hafizan; Azid, Azman; Mohd Sharif, Sharifah; Latif, Mohd Talib; Aris, Ahmad Zaharin; Zain, Sharifuddin M; Dominick, Doreena

2013-09-01

349

Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends.  

PubMed

Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing. PMID:24676572

Haller, Sven; Lovblad, Karl-Olof; Giannakopoulos, Panteleimon; Van De Ville, Dimitri

2014-05-01

350

Oxidation-specific epitopes are danger-associated molecular patterns recognized by pattern recognition receptors of innate immunity.  

PubMed

Oxidation reactions are vital parts of metabolism and signal transduction. However, they also produce reactive oxygen species, which damage lipids, proteins and DNA, generating "oxidation-specific" epitopes. In this review, we discuss the hypothesis that such common oxidation-specific epitopes are a major target of innate immunity, recognized by a variety of "pattern recognition receptors" (PRRs). By analogy with microbial "pathogen-associated molecular patterns" (PAMPs), we postulate that host-derived, oxidation-specific epitopes can be considered to represent "danger (or damage)-associated molecular patterns" (DAMPs). We also argue that oxidation-specific epitopes present on apoptotic cells and their cellular debris provided the primary evolutionary pressure for the selection of such PRRs. Furthermore, because many PAMPs on microbes share molecular identity and/or mimicry with oxidation-specific epitopes, such PAMPs provide a strong secondary selecting pressure for the same set of oxidation-specific PRRs as well. Because lipid peroxidation is ubiquitous and a major component of the inflammatory state associated with atherosclerosis, the understanding that oxidation-specific epitopes are DAMPs, and thus the target of multiple arcs of innate immunity, provides novel insights into the pathogenesis of atherosclerosis. As examples, we show that both cellular and soluble PRRs, such as CD36, toll-like receptor-4, natural antibodies, and C-reactive protein recognize common oxidation-specific DAMPs, such as oxidized phospholipids and oxidized cholesteryl esters, and mediate a variety of immune responses, from expression of proinflammatory genes to excessive intracellular lipoprotein accumulation to atheroprotective humoral immunity. These insights may lead to improved understanding of inflammation and atherogenesis and suggest new approaches to diagnosis and therapy. PMID:21252151

Miller, Yury I; Choi, Soo-Ho; Wiesner, Philipp; Fang, Longhou; Harkewicz, Richard; Hartvigsen, Karsten; Boullier, Agnčs; Gonen, Ayelet; Diehl, Cody J; Que, Xuchu; Montano, Erica; Shaw, Peter X; Tsimikas, Sotirios; Binder, Christoph J; Witztum, Joseph L

2011-01-21

351

Automation of pattern recognition in cDNA microarray data: an application to gene-based diagnostic prediction of cancers  

NASA Astrophysics Data System (ADS)

The purpose of this paper is two-fold: (a) to demonstrate that pattern recognition methods in image processing leads to a noticeable improvement in bioinformatics, specifically, analysis of microarray data in gene expression correlated to cancer; (b) to bring the utility of geometric methods in pattern recognition to attention of researchers in bioinformatics and molecular biologists. Our method of analysis is seen to readily provide great improvement over the latest published methods in bioinformatics. We also hope that in the process, we provide mathematical insight into the problems of microarray data analysis from the point of view of signal processing and learning theory.

Wang, Liya; Assadi, Amir H.

2002-11-01

352

Global patterns of cloud optical thickness variation with temperature  

NASA Technical Reports Server (NTRS)

The International Satellite Cloud Climatology Project dataset is used to correlate variations of cloud optical thickness and cloud temperature in today's atmosphere. The analysis focuses on low clouds in order to limit the importance of changes in cloud vertical extent, particle size, and water phase. Coherent patterns of change are observed on several time and space scales. On the planetary scale, clouds in colder, higher latitudes are found to be optically thicker than clouds in warmer, lower latitudes. On the seasonal scale, winter clouds are, for the most part, optically thicker than summer clouds. The logarithmic derivative of cloud optical thickness with temperature is used to describe the sign and magnitude of the optical thickness-temperature correlation. The seasonal, latitudinal, and day-to-day variations of this relation are examined for Northern Hemisphere clouds in 1984. In cold continental clouds, optical thickness increases with temperature, consistent with the temperature variation of the adiabatic cloud water content. In warm continental and in almost all maritime clouds, however, optical thickness decreases with temperature.

Tselioudis, George; Rossow, William B.; Rind, David

1992-01-01

353

[Proposal for recognition of the comfort pattern in clients with pemphigus vulgaris using Fuzzy Logic].  

PubMed

The objective was to propose the use of Fuzzy Logic for recognition of comfort patterns in people undergoing a technology of nursing care because of pemphigus vulgaris, a rare mucocutaneous disease that affects mainly adults. The proposal applied experimental methods, with subjects undergoing a qualitative-quantitative comparison (taxonomy/relevance) of the comfort patterns before and after the intervention. A record of a chromatic scale corresponding to the intensity of each attribute was required: pain, mobility and impaired self-image. The Fuzzy rules established by an inference engine set the standard for comfort in maximum, median and minimum discomfort, reflecting the effectiveness of nursing care. Although rarely used in the area of nursing, this logic enabled viable research without a priori scaling of the number of subjects depending on the estimation of population parameters. It is expected to evaluate the pattern of comfort in the client with pemphigus, before the applied technology, in a personalized way, leading to a comprehensive evaluation. PMID:24310696

Brandăo, Euzeli da Silva; dos Santos, Iraci; Lanzillotti, Regina Serrăo; Moreira, Augusto Júnior

2013-08-01

354

Histone deacetylase expression patterns in developing murine optic nerve  

PubMed Central

Background Histone deacetylases (HDACs) play important roles in glial cell development and in disease states within multiple regions of the central nervous system. However, little is known about HDAC expression or function within the optic nerve. As a first step in understanding the role of HDACs in optic nerve, this study examines the spatio-temporal expression patterns of methylated histone 3 (K9), acetylated histone 3 (K18), and HDACs 1–6 and 8–11 in the developing murine optic nerve head. Results Using RT-qPCR, western blot and immunofluorescence, three stages were analyzed: embryonic day 16 (E16), when astrocyte precursors are found in the optic stalk, postnatal day 5 (P5), when immature astrocytes and oligodendrocytes are found throughout the optic nerve, and P30, when optic nerve astrocytes and oligodendrocytes are mature. Acetylated and methylated histone H3 immunoreactivity was co-localized in the nuclei of most SOX2 positive glia within the optic nerve head and adjacent optic nerve at all developmental stages. HDACs 1–11 were expressed in the optic nerve glial cells at all three stages of optic nerve development in the mouse, but showed temporal differences in overall levels and subcellular localization. HDACs 1 and 2 were predominantly nuclear throughout optic nerve development and glial cell maturation. HDACs 3, 5, 6, 8, and 11 were predominantly cytoplasmic, but showed nuclear localization in at least one stage of optic nerve development. HDACs 4, 9 and10 were predominantly cytoplasmic, with little to no nuclear expression at any time during the developmental stages examined. Conclusions Our results showing that HDACs 1, 2, 3, 5, 6, 8, and 11 were each localized to the nuclei of SOX2 positive glia at some stages of optic nerve development and maturation and extend previous reports of HDAC expression in the aging optic nerve. These HDACs are candidates for further research to understand how chromatin remodeling through acetylation, deacetylation and methylation contributes to glial development as well as their injury response. PMID:25011550

2014-01-01

355

Novel Zooming Scale Hough Transform Pattern Recognition Algorithm for the PHENIX Detector  

NASA Astrophysics Data System (ADS)

Single ultra-relativistic heavy ion collisions at RHIC and the LHC and multiple overlapping proton-proton collisions at the LHC present challenges to pattern recognition algorithms for tracking in these high multiplicity environments. One must satisfy many constraints including high track finding efficiency, ghost track rejection, and CPU time and memory constraints. A novel algorithm based on a zooming scale Hough Transform is now available in Ref [1] that is optimized for efficient high speed caching and flexible in terms of its implementation. In this presentation, we detail the application of this algorithm to the PHENIX Experiment silicon vertex tracker (VTX) and show initial results from Au+Au at ?sNN = 200 GeV collision data taken in 2011. We demonstrate the current algorithmic performance and also show first results for the proposed sPHENIX detector. [4pt] Ref [1] Dr. Dion, Alan. ``Helix Hough'' http://code.google.com/p/helixhough/

Koblesky, Theodore

2012-03-01

356

Research on the selection of innovation compound using Possibility Construction Space Theory and fuzzy pattern recognition  

NASA Astrophysics Data System (ADS)

There are a large number of fuzzy concepts and fuzzy phenomena in traditional Chinese medicine, which have led to great difficulties for study of traditional Chinese medicine. In this paper, the mathematical methods are used to quantify fuzzy concepts of drugs and prescription. We put forward the process of innovation formulations and selection method in Chinese medicine based on the Possibility Construction Space Theory (PCST) and fuzzy pattern recognition. Experimental results show that the method of selecting medicines from a number of characteristics of traditional Chinese medicine is consistent with the basic theory of traditional Chinese medicine. The results also reflect the integrated effects of the innovation compound. Through the use of the innovation formulations system, we expect to provide software tools for developing new traditional Chinese medicine and to inspire traditional Chinese medicine researchers to develop novel drugs.

Xie, Songhua; Li, Dehua; Nie, Hui

2009-10-01

357

[Classification of Cimicifuga species based on 1H-NMR fingerprint combined with pattern recognition technique].  

PubMed

The metabolomic analysis of three Cimicifuga species was performed using H-NMR spectroscopy and pattern recognition (PR) techniques. A broad range of metabolites could be detected by 'H-NMR spectroscopy without any chromatographic separation. The analysis using principal component analysis (PCA) and discriminant partial least square (DPLS) of the 1H-NMR spectrum showed a clear discrimination between C. foetida and the other two species. The major metabolites responsible for the discrimination were triterpenoid saponins and saccharides. These results indicated that the combination of 1H-NMR and PR provides a useful tool for chemotaxonomic analysis and authentification of Cimicifuga species, and could used for the quality control of plant materials. PMID:23672045

Shen, Li; Zhao, Yan-Yan; Xie, Hong-Ping; Liu, Wan-Hui

2013-01-01

358

Pattern recognition system invariant to rotation and scale to identify color images  

NASA Astrophysics Data System (ADS)

This work presents a pattern recognition digital system based on nonlinear correlations. The correlation peak values given by the system were analyzed by the peak-to-correlation energy (PCE) metric to determine the optimal value of the non-linear coefficient kin the k-law. The system was tested with 18 different color images of butterflies; each image was rotated from 0° to 180° with increments of 1° and scaled ±25% with increments of 1% and to take advantage of the color property of the images the RGB model was employed. The boxplot statistical analysis of the mean with ±2*EE (standard errors) for the PCE values set that the system invariant to rotation and scale has a confidence level at least of 95.4%.

Coronel-Beltrán, Angel

2014-10-01

359

Discrimination of Beef Samples by Electronic Nose and Pattern Recognition Techniques Preliminary Results  

NASA Astrophysics Data System (ADS)

In this paper a study about the possibility of beef characterization with electronic nose is presented. Three beef classes were compared: Piemontese (PIE), Limousin (FRA) and meat from Argentine (ARG). 150 meat samples were put in glass vials and analysed with a commercial electronic nose instrument based on 10 metal oxide semiconductor sensors. Sensors response of beef classes seemed to be different. Different supervised and unsupervised pattern recognition procedures were applied to sensors signal: principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). Multivariate analysis pointed out promising classification and prediction results. Three clusters (according to the beef classes) can be clearly discriminated in PCA score plot. Statistical parameters from calibration, validation and prediction of PLS-DA model revealed themselves to be indices of a good model. These results demonstrate that electronic nose technology with multivariate analysis models is promising for the rapid determination of differences in meat aroma.

Cornale, P.; Barbera, S.

2009-05-01

360

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.  

PubMed

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned. PMID:24287428

Sansone, Mario; Fusco, Roberta; Pepino, Alessandro; Sansone, Carlo

2013-01-01

361

Pattern Recognition Techniques Applied to the Study of Leishmanial Glyceraldehyde-3-Phosphate Dehydrogenase Inhibition  

PubMed Central

Chemometric pattern recognition techniques were employed in order to obtain Structure-Activity Relationship (SAR) models relating the structures of a series of adenosine compounds to the affinity for glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). A training set of 49 compounds was used to build the models and the best ones were obtained with one geometrical and four electronic descriptors. Classification models were externally validated by predictions for a test set of 14 compounds not used in the model building process. Results of good quality were obtained, as verified by the correct classifications achieved. Moreover, the results are in good agreement with previous SAR studies on these molecules, to such an extent that we can suggest that these findings may help in further investigations on ligands of LmGAPDH capable of improving treatment of leishmaniasis. PMID:24566143

Lozano, Norka B. H.; Oliveira, Rafael F.; Weber, Karen C.; Honorio, Kathia M.; Guido, Rafael V. C.; Andricopulo, Adriano D.; de Sousa, Alexsandro G.; da Silva, Albérico B. F.

2014-01-01

362

Technical analysis : neural network based pattern recognition of technical trading indicators, statistical evaluation of their predictive value and a historical overview of the field  

E-print Network

We revisit the kernel regression based pattern recognition algorithm designed by Lo, Mamaysky, and Wang (2000) to extract nonlinear patterns from the noisy price data, and develop an analogous neural network based one. We ...

Hasanhodzic, Jasmina, 1979-

2004-01-01

363

Radiation pattern analysis for optical horn antenna e-mail : shhan@stargate.snu.ac.kr  

E-print Network

Radiation pattern analysis for optical horn antenna *, , , 1) , , 1) e-mail : shhan@stargate.snu.ac.kr Abstract We analyzed the radiation pattern for 2 dimensional horn antenna in optical region by using) , . . (microwave) (horn antenna). . , (coupler) . . [ 1] 2

Park, Namkyoo

364

Pattern recognition-assisted infrared library searching of automotive clear coats.  

PubMed

Pattern recognition techniques have been developed to search the infrared (IR) spectral libraries of the paint data query (PDQ) database to differentiate between similar but nonidentical IR clear coat paint spectra. The library search system consists of two separate but interrelated components: search prefilters to reduce the size of the IR library to a specific assembly plant or plants corresponding to the unknown paint sample and a cross-correlation searching algorithm to identify IR spectra most similar to the unknown in the subset of spectra identified by the prefilters. To develop search prefilters with the necessary degree of accuracy, IR spectra from the PDQ database were preprocessed using wavelets to enhance subtle but significant features in the data. Wavelet coefficients characteristic of the assembly plant of the vehicle were identified using a genetic algorithm for pattern recognition and feature selection. A search algorithm was then used to cross-correlate the unknown with each IR spectrum in the subset of library spectra identified by the search prefilters. Each cross-correlated IR spectrum was simultaneously compared to an autocorrelated IR spectrum of the unknown using several spectral windows that span different regions of the cross-correlated and autocorrelated data from the midpoint. The top five hits identified in each search window are compiled, and a histogram is computed that summarizes the frequency of occurrence for each selected library sample. The five library samples with the highest frequency of occurrence are selected as potential hits. Even in challenging trials where the clear coat paint samples evaluated were all the same make (e.g., General Motors) within a limited production year range, the model of the automobile from which the unknown paint sample was obtained could be identified from its IR spectrum. PMID:25506887

Fasasi, Ayuba; Mirjankar, Nikhil; Stoian, Razvan-Ionut; White, Collin; Allen, Matthew; Sandercock, Mark P; Lavine, Barry K

2015-01-01

365

Identification of natural metabolites in mixture: a pattern recognition strategy based on (13)C NMR.  

PubMed

Because of their highly complex metabolite profile, the chemical characterization of bioactive natural extracts usually requires time-consuming multistep purification procedures to achieve the structural elucidation of pure individual metabolites. The aim of the present work was to develop a dereplication strategy for the identification of natural metabolites directly within mixtures. Exploiting the polarity range of metabolites, the principle was to rapidly fractionate a multigram quantity of a crude extract by centrifugal partition extraction (CPE). The obtained fractions of simplified chemical composition were subsequently analyzed by (13)C NMR. After automatic collection and alignment of (13)C signals across spectra, hierarchical clustering analysis (HCA) was performed for pattern recognition. As a result, strong correlations between (13)C signals of a single structure within the mixtures of the fraction series were visualized as chemical shift clusters. Each cluster was finally assigned to a molecular structure with the help of a locally built (13)C NMR chemical shift database. The proof of principle of this strategy was achieved on a simple model mixture of commercially available plant secondary metabolites and then applied to a bark extract of the African tree Anogeissus leiocarpus Guill. & Perr. (Combretaceae). Starting from 5 g of this genuine extract, the fraction series was generated by CPE in only 95 min. (13)C NMR analyses of all fractions followed by pattern recognition of (13)C chemical shifts resulted in the unambiguous identification of seven major compounds, namely, sericoside, trachelosperogenin E, ellagic acid, an epimer mixture of (+)-gallocatechin and (-)-epigallocatechin, 3,3'-di-O-methylellagic acid 4'-O-xylopyranoside, and 3,4,3'-tri-O-methylflavellagic acid 4'-O-glucopyranoside. PMID:24555703

Hubert, Jane; Nuzillard, Jean-Marc; Purson, Sylvain; Hamzaoui, Mahmoud; Borie, Nicolas; Reynaud, Romain; Renault, Jean-Hugues

2014-03-18

366

Centroid Detection by Gaussian Pattern Matching in Adaptive Optics  

E-print Network

Shack Hartmann wavefront sensor is a two dimensional array of lenslets which is used to detect the incoming phase distorted wavefront through local tilt measurements made by recording the spot pattern near the focal plane. Wavefront reconstruction is performed in two stages - (a) image centroiding to calculate local slopes, (b) formation of the wavefront shape from local slope measurement. Centroiding accuracy contributes to most of the wavefront reconstruction error in Shack Hartmann sensor based adaptive optics system with readout and background noise. It becomes even more difficult in atmospheric adaptive optics case, where scintillation effects may also occur. In this paper we used a denoising technique based on thresholded Zernike reconstructor to minimize the effects due to readout and background noise. At low signal to noise ratio, this denoising technique can be improved further by taking the advantage of the shape of the spot. Assuming a Gaussian pattern for individual spots, it is shown that the cen...

Vyas, Akondi; Prasad, B Raghavendra

2009-01-01

367

Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control  

PubMed Central

Background The nonstationary property of electromyography (EMG) signals usually makes the pattern recognition (PR) based methods ineffective after some time in practical application for multinational prosthesis. The conventional EMG PR, which is accomplished in two separate steps: training and testing, ignores the mismatch between training and testing conditions and often discards the useful information in testing dataset. Method This paper presents a novel self-enhancing approach to improve the classification performance of the electromyography (EMG) pattern recognition (PR). The proposed self-enhancing method incorporates the knowledge beyond the training condition to the classifiers from the testing data. The widely-used linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are extended to self-enhancing LDA (SELDA) and self-enhancing QDA (SEQDA) by continuously updating their model parameters such as the class mean vectors, the class covariances and the pooled covariance. Autoregressive (AR) and Fourier-derived cepstral (FC) features are adopted. Experimental data in two different protocols are used to evaluate performance of the proposed methods in short-term and long-term application respectively. Results In protocol of short-term EMG, based on AR and FC, the recognition accuracy of SEQDA and SELDA is 2.2% and 1.6% higher than conventional that of QDA and LDA respectively. The mean results of SEQDA(C) and SEQDA (M) are improved by 2.2% and 0.75% for AR, and 1.99% and 1.1% for FC respectively when compared to QDA. The mean results of SELDA(C) and SELDA (M) are improved by 0.48% and 1.55% for AR, and 0.67% and 1.22% for FC when compared to LDA. In protocol of long-term EMG, the mean result of SEQDA is 3.15% better than that of QDA. Conclusion The experimental results show that the self-enhancing classifiers significantly outperform the original versions using both AR and FC coefficient feature sets. The performance of SEQDA is superior to SELDA. In addition, preliminary study on long-term EMG data is conducted to verify the performance of SEQDA. PMID:23634939

2013-01-01

368

Combing rough set and RBF neural network for large-scale ship recognition in optical satellite images  

NASA Astrophysics Data System (ADS)

Large scale ship recognition in optical remote sensing images is of great importance for many military applications. It aims to recognize the category information of the detected ships for effective maritime surveillance. The contributions of the paper can be summarized as follows: Firstly, based on the rough set theory, the common discernibility degree is used to compute the significance weight of each candidate feature and select valid recognition features automatically; Secondly, RBF neural network is constructed based on the selected recognition features. Experiments on recorded optical satellite images show the proposed method is effective and can get better classification rates at a higher speed than the state of the art methods.

Chunyan, Lu; Huanxin, Zou; Hao, Sun; Shilin, Zhou

2014-03-01

369

Designing a new optical sensor using wide band speckle patterns  

NASA Astrophysics Data System (ADS)

The basic elements of the optical computer mouse (OCM) are; a light emitting diode (LED), image acquisition system (IAS) which acquires images via the lens and a digital signal processor (DSP) to implement the algorithm to determine direction and distance of motion. Here, we describe the light speckles produced from different colour LEDs to design and implement a new optical computer mouse. The speckle pattern will be used also to determine the velocity of the device relative to the surface it slides on it. The most important and critical property of speckles is their average diameter, which is independent of the type of the surface being illuminated by coherent (He-Ne laser and diode laser) or partially coherent light (LEDs). The average diameter of a speckle pattern is function of the diameter of the illuminated area of the surface, the distance between the surface and the detector, and the wavelength of the used light. In this work, we replaced the laser source by a small powerful white light lamp with different optical coloured filters and studying the resulting coloured speckle patterns to investigate the effect of different wavelengths on the velocity of the device relative to the surface it slides on it.

El Ghandoor, Hatem; El Sherif, Ashraf F.; Darwish, M.

2007-06-01

370

Foundations for a syntatic pattern recognition system for genomic DNA sequences. [Annual] report, 1 December 1991--31 March 1993  

SciTech Connect

The goal of the proposed work is the creation of a software system that will perform sophisticated pattern recognition and related functions at a level of abstraction and with expressive power beyond current general-purpose pattern-matching systems for biological sequences; and with a more uniform language, environment, and graphical user interface, and with greater flexibility, extensibility, embeddability, and ability to incorporate other algorithms, than current special-purpose analytic software.

Searles, D.B.

1993-03-01

371

Global patterns of cloud optical thickness variation with temperature  

NASA Technical Reports Server (NTRS)

A global cloud climatology dataset is used to study patterns of cloud optical thickness variation with temperature. The data, which cover the period from July 1983 through June 1995, contain detailed information on the distribution of cloud radiative properties and their diurnal and seasonal variations, as well as information on the vertical distribution of temperature and humidity in the troposphere. For cold low clouds over land, the temperature coefficient of change in optical thickness has a value of about 0.04, which is similar to that deduced from Soviet aircraft observations and derived from thermodynamic considerations for the change of cloud liquid water with temperature. It is suggested that, in this cold-temperature range, cloud optical thickness variations are dominated by changes in the liquid water content of the cloud and that the liquid water content changes in accordance with the thermodynamic theory.

Tselioudis, George; Rind, David; Rossow, William B.

1990-01-01

372

Investigation of pattern recognition techniques for the indentification of splitting surfaces in Monte Carlo particle transport calculations  

Microsoft Academic Search

Statistical and deterministic pattern recognition systems are designed ; to classify the state space of a Monte Carlo transport problem into importance ; regions. The surfaces separating the regions can be used for particle splitting ; and Russian roulette in state space in order to reduce the variance of the Monte ; Carlo tally. Computer experiments are performed to evaluate

1975-01-01

373

Copyright 1998 IEEE. Published in the 1998 International Conference on Pattern Recognition (ICPR'98), 1720 August, 1998, Brisbane, Australia  

E-print Network

Copyright 1998 IEEE. Published in the 1998 International Conference on Pattern Recognition (ICPR'98), 17­20 August, 1998, Brisbane, Australia Use of Explicit Knowledge and GIS Data for the 3D Evaluation tasks the presented system exploits prior knowledge, represented explicitly by semantic nets, and uses

374

Fundamental remote sensing science research program. Part 1: Status report of the mathematical pattern recognition and image analysis project  

NASA Technical Reports Server (NTRS)

The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of the Earth from remotely sensed measurement of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inference about the Earth.

Heydorn, R. D.

1984-01-01

375

PREPROCESSING, VARIABLE SELECTION AND CLASSIFICATION RULES IN THE APPLICATION OF SIMCA PATTERN RECOGNITION TO MASS SPECTRAL DATA  

EPA Science Inventory

In a recent report a strategy was proposed for the classification and identification of toxic organic compounds observed in ambient air from mass spectra using computational pattern recognition based on SlMCA principal components modeling of the autocorrelation transformed mass s...

376

Method of identification of rings in spectra from RICH detectors using a deconvolution-based pattern recognition algorithm  

NASA Astrophysics Data System (ADS)

A new pattern recognition algorithm applied for determination of rings in two-dimensional spectra from RICH detectors is presented. The method is based on Gold's deconvolution algorithm. It enables one to concentrate the contents of one ring into a point located at its center. The algorithm is capable of identifying curves of any shape, even of an irregular one.

Morhá?, Miroslav; Matoušek, Vladislav

2011-05-01

377

Induction of intracellular cytokine production in human monocytes\\/macrophages stimulated with ligands of pattern recognition receptors  

Microsoft Academic Search

Objective: This study addressed the role of the pattern recognition receptors (PRR), which recognize different molecular structures present on microorganisms, apoptotic, senescent and tumor cells, in the stimulation of human monocyte and monocyte-derived macrophages (MDM) for the production of intracellular cytokines. Materials and methods: Monocytes and MDM were stimulated with different ligands of scavenger receptors (SR) and mannose receptor (MR).

B. Mytar; M. Gawlicka; R. Szatanek; M. Wo?oszyn; I. Ruggiero; B. Piekarska; M. Zembala

2004-01-01

378

Classification of food, beverages and perfumes by WO 3 thin-film sensors array and pattern recognition techniques  

Microsoft Academic Search

We have designed and fabricated a sensors array using WO3 thin films operating at the temperature of 180°C for food and perfume analysis purposes. The sensing surface of the rf sputtered WO3 thin films has been differently activated by evaporated Pd, Au, Bi, Sb catalysts. Pattern recognition (PARC) techniques as principal components analysis (PCA) and cluster analysis (CA) have been

M. Penza; G. Cassano; F. Tortorella; G. Zaccaria

2001-01-01

379

Perfume and flavor identification by odor sensing system using quartz-resonator sensor array and neural-network pattern recognition  

Microsoft Academic Search

A quartz-resonator sensor array and neural-network pattern recognition system previously used for whisky-aroma identification has been applied to perfume and flavor identification. Perfume and flavor identification by this odor sensing system was successfully performed although sensing membranes tuned up for whisky-aroma identification were used. Good separation among the samples was obtained

T. Nakamoto; A. Fukuda; T. Moriizumi

1991-01-01

380

Smart dosimetry by pattern recognition using a single photon counting detector system in time over threshold mode  

Microsoft Academic Search

The function of a dosimeter is to determine the absorbed dose of radiation, for those cases in which, generally, the particular type of radiation is already known. Lately, a number of applications have emerged in which all kinds of radiation are absorbed and are sorted by pattern recognition, such as the Medipix2 application in [1]. This form of smart dosimetry

S Reza; W S Wong; E Fröjdh; B Norlin; C Fröjdh; G Thungström; J Thim

2012-01-01

381

SWIMMING PATTERN AS AN INDICATOR OF THE ROLES OF COPEPOD SENSORY SYSTEMS IN THE RECOGNITION OF FOOD  

EPA Science Inventory

The roles of copepod sensory systems in the recognition of food were investigated using the 'Bugwatcher', a video-computer system designed to track and describe quantitatively the swimming patterns of aquatic organisms. Copepods acclimated, or non-acclimated to a chemosensory sti...

382

Research on the Algorithm of Object Recognition of Ship noise Based on Auditory Feature and Pattern-matching  

Microsoft Academic Search

In this paper, an object recognition algorithm of ship noise is presented combined a new kind of information processing technology with pattern matching. The procedure of processing acoustic signal of human being's hearing is imitated to extract the auditory correlation spectrum (ARS) as feature in the algorithm. Then, a novel feature named as summation of auditory correlation spectrum (SARS) is

Pan Xiuqin; Li XiaLi; Lu Yong; Cao Yongcun; Zhao Yue

2006-01-01

383

A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps  

PubMed Central

In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features. PMID:25333290

Yin, Shouyi; Dai, Xu; Ouyang, Peng; Liu, Leibo; Wei, Shaojun

2014-01-01

384

Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincaré plots.  

PubMed

The Poincaré plot emerges as an effective tool for assessing cardiovascular autonomic regulation. It displays nonlinear characteristics of heart rate variability (HRV) from electrocardiographic (ECG) recordings and gives a global view of the long range of ECG signals. In the telemedicine or computer-aided diagnosis system, it would offer significant auxiliary information for diagnosis if the patterns of the Poincaré plots can be automatically classified. Therefore, we developed an automatic classification system to distinguish five geometric patterns of the Poincaré plots from four types of cardiac arrhythmias. The statistics features are designed on measurements and an ensemble classifier of three types of neural networks is proposed. Aiming at the difficulty to set a proper threshold for classifying the multiple categories, the threshold selection strategy is analyzed. 24?h ECG monitoring recordings from 674 patients, which have four types of cardiac arrhythmias, are adopted for recognition. For comparison, Support Vector Machine (SVM) classifiers with linear and Gaussian kernels are also applied. The experiment results demonstrate the effectiveness of the extracted features and the better performance of the designed classifier. Our study can be applied to diagnose the corresponding sinus rhythm and arrhythmia substrates disease automatically in the telemedicine and computer-aided diagnosis system. PMID:25582837

Zhang, Lijuan; Guo, Tianci; Xi, Bin; Fan, Yang; Wang, Kun; Bi, Jiacheng; Wang, Ying

2015-02-01

385

The long pentraxin PTX3: a paradigm for humoral pattern recognition molecules.  

PubMed

Pattern recognition molecules (PRMs) are components of the humoral arm of innate immunity; they recognize pathogen-associated molecular patterns (PAMP) and are functional ancestors of antibodies, promoting complement activation, opsonization, and agglutination. In addition, several PRMs have a regulatory function on inflammation. Pentraxins are a family of evolutionarily conserved PRMs characterized by a cyclic multimeric structure. On the basis of structure, pentraxins have been operationally divided into short and long families. C-reactive protein (CRP) and serum amyloid P component are prototypes of the short pentraxin family, while pentraxin 3 (PTX3) is a prototype of the long pentraxins. PTX3 is produced by somatic and immune cells in response to proinflammatory stimuli and Toll-like receptor engagement, and it interacts with several ligands and exerts multifunctional properties. Unlike CRP, PTX3 gene organization and regulation have been conserved in evolution, thus allowing its pathophysiological roles to be evaluated in genetically modified animals. Here we will briefly review the general properties of CRP and PTX3 as prototypes of short and long pentraxins, respectively, emphasizing in particular the functional role of PTX3 as a prototypic PRM with antibody-like properties. PMID:23527487

Mantovani, Alberto; Valentino, Sonia; Gentile, Stefania; Inforzato, Antonio; Bottazzi, Barbara; Garlanda, Cecilia

2013-05-01

386

A pattern recognition system for locating small volvanoes in Magellan SAR images of Venus  

NASA Technical Reports Server (NTRS)

The Magellan data set constitutes an example of the large volumes of data that today's instruments can collect, providing more detail of Venus than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations put together. However, data analysis technology has not kept pace with data collection and storage technology. Due to the sheer size of the data, complete and comprehensive scientific analysis of such large volumes of image data is no longer feasible without the use of computational aids. Our progress towards developing a pattern recognition system for aiding in the detection and cataloging of small-scale natural features in large collections of images is reported. Combining classical image processing, machine learning, and a graphical user interface, the detection of the 'small-shield' volcanoes (less than 15km in diameter) that constitute the most abundant visible geologic feature in the more that 30,000 synthetic aperture radar (SAR) images of the surface of Venus are initially targeted. Our eventual goal is to provide a general, trainable tool for locating small-scale features where scientists specify what to look for simply by providing examples and attributes of interest to measure. This contrasts with the traditional approach of developing problem specific programs for detecting Specific patterns. The approach and initial results in the specific context of locating small volcanoes is reported. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanoes visible in the Magellan data. Identifying and studying these volcanoes is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is forbiddingly time-consuming. Hence, the development of techniques to partially automate this task were undertaken. The primary constraints for this particular problem are that the method must be reasonably robust and fast. Unlike most geological features, the small volcanoes of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include (1) a circular planimetric outline, (2) known diameter frequency distribution from preliminary studies, (3) a limited number of basic morphological shapes, and (4) the common occurrence of a single, circular summit pit at the center of the edifice.

Burl, M. C.; Fayyad, U. M.; Smyth, P.; Aubele, J. C.; Crumpler, L. S.

1993-01-01

387

A real-time comparison between direct control, sequential pattern recognition control and simultaneous pattern recognition control using a Fitts’ law style assessment procedure  

PubMed Central

Background Pattern recognition (PR) based strategies for the control of myoelectric upper limb prostheses are generally evaluated through offline classification accuracy, which is an admittedly useful metric, but insufficient to discuss functional performance in real time. Existing functional tests are extensive to set up and most fail to provide a challenging, objective framework to assess the strategy performance in real time. Methods Nine able-bodied and two amputee subjects gave informed consent and participated in the local Institutional Review Board approved study. We designed a two-dimensional target acquisition task, based on the principles of Fitts’ law for human motor control. Subjects were prompted to steer a cursor from the screen center of into a series of subsequently appearing targets of different difficulties. Three cursor control systems were tested, corresponding to three electromyography-based prosthetic control strategies: 1) amplitude-based direct control (the clinical standard of care), 2) sequential PR control, and 3) simultaneous PR control, allowing for a concurrent activation of two degrees of freedom (DOF). We computed throughput (bits/second), path efficiency (%), reaction time (second), and overshoot (%)) and used general linear models to assess significant differences between the strategies for each metric. Results We validated the proposed methodology by achieving very high coefficients of determination for Fitts’ law. Both PR strategies significantly outperformed direct control in two-DOF targets and were more intuitive to operate. In one-DOF targets, the simultaneous approach was the least precise. The direct control was efficient in one-DOF targets but cumbersome to operate in two-DOF targets through a switch-depended sequential cursor control. Conclusions We designed a test, capable of comprehensively describing prosthetic control strategies in real time. When implemented on control subjects, the test was able to capture statistically significant differences (p?

2014-01-01

388

A VGA 30-fps Realtime Optical-Flow Processor Core for Moving Picture Recognition  

NASA Astrophysics Data System (ADS)

This paper describes an optical-flow processor core for real-time video recognition. The processor is based on the Pyramidal Lucas and Kanade (PLK) algorithm. It features a smaller chip area, higher pixel rate, and higher accuracy than conventional optical-flow processors. Introduction of search range limitation and the Carman filter to the original PLK algorithm improve the optical-flow accuracy, and reduce the processor hardware cost. Furthermore, window interleaving and window overlap methods reduces the necessary clock frequency of the processor by 70%, allowing low-power characteristics. We first verified the PLK algorithm and architecture with a proto-typed FPGA implementation. Then, we designed a VLSI processor that can handle a VGA 30-fps image sequence at a clock frequency of 332MHz. The core size and power consumption are estimated at 3.50×3.00mm2 and 600mW, respectively, in a 90-nm process technology.

Murachi, Yuichiro; Fukuyama, Yuki; Yamamoto, Ryo; Miyakoshi, Junichi; Kawaguchi, Hiroshi; Ishihara, Hajime; Miyama, Masayuki; Matsuda, Yoshio; Yoshimoto, Masahiko

389

Gene polymorphisms in pattern recognition receptors and susceptibility to idiopathic recurrent vulvovaginal candidiasis  

PubMed Central

Objective: Approximately 5% of women suffer from recurrent vulvovaginal candidiasis (RVVC). It has been hypothesized that genetic factors play an important role in the susceptibility to RVVC. The aim of this study was to assess the effect of genetic variants of genes encoding for pattern recognition receptors (PRRs) on susceptibility to RVVC. Study design: For the study, 119 RVVC patients and 263 healthy controls were recruited. Prevalence of polymorphisms in five PRRs involved in recognition of Candida were investigated in patients and controls. In silico and functional studies were performed to assess their functional effects. Results: Single nucleotide polymorphisms (SNPs) in TLR1, TLR4, CLEC7A, and CARD9 did not affect the susceptibility to RVVC. In contrast, a non-synonymous polymorphism in TLR2 (rs5743704, Pro631His) increased the susceptibility to RVVC almost 3-fold. Furthermore, the TLR2 rs5743704 SNP had deleterious effects on protein function as assessed by in silico analysis, and in vitro functional assays suggested that it reduces production of IL-17 and IFN? upon stimulation of peripheral blood mononuclear cells with Candida albicans. No effects were observed on serum mannose-binding lectin concentrations. Condensation: This study demonstrates the association of susceptibility to RVVC with genetic variation in TLR2, most likely caused by decreased induction of mucosal antifungal host defense. Conclusion: Genetic variation in TLR2 may significantly enhance susceptibility to RVVC by modulating host defense mechanisms against Candida. Additional studies are warranted to assess systematically the role of host genetic variation for susceptibility to RVVC. PMID:25295030

Rosentul, Diana C.; Delsing, Corine E.; Jaeger, Martin; Plantinga, Theo S.; Oosting, Marije; Costantini, Irene; Venselaar, Hanka; Joosten, Leo A. B.; van der Meer, Jos W. M.; Dupont, Bertrand; Kullberg, Bart-Jan; Sobel, Jack D.; Netea, Mihai G.

2014-01-01

390

Ngram-Derived Pattern Recognition for the Detection and Prediction of Epileptic Seizures  

PubMed Central

This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70–100% and low false predictions (dependant on training procedure). The cases of highest false predictions are found in the frontal origin with 0.31–0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40–50% for a false prediction rate of less than 0.15/hour. PMID:24886714

Eftekhar, Amir; Juffali, Walid; El-Imad, Jamil; Constandinou, Timothy G.; Toumazou, Christofer

2014-01-01

391

Defect Localization Capabilities of a Global Detection Scheme: Spatial Pattern Recognition Using Full-field Vibration Test Data in Plates  

NASA Technical Reports Server (NTRS)

Recently, a conceptually simple approach, based on the notion of defect energy in material space has been developed and extensively studied (from the theoretical and computational standpoints). The present study focuses on its evaluation from the viewpoint of damage localization capabilities in case of two-dimensional plates; i.e., spatial pattern recognition on surfaces. To this end, two different experimental modal test results are utilized; i.e., (1) conventional modal testing using (white noise) excitation and accelerometer-type sensors and (2) pattern recognition using Electronic speckle pattern interferometry (ESPI), a full field method capable of analyzing the mechanical vibration of complex structures. Unlike the conventional modal testing technique (using contacting accelerometers), these emerging ESPI technologies operate in a non-contacting mode, can be used even under hazardous conditions with minimal or no presence of noise and can simultaneously provide measurements for both translations and rotations. Results obtained have clearly demonstrated the robustness and versatility of the global NDE scheme developed. The vectorial character of the indices used, which enabled the extraction of distinct patterns for localizing damages proved very useful. In the context of the targeted pattern recognition paradigm, two algorithms were developed for the interrogation of test measurements; i.e., intensity contour maps for the damaged index, and the associated defect energy vector field plots.

Saleeb, A. F.; Prabhu, M.; Arnold, S. M. (Technical Monitor)

2002-01-01

392

Event-related fast optical signal in a rapid object recognition task: Improving detection by the independent component analysis  

Microsoft Academic Search

Noninvasive recording of fast optical signals presumably reflecting neuronal activity is a challenging task because of a relatively low signal-to-noise ratio. To improve detection of those signals in rapid object recognition tasks, we used the independent component analysis (ICA) to reduce “global interference” (heartbeat and contribution of superficial layers). We recorded optical signals from the left prefrontal cortex in 10

Andrei V. Medvedev; Jana Kainerstorfer; Sergey V. Borisov; Randall L. Barbour; John VanMeter

2008-01-01

393

Ultrafast Forwarding Architecture Using a Single Optical Processor for Multiple SAC-Label Recognition Based on FWM  

Microsoft Academic Search

We propose and demonstrate a novel ultrafast label processor that can recognize multiple spectral amplitude coded (SAC) labels using four wave mixing (FWM) sideband allocation and selective optical filtering. Our proposed solution favors hardware simplicity over bandwidth efficiency in order to achieve ultra- fast label recognition at reasonable cost. Our implementation, unlike all other optical label processing techniques, does not

JosÉ Bernardo Rosas-FernÁndez; Simon Ayotte; Leslie A. Rusch; Sophie LaRochelle

2008-01-01

394

Multiple degree of freedom object recognition using optical relational graph decision nets  

NASA Technical Reports Server (NTRS)

Multiple-degree-of-freedom object recognition concerns objects with no stable rest position with all scale, rotation, and aspect distortions possible. It is assumed that the objects are in a fairly benign background, so that feature extractors are usable. In-plane distortion invariance is provided by use of a polar-log coordinate transform feature space, and out-of-plane distortion invariance is provided by linear discriminant function design. Relational graph decision nets are considered for multiple-degree-of-freedom pattern recognition. The design of Fisher (1936) linear discriminant functions and synthetic discriminant function for use at the nodes of binary and multidecision nets is discussed. Case studies are detailed for two-class and multiclass problems. Simulation results demonstrate the robustness of the processors to quantization of the filter coefficients and to noise.

Casasent, David P.; Lee, Andrew J.

1988-01-01

395

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques  

NASA Technical Reports Server (NTRS)

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed.

Smith, William L.; Ebert, Elizabeth

1990-01-01

396

Visual function and pattern visual evoked response in optic neuritis.  

PubMed Central

The disparity between clinical visual function and pattern visual evoked response (VER) was studied in 53 patients who had suffered an attack of optic neuritis (ON) more than six months before. The visual functions tested included Snellen visual acuity, colour vision, visual field, and contrast sensitivity. The effect of pattern presentation, check size, and luminance was tested by recording VERs with several stimulus configurations. VER amplitudes were found to be associated with the outcome of all four clinical tests, independently of check size, luminance, or the presentation method used. On the other hand VER latencies were hardly ever related to the results of any of the four clinical visual tests. These findings support the idea that VER amplitude provides information about visual spatial perception, while VER latency is more related to the extent of demyelination. PMID:3651376

Sanders, E A; Volkers, A C; van der Poel, J C; van Lith, G H

1987-01-01

397

Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition  

NASA Technical Reports Server (NTRS)

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Kiang, Richard K.

1992-01-01

398

Inherently Chiral Calixarenes: Synthesis, Optical Resolution, Chiral Recognition and Asymmetric Catalysis  

PubMed Central

Inherently chiral calixarenes, whose chirality is based on the absence of a planar symmetry or an inversion center in the molecules as a whole through the asymmetric array of several achiral groups upon the three-dimensional calix-skeletons, are challenging and attractive chiral molecules, because of their potential in supramolecular chemistry. The synthesis and optical resolution of all varieties of inherently chiral calixarenes are systematically discussed and classified, and their applications in chiral recognition and asymmetric catalysis are thoroughly illustrated in this review. PMID:21339996

Li, Shao-Yong; Xu, Yao-Wei; Liu, Jun-Min; Su, Cheng-Yong

2011-01-01

399

Using pattern recognition as a method for predicting extreme events in natural and socio-economic systems  

NASA Astrophysics Data System (ADS)

Vladimir (Volodya) Keilis-Borok has pioneered the use of pattern recognition as a technique for analyzing and forecasting developments in natural as well as socio-economic systems. Keilis-Borok's work on predicting earthquakes and landslides using this technique as a leading geophysicist has been recognized around the world. Keilis-Borok has also been a world leader in the application of pattern recognition techniques to the analysis and prediction of socio-economic systems. He worked with Allan Lichtman of American University in using such techniques to predict presidential elections in the U.S. Keilis-Borok and I have worked together with others on the use of pattern recognition techniques to analyze and to predict socio-economic systems. We have used this technique to study the pattern of macroeconomic indicators that would predict the end of an economic recession in the U.S. We have also worked with officers in the Los Angeles Police Department to use this technique to predict surges of homicides in Los Angeles.

Intriligator, M.

2011-12-01

400

Fuzzy logic and neural networks in artificial intelligence and pattern recognition  

NASA Astrophysics Data System (ADS)

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

Sanchez, Elie

1991-10-01

401

Viral CNS infections: role of glial pattern recognition receptors in neuroinflammation.  

PubMed

Viruses are the major causative agents of central nervous system (CNS) infection worldwide. RNA and DNA viruses trigger broad activation of glial cells including microglia and astrocytes, eliciting the release of an array of mediators that can promote innate and adaptive immune responses. Such responses can limit viral replication and dissemination leading to infection resolution. However, a defining feature of viral CNS infection is the rapid onset of severe neuroinflammation and overzealous glial responses are associated with significant neurological damage or even death. The mechanisms by which microglia and astrocytes perceive neurotropic RNA and DNA viruses are only now becoming apparent with the discovery of a variety of cell surface and cytosolic molecules that serve as sensors for viral components. In this review we discuss the role played by members of the Toll-like family of pattern recognition receptors (PRRs) in the inflammatory responses of glial cells to the principle causative agents of viral encephalitis. Importantly, we also describe the evidence for the involvement of a number of newly described intracellular PRRs, including retinoic acid-inducible gene I and DNA-dependent activator of IFN regulatory factors, that are thought to function as intracellular sensors of RNA and DNA viruses, respectively. Finally, we explore the possibility that cross-talk exists between these disparate viral sensors and their signaling pathways, and describe how glial cytosolic and cell surface/endosomal PRRs could act in a cooperative manner to promote the fulminant inflammation associated with acute neurotropic viral infection. PMID:22723794

Furr, Samantha R; Marriott, Ian

2012-01-01

402

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory  

NASA Astrophysics Data System (ADS)

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

403

Total luminescence spectroscopy with pattern recognition for classification of edible oils.  

PubMed

Total luminescence spectroscopy combined with pattern recognition has been used to discriminate between four different types of edible oils, extra virgin olive (EVO), non-virgin olive (NVO), sunflower (SF) and rapeseed (RS) oils. Simplified fuzzy adaptive resonance theory mapping (SFAM), traditional back propagation (BP) and radial basis function (RBF) neural networks provided 100% classification for 120 samples, SFAM was found to be the most efficient. The investigation was extended to the adulteration of percentage v/v SF or RS in EVO at levels from 5% to 90% creating a total of 480 samples. SFAM was found to be more accurate than RBF and BP for classification of adulterant level. All misclassifications for SFAM occurred at the 5% v/v level resulting in a total of 99.375% correctly classified oil samples. The percentage of adulteration may be described by either RBF network (2.435% RMSE) or a simple Euclidean distance relationship of the principal component analysis (PCA) scores (2.977% RMSE) for v/v RS in EVO adulteration. PMID:12894840

Scott, Simon M; James, David; Ali, Zulfiqur; O'Hare, William T O; Rowell, Fred J

2003-07-01

404

Enter at Your Own Risk: How Enteroviruses Navigate the Dangerous World of Pattern Recognition Receptor Signaling  

PubMed Central

Enteroviruses are the most common human viral pathogens worldwide. This genus of small, non-enveloped, single stranded RNA viruses includes coxsackievirus, rhinovirus, echovirus, and poliovirus species. Infection with these viruses can induce mild symptoms that resemble the common cold, but can also be associated with more severe syndromes such as poliomyelitis, neurological diseases including aseptic meningitis and encephalitis, myocarditis, and the onset of type I diabetes. In humans, polarized epithelial cells lining the respiratory and/or digestive tracts represent the initial sites of infection by enteroviruses. Control of infection in the host is initiated through the engagement of a variety of pattern recognition receptors (PRRs). PRRs act as the sentinels of the innate immune system and serve to alert the host to the presence of a viral invader. This review assembles the available data annotating the role of PRRs in the response to enteroviral infection as well as the myriad ways by which enteroviruses both interrupt and manipulate PRR signaling to enhance their own replication, thereby inducing human disease. PMID:23764548

Harris, Katharine G; Coyne, Carolyn B

2013-01-01

405

Pumping system fault detection and diagnosis utilizing pattern recognition and fuzzy inference techniques  

SciTech Connect

An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and thus has the potential of providing incipient fault detection information to operators sufficiently early to avoid forced process shutdowns. This system also provides a diagnosis of the cause of the initiating fault(s) by a physical-model-derived rule-based expert system in which system and subsystem state uncertainties are handled using fuzzy inference techniques. This system has been initially applied to the monitoring of the operational state of the primary coolant pumping system on the EBR-II nuclear reactor. Early validation studies have shown that a rapidly developing incipient fault on centrifugal pumps can be detected well in advance of any changes in the nominal process signals. 17 refs., 6 figs.

Singer, R.M.; Gross, K.C. (Argonne National Lab., IL (USA)); Humenik, K.E. (Maryland Univ., Baltimore, MD (USA). Dept. of Computer Science)

1991-01-01

406

Pattern recognition receptor-mediated cytokine response in infants across 4 continents??  

PubMed Central

Background Susceptibility to infection as well as response to vaccination varies among populations. To date, the underlying mechanisms responsible for these clinical observations have not been fully delineated. Because innate immunity instructs adaptive immunity, we hypothesized that differences between populations in innate immune responses may represent a mechanistic link to variation in susceptibility to infection or response to vaccination. Objective Determine whether differences in innate immune responses exist among infants from different continents of the world. Methods We determined the innate cytokine response following pattern recognition receptor (PRR) stimulation of whole blood from 2-year-old infants across 4 continents (Africa, North America, South America, and Europe). Results We found that despite the many possible genetic and environmental exposure differences in infants across 4 continents, innate cytokine responses were similar for infants from North America, South America, and Europe. However, cells from South African infants secreted significantly lower levels of cytokines than did cells from infants from the 3 other sites, and did so following stimulation of extracellular and endosomal but not cytosolic PRRs. Conclusions Substantial differences in innate cytokine responses to PRR stimulation exist among different populations of infants that could not have been predicted. Delineating the underlying mechanism(s) for these differences will not only aid in improving vaccine-mediated protection but possibly also provide clues for the susceptibility to infection in different regions of the world. PMID:24290283

Smolen, Kinga K.; Ruck, Candice E.; Fortuno, Edgardo S.; Ho, Kevin; Dimitriu, Pedro; Mohn, William W.; Speert, David P.; Cooper, Philip J.; Esser, Monika; Goetghebuer, Tessa; Marchant, Arnaud; Kollmann, Tobias R.

2014-01-01

407

Pattern recognition of neuron specific enolase and carcinoembryonic antigen in whole blood samples.  

PubMed

New tools and methods for pattern recognition of neuron specific enolase (NSE) and carcinoembryonic antigen (CEA) were proposed for the screening of whole blood samples. The new tools were based on stochastic sensors designed using nanoporous gold microspheres, graphite, graphene, diamond paste as well as ?-CDs, and 5,10,15,20-tetraphenyl-21H,23H-porphyrin. The best sensor for the assay of CEA was the one based on P/graphite (the limit of determination was 16?fg/ml and sensitivity was 2.32?×?10(7) ?s?mg(-1) ?ml), while for the assay of NSE the, best sensor was the one based on P/graphene (the limit of determination was 7.45?pg/ml and sensitivity was 2.49?×?10(8) ?s?mg(-1) ?ml). The sensor of choice for simultaneous detection of NSE and CEA is the one based on P/graphene because we need high sensitivity and low limit of determination for NSE. To our knowledge, this is the only one screening test for early detection of lung cancer, by identification of NSE and CEA in whole blood samples. Copyright © 2015 John Wiley & Sons, Ltd. PMID:25604868

Stefan-van Staden, Raluca-Ioana; Comnea-Stancu, Ionela Raluca; Surdu-Bob, Carmen Cristina; Stanciu-Gavan, Camelia

2015-02-01

408

Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory  

PubMed Central

Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552

Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.

2014-01-01

409

Speciation and the evolution of gamete recognition genes: pattern and process.  

PubMed

Proteins on gamete surfaces are major determinants of fertilization success, particularly in free-spawning animals. Molecular analyses of these simple genetic systems show rapid evolution, positive selection, accelerated coalescence and, sometimes, extensive polymorphism. Careful analysis of the behavior of sperm produced by males with different gamete alleles shows that these alleles can deliver significant functional differences. Three forms of allele-specific fertilization advantage have been shown: assortative mating based on gamete type, rare allele advantage and heterozygote superiority. Models suggest that sperm and egg proteins may be coevolutionary partners that can alternate between directional selection for high fertilization ability and cyclic adaptation of eggs and sperm driven by sexual conflict. These processes act within allopatric populations and may accelerate their divergence if gamete adaptations in separate demes reduce cross-fertilization. Reproductive character displacement by reinforcement may play a diversifying role when previously allopatric populations rejoin. In circumstance that might prove to be common, divergence in sympatry can be driven by sexual conflict or by association of mating types with ecological differences. The ecology of fertilization, especially the degree of sperm competition and egg death via polyspermy, are important determinants of the strength and direction of selection on gametes. Free-spawning animals allow careful analysis of gamete recognition -from the behavior of adults and interactions of gametes, to molecular patterns of allele divergence. Future research efforts on the evolutionary consequences of fertilization ecology, and the interaction between extensive variation in egg surface proteins and sperm fertilization ability, are particularly needed. PMID:19018273

Palumbi, S R

2009-01-01

410

Identification of a ?-glucosidase from the Mucor circinelloides genome by peptide pattern recognition.  

PubMed

Mucor circinelloides produces plant cell wall degrading enzymes that allow it to grow on complex polysaccharides. Although the genome of M. circinelloides has been sequenced, only few plant cell wall degrading enzymes are annotated in this species. We applied peptide pattern recognition, which is a non-alignment based method for sequence analysis to map conserved sequences in glycoside hydrolase families. The conserved sequences were used to identify similar genes in the M. circinelloides genome. We found 12 different novel genes encoding members of the GH3, GH5, GH9, GH16, GH38, GH47 and GH125 families in M. circinelloides. One of the two GH3-encoding genes was predicted to encode a ?-glucosidase (EC 3.2.1.21). We expressed this gene in Pichia pastoris KM71H and found that the purified recombinant protein had relative high ?-glucosidase activity (1.73U/mg) at pH5 and 50°C. The Km and Vmax with p-nitrophenyl-?-d-glucopyranoside as substrate was 0.20mM and 2.41U/mg, respectively. The enzyme was not inhibited by glucose and retained 84% activity at glucose concentrations up to 140mM. Although zygomycetes are not considered to be important degraders of lignocellulosic biomass in nature, the present finding of an active ?-glucosidase in M. circinelloides demonstrates that enzymes from this group of fungi have a potential for cellulose degradation. PMID:25442948

Huang, Yuhong; Busk, Peter Kamp; Grell, Morten Nedergaard; Zhao, Hai; Lange, Lene

2014-12-01

411

Complement activation by ligand-driven juxtaposition of discrete pattern recognition complexes.  

PubMed

Defining mechanisms governing translation of molecular binding events into immune activation is central to understanding immune function. In the lectin pathway of complement, the pattern recognition molecules (PRMs) mannan-binding lectin (MBL) and ficolins complexed with the MBL-associated serine proteases (MASP)-1 and MASP-2 cleave C4 and C2 to generate C3 convertase. MASP-1 was recently found to be the exclusive activator of MASP-2 under physiological conditions, yet the predominant oligomeric forms of MBL carry only a single MASP homodimer. This prompted us to investigate whether activation of MASP-2 by MASP-1 occurs through PRM-driven juxtaposition on ligand surfaces. We demonstrate that intercomplex activation occurs between discrete PRM/MASP complexes. PRM ligand binding does not directly escort the transition of MASP from zymogen to active enzyme in the PRM/MASP complex; rather, clustering of PRM/MASP complexes directly causes activation. Our results support a clustering-based mechanism of activation, fundamentally different from the conformational model suggested for the classical pathway of complement. PMID:25197071

Degn, Sřren E; Kjaer, Troels R; Kidmose, Rune T; Jensen, Lisbeth; Hansen, Annette G; Tekin, Mustafa; Jensenius, Jens C; Andersen, Gregers R; Thiel, Steffen

2014-09-16

412

A pattern recognition system for prostate mass spectra discrimination based on the CUDA parallel programming model  

NASA Astrophysics Data System (ADS)

The aim of the present study was to implement a pattern recognition system for the discrimination of healthy from malignant prostate tumors from proteomic Mass Spectroscopy (MS) samples and to identify m/z intervals of potential biomarkers associated with prostate cancer. One hundred and six MS-spectra were studied in total. Sixty three spectra corresponded to healthy cases (PSA < 1) and forty three spectra were cancerous (PSA > 10). The MS-spectra are publicly available from the NCI Clinical Proteomics Database. The pre-processing comprised the steps: denoising, normalization, peak extraction and peak alignment. Due to the enormous number of features that rose from MS-spectra as informative peaks, and in order to secure optimum system design, the classification task was performed by programming in parallel the multiprocessors of an nVIDIA GPU card, using the CUDA framework. The proposed system achieved 98.1% accuracy. The identified m/z intervals displayed significant statistical differences between the two classes and were found to possess adequate discriminatory power in characterizing prostate samples, when employed in the design of the classification system. Those intervals should be further investigated since they might lead to the identification of potential new biomarkers for prostate cancer.

Kostopoulos, Spiros; Glotsos, Dimitris; Sidiropoulos, Konstantinos; Asvestas, Pantelis; Cavouras, Dionisis; Kalatzis, Ioannis

2014-03-01

413

Constructing a three-dimensional face model by a stereo method and its application to optical parallel face recognition  

Microsoft Academic Search

The authors have proposed and fabricated the FARCO (Fast Face Recognition Optical Correlator) based on Vanderlugt Correlator1 with a super-higher-speed of 1000 faces\\/s2,3. Using its rapid data processing capability, a robust recognition system can be constructed by registering three-dimensional (3-D) face data. There are a number of techniques to obtain 3-D data. Since the system deals with people, it is

Eriko Watanabe; Nobuko Arima; Kashiko Kodate

2006-01-01

414

Reactive liquid crystal materials for optically anisotropic patterned retarders  

NASA Astrophysics Data System (ADS)

Merck has developed a range of reactive liquid crystal materials (Reactive Mesogens) that are designed to form thin, birefringent, coatable films for optical applications. Reactive Mesogen (RM) films are typically coated from solution and polymerized in-situ to form thin, optics-grade coatings. Merck RM materials are customized formulations including reactive liquid crystals, surfactants, photoinitiators and other proprietary additives. Merck have optimized the materials to achieve the optimum physical performance in each application. In this paper we focus on the optimization of RM materials to achieve the finest patterning resolution and defined feature shape whilst maintaining good physical properties of the films. Several conventional trade-offs are investigated and circumvented using novel material concepts. Different methods of patterning RM materials are discussed and the merits of each considered. Thermal annealing of non-polymerized regions can create isotropic islands within the polymerized anisotropic matrix. Alternatively, the non polymerized material can be re-dissolved in the coating solvent and rinsed away. Each of these techniques has benefits depending on the processing conditions and these are discussed in depth.

Harding, Richard; Gardiner, Iain; Yoon, Hyun-Jin; Perrett, Tara; Parri, Owain; Skjonnemand, Karl

2008-11-01

415

[Effect of baicalin on pattern recognition receptor TLR2/4-NOD2 and its significance of druggability].  

PubMed

Activation pattern recognition receptors can cause the startup of downstream signaling pathways, the expression of inflammatory factors, and finally immunological inflammatory reaction. Either exogenous pathogenic microorganisms or endogenous tissue components can activate these pattern recognition receptors as ligands at varying degrees, and then cause the immunological inflammatory reaction. Therefore, it is of great significance to inhibit relevant receptors, as well as the immunological inflammatory reaction, in order to avoid tissue injury during the course of disease. Baicalin is able to specifically inhibit the expression of TLR2/4-NOD2, inhibit the expression of inflammatory factors IL-1beta, IL-6 and TNF-alpha, and thereby reducing the injury of the tissue cells during the course of disease. This effect is non-specific with tissues, which is of great theoretical and practical significance in druggability. In addition, the drug metabolism and toxicity of baicalin are also discussed for its druggability in this article. PMID:24228579

Chai, Yu-Shuang; Lei, Fan; Xing, Dong-Ming; Ding, Yi; Du, Li-Jun

2013-08-01

416

Oxidative burst and nitric oxide responses in carp macrophages induced by zymosan, MacroGard(®) and selective dectin-1 agonists suggest recognition by multiple pattern recognition receptors.  

PubMed

?-Glucans are glucose polymers that are found in the cell walls of plants, bacteria, certain fungi, mushrooms and the cell wall of baker's yeast. In mammals, myeloid cells express several receptors capable of recognizing ?-glucans, with the C-type lectin receptor dectin-1 in conjunction with Toll-like receptor 2 (TLR2), considered key receptors for recognition of ?-glucan. In our studies to determine the possible involvement of these receptors on carp macrophages a range of sources of ?-glucans were utilized including particulate ?-glucan preparations of baker's yeast such as zymosan, which is composed of insoluble ?-glucan and mannan, and MacroGard(®), a ?-glucan-based feed ingredient for farmed animals including several fish species. Both preparations were confirmed TLR2 ligands by measuring activation of HEK293 cells transfected with human TLR2 and CD14, co-transfected with a secreted embryonic alkaline phosphatase (SEAP) reporter gene. In addition, dectin-1-specific ligands in mammals i.e. zymosan treated to deplete the TLR-stimulating properties and curdlan, were monitored for their effects on carp macrophages by measuring reactive oxygen and nitrogen radicals production, as well as cytokine gene expression by real-time PCR. Results clearly show the ability of carp macrophages to strongly react to particulate ?-glucans with an increase in the production of reactive oxygen and nitrogen radicals and an increase in cytokine gene expression, in particular il-1?, il-6 and il-11. We identified carp il-6, that was previously unknown. In addition, carp macrophages are less, but not unresponsive to selective dectin-1 agonists, suggesting recognition of ?-glucans by multiple pattern recognition receptors that could include TLR but also non-TLR receptors. Candidate receptors for recognition of ?-glucans are discussed. PMID:23831551

Pietretti, D; Vera-Jimenez, N I; Hoole, D; Wiegertjes, G F

2013-09-01

417

Using of FPGA Coprocessor for Improving the Execution Speed of the Pattern Recognition Algorithm for ATLAS - High Energy Physics Experiment  

Microsoft Academic Search

\\u000a Pattern recognition algorithms are used in experimental High Energy physics for getting parameters (features) of particles\\u000a tracks in detectors. It is particularly important to have fast algorithms in trigger system. This paper investigates the suitability\\u000a of using FPGA coprocessor for speedup of the TRT-LUT algorithm – one of the feature extraction algorithms for second level\\u000a trigger for ATLAS experiment (CERN). Two

Christian Hinkelbein; Andrei Khomich; Andreas Kugel; Reinhard Männer; Matthias Müller

2004-01-01

418

Optical Patterning of Three-Dimensional Carbon Nanotube Microstructures  

NASA Astrophysics Data System (ADS)

We present an optical, non-contact method for patterning three-dimensional carbon nanotube microstructures. In this method, a 1?m diameter focused laser spot is used to burn patterns in dense arrays of vertically grown multiwalled carbon nanotubes. The threshold for laser burnout and the depth of burnout are determined by Raman spectroscopy and scanning electron microscopy. Using a high precision translation stage to control the position of the laser spot on the sample, we create several 3D patterns to illustrate this method's potential use for the rapid prototyping of carbon nanotube microstructures [1]. After laser surface treatment, we observe undercut profiles, changes in nanotube density, and nanoparticle formation, which provide insight into the unique evolution of the nanotube microstructures during the burnout process. This non-lithographic method provides new opportunities for chemically sensitive applications of nanotubes and expands their possible applications into new areas. [1] Hung, Wei Hsuan, Kumar, Rajay, Bushmaker, Adam, Cronin, Stephen B., and Bronikowski, Michael J. Rapid prototyping of three-dimensional microstructures from multiwalled carbon nanotubes. Applied Physics Letters 91, 093121 (2007).

Hung, Wei-Husuan; Kumar, Rajay; Bushmaker, Adam; Bronikowski, Michael J.; Cronin, Stephen B.

2008-03-01

419

Polarization based optical sectioning of multilayer cell patterns  

NASA Astrophysics Data System (ADS)

In this paper we present a polarization based technique for optical sectioning and imaging of multi-layer cell patterns separated by a weakly diffused media. Multi-layer cell pattern is important to study because this type of structure is often used for heterogeneous three dimensional cell culture and bio-chips applications, where information at different depths would be crucial. Functioning of this type of bi-layer or multilayer cell patterns can easily be monitored using polarization based imaging techniques. For polarization based imaging, samples are excited by white light source with different set of band-pass filter and linear polarizer, and images are collected through corresponding long-pass filters and analyzer by CCD camera. Preliminary experiments are carried out using absorption inhomogeneity separated by a weakly diffused thin polymer layers. Polarized images at various angles are collected at a set of excitation wavelength. Such measurements can identify 3x3 sub-matrix elements out of the full 4x4 sixteen elements of Mueller matrix. In order to enhance the image contrast, the 3x3 Mueller components are further decomposed into diattenuation and depolarization power images. Superficial layer image information is found to be more prominent in the depolarization power images, and diattenuation images provide sub layer information. By comparing the decomposition images at various wavelengths, we can observe sub-layer structures at different depths.

Gupta, Sharad; Ye, Jong Chul; Cho, David Jaeyun

2006-02-01

420

EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury.  

PubMed

Myoelectric pattern recognition with a large number of electromyogram (EMG) channels provides an approach to assessing motor control information available from the recorded muscles. In order to develop a practical myoelectric control system, a feature dependent channel reduction method was developed in this study to determine a small number of EMG channels for myoelectric pattern recognition analysis. The method selects appropriate raw EMG features for classification of different movements, using the minimum Redundancy Maximum Relevance (mRMR) and the Markov random field (MRF) methods to rank a large number of EMG features, respectively. A k-nearest neighbor (KNN) classifier was used to evaluate the performance of the selected features in terms of classification accuracy. The method was tested using 57 channels' surface EMG signals recorded from forearm and hand muscles of individuals with incomplete spinal cord injury (SCI). Our results demonstrate that appropriate selection of a small number of raw EMG features from different recording channels resulted in similar high classification accuracies as achieved by using all the EMG channels or features. Compared with the conventional sequential forward selection (SFS) method, the feature dependent method does not require repeated classifier implementation. It can effectively reduce redundant information not only cross different channels, but also cross different features in the same channel. Such hybrid feature-channel selection from a large number of EMG recording channels can reduce computational cost for implementation of a myoelectric pattern recognition based control system. PMID:24844608

Liu, Jie; Li, Xiaoyan; Li, Guanglin; Zhou, Ping

2014-07-01

421

Comparison of two exploratory data analysis methods for classification of Phyllanthus chemical fingerprint: unsupervised vs. supervised pattern recognition technologies.  

PubMed

In this study, unsupervised and supervised classification methods were compared for comprehensive analysis of the fingerprints of 26 Phyllanthus samples from different geographical regions and species. A total of 63 compounds were identified and tentatively assigned structures for the establishment of fingerprints using high-performance liquid chromatography time-of-flight mass spectrometry (HPLC/TOFMS). Unsupervised and supervised pattern recognition technologies including principal component analysis (PCA), nearest neighbors algorithm (NN), partial least squares discriminant analysis (PLS-DA), and artificial neural network (ANN) were employed. Results showed that Phyllanthus could be correctly classified according to their geographical locations and species through ANN and PLS-DA. Important variables for clusters discrimination were also identified by PCA. Although unsupervised and supervised pattern recognitions have their own disadvantage and application scope, they are effective and reliable for studying fingerprints of traditional Chinese medicines (TCM). These two technologies are complementary and can be superimposed. Our study is the first holistic comparison of supervised and unsupervised pattern recognition technologies in the TCM chemical fingerprinting. They showed advantages in sample classification and data mining, respectively. PMID:25504091

Guo, Jianru; Chen, QianQian; Wang, Caiyun; Qiu, Hongcong; Liu, Buming; Jiang, Zhi-Hong; Zhang, Wei

2015-02-01

422

A decision-based velocity ramp for minimizing the effect of misclassifications during real-time pattern recognition control  

PubMed Central

Real-time pattern recognition control is frequently affected by misclassifications. This study investigated the use of a decision-based velocity ramp that attenuated movement speed after a change in classifier decision. The goal was to improve prosthesis positioning by minimizing the effect of unintended movements. Non-amputee and amputee subjects controlled a prosthesis in real-time using pattern recognition. While performing a target achievement test in a virtual environment, subjects had a significantly higher completion rate (p < 0.05) and a more direct path (p < 0.05) to the target with the velocity ramp than without it. Using a physical prosthesis, subjects stacked a greater average number of 1” cubes (p < 0.05) in three minutes with the velocity ramp than without it (76% more blocks for non-amputees; 89% more blocks for amputees). Real-time control using the velocity ramp also showed significant performance improvements above using majority vote. Eighty-three percent of subjects preferred to control the prosthesis using the velocity ramp. These results suggest that using a decision-based velocity ramp with pattern recognition may improve user performance. Since the velocity ramp is a post-processing step, it has the potential to be used with a variety of classifiers for many applications. PMID:21592916

Simon, Ann M.; Hargrove, Levi J.; Lock, Blair A.; Kuiken, Todd A.

2014-01-01

423

Pattern recognition control of multifunction myoelectric prostheses by patients with congenital transradial limb defects: A preliminary study  

PubMed Central

Background Electromyography (EMG) pattern recognition offers the potential for improved control of multifunction myoelectric prostheses. However, it is unclear whether this technology can be successfully used by congenital amputees. Objective The purpose of this investigation was to assess the ability of congenital transradial amputees to control a virtual multifunction prosthesis using EMG pattern recognition and compare their performance to that of acquired amputees from a previous study. Study Design Preliminary cross-sectional study. Methods Four congenital transradial amputees trained and tested a linear discriminant analysis (LDA) classifier with four wrist movements, five hand movements, and a no movement class. Subjects then tested the classifier in real time using a virtual arm. Results Performance metrics for the residual limb were poorer than those with the intact limb (classification accuracy: 52.1% ± 15.0% vs. 93.2% ± 15.8%; motion-completion rate: 49.0% ± 23.0% vs. 84.0% ± 9.4%; motion-completion time: 2.05 ± 0.75 s vs. 1.13 ± 0.05 s, respectively). On average, performance with the residual limb by congenital amputees was reduced compared to that reported for acquired transradial amputees. However, one subject performed similarly to acquired amputees. Conclusions Pattern recognition control may be a viable option for some congenital amputees. Further study is warranted to determine success factors. PMID:21960053

Kryger, Michael; Schultz, Aimee E; Kuiken, Todd A

2015-01-01

424

Emotion Recognition Pattern in Adolescent Boys with Attention-Deficit/Hyperactivity Disorder  

PubMed Central

Background. Social and emotional deficits were recently considered as inherent features of individuals with attention-deficit hyperactivity disorder (ADHD), but only sporadic literature data exist on emotion recognition in adolescents with ADHD. The aim of the present study was to establish emotion recognition profile in adolescent boys with ADHD in comparison with control adolescents. Methods. Forty-four adolescent boys (13–16 years) participated in the study after informed consent; 22 boys had a clinical diagnosis of ADHD, while data were also assessed from 22 adolescent control boys matched for age and Raven IQ. Parent- and self-reported behavioral characteristics were assessed by the means of the Strengths and Difficulties Questionnaire. The recognition of six basic emotions was evaluated by the “Facial Expressions of Emotion-Stimuli and Tests.” Results. Compared to controls, adolescents with ADHD were more sensitive in the recognition of disgust and, worse in the recognition of fear and showed a tendency for impaired recognition of sadness. Hyperactivity measures showed an inverse correlation with fear recognition. Conclusion. Our data suggest that adolescent boys with ADHD have alterations in the recognition of specific emotions. PMID:25110694

Bozsik, Csilla; Gadoros, Julia; Inantsy-Pap, Judit

2014-01-01

425

Biometric personal identification based on iris pattern recognition using Wavelet Packet Transform  

Microsoft Academic Search

A new iris recognition system based on Wavelet Packet Analysis and Morlet wavelet is described. Morlet wavelet calculations are easy compared to Gabor wavelets. Moreover Gabor wavelet based iris recognition system is patented which blocks its further development. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of

S. Hariprasath; V. Mohan

2010-01-01

426

Gas-liquid Flow Pattern Recognition Based on Wavelet Packet Energy Entropy of Vortex-induced Pressure Fluctuation  

NASA Astrophysics Data System (ADS)

Here we report a novel flow-pattern map to distinguish the gas-liquid flow patterns in horizontal pipes at ambient temperature and atmospheric pressure. The map is constructed using the coordinate system of wavelet packet energy entropy versus total mass flow rate. The wavelet packet energy entropy is obtained from the coefficients of vortex-induced pressure fluctuation decomposed by the wavelet packet transform. A triangular bluff body perpendicular to the flow direction is employed to generate the pressure fluctuation. Experimental tests confirm the suitability of the wavelet packet energy entropy as an ideal indicator of the gas-liquid flow patterns. The overall identification rate of the map is 92.86%, which can satisfy most engineering applications. This method provides a simple, practical, and robust solution to the problem of gas-liquid flow pattern recognition.

Sun, Zhiqiang; Shao, Shuai; Gong, Hui

2013-04-01

427

Sequence variability of the pattern recognition receptor Mermaid mediates specificity of marine nematode symbioses  

PubMed Central

Selection of a specific microbial partner by the host is an all-important process. It guarantees the persistence of highly specific symbioses throughout host generations. The cuticle of the marine nematode Laxus oneistus is covered by a single phylotype of sulfur-oxidizing bacteria. They are embedded in a layer of host-secreted mucus containing the mannose-binding protein Mermaid. This Ca2+-dependent lectin mediates symbiont aggregation and attachment to the nematode. Here, we show that Stilbonema majum—a symbiotic nematode co-occurring with L. oneistus in shallow water sediment—is covered by bacteria phylogenetically distinct to those covering L. oneistus. Mermaid cDNA analysis revealed extensive protein sequence variability in both the nematode species. We expressed three recombinant Mermaid isoforms, which based on the structural predictions display the most different carbohydrate recognition domains (CRDs). We show that the three CRDs (DNT, DDA and GDA types) possess different affinities for L. oneistus and S. majum symbionts. In particular, the GDA type, exclusively expressed by S. majum, displays highest agglutination activity towards its symbionts and lowest towards its L. oneistus symbionts. Moreover, incubation of L. oneistus in the GDA type does not result in complete symbiont detachment, whereas incubation in the other types does. This indicates that the presence of particular Mermaid isoforms on the nematode surface has a role in the attachment of specific symbionts. This is the first report of the functional role of sequence variability in a microbe-associated molecular patterns receptor in a beneficial association. PMID:21228893

Bulgheresi, Silvia; Gruber-Vodicka, Harald R; Heindl, Niels R; Dirks, Ulrich; Kostadinova, Maria; Breiteneder, Heimo; Ott, Joerg A

2011-01-01

428

Type I interferon and pattern recognition receptor signaling following particulate matter inhalation  

PubMed Central

Background Welding, a process that generates an aerosol containing gases and metal-rich particulates, induces adverse physiological effects including inflammation, immunosuppression and cardiovascular dysfunction. This study utilized microarray technology and subsequent pathway analysis as an exploratory search for markers/mechanisms of in vivo systemic effects following inhalation. Mice were exposed by inhalation to gas metal arc – stainless steel (GMA-SS) welding fume at 40?mg/m3 for 3?hr/d for 10?d and sacrificed 4?hr, 14?d and 28?d post-exposure. Whole blood cells, aorta and lung were harvested for global gene expression analysis with subsequent Ingenuity Pathway Analysis and confirmatory qRT-PCR. Serum was collected for protein profiling. Results The novel finding was a dominant type I interferon signaling network with the transcription factor Irf7 as a central component maintained through 28?d. Remarkably, these effects showed consistency across all tissues indicating a systemic type I interferon response that was complemented by changes in serum proteins (decreased MMP-9, CRP and increased VCAM1, oncostatin M, IP-10). In addition, pulmonary expression of interferon ? and ? and Irf7 specific pattern recognition receptors (PRR) and signaling molecules (Ddx58, Ifih1, Dhx58, ISGF3) were induced, an effect that showed specificity when compared to other inflammatory exposures. Also, a canonical pathway indicated a coordinated response of multiple PRR and associated signaling molecules (Tlr7, Tlr2, Clec7a, Nlrp3, Myd88) to inhalation of GMA-SS. Conclusion This methodological approach has the potential to identify consistent, prominent and/or novel pathways and provides insight into mechanisms that contribute to pulmonary and systemic effects following toxicant exposure. PMID:22776377

2012-01-01

429

Pattern recognition of electromagnetic field scattering from anthropogenic objects on underlying surface  

NASA Astrophysics Data System (ADS)

In the paper we have proposed recognition of object by RCS diagrams method. For modeling the scattering field of 3D objects on underlying surface we had use widely known FDTD method. We have used for distance function in developing method conjugation indices with so-called support plane, is formed within feature vectors of recognition class. We have given the results of recognition experiments with three different methods: support vector method, correlation method with the average class vector and a new support plane method.

Zherdev, Denis A.; Fursov, Vladimir A.

2014-09-01

430

TreeRipper web application: towards a fully automated optical tree recognition software  

PubMed Central

Background Relationships between species, genes and genomes have been printed as trees for over a century. Whilst this may have been the best format for exchanging and sharing phylogenetic hypotheses during the 20th century, the worldwide web now provides faster and automated ways of transferring and sharing phylogenetic knowledge. However, novel software is needed to defrost these published phylogenies for the 21st century. Results TreeRipper is a simple website for the fully-automated recognition of multifurcating phylogenetic trees (http://linnaeus.zoology.gla.ac.uk/~jhughes/treeripper/). The program accepts a range of input image formats (PNG, JPG/JPEG or GIF). The underlying command line c++ program follows a number of cleaning steps to detect lines, remove node labels, patch-up broken lines and corners and detect line edges. The edge contour is then determined to detect the branch length, tip label positions and the topology of the tree. Optical Character Recognition (OCR) is used to convert the tip labels into text with the freely available tesseract-ocr software. 32% of images meeting the prerequisites for TreeRipper were successfully recognised, the largest tree had 115 leaves. Conclusions Despite the diversity of ways phylogenies have been illustrated making the design of a fully automated tree recognition software difficult, TreeRipper is a step towards automating the digitization of past phylogenies. We also provide a dataset of 100 tree images and associated tree files for training and/or benchmarking future software. TreeRipper is an open source project licensed under the GNU General Public Licence v3. PMID:21599881

2011-01-01

431

Molecular recognition of AT-DNA sequences by the induced CD pattern of dibenzotetraaza[14]annulene (DBTAA)–adenine derivatives  

PubMed Central

Summary An investigation of the interactions of two novel and several known DBTAA–adenine conjugates with double-stranded DNA and RNA has revealed the DNA/RNA groove as the dominant binding site, which is in contrast to the majority of previously studied DBTAA analogues (DNA/RNA intercalators). Only DBTAA–propyladenine conjugates revealed the molecular recognition of AT-DNA by an ICD band pattern > 300 nm, whereas significant ICD bands did not appear for other ds-DNA/RNA. A structure–activity relation for the studied series of compounds showed that the essential structural features for the ICD recognition are a) the presence of DNA-binding appendages (adenine side chain and positively charged side chain) on both DBTAA side chains, and b) the presence of a short propyl linker, which does not support intramolecular aromatic stacking between DBTAA and adenine. The observed AT-DNA-ICD pattern differs from previously reported ss-DNA (poly dT) ICD recognition by a strong negative ICD band at 350 nm, which allows for the dynamic differentiation between ss-DNA (poly dT) and coupled ds-AT-DNA. PMID:25246976

Stojkovi?, Marijana Radi?; Škugor, Marko; Dudek, ?ukasz; Grolik, Jaros?aw; Eilmes, Julita

2014-01-01

432

21/8/05 Dissimilarity Representation 1 Dissimilarity Representations in Pattern Recognition  

E-print Network

dependence f(x{apple}) f(x{pear}) such that new examples are correctly classified Representation Functional relationMeasurements 765321 774311 f(x{ }) pear X pear apple Given a set of examples Recognition: Learning

Duin, Robert P.W.

433

Criteria for pathology recognition in optical coherence tomography of fallopian tubes  

NASA Astrophysics Data System (ADS)

An increase of infertility and chronic pelvic pains syndrome, a growing level of latent diseases of this group, as well as a stably high percentage (up to 25% for infertility and up to 60% for the chronic pelvic pains syndrome) of undetermined origin raises the requirement for novel introscopic diagnostic techniques. We demonstrate abilities of optical coherence tomography (OCT) as a complementary technique to laparoscopy in diagnostics of fallopian tubes pathologies. We have acquired OCT images of different parts of fallopian tubes in norm and with morphologically proven pathology. Based on comparative analysis of the OCT data and the results of histological studies, we have worked out the subjective OCT criteria for distinguishing between unaltered and pathologic tissues. The developed criteria are verified in blind recognition tests. Diagnostic efficacy of OCT diagnostics in the case ofpelvic inflammatory diseases has been statistically evaluated, and high diagnostic accuracy (88%) is shown. Basing of the subjective criteria, an attempt to develop independent criteria aimed for automated recognition of pathological states in fallopian tubes is undertaken. Enhanced diagnostic accuracy (96%) of the developed independent criteria is demonstrated.

Kirillin, Mikhail; Panteleeva, Olga; Yunusova, Ekaterina; Donchenko, Ekaterina; Shakhova, Natalia

2012-08-01

434

Optical Font Recognition for Multi-Font OCR and Document Processing Serena La Manna** Anna Maria Colla* Alessandro Sperduti**  

E-print Network

Optical Font Recognition for Multi-Font OCR and Document Processing Serena La Manna** Anna Maria,persog@di.unipi.it Abstract In this paper we present a Multi-font OCR system to be employed for document processing, which- mental results and prospective use in document processing applications are presented. 1. Introduction

Sperduti, Alessandro

435

Leucine-rich Repeats of Bacterial Surface Proteins Serve as Common Pattern Recognition Motifs of Human Scavenger Receptor gp340*  

PubMed Central

Scavenger receptors are innate immune molecules recognizing and inducing the clearance of non-host as well as modified host molecules. To recognize a wide pattern of invading microbes, many scavenger receptors bind to common pathogen-associated molecular patterns, such as lipopolysaccharides and lipoteichoic acids. Similarly, the gp340/DMBT1 protein, a member of the human scavenger receptor cysteine-rich protein family, displays a wide ligand repertoire. The peptide motif VEVLXXXXW derived from its scavenger receptor cysteine-rich domains is involved in some of these interactions, but most of the recognition mechanisms are unknown. In this study, we used mass spectrometry sequencing, gene inactivation, and recombinant proteins to identify Streptococcus pyogenes protein Spy0843 as a recognition receptor of gp340. Antibodies against Spy0843 are shown to protect against S. pyogenes infection, but no function or host receptor have been identified for the protein. Spy0843 belongs to the leucine-rich repeat (Lrr) family of eukaryotic and prokaryotic proteins. Experiments with truncated forms of the recombinant proteins confirmed that the Lrr region is needed in the binding of Spy0843 to gp340. The same motif of two other Lrr proteins, LrrG from the Gram-positive S. agalactiae and BspA from the Gram-negative Tannerella forsythia, also mediated binding to gp340. Moreover, inhibition of Spy0843 binding occurred with peptides containing the VEVLXXXXW motif, but also peptides devoid of the XXXXW motif inhibited binding of Lrr proteins. These results thus suggest that the conserved Lrr motif in bacterial proteins serves as a novel pattern recognition motif for unique core peptides of human scavenger receptor gp340. PMID:19465482

Loimaranta, Vuokko; Hytönen, Jukka; Pulliainen, Arto T.; Sharma, Ashu; Tenovuo, Jorma; Strömberg, Nicklas; Finne, Jukka

2009-01-01

436

Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol.  

PubMed

Research on pattern recognition for myoelectric control has usually focused on a small number of EMG channels, because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrodes shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study was to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on 7 able-bodied subjects and 1 subject with amputation, for the classification of 9 and 7 classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods ~95% for 9 classes). However, the new spatial features demonstrated lower sensitivity to electrode shift (±1cm) with respect to the classic features (p<0.05). When even just one channel was noisy, the classification accuracy dropped by ~10% for all methods. However, the new method could be applied without any re-training to a subset of high-quality channels whereas the classic methods require re-training when some channels are omitted. In conclusion, the new spatial feature space proposed in this study improved the robustness to electrode number and shift in myocontrol with respect to previous approaches. PMID:25389242

Stango, Antonietta; Negro, Francesco; Farina, Dario

2014-11-01

437

Fundamental remote science research program. Part 2: Status report of the mathematical pattern recognition and image analysis project  

NASA Technical Reports Server (NTRS)

The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of he Earth from remotely sensed measurements of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inferences about the Earth. This report summarizes the progress that has been made toward this program goal by each of the principal investigators in the MPRIA Program.

Heydorn, R. P.

1984-01-01

438

Lateral Inhibition in Accumulative Computation and Fuzzy Sets for Human Fall Pattern Recognition in Colour and Infrared Imagery  

PubMed Central

Fall detection is an emergent problem in pattern recognition. In this paper, a novel approach which enables to identify a type of a fall and reconstruct its characteristics is presented. The features detected include the position previous to a fall, the direction and velocity of a fall, and the postfall inactivity. Video sequences containing a possible fall are analysed image by image using the lateral inhibition in accumulative computation method. With this aim, the region of interest of human figures is examined in each image, and geometrical and kinematic characteristics for the sequence are calculated. The approach is valid in colour and in infrared video. PMID:24294142

Sokolova, Marina V.; Serrano-Cuerda, Juan

2013-01-01

439

NLRP7 and related inflammasome activating pattern recognition receptors and their function in host defense and disease  

PubMed Central

Host defense requires the maturation and release of the pro-inflammatory cytokines interleukin (IL)-1? and IL-18 and the induction of pyroptotic cell death, which depends on the activation of inflammatory Caspases within inflammasomes by innate immune cells. Several cytosolic Pattern recognition receptors (PRRs) have been implicated in this process in response to infectious and sterile agonists. Here we summarize the current knowledge on inflammasome-organizing PRRs, emphasizing the recently described NLRP7, and their implications in human disease. PMID:23618810

Radian, Alexander D.; de Almeida, Lucia; Dorfleutner, Andrea; Stehlik, Christian

2013-01-01

440

Using geometric algebra to understand pattern rotations in multiple mirror optical systems  

SciTech Connect

Geometric Algebra (GA) is a new formulation of Clifford Algebra that includes vector analysis without notation changes. Most applications of Ga have been in theoretical physics, but GA is also a very good analysis tool for engineering. As an example, the authors use GA to study pattern rotation in optical systems with multiple mirror reflections. The common ways to analyze pattern rotations are to use rotation matrices or optical ray trace codes, but these are often inconvenient. The authors use GA to develop a simple expression for pattern rotation that is useful for designing or tolerancing pattern rotations in a multiple mirror optical system by inspection. Pattern rotation is used in many optical engineering systems, but it is not normally covered in optical system engineering texts. Pattern rotation is important in optical systems such as: (1) the 192 beam National ignition Facility (NIF), which uses square laser beams in close packed arrays to cut costs; (2) visual optical systems, which use pattern rotation to present the image to the observer in the appropriate orientation, and (3) the UR90 unstable ring resonator, which uses pattern rotation to fill a rectangular laser gain region and provide a filled-in laser output beam.

Hanlon, J.; Ziock, H.

1997-05-01

441

Fingerprints of Learned Object Recognition Seen in the fMRI Activation Patterns of Lateral Occipital Complex.  

PubMed

One feature of visual processing in the ventral stream is that cortical responses gradually depart from the physical aspects of the visual stimulus and become correlated with perceptual experience. Thus, unlike early retinotopic areas, the responses in the object-related lateral occipital complex (LOC) are typically immune to parameter changes (e.g., contrast, location, etc.) when these do not affect recognition. Here, we use a complementary approach to highlight changes in brain activity following a shift in the perceptual state (in the absence of any alteration in the physical image). Specifically, we focus on LOC and early visual cortex (EVC) and compare their functional magnetic resonance imaging (fMRI) responses to degraded object images, before and after fast perceptual learning that renders initially unrecognized objects identifiable. Using 3 complementary analyses, we find that, in LOC, unlike EVC, learned recognition is associated with a change in the multivoxel response pattern to degraded object images, such that the response becomes significantly more correlated with that evoked by the intact version of the same image. This provides further evidence that the coding in LOC reflects the recognition of visual objects. PMID:24692511

Roth, Zvi N; Zohary, Ehud

2014-03-31

442

Distortion invariant pattern recognition with phase filters Joseph Rosen and Joseph Shamir  

E-print Network

(u,v), and evaluate the transfer characteristics ofthe The authors are with Technion-Israel Institute of Technology, the recognition levels were decreased,but they remain adequate for unambiguous identification together efficiency and selectivity ob- tained with phase-only filters4 the objective of this work is to use the phase

Rosen, Joseph

443

Speechreading: an overview of image processing, feature extraction, sensory integration and pattern recognition techniques  

Microsoft Academic Search

We give an overview of speechreading systems from the perspective of the face and gesture recognition community, paying particular attention to approaches to key design decisions and the benefits and drawbacks. We discuss the central issue of sensory integration how much processing of the acoustic and the visual information should go on before integration how should it be integrated. We

David G. Stork; Marcus E. Hennecke

1996-01-01

444

The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors  

Microsoft Academic Search

The discovery of Toll-like receptors (TLRs) as components that recognize conserved structures in pathogens has greatly advanced understanding of how the body senses pathogen invasion, triggers innate immune responses and primes antigen-specific adaptive immunity. Although TLRs are critical for host defense, it has become apparent that loss of negative regulation of TLR signaling, as well as recognition of self molecules

Taro Kawai; Shizuo Akira

2010-01-01

445

Damaged Character Pattern Recognition on Wooden Tablets Excavated from The Heijyo Palace Site  

E-print Network

recognition, image processing, non-linear normalization, feature extraction. 1. Introduction A "mokkan to read mokkans. Section 3 presents its image processing libraries. Section 4 describes user of Agriculture and Technology National Research Institute for Cultural Properties nakagawa@cc.tuat.ac.jp, {kei

Paris-Sud XI, Université de

446

Pattern Recognition 42 (2009) 2460 --2469 Contents lists available at ScienceDirect  

E-print Network

Recognition journal homepage: www.elsevier.com/locate/pr An improved box-counting method for image fractal Equipment & System Security and New Tec