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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

Optical Pattern Recognition With Self-Amplification  

NASA Technical Reports Server (NTRS)

In optical pattern recognition system with self-amplification, no reference beam used in addressing mode. Polarization of laser beam and orientation of photorefractive crystal chosen to maximize photorefractive effect. Intensity of recognition signal is orders of magnitude greater than other optical correlators. Apparatus regarded as real-time or quasi-real-time optical pattern recognizer with memory and reprogrammability.

Liu, Hua-Kuang

1994-01-01

3

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

4

Multiple degree of freedom optical pattern recognition  

NASA Technical Reports Server (NTRS)

Three general optical approaches to multiple degree of freedom object pattern recognition (where no stable object rest position exists) are advanced. These techniques include: feature extraction, correlation, and artificial intelligence. The details of the various processors are advanced together with initial results.

Casasent, D.

1987-01-01

5

Simplified Pattern Recognition Based On Multiaperture Optics  

NASA Astrophysics Data System (ADS)

Multiaperture optics systems are similar in design to the concepts applying to the insect eye. Digitizing at the detector level is inherent in these systems. The fact that each eyelet forms one pixel of the overall image lends itself to optical preprocessing. There-fore a simplified pattern recognition scheme can be used in connection with multiaperture optics systems. The pattern recognition system used is based on the conjecture that all shapes encountered can be dissected into a set of rectangles. This is accomplished by creating a binary image and comparing each row of numbers starting at the top of the frame with the next row below. A set of rules is established which decides if the binary ones of the next row are to be incorporated in the present rectangle or start a new rectangle. The number and aspect ratios of the rectangles formed constitute a recognition code. These codes are kept and updated in a library. Since the same shape may give rise to different recognition codes depending on the attitude of the shape in respect to the detector grid, all shapes are rotated and normalized prior to dissecting. The rule is that the pattern is turned to maximize the number of straight edges which line up with the detector grid. The mathematical mechanism for rotation of the shape is described. Assuming a-priori knowledge of the size of the object exists, the normalization procedure can be used for distance determination. The description of the hardware for acquisition of the image is provided.

Schneider, Richard T.; Lin, Shih-Chao

1987-05-01

6

Pattern Recognition Relevant To Multiaperture Optics  

NASA Astrophysics Data System (ADS)

Multiaperture optics deals with image formation employing a large number of optical elements, the insect eye being an example. If the apposition design is used, each optical element creates one pixel only, although due to FOV overlap, there may be some additional information collected. The consequence is that multiaperture optics devices produce a relatively small number of pixels. This presents a challenge to any pattern recognition procedure by requiring it to make assumptions on the information content concerning the spaces between the pixels. Another challenge for pattern recognition, common for all systems, is speed. Therefore, time should not be wasted in examining dead zones in the field. The algorithm presented here meets the first challenge by transforming the presented image into a combination of rectangles of varying aspect ratios. The second challenge is met by selectively examining only those data locations known to be responsive and eliminating any blank space above and below the pattern. This process consists of three stages: 1) an essentially random distribution of data points is converted into rectangular form; 2) these rectangles are then converted into a series of "code elements" which are actually equal to the value of their aspect ratios; 3) this pattern of code elements is then compared to numbers representing some known patterns to achieve identification. The algorithm relies heavily on the concept of a-priori knowl-edge as well as recognizing a fairly small universe of patterns.

Schneider, Richard T.; Meyers, Kenneth L.

1986-12-01

7

Bifurcating optical pattern recognition in photorefractive crystals  

NASA Technical Reports Server (NTRS)

A new concept and experimental demonstration of a bifurcating optical pattern recognizer that uses a nonlinear gain saturation memory medium such as a high-gain photorefractive crystal are presented. A barium titanate crystal is used as a typical example of the nonlinear medium for the demonstration of the bifurcating optical pattern recognizer.

Liu, Hua-Kuang

1993-01-01

8

Bifurcating optical pattern recognition in photorefractive crystals  

NASA Technical Reports Server (NTRS)

A concept of bifurcating optical pattern rocognizer (BIOPAR) is described and demonstrated experimentally, using barium titanate crystal. When an input is applied to BIOPAR, the output may be directed to two ports.

Liu, Hua-Kuang

1993-01-01

9

A Compact Prototype of an Optical Pattern Recognition System  

NASA Technical Reports Server (NTRS)

In the Technology 2006 Case Studies/Success Stories presentation, we will describe and demonstrate a prototype of a compact optical pattern recognition system as an example of a successful technology transfer and continuuing development of state-of-the-art know-how by the close collaboration among government, academia, and small business via the NASA SBIR program. The prototype consists of a complete set of optical pattern recognition hardware with multi-channel storage and retrieval capability that is compactly configured inside a portable 1'X 2'X 3' aluminum case.

Jin, Y.; Liu, H. K.; Marzwell, N. I.

1996-01-01

10

Self-amplified optical pattern recognition system  

NASA Technical Reports Server (NTRS)

A self amplifying optical pattern recognizer includes a geometric system configuration similar to that of a Vander Lugt holographic matched filter configuration with a photorefractive crystal specifically oriented with respect to the input beams. An extraordinarily polarized, spherically converging object image beam is formed by laser illumination of an input object image and applied through a photorefractive crystal, such as a barium titanite (BaTiO.sub.3) crystal. A volume or thin-film dif ORIGIN OF THE INVENTION The invention described herein was made in the performance of work under a NASA contract, and is subject to the provisions of Public Law 96-517 (35 USC 202) in which the Contractor has elected to retain title.

Liu, Hua-Kuang (Inventor)

1994-01-01

11

Optical pattern recognition with adjustable sensitivity to shape and texture  

NASA Astrophysics Data System (ADS)

In this paper, an optical pattern recognition system with adjustable sensitivity to shape distortions and texture changes of the objects is presented. Application to a recognition task where the information of texture is the most decisive feature for a given object to be detected is provided. We apply the dual nonlinear correlation (DNC) model along with a support function acting in the frequency domain. This support function performs as an additional nonlinearity that enhances the information of some selected frequency bands related to the textural content of the target. A mathematical analysis allows the authors to show the usefulness of the proposed support function in the frame of the DNC model. The recognition system is applied to accomplish different recognition tasks involving model and real textured objects. The proposed optoelectronic correlator has been used to obtain successful experimental optical results, which are in accordance with the simulated results also provided.

Pérez, Elisabet; Sagrario Millán, María.; Chalasinska-Macukow, Katarzyna

2002-02-01

12

Self-amplified optical pattern-recognition technique  

NASA Technical Reports Server (NTRS)

A self-amplified optical pattern-recognition technique that utilizes a photorefractive crystal as a real-time volume holographic filter with recording accomplished by means of laser beams of proper polarization and geometric configuration is described. After the holographic filter is recorded, it can be addressed with extremely weak object beams and an even weaker reference beam to obtain a pattern-recognition signal. Because of beam-coupling energy transfer from the input object beam to the diffracted beam, the recognition signal is greatly amplified. Experimental results of this technique using BaTiO3 crystal show that 5 orders of magnitude of amplification of a recognition signal can be obtained.

Liu, Hua-Kuang

1992-01-01

13

Achromatic optical correlator for white light pattern recognition  

NASA Technical Reports Server (NTRS)

An achromatic optical correlator using spatially multiplexed achromatic matched spatial filters (MSFs) for white light optical pattern recognition is presented. The MSF array is synthesizd using a monochromatic laser and its achromaticity is achieved by adjusting the scale and spatial carrier frequency of each MSF to accommodate the wavelength variations in white light correlation detections. Systems analysis and several experimental results showing the correlation peak intensity using white-light illumination are presented.

Chao, Tien-Hsin; Liu, Hua-Kuang; Chen, Ming; Cai, Luzhong

1987-01-01

14

Real-valued composite filters for optical pattern recognition  

NASA Technical Reports Server (NTRS)

The design of real-valued composite filters for optical pattern recognition and classification is considered. A procedure to design a real-valued minimum average correlation energy (MACE) filter is developed. Also, the design of a real MVSDF-MACE filter that minimizes the output variance due to input noise while maintaining a sharp correlation peak is developed. Computer simulation indicates that the performance of these real filters is almost as good as that of the complex filters.

Balendra, A.; Rajan, P. K.

1993-01-01

15

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

16

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

17

Pattern Recognition  

Microsoft Academic Search

Pattern recognition methods have been applied to a wide variety of chemical problems. In a typical pattern recognition study, samples are classified according to a specific property using measurements that are indirectly related to the property of interest. An empirical relationship or classification rule is developed from a set of samples for which the property of interest and the measurements

Barry K. Lavine

2006-01-01

18

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

19

Binary optical filters for scale invariant pattern recognition  

NASA Technical Reports Server (NTRS)

Binary synthetic discriminant function (BSDF) optical filters which are invariant to scale changes in the target object of more than 50 percent are demonstrated in simulation and experiment. Efficient databases of scale invariant BSDF filters can be designed which discriminate between two very similar objects at any view scaled over a factor of 2 or more. The BSDF technique has considerable advantages over other methods for achieving scale invariant object recognition, as it also allows determination of the object's scale. In addition to scale, the technique can be used to design recognition systems invariant to other geometric distortions.

Reid, Max B.; Downie, John D.; Hine, Butler P.

1992-01-01

20

Optical pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989  

NASA Technical Reports Server (NTRS)

Papers on optical pattern recognition are presented, covering topics such as the estimation of satellite pose and motion parameters using a neural net tracker, associative memory, optical implmentation of programmable neural networks, optoelectronic neural networks, dynamic autoassociative neural memory, heteroassociative memory, bilinear pattern recognition processors, optical processing of optical correlation plane data, and a synthetic discriminant function-based nonlinear optical correlator. Other topics include an interactive optical-digital image processor, geometric transformations for video compression and human teleoperator display, quasiconformal remapping for compensation of human visual field defects, hybrid vision for automated spacecraft landing, advanced symbolic and inference optical correlation filters, and a rotationally invariant holographic tracking system. Additional topics include the detection of rotational and scale-varying objects with a programmable joint transform correlator, a single spatial light modulator binary nonlinear optical correlator, optical joint transform correlation, linear phase coefficient composite filters, and binary phase-only filters.

Liu, Hua-Kuang (editor)

1989-01-01

21

Optical pattern recognition for validation and security verification  

NASA Astrophysics Data System (ADS)

We propose an idea for security verification of credit cards, passports, and other ID so that they cannot easily be reproduced. A new scheme of complex phase/amplitude patterns that cannot be seen and cannot be copied by an intensity sensitive detector such as a CCD camera is used. The basic idea is to permanently and irretrievably bond a phase mask to a primary identification amplitude pattern such as a fingerprint, a picture of a face, or a signature. Computer simulation results and tests of the proposed system will be provided to verify that both the phase mask and the primary pattern are separately readable and identifiable in an optical processor or correlator.

Javidi, Bahram; Horner, Joseph L.

1994-03-01

22

Optical pattern recognition for validation and security verification  

NASA Astrophysics Data System (ADS)

We propose an idea for security verification of credit cards, passports, and other forms of identification so that they cannot easily be reproduced. A new scheme of complex phase/amplitude patterns that cannot be seen and cannot be coped by an intensity-sensitive detector such as a CCD camera is used. The basic idea is to permanently and irretrievably bond a phase mask to a primary identification amplitude pattern such as fingerprint, a picture of a face, or a signature. Computer simulation results and tests of the proposed system are provided to verify that both the phase mask and the primary pattern are separately readable and identifiable in an optical processor or correlator.

Javidi, Bahram; Horner, Joseph L.

1994-06-01

23

Optical pattern recognition III; Proceedings of the Meeting, Orlando, FL, Apr. 21, 22, 1992  

NASA Technical Reports Server (NTRS)

Consideration is given to transitioning of optical processing into systems (TOPS), optical correlator hardware, phase-only optical correlation filters, optical distortion-invariant correlation filters, and optical neural networks. Particular attention is given to a test target for optical correlators, a TOPS electronic warfare channelizer program, a portable video-rate optical correlator, a joint transform correlator employing electron trapping materials, a novelty filtered optical correlator using a photorefractive crystal, a comparison of correlation performance of smart ternary phase-amplitude filters with gray-scale and binary input scenes, real-time distortion-tolerant composite filters for automatic target identification, landscaping the correlation surface, fast designing of a circular harmonic filter using simulated annealing, feature-based correlation filters for distortion invariance, automatic target recognition using a feature-based optical neural network, and a holographic inner-product processor for pattern recognition.

Casasent, David P. (editor); Chao, Tien-Hsin (editor)

1992-01-01

24

Hybrid optical/digital architecture for distortion-invariant pattern recognition. Master's thesis  

SciTech Connect

This research investigated optical techniques for pattern recognition. An optical joint transform correlator was implemented using a magneto-optic spatial light modulator, and a charge coupled device (CCD) camera and frame grabber under personal computer (PC) control. A hybrid optical/digital architecture that could potentially perform position, scale, and rotation invariant pattern recognition using a computer generated hologram (CGH) was also implemented. The joint transform correlator was tested using forward looking infrared (FLIR) imagery containing tactical targets, and gave very good results. New techniques for binarizing the FLIR inputs and the fringe pattern of the joint transform were discovered. The input binarization used both scene average and a localized energy normalization technique for binarization. This resulted in reduced scene background, while retaining target detail. The fringe binarization technique subtracted the Fourier transform of the scene from the joint transform, and binarized on the average difference. This new technique was a significant improvement over recent published designs.

Cline, J.D.

1989-12-01

25

Passive and active optical bit-pattern recognition structures for multiwavelength optical packet switching networks  

NASA Astrophysics Data System (ADS)

Next generation High-Speed optical packet switching networks require components capable of recognising the optical header to enable on-the-fly accurate switching of incoming data packets to their destinations. This paper experimentally demonstrates a comparison between two different optical header recognition structures; A passive structure based on the use of Fiber Bragg Gratings (FBGs), whereas the active structure employs Opto-VLSI processors that synthesise dynamic wavelength profile through digital phase holograms. The structures are experimentally demonstrated at 10Gbps. Performance comparison between the two structures is also discussed. These optical header recognition structures are attractive for multiwavelength optical network and applications.

Aljada, Muhsen; Alameh, Kamal

2007-05-01

26

Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems  

NASA Astrophysics Data System (ADS)

A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase-only implementation with lower detection performance than full complex electronic systems. Our study includes pseudo-random pixel encoding techniques for approximating full complex filtering. Optical filter bank implementation is possible and they have the advantage of time averaging the entire filter bank at real time rates. Time-averaged optical filtering is computational comparable to billions of digital operations-per-second. For this reason, we believe future trends in high speed pattern recognition will involve hybrid architectures of both optical and DSP elements.

Lhamon, Michael Earl

27

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

28

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

29

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

30

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

31

Optical and digital pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 13-15, 1987  

NASA Technical Reports Server (NTRS)

The papers presented in this volume provide an overview of current research in both optical and digital pattern recognition, with a theme of identifying overlapping research problems and methodologies. Topics discussed include image analysis and low-level vision, optical system design, object analysis and recognition, real-time hybrid architectures and algorithms, high-level image understanding, and optical matched filter design. Papers are presented on synthetic estimation filters for a control system; white-light correlator character recognition; optical AI architectures for intelligent sensors; interpreting aerial photographs by segmentation and search; and optical information processing using a new photopolymer.

Liu, Hua-Kuang (editor); Schenker, Paul (editor)

1987-01-01

32

Soviet image pattern recognition research  

Microsoft Academic Search

This report is an assessment of the published Soviet image pattern recognition (IPR) research and was written by a panel of six US academic experts in that research field. Image pattern recognition is a set of technological research topics involving automatic or interactive computer processing of pictorial information, utilizing optical, electronic, and computer technologies. This report focuses on IPR system

B. L. McKenney; M. McGrain; A. Klinger; J. K. Aggarwal; N. J. George; R. M. Haralick

1989-01-01

33

Pattern recognition principles  

NASA Technical Reports Server (NTRS)

The present work gives an account of basic principles and available techniques for the analysis and design of pattern processing and recognition systems. Areas covered include decision functions, pattern classification by distance functions, pattern classification by likelihood functions, the perceptron and the potential function approaches to trainable pattern classifiers, statistical approach to trainable classifiers, pattern preprocessing and feature selection, and syntactic pattern recognition.

Tou, J. T.; Gonzalez, R. C.

1974-01-01

34

Real-valued composite filters for correlation-based optical pattern recognition  

NASA Technical Reports Server (NTRS)

Advances in the technology of optical devices such as spatial light modulators (SLMs) have influenced the research and growth of optical pattern recognition. In the research leading to this report, the design of real-valued composite filters that can be implemented using currently available SLMs for optical pattern recognition and classification was investigated. The design of real-valued minimum average correlation energy (RMACE) filter was investigated. Proper selection of the phase of the output response was shown to reduce the correlation energy. The performance of the filter was evaluated using computer simulations and compared with the complex filters. It was found that the performance degraded only slightly. Continuing the above investigation, the design of a real filter that minimizes the output correlation energy and the output variance due to noise was developed. Simulation studies showed that this filter had better tolerance to distortion and noise compared to that of the RMACE filter. Finally, the space domain design of RMACE filter was developed and implemented on the computer. It was found that the sharpness of the correlation peak was slightly reduced but the filter design was more computationally efficient than the complex filter.

Rajan, P. K.; Balendra, Anushia

1992-01-01

35

High-speed optical bit-pattern recognition employing fibre Bragg grating and Opto-VLSI processing  

Microsoft Academic Search

We propose a novel optical bit-pattern recognition employing an Opto-VLSI processor in conjunction with an array of fibre Bragg gratings (FBGs) with different Bragg wavelengths and a coherent-to-incoherent light converter. The FBG array slices the spectrum of the incoherent optical header and provides wavelength-dependent time delays, whereas the Opto-VLSI processor generates wavelength intensity profiles that match specific bit patterns. The

Muhsen Aljada; Kamal E. Alameh; Rong Zheng; Khalid Al-Begain

2006-01-01

36

Moment-Based Methods for Hybrid Optical/digital Pattern Recognition.  

NASA Astrophysics Data System (ADS)

Optical pre-processing of image data offers the opportunity for a tremendous data compression at rates unattainable through conventional electronic digital computing. Among the most well-known operations of this type is optical feature extraction for pattern recognition. This dissertation has optical feature extraction as its starting point. Taking advantage of the fundamental optical operation of Fourier transformation, the improvement in performance which can be obtained from extracting a set of moment invariants from both an image and its Fourier transform is studied from theoretical and experimental standpoints. Basically, moment invariants of the space domain are useful for distinguishing differences in shape and other gross features while Fourier domain moment invariants are useful for distinguishing differences in fine detail. Taken together, this dual -domain feature set is useful for a wider range of pattern recognition tasks than moment invariants from either domain taken by itself. A compact optical system for extracting the dual-domain complex moments which utilizes polarization and random phase multiplexing techniques is introduced. Feature extraction operations are usually both space-variant and global necessitating some form of scene segmentation and registration prior to extracting the feature. A windowed correlation method for optically locating the centroids of non-overlapping objects in a scene is demonstrated which accomplishes these segmentation and registration functions. It is further shown that this same approach can be used for tracking the movement of rigid and non -rigid objects. The centroid location technique can be extended into a general image understanding tool. Centroids are tracked over a continuous range of window sizes to produce a centroid scale-space map. One of the strongest advantages of the centroid scale-space approach is that the contours which are generated for 2-D inputs take the form of curved lines as compared with complicated surfaces for other well -known scale-space methods. The use of the centroid scale -space maps for obtaining the locations and size distribution of objects in a scene and for obtaining the medial axis transform of a scene is demonstrated. A hybrid optical/digital approach for computing the centroid scale-space maps is suggested.

Freeman, Mark Olmsted

37

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

38

Smart pattern recognition  

NASA Astrophysics Data System (ADS)

The purpose of this paper is to test correlation methods for pattern recognition applications. A broad overview of the main correlation architectures is first given. Many correlation data are compared with those obtained from standard pattern recognition methods. We used our simulations to predict improved decisional performance from correlation methods. More specifically, we are focused on the POF filter and composite filter family. We present an optimized composite correlation filter, called asymmetric segmented phase-only filter (ASPOF) for mobile target recognition applications. The main objective is to find a compromise between the number of references to be merged in the correlation filter and the time needed for making a decision. We suggest an all-numerical implementation of a VanderLugt (VLC) type composite filter. The aim of this all-numerical implementation is to take advantage of the benefits of the correlation methods and make the correlator easily reconfigurable for various scenarios. The use of numerical implementation of the optical Fourier transform improves the decisional performance of the correlator. Further, it renders the correlator less sensitive to the saturation phenomenon caused by the increased number of references used for fabricating the composite filter. Different tests are presented making use of the peak-to-correlation energy criterion and ROC curves. These tests confirm the validity ofour technique. Elderly fall detection and underwater mine detection are two applications which are considered for illustrating the benefits of our approach. The present work is motivated by the need for detailed discussions of the choice of the correlation architecture for these specific applications, pre-processing in the input plane and post processing in the output plane techniques for such analysis.

Alfalou, A.; Brosseau, C.; Alam, M. S.

2013-03-01

39

Image Recognition Based on Biometric Pattern Recognition  

Microsoft Academic Search

A new method, biomimetric pattern recognition, is mentioned to recognize images. At first, the image is pretreatment and feature extraction, then a high vector is got. A biomimetric pattern recognition model is designed. The judgment function is used to discriminate the classification of the samples. It is showed that the method is effective for little samples by experiment. It would

Shuliang Sun; Zhong Chen; Chenglian Liu; Yongning Guo; Xueyun Lin

2011-01-01

40

Inquisitive pattern recognition  

Microsoft Academic Search

In nature, inquisitiveness is the drive to question, to seek a deeper understanding and to challenge assumptions. Within the discrete world of computers, inquisitive pattern recognition (IPR) is the constructive investigation and exploitation of conflict in information. Data fusion is fertile proving-ground for inquisitive technologies. Multi-source, multi-modal data inherently contain conflicting information. As data fusion advances capabilities in situation assessment,

Amy L. Magnus; Steven C. Gustafson

2000-01-01

41

Achievable Rates for Pattern Recognition  

Microsoft Academic Search

Biological and machine pattern recognition systems face a common challenge: Given sensory data about an unknown object, classify the object by comparing the sensory data with a library of internal representations stored in memory. In many cases of interest, the number of patterns to be discriminated and the richness of the raw data force recognition systems to internally represent memory

M. Brandon Westover; Joseph A. O'sullivan

2005-01-01

42

Pattern recognition and neural networks  

Microsoft Academic Search

ashing powders (Carstensen, 1992).In all these of tasks there is a predefined set of classes of patterns which might bepresented, and the task is to classify a future pattern as one of these classes. Suchtasks are called classification or supervised pattern recognition1. Clearly someonehad to determine the classes in the first place, and seeking groupings of patterns iscalled cluster analysis

Brian D. Ripley

1996-01-01

43

A dual element multipoint optical fibre water contamination sensor system utilising artificial neural network pattern recognition  

Microsoft Academic Search

A dual-element multipoint optical fibre sensor system capable of detecting ethanol in water supplies is reported. The sensor system utilises a U-bend configuration for each sensor element in order to maximise the sensitivity of the system and is interrogated using a technique known as optical time domain reflectometry, OTDR, as this method is capable of detecting attenuation over distance. Analysis

D. King; W. B. Lyons; C. Flanagan; E. Lewi

2003-01-01

44

PATTERN RECOGNITION ROBI POLIKAR  

E-print Network

recognition problems are considerably more difficult then even the one illustrated above, and such problems features can we use to identify above pictured people as males or females? 1 Wiley Encyclopedia

Polikar, Robi

45

Pattern Recognition from Satellite Altitudes  

Microsoft Academic Search

Several decision algorithms were used to classify complex patterns recorded by TV cameras aboard unmanned, scientific satellites. Recognition experiments were performed with two kinds of patterns: lunar topographic features and clouds in the earth's atmosphere. Classification accuracies ranged from 53 percent to 99 percent on independent data.

Eugene Darling; R. D. Joseph

1968-01-01

46

Building an optical pattern recognizer  

NASA Astrophysics Data System (ADS)

The present portable solid-optics correlator for real-time pattern recognition uses pixelated spatial light modulators and phase-only filters, and will operate on sensor information extracted from any sensor system. Prospective operations of such a rugged and portable optical pattern recognizer include smart weapon midcourse guidance and navigation, target recognition, aim-point selection, and precise terminal homing. An account is given of the testing procedure being used by the U.S. Army missile command for a missile-guidance appligation of this optical correlator.

Lindberg, Perry C.; Gregory, Don A.

1991-08-01

47

Optical pattern recognition architecture implementing the mean-square error correlation algorithm  

DOEpatents

An optical architecture implementing the mean-square error correlation algorithm, MSE=.SIGMA.[I-R].sup.2 for discriminating the presence of a reference image R in an input image scene I by computing the mean-square-error between a time-varying reference image signal s.sub.1 (t) and a time-varying input image signal s.sub.2 (t) includes a laser diode light source which is temporally modulated by a double-sideband suppressed-carrier source modulation signal I.sub.1 (t) having the form I.sub.1 (t)=A.sub.1 [1+.sqroot.2m.sub.1 s.sub.1 (t)cos (2.pi.f.sub.o t)] and the modulated light output from the laser diode source is diffracted by an acousto-optic deflector. The resultant intensity of the +1 diffracted order from the acousto-optic device is given by: I.sub.2 (t)=A.sub.2 [+2m.sub.2.sup.2 s.sub.2.sup.2 (t)-2.sqroot.2m.sub.2 (t) cos (2.pi.f.sub.o t] The time integration of the two signals I.sub.1 (t) and I.sub.2 (t) on the CCD deflector plane produces the result R(.tau.) of the mean-square error having the form: R(.tau.)=A.sub.1 A.sub.2 {[T]+[2m.sub.2.sup.2.multidot..intg.s.sub.2.sup.2 (t-.tau.)dt]-[2m.sub.1 m.sub.2 cos (2.tau.f.sub.o .tau.).multidot..intg.s.sub.1 (t)s.sub.2 (t-.tau.)dt]} where: s.sub.1 (t) is the signal input to the diode modulation source: s.sub.2 (t) is the signal input to the AOD modulation source; A.sub.1 is the light intensity; A.sub.2 is the diffraction efficiency; m.sub.1 and m.sub.2 are constants that determine the signal-to-bias ratio; f.sub.o is the frequency offset between the oscillator at f.sub.c and the modulation at f.sub.c +f.sub.o ; and a.sub.o and a.sub.1 are constant chosen to bias the diode source and the acousto-optic deflector into their respective linear operating regions so that the diode source exhibits a linear intensity characteristic and the AOD exhibits a linear amplitude characteristic.

Molley, Perry A. (Albuquerque, NM)

1991-01-01

48

Neural Networks for Pattern Recognition  

Microsoft Academic Search

his is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization,

Christopher M. Bishop

1995-01-01

49

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

50

Photonic correlator pattern recognition: Application to autonomous docking  

NASA Technical Reports Server (NTRS)

Optical correlators for real-time automatic pattern recognition applications have recently become feasible due to advances in high speed devices and filter formulation concepts. The devices are discussed in the context of their use in autonomous docking.

Sjolander, Gary W.

1991-01-01

51

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

52

Adaptive pattern recognition and neural networks  

Microsoft Academic Search

The application of neural-network computers to pattern-recognition tasks is discussed in an introduction for advanced students. Chapters are devoted to the nature of the pattern-recognition task, the Bayesian approach to the estimation of class membership, the fuzzy-set approach, patterns with nonnumeric feature values, learning discriminants and the generalized perceptron, recognition and recall on the basis of partial cues, associative memories,

Yoh-Han Pao; Yohhan

1989-01-01

53

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

54

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

55

The Fuzzy Sets Approach to Pattern Recognition  

NASA Technical Reports Server (NTRS)

The fuzzy set concept is defined and its application to pattern recognition is illustrated. An iterative procedure for learning the equi-membership surfaces and for generating a set of discriminate functions for two pattern classes is given.

Wilson, T.

1972-01-01

56

Pattern recognition systems and procedures  

NASA Technical Reports Server (NTRS)

The objectives of the pattern recognition tasks are to develop (1) a man-machine interactive data processing system; and (2) procedures to determine effective features as a function of time for crops and soils. The signal analysis and dissemination equipment, SADE, is being developed as a man-machine interactive data processing system. SADE will provide imagery and multi-channel analog tape inputs for digitation and a color display of the data. SADE is an essential tool to aid in the investigation to determine useful features as a function of time for crops and soils. Four related studies are: (1) reliability of the multivariate Gaussian assumption; (2) usefulness of transforming features with regard to the classifier probability of error; (3) advantage of selecting quantizer parameters to minimize the classifier probability of error; and (4) advantage of using contextual data. The study of transformation of variables (features), especially those experimental studies which can be completed with the SADE system, will be done.

Nelson, G. D.; Serreyn, D. V.

1972-01-01

57

Inverse Scattering Approach to Improving Pattern Recognition  

SciTech Connect

The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the ''wake-sleep'' algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensory feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.

Chapline, G; Fu, C

2005-02-15

58

Inverse scattering approach to improving pattern recognition  

NASA Astrophysics Data System (ADS)

The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the "wake-sleep" algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensory feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.

Chapline, George; Fu, Chi-Yung

2005-05-01

59

Spectral feature classification and spatial pattern recognition  

NASA Technical Reports Server (NTRS)

This paper introduces a spatial pattern recognition processing concept involving the use of spectral feature classification technology and coherent optical correlation. The concept defines a hybrid image processing system incorporating both digital and optical technology. The hybrid instrument provides simplified pseudopattern images as functions of pixel classification from information embedded within a real-scene image. These pseudoimages become simplified inputs to an optical correlator for use in a subsequent pattern identification decision useful in executing landmark pointing, tracking, or navigating functions. Real-time classification is proposed as a research tool for exploring ways to enhance input signal-to-noise ratio as an aid in improving optical correlation. The approach can be explored with developing technology, including a current NASA Langley Research Center technology plan that involves a series of related Shuttle-borne experiments. A first-planned experiment, Feature Identification and Location Experiment (FILE), is undergoing final ground testing, and is scheduled for flight on the NASA Shuttle (STS2/flight OSTA-1) in 1980. FILE will evaluate a technique for autonomously classifying earth features into the four categories: bare land; water; vegetation; and clouds, snow, or ice.

Sivertson, W. E., Jr.; Wilson, R. G.

1979-01-01

60

Pattern recognition for statistical process control charts  

Microsoft Academic Search

Control charts are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Patterns displayed on control charts can provide information about the process. This paper describes the development of a pattern recognition system designed to detect and analyse various patterns that can occur on statistical quality

Jiemin Wang; A. K. Kochhar; R. G. Hannam

1998-01-01

61

Distortion invariant pattern recognition with phase filters  

NASA Technical Reports Server (NTRS)

A recently developed approach for pattern recognition using spatial filters with reduced tolerance requirements is employed for the generation of filters containing mainly phase information. As anticipated, the recognition levels were decreased, but they remain adequate for unambiguous identification together with appreciable amounts of distortion immunity. Furthermore, the information content of the filters is compatible with low devices like spatial light modulators.

Rosen, Joseph; Shamir, Joseph

1987-01-01

62

An Exercise in Critical Thinking: Pattern Recognition.  

ERIC Educational Resources Information Center

Describes an activity designed to allow secondary students to practice pattern recognition using the proof-of-purchase scanning lines found on many consumer products. Provided are a list of materials, procedures, and student activity sheets. (CW)

Postiglione, Ralph

1988-01-01

63

Adaptive pattern recognition and neural networks  

SciTech Connect

The application of neural-network computers to pattern-recognition tasks is discussed in an introduction for advanced students. Chapters are devoted to the nature of the pattern-recognition task, the Bayesian approach to the estimation of class membership, the fuzzy-set approach, patterns with nonnumeric feature values, learning discriminants and the generalized perceptron, recognition and recall on the basis of partial cues, associative memories, self-organizing nets, the functional-link net, fuzzy logic in the linking of symbolic and subsymbolic processing, and adaptive pattern recognition and its applications. Also included are C-language programs for (1) a generalized delta-rule net for supervised learning and (2) unsupervised learning based on the discovery of clustered structure. 183 refs.

Pao, Yohhan.

1989-01-01

64

Generalized Feature Extraction for Structural Pattern Recognition  

E-print Network

) and the Air Force Research Laboratory (AFRL) under grant #F30602-96-1-0349, the National Science Foundation, AFRL, NSF, AFOSR, SCEEE, or the U.S. government. #12;Keywords: Structural pattern recognition

65

Generalized Feature Extraction for Structural Pattern Recognition  

E-print Network

(DARPA) and the Air Force Research Laboratory (AFRL) under grant #F30602­96­1­0349, the National Science, of DARPA, AFRL, NSF, AFOSR, SCEEE, or the U.S. government. #12; Keywords: Structural pattern recognition

66

Learned pattern recognition using synthetic-discriminant-functions  

NASA Technical Reports Server (NTRS)

A method of using synthetic-discriminant-functions to facilitate learning in a pattern recognition system is discussed. Learning is accomplished by continually adding images to the training set used for synthetic discriminant functions (SDF) construction. Object identification is performed by efficiently searching a library of SDF filters for the maximum optical correlation. Two library structures are discussed - binary tree and multilinked graph - along with maximum ascent, back-tracking, perturbation, and simulated annealing searching techniques. By incorporating the distortion invariant properties of SDFs within a library structure, a robust pattern recognition system can be produced.

Jared, David A.; Ennis, David J.

1986-01-01

67

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

68

Word recognition using ideal word patterns  

NASA Astrophysics Data System (ADS)

The word shape analysis approach to text recognition is motivated by discoveries in psychological studies of the human reading process. It attempts to describe and compare the shape of the word as a whole object without trying to segment and recognize the individual characters, so it bypasses the errors committed in character segmentation and classification. However, the large number of classes and large variation and distortion expected in all patterns belonging to the same class make it difficult for conventional, accurate, pattern recognition approaches. A word shape analysis approach using ideal word patterns to overcome the difficulty and improve recognition performance is described in this paper. A special word pattern which characterizes a word class is extracted from different sample patterns of the word class and stored in memory. Recognition of a new word pattern is achieved by comparing it with the special pattern of each word class called ideal word pattern. The process of generating the ideal word pattern of each word class is proposed. The algorithm was tested on a set of machine printed gray scale word images which included a wide range of print types and qualities.

Zhao, Sheila X.; Srihari, Sargur N.

1994-03-01

69

Optical correlation recognition based on LCOS  

NASA Astrophysics Data System (ADS)

Vander-Lugt correlator[1] plays an important role in optical pattern recognition due to the characteristics of accurate positioning and high signal-to-noise ratio. The ideal Vander-Lugt correlator should have the ability of outputting strong and sharp correlation peak in allusion to the true target, in the existing Spatial Light Modulators[2], Liquid Crystal On Silicon(LCOS) has been the most competitive candidate for the matched filter owing to the continuous phase modulation peculiarity. Allowing for the distortions of the target to be identified including rotations, scaling changes, perspective changes, which can severely impact the correlation recognition results, herein, we present a modified Vander-Lugt correlator based on the LCOS by means of applying an iterative algorithm to the design of the filter so that the correlator can invariant to the distortions while maintaining good performance. The results of numerical simulation demonstrate that the filter could get the similar recognition results for all the training images. And the experiment shows that the modified correlator achieves the 180° rotating tolerance significantly improving the recognition efficiency of the correlator.

Tang, Mingchuan; Wu, Jianhong

2013-08-01

70

Pattern-Recognition Processor Using Holographic Photopolymer  

NASA Technical Reports Server (NTRS)

proposed joint-transform optical correlator (JTOC) would be capable of operating as a real-time pattern-recognition processor. The key correlation-filter reading/writing medium of this JTOC would be an updateable holographic photopolymer. The high-resolution, high-speed characteristics of this photopolymer would enable pattern-recognition processing to occur at a speed three orders of magnitude greater than that of state-of-the-art digital pattern-recognition processors. There are many potential applications in biometric personal identification (e.g., using images of fingerprints and faces) and nondestructive industrial inspection. In order to appreciate the advantages of the proposed JTOC, it is necessary to understand the principle of operation of a conventional JTOC. In a conventional JTOC (shown in the upper part of the figure), a collimated laser beam passes through two side-by-side spatial light modulators (SLMs). One SLM displays a real-time input image to be recognized. The other SLM displays a reference image from a digital memory. A Fourier-transform lens is placed at its focal distance from the SLM plane, and a charge-coupled device (CCD) image detector is placed at the back focal plane of the lens for use as a square-law recorder. Processing takes place in two stages. In the first stage, the CCD records the interference pattern between the Fourier transforms of the input and reference images, and the pattern is then digitized and saved in a buffer memory. In the second stage, the reference SLM is turned off and the interference pattern is fed back to the input SLM. The interference pattern thus becomes Fourier-transformed, yielding at the CCD an image representing the joint-transform correlation between the input and reference images. This image contains a sharp correlation peak when the input and reference images are matched. The drawbacks of a conventional JTOC are the following: The CCD has low spatial resolution and is not an ideal square-law detector for the purpose of holographic recording of interference fringes. A typical state-of-the-art CCD has a pixel-pitch limited resolution of about 100 lines/mm. In contrast, the holographic photopolymer to be used in the proposed JTOC offers a resolution > 2,000 lines/mm. In addition to being disadvantageous in itself, the low resolution of the CCD causes overlap of a DC term and the desired correlation term in the output image. This overlap severely limits the correlation signal-to-noise ratio. The two-stage nature of the process limits the achievable throughput rate. A further limit is imposed by the low frame rate (typical video rates) of low- and medium-cost commercial CCDs.

Chao, Tien-Hsin; Cammack, Kevin

2006-01-01

71

Interpolating vectors for robust pattern recognition.  

PubMed

This paper proposes a powerful algorithm for pattern recognition, which uses interpolating vectors for classifying patterns. Labeled reference vectors in a multi-dimensional feature space are first produced by a kind of competitive learning. We then assume a situation where virtual vectors, called interpolating vectors, are densely placed along line segments connecting all pairs of reference vectors of the same label. From these interpolating vectors, we choose the one that has the largest similarity to the test vector. Its label shows the result of pattern recognition. In practice, we can get the same result with a simpler process. We applied this method to the neocognitron for handwritten digit recognition and reduced the error rate from 1.52% to 1.02% for a blind test set of 5000 digits. PMID:17714913

Fukushima, Kunihiko

2007-10-01

72

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

73

Algorithms for adaptive nonlinear pattern recognition  

NASA Astrophysics Data System (ADS)

In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral remote sensing and target recognition applications. The authors' recent research in the development of adaptive TNE and adaptive LAMs is overviewed, with experimental results that show utility for a wide variety of pattern classification applications. Performance results are presented in terms of measured computational cost, noise tolerance, and classification accuracy.

Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary

2011-09-01

74

Process monitoring in milling by pattern recognition  

Microsoft Academic Search

The application of the pattern recognition technique for process monitoring in end milling is discussed in this paper. Cutting forces, torque, and spindle vibrations are monitored during machining, and are used to generate several signal features which are shown to be rather sensitive to the process conditions under consideration. Five machining conditions (classes) are considered in this study, namely; tool

M. A. Elbestawi; J. Marks; T. Papazafiriou

1989-01-01

75

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.

76

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.

77

Pattern recognition for electric power system protection  

Microsoft Academic Search

The objective of this research is to demonstrate pattern recognition tools such as decision trees (DTs) and neural networks that will improve and automate the design of relay protection functions in electric power systems. Protection functions that will benefit from the research include relay algorithms for high voltage transformer protection (TP) and for high impedance fault (HIF) detection. A methodology,

Yong Sheng

2002-01-01

78

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

79

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

80

Behavior Pattern Recognition and Aviation Security  

Microsoft Academic Search

New and evolving threats to aviation security require innovative and proactive solutions. Boston's Logan International Airport is adopting new methods to screen passengers based on techniques developed in Israel over the past 30 years. Behavior pattern recognition is a form of profiling passengers based not on race or religion but on suspicious and deceitful behavior. This technique is being used

Brian Seymour

2005-01-01

81

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

82

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

83

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

84

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

2015-03-01

85

[Comprehensive chemical pattern recognition of atractylodis rhizoma].  

PubMed

A method of comprehensive chemical pattern recognition of Atractylodis Rhizoma was established by GC-MS fingerprint, principal component analysis, cluster analysis and discriminant analysis. A DB-wax column (0.25 mm x 60 m, 0.25 microm) with El ion source and 70 V electron multiplier were used for GC-MS analysis. Using principal component analysis, cluster analysis, and discriminant analysis, 15 common peaks of sample fingerprints for chemical pattern recognition research were analysed. The same results were obtained from the fingerprint, principal component analysis and cluster analysis, which could use to distinguish genuine Atractylodes lancea, ungenuine A. lancea and A. chinensis. Thus, this method could be used for the quality control and comprehensive evaluation of Atractylodis Rhizoma. PMID:25276978

Wang, Fan; Ouyang, Zhen; Guo, Lan-Ping; Zhao, Ming; Peng, Hua-Sheng; Liao, Jing-Lin; Liang, Zhong-Ping

2014-07-01

86

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

87

VLSI Microsystem for Rapid Bioinformatic Pattern Recognition  

NASA Technical Reports Server (NTRS)

A system comprising very-large-scale integrated (VLSI) circuits is being developed as a means of bioinformatics-oriented analysis and recognition of patterns of fluorescence generated in a microarray in an advanced, highly miniaturized, portable genetic-expression-assay instrument. Such an instrument implements an on-chip combination of polymerase chain reactions and electrochemical transduction for amplification and detection of deoxyribonucleic acid (DNA).

Fang, Wai-Chi; Lue, Jaw-Chyng

2009-01-01

88

Pattern Recognition in Time Series  

NASA Astrophysics Data System (ADS)

Perhaps the most commonly encountered data types are time series, touching almost every aspect of human life, including astronomy. One obvious problem of handling time-series databases concerns with its typically massive size—gigabytes or even terabytes are common, with more and more databases reaching the petabyte scale. For example, in telecommunication, large companies like AT&T produce several hundred millions long-distance records per day [Cort00]. In astronomy, time-domain surveys are relatively new—these are surveys that cover a significant fraction of the sky with many repeat observations, thereby producing time series for millions or billions of objects. Several such time-domain sky surveys are now completed, such as the MACHO [Alco01],OGLE [Szym05], SDSS Stripe 82 [Bram08], SuperMACHO [Garg08], and Berkeley’s Transients Classification Pipeline (TCP) [Star08] projects. The Pan-STARRS project is an active sky survey—it began in 2010, a 3-year survey covering three-fourths of the sky with ˜60 observations of each field [Kais04]. The Large Synoptic Survey Telescope (LSST) project proposes to survey 50% of the visible sky repeatedly approximately 1000 times over a 10-year period, creating a 100-petabyte image archive and a 20-petabyte science database (http://www.lsst.org/). The LSST science database will include time series of over 100 scientific parameters for each of approximately 50 billion astronomical sources—this will be the largest data collection (and certainly the largest time series database) ever assembled in astronomy, and it rivals any other discipline’s massive data collections for sheer size and complexity. More common in astronomy are time series of flux measurements. As a consequence of many decades of observations (and in some cases, hundreds of years), a large variety of flux variations have been detected in astronomical objects, including periodic variations (e.g., pulsating stars, rotators, pulsars, eclipsing binaries, planetary transits), quasi-periodic variations (e.g., star spots, neutron star oscillations, active galactic nuclei), outburst events (e.g., accretion binaries, cataclysmic variable stars, symbiotic stars), transient events (e.g., gamma-ray bursts (GRB), flare stars, novae, supernovae (SNe)), stochastic variations (e.g., quasars, cosmic rays, luminous blue variables (LBVs)), and random events with precisely predictable patterns (e.g., microlensing events). Several such astrophysical phenomena are wavelength-specific cases, or were discovered as a result of wavelength-specific flux variations, such as soft gamma ray repeaters, x-ray binaries, radio pulsars, and gravitational waves. Despite the wealth of discoveries in this space of time variability, there is still a vast unexplored region, especially at low flux levels and short time scales (see also the chapter by Bloom and Richards in this book). Figure 28.1 illustrates the gap in astronomical knowledge in this time-domain space. The LSST project aims to explore phenomena in the time gap. In addition to flux-based time series, astronomical data also include motion-based time series. These include the trajectories of planets, comets, and asteroids in the Solar System, the motions of stars around the massive black hole at the center of the Milky Way galaxy, and the motion of gas filaments in the interstellar medium (e.g., expanding supernova blast wave shells). In most cases, the motions measured in the time series correspond to the actual changing positions of the objects being studied. In other cases, the detected motions indirectly reflect other changes in the astronomical phenomenon, such as light echoes reflecting across vast gas and dust clouds, or propagating waves.

Lin, Jessica; Williamson, Sheri; Borne, Kirk D.; DeBarr, David

2012-03-01

89

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

90

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

91

Face recognition with directional ternary pattern (DTP)  

NASA Astrophysics Data System (ADS)

This paper presents an effective and robust facial feature descriptor based on the directional ternary pattern (DTP) for face recognition. The DTP operator encodes the texture information of a local region by labeling the edge response values in all eight directions around a pixel with three different levels. The coding scheme exploits a threshold in order to differentiate between smooth and high-textured face regions while forming the ternary code. The location and occurrence information of the DTP micro-patterns within the facial image is then used as the feature descriptor. The effectiveness of the proposed method is evaluated with the FERET face image database using template matching (TM). Extensive experiments show the superiority of the DTP feature descriptor against some well-known local pattern-based feature representation methods.

Kabir, Md. H.; Ahmed, Faisal

2013-03-01

92

HUSSAIN ET. AL: FACE RECOGNITION USING LOCAL QUANTIZED PATTERNS 1 Face Recognition using Local Quantized  

E-print Network

HUSSAIN ET. AL: FACE RECOGNITION USING LOCAL QUANTIZED PATTERNS 1 Face Recognition using Local to illumination variations. Extensive experiments on several challenging face recognition datasets (such as FERET representa- tions in a projected space. 1 Introduction Face recognition is an important and popular visual

Paris-Sud XI, Université de

93

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

94

Success potential of automated star pattern recognition  

NASA Technical Reports Server (NTRS)

A quasi-analytical model is presented for calculating the success probability of automated star pattern recognition systems for attitude control of spacecraft. The star data is gathered by an imaging star tracker (STR) with a circular FOV capable of detecting 20 stars. The success potential is evaluated in terms of the equivalent diameters of the FOV and the target star area ('uniqueness area'). Recognition is carried out as a function of the position and brightness of selected stars in an area around each guide star. The success of the system is dependent on the resultant pointing error, and is calculated by generating a probability distribution of reaching a threshold probability of an unacceptable pointing error. The method yields data which are equivalent to data available with Monte Carlo simulatins. When applied to the recognition system intended for use on the Space IR Telescope Facility it is shown that acceptable pointing, to a level of nearly 100 percent certainty, can be obtained using a single star tracker and about 4000 guide stars.

Van Bezooijen, R. W. H.

1986-01-01

95

Visual pattern recognition using coupled filters  

NASA Astrophysics Data System (ADS)

We discuss the use of an optical correlator with a highly coupled filter and dappled targets to track an object in a field of view cluttered by background noise and/or similar objects. The dappled targets are fractal images whose statistics are independent of scale. Each is unique for tracking the targets. We report the drop in correlation (hence recognition) of an object as a function of in-plane rotation and as a function of range. We discuss plans for an application in Johnson Space Center's Automation and Robotics group, in which correlation processing of these targets would distinguish an object and pass its position and orientation to a robot control system. Using MEDOF (minimum Euclidean distance optimal filter) to create filters on the coupled filter modulator, we show that background clutter can be optically filtered out.

Monroe, Stanley E., Jr.; Juday, Richard D.; Barton, R. Shane; Qin, Michael K.

1995-06-01

96

Assisted Peptide Folding by Surface Pattern Recognition  

PubMed Central

Natively disordered proteins belong to a unique class of biomolecules whose function is related to their flexibility and their ability to adopt desired conformations upon binding to substrates. In some cases these proteins can bind multiple partners, which can lead to distinct structures and promiscuity in functions. In other words, the capacity to recognize molecular patterns on the substrate is often essential for the folding and function of intrinsically disordered proteins. Biomolecular pattern recognition is extremely relevant both in vivo (e.g., for oligomerization, immune response, induced folding, substrate binding, and molecular switches) and in vitro (e.g., for biosensing, catalysis, chromatography, and implantation). Here, we use a minimalist computational model system to investigate how polar/nonpolar patterns on a surface can induce the folding of an otherwise unstructured peptide. We show that a model peptide that exists in the bulk as a molten globular state consisting of many interconverting structures can fold into either a helix-coil-helix or an extended helix structure in the presence of a complementary designed patterned surface at low hydrophobicity (3.7%) or a uniform surface at high hydrophobicity (50%). However, we find that a carefully chosen surface pattern can bind to and catalyze the folding of a natively unfolded protein much more readily or effectively than a surface with a noncomplementary or uniform distribution of hydrophobic residues. PMID:21354404

Zhuang, Zhuoyun; Jewett, Andrew I.; Kuttimalai, Silvan; Bellesia, Giovanni; Gnanakaran, S.; Shea, Joan-Emma

2011-01-01

97

Pattern recognition for electric power system protection  

NASA Astrophysics Data System (ADS)

The objective of this research is to demonstrate pattern recognition tools such as decision trees (DTs) and neural networks that will improve and automate the design of relay protection functions in electric power systems. Protection functions that will benefit from the research include relay algorithms for high voltage transformer protection (TP) and for high impedance fault (HIF) detection. A methodology, which uses DTs and wavelet analysis to distinguish transformer internal faults from other conditions that are easily mistaken for internal faults, has been developed. Also, a DT based solution is proposed to discriminate HIFs from normal operations that may confuse relays. Both methods have been verified with simulation data generated by the Electromagnetic Transients Program. Compared with traditional methods, both show better performance. After being trained with a large number of carefully selected features, the desired DTs can obtain an accuracy of greater than 95%. Further, no special equipment is necessary; the DT-based controller only needs the standard relay input signals sampled at 1920 Hz. So far, no one has applied the same methodologies to solve these problems. Even though some future work with experimental data is needed to make the methods more convincing for utilities, the research has already shown that pattern recognition is a promising direction in developing power system protection algorithms.

Sheng, Yong

2002-11-01

98

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

99

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.

100

Research strategies for archaeological pattern recognition on historic sites  

Microsoft Academic Search

Various strategies now being used to delineate pattern using data from historic sites in the British colonial system are outlined. The formula concept of pattern recognition seen in the Mean Ceramic Date Formula illustrates the highly regular patterning of discarded ceramics through time. The patterns of refuse disposal on British colonial sites are illustrated by means of the Brunswick Pattern

1978-01-01

101

28 October 2011 1Ups and Downs in Pattern Recognition Ups and Downs in  

E-print Network

28 October 2011 1Ups and Downs in Pattern Recognition Ups and Downs in Pattern Recognition Bob Duin, 28 October 2011 Learning about the world 28 October 2011 2Ups and Downs in Pattern Recognition Human October 2011 3Ups and Downs in Pattern Recognition The Pattern Recognition Problem A B ? The four

Duin, Robert P.W.

102

Pattern-Recognition Algorithm for Locking Laser Frequency  

NASA Technical Reports Server (NTRS)

A computer program serves as part of a feedback control system that locks the frequency of a laser to one of the spectral peaks of cesium atoms in an optical absorption cell. The system analyzes a saturation absorption spectrum to find a target peak and commands a laser-frequency-control circuit to minimize an error signal representing the difference between the laser frequency and the target peak. The program implements an algorithm consisting of the following steps: Acquire a saturation absorption signal while scanning the laser through the frequency range of interest. Condition the signal by use of convolution filtering. Detect peaks. Match the peaks in the signal to a pattern of known spectral peaks by use of a pattern-recognition algorithm. Add missing peaks. Tune the laser to the desired peak and thereafter lock onto this peak. Finding and locking onto the desired peak is a challenging problem, given that the saturation absorption signal includes noise and other spurious signal components; the problem is further complicated by nonlinearity and shifting of the voltage-to-frequency correspondence. The pattern-recognition algorithm, which is based on Hausdorff distance, is what enables the program to meet these challenges.

Karayan, Vahag; Klipstein, William; Enzer, Daphna; Yates, Philip; Thompson, Robert; Wells, George

2006-01-01

103

WPTER: wavelet packet transform for efficient pattern recognition of signals  

Microsoft Academic Search

In the present work, we propose a novel algorithm based on the Wavelet Packet Transform (WPT) for pattern recognition of signals, which operates both feature selection and classification at the same time: Wavelet Packet Transform for Efficient pattern Recognition of signals (WPTER). The distinctive characteristics of WPTER with respect to the previously proposed algorithms for the WPT-based classification of signals

Marina Cocchi; Renato Seeber; Alessandro Ulrici

2001-01-01

104

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

105

DTWRadon-based Shape Descriptor for Pattern Recognition  

E-print Network

in many disciplines such as biology, computer vision, artificial intelligence or remote sensing where Author manuscript, published in "International Journal of Pattern Recognition and Artificial Intelligence present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon

Paris-Sud XI, Université de

106

Evolving Cellular Automata Based Associative Memory For Pattern Recognition  

E-print Network

Evolving Cellular Automata Based Associative Memory For Pattern Recognition Niloy Ganguly 1 Arijit@,pradipta@,biplab@,ppc@ppc.gbecs.ac.in Abstract. This paper reports a Cellular Automata (CA) model for pattern recognition. The special class of CA, referred to as GMACA (Generalized Multiple Attractor Cellular Automata), is employed to de- sign

Ganguly, Niloy

107

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 database, and the highest accuracy of 99.13% is obtained with the proposed method. Keywords-- Gender

Bebis, George

108

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

109

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. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Apple; Stem

Ünay, Devrim

110

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

111

Pattern recognition in the database of a mask layout  

NASA Astrophysics Data System (ADS)

The request of pattern recognition has been frequently brought up by both mask and wafer engineers. Despite different intentions, pattern recognition is usually the first step of many applications and hence plays a major role to accomplish certain tasks. For the purpose of this work, pattern recognition is defined as searching a specific polygon or a group of particular patterns from a chip layout. Operator scan is truly not an efficient approach of pattern recognition, in particular, for cases with huge design database of advanced semiconductor integrated circuits. Obviously, an automation system of pattern recognition is necessary and benefits the data preparation process. Two categories of pattern recognition are discussed in the present study, 'fuzzy search' and 'exact match.' Each category has its own application, but the searching algorithms could be much different. Details of searching algorithms are given for both categories of pattern recognition. Due to the nature of industrial standard, the scope of the present application is limited to database with GDSII format. Hence, coordinate searching is internally used inside the searching engine.

Chen, Shih-Ying; Lynn, Eric C.; Shin, Jaw-Jung

2002-07-01

112

PARALLEL SELF-ORGANIZING FEATURE MAPS FOR UNSUPERVISED PATTERN RECOGNITION  

Microsoft Academic Search

Neural network research has recently undergone a revival for use in pattern recognition applications. If a training set of data can be provided, the supervised types of networks, such as the Hopfield nets or perceptrons, can be used to recognize patterns. For unsupervised pattern recognition, systems such as those of the Carpenter\\/Grossberg ART2 system and Kohonens’ self-organizing feature maps are

TERRANCE L. HUNTSBERGER; PONGSAK AJJIMARANGSEE

1990-01-01

113

Optical sensing: recognition elements and devices  

NASA Astrophysics Data System (ADS)

The requirements in chemical and biochemical sensing with respect to recognition elements, avoiding non-specific interactions, and high loading of the surface for detection of low concentrations as well as optimized detection systems are discussed. Among the many detection principles the optical techniques are classified. Methods using labeled compounds like Total Internal Reflection Fluorescence (TIRF) and direct optical methods like micro reflectometry or refractometry are discussed in comparison. Reflectometric Interference Spectroscopy (RIfS) is presented as a robust simple method for biosensing. As applications, trace analysis of endocrine disruptors in water, hormones in food, detection of viruses and bacteria in food and clinical diagnostics are discussed.

Gauglitz, Guenter G.

2012-09-01

114

BI DIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORK METHOD IN THE CHARACTER RECOGNITION  

Microsoft Academic Search

Pattern recognition techniques are associated a symbolic identity with the image of the pattern. In this work we will analyze different neural network methods in pattern recognition. This problem of replication of patterns by machines (computers) involves the machine printed patterns. The pattern recognition is better known as optical pattern recognition. Since, it deals with recognition of optically processed patterns

Yash Pal Singh; V. S. Yadav; Amit Gupta; Abhilash Khare

2009-01-01

115

Pattern recognition in the satellite temperature retrieval problem  

NASA Technical Reports Server (NTRS)

Pattern recognition procedures have been developed in order to improve the first-guess fields for satellite temperature retrievals. The first procedure is used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. The second procedure is used to organize a priori data into shape-coherent pattern libraries using structural information inherent in the data itself. On the basis of independent tests of about 800 temperature retrievals, it was found that: (1) the pattern recognition techniques reduced first-guess profile errors by nearly 50 percent in comparison with traditional partitioning schemes; and (2) with regression and physical-iterative retrieval algorithms, however, the effect of pattern recognition on temperature retrieval error was insignificant. Analysis of individual retrieval errors showed that poor retrievals may outweigh the potential benefits of both pattern recognition techniques.

Thompson, O. E.; Goldberg, M. D.; Dazlich, D. A.

1985-01-01

116

Pattern-Recognition Receptors and Gastric Cancer  

PubMed Central

Chronic inflammation has been associated with an increased risk of several human malignancies, a classic example being gastric adenocarcinoma (GC). Development of GC is known to result from infection of the gastric mucosa by Helicobacter pylori, which initially induces acute inflammation and, in a subset of patients, progresses over time to chronic inflammation, gastric atrophy, intestinal metaplasia, dysplasia, and finally intestinal-type GC. Germ-line encoded receptors known as pattern-recognition receptors (PRRs) are critical for generating mature pro-inflammatory cytokines that are crucial for both Th1 and Th2 responses. Given that H. pylori is initially targeted by PRRs, it is conceivable that dysfunction within genes of this arm of the immune system could modulate the host response against H. pylori infection, and subsequently influence the emergence of GC. Current evidence suggests that Toll-like receptors (TLRs) (TLR2, TLR3, TLR4, TLR5, and TLR9), nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) (NOD1, NOD2, and NLRP3), a C-type lectin receptor (DC-SIGN), and retinoic acid-inducible gene (RIG)-I-like receptors (RIG-I and MDA-5), are involved in both the recognition of H. pylori and gastric carcinogenesis. In addition, polymorphisms in genes involved in the TLR (TLR1, TLR2, TLR4, TLR5, TLR9, and CD14) and NLR (NOD1, NOD2, NLRP3, NLRP12, NLRX1, CASP1, ASC, and CARD8) signaling pathways have been shown to modulate the risk of H. pylori infection, gastric precancerous lesions, and/or GC. Further, the modulation of PRRs has been suggested to suppress H. pylori-induced inflammation and enhance GC cell apoptosis, highlighting their potential relevance in GC therapeutics. In this review, we present current advances in our understanding of the role of the TLR and NLR signaling pathways in the pathogenesis of GC, address the involvement of other recently identified PRRs in GC, and discuss the potential implications of PRRs in GC immunotherapy. PMID:25101079

Castaño-Rodríguez, Natalia; Kaakoush, Nadeem O.; Mitchell, Hazel M.

2014-01-01

117

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

118

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

119

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

120

Maximum Likelihood Study for Sound Pattern Separation and Recognition  

Microsoft Academic Search

The increasing needs of content-based automatic indexing for large musical repositories have led to extensive investigation in musical sound pattern recognition. Numerous acoustical sound features have been developed to describe the characteristics of a sound piece. Many of these features have been successfully applied to monophonic sound timbre recognition. However, most of those features failed to describe enough characteristics of

Xin Zhang; Krzysztof Marasek; Zbigniew W. Ras

2007-01-01

121

Insect vision: emergence of pattern recognition from coarse encoding.  

PubMed

Neurogenetic tools of Drosophila research allow unique access to the neural circuitry underpinning visually guided behaviours. New research is highlighting how particular areas in the fly's central brain needed for pattern recognition provide a coarse visual encoding. PMID:24456981

Wystrach, Antoine; Dewar, Alex D M; Graham, Paul

2014-01-20

122

Visual cluster analysis and pattern recognition template and methods  

DOEpatents

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

Osbourn, Gordon Cecil (Albuquerque, NM); Martinez, Rubel Francisco (Albuquerque, NM)

1999-01-01

123

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 ...

124

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

125

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

126

Markov logic networks for optical chemical structure recognition.  

PubMed

Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image data sets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.dinfo.unifi.it. PMID:25068386

Frasconi, Paolo; Gabbrielli, Francesco; Lippi, Marco; Marinai, Simone

2014-08-25

127

Applications of Moment Invariants to Neurocomputing for Pattern Recognition.  

NASA Astrophysics Data System (ADS)

This thesis records a continuous effort during the past several years in the exploitation of using moment invariants in neurocomputing for pattern recognition. In the introductory section of this thesis, the neuron as a computational unit and the concept of algebraic invariants are discussed in the common sense. As the underlying body of the thesis, Hu's invariants of visual patterns are reviewed. A unique explanation of the significance of moments and moment invariants of different order is proposed. This explanation forms the basis of a new method of character recognition, which may provide additional information about feature selection or discrimination between characters. Image descriptors for character recognition are also considered, they are circular harmonic expansions for rotation invariant pattern recognition, Mellin transform for scale invariant pattern recognition and the combination the two, namely the Fourier -Mellin image descriptors (FMDs), for rotation and scale invariant pattern recognition. A method for accurately calculating the FMDs is proposed and is applied to the calculations of all the alphabetic-numeric characters. These 36 characters are designed as the reference patterns for pattern recognition, for which the geometrical parameters, Hu's invariants and the FMDs have been calculated and listed in the appendix. Attention is then turned to three neural network models (Hopfield, Fukushima and Inter-Pattern Association) which are described in terms of the correspondence between these models and the biological nerve systems and the effectiveness of applying these models to pattern recognition. The information storage capacity of these models is also estimated. Application of the moment invariants with neurocomputing begins with an investigation of the feasibility of using the image irradiance moments to replace the Hamming distance which is generally used in the criterion that shows the convergence in neurocomputing. Moreover, invariant pattern recognition is obtained by introducing the binary codes of moment invariants to neurocomputing. Combining moment invariants with neural network processing allows us to recognize patterns which have been subjected to various distortions, such as noise, translation, rotation and scale variation. Finally, a brief discussion about a future study of the invariant pattern recognition and a summary conclude this thesis.

Li, Yajun

1990-01-01

128

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

129

The Relationship between Arithmetic and Reading Achievement and Visual Pattern Recognition in First Grade Children.  

ERIC Educational Resources Information Center

Results from testing 20 first graders in a remedial class in Maryland indicated that: same pattern recognition was significantly higher than reverse pattern recognition; identical pattern recognition did not affect performance on reading and arithmetic achievement; reverse pattern recognition significantly affected performance on reading and…

Bragman, Ruth; Hardy, Robert C.

1982-01-01

130

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...

131

Optical Computer Recognition of Facial Expressions Associated with Stress Induced by Performance  

E-print Network

Optical Computer Recognition of Facial Expressions Associated with Stress Induced by Performance, METAXAS DN. Optical computer recognition of facial expressions associated with stress induced optical computer recognition (OCR) algorithms for detecting facial changes during performance while people

Pennsylvania, University of

132

Size Scaling in Visual Pattern Recognition  

ERIC Educational Resources Information Center

Human visual recognition on the basis of shape but regardless of size was investigated by reaction time methods. Results suggested two processes of size scaling: mental-image transformation and perceptual-scale transformation. Image transformation accounted for matching performance based on visual short-term memory, whereas scale transformation…

Larsen, Axel; Bundesen, Claus

1978-01-01

133

Complements to 'Pattern Recognition and Neural Networks  

Microsoft Academic Search

Introduction Page 4: The book by Przytula & Prasanna (1993) discusses in detail the parallel implementation of neural networks. Page 16: Langley (1996) provides a book-length introduction to one viewpointon machine learning. Langley & Simon (1995) and Bratko & Muggleton (1995) discuss applications of machine learning with claimed real-world benefits. Valentin et al. (1994) survey recent developments in face recognition.

B. d. Ripley

1996-01-01

134

Pigment Melanin: Pattern for Iris Recognition  

Microsoft Academic Search

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, which is unavailable in near-infrared (NIR) imaging. This is due to the biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to

S. Mahdi Hosseini; Babak Nadjar Araabi; Hamid Soltanian-Zadeh

2010-01-01

135

Handwritten picture language for effective pattern recognition  

Microsoft Academic Search

There exist many techniques for automatic beautification from handwritten pictures. Some pictures, however, still remain after any sort of recognition is used. In fact, some handwritten pictures can be recognized easily and others are difficult to recognize. We design a handwritten picture language which is composed of pictures already known to be recognized easily. The user draws an original picture

Sachiko Kawachiya

1996-01-01

136

Cerebellar LTD and Pattern Recognition by Purkinje Cells  

E-print Network

,3,7, * Wolfgang Mittmann,2,3,7 Freek E. Hoebeek,4 R. Angus Silver,3 Chris I. De Zeeuw,4,5 Michael Ha¨ usser,2 patterns. We have studied the LTD-based recognition of PF patterns in a biophysically realistic Purkinje to the Purkinje cell (Ito, 2001; Ito et al., 1982; Sakurai, 1987). Thus, a PF activity pattern that is paired

Steuber, Volker

137

Local binary patterns for multi-view facial expression recognition , R. Bowden  

E-print Network

Local binary patterns for multi-view facial expression recognition S. Moore , R. Bowden Centre: Facial expression recognition Multi-view facial expression recognition Head pose estimation Local binary patterns Local gabor binary patterns a b s t r a c t Research into facial expression recognition has

Bowden, Richard

138

Pattern recognition at different scales: A statistical perspective  

NASA Astrophysics Data System (ADS)

In this paper we borrow concepts from Information Theory and Statistical Mechanics to perform a pattern recognition procedure on a set of x-ray hazelnut images. We identify two relevant statistical scales, whose ratio affects the performance of a machine learning algorithm based on statistical observables, and discuss the dependence of such scales on the image resolution. Finally, by averaging the performance of a Support Vector Machines algorithm over a set of training samples, we numerically verify the predicted onset of an optimal scale of resolution, at which the pattern recognition is favoured.

Colangeli, Matteo; Rugiano, Francesco; Pasero, Eros

2014-07-01

139

Pattern recognition receptors in innate immunity, host defense, and immunopathology  

PubMed Central

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. An improved understanding of the pattern recognition receptors that mediate innate responses and their downstream effects after receptor ligation has the potential to lead to new ways to improve vaccines and prevent autoimmunity. This review focuses on the control of innate immune activation and the role that innate immune receptors play in helping to maintain tissue homeostasis. PMID:24292903

Suresh, Rahul

2013-01-01

140

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

141

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

142

Auditory orientation in crickets: pattern recognition controls reactive steering.  

PubMed

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 species-specific 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. PMID:16227440

Poulet, James F A; Hedwig, Berthold

2005-10-25

143

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

144

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

145

Biometric verification based on grip-pattern recognition  

NASA Astrophysics Data System (ADS)

This paper describes the design, implementation and evaluation of a user-verification system for a smart gun, which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 × 44 piezoresistive elements is used to measure the grip pattern. An interface has been developed to acquire pressure images from the sensor. The values of the pixels in the pressure-pattern images are used as inputs for a verification algorithm, which is currently implemented in software on a PC. The verification algorithm is based on a likelihoodratio classifier for Gaussian probability densities. First results indicate that it is feasible to use grip-pattern recognition for biometric verification.

Veldhuis, Raymond N.; Bazen, Asker M.; Kauffman, Joost A.; Hartel, Pieter

2004-06-01

146

Optical correlators for recognition of human face thermal images  

NASA Astrophysics Data System (ADS)

In this paper, the application of the optical correlators for face thermograms recognition is described. The thermograms were colleted from 27 individuals. For each person 10 pictures in different conditions were recorded and the data base composed of 270 images was prepared. Two biometric systems based on joint transform correlator and 4f correlator were built. Each system was designed for realizing two various tasks: verification and identification. The recognition systems were tested and evaluated according to the Face Recognition Vendor Tests (FRVT).

Bauer, Joanna; Podbielska, Halina; Suchwalko, Artur; Mazurkiewicz, Jacek

2005-09-01

147

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

148

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

149

Pattern recognition based digital relaying for advanced series compensated line  

Microsoft Academic Search

This paper presents a new approach for fault analysis of advanced series compensated (thyristor controlled series compensated) line using pattern recognition approach. Here, S-transform (ST) is used to process the post fault current signal samples and features are extracted to identify the faulty phase and the faulty section involved in the fault process. The S-transform is an extension of wavelet

S. R. Samantaray; P. K. Dash

2008-01-01

150

The Illinois Pattern Recognition Computer-ILLIAC III  

Microsoft Academic Search

This report describes the system design of an all-digital computer for visual recognition. One processor, the Pattern Articulation Unit (PAU), has been singled out for detailed discussion. Other units, in particular the Arithmetic Unit and the Taxicrinic Unit, are treated in reports listed in the bibliography. The PAU has been shown to be a processor of fundamentally new design-its logical

BRUCE H. McCORMICKt

1963-01-01

151

Quantification of pattern recognition quality by multivariate normal distribution functions  

Microsoft Academic Search

Analysis of the multivariate data distributions can be helpful or directly applicable in pattern recognition tests. Estimate of the volume of the critical region of overlapping distributions is essential in determination of the condence level of classica- tion. Mathematical tools for analysis of the multivariate distributions (included probability, false positives and false negatives, means for calculation of the critical region)

P. Serapinas; A. Acus; A. Goötauto

2008-01-01

152

Visual Pattern Recognition in Drosophila Is Invariant for  

E-print Network

of their visual field where they had originally seen them. Tethered flies (Drosophila melanogaster) in a flight before. In the flight simulator (Fig. 1A), the fly's (Drosophila melanogaster) head and thorax and, henceVisual Pattern Recognition in Drosophila Is Invariant for Retinal Position Shiming Tang,1

Field, David

153

Pattern Recognition Techniques for Odor Discrimination in Gas Sensor Array  

E-print Network

sensors for the detection of single gases (such as CO, CH4, H2, SO2, NOx, O3 etc...) has seenPattern Recognition Techniques for Odor Discrimination in Gas Sensor Array Amine Bermak, Sofiane of the most recent as well as tradi- tional applications for gas sensors. The table also reports the gases

Martinez, Dominique

154

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

155

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

156

Shape variability and spatial relationships modeling in statistical pattern recognition  

Microsoft Academic Search

We focus on the problem of shape variability modeling in statistical pattern recognition. We present a nonlinear statistical model invariant to affine transformations. This model is learned on an ordinate set of points. The concept of relations between model components is also taken in account. This model is used to find curves and points partially occulted in the image. We

Barbara Romaniuk; Michel Desvignes; Marinette Revenu; Marie-josèphe Deshayes

2004-01-01

157

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

158

AANN: an alternative to GMM for pattern recognition  

Microsoft Academic Search

The objective in any pattern recognition problem is to capture the characteristics common to each class from feature vectors of the training data. While Gaussian mixture models appear to be general enough to characterize the distribution of the given data, the model is constrained by the fact that the shape of the components of the distribution is assumed to be

B. Yegnanarayana; S. P. Kishore

2002-01-01

159

Fuzzy pattern recognition of circadian cycles in ecosystems  

Microsoft Academic Search

Many ecological variables show a wide range of fluctuations, the most important of which is the diurnal variation. This cycling may contain important information regarding the ecosystem’s functioning and, if properly interpreted, can represent a valuable predictive tool in ecosystems management. This paper describes a simple algorithm for extracting meaningful information from daily cycles using fuzzy pattern recognition techniques. The

S. Marsili-Libelli

2004-01-01

160

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

161

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

162

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

163

Pattern Recognition 38 (2005) 10331043 www.elsevier.com/locate/patcog  

E-print Network

Pattern Recognition 38 (2005) 1033­1043 www.elsevier.com/locate/patcog Behavior classification to solve a #12;1034 R. Goldenberg et al. / Pattern Recognition 38 (2005) 1033­1043 number of basic computer

Kimmel, Ron

2005-01-01

164

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

165

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

166

Sophisticated temporal pattern recognition in retinal ganglion cells.  

PubMed

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

2008-04-01

167

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

168

Partial rotation invariance in retinal pattern recognition  

NASA Astrophysics Data System (ADS)

We desire to have a joint transform correlator track features in the image of a human retina. Previous binarized digital methods indicated unacceptable limitations in tracking through torsion motions of the eye. To create an extended range of response to eyeball rotation we tried several methods of processing the reference image. We compared laboratory measurements with digital simulations. Based on small statistics and our noiseless models, the results disagree; the digital method has less range, and the optical method has sufficient range (+/- 5 degree(s)) for our purpose.

Juday, Richard D.; Knopp, Jerome; Soutar, Colin; Barton, R. Shane

1994-06-01

169

Pattern recognition with parallel associative memory  

NASA Technical Reports Server (NTRS)

An examination is conducted of the feasibility of searching targets in aerial photographs by means of a parallel associative memory (PAM) that is based on the nearest-neighbor algorithm; the Hamming distance is used as a measure of closeness, in order to discriminate patterns. Attention has been given to targets typically used for ground-control points. The method developed sorts out approximate target positions where precise localizations are needed, in the course of the data-acquisition process. The majority of control points in different images were correctly identified.

Toth, Charles K.; Schenk, Toni

1990-01-01

170

Object detection by optical correlator and intelligence recognition surveillance systems  

NASA Astrophysics Data System (ADS)

We report a recent work on robust object detection in high-resolution aerial imagery in urban environment for Intelligence, Surveillance and Recognition (ISR) missions. Our approaches used the simple linear iterative clustering (SLIC) algorithm, which combines regional and edge information to form the superpixels. The irregularity in size and shape of the superpixels measured with the Hausdorff distance served to determine the salient regions in the very large aerial images. Then, the car detection was performed with both the component-based approach and the featurebased approaches. We merged the superpixels with the statistical region merging (SRM) algorithm. The regions were described by the radiometric, geometrical moments and shape features, and classified using the Support Vector Machine (SVM). The cast shadow were detected and removed by a radiometry based tricolor attenuation model (TAM). Detection of object parts is less sensitive to occlusion, rotation, and changes in scale, view angle and illumination than detection of the object as whole. The object parts were combined to the object according to their unique spatial relations. On the other hand, we used the invariant scale invariant feature transform (SIFT) features to describe superpixels and classed them by the SVM as belong or not to the object. All along our recent work we still trace the brilliant ideas in early days by H. John Caulfield and other pioneers of optical pattern recognition, for improving the discrimination of the matched spatial filter with linear combinations of cross-correlations, which have been inherited transformed and reinvented to achieve tremendous progress.

Sheng, Yunlong

2013-09-01

171

Opportunity Recognition as the Detection of Meaningful Patterns: Evidence from Comparisons of Novice and Experienced Entrepreneurs  

Microsoft Academic Search

It is suggested that the recognition of new business opportunities often involves pattern recognition--the cognitive process through which individuals identify meaningful patterns in complex arrays of events or trends. Basic research on pattern recognition indicates that cognitive frameworks acquired through experience (e.g., prototypes) play a central role in this process. Such frameworks provide individuals with a basis for noticing connections

Robert A. Baron; Michael D. Ensley

2006-01-01

172

Optical Character Recognition for Cursive Handwriting  

Microsoft Academic Search

A new analytic scheme, which uses a sequence of image segmentation and recognition algorithms, is proposed for the off-line cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, stroke width and height, are estimated. Second, a segmentation method finds character segmentation paths by combining gray-scale and binary information. Third, a hidden Markov model (HMM) is employed

Nafiz Arica; Fatos T. Yarman-vural

2002-01-01

173

Detection and recognition of radial frequency patterns.  

PubMed

Detection thresholds for radial deformations of circular contours were measured using a range of radii and contour peak spatial frequencies. For radial frequencies above two cycles, thresholds were found to be a constant fraction of the mean radius across a four-octave range of pattern radii and peak spatial frequencies (mean Weber fraction: 0.003-0.004). At low radial frequencies, thresholds were unaffected by contrast reduction. In 167 ms presentations, subjects were able to identify radial frequencies of six cycles and below with an accuracy of over 90% correct even when phase was randomized. The extreme sensitivity of subjects to these radial deformations (as low as 2-4 s of arc) cannot be explained by local orientation or curvature analysis, and points instead to the global pooling of contour information at intermediate levels of form vision. PMID:9893789

Wilkinson, F; Wilson, H R; Habak, C

1998-11-01

174

Pattern Recognition Letters 20(1):89-96 (1999) The Role of Topographical Constraints in Face Recognition  

E-print Network

Pattern Recognition Letters 20(1):89-96 (1999) The Role of Topographical Constraints in Face for the case of face recognition. Images are represented by rectangular graphs labeled with jets, based in face recognition is to reliably find the face and its landmarks in the first place. In practical

Wiskott, Laurenz

175

Pattern Recognition Software and Techniques for Biological Image Analysis  

PubMed Central

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays. PMID:21124870

Shamir, Lior; Delaney, John D.; Orlov, Nikita; Eckley, D. Mark; Goldberg, Ilya G.

2010-01-01

176

An optical processor for object recognition and tracking  

NASA Technical Reports Server (NTRS)

The design and development of a miniaturized optical processor that performs real time image correlation are described. The optical correlator utilizes the Vander Lugt matched spatial filter technique. The correlation output, a focused beam of light, is imaged onto a CMOS photodetector array. In addition to performing target recognition, the device also tracks the target. The hardware, composed of optical and electro-optical components, occupies only 590 cu cm of volume. A complete correlator system would also include an input imaging lens. This optical processing system is compact, rugged, requires only 3.5 watts of operating power, and weighs less than 3 kg. It represents a major achievement in miniaturizing optical processors. When considered as a special-purpose processing unit, it is an attractive alternative to conventional digital image recognition processing. It is conceivable that the combined technology of both optical and ditital processing could result in a very advanced robot vision system.

Sloan, J.; Udomkesmalee, S.

1987-01-01

177

Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays  

PubMed Central

This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals. PMID:23443378

Kim, Eungyeong; Lee, Seok; Kim, Jae Hun; Kim, Chulki; Byun, Young Tae; Kim, Hyung Seok; Lee, Taikjin

2012-01-01

178

Markov sequential pattern recognition : dependency and the unknown class.  

SciTech Connect

The sequential probability ratio test (SPRT) minimizes the expected number of observations to a decision and can solve problems in sequential pattern recognition. Some problems have dependencies between the observations, and Markov chains can model dependencies where the state occupancy probability is geometric. For a non-geometric process we show how to use the effective amount of independent information to modify the decision process, so that we can account for the remaining dependencies. Along with dependencies between observations, a successful system needs to handle the unknown class in unconstrained environments. For example, in an acoustic pattern recognition problem any sound source not belonging to the target set is in the unknown class. We show how to incorporate goodness of fit (GOF) classifiers into the Markov SPRT, and determine the worse case nontarget model. We also develop a multiclass Markov SPRT using the GOF concept.

Malone, Kevin Thomas; Haschke, Greg Benjamin; Koch, Mark William

2004-10-01

179

Neural nets for adaptive filtering and adaptive pattern recognition  

SciTech Connect

The fields of adaptive signal processing and adaptive neural networks have been developing independently but have that adaptive linear combiner (ALC) in common. With its inputs connected to a tapped delay line, the ALC becomes a key component of an adaptive filter. With its output connected to a quantizer, the ALC becomes an adaptive threshold element of adaptive neuron. Adaptive threshold elements, on the other hand, are the building blocks of neural networks. Today neural nets are the focus of widespread research interest. Areas of investigation include pattern recognition and trainable logic. Neural network systems have not yet had the commercial impact of adaptive filtering. The commonality of the ALC to adaptive signal processing and adaptive neural networks suggests the two fields have much to share with each other. This article describes practical applications of the ALC in signal processing and pattern recognition.

Widrow, B.; Winter, R.

1988-03-01

180

Face Recognition with Patterns of Oriented Edge Magnitudes  

Microsoft Academic Search

\\u000a This paper addresses the question of computationally inexpensive yet discriminative and robust feature sets for real-world\\u000a face recognition. The proposed descriptor named Patterns of Oriented Edge Magnitudes (POEM) has desirable properties: POEM\\u000a (1) is an oriented, spatial multi-resolution descriptor capturing rich information about the original image; (2) is a multi-scale\\u000a self-similarity based structure that results in robustness to exterior variations;

Ngoc-Son Vu; Alice Caplier

2010-01-01

181

A new paradigm for pattern recognition of drugs.  

PubMed

A new paradigm is suggested for pattern recognition of drugs. The approach is based on the combined application of the 4D/3D quantitative structure-activity relationship (QSAR) algorithms BiS and ConGO. The first algorithm, BiS/MC (multiconformational), is used for the search for the conformers interacting with a receptor. The second algorithm, ConGO, has been suggested for the detailed study of the selected conformers' electron density and for the search for the electron structure fragments that determine the pharmacophore and antipharmacophore parts of the compounds. In this work we suggest using a new AlteQ method for the evaluation of the molecular electron density. AlteQ describes the experimental electron density (determined by low-temperature highly accurate X-ray analysis) much better than a number of quantum approaches. Herein this is shown using a comparison of the computed electron density with the results of highly accurate X-ray analysis. In the present study the desirability function is used for the first time for the analysis of the effects of the electron structure in the process of pattern recognition of active and inactive compounds. The suggested method for pattern recognition has been used for the investigation of various sets of compounds such as DNA-antimetabolites, fXa inhibitors, 5-HT(1A), and alpha(1)-AR receptors inhibitors. The pharmacophore and antipharmacophore fragments have been found in the electron structures of the compounds. It has been shown that the pattern recognition cross-validation quality for the datasets is unity. PMID:18357415

Potemkin, Vladimir A; Grishina, Maria A

2008-01-01

182

Pattern recognition for Space Applications Center director's discretionary fund  

NASA Technical Reports Server (NTRS)

Results and conclusions are presented on the application of recent developments in pattern recognition to spacecraft star mapping systems. Sensor data for two representative starfields are processed by an adaptive shape-seeking version of the Fc-V algorithm with good results. Cluster validity measures are evaluated, but not found especially useful to this application. Recommendations are given two system configurations worthy of additional study,

Singley, M. E.

1984-01-01

183

System integration of pattern recognition, adaptive aided, upper limb prostheses  

NASA Technical Reports Server (NTRS)

The requirements for successful integration of a computer aided control system for multi degree of freedom artificial arms are discussed. Specifications are established for a system which shares control between a human amputee and an automatic control subsystem. The approach integrates the following subsystems: (1) myoelectric pattern recognition, (2) adaptive computer aiding; (3) local reflex control; (4) prosthetic sensory feedback; and (5) externally energized arm with the functions of prehension, wrist rotation, elbow extension and flexion and humeral rotation.

Lyman, J.; Freedy, A.; Solomonow, M.

1975-01-01

184

Pattern recognition for remote sensing - Progress and prospects  

NASA Technical Reports Server (NTRS)

An overview is given of the current state of automatic image pattern recognition as applied to remote sensing of the earth's resources. The framework for the discussion is provided by four important aspects of the remote sensing problem: scene information content, characterization of scene information, information extraction methods, and the net value of extractable information. Outstanding problems are surveyed, as are the prospects for future developments. The effect of increasingly complex data bases and the rapidly evolving digital computer technology are highlighted.

Swain, P. H.

1980-01-01

185

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

186

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

187

Neurocomputing methods for pattern recognition in nuclear physics  

SciTech Connect

We review recent progress on the development and applications of novel neurocomputing techniques for pattern recognition problems of relevance to RHIC experiments. The Elastic Tracking algorithm is shown to achieve sub-pad two track resolution without preprocessing. A high pass neural filter is developed for jet analysis and singular deconvolution methods are shown to recover the primordial jet distribution to a surprising high degree of accuracy.

Gyulassy, M.; Dong, D.; Harlander, M. [Lawrence Berkeley Lab., CA (United States)

1991-12-31

188

A Web-based Learning System for Statistical Pattern Recognition  

Microsoft Academic Search

This paper presents a web-based learning system in support of a Ph.D. course in Statistical Pattern Recognition. More specifically, the learning system allows the user to know the main concepts related with several distance-based classification and prototype selection algorithms, as well as to interactively experiment with them by means of Java applets. The aim of such a web-based learning system

J. LOBATO; J. S. SÁNCHEZ; J. M. SOTOCA

189

A Family of Novel Graph Kernels for Structural Pattern Recognition  

Microsoft Academic Search

Recently, an emerging trend of representing objects by graphs can be observed. As a matter of fact, graphs offer a versatile\\u000a alternative to feature vectors in pattern recognition, machine learning and data mining. However, the space of graphs contains\\u000a almost no mathematical structure, and consequently, there is a lack of suitable methods for graph classification. Graph kernels,\\u000a a novel class

Horst Bunke; Kaspar Riesen

2007-01-01

190

Unbiased SVM Density Estimation with Application to Graphical Pattern Recognition  

Microsoft Academic Search

Classification of structured data (i.e., data that are repre- sented as graphs) is a topic of interest in the machine learning community. This paper presents a different, simple approach to the problem of struc- tured pattern recognition, relying on the description of graphs in terms of algebraic binary relations. Maximum-a-posteriori decision rules over relations require the estimation of class-conditional probability

Edmondo Trentin; Ernesto Di Iorio

2007-01-01

191

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

192

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

193

Adaptive myoelectric pattern recognition toward improved multifunctional prosthesis control.  

PubMed

The non-stationary property of electromyography (EMG) signals in real life settings usually hinders the clinical application of the myoelectric pattern recognition for prosthesis control. The classical EMG pattern recognition approach consists of two separate steps: training and testing, without considering the changes between training and testing data induced by electrode shift, fatigue, impedance changes and psychological factors, and often results in performance degradation. The aim of this study was to develop an adaptive myoelectric pattern recognition system, aiming to retrain the classifier online with the testing data without supervision, providing a self-correction mechanism for suppressing misclassifications. This paper presents an adaptive unsupervised classifier based on support vector machine (SVM) to improve the classification performance. Experimental data from 15 healthy subjects were used to evaluate performance. Preliminary study on intra-session and inter-session EMG data was conducted to verify the performance of the unsupervised adaptive SVM classifier. The unsupervised adaptive SVM classifier outperformed the conventional SVM by 3.3% and 8.0% for the combination of time-domain and autoregressive features in the intra-session and inter-session tests, respectively. The proposed approach is capable of incorporating the useful information in testing data to the classification model by taking into account the overtime changes in the testing data with respect to the training data to retrain the original classifier, therefore providing a self-correction mechanism for suppressing misclassifications. PMID:25749182

Liu, Jie

2015-04-01

194

Spatial pattern recognition of seismic events in South West Colombia  

NASA Astrophysics Data System (ADS)

Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

Benítez, Hernán D.; Flórez, Juan F.; Duque, Diana P.; Benavides, Alberto; Lucía Baquero, Olga; Quintero, Jiber

2013-09-01

195

Classification of simultaneous movements using surface EMG pattern recognition.  

PubMed

Advanced upper limb prostheses capable of actuating multiple degrees of freedom (DOFs) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one DOF at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using nonamputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for nonamputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less (p < 0.05) than a single linear discriminant analysis (LDA) classifier or a parallel approach. For three-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements. PMID:23247839

Young, Aaron J; Smith, Lauren H; Rouse, Elliott J; Hargrove, Levi J

2013-05-01

196

Assessment of bioinspired models for pattern recognition in biomimetic systems.  

PubMed

The increasing complexity of the artificial implementations of biological systems, such as the so-called electronic noses (e-noses) and tongues (e-tongues), poses issues in sensory feature extraction and fusion, drift compensation and pattern recognition, especially when high reliability is required. In particular, in order to achieve effective results, the pattern recognition system must be carefully designed. In order to investigate a novel biomimetic approach for the pattern recognition module of such systems, the classification capabilities of an artificial model inspired by the mammalian cortex, a cortical-based artificial neural network (CANN), are compared with several artificial neural networks present in the e-nose and e-tongue literature, a multilayer perceptron (MLP), a Kohonen self-organizing map (KSOM) and a fuzzy Kohonen self-organizing map (FKSOM). Each network was tested with large datasets coming from a conducting polymer-sensor-based e-nose and a composite array-based e-tongue. The comparison of results showed that the CANN model is able to strongly enhance the performances of both systems. PMID:18364563

Pioggia, G; Ferro, M; Francesco, F Di; Ahluwalia, A; De Rossi, D

2008-03-01

197

Classification of Simultaneous Movements using Surface EMG Pattern Recognition  

PubMed Central

Advanced upper-limb prostheses capable of actuating multiple degrees of freedom (DOF) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one degree of freedom at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using non-amputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for non-amputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less (p<0.05) than a single LDA classifier or a parallel approach. For 3-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements. PMID:23247839

Young, Aaron J.; Smith, Lauren H.; Rouse, Elliott J.; Hargrove, Levi J.

2014-01-01

198

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

199

Artificial retinal neural network for visual pattern recognition  

NASA Astrophysics Data System (ADS)

With feed-forward adaptive network (FFAN) and feed-back associative network (FBAN) respectively imitating the performances of retina and cerebral cortex, an artificial retinal neural network (ARNN) was presented in this paper for fast recognition of visual patterns. In our ARNN model to be implemented with neural network chip MD1200, every synaption of neurons can be arbitrarily given as an integer value from minus 215 to 215. After these synaptions are trained, the visual pattern not only under geometric transformation but also in the presence of noise can be recognized by the ARNN's system.

Guo, Donghui; Cheng, Lee Ming; Cheng, L. L.; Chen, Zhenxiang; Liu, Ruitang; Wu, Boxi

1996-03-01

200

Pattern recognition of earthquake prone area in North China  

E-print Network

PATTERN RECOGNITIOV OF EARTHQUAKE PRONE AREA IV NORTH CHINA A Thesis JI-XiiiV GU Submitted to the Office of Graduate Studies of Texas AkM University in partial fulfillment of the requirements for the degree of XIASTER OF SCIE'VCE August 1989... ( Xlember) J. E. Russell (lvtember) Joel S. Watkins (Head of Department) August 1989 ABSTRACT Pattern Recognition of Earthquake-Prone Areas in North China, ( August 1989) Ji-min Gu Shanghai University of Science and Technology, Shanghai, China Co...

Gu, Ji-Min

1989-01-01

201

Rapid detection of malignant bio-species using digital holographic pattern recognition and nano-photonics  

NASA Astrophysics Data System (ADS)

There is a great need for rapid detection of bio-hazardous species particularly in applications to food safety and biodefense. It has been recently demonstrated that the colonies of various bio-species could be rapidly detected using culture-specific and reproducible patterns generated by scattered non-coherent light. However, the method heavily relies on a digital pattern recognition algorithm, which is rather complex, requires substantial computational power and is prone to ambiguities due to shift, scale, or orientation mismatch between the analyzed pattern and the reference from the library. The improvement could be made, if, in addition to the intensity of the scattered optical wave, its phase would be also simultaneously recorded and used for the digital holographic pattern recognition. In this feasibility study the research team recorded digital Gabor-type (in-line) holograms of colonies of micro-organisms, such as Salmonella with a laser diode as a low-coherence light source and a lensless high-resolution (2.0x2.0 micron pixel pitch) digital image sensor. The colonies were grown in conventional Petri dishes using standard methods. The digitally recorded holograms were used for computational reconstruction of the amplitude and phase information of the optical wave diffracted on the colonies. Besides, the pattern recognition of the colony fragments using the cross-correlation between the digital hologram was also implemented. The colonies of mold fungi Altenaria sp, Rhizophus, sp, and Aspergillus sp have been also generating nano-colloidal silver during their growth in specially prepared matrices. The silver-specific plasmonic optical extinction peak at 410-nm was also used for rapid detection and growth monitoring of the fungi colonies.

Sarkisov, Sergey S.; Kukhtareva, Tatiana; Kukhtarev, Nickolai V.; Curley, Michael J.; Edwards, Vernessa; Creer, Marylyn

2013-03-01

202

Innate sensing of viruses by pattern recognition receptors in birds  

PubMed Central

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

2013-01-01

203

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

204

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

205

Recognition of unnatural patterns in process control charts through combining two types of neural networks  

Microsoft Academic Search

Unnatural patterns in control charts associate a set of assignable causes in production process. Therefore, effective discrimination of these patterns has special importance in on-line statistical process control. In resent years, artificial neural networks because of their abilities in patterns recognition have been used to detect control charts patterns. The correct and precise recognition in a real-time is difficult, because

S. M. T. Fatemi Ghomi; S. A. Lesany; A. Koochakzadeh

2011-01-01

206

Facial expression recognition based on Local Binary Patterns: A comprehensive study  

E-print Network

Facial expression recognition based on Local Binary Patterns: A comprehensive study Caifeng Shan a 2008 Accepted 16 August 2008 Keywords: Facial expression recognition Local Binary Patterns Support representation from original face images is a vital step for successful facial expression recognition

Kim, Tae-Kyun

207

High-Precise and Robust Face-Recognition System Based on Optical Parallel Correlator  

NASA Astrophysics Data System (ADS)

Facial recognition is applied in a wide range of security systems, and has been studied since the 1970s, with extensive research into and development of digital processing. However, there is only available a 1:1 verification system combined with ID card identification, or an ID-less system with a small number of images in the database. The number of images that can be stored is limited, and recognition has to be improved to account for photos taken at different angles. Commercially available facial recognition systems for the most part utilize digital computers performing electronic pattern recognition. In contrast, optical analog operations can process two-dimensional images instantaneously in parallel using a lens-based Fourier transform function. In the 1960s two methods were proposed, the Vanderlugt correlator and the joint transform correlator (JTC). We present a new scheme using a multi-channel parallel JTC to make better use of spatial parallelism, through the use of a diffraction-type multi-level zone-plate array to extend a single-channel JTC. Our project's objectives were: (i) to design a matched filter which equips the system with high recognition capability at a faster calculation speed by analyzing the spatial frequency of facial image elements, and (ii) to create a four-channel Vanderlugt correlator with super-high-speed (1000 frame/s) optical parallel facial recognition system, robust enough for 1:N identification, for a large database with 4000 images. Automation was also achieved for the entire process via a practical controlling system. The achieved super-high-speed facial recognition system based on optical parallelism is faster in its processing time than the JTC optical correlator.

Kodate, Kashiko

2005-10-01

208

A star pattern recognition algorithm for autonomous attitude determination  

NASA Technical Reports Server (NTRS)

The star-pattern recognition algorithm presented allows the advanced Full-sky Autonomous Star Tracker (FAST) device, such as the projected ASTROS II system of the Mariner Mark II planetary spacecraft, to reliably ascertain attitude about all three axes. An ASTROS II-based FAST, possessing an 11.5 x 11.5 deg field of view and 8-arcsec accuracy, can when integrated with an all-sky data base of 4100 guide stars determine its attitude in about 1 sec, with a success rate close to 100 percent. The present recognition algorithm can also be used for automating the acquisition of celestial targets by astronomy telescopes, autonomously updating the attitude of gyro-based attitude control systems, and automating ground-based attitude reconstruction.

Van Bezooijen, R. W. H.

1990-01-01

209

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

210

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

211

Implementation of pattern recognition algorithm based on RBF neural network  

NASA Astrophysics Data System (ADS)

In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital Signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240x320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.

Bouchoux, Sophie; Brost, Vincent; Yang, Fan; Grapin, Jean Claude; Paindavoine, Michel

2002-12-01

212

Optical character recognition of handwritten Arabic using hidden Markov models  

SciTech Connect

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. [University of Jordan; Natsheh, Asem M. [University of Jordan; Abandah, Gheith A. [University of Jordan; Olama, Mohammed M [ORNL

2011-01-01

213

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

214

Optical fingerprint recognition based on local minutiae structure coding.  

PubMed

A parallel volume holographic optical fingerprint recognition system robust to fingerprint translation, rotation and nonlinear distortion is proposed. The optical fingerprint recognition measures the similarity by using the optical filters of multiplexed holograms recorded in the holographic media. A fingerprint is encoded into multiple template data pages based on the local minutiae structure coding method after it is adapted for the optical data channel. An improved filter recording time schedule and a post-filtering calibration technology are combined to suppress the calculating error from the large variations in data page filling ratio. Experimental results tested on FVC2002 DB1 and a forensic database comprising 270,216 fingerprints demonstrate the robustness and feasibility of the system. PMID:23938559

Yi, Yao; Cao, Liangcai; Guo, Wei; Luo, Yaping; Feng, Jianjiang; He, Qingsheng; Jin, Guofan

2013-07-15

215

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

216

Auditory Pattern Recognition and Brief Tone Discrimination of Children with Reading Disorders  

ERIC Educational Resources Information Center

Auditory pattern recognition skills in children with reading disorders were investigated using perceptual tests involving discrimination of frequency and duration tonal patterns. A behavioral test battery involving recognition of the pattern of presentation of tone triads was used in which individual components differed in either frequency or…

Walker, Marianna M.; Givens, Gregg D.; Cranford, Jerry L.; Holbert, Don; Walker, Letitia

2006-01-01

217

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

218

Recognition of subsurface defects in machined ceramics by application of neural networks to laser scatter patterns  

SciTech Connect

Laser scatter has shown promise as a method to characterize damage microstructural variations as well as a method to characterize surfaces in optical translucent ceramics. Because large volumes of data need to be handled (and sorted) quickly, automated pattern recognition methods using neural networks have been implemented to recognize differences in patterns. A He-Ne laser ({lambda}=0.632{mu}) has been used to obtain scatter patterns from hot pressed Si{sub 3}N{sub 4} with various microstructural variations. By use of a backpropagation neural network running on an IBM PC clone 486/33 machine, a correlation was established between subsurface microstructure and position in Si{sub 3}N{sub 4} ball bearings. The data were confirmed by destructive analysis.

Stinson, M.C. [Central Michigan Univ., (United States). Dept. of Computer Science; Lee, O.W. [Univ. of California, Los Angeles, CA (United States). Dept. of Electrical Engineering; Steckenrider, J.S.; Ellingson, W.A. [Argonne National Lab., IL (United States)

1994-09-01

219

Gene prediction by pattern recognition and homology search  

SciTech Connect

This paper presents an algorithm for combining pattern recognition-based exon prediction and database homology search in gene model construction. The goal is to use homologous genes or partial genes existing in the database as reference models while constructing (multiple) gene models from exon candidates predicted by pattern recognition methods. A unified framework for gene modeling is used for genes ranging from situations with strong homology to no homology in the database. To maximally use the homology information available, the algorithm applies homology on three levels: (1) exon candidate evaluation, (2) gene-segment construction with a reference model, and (3) (complete) gene modeling. Preliminary testing has been done on the algorithm. Test results show that (a) perfect gene modeling can be expected when the initial exon predictions are reasonably good and a strong homology exists in the database; (b) homology (not necessarily strong) in general helps improve the accuracy of gene modeling; (c) multiple gene modeling becomes feasible when homology exists in the database for the involved genes.

Xu, Y.; Uberbacher, E.C.

1996-05-01

220

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

221

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

222

Proposal for the development of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)  

SciTech Connect

Future particle physics experiments looking for rare processes will have no choice but 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 process. The authors propose to develop a 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) chip for HEP applications, to advance the state-of-the-art for pattern recognition and track reconstruction for fast triggering.

Deptuch, Gregory; Hoff, Jim; Kwan, Simon; Lipton, Ron; Liu, Ted; Ramberg, Erik; Todri, Aida; Yarema, Ray; /Fermilab; Demarteua, Marcel,; Drake, Gary; Weerts, Harry; /Argonne /Chicago U. /Padua U. /INFN, Padua

2010-10-01

223

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

224

Proceedings of the Second Annual Symposium on Mathematical Pattern Recognition and Image Analysis Program  

NASA Technical Reports Server (NTRS)

Several papers addressing image analysis and pattern recognition techniques for satellite imagery are presented. Texture classification, image rectification and registration, spatial parameter estimation, and surface fitting are discussed.

Guseman, L. F., Jr. (principal investigator)

1984-01-01

225

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

226

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

227

Pattern recognition analysis of polar clouds during summer and winter  

NASA Technical Reports Server (NTRS)

A pattern recognition algorithm is demonstrated which classifies eighteen surface and cloud types in high-latitude AVHRR imagery based on several spectral and textural features, then estimates the cloud properties (fractional coverage, albedo, and brightness temperature) using a hybrid histogram and spatial coherence technique. The summertime version of the algorithm uses both visible and infrared data (AVHRR channels 1-4), while the wintertime version uses only infrared data (AVHRR channels 3-5). Three days of low-resolution AVHRR imagery from the Arctic and Antarctic during January and July 1984 were analyzed for cloud type and fractional coverage. The analysis showed significant amounts of high cloudiness in the Arctic during one day in winter. The Antarctic summer scene was characterized by heavy cloud cover in the southern ocean and relatively clear conditions in the continental interior. A large region of extremely low brightness temperatures in East Antarctica during winter suggests the presence of polar stratospheric cloud.

Ebert, Elizabeth E.

1992-01-01

228

Recognition of lipopolysaccharide pattern by TLR4 complexes  

PubMed Central

Lipopolysaccharide (LPS) is a major component of the outer membrane of Gram-negative bacteria. Minute amounts of LPS released from infecting pathogens can initiate potent innate immune responses that prime the immune system against further infection. However, when the LPS response is not properly controlled it can lead to fatal septic shock syndrome. The common structural pattern of LPS in diverse bacterial species is recognized by a cascade of LPS receptors and accessory proteins, LPS binding protein (LBP), CD14 and the Toll-like receptor4 (TLR4)–MD-2 complex. The structures of these proteins account for how our immune system differentiates LPS molecules from structurally similar host molecules. They also provide insights useful for discovery of anti-sepsis drugs. In this review, we summarize these structures and describe the structural basis of LPS recognition by LPS receptors and accessory proteins. PMID:24310172

Park, Beom Seok; Lee, Jie-Oh

2013-01-01

229

Dense pattern optical multipass cell  

DOEpatents

A multiple pass optical cell and method comprising providing a pair of opposed cylindrical mirrors having curved axes with substantially equal focal lengths, positioning an entrance hole for introducing light into the cell and an exit hole for extracting light from the cell, wherein the entrance hole and exit hole are coextensive or non-coextensive, introducing light into the cell through the entrance hole, and extracting light from the cell through the exit hole.

Silver, Joel A [Santa Fe, NM

2009-01-13

230

Dense Pattern Optical Multipass Cell  

NASA Technical Reports Server (NTRS)

A multiple pass optical cell and method comprising providing a pair of opposed cylindrical mirrors having curved axes with substantially equal focal lengths, positioning an entrance hole for introducing light into the cell and an exit hole for extracting light from the cell, wherein the entrance hole and exit hole are coextensive or non-coextensive, introducing light into the cell through the entrance hole, and extracting light from the cell through the exit hole.

Silver, Joel A. (Inventor)

2009-01-01

231

Damaged Character Pattern Recognition on Wooden Tablets Excavated from The Heijyo Palace Site  

E-print Network

Damaged Character Pattern Recognition on Wooden Tablets Excavated from The Heijyo Palace Site damaged character pattern recognition on wooden tablets excavated from the Heijyo palace site (the ancient palace in the Nara period from AD. 710 to 794). Since most of excavated tablets have been stained

Paris-Sud XI, Université de

232

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

E-print Network

Pattern Recognition Letters 15 (1994) 261-271 North-Holland PATREC 1172 Genetic algorithms 23 August 1993 Abstract Pal, S .K., Genetic algorithms for optimal image enhancement, Pattern Recognition Letters 15 (1994) 261-271. Genetic algorithms represent a class of highly parallel adaptive search

Pal, Sankar Kumar

233

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

234

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

235

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

236

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.

237

Teaching image processing and pattern recognition with the Intel OpenCV library  

Microsoft Academic Search

In this paper we present an approach to teaching image processing and pattern recognition with the use of the OpenCV library. Image processing, pattern recognition and computer vision are important branches of science and apply to tasks ranging from critical, involving medical diagnostics, to everyday tasks including art and entertainment purposes. It is therefore crucial to provide students of image

Adam Kozlowski; Aleksandra Królak

2009-01-01

238

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

239

Automatic identification of oculomotor behavior using pattern recognition techniques.  

PubMed

In this paper, a methodological scheme for identifying distinct patterns of oculomotor behavior such as saccades, microsaccades, blinks and fixations from time series of eye?s angular displacement is presented. The first step of the proposed methodology involves signal detrending for artifacts removal and estimation of eye?s angular velocity. Then, feature vectors from fourteen first-order statistical features are formed from each angular displacement and velocity signal using sliding, fixed-length time windows. The obtained feature vectors are used for training and testing three artificial neural network classifiers, connected in cascade. The three classifiers discriminate between blinks and non-blinks, fixations and non-fixations and saccades and microsaccades, respectively. The proposed methodology was tested on a dataset from 1392 subjects, each performing three oculomotor fixation conditions. The average overall accuracy of the three classifiers, with respect to the manual identification of eye movements by experts, was 95.9%. The proposed methodological scheme provided better results than the well-known Velocity Threshold algorithm, which was used for comparison. The findings of the present study indicate that the utilization of pattern recognition techniques in the task of identifying the various eye movements may provide accurate and robust results. PMID:25836568

Korda, Alexandra I; Asvestas, Pantelis A; Matsopoulos, George K; Ventouras, Errikos M; Smyrnis, Nikolaos P

2015-05-01

240

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; Mourão-Miranda, J

2013-07-01

241

Silicon retina for real-time pattern recognition  

NASA Astrophysics Data System (ADS)

We present in this paper a programmable silicon retina designed for real-time pattern recognition. Its working principle is based on the comparison between an image projected on the retina by some opt9ical means and a reference binary image or mask memorized in the circuit. The result of the comparison is two signals corresponding to the sum of the currents produced by the pixels pertaining to the black and white zones of the reference binary image, this image when projected on the retina will produce a maximum white pixel current and a minimum black pixel current if it coincides perfectly with the reference binary image. If the projected image is shifted with respect to the reference binary image or if it is different then the black and white pixel currents will be different also. By measuring these two currents and by comparing them to expected values, a shift of the pattern or a difference between the observed and programmed pattern can be detected. Extensive computer simulations have been done in order to validate the working principle of the retina. Moreover, in order to verify the feasibility of the circuit in CMOS technology, we have fabricated a prototype non-programmable circuit in 1.2 micrometers standard CMOS technology. The measurements done on this circuit are quite encouraging and have been found to correspond to our expectations. Finally, the architecture of the programmable silicon retina, designed in a more recent 0.6 micrometers CMOS technology, is presented. This circuit is currently being fabricated.

Voon, Lew F. L. Y.; Cathebras, Guy; Bellach, Benaissa; Lamalle, Bernard; Gorria, Patrick

2001-05-01

242

Coaxial optical structure for iris recognition from a distance  

NASA Astrophysics Data System (ADS)

Supporting an unconstrained user interface is an important issue in iris recognition. Various methods try to remove the constraint of the iris being placed close to the camera, including portal-based and pan-tilt-zoom (PTZ)-based solutions. Generally speaking, a PTZ-based system has two cameras: one scene camera and one iris camera. The scene camera detects the eye's location and passes this information to the iris camera. The iris camera captures a high-resolution image of the person's iris. Existing PTZ-based systems are divided into separate types and parallel types, according to how the scene camera and iris camera combine. This paper proposes a novel PTZ-based iris recognition system, in which the iris camera and the scene camera are combined in a coaxial optical structure. The two cameras are placed together orthogonally and a cold mirror is inserted between them, such that the optical axes of the two cameras become coincident. Due to the coaxial optical structure, the proposed system does not need the optical axis displacement-related compensation required in parallel-type systems. Experimental results show that the coaxial type can acquire an iris image more quickly and accurately than a parallel type when the stand-off distance is between 1.0 and 1.5 m.

Jung, Ho Gi; Jo, Hyun Su; Park, Kang Ryoung; Kim, Jaihie

2011-05-01

243

Optically Patternable Polymer Light-Emitting Device  

NASA Astrophysics Data System (ADS)

Optically patternable polymer light-emitting devices with an indium-tin-oxide/poly(2-methoxy-5-dodecyloxy-p-phenylene vinylene)/semitransparent-Al structure are fabricated and characterized. When the optical transmittance of the Al electrode is about 30% at a 500 nm wavelength, the emission from the device is rapidly reduced by photoirradiation in air, and the emission is completely suppressed after irradiation for 5 min. Considerable bleaching of the optical absorption of the polymer film of the devices due to photoirradiation is also observed. These effects originate from photooxidation of the polymer by the atmospheric oxygen passing through the semitransparent-Al electrode. It is also shown that these effects are considerably moderated by using a thicker Al electrode, indicating the importance of the Al electrode thickness on the patternability of the emission.

Tada, Kazuya; Onoda, Mitsuyoshi; Nakayama, Hiroshi

1999-07-01

244

Programmable accelerating chip for optical Chinese character recognition  

NASA Astrophysics Data System (ADS)

In this paper, a programmable accelerating chip with parallel and pipelined computation capabilities for optical Chinese character recognition (OCCR) is presented. The chip contains eight matching processing elements working in parallel and a memory-saving sorter which can choose the best 32 candidates. The structure of the design is flexible and modular, which is capable of processing different start points and end points of the template feature. Fabricated in 1.0 micron CMOS gate array, the chip contains approximately 16,000 gates. It has been tested to be fully functional. By using this chip, a powerful optical Chinese character recognition system with high speed, high recognition rate, and accumulated learning ability is developed. From the experimental results, the process speed of this chip can be up to 200 characters per second, which is one hundred times the conventional pure software process speed. The chips can be used in parallel for a larger template character system and will not increase the process time.

Chang, Ming-Wen; Jeng, Bor-Shenn; Miou, Char-Shin; Wen, Chi-Jain; Lee, Cheng-Tzong; Shieh, Dung-Ming; Shy, Shih-Fu

1994-09-01

245

Effect of spectral resolution on pattern recognition analysis using passive fourier transform infrared sensor data  

SciTech Connect

The Fourier transform infrared (FT-IR) spectral data of two nerve agent simulants, diisopropyl methyl phosphonate (DIMP) and dimethyl methyl phosphonate (DMMP), are used as test cases to determine the spectral resolution that gives optimal pattern recognition performance. DIMP is used as the target analyte for detection, while DMMP is used to test the ability of the automated pattern recognition methodology to detect the analyte selectively. Interferogram data are collected by using a Midac passive FT-IR instrument. The methodology is based on the application of pattern recognition techniques to short segments of single-beam spectra obtained by Fourier processing the collected interferogram data. The work described in this article evaluates the effect of varying spectral resolution on the pattern recognition results. The objective is to determine the optimal spectral resolution to be used for data collection. The results of this study indicate that the data with a nominal spectral resolution of 16 cm{sup -1} provide sufficient selectivity to give pattern recognition results comparable to that obtained by using higher resolution data. We found that, while higher resolution does not increase selectivity sufficiently to provide better pattern recognition results, lower resolution decreases selectivity and degrades the pattern recognition results. These results can be used as guidelines to maximize detection sensitivity, to minimize the time needed for data collection, and to reduce data storage requirements. (c) 2000 Society for Applied Spectroscopy.

Bangalore, Arjun S. [Chemical Technology Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, Illinois 60439 (United States)] [Chemical Technology Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, Illinois 60439 (United States); Demirgian, Jack C. [Environmental Research Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, Illinois 60439 (United States)] [Environmental Research Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, Illinois 60439 (United States); Boparai, Amrit S. [Chemical Technology Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, Illinois 60439 (United States)] [Chemical Technology Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, Illinois 60439 (United States); Small, Gary W. [Center for Intelligent Chemical Instrumentation, Department of Chemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701-2979 (United States)] [Center for Intelligent Chemical Instrumentation, Department of Chemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701-2979 (United States)

1999-11-01

246

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

247

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

248

Pattern recognition of amino acid signatures in retinal neurons.  

PubMed

Pattern recognition of amino acid signals partitions the cells of the goldfish retina into nine statistically unique biochemical theme classes and permits a first-order chemical mapping of virtually all cellular space. Photoreceptors, bipolar cells, and ganglion cells display a set of unique, nominally glutamatergic type E1, E1+E2, and E4 signatures, respectively. All horizontal cells are assignable to a GABAergic gamma 2 class or a non-GABAergic class with a glutamate-rich E3 signature. The amacrine cell layer is largely a mixture of (1) a taurine-dominated T1 Müller's cell signature and (2) GABAergic gamma 1, glycinergic G1, and dual glycinergic/GABAergic G gamma 1 amacrine cell signatures. Several major conclusions emerge from this work. (1) Glutamatergic, GABAergic, and glycinergic neural signatures and glial signatures account for over 99% of the cellular space in the retina. (2) All known neurons in the goldfish retina are associated with a set of conventional nonpeptide neurotransmitters. (3) Multiple forms of metabolic profiles are associated with a single nominal neurotransmitter category. (4) Glutamate and aspartate contents exhibit overlapping distributions and are not adequate univariate probes for identifying cell classes. (5) Signatures can serve as quantitative measures of cell state. PMID:7623139

Marc, R E; Murry, R F; Basinger, S F

1995-07-01

249

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

250

On Assisting a Visual-Facial Affect Recognition System with Keyboard-Stroke Pattern Information  

NASA Astrophysics Data System (ADS)

Towards realizing a multimodal affect recognition system, we are considering the advantages of assisting a visual-facial expression recognition system with keyboard-stroke pattern information. Our work is based on the assumption that the visual-facial and keyboard modalities are complementary to each other and that their combination can significantly improve the accuracy in affective user models. Specifically, we present and discuss the development and evaluation process of two corresponding affect recognition subsystems, with emphasis on the recognition of 6 basic emotional states, namely happiness, sadness, surprise, anger and disgust as well as the emotion-less state which we refer to as neutral. We find that emotion recognition by the visual-facial modality can be aided greatly by keyboard-stroke pattern information and the combination of the two modalities can lead to better results towards building a multimodal affect recognition system.

Stathopoulou, I.-O.; Alepis, E.; Tsihrintzis, G. A.; Virvou, M.

251

Fusion of optical flow based motion pattern analysis and silhouette classification for person tracking and detection  

NASA Astrophysics Data System (ADS)

This paper presents a novel approach to detect persons in video by combining optical flow based motion analysis and silhouette based recognition. A new fast optical flow computation method is described, and its application in a motion based analysis framework unifying human tracking and detection is outlined. Our optical flow algorithm represents optical flow by grid based motion vectors, which are computed very efficiently and robustly applying template matching. We model the motion patterns of the tracked human and non-human objects by the positions, velocities, motion magnitudes, and motion directions of their optical flow vectors, and build a random forest on these features. For recognition, the random forest computes a normalized score measuring the similarity of a track to a human track. Using edge detection on a motion image for each motion blob its silhouette is computed. Recognition scores are computed, which measure the similarity of the silhouettes with human silhouettes. The optical flow classifier and the silhouette classifier are used as a combined classifier. We analyze the ROC curve to set different decision thresholds on the recognition score for different scenarios. The experiments on the VIRAT test set demonstrate that for human detection the combination of the optical flow based motion method with one based on human silhouette analysis, obtains superior results, compared to the constituent methods.

Tangelder, Johan W. H.; Lebert, Ed; Burghouts, Gertjan J.; van Zon, Kasper; den Uyl, Marten J.

2014-10-01

252

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

253

Classification of cultured mammalian cells by shape analysis and pattern recognition.  

PubMed Central

We have developed a method for classifying cultured cells on the basis of shape characteristics. High-resolution optical information on three-dimensional shape was obtained by anodic oxide interferometry. Each interference order formed in a cell was considered as a closed figure; measurement of 37 mathematical descriptors was carried out for each figure. The individual cells were classified according to the values of their descriptors. We used standard principles of pattern recognition, such as hierarchical cluster analysis and nearest neighbor analysis, as a basis for ordering the cells into groups. Alternatively, linear discriminant functions could be used, but they provided only a slight improvement in correct classification of the cells. We anticipate that the method will be appropriate for classification of cultured cell lines and for determination of the magnitude and direction of cell shape changes implicated in various biological processes. Images PMID:6929502

Olson, A C; Larson, N M; Heckman, C A

1980-01-01

254

Vision-Based Hand Gesture Recognition for Understanding Musical Time Pattern and Tempo  

Microsoft Academic Search

We introduce a method of understanding of four musical time patterns and three tempos that are generated by a human conductor of robot orchestra or an operator of computer- based music play system using the hand gesture recognition. We use only a stereo vision camera with no extra special devices. We suggest a simple and reliable vision-based hand gesture recognition

Hongmo Je; Jiman Kim; Daijin Kim

2007-01-01

255

Human Activity Recognition and Pattern Eunju Kim, Sumi Helal and Diane Cook  

E-print Network

1 Human Activity Recognition and Pattern Discovery Eunju Kim, Sumi Helal and Diane Cook Activity recognition is an important technology in pervasive computing because it can be applied to many real be interleaved [3]. For instance, while cooking, if there is a call from a friend, people pause cooking

Cook, Diane J.

256

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

257

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...

258

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...

259

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques  

Technology Transfer Automated Retrieval System (TEKTRAN)

The combination of gas chromatography and pattern recognition (GC/PR) analysis is a powerful tool for investigating complicated biological problems. Clustering, mapping, discriminant development, etc. are necessary to analyze realistically large chromatographic data sets and to seek meaningful relat...

260

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

261

Multiresolution pattern recognition of small volcanos in Magellan data  

NASA Technical Reports Server (NTRS)

The Magellan data is a treasure-trove for scientific analysis of venusian geology, providing far more detail than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations. However, at this point, planetary scientists are being overwhelmed by the sheer quantities of data collected--data analysis technology has not kept pace with our ability to collect and store it. In particular, 'small-shield' volcanos (less than 20 km in diameter) are the most abundant visible geologic feature on the planet. 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 volcanos visible in the Magellan data. Identifying and studying these volcanos is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is very time-consuming. Hence, we have undertaken the development of techniques to partially automate this task. The goal is not the unrealistic one of total automation, but rather the development of a useful tool to aid the project scientists. The primary constraints for this particular problem are as follows: (1) the method must be reasonably robust; and (2) the method must be reasonably fast. Unlike most geological features, the small volcanos 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 the following: (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.

Smyth, P.; Anderson, C. H.; Aubele, J. C.; Crumpler, L. S.

1992-01-01

262

Characteristics and Biological Variations of M-Ficolin, a Pattern Recognition Molecule, in Plasma  

Microsoft Academic Search

The three human ficolins, H-ficolin, L-ficolin and M-ficolin, are pattern recognition molecules of the innate immune system. All three ficolins can activate the lectin pathway of the complement system after binding to pathogens. H- and L-ficolin are serum proteins with an average concentration of 18 and 3 ?g\\/ml, respectively. M-ficolin has been described as a membrane-associated pattern recognition receptor of

Thomas Wittenborn; Steffen Thiel; Lisbeth Jensen; Hans J. Nielsen; Jens C. Jensenius

2010-01-01

263

Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration.  

PubMed

Pattern recognition of myoelectric signals for prosthesis control has been extensively studied in research settings and is close to clinical implementation. These systems are capable of intuitively controlling the next generation of dexterous prosthetic hands. However, pattern recognition systems perform poorly in the presence of electrode shift, defined as movement of surface electrodes with respect to the underlying muscles. This paper focused on investigating the optimal interelectrode distance, channel configuration, and electromyography feature sets for myoelectric pattern recognition in the presence of electrode shift. Increasing interelectrode distance from 2 to 4 cm improved pattern recognition system performance in terms of classification error and controllability (p < 0.01). Additionally, for a constant number of channels, an electrode configuration that included electrodes oriented both longitudinally and perpendicularly with respect to muscle fibers improved robustness in the presence of electrode shift (p < 0.05). We investigated the effect of the number of recording channels with and without electrode shift and found that four to six channels were sufficient for pattern recognition control. Finally, we investigated different feature sets for pattern recognition control using a linear discriminant analysis classifier and found that an autoregressive set significantly (p < 0.01) reduced sensitivity to electrode shift compared to a traditional time-domain feature set. PMID:22147289

Young, Aaron J; Hargrove, Levi J; Kuiken, Todd A

2012-03-01

264

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

265

Cross-kingdom patterns of alternative splicing and splice recognition  

E-print Network

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 ...

McGuire, Abigail Manson

266

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

267

Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature  

PubMed Central

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

268

A Training Strategy for Learning Pattern Recognition Control for Myoelectric Prostheses  

PubMed Central

Pattern recognition-based control of myoelectric prostheses offers amputees a natural, intuitive way of controlling the increasing functionality of modern myoelectric prostheses. While this approach to prosthesis control is certainly attractive, it is a significant departure from existing control methods. The transition from the more traditional methods of direct or proportional control to pattern recognition-based control presents a training challenge that will be unique to each amputee. In this paper we describe specific ways that a transradial amputee, prosthetist, and occupational therapist team can overcome these challenges by developing consistent and distinguishable muscle patterns. A central part of this process is the employment of a computer-based pattern recognition training system with which an amputee can learn and improve pattern recognition skills throughout the process of prosthesis fitting and testing. We describe in detail the manner in which four transradial amputees trained to improve their pattern recognition-based control of a virtual prosthesis by focusing on building consistent, distinguishable muscle patterns. We also describe a three-phase framework for instruction and training: 1) initial demonstration and conceptual instruction, 2) in-clinic testing and initial training, and 3) at-home training. PMID:23459166

Powell, Michael A.; Thakor, Nitish V.

2012-01-01

269

The application of pattern recognition in wood processing industry  

NASA Astrophysics Data System (ADS)

In order to improve the level of automation in wood production, the parameters of Gray level co-occurrence matrix(GLCM) and Gauss - Markov random field(GMRF) were extracted. S - NFS algorithm was applied to data fusion. And the redundancy and complementarities of two texture parameters were used to build the wood texture parameter system. An integrated measurement rule based on BP neural network classifier's overall recognition rate of samples was advanced to design its integrated classifier. Experiments show that the recognition rate of integrated neural network classifier is superior to the individual network and the nearby classifier, and the average recognition rate of 10 texture samples have reached up to 97%, which could meet the needs of industrial production. And it shows that the established parameter system for wood texture description is effective.

Wang, Yeqin; Wang, Hui

2010-08-01

270

Pattern Recognition System with Top-Down Process of Mental Rotation  

Microsoft Academic Search

A new model which can recognize rotated, distorted, scaled, shifted and noised patterns is proposed. The model is constructed based on psycholog- ical experiments in a mental rotation. The model has two types of processes: (i) one is a bottom-up process in which pattern recognition is realized by means of a rotation-invariant neocognitron and a standard neocognitron and (ii) the

Shunji Satoh; Hirotomo Aso; Shogo Miyake; Jousuke Kuroiwa

1999-01-01

271

Inhibition of pattern recognition receptor-mediated inflammation by bioactive phytochemicals  

Technology Transfer Automated Retrieval System (TEKTRAN)

Emerging evidence reveals that pattern-recognition receptors (PRRs), Toll-like receptors (TLRs) and Nucleotide-binding oligomerization domain proteins (NODs) mediate both infection-induced and sterile inflammation by recognizing pathogen-associated molecular patterns (PAMPs) and endogenous molecules...

272

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

273

Finger Force Direction Recognition by Principal Component Analysis of Fingernail Coloration Pattern  

E-print Network

in the fingernails that are related to force directions on the fingertip. The development of a tech- nique that canFinger Force Direction Recognition by Principal Component Analysis of Fingernail Coloration Pattern pattern is presented to infer fingertip force direction during planar contact. Images from 7 sub- jects

Hollerbach, John M.

274

Pattern Recognition Based on Stability of Discrete Time Cellular Neural Networks  

E-print Network

Pattern Recognition Based on Stability of Discrete Time Cellular Neural Networks Zhigang Zeng1, some sufficient conditions are obtained to guar- antee that discrete time cellular neural networks (DTCNNs) can have some stable memory patterns. These conditions can be directly derived from the structure

Hefei Institute of Intelligent Machines

275

DESIGN AND CHARACTERIZATION OF CELLULAR AUTOMATA BASED ASSOCIATIVE MEMORY FOR PATTERN RECOGNITION  

E-print Network

DESIGN AND CHARACTERIZATION OF CELLULAR AUTOMATA BASED ASSOCIATIVE MEMORY FOR PATTERN RECOGNITION@,biplab@,ppc@gcs.becs.ac.in Abstract| This paper reports a Cellular Automata (CA) based model of associative memory. The model has been a Cellular Automata (CA) based model of Associative Memory designed to recognize patterns. Characterization

Ganguly, Niloy

276

Self-Efficacy of Pattern Recognition in Science of Middle School Students.  

ERIC Educational Resources Information Center

The goal of this research was to determine the effectiveness of a middle school science curriculum designed to inspire students to think about science through studying the patterns of humans. The curriculum focuses on human behavior, evolution, ecology, and performance and is based on the notion that pattern recognition is highly correlated with…

Smist, J. M.; Barkman, R. C.

277

Circadian Patterns Recognition in Ecosystems by Wavelet Filtering and Fuzzy Clustering  

E-print Network

Circadian Patterns Recognition in Ecosystems by Wavelet Filtering and Fuzzy Clustering Stefano a method for extracting representative patterns from a set of data representing circadian cycles with a discrete wavelet decomposition in order to filter out the noise and isolate the relevant circadian cycle

278

Temporal Pattern as a Cue for Species-Specific Calling Song Recognition in Crickets  

Microsoft Academic Search

Female crickets can recognize conspecific calling song from its temporal pattern alone. In Teleogryllus oceanicus, the song pattern consists of three classes of interpulse intervals arranged in a stereotyped sequence. Females recognize a model song in which the sequential order of intervals is random. This argues against the hypothesis that recognition results from matching auditory input to an internal template

Gerald S. Pollack; Ronald R. Hoy

1979-01-01

279

HotSpotter -Patterned Species Instance Recognition Jonathan P. Crall  

E-print Network

. 1. Introduction Motivation: Conducting research on animal populations requires reliable information step requires comparing each animal image to a database of pic- tures of animals that have already been for automatic animal identification are needed. This need has spawned research on recognition of an- imals

Bystroff, Chris

280

Team Pattern Recognition: Sharing Cognitive Chunks Under Time Pressure  

Microsoft Academic Search

This study extends the theory of Recognition Primed Decision-Making by applying it to groups. Furthermore, we explore the application of Template Theory to collaboration. An experiment was conducted in which teams made resource allocation decisions while physically dispersed and supported with a shared virtual work surface (What You See Is What I See - WYSIWIS) both with and without time-pressure.

Stephen C. Hayne; C. A. P. Smith; Leo R. Vijayasarathy

2005-01-01

281

Optical control and dynamic patterning of zeolites  

NASA Astrophysics Data System (ADS)

Zeolite crystals have a wide use as model systems for artificial light harvesting systems, as nano-containers for supramolecular organization or as building blocks for 1D and 2D assemblies of several crystals. In particular the assembly of zeolite L crystals with the aim to bridge the gap between the nano- and the macroscopic world has been a focus of research during the last years. However, almost all available approaches to order, assemble and pattern Zeolite L are restricted to large amounts of crystals. Although these approaches have proven to be powerful for many applications, but they have only limited control over positioning or orientation of single crystals and are lacking if patterns or structures are required which are composed of a few or up to a few hundred individual crystals. We demonstrate here that holographic optical tweezers are a powerful and versatile instrument to control zeolite L on the single crystal level. It is shown that full three-dimensional positioning, including rotational control, of any zeolite L crystal can be achieved. Finally, we demonstrate fully reversible, dynamic patterning of a multitude of individually controlled zeolite L crystals.

Woerdemann, Mike; Alpmann, Christina; Hörner, Florian; Devaux, André; De Cola, Luisa; Denz, Cornelia

2010-08-01

282

Functional networks training algorithm for statistical pattern recognition  

Microsoft Academic Search

Pattern classification is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make reasonable decisions about the categories of the patterns. It is a very important in a variety of engineering and scientific disciplines such as computer vision, artificial intelligence, and medicine. New and emerging applications, such as Web searching,

Emad A. El-Sebakhy

2004-01-01

283

Relative gradient local binary patterns method for face recognition under varying illuminations  

NASA Astrophysics Data System (ADS)

Local binary patterns (LBPs) are effective facial texture feature descriptors in face recognition. However, the performance of original LBP-based face recognition methods rapidly deteriorates in the condition of nonmonotonic illumination variations. In order to overcome this drawback, we propose a LBP-based face recognition approach, namely relative gradient LBPs (RGLBPs), in which the relative gradient is first applied to the original face images to extract illumination invariant features. Then, an LBP describes textural and structural features for face recognition. Finally, the chi-square dissimilarity measure and the nearest neighbor classifier are used for classification. The experimental results validate that the proposed approach is efficient for the illumination problem in face recognition and also robust to expression and age variations.

HuoRong, Ren; XinXin, Yan; Yan, Zhou; Rui, Cui; JianWei, Sun; Yang, Liu

2013-10-01

284

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

285

Principal patterns of fractional-order differential gradients for face recognition  

NASA Astrophysics Data System (ADS)

We investigate the ability of fractional-order differentiation (FD) for facial texture representation and present a local descriptor, called the principal patterns of fractional-order differential gradients (PPFDGs), for face recognition. In PPFDG, multiple FD gradient patterns of a face image are obtained utilizing multiorientation FD masks. As a result, each pixel of the face image can be represented as a high-dimensional gradient vector. Then, by employing principal component analysis to the gradient vectors over the centered neighborhood of each pixel, we capture the principal gradient patterns and meanwhile compute the corresponding orientation patterns from which oriented gradient magnitudes are computed. Histogram features are finally extracted from these oriented gradient magnitude patterns as the face representation using local binary patterns. Experimental results on face recognition technology, A.M. Martinez and R. Benavente, Extended Yale B, and labeled faces in the wild face datasets validate the effectiveness of the proposed method.

Yu, Lei; Cao, Qi; Zhao, Anping

2015-01-01

286

Visual pattern recognition in Drosophila is invariant for retinal position.  

PubMed

Vision relies on constancy mechanisms. Yet, these are little understood, because they are difficult to investigate in freely moving organisms. One such mechanism, translation invariance, enables organisms to recognize visual patterns independent of the region of their visual field where they had originally seen them. Tethered flies (Drosophila melanogaster) in a flight simulator can recognize visual patterns. Because their eyes are fixed in space and patterns can be displayed in defined parts of their visual field, they can be tested for translation invariance. Here, we show that flies recognize patterns at retinal positions where the patterns had not been presented before. PMID:15310908

Tang, Shiming; Wolf, Reinhard; Xu, Shuping; Heisenberg, Martin

2004-08-13

287

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

288

Fractal Features-Based Partial Discharge Pattern Recognition Using Extension Method  

Microsoft Academic Search

Partial discharge (PD) may cause the insulation deterioration in power equipments and impact the reliability. Therefore, the PD detection with pattern recognition is an important tool in high-voltage insulation diagnosis of power systems. A PD recognition system for high-voltage power cable based on extension method is proposed in this paper. A PD detector is used to measure the raw three-dimension

Hung-Cheng Chen

2012-01-01

289

Possible use of pattern recognition for the analysis of Mars rover X-ray fluorescence spectra  

NASA Technical Reports Server (NTRS)

On the Mars rover sample-return mission, the rover vehicle will collect and select samples from different locations on the Martian surface to be brought back to earth for laboratory studies. It is anticipated that an in situ energy-dispersive X-ray fluorescence (XRF) spectrometer will be on board the rover. On such a mission, sample selection is of higher priority than in situ quantitative chemical anlaysis. With this in mind, a pattern recognition technique is proposed as a simple, direct, and speedy alternative to detailed chemical analysis of the XRF spectra. The validity and efficacy of the pattern recognition technique are demonstrated by the analyses of laboratory XRF spectra obtained from a series of geological samples, in the form both of standardized pressed pellets and as unprepared rocks. It is found that pattern recognition techniques applied to the raw XRF spectra can provide for the same discrimination among samples as a knowledge of their actual chemical composition.

Yin, Lo I; Trombka, Jacob I.; Seltzer, Stephen M.; Johnson, Robert G.; Philpotts, John A.

1989-01-01

290

Pattern recognition of HER-1 in biological fluids using stochastic sensing.  

PubMed

Stochastic sensing was employed for pattern recognition of HER-1 in biological fluids. Nanostructured materials such as 5,10,15,20-tetraphenyl-21H,23H-porphyrin, maltodextrin and ?-cyclodextrin were used to modify diamond paste for stochastic sensing of HER-1. Pattern recognition of HER-1 in biological fluids was performed in a linear concentration range between 5.60?×?10(-11) and 9.72?×?10(-7?)mg?ml(-1). The lower limits of determination (10(-12?)mg?ml(-1) magnitude order) were recorded when maltodextrin and ?-cyclodextrin were used for stochastic sensing. The pattern recognition test of HER-1 in biological fluids samples shows high reliability for both qualitative and quantitative assay. PMID:24964343

Stefan-van Staden, Raluca-Ioana; Moldoveanu, Iuliana; Stanciu Gavan, Camelia

2015-04-01

291

ROBUST FACIAL EXPRESSION RECOGNITION USING LOCAL BINARY PATTERNS Caifeng Shan, Shaogang Gong and Peter W. McOwan  

E-print Network

ROBUST FACIAL EXPRESSION RECOGNITION USING LOCAL BINARY PATTERNS Caifeng Shan, Shaogang Gong- troduced for facial expression recognition capable of ro- bust performance over a rang of image resolutions to the com- plexity and variety of facial expressions. Automatic facial expression recognition involves two

Kim, Tae-Kyun

292

The Recognition Of Myoelectric Patterns For Prosthetic Limb Control  

Microsoft Academic Search

This paper describes a novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using

Bernard Hudgins; Philip Parker; Robert N. Scott

1991-01-01

293

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

294

The use of pattern recognition techniques in analyzing down-hole dynamometer cards  

E-print Network

problem, the actual methods that are used are quite different due to the basic differences which exist between the shape of well logs and the shape of down-hole cards. A well log is a plot of the rock or fluid property measured by the logging instrument... very simple pattern recognition scheme that is designed only to recognize cases where fluid pound exists. It does not have the capability of distinguishing the many other down-hole problems that can occur in a rod pumped well. PATTERN RECOGNITION...

Dickinson, Roderick Raymond

1987-01-01

295

Detection of Ambiguous Patterns Using SVMs: Application to Handwritten Numeral Recognition  

NASA Astrophysics Data System (ADS)

This work presents a pattern recognition system that is able to detect ambiguous patterns and explain its answers. The system consists of a set of parallel Support Vector Machine (SVM) classifiers, each one dedicated to a representative feature extracted from the input, followed by an analysing module based on a bayesian strategy in charge of defining the system answer. We apply the system to the recognition of handwritten numerals. Experiments were carried out on the MNIST database, which is generally accepted as one of the standards in most of the literature in the field.

Seijas, Leticia; Segura, Enrique

296

Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients  

E-print Network

Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients Johanna Carvajal, multi-action recognition, segmen- tation, stochastic modelling, Gaussian mixture models 1. INTRODUCTION segmentation and classification by classifying temporal regions using a multi- class SVM and performing

Sanderson, Conrad

297

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

298

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

299

Analog design of a new neural network for optical character recognition  

Microsoft Academic Search

An electronic circuit is presented for a new type of neural network, which gives a recognition rate of over 100 kHz. The network is used to classify handwritten numerals, presented as Fourier and wavelet descriptors, and has been shown to train far quicker than the popular backpropagation network while maintaining classification accuracy. In neural-network pattern recognition studies, the backpropagation network

Ian P. Morns; Satnam Singh Dlay

1999-01-01

300

A Novel Feature Extraction for Robust EMG Pattern Recognition  

E-print Network

Varieties of noises are major problem in recognition of Electromyography (EMG) signal. Hence, methods to remove noise become most significant in EMG signal analysis. White Gaussian noise (WGN) is used to represent interference in this paper. Generally, WGN is difficult to be removed using typical filtering and solutions to remove WGN are limited. In addition, noise removal is an important step before performing feature extraction, which is used in EMG-based recognition. This research is aimed to present a novel feature that tolerate with WGN. As a result, noise removal algorithm is not needed. Two novel mean and median frequencies (MMNF and MMDF) are presented for robust feature extraction. Sixteen existing features and two novelties are evaluated in a noisy environment. WGN with various signal-to-noise ratios (SNRs), i.e. 20-0 dB, was added to the original EMG signal. The results showed that MMNF performed very well especially in weak EMG signal compared with others. The error of MMNF in weak EMG signal with...

Phinyomark, Angkoon; Phukpattaranont, Pornchai

2009-01-01

301

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

302

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

303

Affine Invariant Pattern Recognition Using Multi-Scale Autoconvolution  

E-print Network

-Scale Au- toconvolution (MSA). The proposed transform is based on a probabilistic interpretation the dream of a general pattern recognizer. While it is difficult to directly achieve such a system, many

Salo, Mikko

304

Detection of tool wear using gradient adaptive lattice and pattern recognition analysis  

NASA Astrophysics Data System (ADS)

A method of recognising tool wear states in a turning operation from the integrated information of cutting force and acoustic emission signals is presented. The approach, which employs gradient adaptive lattice analysis and pattern recognition techniques, is fast and yields accurate recognition of tool wear states in a wide range of cutting conditions. The gradient adaptive lattice algorithm is applied to recursively compute the autoregressive as well as partial correlation coefficients for both signals with high computational efficiency. Those coefficients are chosen to characterise the sensing signals and used as the feature inputs for pattern recognition. A stepwise search procedure was applied to select the most useful features for the recognition process. Both unsupervised learning technique (fuzzy C-means algorithm) and supervised learning technique (linear discriminant analysis) are used in the pattern recognition analysis. Characteristics of each method are discussed and performance in recognising the states of cutting tool (fresh or worn) is evaluated. The results show that this approach was successfully applied to signify a fresh or worn tool state with a high percentage of correct classification (over 90%).

Jiaa, C. L.; Dornfeld, D. A.

1992-03-01

305

Recognition of Crowd Behavior from Mobile Sensors with Pattern Analysis and Graph Clustering Methods  

E-print Network

Mobile on-body sensing has distinct advantages for the analysis and understanding of crowd dynamics: sensing is not geographically restricted to a specific instrumented area, mobile phones offer on-body sensing and they are already deployed on a large scale, and the rich sets of sensors they contain allows one to characterize the behavior of users through pattern recognition techniques. In this paper we present a methodological framework for the machine recognition of crowd behavior from on-body sensors, such as those in mobile phones. The recognition of crowd behaviors opens the way to the acquisition of large-scale datasets for the analysis and understanding of crowd dynamics. It has also practical safety applications by providing improved crowd situational awareness in cases of emergency. The framework comprises: behavioral recognition with the user's mobile device, pairwise analyses of the activity relatedness of two users, and graph clustering in order to uncover globally, which users participate in a gi...

Roggen, Daniel; Tröster, Gerhard; Helbing, Dirk; 10.3934/nhm.2011.6.521

2011-01-01

306

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

307

Pattern Recognition of Longitudinal Trial Data with Nonignorable Missingness: An Empirical Case Study  

PubMed Central

Methods for identifying meaningful growth patterns of longitudinal trial data with both nonignorable intermittent and drop-out missingness are rare. In this study, a combined approach with statistical and data mining techniques is utilized to address the nonignorable missing data issue in growth pattern recognition. First, a parallel mixture model is proposed to model the nonignorable missing information from a real-world patient-oriented study and concurrently to estimate the growth trajectories of participants. Then, based on individual growth parameter estimates and their auxiliary feature attributes, a fuzzy clustering method is incorporated to identify the growth patterns. This case study demonstrates that the combined multi-step approach can achieve both statistical gener ality and computational efficiency for growth pattern recognition in longitudinal studies with nonignorable missing data. PMID:20336179

Fang, Hua; Espy, Kimberly Andrews; Rizzo, Maria L.; Stopp, Christian; Wiebe, Sandra A.; Stroup, Walter W.

2010-01-01

308

Syntactic reasoning and pattern recognition for analysis of coronary artery images.  

PubMed

This paper presents a new approach to the application of structural pattern recognition methods for image understanding, based on content analysis and knowledge discovery performed on medical images. This presents in particular computer analysis and recognition of local stenoses of the coronary arteries lumen. These stenoses are the result of the appearance of arteriosclerosis plaques, which in consequence lead to different forms of ischemic cardiovascular diseases. Such diseases may be seen in the form of stable or unstable disturbances of heart rhythm or infarctions. Analysis of the correct morphology of these arteries lumen is possible with the application of the syntactic analysis and pattern recognition methods, in particular with the attributed grammar of LALR type. In the paper, we shall describe all stages of analysis and understanding of images in the context of obtained features, and we shall also present the proper algorithm of syntactic reasoning based on the acquired knowledge. PMID:12234721

Ogiela, Marek R; Tadeusiewicz, Ryszard

2002-01-01

309

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

310

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

311

EEGbased communication: a pattern recognition approach William D. Penny a , Stephen J. Roberts a and Maria J. Stokes b  

E-print Network

by our emphasis on pattern recognition methods rather than on biofeedback training. Two key technical to drive cursor movements via a biofeedback process. Though successful, the process is rather long, taking device. Our approach relies less on biofeedback training and more on the use of pattern recognition

Roberts, Stephen

312

Sign Language Recognition using Sequential Pattern Trees Eng-Jon Ong Helen Cooper Nicolas Pugeault Richard Bowden  

E-print Network

Sign Language Recognition using Sequential Pattern Trees Eng-Jon Ong Helen Cooper Nicolas Pugeault it is well suited to Sign Language Recognition. Us- ing deterministic robust features based on hand Pattern (SP) techniques. 1. Introduction This paper attempts to tackle the problem of indepen- dent sign-language

Bowden, Richard

313

Discrimination between Inrush current and Internal Faults using Pattern Recognition Approach  

Microsoft Academic Search

This paper presents a new approach to distinguish between inrush current and internal faults of power transformer using pattern recognition approach. The HS-transform (hyperbolic S-transform) is used to extract patterns of inrush current and internal faults from the captured transformer current. HS-transform is a very powerful tool for non-stationary signal analysis giving the information of transient currents both in time

B. K. Panigrahi; S. R. Samantaray; P. K. Dash; G. Panda

2006-01-01

314

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

315

Identification of Patients with Congestive Heart Failure by Recognition of Sub-Bands Spectral Patterns  

Microsoft Academic Search

A new simple technique based on recognition of power spectral densities patterns of decomposed sub-bands of R-R interval (RRI) data for identification of patients with Congestive Heart Failure (CHF), is investigated. This method uses a soft- decision algorithm for estimating the PSD of the decomposed sub- bands by a probability measure of energy contents in those sub- bands. Both trial

Abdulnasir Hossen; Bader Al-Ghunaimi

2008-01-01

316

Human monocyte scavenger receptors are pattern recognition receptors for (133)--D-glucans  

Microsoft Academic Search

Glucans are cell wall constituents of fungi and bacteria that bind to pattern recognition receptors and modulate innate immunity, in part, by macrophage activation. We used surface plas- mon resonance to examine the binding of glucans, differing in fine structure and charge density, to scavenger receptors on membranes isolated from human monocyte U937 cells. Experiments were performed at 25°C using

Peter J. Rice; Jim L. Kelley; Grigorij Kogan; Harry E. Ensley; John H. Kalbfleisch; I. William Browder; David L. Williams

2002-01-01

317

A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control  

Microsoft Academic Search

This paper presents a heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control. Basic signal statistics (mean and standard deviation) are used for membership function construction, and fuzzy c-means (FCMs) data clustering is used to automate the construction of a simple amplitude-driven inference rule base. The result is a system that is transparent to, and

A. B. Ajiboye; R. Fff. Weir

2005-01-01

318

Abstract--Applying electromyographic (EMG) signal pattern recognition to artificial leg control is challenging because leg  

E-print Network

Abstract--Applying electromyographic (EMG) signal pattern recognition to artificial leg control is challenging because leg EMGs are non-stationary. Time-frequency features are suitable for representing non of this study was to quantify the computation speed of Graphic Processor Unit (GPU) on EMG time

Sun, Yan Lindsay

319

High impedance fault detection based on wavelet transform and statistical pattern recognition  

Microsoft Academic Search

A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high\\/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and

Ali-Reza Sedighi; Mahmood-Reza Haghifam; O. P. Malik; Mohammad-Hassan Ghassemian

2005-01-01

320

Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction  

ERIC Educational Resources Information Center

Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study…

Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

2012-01-01

321

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions  

Microsoft Academic Search

Dynamic texture is an extension of texture to the temporal domain. Description and recognition of dynamic tex- tures have attracted growing attention. In this paper, a novel approach for recognizing dynamic textures is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an

Guoying Zhao; Matti Pietikäinen

2007-01-01

322

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

Microsoft Academic Search

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

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

2003-01-01

323

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

324

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

325

How Groups Learn: The Role of Communication Patterns, Cue Recognition, Context Facility, and Cultural Intelligence  

ERIC Educational Resources Information Center

This article explores the role of group learning by focusing on how intragroup communication patterns (implicit and explicit) influence learning readiness dimensions (cue recognition, context facility, and cultural intelligence), which in turn influences the group's ability to learn and the type of leaning that occurs. Groups with high levels of…

Silberstang, Joyce; London, Manuel

2009-01-01

326

A strip chart recorder pattern recognition tool kit for Shuttle operations  

NASA Technical Reports Server (NTRS)

During Space Shuttle operations, Mission Control personnel monitor numerous mission-critical systems such as electrical power; guidance, navigation, and control; and propulsion by means of paper strip chart recorders. For example, electrical power controllers monitor strip chart recorder pen traces to identify onboard electrical equipment activations and deactivations. Recent developments in pattern recognition technologies coupled with new capabilities that distribute real-time Shuttle telemetry data to engineering workstations make it possible to develop computer applications that perform some of the low-level monitoring now performed by controllers. The number of opportunities for such applications suggests a need to build a pattern recognition tool kit to reduce software development effort through software reuse. We are building pattern recognition applications while keeping such a tool kit in mind. We demonstrated the initial prototype application, which identifies electrical equipment activations, during three recent Shuttle flights. This prototype was developed to test the viability of the basic system architecture, to evaluate the performance of several pattern recognition techniques including those based on cross-correlation, neural networks, and statistical methods, to understand the interplay between an advanced automation application and human controllers to enhance utility, and to identify capabilities needed in a more general-purpose tool kit.

Hammen, David G.; Moebes, Travis A.; Shelton, Robert O.; Savely, Robert T.

1993-01-01

327

PATTERN RECOGNITION/EXPERT SYSTEM FOR MASS SPECTRA OF VOLATILE TOXIC AND OTHER ORGANIC COMPOUNDS  

EPA Science Inventory

A system based on principles of pattern recognition has been developed for identifying toxic and other volatile organic pollutants in complex environmental samples. t interprets the most commonly used monitoring data, mass spectral data, and produces a class designation, an estim...

328

Real-time performance of textile electrodes in electromyogram pattern-recognition based prosthesis control  

Microsoft Academic Search

Textile electrode is flexible, folding, washable and biocompatible with skin. With these advantages, the textile electrodes should be an ideal alternative for electromyogram (EMG) recordings in clinical applications. In this study, a textile electrode system was used for EMG signal recordings and its usability and performance in classifying different arm and hand movements were evaluated through real-time pattern recognition control

Dandan Tao; Haoshi Zhang; Zhenxing Wu; Guanglin Li

2012-01-01

329

A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation  

E-print Network

' Rule in those cases where classes had features with overlapped distributions. Twenty-one out of 26A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation José L for recognizing the 26 hand shapes of the American Sign Language alphabet, using a novel glove-like device. Two

Lindeman, Robert W.

330

A new statistical pattern recognition distance rejection model: application to the monitoring of car catalytic converters  

Microsoft Academic Search

A novel statistical pattern recognition model is proposed in order to solve specific industrial diagnosis problems. The representation of discriminative parameters as a function of the operating point parameters enables accurate operating mode distance rejection. Adapted distance rejection options are presented in order to deal with unknown classes. These methods have been applied to a real world diagnosis problem: the

A. Boatas; B. Dubuisson; M. A. Dillies-Peltier

2000-01-01

331

Dual-band, infrared buried mine detection using a statistical pattern recognition approach  

SciTech Connect

The main objective of this work was to detect surrogate land mines, which were buried in clay and sand, using dual-band, infrared images. A statistical pattern recognition approach was used to achieve this objective. This approach is discussed and results of applying it to real images are given.

Buhl, M.R.; Hernandez, J.E.; Clark, G.A.; Sengupta, S.K.

1993-08-01

332

Advances in the Use of Neurophysiologycally-based Fusion for Visualization and Pattern Recognition of  

E-print Network

.e. MRI, CT, PET, SPECT, etc.). By providing a single fused image, this architecture can increase userAdvances in the Use of Neurophysiologycally-based Fusion for Visualization and Pattern Recognition and information maximization. To this end, we have improved on an image fusion architecture first developed

Wang, Xiaorui "Ray"

333

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...

334

A WAVELET-BASED PATTERN RECOGNITION ALGORITHM TO CLASSIFY POSTURAL TRANSITIONS IN HUMANS  

E-print Network

A WAVELET-BASED PATTERN RECOGNITION ALGORITHM TO CLASSIFY POSTURAL TRANSITIONS IN HUMANS Anthony and workers in institutions equipped to care of elderly people. To prevent overpopulation problems, researcher to detect and reproduce move- ments of a part of the human body (a limb for instance) with uses in virtual

Boyer, Edmond

335

Pattern recognition using neural networks. Technical report, August 1, 1994--September 11, 1994  

SciTech Connect

I am pleased to submit the following technical report to Oak Ridge National Laboratories as an accomplishment of the 6 (six) week appointment in the U.S. Nuclear Regulatory Commission`s Historically Black College and Universities Faculty Research Participation Program, Summer 1994 (August - September 11, 1994). In this project, an approach for pattern recognition using neural networks is proposed. Particularly, a Boltzmann machine, a Hopfield neural net model, is used in pattern recognition with desirable learning ability. The Boltzmann machine features stochastic learning, which acts as the connection dynamics for determining the weights on the connections between the neuron-like cells (processing elements) of different layers in the neural network. An algorithm for pattern recognition using Boltzmann machine is also presented, which could be coded with C programming language or others to implement the approach for efficient pattern recognition. Finally, a follow-on research work derived from this project is planned if the author could win another summer appointment in 1995 from the Science/Engineering Education Division, Oak Ridge Institute for Science and Education, Oak Ridge National Laboratories.

Ma, H.

1994-12-31

336

Reduction of Non Deterministic Automata for Hidden Markov Model Based Pattern Recognition Applications  

E-print Network

underlying graph is a tree). In a previous paper, we showed that non-deterministic automata wereReduction of Non Deterministic Automata for Hidden Markov Model Based Pattern Recognition small non-deterministic automata from lexicons. We present experimental results demonstrating

Paris-Sud XI, Université de

337

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...

338

Pattern Recognition 39 (2006) 5773 www.elsevier.com/locate/patcog  

E-print Network

for some zones. For some types of zones, we use a hidden Markov model (HMM) and #12;58 Y. Wang et alPattern Recognition 39 (2006) 57­73 www.elsevier.com/locate/patcog Document zone content describes an algorithm for the determination of zone content type of a given zone within a document image

Wang, Yalin

2006-01-01

339

Differential expression of pattern recognition receptors in sheep tissues and leukocyte subsets  

Microsoft Academic Search

The various members of the different pattern recognition receptor families are now recognized as playing a crucial role in the initial interactions between a pathogen and the host. This paper identifies all 10 members of the TLR family in sheep as well as key members of the C-type lectin and NLR families. Our data show that sheep possess the ‘human’

King S. Nalubamba; Anton G. Gossner; Robert G. Dalziel; John Hopkins

2007-01-01

340

Pattern recognition of genomic features with microarrays: site typing of Mycobacterium tuberculosis strains.  

E-print Network

Pattern recognition of genomic features with microarrays: site typing of Mycobacterium tuberculosis-5479 Abstract Mycobacterium tuberculosis (M. tb.) strains differ in the number and locations of a transposon sequences in genomes. Mycobacterium tuberculosis (M. tb.) is an infectious pathogen reported in 8 million

Raychaudhuri, Soumya

341

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

Microsoft Academic Search

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,

Fuhui Long; Nanning Zheng; Jiande Jiang

1998-01-01

342

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...

343

Proceedings of the IEEE Computer Society conference on computer vision and pattern recognition  

SciTech Connect

This book presents the papers given at a conference on image processing and pattern recognition. Topics considered at the conference included stereovision, vision applications, parallel processing, algorithms, artificial intelligence, structure from motion, three-dimensional shape representation, array processors, industrial vision systems, computer architecture, homogeneous multiprocessors, shape from texture, and optimal likelihood generators for edge detection under Gaussian additive noise.

Not Available

1986-01-01

344

Proceedings of the IEEE conference on computer vision and pattern recognition  

SciTech Connect

This book presents the papers given at a conference on image processing, pattern recognition, and robotic vision. Topics considered at the conference included expert systems, artificial intelligence, knowledge bases, computerized simulation, computer architecture, vision models and texture, algorithms, parallel algorithms, data processing, circuit theory, array processors, and distributed data processing, and data-flow processing.

Not Available

1985-01-01

345

Automatic Segmentation of Menisci in MR Images Using Pattern Recognition and Graph  

E-print Network

tool to evaluate soft tissue, tendon, ligaments and menisci in the knee. The menisci are locatedAutomatic Segmentation of Menisci in MR Images Using Pattern Recognition and Graph Cuts Fredrik is a non invasive method with no ionization and with good soft tissue contrast and constitutes an important

Lunds Universitet

346

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 ...

347

Fast Pattern Recognition Using Gradient-Descent Search in an Image Pyramid James MacLean  

E-print Network

method is normalized grey-scale correlation (NGC), in which a target image is cor- related@cs.yorku.ca Abstract A new technique for fast pattern recognition using normal- ized grey-scale correlation (NGC, or accurately determine its location for registration purposes is a valuable tool in manufacturing. One popular

MacLean, W. James

348

Foreword to the Special Issue on Pattern Recognition in Remote Sensing  

E-print Network

1 Foreword to the Special Issue on Pattern Recognition in Remote Sensing The constant increase in the amount of remotely sensed images as well as the urgent need for the extraction of useful information from techniques to unsolved problems in remote sensing image analysis that cannot be handled by using traditional

Aksoy, Selim

349

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

Microsoft Academic Search

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 novices performing chess-related and -unrelated (visual) search tasks. As expected, the

Merim Bilali?; Robert Langner; Michael Erb; Wolfgang Grodd

2010-01-01

350

Pattern Recognition Letters 00 (2012) 125 Intelligent Multi-Camera Video Surveillance: A Review  

E-print Network

Pattern Recognition Letters 00 (2012) 1­25 Journal Logo Intelligent Multi-Camera Video Surveillance, Hong Kong Abstract Intelligent multi-camera video surveillance is a multidisciplinary field related analysis and co- operative video surveillance both with active and static cameras. Detailed descriptions

Wang, Xiaogang

351

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...

352

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

353

Pattern Recognition 39 (2006) 624634 www.elsevier.com/locate/patcog  

E-print Network

2 2788 3799X1819; fax: +886 2 2782 4814. E-mail addresses: ister@iis.sinica.edu.tw (C.-H. Chou-Chin Linb ,Ying-Ho Liua , Fu Changa, aInstitute of Information Science, Academia Sinica, 128 Section 2.-H. Chou et al. / Pattern Recognition

Chou, Chien-Hsing (Ister)

2006-01-01

354

New pattern recognition methods for identifying oil spills from satellite remote sensing data  

NASA Astrophysics Data System (ADS)

The early detection and identification of oil spills are critical prerequisites for performing cost-effective maritime salvage operations. This paper presents a new approach for distinguishing oil spills that are produced by stationary offshore sources, during their early phase of occurrence. The results were reached after analyzing over 100 images of satellite remote sensing data that were produced by either active microwave sensors like the Synthetic Aperture Radar (SAR) sensors or passive optical sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS). The laws of conservation of mass and momentum that describe the dynamics of an oil spill over the water surface, were used for the development of a new detection algorithm that encompasses a parallel concept of shape conservation. The validity of this new empirical algorithm depends upon a number of assumptions that were made about the oil viscosity, temperature, water currents, wind speeds and the spills' spatial extent and duration. It can also be shown that unique texture differences can be revealed between an oil spill and other look-alikes' features like, for example, wind patterns, rain cells and algal mats by applying edge filtering operations on the patches that are under investigation, and therefore the reduction of false positives. The work presented here may have profound implications on future studies that examine the use of automatic recognitions methods, that are based on pattern and texture analysis. The results may also lead to new methodologies by which the dispersion and trajectory models of oil spills can be studied in new detail and ultimately used in environmental impact assessment operations.

Alawadi, Fahad

2009-09-01

355

A baseball exploration system using spatial pattern recognition  

Microsoft Academic Search

Despite a lot of research efforts in baseball video processing, little work has been done in analyzing the detailed process and ball movement of the batting content. This paper proposes a novel system to automatically summarize the progress of each batting in baseball videos. Utilizing the strictly-defined specifications of the baseball field, the system recognizes the spatial patterns in each

Hua-tsiing Chen; Ming-ho Hsiao; Hsuan-sheng Chen; Wen-Jiin Tsai; Suh-yin Lee

2008-01-01

356

Artificial neural network & pattern recognition approach for narrowband signal extraction  

Microsoft Academic Search

Estimation of unknown frequency, extraction of narrowband signals buried under noise and periodic interference are accomplished by employing existing techniques. However, the authors propose an artificial neural net based scheme together with pattern classification algorithm for narrowband signal extraction. A three layer feedforward net is trained with three different algorithms namely backpropagation, Cauchy's algorithm with Boltzmann's probability distribution feature and

P. K. Dash; P. K. Nanda; S. Saha; R. Doraiswami

1991-01-01

357

Earth Observations: Pattern Recognition of the Earth System  

NSDL National Science Digital Library

Earth Observation is an activity where students observe and patterns in datasets of the different Earth spheres. Correlations between datasets are examined to stimulate student thinking of the interrelations in the Earth system. This activity is connect to process and concepts covered later in the class.

Matthew Nyman

358

Auditory orientation in crickets: Pattern recognition controls reactive steering  

Microsoft Academic Search

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,

James F. A. Poulet; Berthold Hedwig

2005-01-01

359

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

360

Recognition of haptic interaction patterns in dyadic joint object manipulation.  

PubMed

The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method. PMID:25532210

Madan, Cigil Ece; Kucukyilmaz, Ayse; Sezgin, Tevfik Metin; Basdogan, Cagatay

2015-01-01

361

Optical character recognition: an illustrated guide to the frontier  

Microsoft Academic Search

We offer a perspective on the performance of current OCR systems by illustrating and explaining actual OCR errors made by three commercial devices. After discussing briefly the character recognition abilities of humans and computers, we present illustrated examples of recognition errors. The top level of our taxonomy of the causes of errors consists of Imaging Defects, Similar Symbols, Punctuation, and

George Nagy; Thomas A. Nartker; Stephen V. Rice

1999-01-01

362

Modal Control in Semiconductor Optical Waveguides With Uniaxially Patterned Layers  

NASA Astrophysics Data System (ADS)

Uniaxially patterned (UAP) dielectric layers have an optical anisotropy that can be externally controlled. This paper examines the effects of patterning the cladding or the core layer of a three-layer optical waveguide on the polarization properties of propagating radiation. Particular attention is paid to the case when the core material is a semiconductor with optical gain. A number of devices are discussed based on incorporating a UAP layer in the structure design, such as a polarization-insensitive amplifier, a polarizer, an optically controlled polarization switch, and an optically controlled modal coupler.

Subashiev, Arsen V.; Luryi, Serge

2006-03-01

363

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

364

Artificial Neural Networks Lab 2 Classical Pattern Recognition  

E-print Network

-task appears with a box 1. then your respective answer must be presented. To obtain 1 extra point to be added are boc_decision.m dis.mat landsat.mat plot_dline.m plot_levels.m plot_surface.m sat_features.m sat to the two features, and a number of columns, corresponding to the number of patterns. Plotting the data

Duckett, Tom

365

Maximum-likelihood density modification using pattern recognition of structural motifs  

PubMed Central

The likelihood-based approach to density modification [Terwilliger (2000 ?), Acta Cryst. D56, 965–972] is extended to include the recognition of patterns of electron density. Once a region of electron density in a map is recognized as corresponding to a known structural element, the likelihood of the map is reformulated to include a term that reflects how closely the map agrees with the expected density for that structural element. This likelihood is combined with other aspects of the likelihood of the map, including the presence of a flat solvent region and the electron-density distribution in the protein region. This likelihood-based pattern-recognition approach was tested using the recognition of helical segments in a largely helical protein. The pattern-recognition method yields a substantial phase improvement over both conventional and likelihood-based solvent-flattening and histogram-matching methods. The method can potentially be used to recognize any common structural motif and incorporate prior knowledge about that motif into density modification. PMID:11717487

Terwilliger, Thomas C.

2001-01-01

366

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

367

Local directional pattern of phase congruency features for illumination invariant face recognition  

NASA Astrophysics Data System (ADS)

An illumination-robust face recognition system using Local Directional Pattern (LDP) descriptors in Phase Congruency (PC) space is proposed in this paper. The proposed Directional Pattern of Phase Congruency (DPPC) is an oriented and multi-scale local descriptor that is able to encode various patterns of face images under different lighting conditions. It is constructed by applying LDP on the oriented PC images. A LDP feature is obtained by computing the edge response values in eight directions at each pixel position and encoding them into an eight bit binary code using the relative strength magnitude of these edge responses. Phase congruency and local directional pattern have been independently used in the field of face and facial expression recognition, since they are robust to illumination changes. When the PC extracts the discontinuities in the image such as edges and corners, the LDP computes the edge response values in different directions and uses these to encode the image texture. The local directional pattern descriptor on the phase congruency image is subjected to principal component analysis (PCA) for dimensionality reduction for fast and effective face recognition application. The performance evaluation of the proposed DPPC algorithm is conducted on several publicly available databases and observed promising recognition rates. Better classification accuracy shows the superiority of the LDP descriptor against other appearance-based feature descriptors such as Local Binary Pattern (LBP). In other words, our result shows that by using the LDP descriptor the Euclidean distance between reference image and testing images in the same class is much less than that between reference image and testing images from the other classes.

Essa, Almabrok E.; Asari, Vijayan K.

2014-04-01

368

Critical song features for auditory pattern recognition in crickets.  

PubMed

Many different invertebrate and vertebrate species use acoustic communication for pair formation. In the cricket Gryllus bimaculatus, females recognize their species-specific calling song and localize singing males by positive phonotaxis. The song pattern of males has a clear structure consisting of brief and regular pulses that are grouped into repetitive chirps. Information is thus present on a short and a long time scale. Here, we ask which structural features of the song critically determine the phonotactic performance. To this end we employed artificial neural networks to analyze a large body of behavioral data that measured females' phonotactic behavior under systematic variation of artificially generated song patterns. In a first step we used four non-redundant descriptive temporal features to predict the female response. The model prediction showed a high correlation with the experimental results. We used this behavioral model to explore the integration of the two different time scales. Our result suggested that only an attractive pulse structure in combination with an attractive chirp structure reliably induced phonotactic behavior to signals. In a further step we investigated all feature sets, each one consisting of a different combination of eight proposed temporal features. We identified feature sets of size two, three, and four that achieve highest prediction power by using the pulse period from the short time scale plus additional information from the long time scale. PMID:23437054

Meckenhäuser, Gundula; Hennig, R Matthias; Nawrot, Martin P

2013-01-01

369

Two-level DP-matching--A dynamic programming-based pattern matching algorithm for connected word recognition  

Microsoft Academic Search

This paper reports a pattern matching approach to connected word recognition. First, a general principle of connected word recognition is given based on pattern matching between unknown continuous speech and artificially synthesized connected reference patterns. Time-normalization capability is allowed by use of dynamic programming-based time-warping technique (DP-matching). Then, it is shown that the matching process is efficiently carried out by

HIROAKI SAKOE

1979-01-01

370

Multiwavelength correlator for optical header recognition based on arrays of fibre Bragg gratings  

Microsoft Academic Search

We demonstrate a novel optical correlator for multiwavelength header recognition employing arrays of fibre Bragg gratings (FBGs). One FBG array is used to appropriately delay the entire different header wavelengths, and the others are used as a header bank, in which each one is designed with a different wavelength profile. The proof of principle of the optical correlator is experimentally

Muhsen Aljada; Rong Zheng; Kamal Alameh; Khalid Al-Begain

2006-01-01

371

Femtosecond Laser Processing by Using Patterned Vector Optical Fields  

PubMed Central

We present and demonstrate an approach for femtosecond laser processing by using patterned vector optical fields (PVOFs) composed of multiple individual vector optical fields. The PVOFs can be flexibly engineered due to the diversity of individual vector optical fields in spatial arrangement and distribution of states of polarization, and it is easily created with the aid of a spatial light modulator. The focused PVOFs will certainly result in various interference patterns, which are then used to fabricate multi-microholes with various patterns on silicon. The present approach can be expanded to fabricate three-dimensional microstructures based on two-photon polymerization. PMID:23884360

Lou, Kai; Qian, Sheng-Xia; Ren, Zhi-Cheng; Tu, Chenghou; Li, Yongnan; Wang, Hui-Tian

2013-01-01

372

Accurate, fast, and secure biometric fingerprint recognition system utilizing sensor fusion of fingerprint patterns  

NASA Astrophysics Data System (ADS)

Fingerprint recognition is one of the first techniques used for automatically identifying people and today it is still one of the most popular and effective biometric techniques. With this increase in fingerprint biometric uses, issues related to accuracy, security and processing time are major challenges facing the fingerprint recognition systems. Previous work has shown that polarization enhancementencoding of fingerprint patterns increase the accuracy and security of fingerprint systems without burdening the processing time. This is mainly due to the fact that polarization enhancementencoding is inherently a hardware process and does not have detrimental time delay effect on the overall process. Unpolarized images, however, posses a high visual contrast and when fused (without digital enhancement) properly with polarized ones, is shown to increase the recognition accuracy and security of the biometric system without any significant processing time delay.

El-Saba, Aed; Alsharif, Salim; Jagapathi, Rajendarreddy

2011-04-01

373

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

374

Visual pattern recognition based on spatio-temporal patterns of retinal ganglion cells' activities.  

PubMed

Neural information is processed based on integrated activities of relevant neurons. Concerted population activity is one of the important ways for retinal ganglion cells to efficiently organize and process visual information. In the present study, the spike activities of bullfrog retinal ganglion cells in response to three different visual patterns (checker-board, vertical gratings and horizontal gratings) were recorded using multi-electrode arrays. A measurement of subsequence distribution discrepancy (MSDD) was applied to identify the spatio-temporal patterns of retinal ganglion cells' activities in response to different stimulation patterns. The results show that the population activity patterns were different in response to different stimulation patterns, such difference in activity pattern was consistently detectable even when visual adaptation occurred during repeated experimental trials. Therefore, the stimulus pattern can be reliably discriminated according to the spatio-temporal pattern of the neuronal activities calculated using the MSDD algorithm. PMID:21886670

Jing, Wei; Liu, Wen-Zhong; Gong, Xin-Wei; Gong, Hai-Qing; Liang, Pei-Ji

2010-09-01

375

Visual pattern recognition based on spatio-temporal patterns of retinal ganglion cells’ activities  

PubMed Central

Neural information is processed based on integrated activities of relevant neurons. Concerted population activity is one of the important ways for retinal ganglion cells to efficiently organize and process visual information. In the present study, the spike activities of bullfrog retinal ganglion cells in response to three different visual patterns (checker-board, vertical gratings and horizontal gratings) were recorded using multi-electrode arrays. A measurement of subsequence distribution discrepancy (MSDD) was applied to identify the spatio-temporal patterns of retinal ganglion cells’ activities in response to different stimulation patterns. The results show that the population activity patterns were different in response to different stimulation patterns, such difference in activity pattern was consistently detectable even when visual adaptation occurred during repeated experimental trials. Therefore, the stimulus pattern can be reliably discriminated according to the spatio-temporal pattern of the neuronal activities calculated using the MSDD algorithm. PMID:21886670

Jing, Wei; Liu, Wen-Zhong; Gong, Xin-Wei; Gong, Hai-Qing

2010-01-01

376

Host-Viral Interactions: Role of Pattern Recognition Receptors (PRRs) in Human Pneumovirus Infections  

PubMed Central

Acute respiratory tract infection (RTI) is a leading cause of morbidity and mortality worldwide and the majority of RTIs are caused by viruses, among which respiratory syncytial virus (RSV) and the closely related human metapneumovirus (hMPV) figure prominently. Host innate immune response has been implicated in recognition, protection and immune pathological mechanisms. Host-viral interactions are generally initiated via host recognition of pathogen-associated molecular patterns (PAMPs) of the virus. This recognition occurs through host pattern recognition receptors (PRRs) which are expressed on innate immune cells such as epithelial cells, dendritic cells, macrophages and neutrophils. Multiple PRR families, including Toll-like receptors (TLRs), RIG-I-like receptors (RLRs) and NOD-like receptors (NLRs), contribute significantly to viral detection, leading to induction of cytokines, chemokines and type I interferons (IFNs), which subsequently facilitate the eradication of the virus. This review focuses on the current literature on RSV and hMPV infection and the role of PRRs in establishing/mediating the infection in both in vitro and in vivo models. A better understanding of the complex interplay between these two viruses and host PRRs might lead to efficient prophylactic and therapeutic treatments, as well as the development of adequate vaccines. PMID:24244872

Kolli, Deepthi; Velayutham, Thangam Sudha; Casola, Antonella

2013-01-01

377

Direct Nano-Patterning With Nano-Optic Devices  

E-print Network

thermal oxidation and nano-scale melting/recrystallization of the targets. Furthermore, the resulting nano-patterns also showed a significant dependence on the optical properties (i.e., absorption coefficient and surface reflectivity) of the target...

Meenashi Sundaram, Vijay

2011-08-08

378

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

379

Pattern-recognition software detecting the onset of failures in complex systems  

SciTech Connect

A very general mathematical framework for embodying learned data from a complex system and combining it with a current observation to estimate the true current state of the system has been implemented using nearly universal pattern-recognition algorithms and applied to surveillance of the EBR-II power plant. In this application the methodology can provide signal validation and replacement of faulty signals on a near-real-time basis for hundreds of plant parameters. The mathematical framework, the pattern-recognition algorithms, examples of the learning and estimating process, and plant operating decisions made using this methodology are discussed. The entire methodology has been reduced to a set of FORTRAN subroutines which are small, fast, robust and executable on a personal computer with a serial link to the system's data acquisition computer, or on the data acquisition computer itself.

Mott, J.; King, R.

1987-01-01

380

Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations  

NASA Astrophysics Data System (ADS)

We report the development of a pattern recognition scheme that takes into account both fcc and hcp adsorption sites in performing self-learning kinetic Monte Carlo (SLKMC-II) simulations on the fcc(111) surface. In this scheme, the local environment of every under-coordinated atom in an island is uniquely identified by grouping fcc sites, hcp sites and top-layer substrate atoms around it into hexagonal rings. As the simulation progresses, all possible processes, including those such as shearing, reptation and concerted gliding, which may involve fcc-fcc, hcp-hcp and fcc-hcp moves are automatically found, and their energetics calculated on the fly. In this article we present the results of applying this new pattern recognition scheme to the self-diffusion of 9-atom islands (M9) on M(111), where M = Cu, Ag or Ni.

Islamuddin Shah, Syed; Nandipati, Giridhar; Kara, Abdelkader; Rahman, Talat S.

2012-09-01

381

Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations.  

PubMed

We report the development of a pattern recognition scheme that takes into account both fcc and hcp adsorption sites in performing self-learning kinetic Monte Carlo (SLKMC-II) simulations on the fcc(111) surface. In this scheme, the local environment of every under-coordinated atom in an island is uniquely identified by grouping fcc sites, hcp sites and top-layer substrate atoms around it into hexagonal rings. As the simulation progresses, all possible processes, including those such as shearing, reptation and concerted gliding, which may involve fcc-fcc, hcp-hcp and fcc-hcp moves are automatically found, and their energetics calculated on the fly. In this article we present the results of applying this new pattern recognition scheme to the self-diffusion of 9-atom islands (M(9)) on M(111), where M  =  Cu, Ag or Ni. PMID:22898941

Shah, Syed Islamuddin; Nandipati, Giridhar; Kara, Abdelkader; Rahman, Talat S

2012-09-01

382

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. PMID:25674081

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

2015-01-01

383

Application of pattern recognition in the forecast of outburst area of coal and gas  

NASA Astrophysics Data System (ADS)

Based on the intrinsic relation between a number of outburst factors and outburst dangers, multi-factor pattern recognition model has been established. Furthermore, the prediction rules of outburst probability of coal and gas have been determined. By adopting multi-factor pattern recognition probabilistic prediction methods, the regional forecast of dangerous areas of coal and gas has been finished; risky areas, threatening areas and areas without obvious dangers of coal and gas inside the coal field has been divided; assessment has been made on the outburst danger of coal and gas; the accuracy of gas disaster prediction has been improved. The establishment of a relatively scientific prediction method of outburst areas of coal and gas would make it possible to enable coal mine safety workers to make accurate judgment and prevent the outburst of coal and gas.

Lan, T. W.; Zhang, H. W.; Chen, Y.; Ren, X.

2010-08-01

384

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

385

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

386

Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data  

E-print Network

FACILITATION OF VISUAL PATTERN RECOGNITION BY EXTRACTION OF RELEVANT FEATURES FROM MICROSCOPIC TRAFFIC DATA A Thesis by MATTHEW JAMES FIELDS Submitted to the Office of Graduate Studies of Texas A&M University in partial... Thesis by MATTHEW JAMES FIELDS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Approved by: Chair of Committee, Paul Nelson Committee Members...

Fields, Matthew James

2009-05-15

387

Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data  

E-print Network

FACILITATION OF VISUAL PATTERN RECOGNITION BY EXTRACTION OF RELEVANT FEATURES FROM MICROSCOPIC TRAFFIC DATA A Thesis by MATTHEW JAMES FIELDS Submitted to the Office of Graduate Studies of Texas A&M University in partial... Thesis by MATTHEW JAMES FIELDS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Approved by: Chair of Committee, Paul Nelson Committee Members...

Fields, Matthew James

2008-10-10

388

IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning  

Microsoft Academic Search

In recent years the use of graph based representation has gained popularity in pattern recognition and machine learning. As\\u000a a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various\\u000a algorithms for graph based machine learning have been proposed in the literature. However, in contrast with the emerging interest\\u000a in graph

Kaspar Riesen; Horst Bunke

2008-01-01

389

Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition  

Microsoft Academic Search

The task of locating buried utilities using ground penetrating radar is addressed, and a novel processing technique computationally suitable for on-site imaging is proposed. The developed system comprises a neural network classifier, a pattern recognition stage, and additional pre-processing, feature-extraction and image processing stages. Automatic selection of the areas of the radargram containing useful information results in a reduced data

W. Alnuaimy; Y. Huang; M. Nakhkash; M. T. C Fang; V. T Nguyen; A. Eriksen

2000-01-01

390

A theoretical analysis of a pattern recognition algorithm for bank failure prediction  

E-print Network

and MDA models, sometimes by very wide margins. The EWS obtained weighted scores of 98/100 compared to 66/100 and 61/100 for the MDA and logit models, respectively, in identical experiments (Kolari et al. 1994). These results require a strong.... The features selected by these systems should identify the intrinsic characteristics of the objects being classified (Bongard 1970). For example, a pattern recognition system classifying bananas from lemons will not use color as a feature, it will however...

Prieto Orlando, Rodrigo Javier

1994-01-01

391

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

392

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

393

Recognition and tracking of spatial–temporal congested traffic patterns on freeways  

Microsoft Academic Search

The two models FOTO (Forecasting of Traffic Objects) and ASDA (Automatische Staudynamikanalyse: Automatic Tracking of Moving Traffic Jams) for the automatic recognition and tracking of congested spatial–temporal traffic flow patterns on freeways are presented. The models are based on a spatial–temporal traffic phase classification made in the three-phase traffic theory by Kerner. In this traffic theory, in congested traffic two

Boris S. Kerner; Hubert Rehborn; Mario Aleksic; Andreas Haug

2004-01-01

394

Evolvable Hardware and its application to pattern recognition and fault-tolerant systems  

Microsoft Academic Search

This paper describes Evolvable Hardware (EHW) and its applications to pattern recognition and fault-torelant systems. EHW can change its own hardware structure to adapt to the environment whenever environmental changes (including hardware malfunction) occur. EHW is implemented on a PLD(Programmable Logic Device)-like device whose architecture can be altered by re-programming the architecture bits. Through genetic algorithms, EHW finds the architecture

Tetsuya Higuchi; Masaya Iwata; Isamu Kajitani; Hitoshi Iba; Yuji Hirao; Tatsumi Furuya; Bernard Manderick

395

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

396

Functional Pattern Recognition of 3D Laser Scanned Images of Wood-Pulp Chips  

Microsoft Academic Search

We evaluate the appropriateness of applying a functional rather than the typical vectorial approach to a pattern recognition\\u000a problem. The problem to be resolved was to construct an online system for controlling wood-pulp chip granulometry quality\\u000a for implementation in a wood-pulp factory. A functional linear model and a functional logistic model were used to classify\\u000a the hourly empirical distributions of

Marcos López; José M. Matías; José A. Vilán; Javier Taboada

2007-01-01

397

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

398

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

399

Classification of Camellia (Theaceae) species using leaf architecture variations and pattern recognition techniques.  

PubMed

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

Lu, Hongfei; Jiang, Wu; Ghiassi, M; Lee, Sean; Nitin, Mantri

2012-01-01

400

Knowledge fusion: An approach to time series model selection followed by pattern recognition  

SciTech Connect

This report describes work done during FY 95 that was sponsored by the Department of Energy, Office of Nonproliferation and National Security, Knowledge Fusion Project. The project team selected satellite sensor data to use as the one main example for the application of its analysis algorithms. The specific sensor-fusion problem has many generic features, which make it a worthwhile problem to attempt to solve in a general way. The generic problem is to recognize events of interest from multiple time series that define a possibly noisy background. By implementing a suite of time series modeling and forecasting methods and using well-chosen alarm criteria, we reduce the number of false alarms. We then further reduce the number of false alarms by analyzing all suspicious sections of data, as judged by the alarm criteria, with pattern recognition methods. An accompanying report (Ref 1) describes the implementation and application of this 2-step process for separating events from unusual background and applies a suite of forecasting methods followed by a suite of pattern recognition methods. This report goes into more detail about one of the forecasting methods and one of the pattern recognition methods and is applied to the same kind of satellite-sensor data that is described in Ref. 1.

Bleasdale, S.A.; Burr, T.L.; Scovel, J.C.; Strittmatter, R.B.

1996-03-01

401

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

402

Teaching image processing and pattern recognition with the Intel OpenCV library  

NASA Astrophysics Data System (ADS)

In this paper we present an approach to teaching image processing and pattern recognition with the use of the OpenCV library. Image processing, pattern recognition and computer vision are important branches of science and apply to tasks ranging from critical, involving medical diagnostics, to everyday tasks including art and entertainment purposes. It is therefore crucial to provide students of image processing and pattern recognition with the most up-to-date solutions available. In the Institute of Electronics at the Technical University of Lodz we facilitate the teaching process in this subject with the OpenCV library, which is an open-source set of classes, functions and procedures that can be used in programming efficient and innovative algorithms for various purposes. The topics of student projects completed with the help of the OpenCV library range from automatic correction of image quality parameters or creation of panoramic images from video to pedestrian tracking in surveillance camera video sequences or head-movement-based mouse cursor control for the motorically impaired.

Koz?owski, Adam; Królak, Aleksandra

2009-06-01

403

Trackless ring identification and pattern recognition in Ring Imaging Cherenkov (RICH) detectors  

E-print Network

shows 100 circles proposed by the three hit selection method for an example event. As desired they are concentrated mostly in areas where rings appear to be – not much time is being wasted proposing wildly unreal- istic circles. Note that not all... Recognition and Ring Identification The single most important thing to recognise when pattern matching is that: It is impossible to recognise a pattern of any kind until you have an idea of what it is you are looking for. To give a simple example from...

Lester, Christopher G

404

Observation of phase boundaries in spontaneous optical pattern formation  

NASA Astrophysics Data System (ADS)

With measured optical images in spontaneous pattern formations, we observe the phase boundaries in the phase diagram, defining by the degree of coherence and biased voltage. Pattern transitions in the form of stripes, reoriented stripes, hexagons, and spots are revealed experimentally and theoretically for incoherent beams in noninstantaneous anisotropic photorefractive crystals, with demonstrations in the boundary of mixed-phase states.

Shen, Ming; Su, Yonan; Hong, Ray-Ching; Lin, YuanYao; Jeng, Chien-Chung; Shih, Ming-Feng; Lee, Ray-Kuang

2015-02-01

405

Fabrication of Large-Area Patterned Nanostructures for Optical Applications  

E-print Network

Fabrication of Large-Area Patterned Nanostructures for Optical Applications by Nanoskiving Qiaobing for fabrication of patterned metallic nanostructures over the large (mm2) areas required for applications in photonics are much needed. In this paper, we demonstrate the fabrication of arrays of closed and open, loop

Prentiss, Mara

406

A geometrical optics method of pattern synthesis for linear arrays  

Microsoft Academic Search

A unique phase expression is given which allows synthesis of a wide variety of shaped radiation patterns for a linear array. The accompanying amplitude distribution is found by a stationary phase evaluation of the radiation integral and is shown to be functionally related to the desired radiation pattern. Because of the optical type of approximation (stationary phase) used to evaluate

H. Shanks

1960-01-01

407

Versatile functional microstructured polystyrene-based platforms for protein patterning and recognition.  

PubMed

We report the preparation of different functional surface patterns based on the optimization of the photo-cross-linking/degradation kinetics of polystyrene (PS) upon exposure to UV-light. We employed a PS-b-PGA (polystyrene-block-poly(l-glutamic acid)) block copolymer that will, in addition to the surface pattern, provide functionality. By using short irradiation times, PS can be initially cross-linked, whereas an excess of the exposure time provokes the degradation of the material. As a result of the optimization of time of exposure, the use of an appropriate cover, or the incorporation of an appropriate amount of absorbing active species (photoinitiator), different tailor-made surface patterns can be obtained, from boxes to needles. Moreover, in addition to the surface pattern, we introduced changes on the chemical composition of the polystyrene using an amphiphilic block copolymer (for instance, we employ PS-b-PGA) that will provide functional surfaces with major advantages. In particular, the presence of carboxylic functional groups provides a unique opportunity to anchor, for instance polypeptide sequences. We describe the immobilization of polypeptide sequences in precise surface positions that allows the use of the surfaces for protein recognition purposes. The immobilization of the proteins evidence the success of the recognition and opens a new alternative for protein patterning on surfaces for many biotechnological and biomedical applications. PMID:23901941

Palacios-Cuesta, Marta; Cortajarena, Aitziber L; García, Olga; Rodríguez-Hernández, Juan

2013-09-01

408

Patterns of diabetic macular edema with optical coherence tomography  

Microsoft Academic Search

PURPOSE: We report cross-sectional images of diabetic macular edema and correlation between tomographic features and visual acuity with best correction by means of optical coherence tomography.METHOD: In a prospective study, optical coherence tomography was performed in 59 eyes of 42 patients with diabetic macular edema and in 10 eyes of 10 normal control subjects.RESULTS: Optical coherence tomography showed three patterns

Tomohiro Otani; Shoji Kishi; Yasuhiro Maruyama

1999-01-01

409

Structural Pattern Recognition Techniques for Data Retrieval in Massive Fusion Databases  

NASA Astrophysics Data System (ADS)

Diagnostics of present day reactor class fusion experiments, like the Joint European Torus (JET), generate thousands of signals (time series and video images) in each discharge. There is a direct correspondence between the physical phenomena taking place in the plasma and the set of structural shapes (patterns) that they form in the signals: bumps, unexpected amplitude changes, abrupt peaks, periodic components, high intensity zones or specific edge contours. A major difficulty related to data analysis is the identification, in a rapid and automated way, of a set of discharges with comparable behavior, i.e. discharges with "similar" patterns. Pattern recognition techniques are efficient tools to search for similar structural forms within the database in a fast an intelligent way. To this end, classification systems must be developed to be used as indexation methods to directly fetch the more similar patterns.

Vega, J.; Murari, A.; Rattá, G. A.; Castro, P.; Pereira, A.; Portas, A.

2008-03-01

410

Hand Biometric Recognition Based on Fused Hand Geometry and Vascular Patterns  

PubMed Central

A hand biometric authentication method based on measurements of the user's hand geometry and vascular pattern is proposed. To acquire the hand geometry, the thickness of the side view of the hand, the K-curvature with a hand-shaped chain code, the lengths and angles of the finger valleys, and the lengths and profiles of the fingers were used, and for the vascular pattern, the direction-based vascular-pattern extraction method was used, and thus, a new multimodal biometric approach is proposed. The proposed multimodal biometric system uses only one image to extract the feature points. This system can be configured for low-cost devices. Our multimodal biometric-approach hand-geometry (the side view of the hand and the back of hand) and vascular-pattern recognition method performs at the score level. The results of our study showed that the equal error rate of the proposed system was 0.06%. PMID:23449119

Park, GiTae; Kim, Soowon

2013-01-01

411

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

412

Digital holographic moiré pattern for optical numerical code generation  

NASA Astrophysics Data System (ADS)

In the present paper low frequency moiré fringe patterns are used as secure numerical code generator. These moiré patterns are experimentally obtained by the superposition of two sinusoidal gratings with slightly different pitches. The Bi12TiO20 photorefractive crystal sample is used as holographic medium An optical numerical base was defined with patterns representing 0,1 and -1 digits like bits. Then, the complete set of these optical bits are combined to form bytes, where a numerical sequence is represented. The results show that the proposed numerical code could be used as standard numerical identification in robotic vision or in transmition of security numerical keys.

de Oliveira, G. N.; de Oliveira, M. E.; da Rocha Freire, R. B., Jr.; dos Santos, P. A. M.

2014-07-01

413

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

Dr Gene Tagliarini

414

Optical character recognition: an illustrated guide to the frontier  

NASA Astrophysics Data System (ADS)

We offer a perspective on the performance of current OCR systems by illustrating and explaining actual OCR errors made by three commercial devices. After discussing briefly the character recognition abilities of humans and computers, we present illustrated examples of recognition errors. The top level of our taxonomy of the causes of errors consists of Imaging Defects, Similar Symbols, Punctuation, and Typography. The analysis of a series of 'snippets' from this perspective provides insight into the strengths and weaknesses of current systems, and perhaps a road map to future progress. The examples were drawn from the large-scale tests conducted by the authors at the Information Science Research Institute of the University of Nevada, Las Vegas. By way of conclusion, we point to possible approaches for improving the accuracy of today's systems. The talk is based on our eponymous monograph, recently published in The Kluwer International Series in Engineering and Computer Science, Kluwer Academic Publishers, 1999.

Nagy, George; Nartker, Thomas A.; Rice, Stephen V.

1999-12-01

415

The object pattern separation (OPS) task: A behavioral paradigm derived from the object recognition task.  

PubMed

The object recognition task (ORT) is widely used to measure object memory processes in rodents. Recently, the memory process known as pattern separation has received increasing attention, as impaired pattern separation can be one of the cognitive symptoms of multiple neurological and psychiatric disorders. Pattern separation is the formation of distinct representations out of similar inputs. In the search for an easily implemented task for rodents that can be used to measure pattern separation, we developed a task derived from the ORT and the object location task (OLT), which we called the object pattern separation (OPS) task. This task aims to measure spatial pattern separation per se, which utilizes memory processes centered in the DG and CA3 region of the hippocampus. Adult male C57BL/6 mice and adult male Wistar rats were used to validate different object locations which can be used to measure spatial pattern separation. Furthermore, different inter-trial time intervals were tested with the most optimal object location, to further evaluate pattern separation-related memory in mice. We found that specific object locations show gradual effects, which is indicative of pattern separation, and that the OPS task allows the detection of spatial pattern separation bi-directionally at intermediate spatial separations. Thus, object locations and time intervals can be specifically adjusted as needed, in order to investigate an expected improvement or impairment. We conclude that the current spatial OPS task can be best described as a specific version of the ORT, which can be used to investigate pattern separation processes. PMID:25446746

van Hagen, B T J; van Goethem, N P; Lagatta, D C; Prickaerts, J

2015-05-15

416

Dermoscopic patterns of Melanoma Metastases: inter-observer consistency and accuracy for metastases recognition  

PubMed Central

Background Cutaneous metastases of malignant melanoma (CMMM) can be confused with other skin lesions. Dermoscopy could be helpful in the differential diagnosis. Objective To describe distinctive dermoscopic patterns that are reproducible and accurate in the identification of CMMM Methods A retrospective study of 146 dermoscopic images of CMMM from 42 patients attending a Melanoma Unit between 2002 and 2009 was performed. Firstly, two investigators established six dermoscopic patterns for CMMM. The correlation of 73 dermoscopic images with their distinctive patterns was assessed by four independent dermatologists to evaluate the reproducibility in the identification of the patterns. Finally, 163 dermoscopic images, including CMMM and non-metastatic lesions, were evaluated by the same four dermatologists to calculate the accuracy of the patterns in the recognition of CMMM. Results Five CMMM dermoscopic patterns had a good inter-observer agreement (blue nevus-like, nevus-like, angioma like, vascular and unspecific). When CMMM were classified according to these patterns, correlation between the investigators and the four dermatologists ranged from ? = 0.56 to 0.7. 71 CMMM, 16 angiomas, 22 blue nevus, 15 malignant melanoma, 11 seborrheic keratosis, 15 melanocytic nevus with globular pattern and 13 pink lesions with vascular pattern were evaluated according to the previously described CMMM dermoscopy patterns, showing an overall sensitivity of 68% (between 54.9–76%) and a specificity of 81% (between 68.6–93.5) for the diagnosis of CMMM. Conclusion Five dermoscopic patterns of CMMM with good inter-observer agreement obtained a high sensitivity and specificity in the diagnosis of metastasis, the accuracy varying according to the experience of the observer. PMID:23495915

Costa, J.; Ortiz-Ibañez, K.; Salerni, G.; Borges, V.; Carrera, C.; Puig, S.; Malvehy, J.

2013-01-01

417

Study of stability of time-domain features for electromyographic pattern recognition  

PubMed Central

Background Significant progress has been made towards the clinical application of human-machine interfaces (HMIs) based on electromyographic (EMG) pattern recognition for various rehabilitation purposes. Making this technology practical and available to patients with motor deficits requires overcoming real-world challenges, such as physical and physiological changes, that result in variations in EMG signals and systems that are unreliable for long-term use. In this study, we aimed to address these challenges by (1) investigating the stability of time-domain EMG features during changes in the EMG signals and (2) identifying the feature sets that would provide the most robust EMG pattern recognition. Methods Variations in EMG signals were introduced during physical experiments. We identified three disturbances that commonly affect EMG signals: EMG electrode location shift, variation in muscle contraction effort, and muscle fatigue. The impact of these disturbances on individual features and combined feature sets was quantified by changes in classification performance. The robustness of feature sets was evaluated by a stability index developed in this study. Results Muscle fatigue had the smallest effect on the studied EMG features, while electrode location shift and varying effort level significantly reduced the classification accuracy for most of the features. Under these disturbances, the most stable EMG feature set with combination of four features produced at least 16.0% higher classification accuracy than the least stable set. EMG autoregression coefficients and cepstrum coefficients showed the most robust classification performance of all studied time-domain features. Conclusions Selecting appropriate EMG feature combinations can overcome the impact of the studied disturbances on EMG pattern classification to a certain extent; however, this simple solution is still inadequate. Stabilizing electrode contact locations and developing effective classifier training strategies are suggested to further improve the robustness of HMIs based on EMG pattern recognition. PMID:20492713

2010-01-01

418

International Union of Basic and Clinical Pharmacology. XCVI. Pattern Recognition Receptors in Health and Disease.  

PubMed

Since the discovery of Toll, in the fruit fly Drosophila melanogaster, as the first described pattern recognition receptor (PRR) in 1996, many families of these receptors have been discovered and characterized. PRRs play critically important roles in pathogen recognition to initiate innate immune responses that ultimately link to the generation of adaptive immunity. Activation of PRRs leads to the induction of immune and inflammatory genes, including proinflammatory cytokines and chemokines. It is increasingly clear that many PRRs are linked to a range of inflammatory, infectious, immune, and chronic degenerative diseases. Several drugs to modulate PRR activity are already in clinical trials and many more are likely to appear in the near future. Here, we review the different families of mammalian PRRs, the ligands they recognize, the mechanisms of activation, their role in disease, and the potential of targeting these proteins to develop the anti-inflammatory therapeutics of the future. PMID:25829385

Bryant, Clare E; Orr, Selinda; Ferguson, Brian; Symmons, Martyn F; Boyle, Joseph P; Monie, Tom P

2015-04-01

419

Increasing robustness against background noise: visual pattern recognition by a neocognitron.  

PubMed

The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It has been demonstrated that recent versions of the neocognitron exhibit excellent performance for recognizing handwritten digits. When characters are written on a noisy background, however, recognition rate was not always satisfactory. To find out the causes of vulnerability to noise, this paper analyzes the behavior of feature-extracting S-cells. It then proposes the use of subtractive inhibition to S-cells from V-cells, which calculate the average of input signals to the S-cells with a root-mean-square. Together with this, several modifications have also been applied to the neocognitron. Computer simulation shows that the new neocognitron is much more robust against background noise than the conventional ones. PMID:21482455

Fukushima, Kunihiko

2011-09-01

420

Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms.  

PubMed

We have employed two pattern recognition methods used commonly for face recognition in order to analyse digital mammograms. The methods are based on novel classification schemes, the AdaBoost and the support vector machines (SVM). A number of tests have been carried out to evaluate the accuracy of these two algorithms under different circumstances. Results for the AdaBoost classifier method are promising, especially for classifying mass-type lesions. In the best case the algorithm achieved accuracy of 76% for all lesion types and 90% for masses only. The SVM based algorithm did not perform as well. In order to achieve a higher accuracy for this method, we should choose image features that are better suited for analysing digital mammograms than the currently used ones. PMID:15925425

Arod?, Tomasz; Kurdziel, Marcin; Sevre, Erik O D; Yuen, David A

2005-08-01

421

Image projection optical system for measuring pattern electroretinograms  

NASA Astrophysics Data System (ADS)

The use of the pattern-electroretinogram (PERG) as a noninvasive diagnostic tool for the early detection of glaucoma has been supported by a number of recent studies. We have developed a unique device which uses a laser interferometer to generate a sinusoidal fringe pattern that is presented to the eye in Maxwellian view for the purpose of producing a PERG response. The projection system stimulates a large visual field and is designed to bypass the optics of the eye in order to measure the true retinal response to a temporally alternating fringe pattern. The contrast, spatial frequency, total power output, orientation, alternating temporal frequency, and field location of the fringe pattern presented to the eye can all be varied by the device. It is critical for these parameters to be variable so that optimal settings may be determined for the normal state and any deviation from it, i.e. early or preclinical glaucoma. Several interferometer designs and optical projection systems were studied in order to design a compact system which provided the desired variable pattern stimulus to the eye. This paper will present a description of the clinical research instrument and its performance with the primary emphasis on the optical system design as it relates to the fringe pattern generation and other optical parameters. Examples of its use in the study of glaucoma diagnosis will also be presented.

Starkey, Douglas E.; Taboada, John; Peters, Daniel

1994-06-01

422

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

423

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

424

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

425

Towards a Standard Testbed for Optical Music Recognition: Definitions, Metrics, and Page Images  

E-print Network

1 Towards a Standard Testbed for Optical Music Recognition: Definitions, Metrics, and Page Images by the absence of anything like the standard testbeds in use in other fields that face difficult evaluation be mitigated or solved outright. To aid in the establishment of a standard testbed, we devise performance

Indiana University

426

A virtual character recognition system based on optical detection of red light and its embedded implementation  

Microsoft Academic Search

This paper proposes a novel ldquoair-writingrdquo character recognition system (ACRS) based on optical detection of red light, with which a user can write a character in the air with a red light-emitting device. The trajectories of the light can be captured and detected by a camera during the writing process and then a character reconstruction algorithm is employed to convert

Kai Ding; Lianwen Jin; Hanyu Yan

2008-01-01

427

Optical Recognition of Converted DNA Nucleotides for Single-Molecule DNA  

E-print Network

Optical Recognition of Converted DNA Nucleotides for Single-Molecule DNA Sequencing Using Nanopore ABSTRACT We demonstrate the feasibility of a nanopore based single-molecule DNA sequencing method, which conversion N anopore-based DNA sequencing is widely consid- ered to be a promising next generation sequencing

428

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

429

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

430

Pattern recognition analysis of differential mobility spectra with classification by chemical family.  

PubMed

Differential mobility spectra for alkanes, alcohols, ketones, cycloalkanes, substituted ketones, and substituted benzenes with carbon numbers between 3 and 10 were obtained from gas chromatography-differential mobility spectrometry (GC-DMS) analyses of mixtures in dilute solution. Spectra were produced in a supporting atmosphere of purified air with 0.6-0.8 ppm moisture, gas temperature of 120 degrees C, sample concentrations of approximately 0.2-5 ppm, and ion source of 5 mCi (185 MBq) 63Ni. Multiple spectra were extracted from chromatographic elution profiles for each chemical providing a library of 390 spectra from 39 chemicals. The spectra were analyzed for structural content by chemical family using two different approaches. In the one approach, the wavelet packet transform was used to denoise and deconvolute the DMS data by decomposing each spectrum into its wavelet coefficients, which represent the sample's constituent frequencies. The wavelet coefficients characteristic of the compound's structural class were identified using a genetic algorithm (GA) for pattern recognition analysis. The pattern recognition GA uses both supervised and unsupervised learning to identify coefficients which optimize clustering of the spectra in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected coefficients is about differences between chemical families in the data set. The principal component analysis routine embedded in the fitness function of the pattern recognition GA acts as an information filter, significantly reducing the size of the search space since it restricts the search to coefficients whose principal component plots show clustering on the basis of chemical family. In a second approach, a back propagation neural network was trained to categorize spectra by chemical families and the network was successfully tested using familiar and unfamiliar chemicals. Performance of the network was associated with a region of the spectrum associated with fragment ions which could be extracted from spectra and were class specific. PMID:17723720

Eiceman, G A; Wang, M; Prasad, S; Schmidt, H; Tadjimukhamedov, F K; Lavine, Barry K; Mirjankar, Nikhil

2006-10-01

431

BIOCAT: a pattern recognition platform for customizable biological image classification and annotation  

PubMed Central

Background Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. Results We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be “chained” in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. Conclusions BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs. PMID:24090164

2013-01-01

432

Spray characterization in high pressure environment using optical line patternator  

NASA Astrophysics Data System (ADS)

For the quantitative measurement in an optically dense spray, the intensity of the attenuated signal should be corrected. Therefore, the optical line patternator was applied to get the original distribution of the dense spray injected from a swirl injector at high ambient pressure up to 4.0 MPa. The optical line patternator is a combined technique of laser extinction measurement and image processing for the spray characterization. The spray was scanned with the laser beam and the line image of Mie scattering was captured simultaneously in the path of each laser beam by using a CCD camera. A photo-diode was used to obtain the transmission data that was the amount of the incident laser beam passing through the spray region. The distribution of the attenuation coefficients in the spray was obtained by processing the transmission data and Mie-scattering distribution data by an algebraic reconstruction technique. From the distribution of attenuation coefficients, we can obtain the accurate surface distribution from the Mie-scattering signal. Because the optical line patternator uses a laser beam instead of a laser sheet to scan the spray, the effect of multiple scattering, due to the increased number density of droplets in a high pressure environment is reduced significantly. The optical line patternator is suitable for investigating the characteristics of a relatively large spray under high pressure environments such as liquid rocket engines.

Koh, Hyeonseok; Kim, Dongjun; Shin, Sanghee; Yoon, Youngbin

2006-08-01

433

Trends in correlation-based pattern recognition and tracking in forward-looking infrared imagery.  

PubMed

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

434

Pyrolysis-mass spectrometry/pattern recognition on a well-characterized suite of humic samples  

USGS Publications Warehouse

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 and fulvic acids can be segregated into two distinct classes. Carbohydrate and phenolic components are more pronounced in the pyrolysis products of the fulvic acids, and saturated and unsaturated hydrocarbons contribute more to the humic acid pyrolysis products. A second factor analysis plot shows a separation which appears to be based primarily on whether the samples are of aquatic or soil origin. ?? 1985.

MacCarthy, P.; DeLuca, S.J.; Voorhees, K.J.; Malcolm, R.L.; Thurman, E.M.

1985-01-01

435

A heirarchy of pattern recognition algorithms for the diagnosis of sucker rod pumped wells  

E-print Network

an oil pump dynagraph. This was a new wav to analvze the subsur- face pump performance using down-hole information. Later, Gibbs and Neely s developed a model that allowed the surface measurements at the polished rod to be used to generate the downhole... to the unit that act on the pumping system. The pattern recognition technique using only downhole dynamometer cards for pump diagnosis can thus be used on any kind and size of sucker rod pumped well. Similar to a properly working pump, an improperly working...

Houang, Anne-Benedicte

1992-01-01

436

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

437

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

438

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...

439

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

440

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

441

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

442

Pattern recognition receptors and cytokines in Mycobacterium tuberculosis infection--the double-edged sword?  

PubMed

Tuberculosis, an infectious disease caused by Mycobacterium tuberculosis (Mtb), remains a major cause of human death worldwide. Innate immunity provides host defense against Mtb. Phagocytosis, characterized by recognition of Mtb by macrophages and dendritic cells (DCs), is the first step of the innate immune defense mechanism. The recognition of Mtb is mediated by pattern recognition receptors (PRRs), expressed on innate immune cells, including toll-like receptors (TLRs), complement receptors, nucleotide oligomerization domain like receptors, dendritic cell-specific intercellular adhesion molecule grabbing nonintegrin (DC-SIGN), mannose receptors, CD14 receptors, scavenger receptors, and FC? receptors. Interaction of mycobacterial ligands with PRRs leads macrophages and DCs to secrete selected cytokines, which in turn induce interferon-?- (IFN?-) dominated immunity. IFN? and other cytokines like tumor necrosis factor-? (TNF?) regulate mycobacterial growth, granuloma formation, and initiation of the adaptive immune response to Mtb and finally provide protection to the host. However, Mtb can evade destruction by antimicrobial defense mechanisms of the innate immune system as some components of the system may promote survival of the bacteria in these cells and facilitate pathogenesis. Thus, although innate immunity components generally play a protective role against Mtb, they may also facilitate Mtb survival. The involvement of selected PRRs and cytokines on these seemingly contradictory roles is discussed. PMID:24350246

Hossain, Md Murad; Norazmi, Mohd-Nor

2013-01-01

443

Infrared face recognition based on intensity of local micropattern-weighted local binary pattern  

NASA Astrophysics Data System (ADS)

The traditional local binary pattern (LBP) histogram representation extracts the local micropatterns and assigns the same weight to all local micropatterns. To combine the different contributions of local micropatterns to face recognition, this paper proposes a weighted LBP histogram based on Weber's law. First, inspired by psychological Weber's law, intensity of local micropattern is defined by the ratio between two terms: one is relative intensity differences of a central pixel against its neighbors and the other is intensity of local central pixel. Second, regarding the intensity of local micropattern as its weight, the weighted LBP histogram is constructed with the defined weight. Finally, to make full use of the space location information and lessen the complexity of recognition, the partitioning and locality preserving projection are applied to get final features. The proposed method is tested on our infrared face databases and yields the recognition rate of 99.2% for same-session situation and 96.4% for elapsed-time situation compared to the 97.6 and 92.1% produced by the method based on traditional LBP.

Xie, Zhihua; Liu, Guodong

2011-07-01

444

Facial expression recognition based on local binary patterns and kernel discriminant isomap.  

PubMed

Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap). PMID:22163713

Zhao, Xiaoming; Zhang, Shiqing

2011-01-01

445

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

446

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

447

Changes in pattern completion--a key mechanism to explain age-related recognition memory deficits?  

PubMed

Accurate memory retrieval from partial or degraded input requires the reactivation of memory traces, a hippocampal mechanism termed pattern completion. Age-related changes in hippocampal integrity have been hypothesized to shift the balance of memory processes in favor of the retrieval of already stored information (pattern completion), to the detriment of encoding new events (pattern separation). Using a novel behavioral paradigm, we investigated the impact of cognitive aging (1) on recognition performance across different levels of stimulus completeness, and (2) on potential response biases. Participants were required to identify previously learned scenes among new ones. Additionally, all stimuli were presented in gradually masked versions to alter stimulus completeness. Both young and older adults performed increasingly poorly as the scenes became less complete, and this decline in performance was more pronounced in elderly participants indicative of a pattern completion deficit. Intriguingly, when novel scenes were shown, only the older adults showed an increased tendency to identify these as familiar scenes. In line with theoretical models, we argue that this reflects an age-related bias towards pattern completion. PMID:25597525

Vieweg, Paula; Stangl, Matthias; Howard, Lorelei R; Wolbers, Thomas

2015-03-01

448

On acoustic emission for failure investigation in CFRP: Pattern recognition and peak frequency analyses  

NASA Astrophysics Data System (ADS)

This paper investigates failure in Carbon Fibre Reinforced Plastics CFRP using Acoustic Emission (AE). Signals have been collected and post-processed for various test configurations: tension, Compact Tension (CT), Compact Compression (CC), Double Cantilever Beam (DCB) and four-point bend End Notched Flexure (4-ENF). The signals are analysed with three different pattern recognition algorithms: k-means, Self Organising Map (SOM) combined with k-means and Competitive Neural Network (CNN). The SOM combined with k-means appears as the most effective of the three algorithms. The results from the clustering analysis follow patterns found in the peak frequencies distribution. A detailed study of the frequency content of each test is then performed and the classification of several failure modes is achieved.

Gutkin, R.; Green, C. J.; Vangrattanachai, S.; Pinho, S. T.; Robinson, P.; Curtis, P. T.

2011-05-01

449

DSP-Based dual-polarity mass spectrum pattern recognition for bio-detection  

SciTech Connect

The Bio-Aerosol Mass Spectrometry (BAMS) instrument analyzes single aerosol particles using a dual-polarity time-of-flight mass spectrometer recording simultaneously spectra of thirty to a hundred thousand points on each polarity. We describe here a real-time pattern recognition algorithm developed at Lawrence Livermore National Laboratory that has been implemented on a nine Digital Signal Processor (DSP) system from Signatec Incorporated. The algorithm first preprocesses independently the raw time-of-flight data through an adaptive baseline removal routine. The next step consists of a polarity dependent calibration to a mass-to-charge representation, reducing the data to about five hundred to a thousand channels per polarity. The last step is the identification step using a pattern recognition algorithm based on a library of known particle signatures including threat agents and background particles. The identification step includes integrating the two polarities for a final identification determination using a score-based rule tree. This algorithm, operating on multiple channels per-polarity and multiple polarities, is well suited for parallel real-time processing. It has been implemented on the PMP8A from Signatec Incorporated, which is a computer based board that can interface directly to the two one-Giga-Sample digitizers (PDA1000 from Signatec Incorporated) used to record the two polarities of time-of-flight data. By using optimized data separation, pipelining, and parallel processing across the nine DSPs it is possible to achieve a processing speed of up to a thousand particles per seconds, while maintaining the recognition rate observed on a non-real time implementation. This embedded system has allowed the BAMS technology to improve its throughput and therefore its sensitivity while maintaining a large dynamic range (number of channels and two polarities) thus maintaining the systems specificity for bio-detection.

Riot, V; Coffee, K; Gard, E; Fergenson, D; Ramani, S; Steele, P

2006-04-21

450

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

PubMed

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. PMID:25699048

Horton, Nathan C; Mathew, Porunelloor A

2015-01-01

451

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. PMID:25699048

Horton, Nathan C.; Mathew, Porunelloor A.

2015-01-01

452

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

453

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

454

A novel algorithm to attack the problem of pattern recognition with near-IR spectroscopy  

SciTech Connect

Near-infrared (near-IR) spectroscopy is a rapid, nondestructive analytical technique that has wide application in industry as well as in academic research. In general, near-IR analysis uses reflectance or absorbance signals to determine chemical information from samples. Near-IR is also a very good technique for differentiating samples from different sources using pattern recognition analysis. In this dissertation, a novel algorithm of the quantile BEST (Boot-strap Error-adjusted Sample Technique) for pattern recognition analysis has been extensively tested with hypothetical data and real samples. A modified model is proposed to improve the system performance in higher dimensional space. The applications to real samples include: (1) the identification of the points of origin of soil samples; (2) near-IR spectrophotometric monitoring of stroke-related changes in the protein and lipid composition of whole gerbil brains; and (3) determination of cholesterol concentration in aqueous and serum samples with principal component analysis. In addition, a new laser spectroscopic system is designed and tested. This system uses Nd-YAG and dye lasers are primary sources. Powerful near-IR radiation is obtained from stimulated Raman scattering. The stability, accuracy, and precision of the system is investigated and an application to known samples is shown.

Zou, Yi.

1993-01-01

455

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

456

Computational Pathology and Telepathology: SY05-1 PATTERN RECOGNITION IN TELEPATHOLOGY.  

PubMed

Telepathology and telecytology (TP/TC) are the most attractive fields for pattern recognition in pathology. One of the major problems of TP/TC is the limitation of information, especially in image size and quality, when data are transmitted via commercially available networks. Commercial companies most often limit the data volume to 20-50 megabytes which is only a fraction of the about 6-10 gigabytes fully digitalized slides have. Therefore both in the synchronous mode used in teleconferencing and in the asynchronous mode used in storing and transmission data volume reduction is an essential prerequisite for TP.To achieve high diagnostic accuracy in remote TP consultation the following requirements have to be fulfilled: 1) Identification of diagnostic hot spots. 2) Standardisation of microscopic image quality captured by digital cameras. 3) Standardisation of the staining quality of slides. 4)Standardisation of additional minimum information, such as macroscopic description of image, X-rays and other imaging procedures.These issues have to follow generally accepted protocols.The pattern recognition technique can be performed on tissue and cellular analysis as well as a combination of cellular/tissues analysis.The above mentioned requirements will be discussed on the basis of histopathological cases with different complex diagnostic questions. PMID:25188189

Stauch, Gerhard; Muenzenmayer, Christian

2014-10-01

457

Pattern recognition applied to infrared images for early alerts in fog  

NASA Astrophysics Data System (ADS)

Fog conditions are the cause of severe car accidents in western countries because of the poor induced visibility. Its forecast and intensity are still very difficult to predict by weather services. Infrared cameras allow to detect and to identify objects in fog while visibility is too low for eye detection. Over the past years, the implementation of cost effective infrared cameras on some vehicles has enabled such detection. On the other hand pattern recognition algorithms based on Canny filters and Hough transformation are a common tool applied to images. Based on these facts, a joint research program between IFSTTAR and Cerema has been developed to study the benefit of infrared images obtained in a fog tunnel during its natural dissipation. Pattern recognition algorithms have been applied, specifically on road signs which shape is usually associated to a specific meaning (circular for a speed limit, triangle for an alert, …). It has been shown that road signs were detected early enough in images, with respect to images in the visible spectrum, to trigger useful alerts for Advanced Driver Assistance Systems.

Boucher, Vincent; Marchetti, Mario; Dumoulin, Jean; Cord, Aurélien

2014-09-01

458

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

459

Design and testing of the first 2D Prototype Vertically Integrated Pattern Recognition Associative Memory  

NASA Astrophysics Data System (ADS)

An associative memory-based track finding approach has been proposed for a Level 1 tracking trigger to cope with increasing luminosities at the LHC. The associative memory uses a massively parallel architecture to tackle the intrinsically complex combinatorics of track finding algorithms, thus avoiding the typical power law dependence of execution time on occupancy and solving the pattern recognition in times roughly proportional to the number of hits. This is of crucial importance given the large occupancies typical of hadronic collisions. The design of an associative memory system capable of dealing with the complexity of HL-LHC collisions and with the short latency required by Level 1 triggering poses significant, as yet unsolved, technical challenges. For this reason, an aggressive R&D program has been launched at Fermilab to advance state of-the-art associative memory technology, the so called VIPRAM (Vertically Integrated Pattern Recognition Associative Memory) project. The VIPRAM leverages emerging 3D vertical integration technology to build faster and denser Associative Memory devices. The first step is to implement in conventional VLSI the associative memory building blocks that can be used in 3D stacking; in other words, the building blocks are laid out as if it is a 3D design. In this paper, we report on the first successful implementation of a 2D VIPRAM demonstrator chip (protoVIPRAM00). The results show that these building blocks are ready for 3D stacking.

Liu, T.; Deptuch, G.; Hoff, J.; Jindariani, S.; Joshi, S.; Olsen, J.; Tran, N.; Trimpl, M.

2015-02-01

460

Recognition of disease-specific patterns in FT-IR spectra of human sera  

NASA Astrophysics Data System (ADS)

Vibrational spectra in the mid-IR region show significant and reproducible correlation with the disease state of the blood donor. When focusing our 'disease pattern recognition (DPR)' approach onto the example of diabetes mellitus we can clearly separate samples obtained from healthy volunteers from those samples which organized from diabetes patients. Furthermore, we are able to differentiate between samples of type-1 diabetics and type-2 diabetics. For disease pattern recognition we use linear and/or regularized discriminant analysis. In a binary, supervised classification of an pair of the three disease states: healthy, diabetes type-1 and diabetes type-2, we consistently achieve sensitivities and specificities >= 80 percent. By setting stricter bounds on the range of acceptable probabilities of belonging to a certain class, we obtain even higher values for the sensitivity and the specificity on the expense of the fraction of 'crisply' classified samples. Since we are able to simultaneously quantify the concentrations of biochemical serum components like glucose, cholesterol and triglycerides from the identical set of spectra with regression coefficients > 90 percent, our approach allows for a direct cross-link between the molecule-based and the disease-based interpretation of the spectra.

Petrich, Wolfgang H.; Dolenko, Brion; Frueh, Johanna; Greger, Helmut; Jacob, Stephan; Keller, Franz; Nikulin, Alexander; Otto, Matthias; Quarder, Ortrud; Somorjai, Raymond L.; Staib, Arnulf; Werner, Gerhard H.; Wielinger, Hans

2000-05-01

461

The role of bacteria and pattern-recognition receptors in Crohn's disease.  

PubMed

Crohn's disease is widely regarded as a multifactorial disease, and evidence from human and animal studies suggests that bacteria have an instrumental role in its pathogenesis. Comparison of the intestinal microbiota of patients with Crohn's disease to that of healthy controls has revealed compositional changes. In most studies these changes are characterized by an increase in the abundance of Bacteroidetes and Proteobacteria and a decrease in that of Firmicutes. In addition, a number of specific mucosa-associated bacteria have been postulated to have a role in Crohn's disease, including Mycobacterium avium subspecies paratuberculosis, adherent and invasive Escherichia coli, Campylobacter and Helicobacter species. The association between mutations in pattern-recognition receptors (Toll-like receptors and Nod-like receptors) and autophagy proteins and Crohn's disease provides further evidence to suggest that defective sensing and killing of bacteria may drive the onset of disease. In this Review, we present recent advances in understanding the role of bacteria and the contribution of pattern-recognition receptors and autophagy in the pathogenesis of Crohn's disease. PMID:21304476

Man, Si Ming; Kaakoush, Nadeem O; Mitchell, Hazel M

2011-03-01