These are representative sample records from Science.gov related to your search topic.
For comprehensive and current results, perform a real-time search at Science.gov.
1

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

2

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

3

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

4

Hybrid optical/digital image pattern recognition - A review  

NASA Astrophysics Data System (ADS)

The architectures, algorithms, and system fabrication of hybrid pattern recognition processors are reviewed. The basic operations achievable in optical systems, two classic optical pattern recognition (OPR) architectures, and conventional feature-based pattern recognition are reviewed. Various optical architectures for feature extraction are discussed, and results obtained with these system concepts are examined. Various new correlator approaches to distortion-invariant OPR are briefly reviewed together with Artificial Intelligence/Image Understanding (AI/IU) research and subpixel target identification research. Synthetic discriminant function techniques to achieve various distortion-invariant 3-D object recognition are described, stressing new results and efficient phase-only and computer-generated hologram techniques to synthesize such filters. System fabrication issues are discussed, emphasizing new results and flight tests on compact architectures and systems for OPR.

Casasent, D.

1985-01-01

5

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

6

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

7

Linear Invariant Multiclass Component Spaces For Optical Pattern Recognition  

NASA Astrophysics Data System (ADS)

Optical processing systems which perform linear transformations on image data at high rates are ideal for image pattern recognition systems. As a result of this processing capability, the linear opera-tion of matched spatial filtering has been explored extensively for pattern recognition. For many practical pattern recognition problems, however, multiclass filtering must be used to overcome the variations of input objects due to image scale changes, image rotations, object aspect differences and sensor differences. Hester and Casasent have shown that a linear mapping can be constructed which images all the class elements of a multiclass set into one out-put element or value. This special multi-class filter concept is extended in this paper to show that a subspace of the multi-class set exists that is invariant with respect to the multiclass mapping under linear operations. The concept of this in-variant space and its generation is detailed and a single example given. A typical optical processing architecture using these invariant elements as filters in an associative pattern recognition system is also presented.

Hester, Charles F.

1983-04-01

8

A polymeric optical pattern-recognition system for security verification  

NASA Astrophysics Data System (ADS)

POLYMERS that exhibit the photorefractive effect-a light-induced modulation of refractive index-are emerging as attractive materials for optical devices and processing systems1,2. Here we demonstrate one such application using our recently developed high-efficiency photorefractive polymer2. The polymer provides a nonlinear medium in which real-time all-optical image correlation, and hence pattern recognition, can be accomplished. This forms the basis of an optical security system, whereby documents are encoded with practically invisible phase masks (such masks are difficult to forge), which may then be rapidly screened to verify the authenticity of the documents. The wavelengths at which our optical system operates are compatible with commercial low-power semiconductor laser diodes, and the system can be integrated into a compact device at low cost.

Volodin, B. L.; Kippelen, B.; Meerholz, K.; Javidi, B.; Peyghambarian, N.

1996-09-01

9

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

10

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

11

Computer Generated Hologram and Magneto-Optic Spatial Light Modulator for Optical Pattern Recognition.  

National Technical Information Service (NTIS)

This thesis investigates the integration of an optical system for real-time position, scale, and rotation invariant pattern recognition. Specifically a Litton Magneto-Optic Spatial Light Modulator is interfaced to a Zenith 248 microcomputer and AT&T frame...

M. W. Mayo

1987-01-01

12

All-optical digital-to-analog conversion using pulse pattern recognition based on optical correlation processing  

Microsoft Academic Search

We propose and demonstrate a novel all-optical digital-to-analog (D\\/A) conversion using pulse pattern recognition based on optical correlation processing. It is composed of pulse pattern recognition based on correlation processing and intensity adjustment using an optical attenuator. We obtain a single pulse as a result of pulse pattern recognition by using correlation processing between a target digital signal and a

T. Nishitani; T. Konishi; H. Furukawa; K. Itoh

2005-01-01

13

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

14

Position, Scale, and Rotation Invariant Optical Pattern Recognition for Target Extraction and Identification.  

National Technical Information Service (NTIS)

This thesis investigates the feasibility of optically implementing a real-time, pattern recognition system using correlation techniques in a position, scale, and rotation invariant (PSRI) feature space. Input target templates were optically Fourier transf...

J. T. Walrond, T. G. Childress

1988-01-01

15

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

16

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

17

Pattern Recognition by an Optical Thin-Film Multilayer Model Xiaodong Li  

E-print Network

@commerce.otago.ac.nz Abstract This paper describes a computational learning model inspired by the technology of optical thinPattern Recognition by an Optical Thin-Film Multilayer Model Xiaodong Li Gippsland School of Computing and Information Technology Monash University Churchill, Victoria, Australia xiaodong

Li, Xiaodong

18

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

19

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

PubMed

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. PMID:19547006

Aljada, Muhsen; Alameh, Kamal

2007-05-28

20

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

21

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

22

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

23

Muscle sensor model using small scale optical device for pattern recognitions.  

PubMed

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

24

An optical 2-dimensional correlator for pattern recognition in embedded computing  

SciTech Connect

Optical processing technology can be applied to a variety of problems in embedded computing. It is particularly well suited for problems involving large two-dimensional arrays of data, for example in correlation based pattern recognition. For large kernel correlations, the parrellelism of optics offers the high throughput necessary to perform the desired correlation or convolution operations in real time. In addition, the latest generation of optical hardware provides the opportunity to construct processors ideally suited to the embedded computer environment because of their potential size, weight, and power consumption advantages over alternative technologies. Using currently available optical devices, one such architecture was constructed which demonstrated the ability to do real-time pattern recognition. The optical processor was able to perform a 2-D correlation of a 64 x 44 pixel reference object with a 256 x 232 pixel input image at standard video rates. This represents an equivalent computation rate of over 10 billion operations per second. Results of the optical processor as well as a discussion of the potential of this technology in the embedded computer enviroment.

Molley, P.A.; Stalker, K.T.

1988-01-01

25

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

26

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

27

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

28

Demonstration and evaluation of all-optical digital-to-analog conversion using pulse pattern recognition based on optical correlation processing  

Microsoft Academic Search

We demonstrate and evaluate the all-optical digital-to-analog conversion using pulse pattern recognition based on optical correlation processing. Experimental results show that four-bit digital signals with 1.65 ps interval can be successfully converted to analog signals

T. Nishitani; T. Konishi; K. Itoh; H. Furukawa

2006-01-01

29

Soviet image pattern recognition research  

SciTech Connect

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 configuration (optical, hybrid, digital), and current research. The topical chapter headings are Image Processing Hardware and Software Preprocessing, Statistical Pattern Recognition, Computer Vision, and Optical Techniques and Systems. Soviet research in all areas of IPR is strong in theory, but limited by poor availability of equipment for generating and handling digital images, and digital computer hardware and software. Nevertheless, some Soviet IPR achievements compare favorably with those of the West. There is strong Soviet research in statistical pattern recognition, where fundamental relationships related to the factors determining error rates in classification of images are being developed. There has been good Soviet work in enhancement and restoration of images (visible and radar) of the surface of Venus. There is a strong Soviet development program in optics and optical processing related to IPR. Nevertheless, the state of Soviet research in computer vision is ten to fifteen years behind the West, because of the lack of adequate hardware and software. The Soviet scientists in the area appear competent and knowledgeable of Western work, so that any improvement in their research output would be derived from access to more capable equipment. 402 refs., 4 figs., 4 tabs.

McKenney, B.L.; McGrain, M. (eds.) (Science Applications International Corp., McLean, VA (USA). Foreign Applied Sciences Assessment Center); Klinger, A. (California Univ., Los Angeles, CA (USA). Dept. of Computer Science); Aggarwal, J.K. (Texas Univ., Austin, TX (USA)); George, N.J. (Rochester Univ., NY (USA). Inst. of Optics); Haralick, R.M. (Washington Univ., Seattle, WA (USA). Dept. of Electric

1989-12-01

30

Pattern recognition in bioinformatics.  

PubMed

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

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

2013-09-01

31

Heliospheric Pattern Recognition  

NASA Astrophysics Data System (ADS)

It is well known that patterns must be recognized before models can be built to aid in our understanding of complex systems. Pattern recognition is one of the pleasures of doing science. Foundational understanding rooted in pattern recognition in the heliospheric system was provided by Professor Parker, famously in the form of the Parker spiral describing the heliospheric magnetic field. Following in that tradition, our community has recognized a profusion of patterns on all scales in the heliosphere, from turbulent flux tubes to the shape of its outer boundaries. It is the magnetic (rather than gravitational) organization of space that affords this wide variety of pattern over a broad range of scales. Examples of the variety of patterns recognized at heliospheric mesoscales will be presented. These involve the structure of the heliospheric current sheet, coronal mass ejections, the coronal hole boundary, and sources of slow solar wind. The primary tools used to recognize these patterns are observations of suprathermal electrons and magnetic fields. From these data one can recognize not only pattern but clues about origin. Of particular interest is the origin of the heliospheric magnetic field and its variation through the solar cycle.

Crooker, N. U.

2013-12-01

32

Optical Character Recognition.  

ERIC Educational Resources Information Center

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

Converso, L.; Hocek, S.

1990-01-01

33

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

34

Multispectral Image Recognition Research Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by biomimetic pattern recognition on information geometry. Biomimetic pattern recognition is a new model of pattern recognition based on ldquomatter cognitionrdquo instead of ldquomatter classificationrdquo. This new model is much closer to the function of human being, than traditional statistical pattern recognition using

Wenming Cao; Hao Feng

2009-01-01

35

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

36

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

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

37

Coverage in Biomimetic Pattern Recognition  

Microsoft Academic Search

Coverage is a kind of method to cover points of same class samples in feature space, which is based on Biomimetic Pattern\\u000a Recognition. The mathematical description of coverage is given and the discriminant boundary of coverage is shown. Coverage\\u000a is tested in face recognition on ORL database. Both the COVERAGE and SVM networks are used for covering. The results show

Wenming Cao; Guoliang Zhao

2007-01-01

38

A method of biomimetic pattern recognition for face recognition  

Microsoft Academic Search

A new method of face recognition, based on biomimetic pattern recognition and multi-weights neuron network, had been proposed. A model for face recognition that is based on biomimetic pattern recognition had been discussed, and a new method of facial feature extraction also had been introduced. The results of experiments with BPR and k-nearest neighbor rules showed that the method based

Wang Zhi-Hai; Mo Hua-Yi; Lu Hua-Xiang; Wang Shou-Jue

2003-01-01

39

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

40

Space Target Recognition Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

Biomimetic Pattern Recognition is a new model of Pattern Recognition based on ldquomatter cognitionrdquo instead of ldquomatter classificationrdquo. This new model is much closer to the function of human being, than traditional statistical Pattern Recognition using ldquooptimal separatingrdquo as its main principle.But it has been investigated in 2D sample space, In this paper, we extend to space target Image sample

Wenming Cao; Hao Feng; Lili Hu; Tiancheng He

2009-01-01

41

504 [Part4, Pattern Recognition  

E-print Network

with comparatively inexpensive equipment. The desire to do the same for non-specialized fonts and non published, optical character reading is by no means a commercial commonplace as yet. Most machines still; Gelernter, 1965; White et al., 1963). This introduces a new factor into the problem. In many recognition

Brown, Gavin

42

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

43

Anthropomorphic pattern recognition systems  

NASA Astrophysics Data System (ADS)

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

Ginzburg, Vera M.

1997-03-01

44

Optical Character Recognition Without Segmentation  

Microsoft Academic Search

A segmentation-free approach for off-line optical character recognition is presented. The proposed method performs the recognition by extracting the characters from the whole word, avoiding the segmentation process. A control point set which includes position and attribute vectors is selected for the features. In the training mode, each sample character is mapped to a set of control points and is

Mehmet Ali Özdil; Fatos T. Yarman-vural; Nafiz Arica

1997-01-01

45

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

46

An Effective Iris Recognition System Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

This paper presents a new method for effective iris recognition using Biomimetic Pattern Recognition (BPR), which is a new theory proposed by academician ShoujueWang. A model for iris recognition that is based on BPR was introduced and thoroughly discussed here. Experimental results on the Chinese Academy of Sciences, Institute of Automation (CASIA) iris image database clearly demonstrates that the use

Junying Zeng; Yikui Zhai; Junying Gan; Ying Xu

2009-01-01

47

Automatic Recognition of Repeating Patterns in Rectified  

E-print Network

Automatic Recognition of Repeating Patterns in Rectified Facade Images DIPLOMARBEIT zur Erlangung Recognition of Repeating Patterns in Rectified Facade Images 16. Dezember 2009 #12;#12;Acknowledgments I would tasks, is able to robustly identify orthogonal repetitive patterns on rectified facade images even

48

All-optical header recognition using variable keyword generator and optical MZI-based XOR  

NASA Astrophysics Data System (ADS)

A novel header recognition scheme using variable keyword generator and optical logic XOR gate with loop is proposed. The proposed optical header recognition is realized all-optically, and is expected to operate in over 40 Gbps. The simulation results show its feasibility to recognize bit patterns of header at 10 Gbit/s and beyond.

Song, SeokSu; Choi, KyoungSun; Lee, NamKyu; Park, Jinwoo

2006-09-01

49

Character and pattern recognition based on moire images  

NASA Astrophysics Data System (ADS)

The paper presents a novel method for recognizing raised or indented characters or patterns on industrial samples by using a combination of moire interferometry technique with optical character recognition (OCR) and pattern recognition. Patterns recognized with this method are of low contrast, and conventional recognition schemes require complex optics and lighting. Raised characters on tires, vin code tags, credit cards, indented characters on metal, wrinkles on skin, and embossment on buttons are some examples. The proposed method uses the moire interferometry technique to obtain a gray scale image of patterns such that their heights are represented in gray scale. This eliminates the need for special optics for each application. 3D images obtained as above, are processed by three sets of algorithms: 1) analytical geometry, 2) pattern recognition, and 3) character recognition. The analytical geometry algorithms consist of constrained and unconstrained fitting methods for scattered data, and transformations between different spaces. The pattern recognition methods consist of feature extraction based on scatter matrices, and classification based on hierarchic classification methods. The OCR algorithm employs gray scale correlation. Extension experiments are conducted to support the method.

Chatterjee, Chanchal; Bieman, Leonard H.

1995-08-01

50

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

51

Syntactic Pattern Recognition of the ECG  

Microsoft Academic Search

An application of the syntactic method to electrocardiogram (ECG) pattern recognition and parameter measurement is presented. Solutions to the related problems of primitive pattern selection, primitive pattern extraction, linguistic representation, and pattern grammar formulation are given. Attribute grammars are used as the model for the pattern grammar because of their descriptive power, founded upon their ability to handle syntactic as

Panagiotis Trahanias; Emmanuel Skordalakis

1990-01-01

52

Biomimetic Pattern Recognition for Speaker-Independent Speech Recognition  

Microsoft Academic Search

In speaker-independent speech recognition, the disadvantage of the most diffused technology (hidden Markov models) is not only the need of many more training samples, but also long train time requirement. This paper describes the use of biomimetic pattern recognition (BPR) in recognizing some Mandarin speech in a speaker-independent manner. The vocabulary of the system consists of 15 Chinese dish's names.

Hong Qin; Shoujue Wang; Hua Sun

2005-01-01

53

Motor imagery EEG Recognition based on Biomimetic Pattern Recognition  

Microsoft Academic Search

This paper improves Biomimetic Pattern Recognition based on Hyper Sausage Neuron and applies it in the study of Motor Imagery EEG recognition. The paper uses the datasets from previous Brain-Computer Interface Competitions to test the accuracy and efficiency of the results, and compares them with those of SVM and BP. The results show that: with sufficient training set, the performance

Kai Xu; Yan Wu

2010-01-01

54

Space reconstruction based Biomimetic Pattern Recognition  

Microsoft Academic Search

The BPR is a new model of pattern recognition principle. It’s based on “matter cognition” instead of “matter classification” in traditional statistical Pattern Recognition. Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Based on the fact of the sample continuity, the classification of feature points is converted into a cover

Ding Jie; Yang Jing-yu

2011-01-01

55

Introduction to the recognition of patterns in compressed data: optical processing of data transformed by block-, transform-, and runlength-encoding, as well as vector quantization  

NASA Astrophysics Data System (ADS)

We have recently shown that the processing of compressed and encrypted imagery can achieve computational speedup and data security by processing fewer data, which are encoded in an obscure format [5- 9]. Our previous work in compressive processing produced numerous image processing algorithms that yielded computational speedups on the order of the compression ratio. In Part 1 of this series [1], we discuss the theoretical basis for pattern recognition over compressed imagery. In this paper, we present theory in support of optical or electro-optical implementations of convolution or correlation operations over block-, transform-, and runlength-encoded imagery, as well as data encoded by vector quantization (VQ). Unlike our previous work in this area, we do not derive operations that return a compressed result. Instead, our algorithms produce a map of correlation coefficients in the image domain, using a compressed image as input. Several of our architectures could, in principle, perform in time that is at least proportional to the compression ratio. Theory is expressed in terms of image algebra, an emerging branch of mathematics that unifies linear and nonlinear mathematics in the image domain. Image algebra has been implemented on a variety of workstations and parallel processors, as well as electro-optical processors. Thus our algorithms are feasible as well as portable Analyses emphasize computational complexity and information loss.

Schmalz, Mark S.

1995-03-01

56

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

57

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

58

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

59

Visual cluster analysis and pattern recognition 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)

2001-01-01

60

Pattern recognition with weighted complex networks  

NASA Astrophysics Data System (ADS)

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

Cheh, Jigger; Zhao, Hong

2008-11-01

61

Pattern recognition applied to uranium prospecting  

Microsoft Academic Search

PATTERN recognition techniques provide one way of uniting quantitative and descriptive geologic data for mineral prospecting. A quantified decision process using computer-selected patterns of geologic data has the potential of selecting areas with undiscovered deposits of uranium or other minerals. When a natural resource is mined more rapidly than it is discovered, its continued production becomes increasingly difficult. For example,

P. L. Briggs

1977-01-01

62

A new approach to iris pattern recognition  

Microsoft Academic Search

An iris identification algorithm is proposed based on adaptive thresholding. The iris images are processed fully in the spatial domain using the distinct features (patterns) of the iris. A simple adaptive thresholding method is used to segment these patterns from the rest of an iris image. This method could possibly be utilized for partial iris recognition since it relaxes the

Yingzi Du; Robert Ives; Delores M. Etter; Thad Welch

2004-01-01

63

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

64

Experiences in Pattern Recognition for Machine Olfaction  

NASA Astrophysics Data System (ADS)

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

Bessant, C.

2011-09-01

65

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

66

Incremental Learning for Multitask Pattern Recognition Problems  

Microsoft Academic Search

This paper presents a learning model of multitask pattern recognition (MTPR) which is constructed by several neural classifiers, long-term memories, and the detector of task changes. In the MTPR problem, several multi-class classification tasks are sequentially given to the learning model without notifying their task categories. This implies that the learning model is supposed to detect task changes by itself

Seiichi Ozawa; Asim Roy

2008-01-01

67

Pattern learning and recognition on statistical manifolds  

E-print Network

;Statistical Pattern Recognition Models data with distributions (generative) or stochastic processes Nielsen Frank.Nielsen@acm.org www.informationgeometry.org Sony Computer Science Laboratories, Inc. July 2013, SIMBAD, York, UK. c 2012 Frank Nielsen, Sony Computer Science Laboratories, Inc. 1/64 #12;Praise

Nielsen, Frank

68

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

69

Reservoir computing for static pattern recognition  

Microsoft Academic Search

This paper introduces reservoir computing for static pattern recognition. Reservoir computing networks are neural networks with a sparsely connected recurrent hidden layer (or reservoir) of neurons. The weights from the inputs to the reservoir and the reservoir weights are ran- domly selected. The weights of the second layer are determined with a linear partial least squares solver. The outputs of

Mark J. Embrechts; A. Alexandre; Jonathan D. Linton

70

ISO ground attitude determination using pattern recognition  

Microsoft Academic Search

This paper describes the attitude determination algorithm developed for the Infrared Space Observatory (ISO) spacecraft ground control. The attitude determination task contains a star pattern recognition algorithm, using a star map generated by the spacecraft Star Tracker and requires no a priori attitude estimates. Derivation of the algorithms for the attitude determination task is given. The practical implementation of the

A. J. Batten

1993-01-01

71

A novel Iris recognition method based on the Contourlet Transform and Biomimetic Pattern Recognition Algorithm  

Microsoft Academic Search

Iris recognition has been a hot research topic in these years. In this paper, an iris recognition method based on the the Contourlet Transform (CT) and Biomimetic Pattern Recognition (BPR) has been proposed. In proposed method, the Contourlet Transform was used to extract the significant features of the preprocessed iris image, and the Biomimetic Pattern Recognition algorithm was used to

Yikui Zhai; Junying Gan; Junying Zeng; Ying Xu

2010-01-01

72

A Feature Extraction Toolbox for Pattern Recognition Application  

Microsoft Academic Search

Feature extraction and evaluation are procedures common to the development of all pattern recognition application. These features are the primary pieces of information used to train the pattern recognition engine, whether that engine is a neural network, a fuzzy logic rulebase, or a genetic algorithm. Careful selection of the features to be used by the pattern recognition engine can significantly

C. W. Baumgart; K. E. Linder; L. K. Nelson

1998-01-01

73

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

74

Characterizing ultrasonic transducers using pattern recognition techniques  

NASA Astrophysics Data System (ADS)

This project's goal was to develop an automated ultrasonic transducer characterization system. A computer-based test system collected the test data for each of the given transducers. This data set was then processed by a number of pattern recognition algorithms. The results from these classifications placed the transducers into groups of similar units. All the transducers in a group will have similar performance characteristics. Each group was isolated from the others.

Ekis, J. W.

1992-04-01

75

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

76

Automated optical recognition of degraded handwritten characters  

NASA Astrophysics Data System (ADS)

This paper reports on a new approach in the field of automated optical recognition of handwritten characters. The approach combines geometrical and topological features, distribution of points, and Alopex based neural network to achieve a high recognition rate. A considerable enhancement in speed is achieved by implementing the process on a compressed image. Distortion tolerant features along with noise removal and region merging permit the handling of degraded documents and characters. Software implementation of the system experimented on the NIST database yields to a recognition rate of 92.4 for numerals and upper-case letters.

Darwiche, Emade; Pandya, Abhijit S.; Mandalia, Anil D.

1992-08-01

77

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

78

Gender Recognition from Faces Using Bandlet and Local Binary Patterns  

E-print Network

(DWT), and principal component analysis (PCA) based gender recognition is proposed in [7]. FirstGender 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

Bebis, George

79

Parsing as Statistical Pattern Recognition David M. Magerman  

E-print Network

Parsing as Statistical Pattern Recognition David M. Magerman IBM T. J. Watson Research Center sentences, one can view NL parsing as simply treebank recognition. The work described in this paper represents a movement away from traditional grammar­based parsing towards parsing as pattern recognition

Pratt, Vaughan

80

ELSEVIER Pattern RecognitionLetters 18 (1997) 119-131 Pattern Recognition  

E-print Network

techniques guided by the principles of evolution and natural genetics. They are efficient, adaptive chromosome differentiation into two classes and a restricted form of crossover operation is defined. Its application to multi-dimensional pattern recognition problems is studied. Superiority of the classifier

Pal, Sankar Kumar

81

Intrusion detection using pattern recognition methods  

NASA Astrophysics Data System (ADS)

Today, cyber attacks such as worms, scanning, active attackers are pervasive in Internet. A number of security approaches are proposed to address this problem, among which the intrusion detection system (IDS) appears to be one of the major and most effective solutions for defending against malicious users. Essentially, intrusion detection problem can be generalized as a classification problem, whose goal is to distinguish normal behaviors and anomalies. There are many well-known pattern recognition algorithms for classification purpose. In this paper we describe the details of applying pattern recognition methods to the intrusion detection research field. Experimenting on the KDDCUP 99 data set, we first use information gain metric to reduce the dimensionality of the original feature space. Two supervised methods, the support vector machine as well as the multi-layer neural network have been tested and the results display high detection rate and low false alarm rate, which is promising for real world applications. In addition, three unsupervised methods, Single-Linkage, K-Means, and CLIQUE, are also implemented and evaluated in the paper. The low computational complexity reveals their application in initial data reduction process.

Jiang, Nan; Yu, Li

2007-09-01

82

Object-Recognition with oblique observation directions Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

In this paper, we propose a new scheme for omnidirectional object-recognition in free space. The proposed scheme divides above problem into several omnidirectional object-recognition with different depression angles. An omnidirectional object-recognition system with oblique observation directions based on a new recognition theory-biomimetic pattern recognition (BPR) is discussed in detail. Based on it, we can get the size of training samples

Wang Shoujue; Chen Xu; Li Weijun

2005-01-01

83

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.

84

Searching for pulsars using image pattern recognition  

E-print Network

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

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

2014-01-01

85

Adaptation in statistical pattern recognition using tangent vectors.  

PubMed

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

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

2004-02-01

86

Application of pattern recognition for Farsi license plate recognition  

Microsoft Academic Search

A Farsi License Plate Recognition (LPR) System is one kind of automatic inspection of transport systems and is of considerable interest because of its potential applications to areas such as automatic toll collection, traffic law enforcement and security control of restricted areas. This paper proposes an automatic license plate recognition system for Persian license plates. We have different type of

A. Broumandnia; M. Fathi

2005-01-01

87

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

88

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

89

EMG pattern recognition based on artificial intelligence techniques  

Microsoft Academic Search

This paper presents an electromyographic (EMG) pattern recognition method to identify motion commands for the control of a prosthetic arm by evidence accumulation based on artificial intelligence with multiple parameters. The integral absolute value, variance, autoregressive (AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of EMG signals. Pattern recognition

Sang-Hui Park; Seok-Pil Lee

1998-01-01

90

Artificial Immune Systems: A Novel Paradigm to Pattern Recognition  

Microsoft Academic Search

This chapter introduces a new computational intelligence paradigm to perform pattern recognition, named Artificial Immune Systems (AIS). AIS take inspiration from the immune system in order to build novel computational tools to solve problems in a vast range of domain areas. The basic immune theories used to explain how the immune system perform pattern recognition are described and their corresponding

L. N. de Castro; J. Timmis

91

Matrix Methods in Data Mining and Pattern Recognition  

E-print Network

Review of Matrix Methods in Data Mining and Pattern Recognition by Lars Eld´en SIAM, 2007 David S´en's book discusses five application areas in data mining and pattern recognition that are amenable about each digit. The effects of simple transformations, such as rotation, stretching, or thick- ing

92

A DNA Computing-Inspired Silicon Chip for Pattern Recognition  

E-print Network

A DNA Computing-Inspired Silicon Chip for Pattern Recognition Joo-Kyung Kim1 , Byung Soo Kim2 , Oh , Jaehyun Park2 , and Byoung-Tak Zhang1 1 Biointelligence Laboratory, School of Computer Science performance on a digit recognition data set of 3760 patterns (10 fold cross validation) of 8 Ã? 8 image

93

EMG pattern recognition control of multifunctional prostheses by transradial amputees  

Microsoft Academic Search

Electromyogram (EMG) pattern recognition approach has been investigated widely with able-bodied subjects for control of multifunctional prostheses and verified with high performance in identifying different movements. However, it remains unclear whether transradial amputees can achieve similar performance. In this study, we investigated the performance of EMG pattern recognition control of multifunctional transradial prostheses in five subjects with unilateral below-elbow amputation.

Guanglin Li; Todd A Kuiken

2009-01-01

94

Multi Pattern Dynamic Time Warping for automatic speech recognition  

Microsoft Academic Search

We are addressing the problem of jointly using multiple noisy speech patterns for automatic speech recognition (ASR), given that they come from the same class. If the user utters a word K times, the ASR system should try to use the information content in all the K patterns of the word simultaneously and improve its speech recognition accuracy compared to

Nishanth Ulhas Nair; T. V. Sreenivas

2008-01-01

95

Study on Speech Recognition of Greeting Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

This paper presents a research method to directly recognize greeting voice without segmentation to avoid error recognition because of error segmentation. The basic principle of biomimetic pattern recognition is applied to speaker-independent and continuous speech recognition of greeting. The high-dimension space covering theory is applied to the learning process of speaker-independent and continuous speech recognition of greeting during the construction

Hong Ye; Youzheng Zhang; Jianwei Shen

2010-01-01

96

Study on Text-Dependent Speaker Recognition Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

\\u000a We studied the application of Biomimetic Pattern Recognition to speaker recognition. A speaker recognition neural network\\u000a using network matching degree as criterion is proposed. It has been used in the system of text-dependent speaker recognition. Experimental results show\\u000a that good effect could be obtained even with lesser samples. Furthermore, the misrecognition caused by untrained speakers\\u000a occurring in testing could be

Shoujue Wang; Yi Huang; Yu Cao

2006-01-01

97

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

98

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

99

Environmental sound recognition using time-frequency intersection patterns  

Microsoft Academic Search

Environmental sound recognition is an important function of robots and intelligent computer systems. In this research, we tried to use a multi-stage perceptron type neural network system for environmental sound recognition. The input data is the one-dimensional combination of instantaneous spectrum at power peak and the power pattern in time domain. Since for almost environmental sounds, their spectrum changes are

Xuan Guol; Yoshiyuki Toyoda; Huankang Li; Jie Huang; Shuxue Ding; Yong Liul

2011-01-01

100

Proceedings of the eighth international conference on pattern recognition  

SciTech Connect

This book presents the papers given at a conference on pattern recognition. Topics considered at the conference included visual inspection, specialized architectures, speech recognition, data processing, image processing, three-dimensional vision, inference and learning, algorithms, robots, knowledge bases, signal processing, texture, shape, artificial intelligence, and expert systems.

Not Available

1986-01-01

101

Human-Computer Interaction for Complex Pattern Recognition Problems  

E-print Network

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

Salama, Khaled

102

Pattern Recognition as Rule-Guided Inductive Inference  

Microsoft Academic Search

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

Ryszard S. Michalski

1980-01-01

103

The control of a prosthetic arm by EMG pattern recognition  

Microsoft Academic Search

An electromyographic (EMG) signal pattern recognition system is constructed for real-time control of a prosthetic arm through precise identification of motion and speed command. A probabilistic model of the EMG patterns is first formulated in the feature space of integral absolute value (IAV). Then, the sample probability density function of pattern classes in the feature space of variance and zero

Sukhan Lee; George N. Saridis

1982-01-01

104

Grip-pattern recognition: applied to a smart gun  

Microsoft Academic Search

The verification performance of a biometric recognition system based on grip patterns, as part of a smart gun for use by the police ocers, has been investigated. The biometric features are extracted from a two-dimensional pattern of the pressure, exerted on the grip of a gun by the hand of a person holding it. Such a grip-pattern verication system is

Xiaoxin Shang

2008-01-01

105

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

106

Scale invariant pattern recognition with logarithmic radial harmonic filters  

E-print Network

Scale invariant pattern recognition with logarithmic radial harmonic filters Joseph Rosen filters with reduced resolution requirements. Partial and complete rotation invariance was demon- strated invariance is possible scale changes must be limited within certain ranges. Previous attempts

Rosen, Joseph

107

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

108

Visual cluster analysis and pattern recognition template and methods  

SciTech Connect

This invention is comprised of 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, G.C.; Martinez, R.F.

1993-12-31

109

Hopfield's Model of Patterns Recognition and Laws of Artistic Perception  

NASA Astrophysics Data System (ADS)

The model of patterns recognition or attractor network model of associative memory, offered by J.Hopfield 1982, is the most known model in theoretical neuroscience. This paper aims to show, that such well-known laws of art perception as the Wundt curve, perception of visual ambiguity in art, and also the model perception of musical tonalities are nothing else than special cases of the Hopfield’s model of patterns recognition.

Yevin, Igor; Koblyakov, Alexander

110

Assessment of bioinspired models for pattern recognition in biomimetic systems  

Microsoft Academic Search

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

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

2008-01-01

111

Face Recognition Based on Biomimetic Pattern Recognition by High-Dimensional Geometry  

Microsoft Academic Search

High-dimensional geometry is a vehicle to achieve biomimetic pattern recognition, which is a new model for recognition science and always to construct convex cell bodies for covering samples points in the space. By this way, in this paper, some k- dimension simplex were constructed for the purpose of covering the sample points of each class in the feature space, and

You-zheng Zhang; Hao Feng; Li-jun Ding

2010-01-01

112

Research on Isolated Word Speech Recognition Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

In this paper, the theories of biomimetic pattern recognition and high-dimension space covering are applied into the isolated word speech recognition. And, based on Hopfield network and RBF network, a new type of neural network model is constructed to realize the coverage of different types of samples which form different geometrical shapes in high-dimension space. Therefore, the purpose of classification

Bin Lu; Jing-jing Xu

2009-01-01

113

Status Recognition for Electrical Parameters of ESPCP Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

Various fault types and difficult diagnosis restricted the improvement of economic benefit and system efficiency of electrical submersible progressing cavity pump (ESPCP) production system. A novel method for status recognition of electrical parameters in fault diagnosis of ESPCP based on biomimetic pattern recognition (BPR) is presented. Application results show the proposed BPR classifier produces significant accuracy for classification of ESPCP

Shi Hai-tao; Yu Yun-hua; Kong Qian-qian

2010-01-01

114

A Feature Extraction Toolbox for Pattern Recognition Application  

SciTech Connect

Feature extraction and evaluation are procedures common to the development of all pattern recognition application. These features are the primary pieces of information used to train the pattern recognition engine, whether that engine is a neural network, a fuzzy logic rulebase, or a genetic algorithm. Careful selection of the features to be used by the pattern recognition engine can significantly streamline the overall development and training of the solution for the pattern recognition application. Presently, AlliedSignal Federal Manufacturing & Technologies (FM&T) is developing an integrated, computer-based software package, called the Feature Extraction Toolbox. This package will be used for developing and deploying solutions to generic pattern recognition problems. The toolbox integrates a variety of software techniques for signal processing, feature extraction and evaluation, and pattern recognition, under a single, user-friendly developmental environment. While a feature extraction toolbox can help in the selection process, it is the user that ultimately must make all decisions. A prototype version of this toolbox has been developed and currently is being used for applications development on several projects in support of the Department of Energy. The toolbox has been developed to run on a laptop computer so that it can be taken to a site and used in the field.

Baumgart, C.W.; Linder, K.E.; Nelson, L.K.

1998-11-23

115

Albedo Pattern Recognition and Time-Series Analyses in Malaysia  

NASA Astrophysics Data System (ADS)

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

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

2012-07-01

116

Improved recognition of control chart patterns using artificial neural networks  

Microsoft Academic Search

Recognition of abnormal patterns in control charts provides clues to reveal potential quality problems in the manufacturing\\u000a processes. One potentially popular approach for recognizing different control chart patterns (CCPs) is to develop heuristics\\u000a based on various shape features of the patterns. The advantage of this approach is that the users can easily understand how\\u000a a particular pattern is identified. However,

Susanta Kumar Gauri; Shankar Chakraborty

2008-01-01

117

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

118

Optical Recognition And Tracking Of Objects  

NASA Technical Reports Server (NTRS)

Separate objects moving independently tracked simultaneously. System uses coherent optical techniques to obtain correlation between each object and reference image. Moving objects monitored by charge-coupled-device television camera, output fed to liquid-crystal television (LCTV) display. Acting as spatial light modulator, LCTV impresses images of moving objects on collimated laser beam. Beam spatially low-pass filtered to remove high-spatial-frequency television grid pattern.

Chao, Tien-Hsin; Liu, Hua-Kuang

1988-01-01

119

Postprocessing for character recognition using pattern features and linguistic information  

NASA Astrophysics Data System (ADS)

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

Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

1993-04-01

120

Gaussian mixture models-based control chart pattern recognition  

Microsoft Academic Search

Abnormal patterns exhibited in control charts can be associated with certain assignable causes for process variation. Hence, accurate and fast control chart pattern recognition (CCPR) is essential for significantly narrowing down the scope of possible causes that must be investigated, and speeds up the troubleshooting process. This study proposes a Gaussian mixture models (GMM)-based CCPR model that employs a collection

Jianbo Yu

2011-01-01

121

Pigment Melanin: Pattern for Iris Recognition  

Microsoft Academic Search

Recognition of iris based on Visible Light (VL) imaging is a difficult\\u000aproblem because of the light reflection from the cornea. Nonetheless, pigment\\u000amelanin provides a rich feature source in VL, unavailable in Near-Infrared\\u000a(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical\\u000anot stimulated in NIR. In this case, a plausible solution to observe such\\u000apatterns

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

2009-01-01

122

Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition: How Iris Recognition Works  

Microsoft Academic Search

Abstract: This paper discusses exploitation ofthis statistical principle, combined with wavelet image coding methods to extract phasedescriptions of incoherent patterns. Demodulation and coarse quantization of the phaseinformation creates decision environments characterized by well-separated clusters, andthis lends itself to rapid and reliable pattern recognition

John Daugman

2003-01-01

123

Quantum pattern recognition with liquid-state nuclear magnetic resonance  

NASA Astrophysics Data System (ADS)

A quantum pattern recognition scheme is presented, which combines the idea of a classic Hopfield neural network with adiabatic quantum computation. Both the input and the memorized patterns are represented by means of the problem Hamiltonian. In contrast to classic neural networks, the algorithm can return a quantum superposition of multiple recognized patterns. A proof of principle for the algorithm for two qubits is provided using a liquid-state NMR quantum computer.

Neigovzen, Rodion; Neves, Jorge L.; Sollacher, Rudolf; Glaser, Steffen J.

2009-04-01

124

Proving theorems by pattern recognition I  

Microsoft Academic Search

:~: plan for carrying the work to more difficult regions, fundamentally new feature beyond tire previous paper i;~ a i; suggestion to replace essentially exhaustive methods t,y ~; study of the patterns according to which extensions i> volved in the search for a proof (or disproof) are contimied The writer feels that the use of pattern recognitio~l, whM~ is in

Hao Wang

1960-01-01

125

Quaternion K-L transform and Biomimetic pattern recognition approaches for color-face recognition  

Microsoft Academic Search

This paper proposed a method of quaternion K-L transform and biomimetic pattern recognition (BPR) for color face recognition. The BPR aimed at optimal covering in the feature space Rn using some complex geometric bodies for cover samples distribution in Rn approximately in order to ¿recognize¿. We used quaternion K-L transform to extract the Eigen-faces of training samples and algebraic feature

Lijun Ding; Hao Feng

2009-01-01

126

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

127

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

128

Pattern Recognition for Automatic Machinery Fault Diagnosis  

Microsoft Academic Search

We present a generic methodology for machinery fault diagnosis through pattern recog- nition techniques. The proposed method has the advantage of dealing with complicated signatures, such as those present in the vibration signals of rolling element bearings with and without defects. The signature varies with the location and severity of bearing defects, load and speed of the shaft, and different

Qiao Sun; Ping Chen; Dajun Zhang; Fengfeng Xi

2004-01-01

129

Peripheral neuropathy: pattern recognition for the pragmatist  

Microsoft Academic Search

Long lists of causes of peripheral neuropathy make peripheral nerve disease a dry and uninspiring subject. A simple scheme based on the answers to just six questions should enable the clinician to recognise characteristic patterns, investigate relevant subgroups appropriately, and identify treatable disorders quickly: which systems are involved? What is the distribution of weakness? What is the nature of the

James R Overell

2011-01-01

130

High speed optical object recognition processor with massive holographic memory  

NASA Technical Reports Server (NTRS)

Real-time object recognition using a compact grayscale optical correlator will be introduced. A holographic memory module for storing a large bank of optimum correlation filters, to accommodate the large data throughput rate needed for many real-world applications, has also been developed. System architecture of the optical processor and the holographic memory will be presented. Application examples of this object recognition technology will also be demonstrated.

Chao, T.; Zhou, H.; Reyes, G.

2002-01-01

131

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

132

EMG pattern recognition based on evidence accumulation for prosthesis control  

Microsoft Academic Search

The authors present an EMG pattern recognition method to identify motion command for the control of a prosthetic arm by evidence accumulation with multiple parameters. The adaptive cepstrum coefficients which the authors propose in this paper, integral absolute value (IAV), difference absolute mean value (DAMV), variance and autoregressive (AR) model coefficients, are extracted as parameters by probabilistic and stochastic models.

Seok-Pil Lee; Sang-Hui Park; Jeong-Seop Kim; Ig-Jae Kim

1996-01-01

133

Pattern recognition applied to earthquake epicenters in California  

Microsoft Academic Search

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

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

1976-01-01

134

International Journal of Pattern Recognition and Artificial Intelligence  

E-print Network

International Journal of Pattern Recognition and Artificial Intelligence Vol. 19, No. 5 (2005) 715 system is now used to study many different aspects of the atmosphere and its components. Among many appli- cations, the Lidar is most widely used in atmospheric research in environments. For instance, the Lidar

Hefei Institute of Intelligent Machines

135

Statistical Pattern Recognition Techniques for Target Differentiation using Infrared Sensor  

E-print Network

Statistical Pattern Recognition Techniques for Target Differentiation using Infrared Sensor Tayfun features in indoor environments, possibly with different surface properties, using simple infrared (IR window is covered with an IR filter to minimize the effect of ambient light on the intensity mea

Barshan, Billur

136

CO2 Sequestration Modeling Using Pattern Recognition and Data Mining;  

E-print Network

CO2 Sequestration Modeling Using Pattern Recognition and Data Mining; Case Study of SACROC field, USA Abstract Capturing carbon dioxide (CO2) from industrial and energy-related sources and depositing it in a geological formation is considered as an efficient way to decrease the CO2 impacts in the atmosphere

Mohaghegh, Shahab

137

Learning Pattern Recognition Through Quasi-Synchronization of Phase Oscillators  

Microsoft Academic Search

The idea that synchronized oscillations are impor- tant in cognitive tasks is receiving significant attention. In this view, single neurons are no longer elementary computational units. Rather, coherent oscillating groups of neurons are seen as nodes of networks performing cognitive tasks. From this assumption, we develop a model of stimulus-pattern learning and recognition. The three most salient features of our

Ekaterina Vassilieva; Guillaume Pinto; José Acacio de Barros; Patrick Suppes

2011-01-01

138

Bearing fault detection in induction motor using pattern recognition techniques  

Microsoft Academic Search

In this paper a procedure based on pattern recognition technique is presented for fault diagnosis of rolling element bearings through artificial neural networks (ANN). The artificial neural networks are trained with a subset of the experimental data for known machine conditions. The networks are tested using the remaining set of data. In this method the characteristic features of time and

J. Zarei; J. Poshtan; M. Poshtan

2008-01-01

139

The Application Research of Pattern Recognition for Failure Detection  

Microsoft Academic Search

Considering the characteristics of the mechanical equipment and the disadvantages of the existing diagnosis method, a new failure detection method based on pattern recognition is defined. Short time signal and linear distinguish function is adopted. Alone feature is combined for deciding. The method of sensitive and efficient is provided for detecting bearing fault. From the analysis result of the actual

Shufen Li; Junli Liu

2009-01-01

140

Driving Pattern Recognition for Control of Hybrid Electric Trucks  

E-print Network

be used to design a control law quickly. The main drawback, however, is the fact that the product fromDriving Pattern Recognition for Control of Hybrid Electric Trucks CHAN-CHIAO LIN1 , SOONIL JEON2 , HUEI PENG3 , AND JANG MOO LEE4 SUMMARY The design procedure for an adaptive power management control

Peng, Huei

141

Pattern Recognition Project : Vessel Detection in Retinal Images  

E-print Network

Pattern Recognition Project : Vessel Detection in Retinal Images Instructor: Wei-Yang Lin Due date study. The goal of this project is to provide a hand-on experience in building a vessel detection methods for detecting retinal blood vessel reported in the literature, e.g., [1, 2, 3]. Each group

Lin, Wei-Yang

142

New Directions in Statistical Physics: Econophysics, Bioinformatics, and Pattern Recognition  

Microsoft Academic Search

This book contains 18 contributions from different authors. Its subtitle `Econophysics, Bioinformatics, and Pattern Recognition' says more precisely what it is about: not so much about central problems of conventional statistical physics like equilibrium phase transitions and critical phenomena, but about its interdisciplinary applications.After a long period of specialization, physicists have, over the last few decades, found more and more

P Grassberger

2004-01-01

143

Problem-Solving Models and Search Strategies for Pattern Recognition  

Microsoft Academic Search

Noting the major limitations of multivariate statistical classification and syntactic pattern recognition models, this paper presents an overview of some recent work using alternate representations for multistage and nearest neighbor multiclass classification, and for structural analysis and feature extraction. These alternate representations are based on generalizations of state-space and AND\\/OR graph models and search strategies developed in artificial intelligence (AI).

Laveen N. Kanal

1979-01-01

144

A text classifier based on biomimetic pattern recognition  

Microsoft Academic Search

In this paper a novel text classification method based on biomimetic pattern recognition (BPR) is proposed. And we implement a BPR text classifier with multi-weight neural network; the model of three-weight neural network and its construction method are described in detail. Some text classification experiments have been done to test the performance of our BPR text classifier; experimental results show

Ji-Bin Zhang; Shuai Cong; Zhi-Ming Xu; Qi-Shu Pan

2009-01-01

145

A More Complex Neuron in Biomimetic Pattern Recognition  

Microsoft Academic Search

Biomimetic pattern recognition has been proposed for several years, but the discussion of its neuron was not very wide and deep. In this paper, we propose a new more complex neuron named Psi3-neuron and give the application in the last part of the paper

Shoujue Wang; Jiangliang Lail

2005-01-01

146

Characterization of ultrasonic transducers using pattern recognition techniques  

SciTech Connect

This paper describes an automated method for characterizing ultrasonic transducers. A computer based test system will collect test data for a given transducer. This data set is then subjected to a pattern recognition algorithm. The results from this classification will place the transducer in a group of similar units. All the transducers in a group will have similar performance characteristics. 9 refs., 1 fig.

Ekis, J.W.

1990-09-01

147

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

148

Classification of radial compressor faults using pattern-recognition techniques  

Microsoft Academic Search

An application of pattern-recognition techniques for the classification of faults in a radial compressor is presented. A number of mechanical alterations, simulating faults, are introduced in a test compressor. They include the insertion of an inlet obstruction, an obstruction in a diffuser passage, variation of impeller tip clearance and impeller fouling. Two kinds of measurements, namely sound emission and casing

N. Aretakis; K. Mathioudakis

1998-01-01

149

Artificial convolution neural network for medical image pattern recognition  

Microsoft Academic Search

We have developed several training methods in conjunction with a convolution neural network for general medical image pattern recognition. An unconventional method of using rotation and shift invariance is also proposed to enhance the neural net performance. The structure of the artificial neural network is a simplified network structure of the neocognitron. Two-dimensional local connection as a group is the

Shih-chung Ben Lo; Heang-ping Chan; Jyh-shyan Lin; Huai Li; Matthew T. Freedman; Seong K. Mun

1995-01-01

150

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

151

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

152

Online pattern recognition in intensive care medicine.  

PubMed Central

In intensive care physiological variables of the critical-ly ill are measured and recorded in short time intervals. The existing alarm systems based on fixed thresholds produce a large number of false alarms. Usually the change of a variable over time is more informative than one pathological value at a particular time point. Intelligent alarm systems which detect important changes within a physiological time series are needed for suitable bedside decision support. There are various approaches to modeling time-dependent data and also several methodologies for pattern detection in time series. We compare several methodologies de-signed for online detection of measurement artifacts, level changes, and trends for a proper classification of the patient s state by means of a comparative case-study. PMID:11825177

Fried, R.; Gather, U.; Imhoff, M.

2001-01-01

153

Quantum Mechanics, Pattern Recognition, and the Mammalian Brain  

NASA Astrophysics Data System (ADS)

Although the usual way of representing Markov processes is time asymmetric, there is a way of describing Markov processes, due to Schrodinger, which is time symmetric. This observation provides a link between quantum mechanics and the layered Bayesian networks that are often used in automated pattern recognition systems. In particular, there is a striking formal similarity between quantum mechanics and a particular type of Bayesian network, the Helmholtz machine, which provides a plausible model for how the mammalian brain recognizes important environmental situations. One interesting aspect of this relationship is that the "wake-sleep" algorithm for training a Helmholtz machine is very similar to the problem of finding the potential for the multi-channel Schrodinger equation. As a practical application of this insight it may be possible to use inverse scattering techniques to study the relationship between human brain wave patterns, pattern recognition, and learning. We also comment on whether there is a relationship between quantum measurements and consciousness.

Chapline, George

2008-10-01

154

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

155

Structural pattern recognition methods based on string comparison for fusion databases  

Microsoft Academic Search

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns

S. Dormido-Canto; G. Farias; R. Dormido; J. Vega; J. Sánchez; N. Duro; H. Vargas; G. Rattá; A. Pereira; A. Portas

2008-01-01

156

Hybrid optical correlator for character recognition  

NASA Astrophysics Data System (ADS)

A hybrid character recognition system is described. It is composed to two parts, the feature extractor and the inner- product correlator. Ten Arabic characters from 0 to 9 are tested for their recognition by the system. The experimental results show that all the printing characters are perfectly recognized with rotational invariance of 15 degree(s) angle, and the recognized ratio for hand-writing characters is over sixty percent.

Chen, Yansong; Li, Dehuan

1994-06-01

157

Centromere and cytoplasmic staining pattern recognition: a local approach.  

PubMed

Autoimmune diseases are very serious and also invalidating illnesses. The benchmark procedure for their diagnosis is the indirect immunofluorescence (IIF) assay performed on the HEp-2 substrate. Medical doctors first determine the fluorescence intensity exhibited by HEp-2 wells and then report the staining pattern. Despite its pivotal role, IIF is affected by inter- and intra-laboratory variabilities demanding for the development of computer-aided-diagnosis tools supporting medical doctor decisions. With reference to staining pattern recognition, state-of-the-art approaches recognize five main patterns characterized by well-defined cell edges. These approaches are based on cell segmentation, a task that recent work suggests to be harder than the classification itself. In this paper, we extend the panel of detectable HEp-2 staining patterns, introducing the recognition of centromere and cytoplasmic patterns, which have a high specific match with certain autoimmune diseases, from other stainings. Since image segmentation algorithms fail on these samples, we developed a classification system integrating local descriptors and the bag of visual word approach, which represents image contents without the burden of segmentation. We tested our approach on a large dataset of HEp-2 images with high variability in both fluorescence intensity and staining patterns correctly recognizing the 97.12 % of samples. The system has also been validated in a daily routine fashion on 108 consecutive IIF analyses of hospital outpatients and inpatients, achieving an accuracy rate of 97.22 %. PMID:23877232

Iannello, Giulio; Onofri, Leonardo; Soda, Paolo

2013-12-01

158

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

159

Real-Time Pattern Recognition - An Industrial Example  

NASA Astrophysics Data System (ADS)

Rapid advancements in cost effective sensors and micro computers are now making practical the on-line implementation of pattern recognition based systems for a variety of industrial applications requiring high processing speeds. One major application area for real time pattern recognition is in the sorting of packaged/cartoned goods at high speed for automated warehousing and return goods cataloging. While there are many OCR and bar code readers available to perform these functions, it is often impractical to use such codes (package too small, adverse esthetics, poor print quality) and an approach which recognizes an item by its graphic content alone is desirable. This paper describes a specific application within the tobacco industry, that of sorting returned cigarette goods by brand and size.

Fitton, Gary M.

1981-11-01

160

Cerebellar involvement in metabolic disorders: a pattern-recognition approach  

Microsoft Academic Search

Inborn errors of metabolism can affect the cerebellum during development, maturation and later during life. We have established\\u000a criteria for pattern recognition of cerebellar abnormalities in metabolic disorders. The abnormalities can be divided into\\u000a four major groups: cerebellar hypoplasia (CH), hyperplasia, cerebellar atrophy (CA), cerebellar white matter abnormalities\\u000a (WMA) or swelling, and involvement of the dentate nuclei (DN) or cerebellar

M. Steinlin; S. Blaser; E. Boltshauser

1998-01-01

161

Viral evasion and subversion of pattern-recognition receptor signalling  

Microsoft Academic Search

The expression of pattern-recognition receptors (PRRs) by immune and tissue cells provides the host with the ability to detect and respond to infection by viruses and other microorganisms. Significant progress has been made from studying this area, including the identification of PRRs, such as Toll-like receptors and RIG-I-like receptors, and the description of the molecular basis of their signalling pathways,

Leonie Unterholzner; Andrew G. Bowie

2008-01-01

162

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

163

An automated system for characterizing ultrasonic transducers using pattern recognition  

Microsoft Academic Search

The system consists of a 3D positioning mechanism, a motion controller, a pulser\\/receiver with gated-peak detector, a digitizing oscilloscope, a spectrum analyzer, and a host computer. Pattern recognition techniques were used to classify and reduce the dimensionality of the transducers. It was found that the K-means algorithm was the most successful algorithm for classifying the transducers, whereas the Baye's decision

M. S. Obaidat; J. W. Ekis

1991-01-01

164

Geometry Algebra Neuron Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

Biometric Pattern Recognition aim at finding the best coverage of per kind of sample’s distribution in the feature space.\\u000a It is based on the analysis of relationship of sample points in the feature space. According to the principle of “same source”,\\u000a research the same kind of samples’ distribution in the feature space can get eigenvector information with low data amount.

Wenming Cao; Feng Hao

165

Medical Image Segmentation Based on Biomimetic Pattern Recognition  

Microsoft Academic Search

A medical image segmentation algorithm based on biomimetic pattern recognition is proposed. First, psi3-neurons' weights are determined according to training samples and then multi-weight neuron networks are established. Second, the neuron networks are used to completely cover the samples' high-dimensional feature space. Finally, medical images are recognized and segmented based on the results of the optimal coverage of the samples.

Jiafu Jiang; He Wei; Qi Qi

2009-01-01

166

Pattern recognition models for spectral reflectance evaluation of apple blemishes  

Microsoft Academic Search

Surface blemishes of various apple varieties were analyzed by their reflectance characteristics between 460 and 1130 nm. Normalized reflectance data were collected at 10 nm increments with liquid crystal tunable filters. Data were utilized as input values for various pattern recognition models specifically multi-layer back propagation, unimodal Gaussian, K-nearest neighbor and nearest cluster algorithms. Partitioning data into 50:50 training and

W. M Miller; J. A Throop; B. L Upchurch

1998-01-01

167

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

168

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

169

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

170

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

171

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

2012-06-07

172

A study on stable operation condition and multi-level recognition of an optical digital-to-analog conversion-type header processor for an optical packet switch  

Microsoft Academic Search

This study investigates the performance of the optical header processor based on the optical digital-to-analog converter. The bit error rate dependence (BER) dependence on the relative polarization angle between splitted signals is demonstrated. The possibility of multi-level recognition of analog pulse amplitudes of header patterns is also shown.

T. Seki; H. Uenohara

2003-01-01

173

Pattern recognition correlator based on digital photo camera  

NASA Astrophysics Data System (ADS)

Diffraction image correlator based on commercial digital SLR photo camera is described. The correlator is proposed for recognition of external 2-D and 3-D scenes illuminated by quasimonochromatic spatially incoherent light. Principal optical scheme of the correlator is analogous to that of incoherent holographic correlator by Lohmann. The correlator hardware consists of digital camera with attached optical correlation filter unit and control computer. No modifications have been introduced in units of commercial digital SLR photo camera. Digital camera was connected via camera interface to computer for controlled camera shooting, transfer of detected correlation signals and post-processing. Two ways were used for correlation filter unit mounting. In the first case, correlation filter was attached to the front of the camera lens. In the second one, it was placed in a free space of the SLR camera body between the interchangeable camera lens and the swing mirror. Computer generated Fourier holograms and kinoforms were used as correlation filters in experiments. The experimental setup of the correlator and experimental results on images recognition are presented. The recognition of test objects of direct and reversed contrast with the same correlation filter was performed.

Starikov, Sergey N.; Balan, Nikita N.; Rodin, Vladislav G.; Solyakin, Ivan V.; Shapkarina, Ekaterina A.

2006-04-01

174

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

175

Design of Optimal Dynamic Analyzers: Mathematical Aspects of Wave Pattern Recognition  

E-print Network

We give a review of the most important results on optimal tomography as mathematical wave-pattern recognition theory emerged in the 70's in connection with the problems of optimal estimation and hypothesis testing in quantum theory. In quantum theory such problems are sometimes referred as the problem of optimal measurement of an unknown quantum state, and are the main subject of the emerging mathematical theory of quantum statistics. We develop the results of quantum pattern recognition theory, most of which belong to VPB, further into the direction of wave, rather than particle statistical estimation and hypothesis testing theory, with the aim to include not only quantum matter waves but also classical wave patterns like optical and acoustic waves. We conclude that Hilbert space and operator methods developed in quantum theory are equally useful in the classical wave theory, as soon as the possible observations are restricted to only intensity distributions of waves, i.e. when the wave states are not the allowed observables, as they are not the observables of individual particles in the quantum theory. We show that all characteristic attributes of quantum theory such as complementarity, entanglement or Heisenberg uncertainty relations are also attributes of the generalized wave pattern recognition theory.

V. P. Belavkin; V. P. Maslov

2004-12-03

176

Facilities for digital pattern recognition: an ECG detective trick.  

PubMed

Algorithms for digital pattern recognition optimized for the demands of the physician are urgently needed. They have to provide high levels of recognition, accuracy, reliability, artefact rejection and flexibility in detecting different types of signal time-course. The microcomputer algorithm presented here works on the principle of Walsh-transformation of signal sections and in-image judging. The algorithm efficiently solves simple tasks, and also recognizes, for instance, ECG P-waves using the same algorithm. A test with 1054 randomly selected outpatient's ECG and with an additional 72 ECGs of inpatients with clinically proved myocardial infarcts produced the following results: The recognition ratio for the R-wave amounted to 98.8% with a failure ratio of 2.3%, while an initial common P-T-pattern was correctly recognized in 80.3% of cases, with a failure ratio of 4.9%. The algorithm was implemented on a Z80 microprocessor and on a single-chip computer Z8. PMID:3349769

Poll, R; Henssge, R

1988-01-01

177

Multiclass pattern recognition using adaptive correlation filters with complex constraints  

NASA Astrophysics Data System (ADS)

An efficient method for reliable multiclass pattern recognition using a bank of adaptive correlation filters is proposed. The method can recognize and classify multiple targets from an input scene by using both the intensity and phase distributions of the output complex correlation plane. The adaptive filters are synthesized with the help of an iterative algorithm based on synthetic discriminant functions with complex constraints. The algorithm optimizes the discrimination capability of the adaptive filters and determines the minimum number of filters in a bank to guarantee a desired classification efficiency. As a result, the computational complexity of the proposed system is low. Computer simulation results obtained with the proposed approach in cluttered and noisy scenes are discussed and compared with those obtained through existing methods in terms of recognition performance, classification efficiency, and computational complexity.

Diaz-Ramirez, Victor H.; Campos-Trujillo, Oliver G.; Kober, Vitaly; Aguilar-Gonzalez, Pablo M.

2012-03-01

178

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

179

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

180

Optical character recognition of handwritten Arabic using hidden Markov models  

NASA Astrophysics Data System (ADS)

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

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

2011-04-01

181

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

182

NLP-inspired structural pattern recognition in chemical application q J. Sidorova  

E-print Network

NLP-inspired structural pattern recognition in chemical application q J. Sidorova , M. Anisimova Computational Biochemistry Research Group, Department of Computer Science, Swiss Federal Institute of Technology recognition Grammar inference Natural language processing Chemical descriptors SMILES Activity prediction a b

Anisimova, Maria

183

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

184

Wavelet-based moment invariants for pattern recognition  

NASA Astrophysics Data System (ADS)

Moment invariants have received a lot of attention as features for identification and inspection of two-dimensional shapes. In this paper, two sets of novel moments are proposed by using the auto-correlation of wavelet functions and the dual-tree complex wavelet functions. It is well known that the wavelet transform lacks the property of shift invariance. A little shift in the input signal will cause very different output wavelet coefficients. The autocorrelation of wavelet functions and the dual-tree complex wavelet functions, on the other hand, are shift-invariant, which is very important in pattern recognition. Rotation invariance is the major concern in this paper, while translation invariance and scale invariance can be achieved by standard normalization techniques. The Gaussian white noise is added to the noise-free images and the noise levels vary with different signal-to-noise ratios. Experimental results conducted in this paper show that the proposed wavelet-based moments outperform Zernike's moments and the Fourier-wavelet descriptor for pattern recognition under different rotation angles and different noise levels. It can be seen that the proposed wavelet-based moments can do an excellent job even when the noise levels are very high.

Chen, Guangyi; Xie, Wenfang

2011-07-01

185

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

186

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

187

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

188

Auditory pattern recognition and brief tone discrimination of children with reading disorders  

Microsoft Academic Search

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 duration. A test involving measurement of difference limens for long and short duration

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

2006-01-01

189

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

NASA Technical Reports Server (NTRS)

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

Chao, Tien-Hsin; Stoner, William W.

1993-01-01

190

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

191

Recognition of Acoustic Emission Patterns from Mixed Mode Wood Fracture.  

NASA Astrophysics Data System (ADS)

Automatic, reactive control of wood drying to maximize drying rate and minimize drying defects would be possible if the development of internal stress associated with micro and macro failure processes due to shrinkage could be detected in real time. Assuming that AE signals due to micro and macro failures during wood fracture testing are the same or similar to signals produced by drying stresses and check formation, it was decided to collect AE signals during wood fracture testing under the type of conditions of moisture content and temperature which might be found during the kiln drying process, and investigate if they may be useful in automatic, reactive kiln control. In particular, it was intended to determine if there were AE patterns associated with specific load levels leading to wood fracture which could give early warning of impending failures. AE signals and load were recorded during fracture testing of Pinus ponderosa and Quercus kelloggii. Single -edge notch tension specimens in the TL orientation were tested in mixed mode (Modes I and II) to determine if there are AE patterns associated with particular loading stages. Tests were made at three levels of temperature--20, 40, and 60 ^circC--and two levels of moisture content--12 and 18%. It was found that (a) maximum event rate increased with increasing load to maximum load and beyond, (b) temperature had a significant effect on number of events to maximum load, (c) moisture content had a significant effect on number of events to conclusion of test, (d) AE signal patterns could be successfully classified by cluster analysis and canonical discriminant analysis, (e) temperature, moisture content, and their interaction had a significant effect on features of AE signal patterns. The AE signal patterns showed very little relationship to stress levels in wood fracture. Pattern recognition of single AE signals therefore does not hold much promise for application to monitoring and control of the kiln drying process. Recognition of key features such as maximum event rate and their critical values therefore appears to be the more useful approach.

Lee, Shih-Hao

192

A New Development on ANN in China - Biomimetic Pattern Recognition and Multi Weight Vector Neurons  

Microsoft Academic Search

A new model of pattern recognition principles—Biomimetic Pattern Recognition, which is based on “matter cognition” instead\\u000a of “matter Classification”, has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical\\u000a model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate\\u000a all kinds of neuron architecture, including RBF and BP

Shoujue Wang

2003-01-01

193

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

194

Tracking speckle patterns with optical correlation  

Microsoft Academic Search

It has been shown that tracking small particle motion can be accomplished by tracking the speckle pattern is produces. This paper describes various methods of real-time tracking of speckle patterns obtained from ultrasonic flow imaging of blood and tissue motion using optical correlation. Results obtained from a gray scale joint transform correlator utilizing a twisted nematic liquid crystal spatial light

Natalie Clark; Michael K. Giles; Sarah H. Harrison; Chris P. Hofer

1993-01-01

195

Vibrotactile pattern recognition: a portable compact tactile matrix.  

PubMed

Compact tactile matrix (CTM) is a vibrotactile device composed of a seven-by-seven array of electromechanical vibrators "tactip" used to represent tactile patterns applied to a small skin area. The CTM uses a dynamic feature to generate spatiotemporal tactile patterns. The design requirements focus particularly on maximizing the transmission of the vibration from one tactip to the others as well as to the skin over a square area of 16 cm (2) while simultaneously minimizing the transmission of vibrations throughout the overall structure of the CTM. Experiments were conducted on 22 unpracticed subjects to evaluate how the CTM could be used to develop a tactile semantics for communication of instructions in order to test the ability of the subjects to identify: 1) directional prescriptors for gesture guidance and 2) instructional commands for operational task requirements in a military context. The results indicate that, after familiarization, recognition accuracies in the tactile patterns were remarkably precise for more 80% of the subjects. PMID:22084044

Thullier, Francine; Bolmont, Benoît; Lestienne, Francis G

2012-02-01

196

Geometry Of Discrete Sets With Applications To Pattern Recognition  

NASA Astrophysics Data System (ADS)

In this paper we present a new framework for discrete black and white images that employs only integer arithmetic. This framework is shown to retain the essential characteristics of the framework for Euclidean images. We propose two norms and based on them, the permissible geometric operations on images are defined. The basic invariants of our geometry are line images, structure of image and the corresponding local property of strong attachment of pixels. The permissible operations also preserve the 3x3 neighborhoods, area, and perpendicularity. The structure, patterns, and the inter-pattern gaps in a discrete image are shown to be conserved by the magnification and contraction process. Our notions of approximate congruence, similarity and symmetry are similar, in character, to the corresponding notions, for Euclidean images [1]. We mention two discrete pattern recognition algorithms that work purely with integers, and which fit into our framework. Their performance has been shown to be at par with the performance of traditional geometric schemes. Also, all the undesired effects of finite length registers in fixed point arithmetic that plague traditional algorithms, are non-existent in this family of algorithms.

Sinha, Divyendu

1990-03-01

197

Pattern recognition on brain magnetic resonance imaging in alpha dystroglycanopathies.  

PubMed

Alpha dystroglycanopathies are heterogeneous group of disorders both phenotypically and genetically. A subgroup of these patients has characteristic brain imaging findings. Four patients with typical imaging findings of alpha dystroglycanopathy are reported. Phenotypic features included: global developmental delay, contractures, hypotonia and oculomotor abnormalities in all. Other manifestations were consanguinity (3), seizures (3), macrocephaly (1), microcephaly (3), retinal changes (2) and hypogenitalism (2). Magnetic resonance imaging (MRI) of the brain revealed polymicrogyria, white matter changes, pontine hypoplasia, and subcortical cerebellar cysts in all the patients, ventriculomegaly, callosal abnormalities, and absent septum pellucidum in two and Dandy -Walker variant malformation in three. Magnetic resonace imaging of the first cousin of one the patient had the same characteristic imaging features. Brain imaging findings were almost identical despite heterogeneity in clinical presentation and histopathological features. Pattern recognition of MR imaging features may serve as a clue to the diagnosis of alpha dystroglycanopathy. PMID:20644281

Bindu, Parayil S; Gayathri, Narayanappa; Bharath, Rose D; Mahadevan, Anita; Sinha, Sanjib; Taly, A B

2010-01-01

198

Using Decision Trees for Comparing Pattern Recognition Feature Sets  

SciTech Connect

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

Proctor, D D

2005-08-18

199

[Application of phenological pattern recognition in ecological dynamic forecasting].  

PubMed

This paper described the principles, methods, and procedures of ecological dynamic forecasting by the automation techniques of pattern recognition and mathematical logic judgment on the basis of phenological data and model output maps from T42L9 numerical weather prediction model. This new forecasting method proposed on the basis of modern meteorology and automation techniques enables the classic phenology to apply to a new field ecological forecasting. It enables phenological forecasting to develop from single-station forecasting stage to regional forecasting stage, which is greatly corresponded to the development stage from single station forecasting stage to synoptic stage in weather forecasting, and enables agro-meteorological forecasting to develop from qualitative and statistical forecasting stage to ecological dynamic forecasting stage. With this new qualitative forecasting method, both the predicted objective and predictors are of considerable bio-physical interests. The ecological dynamic forecasting method could be applied to crop sowing, crop growth, irrigation and fertilization, and diseases and pests PMID:16355779

Pei, Tiefan; Jin, Changiie

2005-09-01

200

Pattern Recognition Of Blood Vessel Networks In Ocular Fundus Images  

NASA Astrophysics Data System (ADS)

We propose a computer method of recognizing blood vessel networks in color ocular fundus images which are used in the mass diagnosis of adult diseases such as hypertension and diabetes. A line detection algorithm is applied to extract the blood vessels, and the skeleton patterns of them are made to analyze and describe their structures. The recognition of line segments of arteries and/or veins in the vessel networks consists of three stages. First, a few segments which satisfy a certain constraint are picked up and discriminated as arteries or veins. This is the initial labeling. Then the remaining unknown ones are labeled by utilizing the physical level knowledge. We propose two schemes for this stage : a deterministic labeling and a probabilistic relaxation labeling. Finally the label of each line segment is checked so as to minimize the total number of labeling contradictions. Some experimental results are also presented.

Akita, K.; Kuga, H.

1982-11-01

201

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

202

Statistical learning theory and its application to pattern recognition  

NASA Astrophysics Data System (ADS)

The problem of pattern recognition is formulated as a classification in the statistic learning theory. Vapnik constructed a class of learning algorithms called support vector machine (SMV) to solve the problem. The algorithm not only has strong theoretical foundation but also provides a powerful tool for solving real-life problems. But it still has some drawbacks. Tow of them are 1) the computational complexity of finding the optimal separating hyperplane is quite high in the linearly separable case, and 2) in the linearly non-separable case, for any given sample set it's hard to choose a proper nonlinear mapping (kernel function) such that the sample set is linearly separable in the new space after the mapping. To overcome these drawbacks, we presented some new approaches. The main idea and some experimental results of the approaches are presented.

Zhang, Ling; Zhang, Bo

2001-09-01

203

Beyond pattern recognition: NOD-like receptors in dendritic cells.  

PubMed

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

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

2013-05-01

204

Pattern recognition receptors and the inflammasome in kidney disease.  

PubMed

Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain receptors (NLRs) are families of pattern recognition receptors that, together with inflammasomes, sense and respond to highly conserved pathogen motifs and endogenous molecules released upon cell damage or stress. Evidence suggests that TLRs, NLRs and the NACHT, LRR and PYD domains-containing protein 3 (NLRP3) inflammasome have important roles in kidney diseases through regulation of inflammatory and tissue-repair responses to infection and injury. In this Review, we discuss the pathological mechanisms that are related to TLRs, NLRs and NLRP3 in various kidney diseases. In general, these receptors are protective in the host defence against urinary tract infection, but can sustain and self-perpetuate tissue damage in sterile inflammatory and immune-mediated kidney diseases. TLRs, NLRs and NLRP3, therefore, have become promising drug targets to enable specific modulation of kidney inflammation and suppression of immunopathology in kidney disease. PMID:24890433

Leemans, Jaklien C; Kors, Lotte; Anders, Hans-Joachim; Florquin, Sandrine

2014-07-01

205

Pattern recognition in a database of cartridge cases  

NASA Astrophysics Data System (ADS)

Several systems exist for collecting spent ammunition for forensic investigation. These databases store images of cartridge cases and the marks on them. The research in this paper is focused on the different methods of feature selection and pattern recognition that can be used for comparison. For automatic comparison of these images it is necessary to extract firstly the useful parts of the images. On databases of 3800 images several processing steps have been tested and compared. The results and methods, which have been implemented, are presented. The usual correlation methods based on gray values of all relevant image data have been tested. They were useful in the database. Further invariant image descriptors and the a trous wavelet transform have been implemented. These methods are promising, however more investigation is needed for the use of these methods.

Geradts, Zeno J.; Bijhold, Jurrien; Hermsen, Rob

1999-02-01

206

Beyond pattern recognition: NOD-like receptors in dendritic cells  

PubMed Central

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

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

2013-01-01

207

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

208

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

209

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

PubMed

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

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

2014-01-01

210

Pattern Recognition Method Zeroes in on Genes that Regulate Cell's Genetic Machinery  

NSF Publications Database

... Fossum (703) 292-8962 bfossum@nsf.gov Pattern Recognition Method Zeroes in on Genes that Regulate ... Using a new technique for recognizing patterns in biological databases, a team of U.S. and Israeli ...

211

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

212

Expanding the Universe of Cytokines and Pattern Recognition Receptors: Galectins and Glycans in Innate Immunity  

Microsoft Academic Search

Effective immunity relies on the recognition of pathogens and tumors by innate immune cells through diverse pattern recognition\\u000a receptors (PRRs) that lead to initiation of signaling processes and secretion of pro- and anti-inflammatory cytokines. Galectins,\\u000a a family of endogenous lectins widely expressed in infected and neoplastic tissues have emerged as part of the portfolio of\\u000a soluble mediators and pattern recognition

Juan P. Cerliani; Sean R. Stowell; Iván D. Mascanfroni; Connie M. Arthur; Richard D. Cummings; Gabriel A. Rabinovich

2011-01-01

213

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

214

Pattern Recognition 42 (2009) 676 --688 Contents lists available at ScienceDirect  

E-print Network

Pattern Recognition 42 (2009) 676 -- 688 Contents lists available at ScienceDirect Pattern Recognition journal homepage: www.elsevier.com/locate/pr A scalable framework for cluster ensembles Prodip Hore, Lawrence O. Hall , Dmitry B. Goldgof Department of Computer Science and Engineering, ENB118

Hall, Lawrence O.

215

Guest Editors' Introduction to the Special Section on Syntactic and Structural Pattern Recognition  

Microsoft Academic Search

This paper presents the guest editors' introduction to the special section which was planned in honor of the memory of the late Professor King-Sun Fu. Dr. King-Sun Fu is widely recognized for his paramount contributions in the field of pattern recognition, especially in the area of syntactic and structural pattern recognition. The paper discusses the problems of interest regarding syntactic

Mitra Basu; Horst Bunke; Alberto Del Bimbo

2005-01-01

216

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

217

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

ERIC Educational Resources Information Center

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

Evans, John M. , Ed.; And Others

218

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

219

Dioxin screening in fish product by pattern recognition of biomarkers.  

PubMed

Two alternative, cost- and time-effective dioxin screening methods relying on two categories of potential lipid biomarkers were investigated. A dioxin range varying from 1.1 to 47.1 pg PCDD/F TEQ-WHO/g lipid using 64 fish meal samples was used for model calibration. The methods were based on multivariate models using either (1) fatty acid composition monitored by GC-FID or (2) fluorescence landscape signals analysed using the PARAFAC model and in both cases predicting dioxin content as pgPCDD/F TEQ-WHO/g lipid. In both cases, Partial Least Squares (PLS) regression was performed for predicting the dioxin content of a sample. The GC-FID data analyses was based on automatic peak alignment and integration, enabling extraction of the area of 140 peaks from the gas chromatograms, as opposed to the 31 fatty acids usually considered for fish oil characterisation. In addition to classic PLS employing the whole dataset for calibration, a two-step local PLS modeling approach was performed based upon an initial selection of k number of calibration samples providing the best match to the prediction sample using a so-called k Nearest Neighbors (kNN) approach, then followed by PLS calibration on these kNN selected samples for dioxin prediction. Fluorescence spectroscopy offers a promising non-invasive and ultra-rapid technique, with less than two minutes analysis time. However, fluorescence spectroscopy using the pattern recognition "kNN-PLS" yielded a correlation of 0.76 (r2) and a high root mean square error of prediction of 11.4 pg PCDD/F TEQ-WHO/g lipid. The predictions were improved when the PLS calibration was performed on all the sample with a root mean square error of prediction of 7.0 pg PCDD/F TEQ-WHO/g lipid. Unfortunately, these results failed to demonstrate the potential of fluorophore monitoring as a screening method. In contrast, the overall best screening performance was obtained with the fatty acid profile, when the kNN-PLS combination employed for pattern recognition (kNN) all the areas of the 140 detected peaks and the PLS regression used the areas of 46 selected peaks. This "kNN-PLS" prediction with three latent variables and based upon the 12 nearest neighbors selected out of the 64 x 2 fatty acid profiles (duplicate analyses), yielded a correlation of 0.85 (r2) and a root mean square error of prediction of 2.1 pg PCDD/F TEQ-WHO/g lipid and resulted in a total analysis time of one and half hour per sample. PMID:17222445

Bassompierre, Marc; Tomasi, Giorgio; Munck, Lars; Bro, Rasmus; Engelsen, Søren Balling

2007-04-01

220

Cerebellar involvement in metabolic disorders: a pattern-recognition approach.  

PubMed

Inborn errors of metabolism can affect the cerebellum during development, maturation and later during life. We have established criteria for pattern recognition of cerebellar abnormalities in metabolic disorders. The abnormalities can be divided into four major groups: cerebellar hypoplasia (CH), hyperplasia, cerebellar atrophy (CA), cerebellar white matter abnormalities (WMA) or swelling, and involvement of the dentate nuclei (DN) or cerebellar cortex. CH can be an isolated typical finding, as in adenylsuccinase deficiency, but is also occasionally seen in many other disorders. Differentiation from CH and CA is often difficult, as in carbohydrate deficient glycoprotein syndrome or 2-L-hydroxyglutaric acidaemia. In cases of atrophy the relationship of cerebellar to cerebral atrophy is important. WMA may be diffuse or patchy, frequently predominantly around the DN. Severe swelling of white matter is present during metabolic crisis in maple syrup urine disease. The DN can be affected by metabolite deposition, necrosis, calcification or demyelination. Involvement of cerebellar cortex is seen in infantile neuroaxonal dystrophy. Changes in DN and cerebellar cortex are rather typical and therefore most helpful; additional features should be sought as they are useful in narrowing down the differential diagnosis. PMID:9689620

Steinlin, M; Blaser, S; Boltshauser, E

1998-06-01

221

Correlation-based pattern recognition for implantable defibrillators.  

PubMed Central

An estimated 300,000 Americans die each year from cardiac arrhythmias. Historically, drug therapy or surgery were the only treatment options available for patients suffering from arrhythmias. Recently, implantable arrhythmia management devices have been developed. These devices allow abnormal cardiac rhythms to be sensed and corrected in vivo. Proper arrhythmia classification is critical to selecting the appropriate therapeutic intervention. The classification problem is made more challenging by the power/computation constraints imposed by the short battery life of implantable devices. Current devices utilize heart rate-based classification algorithms. Although easy to implement, rate-based approaches have unacceptably high error rates in distinguishing supraventricular tachycardia (SVT) from ventricular tachycardia (VT). Conventional morphology assessment techniques used in ECG analysis often require too much computation to be practical for implantable devices. In this paper, a computationally-efficient, arrhythmia classification architecture using correlation-based morphology assessment is presented. The architecture classifies individuals heart beats by assessing similarity between an incoming cardiac signal vector and a series of prestored class templates. A series of these beat classifications are used to make an overall rhythm assessment. The system makes use of several new results in the field of pattern recognition. The resulting system achieved excellent accuracy in discriminating SVT and VT. PMID:8947674

Wilkins, J.

1996-01-01

222

Fixation Patterns During Recognition of Personally Familiar and Unfamiliar Faces  

PubMed Central

Previous studies recording eye gaze during face perception have rendered somewhat inconclusive findings with respect to fixation differences between familiar and unfamiliar faces. This can be attributed to a number of factors that differ across studies: the type and extent of familiarity with the faces presented, the definition of areas of interest subject to analyses, as well as a lack of consideration for the time course of scan patterns. Here we sought to address these issues by recording fixations in a recognition task with personally familiar and unfamiliar faces. After a first common fixation on a central superior location of the face in between features, suggesting initial holistic encoding, and a subsequent left eye bias, local features were focused and explored more for familiar than unfamiliar faces. Although the number of fixations did not differ for un-/familiar faces, the locations of fixations began to differ before familiarity decisions were provided. This suggests that in the context of familiarity decisions without time constraints, differences in processing familiar and unfamiliar faces arise relatively early – immediately upon initiation of the first fixation to identity-specific information – and that the local features of familiar faces are processed more than those of unfamiliar faces. PMID:21607074

van Belle, Goedele; Ramon, Meike; Lefevre, Philippe; Rossion, Bruno

2010-01-01

223

International Symposium on Pattern Recognition and Acoustical Imaging, Newport Beach, CA, Feb. 4-6, 1987, Proceedings  

SciTech Connect

Various papers on pattern recognition and acoustical imaging are presented. The general subjects considered include imaging, texture and speckle analysis in medical ultrasound, parameter estimation, material characterization and NDE, and pattern recognition. Individual topics discussed include: inverse scattering theory foundations of tomography with diffracting wavefields, acoustical image reconstruction algorithms, three-dimensional motion parameter estimation by holographic acoustical systems, pattern recognition in acoustic emission experiments, image reconstruction of flaws using ramp response signatures, and pattern recognition approach to nondestructive evaluation of materials.

Ferrari, L.A.

1987-01-01

224

Human actions recognition using bag of optical flow words  

NASA Astrophysics Data System (ADS)

In this paper, we present an improved approach to recognize human action based on the BOW model and the pLSA model. We propose an improved feature with optical flow to build our bag of words. This feature is able to reduce the high dimension of the pure optical flow template and also has abundant motion information. Then, we use the topic model of pLSA (probabilistic Latent Semantic Analysis) to classify human actions in a special way. We find that the existing methods lead to some mismatching of words due to the k-means clustering approach. To reduce the probability of mismatching, we add the spatial information to each word and improve the training and testing approach. Our approach of recognition is tested on two datasets, the KTH datasets and WEIZMANN datasets. The result shows its good performance.

Zhang, Xu; Miao, Zhenjiang; Wan, Lili

2012-04-01

225

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.

226

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

227

Applications of pattern recognition techniques to online fault detection  

SciTech Connect

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

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

1993-11-01

228

Vascular Dysfunction following Polymicrobial Sepsis: Role of Pattern Recognition Receptors  

PubMed Central

Aims Aim was to elucidate the specific role of pattern recognition receptors in vascular dysfunction during polymicrobial sepsis (colon ascendens stent peritonitis, CASP). Methods and Results Vascular contractility of C57BL/6 (wildtype) mice and mice deficient for Toll-like receptor 2/4/9 (TLR2-D, TLR4-D, TLR9-D) or CD14 (CD14-D) was measured 18 h following CASP. mRNA expression of pro- (Tumor Necrosis Factor-? (TNF?), Interleukin (IL)-1?, IL-6) and anti-inflammatory cytokines (IL-10) and of vascular inducible NO-Synthase (iNOS) was determined using RT-qPCR. Wildtype mice exhibited a significant loss of vascular contractility after CASP. This was aggravated in TLR2-D mice, blunted in TLR4-D animals and abolished in TLR9-D and CD14-D animals. TNF-? expression was significantly up-regulated after CASP in wildtype and TLR2-D animals, but not in mice deficient for TLR4, -9 or CD14. iNOS was significantly up-regulated in TLR2-D animals only. TLR2-D animals showed significantly higher levels of TLR4, -9 and CD14. Application of H154-ODN, a TLR9 antagonist, attenuated CASP-induced cytokine release and vascular dysfunction in wildtype mice. Conclusions Within our model, CD14 and TLR9 play a decisive role for the development of vascular dysfunction and thus can be effectively antagonized using H154-ODN. TLR2-D animals are more prone to polymicrobial sepsis, presumably due to up-regulation of TLR4, 9 and CD14. PMID:22970242

Ehrentraut, Stefan Felix; Dorr, Anne; Ehrentraut, Heidi; Lohner, Ralph; Lee, Sun-Hee; Hoeft, Andreas; Baumgarten, Georg; Knuefermann, Pascal

2012-01-01

229

The SOPC implementation of a keyword spotting algorithm based on biomimetic pattern recognition  

Microsoft Academic Search

A keyword spotting algorithm based on biomimetic pattern recognition is proposed in this paper, including the modeling algorithm and the recognition algoithm. Then the SOPC implementation of the algorithm is given using software and hardware co-design mothodology. The design of two hardware acceleration units are discussed in detail in this paper as well as their contribution to system performance in

Xinyi Zhang; Xiaopeng Yu

2012-01-01

230

Computer-assisted pattern recognition model for the identification of slowly growing mycobacteria including Mycobacterium tuberculosis  

Microsoft Academic Search

We present a computerized pattern recognition model used to speciate mycobacteria based on their restriction fragment length polymorphism (RFLP) banding patterns. DNA fragment migration distances were normalized to minimize lane-to-lane variability of band location both within and among gels through the inclusion of two internal size standards in each sample. The computer model used a library of normalized RFLP patterns

BRIAN D. PLIKAYTIS; BONNIE B. PLIKAYTIS; T. M. Shinnick

1992-01-01

231

Quality estimation of resistance spot welding by using pattern recognition with neural networks  

Microsoft Academic Search

A quality estimation system of resistance spot welding has been developed using a dynamic resistance pattern. Dynamic resistance is monitored in the primary circuit of the welding machine and is mapped into a bipolarized vector for pattern recognition. The Hopfield neural network classifies the pattern vectors and utilizes them to estimate weld quality. Weld strength measurements have been made to

Yongjoon Cho; Sehun Rhee

2004-01-01

232

Recognition of Human Iris Patterns for Biometric Identification  

E-print Network

in false accept and false reject rates of 0.005% and 0.238% respectively. Therefore, iris recognition must thank my parents for their support and encouragement over the years. A special thank you also goes

Kovesi, Peter

233

Computer Simulation Studies of Pattern Recognition in Biomimetic Polymers.  

E-print Network

??The overall aim of this research has been to understand the molecular phenomena governing recognition in biological processes such as antibody-antigen binding, transmembrane signaling, viral-inhibition,… (more)

Jayaraman, Arthi

2006-01-01

234

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

235

Galectins as Pattern Recognition Receptors: Structure, Function, and Evolution  

PubMed Central

Galectins constitute an evolutionary conserved family of ?-galactoside-binding proteins, ubiquitous in mammals and other vertebrate taxa, invertebrates, and fungi. Since their discovery in the 1970s, their biological roles, initially understood as limited to recognition of carbohydrate ligands in embryogenesis and development, have expanded in recent years by the discovery of their immunoregulatory activities. A gradual paradigm shift has taken place in the past few years through the recognition that galectins also bind glycans on the surface of potentially pathogenic microbes, and function as recognition and effector factors in innate immunity. Further, an additional level of functional complexity has emerged with the most recent findings that some parasites “subvert” the recognition roles of the vector/host galectins for successful attachment or invasion. PMID:21948360

Vasta, Gerardo R.

2012-01-01

236

Galectins as pattern recognition receptors: structure, function, and evolution.  

PubMed

Galectins constitute an evolutionary conserved family of ß-galactoside-binding proteins, ubiquitous in mammals and other vertebrate taxa, invertebrates, and fungi. Since their discovery in the 1970s, their biological roles, initially understood as limited to recognition of carbohydrate ligands in embryogenesis and development, have expanded in recent years by the discovery of their immunoregulatory activities. A gradual paradigm shift has taken place in the past few years through the recognition that galectins also bind glycans on the surface of potentially pathogenic microbes, and function as recognition and effector factors in innate immunity. Further, an additional level of functional complexity has emerged with the most recent findings that some parasites "subvert" the recognition roles of the vector/host galectins for successful attachment or invasion. PMID:21948360

Vasta, Gerardo R

2012-01-01

237

Behavioral/Systems/Cognitive Temporal-Pattern Recognition by Single Neurons in a  

E-print Network

Behavioral/Systems/Cognitive Temporal-Pattern Recognition by Single Neurons in a Sensory Pathway- tions devoted to precise temporal coding (Carr, 1993; Carr and Soares, 2002). In addition to conveying

238

9.913-C Pattern Recognition for Machine Vision, Spring 2002  

E-print Network

The course is directed towards advanced undergraduate and beginning graduate students. It will focus on applications of pattern recognition techniques to problems of machine vision. The topics covered in the course include: ...

Poggio, Tomaso

239

New-distinction measure for pattern recognition in fuzzy features space  

NASA Astrophysics Data System (ADS)

Problem of pattern recognition can be interpreted as a problem of acceptance of optimal decision under conditions of uncertainty, caused by absence of the complete and authentic information about a recognized object and its features. The unique adequate method of solving of pattern recognition problem in the conditions of uncertainty is the decisions making by the whole set of available heterogeneous information, taking into account a significance and reliability of each of considered feature and their interrelation. Usually the solution of pattern recognition problem is reduced to the task of minimization of distance from an image of the object up to the standard image of the class of objects. In this paper we offer and review the possible approach to generalization of the Mahalnobis metrics, based on properties of fuzzy number in L-R form. The results of the experimental comparison of the effectiveness of pattern recognition using the considered set of fuzzy features and criteria are discussed.

Zlotnikov, Konstantin A.; Fyodorov, Boris F.

1999-08-01

240

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...

241

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...

242

MAS.622 / 1.126J Pattern Recognition & Analysis, Fall 2000  

E-print Network

Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech ...

Massachusetts Institute of Technology. Media Laboratory.

243

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

E-print Network

This thesis describes a theoretical analysis and a series of empirical tests of a pattern recognition based Early Warning System for bank failure prediction. The theoretical analysis centers on the binarization, feature selection and feature...

Prieto Orlando, Rodrigo Javier

2012-06-07

244

Pattern recognition of porphyry copper deposits in New Mexico and Texas  

E-print Network

PATTERN RECOGNITION OF PORPHYRY COPPER DEPOSITS IN NEW MEXICO AND TEXAS A Thesis by DAVID ALLAN PIATT Submitted to the Graduate College of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE... May 1984 Major Subject: Geophysics PATTERN RECOGNITION OF PORPHYRY COPPER DEPOSITS IN NEW MEXICO AND TEXAS A Thesis by DAVID ALLAN PIATT Approved as to style and content by: c e e aputo (Chairman of Committee) ic ard L. ar son (Member...

Piatt, David Allan

2012-06-07

245

Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery  

E-print Network

NOVEL PATTERN RECOGNITION TECHNIQUES FOR IMPROVED TARGET DETECTION IN HYPERSPECTRAL IMAGERY A Dissertation by WESAM ADEL SAKLA Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment... of the requirements for the degree of DOCTOR OF PHILOSOPHY December 2009 Major Subject: Electrical Engineering NOVEL PATTERN RECOGNITION TECHNIQUES FOR IMPROVED TARGET DETECTION IN HYPERSPECTRAL IMAGERY A Dissertation by WESAM ADEL SAKLA...

Sakla, Wesam Adel

2011-02-22

246

Pattern recognition receptors TLR4 and CD14 mediate response to respiratory syncytial virus  

Microsoft Academic Search

The innate immune system contributes to the earliest phase of the host defense against foreign organisms and has both soluble and cellular pattern recognition receptors for microbial products. Two important members of this receptor group, CD14 and the Toll-like receptor (TLR) pattern recognition receptors, are essential for the innate immune response to components of Gram-negative and Gram-positive bacteria, mycobacteria, spirochetes

Lana Popova; Laura Kwinn; Lia M. Haynes; Les P. Jones; Ralph A. Tripp; Edward E. Walsh; Mason W. Freeman; Douglas T. Golenbock; Larry J. Anderson; Robert W. Finberg; Evelyn A. Kurt-Jones

2000-01-01

247

A new approach to detection of defects in rolling element bearings based on statistical pattern recognition  

Microsoft Academic Search

The paper presents a new approach to the classification of rolling element bearing faults by implementing statistical pattern\\u000a recognition. Diagnostics of rolling element bearing faults actually represents the problem of pattern classification and recognition,\\u000a where the key step is feature extraction from the vibration signal. Characterization of each recorded vibration signal is\\u000a performed by a combination of signal's time-varying statistical

Pavle Stepanic; Ilija V. Latinovic; Zeljko Djurovic

2009-01-01

248

Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Changing Interelectrode Distance and Electrode Configuration  

PubMed Central

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 work focused on investigating the optimal interelectrode distance, channel configuration, and EMG feature sets for myoelectric pattern recognition in the presence of electrode shift. Increasing interelectrode distance from 2 cm 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 LDA 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.

2014-01-01

249

Semi-supervised kernel learning based optical image recognition  

NASA Astrophysics Data System (ADS)

This paper is to propose semi-supervised kernel learning based optical image recognition, called Semi-supervised Graph-based Global and Local Preserving Projection (SGGLPP) through integrating graph construction with the specific DR process into one unified framework. SGGLPP preserves not only the positive and negative constraints but also the local and global structure of the data in the low dimensional space. In SGGLPP, the intrinsic and cost graphs are constructed using the positive and negative constraints from side-information and k nearest neighbor criterion from unlabeled samples. Moreover, kernel trick is applied to extend SGGLPP called KSGGLPP by on the performance of nonlinear feature extraction. Experiments are implemented on UCI database and two real image databases to testify the feasibility and performance of the proposed algorithm.

Li, Jun-Bao; Yang, Zhi-Ming; Yu, Yang; Sun, Zhen

2012-08-01

250

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

251

Investigation of a joint transform correlator characteristics for binary pattern recognition  

NASA Astrophysics Data System (ADS)

The model of the real-time scale-rotation invariant joint transform correlator (JTC) was proposed. The pattern recognition algorithm realized by this correlator was developed. The information-dynamic characteristics of the JTC's input device contained the TV-tube and the joined liquid crystal spatial light modulator are studied. The experimental setup of the JTC for binary image recognition was created. The optimum working modes of the constructed correlator were found. The possibilities of the JTC's experimental setup for the finger-print pattern recognition were investigated.

Batchevsky, Roman S.; Muravsky, Leonid I.; Stefansky, Arkadiy I.; Fitio, Volodymyr M.

1995-11-01

252

Quantum algorithm for optical template recognition with noise filtering  

E-print Network

We propose a probabilistic quantum algorithm that decides whether a monochrome picture matches a given template (or one out of a set of templates). As a major advantage to classical pattern recognition, the algorithm just requires a few incident photons and is thus suitable for very sensitive pictures (similar to the Elitzur-Vaidman problem). Furthermore, for a $2^{n}\\times 2^{m}$ image, $\\ord(n+m)$ qubits are sufficient. Using the quantum Fourier transform, it is possible to improve the fault tolerance of the quantum algorithm by filtering out small-scale noise in the picture. For example images with $512\\times512$ pixels, we have numerically simulated the unitary operations in order to demonstrate the applicability of the algorithm and to analyze its fault tolerance.

Gernot Schaller; Ralf Schützhold

2005-12-07

253

Quantum algorithm for optical-template recognition with noise filtering  

SciTech Connect

We propose a probabilistic quantum algorithm that decides whether a monochrome picture matches a given template (or one out of a set of templates). As a major advantage to classical pattern recognition, the algorithm requires just a few incident photons and is thus suitable for very sensitive pictures (similar to the Elitzur-Vaidman problem). Furthermore, for a 2{sup n}x2{sup m} image, O(n+m) qubits are sufficient. Using the quantum Fourier transform, it is possible to improve the fault tolerance of the quantum algorithm by filtering out small-scale noise in the picture. For example images with 512x512 pixels, we have numerically simulated the unitary operations in order to demonstrate the applicability of the algorithm and to analyze its fault tolerance.

Schaller, Gernot; Schuetzhold, Ralf [Institut fuer Theoretische Physik, Technische Universitaet Dresden, D-01062 Dresden (Germany)

2006-07-15

254

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

255

HotSpotter -Patterned Species Instance Recognition Jonathan P. Crall  

E-print Network

. It is not species specific and has been applied to Grevy's and plains zebras, giraffes, leopards, and lionfish. We. 1. Introduction Motivation: Conducting research on animal populations requires reliable information for animal population analysis depends on locating and recognizing the animals in each image. The recognition

Bystroff, Chris

256

Tissue pattern recognition error rates and tumor heterogeneity in gastric cancer.  

PubMed

The anatomic pathology discipline is slowly moving toward a digital workflow, where pathologists will evaluate whole-slide images on a computer monitor rather than glass slides through a microscope. One of the driving factors in this workflow is computer-assisted scoring, which depends on appropriate selection of regions of interest. With advances in tissue pattern recognition techniques, a more precise region of the tissue can be evaluated, no longer bound by the pathologist's patience in manually outlining target tissue areas. Pathologists use entire tissues from which to determine a score in a region of interest when making manual immunohistochemistry assessments. Tissue pattern recognition theoretically offers this same advantage; however, error rates exist in any tissue pattern recognition program, and these error rates contribute to errors in the overall score. To provide a real-world example of tissue pattern recognition, 11 HER2-stained upper gastrointestinal malignancies with high heterogeneity were evaluated. HER2 scoring of gastric cancer was chosen due to its increasing importance in gastrointestinal disease. A method is introduced for quantifying the error rates of tissue pattern recognition. The trade-off between fully sampling tumor with a given tissue pattern recognition error rate versus randomly sampling a limited number of fields of view with higher target accuracy was modeled with a Monte-Carlo simulation. Under most scenarios, stereological methods of sampling-limited fields of view outperformed whole-slide tissue pattern recognition approaches for accurate immunohistochemistry analysis. The importance of educating pathologists in the use of statistical sampling is discussed, along with the emerging role of hybrid whole-tissue imaging and stereological approaches. PMID:22820657

Potts, Steven J; Huff, Sarah E; Lange, Holger; Zakharov, Vladislav; Eberhard, David A; Krueger, Joseph S; Hicks, David G; Young, George David; Johnson, Trevor; Whitney-Miller, Christa L

2013-01-01

257

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

PubMed

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

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

2012-01-01

258

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

259

COMPUTER TECHNOLOGY: PATTERN RECOGNITION OF BEHAVIORAL EVENTS IN THE NONHUMAN PRIMATE  

EPA Science Inventory

Techniques used in computer graphics and pattern analysis have been applied to the tasks of observing, classifying, and recording spontaneous behavioral activities in the captive primate. The goal in designing this system was to provide a computer-based pattern recognition system...

260

An Overview of Advances of Pattern Recognition Systems in Computer Vision  

E-print Network

17 An Overview of Advances of Pattern Recognition Systems in Computer Vision Kidiyo KPALMA of an individual, a gesture, a fingerprint, a footprint, a human face, the voice of an individual, a speech signal tasks: (1) the analysis (or description) that extracts the characteristics from the pattern being

Paris-Sud XI, Université de

261

Patterns of codon recognition by isoacceptor aminoacyl-tRNAs from wheat germ.  

PubMed Central

Isoacceptors of Ala-, Arg-, Glu-, Gln-, Ile-, Leu-, Lys-, Ser-, Thr- and Val-tRNAs from wheat germ have been resolved by reverse phast chromatography. Codon recognition properties have been determined on isolated fractions of each of these aa-tRNAs and codon assignments have been made to a number of isoacceptors. Evolutionary changes which have occurred in patterns of codon recognition by isoacceptor aa-tRNAs in wheat germ and other organisms are discussed. PMID:251931

Hatfield, D; Rice, M

1978-01-01

262

Applying local Gabor ternary pattern for video-based illumination variable face recognition  

NASA Astrophysics Data System (ADS)

The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. Due to that both Gabor feature and LTP(local ternary pattern) are testified to be robust to illumination variations, we proposed a new approach which achieved illumination variable face recognition by combining Gabor filters with LTP operator. The experimental results compared with the published results on Yale-B and CMU PIE face database of changing illumination verify the validity of the proposed method.

Wang, Huafeng; Han, Yong; Zhang, Zhaoxiang

2012-01-01

263

Applying local Gabor ternary pattern for video-based illumination variable face recognition  

NASA Astrophysics Data System (ADS)

The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. Due to that both Gabor feature and LTP(local ternary pattern) are testified to be robust to illumination variations, we proposed a new approach which achieved illumination variable face recognition by combining Gabor filters with LTP operator. The experimental results compared with the published results on Yale-B and CMU PIE face database of changing illumination verify the validity of the proposed method.

Wang, Huafeng; Han, Yong; Zhang, Zhaoxiang

2011-12-01

264

[Study of feature extraction methods for maize's near infrared spectra in biomimetic pattern recognition].  

PubMed

Near infrared spectrum is an important step in near infrared spectrum qualitative analysis, which influences the qualitative analysis results directly. Diffuse transmittance measurements mode was used to collect spectral data of eight maize varieties. PCA, ICA, PLS-DA and wavelet transformation were used to extract features of pretreated data. Finally, we used the test set data to test the recognition models of eight maize varieties which were built based on biomimetic pattern recognition (BPR). We draw a conclusion that PLS-DA can make models get higher average correct recognition rate than PCA, ICA and Wavelet transformation. PMID:22715757

Shen, Li-feng; Jia, Shi-qiang; Guo, Ting-ting; Wu, Wen-jin; Yan, Yan-lu; An, Dong

2012-04-01

265

Two-dimensional pattern recognition processing of near-wall turbulence  

NASA Astrophysics Data System (ADS)

Two-dimensional pattern recognition processing was introduced into two-dimensional vorticity data obtained by flow visualization and picture processing. Several typical patterns of two-dimensional vorticity distribution over a small unit area were correlated with the vorticity distribution to detect the characteristic structure and arrangement of vortex motions. Ensemble averaged patterns of each velocity component and vorticity around the detected points were calculated.

Ueno, T.; Utami, T.

266

Innate pattern recognition and categorization in a jumping spider.  

PubMed

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

Dolev, Yinnon; Nelson, Ximena J

2014-01-01

267

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

NASA Astrophysics Data System (ADS)

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.

1989-10-01

268

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

269

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

270

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

271

A pattern recognition algorithm for the blind discrimination of liquid and solid filled munitions  

SciTech Connect

A new pattern recognition algorithm is described for the nondestructive evaluation of stockpiled munitions. The algorithm performs blind classification on munitions to determine if they are liquid-filled (chemical) or solid-filled (conventional) munitions. The algorithm uses attributes of the acoustic spectrum of the munition under inspection to perform the discrimination. Results are presented on the performance of the pattern recognition algorithm on a large set of data collected from chemical and conventional munitions at the U.S. Tooele Army Depot.

Roberts, R.S.; Lewis, P.S.; Vela, O.A.

1995-12-01

272

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

273

Real-time optically processed face-recognition system based on arbitrary moiré contours  

NASA Astrophysics Data System (ADS)

A real-time diffraction based optical processed 3-D shape recognition system has been built and demonstrated. The system uses an Ar-ion laser interferometer to project variable spatial frequency structural illumination on 3 dimensional targets which are viewed by a camera. The video data is mixed with a computer generated mask (converted to RS-170 video) and the resulting output video signal is sent to a liquid crystal television (modified to function as a spatial light modulator) which is illuminated by a He-Ne laser. The video mixing process, based on a commercial Chroma-Key circuit, generates an arbitrary moire pattern which is a function of the 3 dimensional shape of the target (indicated by the distorted structured illumination) and of the computer generated mask. As an example, the output pattern could be a 1 dimensional Fresnel zone plate (FZP) when the shape of the object is recognized. In this case, the laser illuminated zone plate produces a bright line focus at the predicted focal distance for the correct target, a reduced intensity line focus for a damaged target, and no output for a totally different target. The result is a mixed video-optical processing system that could be used for real-time quality level sorting or other automated inspection requirements. Other types of diffractive masks are simulated with the goal of increasing the area of target inspection and recognizing and discriminating between different targets with a single mask. Limitations and improvements in the current system are discussed.

Andrade, Rafael A.; Gilbert, Bernard R., III; Dawson, Donald W.; Hart, Chris L.; Kozaitis, Samuel P.; Blatt, Joel H.

1996-06-01

274

All optical switching in semiconductor microresonators based on pattern selection  

Microsoft Academic Search

.  We present here a method for selecting optical patterns in a passive \\u000a semiconductor microresonator, by using a spatial perturbation. A pattern is \\u000a spontaneously generated in the system, and a switching beam causes this \\u000a pattern to rotate even if the power in the switching beam is much lower than \\u000a the power in the pattern. Thus, an all optical switch is realized,

R. Kheradmand; M. Sahrai; H. Tajalli; G. Tissoni; L. A. Lugiato

2008-01-01

275

Input scene restoration in pattern recognition correlator based on digital photo camera  

Microsoft Academic Search

Diffraction image correlator based on commercial digital SLR photo camera was reported earlier. The correlator was proposed for recognition of external scenes illuminated by quasimonochromatic spatially incoherent light. The correlator hardware consists of digital camera with plugged in optical correlation filter unit and control computer. The kinoform used as correlation filter is placed in a free space of the SLR

Sergey N. Starikov; Nikita N. Balan; Mikhail V. Konnik; Vladislav G. Rodin; Ivan V. Solyakin; Ekaterina A. Shapkarina

2007-01-01

276

Recent results on structural pattern recognition for Fusion massive databases  

Microsoft Academic Search

Physics studies in fusion devices require statistical analyses of a large number of discharges. Given the complexity of the plasma and the non-linear interactions between the relevant parameters, connecting a physical phenomenon with the signal patterns that it generates can be quite demanding Up to now, data retrieval has been typically accomplished by means of signal name and shot number.

J. Vega; G. Ratta; A. Murari; P. Castro; S. Dormido-Canto; R. Dormido; G. Farias; A. Pereira; A. Portas; E. de la Luna; I. Pastor; J. Sanchez; N. Duro; R. Castro; M. Santos; H. Vargas

2007-01-01

277

Author's personal copy Pattern Recognition 41 (2008) 10121029  

E-print Network

the distribution of a data set generated by a real-world system is complex and of an unknown shape, especially, is an important aspect of machine learning and pattern analysis. In this paper, we study the widely used spectral and sparse data. An analysis of the characteristics of eigenspace is carried out which shows that (a

Gong, Shaogang

278

Postural pattern recognition in children with unilateral cerebral palsy  

PubMed Central

Background Several different strategies for maintaining upright standing posture in children with cerebral palsy (CP) were observed. Purpose The purpose of the present study was to define two different postural patterns in children with unilateral CP, using moiré topography (MT) parameters. Additionally, another focus of this article was to outline some implications for managing physiotherapy in children with hemiplegia. Patients and methods The study included 45 outpatients with unilateral CP. MT examinations were performed using a CQ Elektronik System device. In addition, a weight distribution analysis on the base of support between unaffected and affected body sides was performed simultaneously. A force plate pressure distribution measurement system (PDM-S) with Foot Print software was used for these measurements. Results The cluster analysis revealed four groups: cluster 1 (n=19; 42.22%); cluster 2 (n=7; 15.56%); cluster 3 (n=9; 20.00%); and cluster 4 (n=10; 22.22%). Conclusion Based on the MT parameters (extracted using a data reduction technique), two postural patterns were described: 1) the pro-gravitational postural pattern; and 2) the anti-gravitational pattern. PMID:24600228

Domagalska-Szopa, Malgorzata; Szopa, Andrzej

2014-01-01

279

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.

Nyman, Matthew

280

Effective hybrid processor to compute image moments for pattern recognition.  

PubMed

A hybrid optical-digital processor is presented for computing the invariant moments of images in real time, which consists of a holographic mask, two lenses, a charge-coupled-device detector, and a microcomputer. The processor is tested by inputting some roman letters, and the produced results show that the invariant moments of a letter are approximately independent of shift and rotation and that the moments are distinct with different letters. PMID:19774028

Chen, Y S; Zheng, S H; Li, D H

1991-05-01

281

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

282

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

283

Kernel Wiener filter and its application to pattern recognition.  

PubMed

The Wiener filter (WF) is widely used for inverse problems. From an observed signal, it provides the best estimated signal with respect to the squared error averaged over the original and the observed signals among linear operators. The kernel WF (KWF), extended directly from WF, has a problem that an additive noise has to be handled by samples. Since the computational complexity of kernel methods depends on the number of samples, a huge computational cost is necessary for the case. By using the first-order approximation of kernel functions, we realize KWF that can handle such a noise not by samples but as a random variable. We also propose the error estimation method for kernel filters by using the approximations. In order to show the advantages of the proposed methods, we conducted the experiments to denoise images and estimate errors. We also apply KWF to classification since KWF can provide an approximated result of the maximum a posteriori classifier that provides the best recognition accuracy. The noise term in the criterion can be used for the classification in the presence of noise or a new regularization to suppress changes in the input space, whereas the ordinary regularization for the kernel method suppresses changes in the feature space. In order to show the advantages of the proposed methods, we conducted experiments of binary and multiclass classifications and classification in the presence of noise. PMID:21047706

Yoshino, Hirokazu; Dong, Chen; Washizawa, Yoshikazu; Yamashita, Yukihiko

2010-11-01

284

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

285

Review: Structural determinants of pattern recognition by lung collectins.  

PubMed

Host defense roles for the lung collectins, surfactant protein A (SP-A) and surfactant protein D (SP-D), were first suspected in the 1980s when molecular characterization revealed their sequence homology to the acute phase reactant of serum, mannose-binding lectin. Surfactant protein A and SP-D have since been shown to play diverse and important roles in innate immunity and pulmonary homeostasis. Their location in surfactant ideally positions them to interact with air-space pathogens. Despite extensive structural similarity, the two proteins show many functional differences and considerable divergence in their interactions with microbial surface components, surfactant lipids, and other ligands. Recent crystallographic studies have provided many new insights relating to these observed differences. Although both proteins can participate in calcium-dependent interactions with sugars and other polyols, they display significant differences in the spatial orientation, charge, and hydrophobicity of their binding surfaces. Surfactant protein D appears particularly adapted to interactions with complex carbohydrates and anionic phospholipids, such as phosphatidylinositol. By contrast, SP-A shows features consistent with its preference for lipid ligands, including lipid A and the major surfactant lipid, dipalmitoylphosphatidylcholine. Current research suggests that structural biology approaches will help to elucidate the molecular basis of pulmonary collectin-ligand recognition and facilitate development of new therapeutics based upon SP-A and SP-D. PMID:20423923

Seaton, Barbara A; Crouch, Erika C; McCormack, Francis X; Head, James F; Hartshorn, Kevan L; Mendelsohn, Richard

2010-06-01

286

Recognition of Higher Order Patterns in Proteins: Immunologic Kernels  

PubMed Central

By applying analysis of the principal components of amino acid physical properties we predicted cathepsin cleavage sites, MHC binding affinity, and probability of B-cell epitope binding of peptides in tetanus toxin and in ten diverse additional proteins. Cross-correlation of these metrics, for peptides of all possible amino acid index positions, each evaluated in the context of a ±25 amino acid flanking region, indicated that there is a strongly repetitive pattern of short peptides of approximately thirty amino acids each bounded by cathepsin cleavage sites and each comprising B-cell linear epitopes, MHC–I and MHC-II binding peptides. Such “immunologic kernel” peptides comprise all signals necessary for adaptive immunologic cognition, response and recall. The patterns described indicate a higher order spatial integration that forms a symbolic logic coordinating the adaptive immune system. PMID:23922927

Bremel, Robert D.; Homan, E. Jane

2013-01-01

287

Recognition of higher order patterns in proteins: immunologic kernels.  

PubMed

By applying analysis of the principal components of amino acid physical properties we predicted cathepsin cleavage sites, MHC binding affinity, and probability of B-cell epitope binding of peptides in tetanus toxin and in ten diverse additional proteins. Cross-correlation of these metrics, for peptides of all possible amino acid index positions, each evaluated in the context of a ±25 amino acid flanking region, indicated that there is a strongly repetitive pattern of short peptides of approximately thirty amino acids each bounded by cathepsin cleavage sites and each comprising B-cell linear epitopes, MHC-I and MHC-II binding peptides. Such "immunologic kernel" peptides comprise all signals necessary for adaptive immunologic cognition, response and recall. The patterns described indicate a higher order spatial integration that forms a symbolic logic coordinating the adaptive immune system. PMID:23922927

Bremel, Robert D; Homan, E Jane

2013-01-01

288

Local binary pattern based texture analysis for visual fire recognition  

Microsoft Academic Search

Color and shape features have difficulty to recognize fire from fire-colored, irregular-shaped and moving objects. An effective algorithm using texture analysis with the Local Binary Pattern (LBP) is proposed in our paper to help deal with this problem. Besides grayscale and rotation invariance, the ability of LBP operator for multi-resolution analysis is enhanced through multiple LBP operators with varying parameters

Jing Huang; Jianhui Zhao; Weiwei Gao; Chengjiang Long; Lu Xiong; Zhiyong Yuan; Shizhong Han

2010-01-01

289

A pattern recognition approach to infer time-lagged genetic interactions  

Microsoft Academic Search

Motivation: For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags.

Cheng-long Chuang; Chih-hung Jen; Chung-ming Chen; Grace S. Shieh

2008-01-01

290

Pattern recognition using rotation-invariant filter-driven template matching  

Microsoft Academic Search

The objective of this work is to propose a new template matching scheme which is able to deal with the recognition issue against rotation. The proposed scheme, rotation-invariant filter-driven template matching (RI-FTM), starts to transform a Cartesian-coordinate pattern to a polar-coordinate pattern. Subsequently, we put our emphasis on how to estimate an appropriate filter which is adopted to establish the

Yi-Chong Zeng

2011-01-01

291

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

E-print Network

classification. Some applications of pattern recognition include diagnosis of medical conditions or weather patterns [Webb 2002]. Techniques are used to create systems, train them using test data, and assess the usefulness of the creation versus the proposed... for present purpose, (eg. vehicle type, color, distance from right edge of the roadway, and vehicle size). This observation is an example of feature selection at work. We conceive a microscopic traffic-flow dataset as organized as follows: Metadata...

Fields, Matthew James

2009-05-15

292

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

E-print Network

classification. Some applications of pattern recognition include diagnosis of medical conditions or weather patterns [Webb 2002]. Techniques are used to create systems, train them using test data, and assess the usefulness of the creation versus the proposed... for present purpose, (eg. vehicle type, color, distance from right edge of the roadway, and vehicle size). This observation is an example of feature selection at work. We conceive a microscopic traffic-flow dataset as organized as follows: Metadata...

Fields, Matthew James

2008-10-10

293

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...

294

First applications of structural pattern recognition methods to the investigation of specific physical phenomena at JET  

Microsoft Academic Search

Structural pattern recognition techniques allow the identification of plasma behaviours. Physical properties are encoded in the morphological structure of signals. Intelligent access methods have been applied to JET databases to retrieve data according to physical criteria. On the one hand, the structural form of signals has been used to develop general purpose data retrieval systems to search for both similar

G. A. Rattá; J. Vega; A. Pereira; A. Portas; E. de la Luna; S. Dormido-Canto; G. Farias; R. Dormido; J. Sánchez; N. Duro; H. Vargas; M. Santos; G. Pajares; A. Murari

2008-01-01

295

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 metal ion content with the geographical origin of coffee and the production mode (organic

Ferreira, Márcia M. C.

296

Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data  

E-print Network

Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data Shu-Kay Ng@maths.uq.edu.au gjm@maths.uq.edu.au Abstract Mixture models implemented via the expectation- maximization (EM struc- ture. The independence assumption in the maximum likeli- hood (ML) learning of mixture models

McLachlan, Geoff

297

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

298

An Improved Algorithm for Linear Inequalities in Pattern Recognition and Switching Theory.  

ERIC Educational Resources Information Center

This thesis presents a new iterative algorithm for solving an n by l solution vector w, if one exists, to a set of linear inequalities, A w greater than zero which arises in pattern recognition and switching theory. The algorithm is an extension of the Ho-Kashyap algorithm, utilizing the gradient descent procedure to minimize a criterion function…

Geary, Leo C.

299

Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  

E-print Network

the construction of these receptors, so that an animal has the genetic capability of expressing over 1010 differentUsing Genetic Algorithms to Explore Pattern Recognition in the Immune System DRAFT July 28, 1993 Mexico Albuquerque, NM 87131 javornik@unmvax.cs.unm.edu Robert E. Smith Dept. of Engineering Mechanics

Forrest, Stephanie

300

Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  

E-print Network

the construction of these receptors, so that an animal has the genetic capability of expressing over 10 10Using Genetic Algorithms to Explore Pattern Recognition in the Immune System DRAFT July 28, 1993 Mexico Albuquerque, NM 87131 javornik@unmvax.cs.unm.edu Robert E. Smith Dept. of Engineering Mechanics

Forrest, Stephanie

301

Adaptive recognition method based on posterior use of distribution pattern of output probabilities  

Microsoft Academic Search

Proposes a new adaptation scheme for speaker-independent recognition. The basic idea lies in the change of the likelihood from ordinary HMM scores to combined observational scores. The new likelihood is computed based on a combination of HMM scores which we call a `pattern of output probabilities distribution' (POPD). The system needs to calculate only the POPD for each new speaker.

Jin-Song Zhang; Beiqian Dai; Changfu Wang; HingKeung Kwan; Keikichi Hirose

1996-01-01

302

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

303

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

304

Two-Dimensional Hydrometeor Image Classification by Statistical Pattern Recognition Algorithms  

Microsoft Academic Search

The investigation reported here involves the automatic classification of binary (black and white) images of hydrometeors (ice particles and raindrops) taken from cloud samples. The goal is to classify such images (both complete and fractional) into the seven most common classes of hydrometeors by statistical pattern recognition techniques. Detailed investigation about the data acquisition system and preprocessing is made. Four

Mizanur M. Rahman; Raymond G. Jacquot; Edmund A. Quincy; Ronald E. Stewart

1981-01-01

305

CATEGORISATION AND PATTERN RECOGNITION METHODS FOR DAMAGE LOCALISATION FROM VIBRATION MEASUREMENTS  

Microsoft Academic Search

This study presents a categorisation (classification) approach towards the damage localisation problem from vibration measurements. A stochastic pattern recognition method for solving such problems is introduced. The method suggests the substructuring in order to reduce the possible damage locations to the number of substructures. It utilises the differences in the frequency response functions of the structure in the damaged and

I. Trendafilova; W. Heylen

2003-01-01

306

Study of Rolling Bearing SVM Pattern Recognition Based on Correlation Dimension of IMF  

Microsoft Academic Search

A method of pattern recognition based on correlation of intrinsic mode function (IMF) and Support Vector Machine (SVM) was proposed. Firstly, the rolling bearing vibration signal was decomposed into a finit series of IMFS by EMD. Secondly, useful IMFS which contained main fault information were chosen through correlation coefficient threshold filtering method. Finally, the correlation dimensions of the main IMFS

Qing Jiang; Ting Li; Yan Yao; Jinhui Cai

2012-01-01

307

Biomimetic Pattern Recognition Based on the Young-Helmholtz Model of Multispectral Image  

Microsoft Academic Search

Biomimetic pattern recognition aim at finding the best coverage of per kind of sample's distribution in the feature space. It is based on the analysis of relationship of sample points in the feature space. According to the principle of ¿same source¿, research the same kind of samples' distribution in the feature space can get eigenvector information with low data amount.

Wenming Cao; Feng Hao

2009-01-01

308

Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis.  

PubMed

Two clinical data sets were applied for pattern recognition in order to discover the correlation between urinary nucleoside profiles and tumours. One data set contains 168 clinical urinary samples, of which 84 specimens are from female thyroid cancer patients (malignant tumour group), and the other samples were collected from healthy women (normal group). However, 168 clinical urinary samples comprised the second data set, too. In all the specimens, each number of the samples for both uterine cervical cancer patients (malignant tumour group) and healthy females (normal group) is 60, and the other 48 samples were collected from uterine myoma patients (benign tumour group). For the two data sets, the separation and quantitative determination of the clinical urinary nucleosides were performed by capillary electrophoresis (CE). The pattern recognition was achieved applying multiple layer perceptron artificial neural networks (MLP ANN) based on conjugate gradient descent training algorithm. Moreover, applying the proposed principal component analysis (PCA) input selection scheme to MLP ANN, the accuracy rate of the pattern recognition was improved to some extent (or without any deterioration) even by much simpler structure of MLP ANN. The study showed that MLP ANN based on PCA input selection was a promising tool for pattern recognition. PMID:19071851

Zhang, Ya Xiong

2007-08-15

309

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...

310

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...

311

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...

312

A pattern recognition method for electronic noses based on an olfactory neural network  

Microsoft Academic Search

Artificial neural networks (ANNs) are generally considered as the most promising pattern recognition method to process the signals from a chemical sensor array of electronic noses, which makes the system more bionics. This paper presents a chaotic neural network entitled KIII, which modeled olfactory systems, applied to an electronic nose to discriminate six typical volatile organic compounds (VOCs) in Chinese

Jun Fu; Guang Li; Yuqi Qin; Walter J. Freeman

2007-01-01

313

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...

314

BOOK REVIEW: New Directions in Statistical Physics: Econophysics, Bioinformatics, and Pattern Recognition  

Microsoft Academic Search

This book contains 18 contributions from different authors. Its subtitle `Econophysics, Bioinformatics, and Pattern Recognition' says more precisely what it is about: not so much about central problems of conventional statistical physics like equilibrium phase transitions and critical phenomena, but about its interdisciplinary applications. After a long period of specialization, physicists have, over the last few decades, found more and

L. T. Wille

2004-01-01

315

The application of statistical pattern recognition methods for damage detection to field data  

Microsoft Academic Search

Recent studies in structural health monitoring have shown that damage detection algorithms based on statistical pattern recognition techniques for ambient vibrations can be used to successfully detect damage in simulated models. However, these algorithms have not been tested on full-scale civil structures, because such data are not readily available. A unique opportunity for examining the effectiveness of these algorithms was

A. Cheung; C. Cabrera; P. Sarabandi; K. K. Nair; A. Kiremidjian; H. Wenzel

2008-01-01

316

Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications  

Microsoft Academic Search

Statistical pattern recognition methodologies have gained considerable attention for Structural Health Monitoring (SHM) applications to detect changes in a structure (e.g. damage). For most of such applications, outlier analysis of the damage sensitive features obtained from the SHM data is used to detect the changes in the structure. There are a number of different approaches used by different research groups

Mustafa Gul; F. Necati Catbas

2009-01-01

317

A Bio-Inspired Pattern Recognition System for Tin-Oxide Gas Sensor Applications  

Microsoft Academic Search

In this paper, a bio-inspired pattern recognition system for tin-oxide gas sensor applications is proposed. To mimic the biological olfactory system, temperature modulation is first used to virtually increase the number of sensors by periodically sampling the sensors' response at different temperatures. A convex microhotplate is used in order to improve the thermal properties of the structure enabling efficient temperature

Aicha Beya Far; Farid Flitti; Bin Guo; Amine Bermak

2009-01-01

318

PATTERN RECOGNITION BASED ANTI COLLISION DEVICE OPTIMIZED FOR ELEPHANT-TRAIN CONFRONTATION  

Microsoft Academic Search

Animal-human conflict is now one of the most burning issues. All over the world animals are losing their lives due to deforestation and other factors. One of the most significant factors is railway line. A huge number of animals are being faced accident on railways track. Among them elephants are the most common victims. Nowadays pattern recognition techniques have been

RAJENDRA NATH DEKA; KANDARPA KUMAR SARMA

2012-01-01

319

Selection of sampling rate for EMG pattern recognition based prosthesis control  

Microsoft Academic Search

Most previous studies of electromyography (EMG) pattern recognition control of multifunctional myoelectric prostheses adopted a conventional sampling rate that is commonly used in EMG research fields. However, it is unknown whether using a lower sampling rate in EMG acquisition still preserves sufficient neural control information for accurate classification of user movement intents. This study investigated the effects of EMG sampling

Guanglin Li; Yaonan Li; Zhiyong Zhang; Yanjuan Geng; Rui Zhou

2010-01-01

320

Study on pattern recognition of EEG based on imagination and hand movement  

Microsoft Academic Search

Electroencephalography (EEG) is the reaction of the overall activities of the brain neurons. In the researches of Brain Computer Interface (BCI), the pattern recognition of EEG which is associated with mental tasks is the most important part of the BCI system. In this paper, data of ¿ wave and ?? wave of C3, C4, P3 and P4 channels are certificated

Yutao Yang; Xiaodong Zhang

2009-01-01

321

Designing Pattern-Recognition Surfaces for Selective Adsorption of Copolymer Sequences Using Lattice Monte Carlo Simulation  

E-print Network

copolymers on a hetero- geneous surface using mean-field theory. Genzer used a 3D self-consistent field model Lattice Monte Carlo Simulation Arthi Jayaraman, Carol K. Hall,* and Jan Genzer Department of Chemical, and in the development of chromatographic materials for target sep- arations. Over the past decade, pattern recognition

Jayaraman, Arthi

322

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

323

Incremental linear discriminant analysis for evolving feature spaces in multitask pattern recognition problems  

Microsoft Academic Search

In this paper, we propose a new incremental linear discriminant analysis (ILDA) for multitask pattern recognition (MTPR) problems in which a chunk of training data for a particular task are given sequentially and the task is switched to another related task one after another. The Pang et al.'s ILDA is extended such that a discriminant space of the current task

Masayuki Hisada; Seiichi Ozawa; Kau Zhang; Nikola Kasabov

2010-01-01

324

Incremental 2-directional 2-dimensional linear discriminant analysis for multitask pattern recognition  

Microsoft Academic Search

In this paper, we propose an incremental 2- directional 2-dimensional linear discriminant analysis (I- (2D) 2 LDA) for multitask pattern recognition (MTPR) problems in which a chunk of training data for a particular task are given sequentially and the task is switched to another related task one after another. In I-(2D) 2 LDA, a discriminant space of the current task

Chunyu Liu; Young-Min Jang; Seiichi Ozawa; Minho Lee

2011-01-01

325

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

E-print Network

-two-dimensional feature vectors of a sensor array consisting of eight sensors, in which four features were extracted from crystal microbalance (QCM) and surface acoustic wave (SAW) sensors [11,12]. However, how to dealSensors and Actuators B 125 (2007) 489­497 A pattern recognition method for electronic noses based

Freeman, Walter J.

326

A Pattern Recognition System Based on Cluster and Discriminant Analysis for Fault Identification during Production  

Microsoft Academic Search

This paper focuses on one stage of a research project concerning online surveillance of the knitting process, which intends to detect faults as soon as possible. The objective of the paper is focused on the pattern recognition stage, i.e, distinguishing faults. For that purpose, discriminant analysis is proposed as the approach to be explored. The general problem is discussed, followed

A. Catarino; A. Rocha; J. L. Monteiro; F. Soares

2007-01-01

327

Size-dependent patterned recognition and extraction of metal ions by a macrocyclic aromatic pyridone pentamer.  

PubMed

A macrocyclic aromatic pyridine pentamer was found to exhibit patterned recognition of metal ions and efficiently extract larger ions, such as Cs(+), Ba(2+), Tl(+), Au(+), K(+) and Rb(+) preferentially over the other 18 smaller metal ions from the aqueous phase into the chloroform layer. PMID:25200048

Shen, Jie; Ma, Wenliang; Yu, Lin; Li, Jin-Bo; Tao, Hu-Chun; Zhang, Kun; Zeng, Huaqiang

2014-10-28

328

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 ...

329

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

Microsoft Academic Search

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

Sholom M. Weiss; Ioannis Kapouleas

1989-01-01

330

Pattern Recognition 40 (2007) 28972907 www.elsevier.com/locate/pr  

E-print Network

systems. 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. Keywords, nonlinear and nonstationary systems often exhibit behavior like strange attraction, chaos, and bifurcation Society. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.patcog.2007.03.007 A novel approach

Ray, Asok

331

Comparison of SIMCA pattern recognition and library search identification of hazardous compounds from mass spectra  

Microsoft Academic Search

SIMCA pattern recognition methods have been applied to mass spectral data from a target list of hazardous chemicals. A scheme has been proposed for classification and identification of five classes of compounds including aromatics, chlorocarbons, bromocarbons, hydrocarbons and polychlorinated biphenyls (PCBs). In addition, partial least squares regression has been used to predict the number of chlorine atoms present in the

M. Sarker; W. G. Glen; L. B. Yin; W. J. Dunn; D. R. Scott

1990-01-01

332

Pattern recognition\\/expert system for mass spectra of volatile toxic and other organic compounds  

Microsoft Academic Search

A system based on principles of pattern recognition has been developed for identifying toxic and other volatile organic pollutants in complex environmental samples. It interprets the most commonly used monitoring data, mass spectral data, and produces a class designation, an estimate of the molecular weight and, if possible, a target pollutant identity. The system was developed and implemented with a

Donald R. Scott

1992-01-01

333

IEEE TRANSACTION ON PATTERN RECOGNITION AND MACHINE INTELLIGENCE (UNDER REVIEW) 1 Tracking People on a Torus  

E-print Network

IEEE TRANSACTION ON PATTERN RECOGNITION AND MACHINE INTELLIGENCE (UNDER REVIEW) 1 Tracking People surveillance, human- machine interface, video archival and retrieval, computer an- imation, autonomous driving in images. Despite the high dimensionality of the body configura- tion space, many human activities lie

Elgammal, Ahmed

334

Extraordinary optical transmission through patterned subwavelength apertures.  

SciTech Connect

Light propagating through a subwavelength aperture can be dramatically increased by etching a grating in the metal around the hole. Moreover, light that would typically broadly diverge when passing through an unpatterned subwavelength hole can be directed into a narrow beam by utilizing a specific pattern around the aperture. While the increased transmission and narrowed angular emission appear to defy far-field diffraction theory, they are consistent with a fortuitous plasmon/photon coupling. In addition, the coupling between photons and surface plasmons affects the emissivity of a surface comprised of such structures. These properties are useful across several strategic areas of interest to Sandia. A controllable emission spectrum could benefit satellite and military application areas. Photolithography and near-field microscopy are natural applications for a system that controls light beyond the diffraction limit in a manner that is easily parallelizable. Over the one year of this LDRD, we have built or modified the numerical tools necessary to model such structures. These numerical codes and the knowledge base for using them appropriately will be available in the future for modeling work on surface plasmons or other optical modeling at Sandia. Using these tools, we have designed and optimized structures for various transmission or emission properties. We demonstrate the ability to design a metallic skin with an emissivity peak at a pre-determined wavelength in the spectrum. We optimize structures for maximum light transmission and show transmitted beams that beat the far-field diffraction limit.

Kemme, Shanalyn A.; El-Kady, Ihab Fathy; Hadley, G. Ronald; Peters, David William; Lanes, Chris E.

2004-12-01

335

Understanding Complexity: Pattern Recognitions, Emergent Phenomena and Causal Coupling  

NASA Astrophysics Data System (ADS)

In teaching and learning complex systems we face a fundamental issue: Simultaneity of causal interactions -where effects are at the same time causes of systems’ behavior. Complex systems’ behavior and evolution are controlled by negative and positive feedback processes, continually changing boundary conditions and complex interaction between systems levels (emergence). These processes cannot be described and understood in a mechanistic framework where causality is conceived of being mostly of cause-effect nature or a linear chain of causes and effects. Mechanist causality by definition is characterized by the assumption that an earlier phenomenon A has a causal effect on the development of a phenomenon B. Since this concept also assumes unidirectional time, B cannot have an effect on A. Since students study science mostly in the lingering mechanistic framework, they have problems understanding complex systems. Specifically, our research on students understanding of complexity indicates that our students seem to have great difficulties in explaining mechanisms underlying natural processes within the current paradigm. Students tend to utilize simple linear model of causality and establish a one-to-one correspondence between cause and effect describing phenomena such as emergence and self-organization as being mechanistically caused. Contrary to experts, when presented with data distribution -spatial and/or temporal-, students first consider or search for a unique cause without describing the distribution or a recognized pattern. Our research suggests that students do not consider a pattern observed as an emergent phenomenon and therefore a causal determinant influencing and controlling the evolution of the system. Changes in reasoning have been observed when students 1) are iteratively asked to recognize and describe patterns in data distribution and 2) subsequently learn to identify these patterns as emergent phenomena and as fundamental causal controls over system evolution and behavior. Since the implementation of the above two strategies in classroom setting has been quite successful, we hypnotized that changing students approach to complex systems requires first the reflection and discussion on the explanatory strategies we utilize in teaching and learning about complexity in Earth Systems Science.

Raia, F.

2010-12-01

336

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

PubMed Central

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

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

2012-01-01

337

Listening for Recollection: A Multi-Voxel Pattern Analysis of Recognition Memory Retrieval Strategies  

PubMed Central

Recent studies of recognition memory indicate that subjects can strategically vary how much they rely on recollection of specific details vs. feelings of familiarity when making recognition judgments. One possible explanation of these results is that subjects can establish an internally directed attentional state (“listening for recollection”) that enhances retrieval of studied details; fluctuations in this attentional state over time should be associated with fluctuations in subjects’ recognition behavior. In this study, we used multi-voxel pattern analysis of fMRI data to identify brain regions that are involved in listening for recollection. We looked for brain regions that met the following criteria: (1) Distinct neural patterns should be present when subjects are instructed to rely on recollection vs. familiarity, and (2) fluctuations in these neural patterns should be related to recognition behavior in the manner predicted by dual-process theories of recognition: Specifically, the presence of the recollection pattern during the pre-stimulus interval (indicating that subjects are “listening for recollection” at that moment) should be associated with a selective decrease in false alarms to related lures. We found that pre-stimulus activity in the right supramarginal gyrus met all of these criteria, suggesting that this region proactively establishes an internally directed attentional state that fosters recollection. We also found other regions (e.g., left middle temporal gyrus) where the pattern of neural activity was related to subjects’ responding to related lures after stimulus onset (but not before), suggesting that these regions implement processes that are engaged in a reactive fashion to boost recollection. PMID:20740073

Quamme, Joel R.; Weiss, David J.; Norman, Kenneth A.

2010-01-01

338

Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms.  

PubMed

EMG pattern classification has been widely studied for decoding user intent for intuitive prosthesis control. However, EMG signals can be easily contaminated by noise and disturbances, which may degrade the classification performance. This study aims to design a real-time self-recovery EMG pattern classification interface to provide reliable user intent recognition for multifunctional prosthetic arm control. A novel self-recovery module consisting of multiple sensor fault detectors and a fast LDA classifier retraining strategy has been developed to immediately recover the classification performance from signal disturbances. The self-recovery EMG pattern recognition (PR) system has been implemented on an embedded system as a working prototype. Experimental evaluation has been performed on an able-bodied subject in real-time to classify three arm movements while signal disturbances were manually introduced. The results of this study may propel the clinical use of EMG PR for multifunctional prosthetic arm control. PMID:24111088

Zhang, Xiaorong; Huang, He; Yang, Qing

2013-01-01

339

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

NASA Astrophysics Data System (ADS)

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

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

340

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

341

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

342

Appeared in the Proceedings of Computer Vision and Pattern Recognition, San Francisco, 1996. Pattern Rejection  

E-print Network

such application in computational vision is face recognition Pentland et al. 94 Sirovich and Kirby 87 Turk by ARPA Contract DACA-76-92-C-007, by DOD ONR MURI Grant N00014-95-1- 0601, and by an NSF National Young

343

Heuristic algorithm for optical character recognition of Arabic script  

NASA Astrophysics Data System (ADS)

In this paper, a heuristic method is developed for segmentation, feature extraction and recognition of the Arabic script. The study is part of a large project for the transcription of the documents in Ottoman Archives. A geometrical and topological feature analysis method is developed for segmentation and feature extraction stages. Chain code transformation is applied to main strokes of the characters which are then classified by the hidden Markov model (HMM) in the recognition stage. Experimental results indicate that the performance of the proposed method is impressive, provided that the thinning process does not yield spurious branches.

Yarman-Vural, Fatos T.; Atici, A.

1996-02-01

344

Input scene restoration in pattern recognition correlator based on digital photo camera  

NASA Astrophysics Data System (ADS)

Diffraction image correlator based on commercial digital SLR photo camera was reported earlier. The correlator was proposed for recognition of external scenes illuminated by quasimonochromatic spatially incoherent light. The correlator hardware consists of digital camera with plugged in optical correlation filter unit and control computer. The kinoform used as correlation filter is placed in a free space of the SLR camera body between the interchangeable camera lens and the swing mirror. On the other hand, this correlator can be considered as a hybrid optical-digital imaging system with wavefront coding. It allows not only to recognize objects in input scene but to restore, if needed, the whole image of input scene from correlation signals distribution registered by SLR camera sensor. Linear methods for image reconstruction in the correlator are discussed. The experimental setup of the correlator and experimental results on images recognition and input scenes restoration are presented.

Starikov, Sergey N.; Balan, Nikita N.; Konnik, Mikhail V.; Rodin, Vladislav G.; Solyakin, Ivan V.; Shapkarina, Ekaterina A.

2007-04-01

345

[Fast discrimination of varieties of corn based on near infrared spectra and biomimetic pattern recognition].  

PubMed

A new method for fast discrimination of varieties of corn by means of near infrared spectroscopy and biomimetic pattern recognition (BPR) was proposed and the recognition models for seven kinds of corn were built. The experiment adopted 140 samples acquired from seven varieties of corn. Firstly, a field spectroradiometer was used for collecting spectra in the wave number range of 4000 to 12,000 cm(-1). Secondly, the original spectral data were pretreated in order to eliminate noise and improve the efficiency of models, and then the characteristic spectral regions were selected by using fixed-sized moving window evolving factor analysis. Thirdly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first five components were more than 99.96%. Finally, according to the first five components, the recognition models were established based on BPR. For the samples in each variety, 10 samples were randomly selected as the training set. The remaining samples of the same variety were used as the first testing set, and the 120 samples of the other varieties were used as the second testing set. Under the condition that almost all the samples in the second set were correctly rejected, the average correct recognition rate was 94.3%. The experimental results demonstrated that the recognition models were effective and efficient. In short, it is feasible to discriminate varieties of corn based on near infrared spectroscopy and BPR. PMID:19950641

Su, Qian; Wu, Wen-jin; Wang, Hong-wu; Wang, Ku; An, Dong

2009-09-01

346

[Fast discrimination of varieties of transgene wheat based on biomimetic pattern recognition and near infrared spectra].  

PubMed

A new method for the fast discrimination of varieties of transgene wheat by means of near infrared spectroscopy and biomimetic pattern recognition (BPR) was proposed and the recognition models of seven varieties of transgene wheat and two varieties of acceptor wheat were built. The experiment adopted 225 samples, which were acquired from nine varieties of wheat. Firstly, a field spectroradiometer was used for collecting spectra in the wave number range from 4 000 to 12 000 cm(-1). Secondly, the original spectral data were pretreated in order to eliminate noise and improve the efficiency of models. Thirdly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 97.28%. Finally, the recognition models were established based on BPR For the every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 200 samples of the other varieties were used as the second testing set. As the 96.7% samples in the second set were correctly rejected, the average correct recognition rate of first testing set was 94.3%. The experimental results demonstrated that the recognition models were effective and efficient. In short, it is feasible to discriminate varieties of transgene wheat based on near infrared spectroscopy and BPR. PMID:20545132

Zhai, Ya-Feng; Su, Qian; Wu, Wen-Jin; He, Zhen-Tian; Zhang, Zong-Ying; An, Jia-Shuang; Dong, Jin; Deng, Xin; Han, Cheng-Gui; Yu, Jia-Lin; Li, Da-Wei; Chen, Xiu-Lan; An, Dong

2010-04-01

347

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

348

Recognition of surface lithologic and topographic patterns in southwest Colorado with ADP techniques  

NASA Technical Reports Server (NTRS)

Analysis of ERTS-1 multispectral data by automatic pattern recognition procedures is applicable toward grappling with current and future resource stresses by providing a means for refining existing geologic maps. The procedures used in the current analysis already yield encouraging results toward the eventual machine recognition of extensive surface lithologic and topographic patterns. Automatic mapping of a series of hogbacks, strike valleys, and alluvial surfaces along the northwest flank of the San Juan Basin in Colorado can be obtained by minimal man-machine interaction. The determination of causes for separable spectral signatures is dependent upon extensive correlation of micro- and macro field based ground truth observations and aircraft underflight data with the satellite data.

Melhorn, W. N.; Sinnock, S.

1973-01-01

349

Pattern recognition minimizes entropy production in a neural network of electrical oscillators  

NASA Astrophysics Data System (ADS)

We investigate the physical principle driving pattern recognition in a previously introduced Hopfield-like neural network circuit (Hölzel and Krischer, 2011 [13]). Effectively, this system is a network of Kuramoto oscillators with a coupling matrix defined by the Hebbian rule. We calculate the average entropy production of all neurons in the network for an arbitrary network state and show that the obtained expression for is a potential function for the dynamics of the network. Therefore, pattern recognition in a Hebbian network of Kuramoto oscillators is equivalent to the minimization of entropy production for the implementation at hand. Moreover, it is likely that all Hopfield-like networks implemented as open systems follow this mechanism.

Hölzel, Robert W.; Krischer, Katharina

2013-11-01

350

Reading the viral signature by Toll-like receptors and other pattern recognition receptors  

Microsoft Academic Search

Successful host defense against viral infections relies on early production of type I interferon (IFN) and subsequent activation of a cellular cytotoxic response. The acute IFN and inflammatory response against virus infections is mediated by cellular pattern-recognition receptors (PRRs) that recognize specific molecular structures on viral particles or products of viral replication. Toll-like receptors (TLRs) constitute a class of membrane-bound

Trine H. Mogensen; Søren R. Paludan

2005-01-01

351

Markov Chains Pattern Recognition Approach Applied to the Medical Diagnosis Tasks  

Microsoft Academic Search

\\u000a In many medical decision problems there exist dependencies between subsequent diagnosis of the same patient. Among the different\\u000a concepts and methods of using “contextual” information in pattern recognition, the approach through Bayes compound decision\\u000a theory is both attractive and efficient from the theoretical and practical point of view. Paper presents the probabilistic\\u000a approach (based on expert rules and learning set)

Michal Wozniak

2005-01-01

352

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

353

Methodologies for characterizing ultrasonic transducers using neural network and pattern recognition techniques  

Microsoft Academic Search

System hardware for characterizing ultrasonic transducers and the associated data acquisition software and characterizing algorithms are considered. The hardware consists mainly of a workstation computer, a receiver\\/pulser with gated peak detector, various monitoring devices, a microcomputer-based 3D positioning controller, and an A\\/D converter. The characterization algorithms are based on neural network and pattern recognition techniques. It is found that artificial

Mohammad S. Obaidat; Dirar S. Abu-Saymeh

1992-01-01

354

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

355

Seismic pattern recognition techniques to predict large eruptions at the Popocatépetl, Mexico, volcano  

Microsoft Academic Search

Using pattern recognition techniques, we formulate a simple prediction rule for a retrospective prediction of the three last largest eruptions of the Popocatépetl, Mexico, volcano that occurred on 23 April–30 June 1997 (Eruption 1; VEI~2–3); 11 December 2000–23 January 2001 (Eruption 2; VEI~3–4) and 7 June–4 September 2002 (Eruption 3; explosive dome extrusion and destruction phase). Times of Increased Probability

D. A. Novelo-Casanova; C. Valdés-González

2008-01-01

356

Discrimination of Reconstructed Milk in Raw Milk by Combining Near Infrared Spectroscopy with Biomimetic Pattern Recognition  

Microsoft Academic Search

A new method for discriminating reconstructed milk in raw milk is proposed by combining NIRS (Near Infrared Spectroscopy)\\u000a with the theory model BPR (Biomimetic Pattern Recognition). In order to compare its discrimination performance, we also carry\\u000a out experiments by using a traditional method combining near infrared spectroscopy with DA (Discrimination Analysis). The\\u000a results indicate that the accuracy of detection is

Ming Sun; Qigao Feng; An Dong; Yaoguang Wei; Jibo Si; Longsheng Fu

2008-01-01

357

Improvement of surface acoustic wave gas sensor response time using neural-network pattern recognition  

Microsoft Academic Search

Surface acoustic wave (SAW) gas-sensor signal processing may allow first, detection of gases, secondly, their identification and thirdly, if possible, their quantification. for a few years now, pattern-recognition techniques using artificial neural networks have been applied to sensor arrays with promising results. Nevertheless, data sets needed for these techniques, are always built with well-established and stable sensor responses. Sometimes, the

Christophe Bordieu; Jacques Pistre´

1995-01-01

358

Multiband Gradient Component Pattern (MGCP): A New Statistical Feature for Face Recognition  

Microsoft Academic Search

A feature extraction method using multi-frequency bands is proposed for face recognition, named as the Multi-band Gradient\\u000a Component Pattern (MGCP). The MGCP captures discriminative information from Gabor filter responses in virtue of an orthogonal\\u000a gradient component analysis method, which is especially designed to encode energy variations of Gabor magnitude. Different\\u000a from some well-known Gabor-based feature extraction methods, MGCP extracts geometry

Yimo Guo; Jie Chen; Guoying Zhao; Matti Pietikäinen; Zhengguang Xu

2009-01-01

359

A Strategy for SPN Detection Based on Biomimetic Pattern Recognition and Knowledge-Based Features  

Microsoft Academic Search

Image processing techniques have proved to be effective in improving the diagnosis of lung nodules. In this paper, we present\\u000a a strategy for solitary pulmonary nodules (SPN) detection using radiology knowledge-based feature extraction scheme and biomimetic\\u000a pattern recognition (BPR). The proposed feature extraction scheme intends to synthesize comprehensive information of SPN according\\u000a to radiology knowledge, e.g. grey level features, morphological,

Yan Liang; Zhongshi He; Ying Liu

2009-01-01

360

The system parameter fusion principle and its application to fuzzy pattern recognition  

NASA Astrophysics Data System (ADS)

Modern technology provides a great amount of information. In computer monitoring systems or computer control systems, especially real-time expert systems, in order to have the situation in hand, we need one or two parameters to express the quality and/or security of the whole system. This paper presents a principle for synthesizing measurements of multiple system parameters into a single parameter and its application to fuzzy pattern recognition.

Pu, Qiangguo

2003-04-01

361

Pattern recognition analysis of anterior cingulate cortex blood flow to classify depression polarity†  

PubMed Central

Differentiating bipolar from recurrent unipolar depression is a major clinical challenge. In 18 healthy females and 36 females in a depressive episode - 18 with bipolar disorder type I, 18 with recurrent unipolar depression - we applied pattern recognition analysis using subdivisions of anterior cingulate cortex (ACC) blood flow at rest, measured with arterial spin labelling. Subgenual ACC blood flow classified unipolar v. bipolar depression with 81% accuracy (83% sensitivity, 78% specificity). PMID:23969484

Almeida, J. R. C.; Mourao-Miranda, J.; Aizenstein, H. J.; Versace, A.; Kozel, F. A.; Lu, H.; Marquand, A.; LaBarbara, E. J.; Brammer, M.; Trivedi, M.; Kupfer, D. J.; Phillips, M. L.

2013-01-01

362

Artificial convolution neural network with wavelet kernels for disease pattern recognition  

Microsoft Academic Search

A two-dimensional convolution neural network (CNN) with wavelet kernels (WK) has been developed for image pattern recognition. The structure of the CNN is a simplified version of the neocognitron. We used only a two-level structure and eliminated all complex-cell layers. Nets between two adjacent layers in the feature selection level of the CNN are selectively interconnected across groups. In this

Shih-Chung B. Lo; Huai Li; Jyh-Shyan Lin; Akira Hasegawa; Chris Y. Wu; Matthew T. Freedman; Seong K. Mun

1995-01-01

363

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

364

Classification of high-resolution manometry data according to videofluoroscopic parameters using pattern recognition  

PubMed Central

Objective To determine if pattern recognition techniques applied to high-resolution manometry (HRM) spatiotemporal plots of the pharyngeal swallow can identify features of disordered swallowing reported on the Modified Barium Swallow Impairment Profile (MBSImP). Study Design Case series evaluating new method of data analysis. Setting University hospital. Subjects and Methods Simultaneous HRM and videofluoroscopy was performed on 30 subjects (335 swallows) with dysphagia. Videofluoroscopic studies were scored according to the MBSImP guidelines while HRM plots were analyzed using a novel program. Pattern recognition using a multilayer perceptron artificial neural network (ANN) was performed to determine if seven pharyngeal components of the MBSImP as well as penetration/aspiration status could be identified from the HRM plot alone. Receiver operating characteristic (ROC) analysis was also performed. Results MBSImP parameters were identified correctly as normal or disordered at an average rate of approximately 91% (area under the ROC curve ranged from 0.902 to 0.981). Classifications incorporating two MBSImP parameters resulted in classification accuracies over 93% (area under the ROC curve ranged from 0.963 to 0.989). Conclusion Pattern recognition coupled with multiparameter quantitative analysis of HRM spatiotemporal plots can be used to identify swallowing abnormalities which are currently assessed using videofluoroscopy. The ability to provide quantitative, functional data at the bedside while avoiding radiation exposure make HRM an appealing tool to supplement and, at times, replace traditional videofluoroscopic studies. PMID:23728150

Hoffman, Matthew R.; Jones, Corinne A.; Geng, Zhixian; Abelhalim, Suzan M.; Walczak, Chelsea C.; Mitchell, Alyssa R.; Jiang, Jack J.; McCulloch, Timothy M.

2013-01-01

365

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

366

Pattern Recognition  

NSDL National Science Digital Library

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

Tagliarini, Gene

2005-04-20

367

Gain, detuning, and radiation patterns of nanoparticle optical antennas  

NASA Astrophysics Data System (ADS)

For their capability to localize and redirect electromagnetic field, metal nanoparticles have been recently viewed as efficient nanoantenna operating in the optical regime. In this article, we experimentally investigated the optical responses of coupled gold antenna pairs and measured the critical parameters defining antenna characteristics: resonant frequencies and bandwidths, detuning and gains, and radiation patterns.

Huang, C.; Bouhelier, A.; Colas Des Francs, G.; Bruyant, A.; Guenot, A.; Finot, E.; Weeber, J.-C.; Dereux, A.

2008-10-01

368

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

369

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

370

Early Recognition and Disease Prediction in the At-Risk Mental States for Psychosis Using Neurocognitive Pattern Classification  

E-print Network

Early Recognition and Disease Prediction in the At-Risk Mental States for Psychosis Using. These patterns may have the potential to substantially improve the early recognition of psychosis. Key words: individualized psychosis prediction/ multivariate analysis/neurocognitive test battery Introduction

Gaser, Christian

371

In IEEE International Conference on Pattern Recognition (CVPR), Alaska, June 2008. Recognizing Primitive Interactions by Exploring Actor-Object States  

E-print Network

and Bio-Inspired Computing Laboratory Department of Computer Sciences Florida Institute of TechnologyIn IEEE International Conference on Pattern Recognition (CVPR), Alaska, June 2008. Recognizing. The literature on activity recognition is extensive. In general, activity analysis methods focus on high

Ribeiro, Eraldo

372

Research on rapid detection of total bacteria in juice based on biomimetic pattern recognition and machine vision  

Microsoft Academic Search

In order to develop an automatic and rapid detection method for enumeration of total bacteria in juice, biomimetic pattern recognition and machine vision were employed. The characteristic data, such as shape, texture and color features, were acquired by using the machine vision technology from bacteria images in varieties of juice. Based on multi-weight higher order neuron network, the recognition models

Shenglang Jin; Yongguang Yin

2010-01-01

373

Applying Error-Correcting Output Coding to Enhance Convolutional Neural Network for Target Detection and Pattern Recognition  

Microsoft Academic Search

This paper views target detection and pattern recognition as a kind of communications problem and applies error-correcting coding to the outputs of a convolutional neural network to improve the accuracy and reliability of detection and recognition of targets. The outputs of the convolutional neural network are designed according to codewords with maximum Hamming distances. The effects of the codewords on

Huiqun Deng; George Stathopoulos; Ching Y. Suen

2010-01-01

374

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

NASA Astrophysics Data System (ADS)

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

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-04-01

375

Z .Pattern Recognition Letters 18 1997 15391548 Linear flaw detection in woven textiles using model-based  

E-print Network

Z .Pattern Recognition Letters 18 1997 1539­1548 Linear flaw detection in woven textiles using to detect linear pattern production faults in woven textiles. Our approach detects a linear pattern Garment production can be divided into two dis- tinct phases: manufacture of the textile fabric, fol

Raftery, Adrian

376

Hybrid Learning Using Multi-objective Genetic Algorithms and Decision Trees for Power Quality Disturbance Pattern Recognition  

Microsoft Academic Search

The objective of this work is to exploit the potential of latest pattern recognition techniques in power quality applications. This paper presents a novel hybrid pattern recognizer for classification of power quality disturbances. The hybrid learning methodology integrates a multiobjective genetic algorithm (GA) and decision trees (CART) in order to evolve optimal subsets of discriminatory features for robust pattern classification.

B. V. Krishna; K. Baskaran

2007-01-01

377

Wavelength Dependence of Optical Waveguide-Type Devices for Recognition of QPSK Routing Labels  

NASA Astrophysics Data System (ADS)

In photonic label routing networks, recognition of optical labels is one of the key functions. We have proposed passive waveguide-type devices for recognition of optical labels coded in quadri-phase-shift-keying (QPSK) form. In this paper, we consider wavelength dependence of the devices. The basic module of the proposed device consists of a 3-dB directional coupler, two Y-junctions, and an asymmetric X-junction. The Y-junction and an asymmetric X-junction have basically no wavelength dependence. Although the 3-dB directional coupler has weak wavelength dependence, the device for two-symbol label recognition is found to work in wavelength 1.5-1.6µm. The performance of the device is confirmed by simulation using beam propagation method (BPM).

Makimoto, Yoshihiro; Hiura, Hitoshi; Goto, Nobuo; Yanagiya, Shin-Ichiro

378

The role of binary mask patterns in automatic speech recognition in background noise  

PubMed Central

Processing noisy signals using the ideal binary mask improves automatic speech recognition (ASR) performance. This paper presents the first study that investigates the role of binary mask patterns in ASR under various noises, signal-to-noise ratios (SNRs), and vocabulary sizes. Binary masks are computed either by comparing the SNR within a time-frequency unit of a mixture signal with a local criterion (LC), or by comparing the local target energy with the long-term average spectral energy of speech. ASR results show that (1) akin to human speech recognition, binary masking significantly improves ASR performance even when the SNR is as low as ?60?dB; (2) the ASR performance profiles are qualitatively similar to those obtained in human intelligibility experiments; (3) the difference between the LC and mixture SNR is more correlated to the recognition accuracy than LC; (4) LC at which the performance peaks is lower than 0?dB, which is the threshold that maximizes the SNR gain of processed signals. This broad agreement with human performance is rather surprising. The results also indicate that maximizing the SNR gain is probably not an appropriate goal for improving either human or machine recognition of noisy speech. PMID:23654411

Narayanan, Arun; Wang, DeLiang

2013-01-01

379

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

380

Optical Music Recognition of Early Typographic Prints using Hidden Markov Models  

Microsoft Academic Search

Music printed with movable type (typographic music) from the 16th and 17th centuries contains specific graphic fea- tures. In this paper, we present a technique and associ- ated experiments for performing optical music recognition on such music prints using Hidden Markov Models (HMM). Our original approach avoids the difficult and unreliable re - moval of staff lines usually required before

Laurent Pugin

381

Arabic Optical Character Recognition (OCR) Evaluation in Order to Develop a Post-OCR Module.  

National Technical Information Service (NTIS)

Optical character recognition (OCR) is the process of converting an image of a document into text. While progress in OCR research has enabled low error rates for English text in low-noise images, performance is still poor for noisy images and documents in...

B. Kjersten

2011-01-01

382

Error-Correcting Output Coding for the Convolutional Neural Network for Optical Character Recognition  

Microsoft Academic Search

It is known that convolutional neural networks (CNNs) are efficient for optical character recognition (OCR) and many other visual classification tasks. This paper applies error-correcting output coding (ECOC) to the CNN for segmentation-free OCR such that: 1) the CNN target outputs are designed according to code words of length N; 2) the minimum Hamming distance of the code words is

Huiqun Deng; George Stathopoulos; Ching Y. Suen

2009-01-01

383

Morphotypic analysis and classification of bacteria and bacterial colonies using laser light-scattering, pattern recognition, and machine-learning system  

NASA Astrophysics Data System (ADS)

Light scattering is one of the most fundamental optical processes whereby electromagnetic waves are forced to deviate from a straight trajectory by non-uniformities in the medium that they traverse. This presentation summarizes our recent research on application of light-scatter measurements paired with machine learning and pattern recognition methodologies for label-free classification of bioparticles. Two separate examples of light scatter-based techniques are discussed: forward-scatter measurements of bacterial colonies in an imaging system, and flow cytometry measurements of scatter signals formed by individual bacterial particles. Recently, we have reported a first practical implementation of a system capable of label-free classification and recognition of pathogenic species of Listeria, Salmonella, Vibrio, Staphylococcus, and E. coli using forward-scatter patterns produced by bacterial colonies irradiated with laser light. Individual bacteria in flow also form complex patterns dependent on particle size, shape, refraction index, density, and morphology. Although commercial flow cytometers allow scatter measurement at two angles this rudimentary approach cannot be used to separate populations of bioparticles of similar shape, size, or structure. The custom-built system used in the presented work collects axial light-loss and scatter signals at five carefully chosen angles. Experimental results obtained from colony scanner, as well from the extended cytometry instrument, were used to train the pattern-recognition algorithm. The results demonstrate that information provided by scatter alone may be sufficient to recognize various bioparticles with 90-99% success rate, both in flow and in imaging systems.

Rajwa, Bartek; Dundar, Murat; Patsekin, Valeri; Huff, Karleigh; Bhunia, Arun; Venkatapathi, Murugesan; Bae, Euiwon; Hirleman, E. Daniel; Robinson, J. Paul

2009-05-01

384

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

PubMed

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

Ranaee, Vahid; Ebrahimzadeh, Ata; Ghaderi, Reza

2010-10-01

385

Vibrodiagnostics of gearboxes using NBV-based classifier: A pattern recognition approach  

NASA Astrophysics Data System (ADS)

Gearbox faults are one of the major factors causing breakdown of industrial machinery and gearbox diagnosing is one of the most important topics in machine condition monitoring. This paper presents a new pattern recognition approach to the condition monitoring of technical objects working under time varying load. The approach shows potential for the fault detection of the high-power planetary gearbox used in bucket wheel excavators. In the presented pattern recognition approach, relations between spectral components of the gearbox vibration signal were investigated in the full range of gearbox operating conditions. A novel Noise-Assisted Feature Subset Evaluation (NAFSE) method addressed for the extraction of diagnostic parameters was introduced. The NAFSE method integrates the feature subset evaluation with the NBV-based classifier and extracts the diagnostic parameter set useful for this classifier. The NBV-based classifier conducted the final recognition of the gearbox condition on the basis of the diagnostic parameters obtained from the NAFSE method. The NBV-based classifier is, in its essence, the condensed 1-NN classifier based on Nearest Boundary Vector algorithm. The elaborated algorithms for determining basic and supplemental boundary vectors together with the original editing procedure of the training set reduction create the original hybrid prototype selection method. The effectiveness of this method has been confirmed in the classification task of the benchmark dataset. In contrast to the traditional hard classifier that assigns only a single-value class label to an investigated pattern, the NBV-based classifier enables the semi-soft classification which offers the possibility of evaluating classification certainty. The offered possibility of evaluating classification certainty has a significant diagnostic meaning. In diagnostic practice it is often not enough merely to recognize the object's condition, but the information about the certainty of the classifier's decision is also necessary. The effectiveness of the proposed pattern recognition approach is illustrated by the fault detection of the high-power planetary gearbox used in a mining machine. It was demonstrated that in order to effectively diagnose machines operating under non-stationary conditions, separate diagnostic relationships at various operating conditions are required. For this reason the extraction of diagnostic parameters was executed for every range of operating conditions separately. This enabled to perform error-free recognition of the gearbox condition including the cases of no load or small load.

Dyba?a, Jacek

2013-07-01

386

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

NASA Astrophysics Data System (ADS)

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

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

2012-06-01

387

Application of pattern recognition in molecular spectroscopy: Automatic line search in high-resolution spectra  

NASA Astrophysics Data System (ADS)

An expert system has been developed for the initial analysis of a recorded spectrum, namely, for the line search and the determination of line positions and intensities. The expert system is based on pattern recognition algorithms. Object recognition learning allows the system to achieve the needed flexibility and automatically detect groups of overlapping lines, whose profiles should be fit together. Gauss, Lorentz, and Voigt profiles are used as model profiles to which spectral lines are fit. The expert system was applied to processing of the Fourier transform spectrum of the D2O molecule in the region 3200-4200 cm-1, and it detected 4670 lines in the spectrum, which consisted of 439000 dots. No one experimentally observed line exceeding the noise level was missed.

Bykov, A. D.; Pshenichnikov, A. M.; Sinitsa, L. N.; Shcherbakov, A. P.

2004-07-01

388

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

389

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

390

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

391

Pattern recognition and cellular immune responses to novel Mycobacterium tuberculosis-antigens in individuals from Belarus  

PubMed Central

Background Tuberculosis (TB) is an enduring health problem worldwide and the emerging threat of multidrug resistant (MDR) TB and extensively drug resistant (XDR) TB is of particular concern. A better understanding of biomarkers associated with TB will aid to guide the development of better targets for TB diagnosis and for the development of improved TB vaccines. Methods Recombinant proteins (n = 7) and peptide pools (n = 14) from M. tuberculosis (M.tb) antigens associated with M.tb pathogenicity, modification of cell lipids or cellular metabolism, were used to compare T cell immune responses defined by IFN-? production using a whole blood assay (WBA) from i) patients with TB, ii) individuals recovered from TB and iii) individuals exposed to TB without evidence of clinical TB infection from Minsk, Belarus. Results We identified differences in M.tb target peptide recognition between the test groups, i.e. a frequent recognition of antigens associated with lipid metabolism, e.g. cyclopropane fatty acyl phospholipid synthase. The pattern of peptide recognition was broader in blood from healthy individuals and those recovered from TB as compared to individuals suffering from pulmonary TB. Detection of biologically relevant M.tb targets was confirmed by staining for intracellular cytokines (IL-2, TNF-? and IFN-?) in T cells from non-human primates (NHPs) after BCG vaccination. Conclusions PBMCs from healthy individuals and those recovered from TB recognized a broader spectrum of M.tb antigens as compared to patients with TB. The nature of the pattern recognition of a broad panel of M.tb antigens will devise better strategies to identify improved diagnostics gauging previous exposure to M.tb; it may also guide the development of improved TB-vaccines. PMID:22336002

2012-01-01

392

Influence of the atmosphere on remotely sensed data. [multispectral pattern recognition effects  

NASA Technical Reports Server (NTRS)

Factors which influence the effects of the atmosphere on the data of remote sensing are examined. A radiative-transfer model is considered and effects of varied optical thickness of the atmosphere are investigated. Effects of varied surface albedo are discussed along with the effects of the sun angle, the effects of the scan angle, and questions regarding the atmospheric effects on the recognition performance. It is found that a multiplicative factor involving the sun angle alone is not sufficient for the correction of space data.

Turner, R. E.; Malila, W. A.; Nalepka, R. F.; Thomson, F. J.

1975-01-01

393

Wavelet-Based Neural Pattern Analyzer for Behaviorally Significant Burst Pattern Recognition  

E-print Network

. Such a compu- tational task for online, real-time data analysis and com- pression requires both efficient conventional microprocessor or DSPs would dissipate too much power and are too large in size for an implantable-loop neural control, the implantable system must also perform real-time analysis of spike patterns from

Bhunia, Swarup

394

Microbial Pattern Recognition Receptors Mediate M-Cell Uptake of a Gram-Negative Bacterium  

PubMed Central

The receptors involved in the sampling of particulate microbial antigens by the gut are largely unknown. Here we demonstrate for the first time in an in vitro M-cell model and in situ in isolated murine intestinal segments that the receptors TLR-4, PAF-R, and ?5?1 integrin are all involved in mediating bacterial uptake associated with transcytosis. The pattern of expression of TLR-4 and ?5?1 integrin differed between M cells and enterocytes. There was increased apical expression of TLR-4 in M-cell cultures, and it was present on the apical surface of murine M cells but not enterocytes in situ. In contrast, PAF-R was expressed equally by both cell types in vitro and was abundantly expressed throughout the intestinal epithelium. Inhibition of TLR-4 and PAF-R, but not TLR-2, reduced gram-negative bacterial uptake by both cell types, whereas inhibition of the apically expressed ?5?1 integrin significantly reduced the ability of M cells to translocate bacteria. Hence, the involvement of each receptor was dependent not only on differences in the level of receptor expression but the?cellular localization. Using bacteria that had mutations that affected the bacterial lipooligosaccharide structure indicated that the oligosaccharide moiety was important in bacterial uptake. Taken together, the data suggest that pathogen-associated molecular pattern interactions with pattern recognition receptors are key factors in M-cell recognition of intestinal antigens for mucosal immune priming. PMID:16369019

Tyrer, Peter; Foxwell, A. Ruth; Cripps, Allan W.; Apicella, Michael A.; Kyd, Jennelle M.

2006-01-01

395

A self-supervised learning system for pattern recognition by sensory integration.  

PubMed

Artificial neural networks are useful tools for pattern recognition because they realize nonlinear mapping between input and output spaces. This ability is tuned by supervised learning methods such as back-propagation. In the supervised learning methods, desired outputs of the neural network are needed. However, the desired outputs are usually unknown in unpredictable environments. To solve this problem, this paper presents a self-supervised learning system for category detection. This system learns categories of objects automatically by integrating information from several sensors. We assume that these sensory inputs are always ambiguous patterns that include some noises according to deformations of the objects. After the learning, the system recognizes objects, also controlling the priority of each sensor, according to the deformation of the sensory input pattern.In the simulation, the system is applied to several learning and recognition tasks using artificial or actual sensory inputs. In all tasks, the system found the categories. Particularly, we applied the new system to the learning of five Japanese vowels with the corresponding shapes of the mouth. As result, the system became to yield specific outputs corresponding to each vowel. PMID:12662619

Yamauchi, K; Oota, M; Ishii, N

1999-12-01

396

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

PubMed Central

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

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

2013-01-01

397

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

398

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

NASA Astrophysics Data System (ADS)

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

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

2011-04-01

399

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

400

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

401

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

SciTech Connect

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

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

1981-01-01

402

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

403

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

404

Evaluating structural pattern recognition for handwritten math via primitive label graphs  

NASA Astrophysics Data System (ADS)

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

Zanibbi, Richard; MoucheÌ?re, Harold; Viard-Gaudin, Christian

2013-01-01

405

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

406

An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition  

Microsoft Academic Search

This paper suggests an automated approach for fault detection and classification in roller bearings, which is based on pattern recognition and principal components analysis of the measured vibration signals. The signals recorded are pre-processed applying a wavelet transform in order to extract the appropriate high frequency (detailed) area needed for ball bearing fault detection. This is followed by a pattern

Irina Trendafilova

2010-01-01

407

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

408

Cross-cultural patterns in emotion recognition: highlighting design and analytical techniques.  

PubMed

This article highlights a range of design and analytical tools for studying the cross-cultural communication of emotion using forced-choice experimental designs. American, Indian, and Japanese participants judged facial expressions from all 3 cultures. A factorial experimental design is used, balanced n x n across cultures, to separate "absolute" cultural differences from "relational" effects characterizing the relationship between the emotion expressor and perceiver. Use of a response bias correction is illustrated for the tendency to endorse particular multiple-choice categories more often than others. Treating response bias also as an opportunity to gain insight into attributional style, the authors examined similarities and differences in response patterns across cultural groups. Finally, the authors examined patterns in the errors or confusions that participants make during emotion recognition and documented strong similarity across cultures. PMID:12899367

Elfenbein, Hillary Anger; Mandal, Manas K; Ambady, Nalini; Harizuka, Susumu; Kumar, Surender

2002-03-01

409

Sub-wavelength patterning of the optical near-field  

NASA Astrophysics Data System (ADS)

We report the sub-wavelength patterning of the optical near-field by total internal reflection illumination of a regular array of resonant gold nano-particles. Under appropriate conditions, the in-plane coupling between Localized Surface Plasmon (LSP) fields gives rise to sub-wavelength light spots between the structures. Measurements performed with an Apertureless Scanning Near-Field Optical Microscope (ASNOM) show a good agreement with theoretical predictions based on the Green dyadic method. This concept might offer a convenient way to elaborate extended optical trap landscapes for manipulation of sub-micrometer systems.

Quidant, Romain; Badenes, Goncal; Cheylan, Stéphanie; Alcubilla, Ramon; Weeber, Jean-Claude; Girard, Christian

2004-01-01

410

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

411

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

PubMed

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

Savchenko, A V

2013-10-01

412

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

PubMed Central

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

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

2013-01-01

413

An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network  

NASA Astrophysics Data System (ADS)

The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

Su, Zhongqing; Ye, Lin

2004-08-01

414

Nucleic acids and endosomal pattern recognition: how to tell friend from foe?  

PubMed Central

The innate immune system has evolved endosomal and cytoplasmic receptors for the detection of viral nucleic acids as sensors for virus infection. Some of these pattern recognition receptors (PRR) detect features of viral nucleic acids that are not found in the host such as long stretches of double-stranded RNA (dsRNA) and uncapped single-stranded RNA (ssRNA) in case of Toll-like receptor (TLR) 3 and RIG-I, respectively. In contrast, TLR7/8 and TLR9 are unable to distinguish between viral and self-nucleic acids on the grounds of distinct molecular patterns. The ability of these endosomal TLR to act as PRR for viral nucleic acids seems to rely solely on the mode of access to the endolysosomal compartment in which recognition takes place. The current dogma states that self-nucleic acids do not enter the TLR-sensing compartment under normal physiological conditions. However, it is still poorly understood how dendritic cells (DC) evade activation by self-nucleic acids, in particular with regard to specific DC subsets, which are specialized in taking up material from dying cells for cross-presentation of cell-associated antigens. In this review we discuss the current understanding of how the immune system distinguishes between foreign and self-nucleic acids and point out some of the key aspects that still require further research and clarification. PMID:23908972

Brencicova, Eva; Diebold, Sandra S.

2013-01-01

415

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

NASA Astrophysics Data System (ADS)

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

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

2013-02-01

416

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

417

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

418

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

NASA Astrophysics Data System (ADS)

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

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

2014-01-01

419

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

420

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

421

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

PubMed

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

Krajewski, Jarek; Batliner, Anton; Golz, Martin

2009-08-01

422

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

423

[Identification of geographical origins of rice with pattern recognition technique by near infrared spectroscopy].  

PubMed

A rapid method was developed for discrimination of the geographical origins of rice with pattern recognition technique by near infrared spectrocopy (NIRS). A total of 119 geography signs product Xiangshui rice samples and 90 rice (Non-Xiangshui rice) samples produced from other places were analyzed by NIRS. After first derivative and smooth processing, principal component analysis (PCA) was used to reduce the dimensionality of the spectral data. Through the loading graph of the first three principal components, characteristic wave band (7 700-6 700, 5 700-4 300 cm(-1)) with max-relativity was determined. In whole wave, using agglomerative hierarchical cluster analysis and Fisher's linear discriminant, the discrimination of Xiangshui rice and Non-Xiangshui rice was all 100%. The correct rate of specific geographical origins of Non-Xiangshui rice was 91.9% by cluster analysis and 96.7% by discriminant analysis. For analysis in the characteristic wave bands, the correct rate of discriminant by cluster analysis was higher than the analysis result through the range of the whole band. Therefore, characteristic wave band has strong representativeness. The results indicate that it is feasible to discriminate the geographical origins of rice with pattern recognition technique by NIRS, and selecting characteristic wave band is one of the validated methods to improve the precision of the discrimination mode. PMID:23586235

Xia, Li-Ya; Shen, Shi-Gang; Liu, Zheng-Hao; Sun, Han-Wen

2013-01-01

424

Parallel processing and VLSI architectures for syntactic pattern recognition and image analysis  

SciTech Connect

Computation speed of syntatic pattern recognition and image analysis algorithms have always been regarded as slow. Several parallel processing techniques are proposed, especially for the syntatic analyzer, to speed up the computation. The distance calculations between strings and trees have been implemented on three different parallel processing systems, namely, the SIMD system, the dedicated SIMD system and the MIMD system. The results show that distance calculation can be sped up when it is implemented on a parallel computer. Earley's algorithm has wide applications in many fields. A parallel Earley's algorithm is proposed, and the recognition algorithm is implemented on a VLSI architecture, the parse extraction algorithm and the complete algorithm on a processor array. This parallel execution only takes linear time. Simulation results prove the correctness of this design. The same Earley's algorithm has been extended to process erroneous input data. This error-correcting syntatic recognizer has also been implemented on a VLSI system. The results from the simulation not only prove the correctness of this design, but also indicate that this recognizer can be used to classify patterns.

Chiang, Y.T.P.

1982-01-01

425

Optical and SAR data integration for automatic change pattern detection  

NASA Astrophysics Data System (ADS)

Automatic change pattern mapping in urban and sub-urban area is important but challenging due to the diversity of urban land use pattern. With multi-sensor imagery, it is possible to generate multidimensional unique information of Earth surface features that allow developing a relationship between a response of each feature to synthetic aperture radar (SAR) and optical sensors to track the change automatically. Thus, a SAR and optical data integration framework for change detection and a relationship for automatic change pattern detection were developed. It was carried out in three steps: (i) Computation of indicators from SAR and optical images, namely: normalized difference ratio (NDR) from multi-temporal SAR images and the normalized difference vegetation index difference (NDVI) from multi-temporal optical images, (ii) computing the change magnitude image from NDR and ?NDVI and delineating the change area and (iii) the development of an empirical relationship, for automatic change pattern detection. The experiment was carried out in an outskirts part of Ho Chi Minh City, one of the fastest growing cities in the world. The empirical relationship between the response of surface feature to optical and SAR imagery has successfully delineated six changed classes in a very complex urban sprawl area that was otherwise impossible with multi-spectral imagery. The improvement of the change detection results by making use of the unique information on both sensors, optical and SAR, is also noticeable with a visual inspection and the kappa index was increased by 0.13 (0.75 to 0.88) in comparison to only optical images.

Mishra, B.; Susaki, J.

2014-09-01

426

Comparative Performance Analysis of Feature(S)- Classifier Combination for Devanagari Optical Character Recognition System  

Microsoft Academic Search

this paper presents a comparative performance analysis of feature(s)-classifier combination for Devanagari optical character recognition system. For performance evaluation, three classifiers namely support vector machines, artificial neural networks and k-nearest neighbors, and seven feature extraction approaches viz. profile direction codes, transition, zoning, directional distance distribution, Gabor filter, discrete cosine transform and gradient features have been used. The first four features

Jasbir Singh; Gurpreet Singh Lehal

2014-01-01

427

Optical recognition of organic vapours through ultrathin calix[4]pyrrole films  

Microsoft Academic Search

Thin films of meso-octaethylporphyrinogen were deposited on suitable surface by Langmuir–Blodgett technique in order to obtain optical sensors using Surface Plasmon Resonance (SPR)technique. In particular such coatings were tested in the molecular recognition of alcohol vapours such as ethanol and methanol. This resulted in a reversible shift in the resonance depth and position of the SPR curves. Different sensitivity and

S Conoci; M Palumbo; B Pignataro; R Rella; L Valli; G Vasapollo

2002-01-01

428

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

PubMed

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

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

2013-09-01

429

Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features.  

PubMed

Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ?8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A). PMID:24721224

Khushaba, Rami N; Takruri, Maen; Miro, Jaime Valls; Kodagoda, Sarath

2014-07-01

430

Temporal Pattern Recognition: A Network Architecture For Multi-Sensor Fusion  

NASA Astrophysics Data System (ADS)

A self-organizing network architecture for the learning and recognition of temporal patterns is proposed. This multi-layered architecture has as its focal point a layer of multi-dimensional Gaussian classification nodes, and the learning scheme employed is based on standard statistical moving mean and moving covariance calculations. The nodes are implemented in the network architecture by using a Gaussian, rather than sigmoidal, transfer function acting on the input from numerous connections. Each connection is analogous to a separate dimension for the Gaussian function. The learning scheme is a one-pass method, eliminating the need for repetitive presentation of the teaching stimuli. The Gaussian classes developed are representative of the statistics of the teaching data and act as templates in classifying novel inputs. The input layer employs a time-based decay to develop a time-ordered representation of the input stimuli. This temporal pattern recognition architecture is used to perform multi-sensor fusion and scene analysis for ROBART II, an autonomous sentry robot employing heterogeneous and homogeneous binary (on / off) sensors. The system receives sensor packets from ROBART indicating which sensors are active. The packets from various sensors are integrated in the input layer. As time progresses these sensor outputs become ordered, allowing the system to recognize activities which are dependent, not only on the individual events which make up the activity, but also on the order in which these events occur and their relative spacing throughout time. Each Gaussian classification node, representing a learned activity as an ordered sequence of sensor outputs, calculates its activation value independently, based on the activity in the input layer. These Gaussian activation values are then used to determine which, if any, of the learned sequences are present and with what confidence. The classification system is capable of recognizing activities despite missing, extraneous or slightly out-of-order inputs. An important predictive quality is also present. This system can predict that an activity may be about to occur prior to receiving confirmation that all component events have occurred. Overall, the temporal pattern recognition system allows the robot to go beyond the alert / no alert stage based on a simple weighted count of the sensors firing. ROBART is now able to determine which activities are occurring, enabling it to intelligently act on this information.

Priebe, C. E.; Marchette, D. J.

1989-03-01

431

Pattern recognition techniques for visualizing the biotropic waveform of air temperature and pressure  

NASA Astrophysics Data System (ADS)

It is known that long periods of adverse weather have a negative effect on the human cardiovascular system. A number of studies have set a lower limit of around 5 days for the duration of these periods. However, the specific features of the negative dynamics of the main weather characteristics—air temperature and atmospheric pressure—remained open. To address this problem, the present paper proposes a conjunctive method of the theory of pattern recognition. It is shown that this method approaches a globally optimal (in the sense of recognition errors) Neumann critical region and can be used to solve various problems in heliobiology. To illustrate the efficiency of this method, we show that some quickly relaxing short sequences of temperature and pressure time series (the so-called temperature waves and waves of atmospheric pressure changes) increase the risk of cardiovascular diseases and can lead to serious organic lesions (particularly myocardial infarction). It is established that the temperature waves and waves of atmospheric pressure changes increase the average morbidity rate of myocardial infarction by 90% and 110%, respectively. Atmospheric pressure turned out to be a more biotropic factor than air temperature.

Ozheredov, V. A.

2012-12-01

432

Extracellular polysaccharides produced by Ganoderma formosanum stimulate macrophage activation via multiple pattern-recognition receptors  

PubMed Central

Background The fungus of Ganoderma is a traditional medicine in Asia with a variety of pharmacological functions including anti-cancer activities. We have purified an extracellular heteropolysaccharide fraction, PS-F2, from the submerged mycelia culture of G. formosanum and shown that PS-F2 exhibits immunostimulatory activities. In this study, we investigated the molecular mechanisms of immunostimulation by PS-F2. Results PS-F2-stimulated TNF-? production in macrophages was significantly reduced in the presence of blocking antibodies for Dectin-1 and complement receptor 3 (CR3), laminarin, or piceatannol (a spleen tyrosine kinase inhibitor), suggesting that PS-F2 recognition by macrophages is mediated by Dectin-1 and CR3 receptors. In addition, the stimulatory effect of PS-F2 was attenuated in the bone marrow-derived macrophages from C3H/HeJ mice which lack functional Toll-like receptor 4 (TLR4). PS-F2 stimulation triggered the phosphorylation of mitogen-activated protein kinases JNK, p38, and ERK, as well as the nuclear translocation of NF-?B, which all played essential roles in activating TNF-? expression. Conclusions Our results indicate that the extracellular polysaccharides produced by G. formosanum stimulate macrophages via the engagement of multiple pattern-recognition receptors including Dectin-1, CR3 and TLR4, resulting in the activation of Syk, JNK, p38, ERK, and NK-?B and the production of TNF-?. PMID:22883599

2012-01-01

433

Characterization of Talbot pattern illumination for scanning optical microscopy  

NASA Astrophysics Data System (ADS)

We studied the use of Talbot pattern illumination in scanning optical microscopy (SOM). Unlike conventional illumination spots used in SOM, the focal spots in Talbot pattern are more complicated and do not have a simple Gaussian intensity distribution. To find out the resolution of SOM using Talbot pattern, we characterized the evolution of the full-width-at-half-maximum spot size of the Talbot focal spots by computer simulation. We then simulated the SOM imaging under Talbot pattern illumination using the razor blade and the U.S. Air Force target as the sample objects, and compared the results with those performed with Gaussian spots as illumination. Using several foci searching algorithms, the optimal focal distances were found to be shorter than the theoretical Talbot distances. The simulation results were consistent with the experiment results published previously. We then provide a practical guidance for searching for optimal focal distances in the SOM based on these studies.

Liu, Guangshuo; Yang, Changhuei; Wu, Jigang

2013-09-01

434

Generation of arbitrary complex quasi-non-diffracting optical patterns.  

PubMed

Due to their unique ability to maintain an intensity distribution upon propagation, non-diffracting light fields are used extensively in various areas of science, including optical tweezers, nonlinear optics and quantum optics, in applications where complex transverse field distributions are required. However, the number and type of rigorously non-diffracting beams is severely limited because their symmetry is dictated by one of the coordinate system where the Helmholtz equation governing beam propagation is separable. Here, we demonstrate a powerful technique that allows the generation of a rich variety of quasi-non-diffracting optical beams featuring nearly arbitrary intensity distributions in the transverse plane. These can be readily engineered via modifications of the angular spectrum of the beam in order to meet the requirements of particular applications. Such beams are not rigorously non-diffracting but they maintain their shape over large distances, which may be tuned by varying the width of the angular spectrum. We report the generation of unique spiral patterns and patterns involving arbitrary combinations of truncated harmonic, Bessel, Mathieu, or parabolic beams occupying different spatial domains. Optical trapping experiments illustrate the opto-mechanical properties of such beams. PMID:24104114

Ortiz-Ambriz, Antonio; Lopez-Aguayo, Servando; Kartashov, Yaroslav V; Vysloukh, Victor A; Petrov, Dmitri; Garcia-Gracia, Hipolito; Gutiérrez-Vega, Julio C; Torner, Lluis

2013-09-23

435

Research of Daily Conversation Transmitting System Based on Mouth Part Pattern Recognition  

NASA Astrophysics Data System (ADS)

The authors are developing a vision-based intension transfer technique by recognizing user’s face expressions and movements, to help free and convenient communications with aged or disabled persons who find difficulties in talking, discriminating small character prints and operating keyboards by hands and fingers. In this paper we report a prototype system, where layered daily conversations are successively selected by recognizing the transition in shape of user’s mouth parts using camera image sequences settled in front of the user. Four mouth part patterns are used in the system. A method that automatically recognizes these patterns by analyzing the intensity histogram data around the mouth region is newly developed. The confirmation of a selection on the way is executed by detecting the open and shut movements of mouth through the temporal change in intensity histogram data. The method has been installed in a desktop PC by VC++ programs. Experimental results of mouth shape pattern recognition by twenty-five persons have shown the effectiveness of the method.

Watanabe, Mutsumi; Nishi, Natsuko

436

The Role of Bacteria and Pattern Recognition Receptors in GvHD  

PubMed Central

Graft-versus-Host Disease (GvHD) is the most serious complication of allogeneic stem cell transplantation (SCT) and results from an activation of donor lymphocytes by recipient antigen-presenting cells (APCs). For a long time, it has been postulated that the intestinal microflora and endotoxin exert a crucial step in this APC activation, as there is early and severe gastrointestinal damage induced by pretransplant conditioning. With the detailed description of pathogen-associated molecular patterns and pathogen recognition receptors single nucleotide polymorphisms of TLRs and especially NOD2 have been identified as potential risk factors of GvHD and transplant related complications thus further supporting the crucial role of innate immunity in SCT, related complications. Gastrointestinal decontamination and neutralization of endotoxin have been used to interfere with this early axis of activation with some success but more specific approaches of modulation of innate immunity are needed for further improvement of clinical outcome. PMID:21188220

Holler, E.; Landfried, K.; Meier, J.; Hausmann, M.; Rogler, G.

2010-01-01

437

Classification of Driving Behavior by Pattern Recognition in Multiclass Users Traffic Flow  

NASA Astrophysics Data System (ADS)

Understanding driving behavior is a complicated researching topic. To describe accurate speed, flow and density of a multiclass users traffic flow, an adequate model is needed. Mostly, user's classes are determined by types of vehicles. However, it is unrealistic to consider drivers with the same type of vehicles have the same driving behavior. Conventionally, classifying driving behavior is obtained through tracking trace of individual vehicles, experimenting by driving simulator or inquiring by questionnaire. It costs a lot and may produce bias because of the design of questionnaire or experiment. Therefore, a new method, which is based on pattern recognition technique, is proposed to classify driving behavior in multiclass user traffic flow. In this study, driving behavior, which performs as speed distributions, is assumed to be Gaussian distributions. According to the assumption, the expectation-maximization algorithm is employed to train and classify different driving behavior. With the method, a economical and automatic way for traffic data processing and parameter extracting is obtained.

Lo, Shih-Ching

2007-12-01

438

Initial results on fault diagnosis of DSN antenna control assemblies using pattern recognition techniques  

NASA Technical Reports Server (NTRS)

Initial results obtained from an investigation using pattern recognition techniques for identifying fault modes in the Deep Space Network (DSN) 70 m antenna control loops are described. The overall background to the problem is described, the motivation and potential benefits of this approach are outlined. In particular, an experiment is described in which fault modes were introduced into a state-space simulation of the antenna control loops. By training a multilayer feed-forward neural network on the simulated sensor output, classification rates of over 95 percent were achieved with a false alarm rate of zero on unseen tests data. It concludes that although the neural classifier has certain practical limitations at present, it also has considerable potential for problems of this nature.

Smyth, P.; Mellstrom, J.

1990-01-01

439

Illumination analysis of the digital pattern recognition system by Bessel masks and one-dimensional signatures  

NASA Astrophysics Data System (ADS)

The effects of illumination variations in digital images are a trend topic of the pattern recognition field. The luminance information of the objects help to classify them, however the environment illumination could cause a lot of problem if the system is not illumination invariant. Some applications of this topic include image and video quality, biometrics classification, etc. In this work an illumination analysis for a digital system invariant to position and rotation based on Fourier transform, Bessel masks, one-dimensional signatures and linear correlations are presented. The digital system was tested using a reference database of 21 fossil diatoms images of gray-scale and 307 x 307 pixels. The digital system has shown an excellent performance in the classification of 60,480 problem images which have different non-homogeneous illumination.

Solorza, S.; Álvarez-Borrego, J.

2013-11-01

440

A fuzzy pattern recognition based system for monitoring laser weld quality  

NASA Astrophysics Data System (ADS)

In-process monitoring of welding has become important as the use of laser welding increases. Plasma and spatter are measured and used as signals for estimating the quality of a weld. The measurement system consists of three photodiode sensors (one IR and two UV) to detect the plasma and spatter signals in CO2 laser welding. The estimating algorithm was constructed using fuzzy pattern recognition considering the amplitudes as well as amounts of data beyond the tolerance boundary. Weld qualities were classified as optimal heat input, slightly low heat input, low heat input and misalignment of focus. Also, an algorithm for detecting spatter was created in order to find the partially produced pit. These algorithms were used for quality monitoring in tailored blank welds with a CO2 laser.

Park, Hyunsung; Rhee, Sehun; Kim, Dongcheol

2001-08-01

441

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

NASA Astrophysics Data System (ADS)

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

Coronel-Beltrán, Angel

2014-10-01

442

Fuel spill identification by gas chromatography -- genetic algorithms/pattern recognition techniques  

SciTech Connect

Gas chromatography and pattern recognition methods were used to develop a potential method for typing jet fuels so a spill sample in the environment can be traced to its source. The test data consisted of 256 gas chromatograms of neat jet fuels. 31 fuels that have undergone weathering in a subsurface environment were correctly identified by type using discriminants developed from the gas chromatograms of the neat jet fuels. Coalescing poorly resolved peaks, which occurred during preprocessing, diminished the resolution and hence information content of the GC profiles. Nevertheless a genetic algorithm was able to extract enough information from these profiles to correctly classify the chromatograms of weathered fuels. This suggests that cheaper and simpler GC instruments ca be used to type jet fuels.

Lavine, B.K.; Moores, A.J. [Clarkson Univ., Potsdam, NY (United States). Dept. of Chemistry; Mayfield, H.T. [AFCESA/RDVC, Tyndall AFB, FL (United States); Faruque, A. [Mississippi Valley State Univ., Itta Bena, MS (United States). Dept. of Computer Science

1998-12-01

443

Pattern recognition approaches for the detection and characterization of discontinuities by eddy current testing  

SciTech Connect

Eddy current signals (ECS) generated under varied experimental conditions from different types of discontinuities like partial/through thickness holes and notches of various dimensions, fatigue cracks, stress corrosion cracks, etc. in AISI type 316 stainless steel sheets/plates have been analyzed using pattern recognition (PR) approaches to understand their quality of performance for detection and characterization of several aspects of the discontinuities. The PR analyses have been carried out using linear discriminant (LD), minimum distance (MD), empirical Bayesian (EB) and K-nearest neighbor (KNN) statistical classifiers, and multilayered perceptron (MLP) and Kohonen's artificial neural network (KANN). The MLP approach has been extended to eddy current images also to achieve deblurring. The practical feasibility and application potential of ANNs is demonstrated through a case study on nuclear fuel cladding tubes where both the online and the offline approaches have been implemented.

Shyamsunder, M.T.; Rajagopalan, C.; Raj, B.; Dewangan, S.K.; Rao, B.P.C.; Ray, K.K.

2000-01-01

444

Infrared spectral classification with artificial neural networks and classical pattern recognition  

NASA Astrophysics Data System (ADS)

Infrared spectroscopy is an important technique for measuring airborne chemicals, for pollution monitoring and to warn of toxic compound releases. Infrared spectroscopy provides both detection and identification of airborne components. Computer-assisted classification tools, including pattern recognition and artificial neural network techniques, have been applied to a collection of infrared spectra of organophosphorus compounds, and these have successfully discriminated commercial pesticide compounds from military nerve agents, precursors, and hydrolysis products. Infrared spectra for previous tests came from a commercial infrared library, with permission, from military laboratories, and from defense contractors. In order to further test such classification tools, additional infrared spectra from the NIST gas-phase infrared library were added to the data set. These additional spectra probed the tendency of the trained classifiers to misidentify unrelated spectra into the trained classes.

Mayfield, Howard T.; Eastwood, DeLyle; Burggraf, Larry W.

2000-07-01

445

Pattern recognition techniques applied to the study of leishmanial glyceraldehyde-3-phosphate dehydrogenase inhibition.  

PubMed

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

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

2014-01-01

446

A Pattern Recognition Feature Optimization Tool Using the Visual Empirical Region of Influence Algorithm  

SciTech Connect

This document is the second in a series that describe graphical user interface tools developed to control the Visual Empirical Region of Influence (VERI) algorithm. In this paper we describe a user interface designed to optimize the VERI algorithm results. The optimization mode uses a brute force method of searching through the combinations of features in a data set for features that produce the best pattern recognition results. With a small number of features in a data set an exact solution can be determined. However, the number of possible combinations increases exponentially with the number of features and an alternate means of finding a solution must be found. We developed and implemented a technique for finding solutions in data sets with both small and large numbers of features. This document illustrates step-by-step examples of how to use the interface and how to interpret the results. It is written in two parts, part I deals with using the interface to find the best combination from all possible sets of features, part II describes how to use the tool to find a good solution in data sets with a large number of features. The VERI Optimization Interface Tool was written using the Tcl/Tk Graphical User Interface (GUI) programming language, version 8.1. Although the Tcl/Tk packages are designed to run on multiple computer platforms, we have concentrated our efforts to develop a user interface for the ubiquitous DOS environment. The VERI algorithms are compiled, executable programs. The optimization interface executes the VERI algorithm in Leave-One-Out mode using the Euclidean metric. For a thorough description of the type of data analysis we perform, and for a general Pattern Recognition tutorial, refer to our website at: http://www.sandia.gov/imrl/XVisionScience/Xusers.htm.

MARTINEZ, RUBEL F.

2002-06-01

447

Solution NMR studies provide structural basis for endotoxin pattern recognition by the innate immune receptor CD14  

SciTech Connect

CD14 functions as a key pattern recognition receptor for a diverse array of Gram-negative and Gram-positive cell-wall components in the host innate immune response by binding to pathogen-associated molecular patterns (PAMPs) at partially overlapping binding site(s). To determine the potential contribution of CD14 residues in this pattern recognition, we have examined using solution NMR spectroscopy, the binding of three different endotoxin ligands, lipopolysaccharide, lipoteichoic acid, and a PGN-derived compound, muramyl dipeptide to a {sup 15}N isotopically labeled 152-residue N-terminal fragment of sCD14 expressed in Pichia pastoris. Mapping of NMR spectral changes upon addition of ligands revealed that the pattern of residues affected by binding of each ligand is partially similar and partially different. This first direct structural observation of the ability of specific residue combinations of CD14 to differentially affect endotoxin binding may help explain the broad specificity of CD14 in ligand recognition and provide a structural basis for pattern recognition. Another interesting finding from the observed spectral changes is that the mode of binding may be dynamically modulated and could provide a mechanism for binding endotoxins with structural diversity through a common binding site.

Albright, Seth; Chen Bin; Holbrook, Kristen [Biochemistry, Cellular and Molecular Biology Department, University of Tennessee, M407 Walters Life Sciences, 1410 Cumberland Avenue, Knoxville, TN 37996-0840 (United States); Jain, Nitin U. [Biochemistry, Cellular and Molecular Biology Department, University of Tennessee, M407 Walters Life Sciences, 1410 Cumberland Avenue, Knoxville, TN 37996-0840 (United States)], E-mail: njain@utk.edu

2008-04-04

448

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

PubMed Central

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

2013-01-01

449

Characteristic of diffractive optical element for arbitrary pattern beam shaping  

NASA Astrophysics Data System (ADS)

Laser materials processing has been used increasingly over the wide area of electronic industries, especially for drilling microvias in printed circuit boards, and poly-silicon annealing for thin film transistor of liquid crystal display. Intensity distribution of laser beam is usually a non-uniform gaussian profile. Therefore, the demand for uniform intensity distribution is rising rapidly in some applications of heat processing. To obtain higher uniformity, beam homogenizer of a diffractive optical element (DOE) has recently been developed and introduced to some promising applications. Through the improvement of optical design algorithms and micro-fabrication techniques of a phase pattern of DOE, it becomes possible to convert a non-uniform gaussian distribution not only into a simple distribution like a square and a line but also into a complicated distribution like a distribution of printed circuit pattern. In this study, we introduce a design and fabrication result of beam shaper of DOE that can convert a gaussian distribution into the distribution of a printed circuit pattern, and present the possibility and the point at issue of new laser material processing by using such optics.

Hirai, Takayuki; Fuse, Keiji; Kurisu, Kenichi; Ebata, Keiji; Matsushima, Kyoji

2004-10-01

450

Enhanced optical magnetoelectric effect in a patterned polar ferrimagnet  

NASA Astrophysics Data System (ADS)

A simple method to dramatically enhance the optical magnetoelectric (ME) effect, i.e., nonreciprocal directional birefringence, is proposed and demonstrated for a polar ferrimagnet GaFeO3 as a typical example. We patterned a simple grating with a pitch of 4 ?m on a surface of GaFeO3 crystal and used the diffracted light as a probe. Optical ME modulation signal for Bragg spot of the order n=1 becomes gigantic in the photon energy 1--4 eV and reaches 1--2% of the bare diffracted light intensity in a magnetic field of 500 Oe. This is amplified by more than three orders of magnitude compared to that for the reflection of bulk GaFeO3. Fabricating a photonic crystal will make it possible to lead a new route for the practical use of the optical ME effect.

Kida, N.; Kaneko, Y.; He, J. P.; Matsubara, M.; Sato, H.; Arima, T.; Akoh, H.; Tokura, Y.

2006-03-01

451

Enhanced Optical Magnetoelectric Effect in a Patterned Polar Ferrimagnet  

NASA Astrophysics Data System (ADS)

A simple method to dramatically enhance the optical magnetoelectric (ME) effect, i.e., nonreciprocal directional birefringence, is proposed and demonstrated for a polar ferrimagnet GaFeO3 as a typical example. We patterned a simple grating with a period of 4?m on a surface of GaFeO3 crystal and used the diffracted light as a probe. The optical ME modulation signal for the Bragg spot of the order n=1 becomes gigantic in the photon energy 1 4 eV and reaches 1 2% of the bare diffracted light intensity in a magnetic field of 500 Oe. This is amplified by more than 3 orders of magnitude compared to that for the reflection of bulk GaFeO3. Fabricating a photonic crystal will make it possible to lead the way for the practical use of the optical ME effect.

Kida, N.; Kaneko, Y.; He, J. P.; Matsubara, M.; Sato, H.; Arima, T.; Akoh, H.; Tokura, Y.

2006-04-01

452

Enhanced optical magnetoelectric effect in a patterned polar ferrimagnet.  

PubMed

A simple method to dramatically enhance the optical magnetoelectric (ME) effect, i.e., nonreciprocal directional birefringence, is proposed and demonstrated for a polar ferrimagnet GaFeO3 as a typical example. We patterned a simple grating with a period of 4 microm on a surface of GaFeO3 crystal and used the diffracted light as a probe. The optical ME modulation signal for the Bragg spot of the order n=1 becomes gigantic in the photon energy 1-4 eV and reaches 1-2% of the bare diffracted light intensity in a magnetic field of 500 Oe. This is amplified by more than 3 orders of magnitude compared to that for the reflection of bulk GaFeO3. Fabricating a photonic crystal will make it possible to lead the way for the practical use of the optical ME effect. PMID:16712266

Kida, N; Kaneko, Y; He, J P; Matsubara, M; Sato, H; Arima, T; Akoh, H; Tokura, Y

2006-04-28

453

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

E-print Network

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

Hasanhodzic, Jasmina, 1979-

2004-01-01

454

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

EPA Science Inventory

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

455

Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity  

Microsoft Academic Search

There are various applications of physical activity monitoring for medical purposes, such as therapeutic rehabilitation, fitness enhancement or the use of physical activity as context information for evaluation of other vital data. Physical activity can be estimated using acceleration sensor-systems fixed on a person's body. By means of pattern recognition methods, it is possible to identify with certain accuracy which

Luciana C. Jatoba; Ulrich Grossmann; Chistophe Kunze; Jorg Ottenbacher; Wilhelm Stork

2008-01-01

456

Automated wafer-scale fabrication of electron beam deposited tips for atomic force microscopes using pattern recognition  

Microsoft Academic Search

We present an automation technique for the growth of electron beam deposited tips on whole wafers of atomic force microscope cantilevers. This technique uses pattern recognition on scanning electron microscope images of successive magnifications to precisely place the tips on the cantilevers. We demonstrate the capabilities of the working system on a four-inch wafer of microfabricated small cantilevers with a

Johannes H Kindt; Georg E Fantner; James B Thompson; Paul K Hansma

2004-01-01

457

Journal of Pattern Recognition Research 1 (2006) 16-32 Image Fusion and Enhancement via Empirical Mode Decomposition  

E-print Network

Journal of Pattern Recognition Research 1 (2006) 16-32 Image Fusion and Enhancement via Empirical, is not common in the literature. Keywords: Data fusion, Empirical mode decomposition, Image fusion, Intrinsic mode function. 1. Introduction Image fusion is the capacity to produce a single fused image from a set

Koschan, Andreas

458

The application of pattern analysis for the recognition of adaptation in a collection of Lolium multiflorum populations  

Microsoft Academic Search

Clustering procedures for the recognition of patterns of adaptation were applied to 43 introduced populations of Lolium multiflorum undergoing evaluation prior to use in breeding programmes. Regular analysis of variance of the productivity revealed considerable interaction between populations and the 15 cuts imposed. The clustering reduced this to a 12 group situation, which maintained 85% of the population variation and

M. D. Hayward; I. H. Delacey; B. F. Tyler; D. W. Drake

1982-01-01

459

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

EPA Science Inventory

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