Sample records for optical pattern recognition

  1. Optical recognition of statistical patterns

    NASA Astrophysics Data System (ADS)

    Lee, S. H.

    1981-12-01

    Optical implementation of the Fukunaga-Koontz transform (FKT) and the Least-Squares Linear Mapping Technique (LSLMT) is described. The FKT is a linear transformation which performs image feature extraction for a two-class image classification problem. The LSLMT performs a transform from large dimensional feature space to small dimensional decision space for separating multiple image classes by maximizing the interclass differences while minimizing the intraclass variations. The FKT and the LSLMT were optically implemented by utilizing a coded phase optical processor. The transform was used for classifying birds and fish. After the F-K basis functions were calculated, those most useful for classification were incorporated into a computer generated hologram. The output of the optical processor, consisting of the squared magnitude of the F-K coefficients, was detected by a T.V. camera, digitized, and fed into a micro-computer for classification. A simple linear classifier based on only two F-K coefficients was able to separate the images into two classes, indicating that the F-K transform had chosen good features. Two advantages of optically implementing the FKT and LSLMT are parallel and real time processing.

  2. Self-amplified optical pattern recognition system

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Inventor)

    1994-01-01

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

  3. Optical pattern recognition algorithms, architecture, and applications: introduction to the feature issue

    NASA Astrophysics Data System (ADS)

    Awwal, Abdul A.; Iftekharuddin, Khan M.; Karim, Mohammad A.; Juday, Richard D.

    2003-08-01

    This issue of Applied Optics features eight papers in the areas of optical pattern recognition. They range from new algorithms for improved classification, rotation and scale invariant recognition to novel architectures and applications of optical correlation filters in information processing.

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

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin

    1991-01-01

    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.

  5. Binary optical filters for scale invariant pattern recognition

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

    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.

  6. Reconfigurable optical differential phase-shift-keying pattern recognition based on incoherent photonic processing.

    PubMed

    Malacarne, Antonio; Ashrafi, Reza; Park, Yongwoo; Azaña, José

    2011-11-01

    We propose and experimentally demonstrate asynchronous optical differential phase-shift-keying (DPSK) pattern recognition using a fully reconfigurable technique. The proposed method uses optical phase-to-bipolar intensity conversion through all-optical differentiation in conjunction with an incoherent time-spectrum convolution system where the pattern to be recognized is implemented directly in the spectral domain through optical amplitude-only linear filtering. Full reconfigurability in terms of bit rate, pattern sequence, and pattern length is achieved using electronically programmable optical filters. We demonstrate dynamically switching recognition of different 64?bit patterns in a continuous 12?Gb/s DPSK pseudorandom optical bit stream with contrast ratio up to 3.8?dB. PMID:22048394

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

    NASA Astrophysics Data System (ADS)

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

    2014-09-01

    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.

  8. Optical pattern recognition for validation and security verification

    Microsoft Academic Search

    Bahram Javidi; Joseph L. Horner

    1994-01-01

    We propose an idea for security verification of credit cards, passports, and other ID so that they cannot easily be reproduced. A new scheme of complex phase\\/amplitude patterns that cannot be seen and cannot be copied by an intensity sensitive detector such as a CCD camera is used. The basic idea is to permanently and irretrievably bond a phase mask

  9. Compact holographic optical neural network system for real-time pattern recognition

    NASA Astrophysics Data System (ADS)

    Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

    1996-08-01

    One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

  10. Automatic pattern recognition

    NASA Astrophysics Data System (ADS)

    Clement, Brian E. P.

    1992-09-01

    A description is first given of the electronic circuitry sufficient and necessary to imitate the action and optical functions of a phase conjugate hologram in the two dimensions of the Euclidean plane. An explanation is then given of its derivation from the underlying principles of UK patent No. GB 2 199 976 (automatic pattern recognition), and of its spatiotemporal (hypercube) applications in neural network form using the phenomena of superconductivity, the genetic code, and a simple geometrical solution of the traveling salesman problem as examples. The presentation includes a discussion of the degree of phase coherence which would be necessary to construct an artificial brain based on a dynamic holographic structure.

  11. Spherical and nonspherical aerosol and particulate characterization using optical pattern recognition techniques

    NASA Astrophysics Data System (ADS)

    Marshall, Martin S.; Benner, Robert E.

    1991-11-01

    Optical pattern recognition techniques for characterizing spherical and nonspherical aerosols and particulates utilized by Dugway Proving Ground in their smoke and obscurant testing programs are presented. The three standard classes of unclassified military smokes and obscurants are included in this study, namely standard liquid aerosol smokes, fibrous obscurants, and flaked brass and graphite IR obscurants. The overall objectives of size distribution in nearly real time under conditions of high particulate loading are specified. A holographic version of the commercially available wedge-ring detector is described to replace the classical matched and phase-only filters, and some of the possible benefits are enumerated for characterizing the two classes of nonspherical obscurants. Computer simulations and experimental data collected for opaque spherical and nonspherical particles are included utilizing the classical matched filter, the phase-only filter and the holographic wedge-ring detector. Computer simulations are presented predicting the performance of a system where the output from the optical correlator utilizing the holographic wedge-ring detector is coupled directly into a optical processor to accomplish the decision making and classification tasks to enhance the speed and performance of the system

  12. Multipoint optical evanescent wave U-bend sensor system based on artificial neural network pattern recognition

    NASA Astrophysics Data System (ADS)

    Lyons, William B.; Flanagan, Colin; Lochman, Steffen; Ewald, Hartmut; Lewis, Elfed

    2001-10-01

    An optical fibre (3 sensor) multipoint U-Bend evanescent wave absorption sensor system is reported which is capable of detecting contaminants in water and depositions by coating on its surface. The sensor is based on a continuous 1Km 62.5micrometers core diameter Polymer Clad Silicon (P.C.S.) fibre which has had its cladding removed in the sensing areas. The sensing fibre is addressed using an Optical Time Domain Reflectometer (OTDR), and is thus capable of resolving distance along its length allowing measurement at multiple points on a single fibre loop. Signals arising from optical fibre sensors can often be complex in nature and this is particularly so in the case of multipoint sensors. Due to cross-coupling effects of interfering parameters, it is difficult to interpret data from such systems using conventional detection techniques. Artificial Neural Network pattern recognition techniques are used for the signal analysis of the sensor, which allow classification of the samples under test, thus allowing the true measurand to be recognized and separated from any cross-coupling effects that may be present. The system described is capable of recognizing cross-sensitivity from interfering parameters such as lime scale coating in hard water and the presence of other species e.g. alcohol in the water. Results are included that have been obtained from the sensors OTDR data. Also presented, are the resulting test outputs that have been obtained from a trained feed-forward neural network designed to interpret the sensor data. The system was 100% successful in classification of all test samples analyzed.

  13. Error Correcting Optical Syntactic Pattern Recognizers

    NASA Astrophysics Data System (ADS)

    Basu, Sanghamitra; Eichmann, George

    1987-08-01

    There. where many pattern recognition problems here the pattern's structural information is important. In these problems, a syntactic method of pattern recognition is of value. In this paper, both parallel syntactic pattern recognition algorithm and optical architecture implementation approaches are described. In particular, the applications of syntactic pattern recognition algorithm to shape classification are illustrated. A number of parallel optical syntactic pattern coding methods; a structural matched filter; an associative memory filter; and an optical symbolic substitution syntactic parser, are discussed.

  14. Pattern Recognition by Pentraxins

    PubMed Central

    Agrawal, Alok; Singh, Prem Prakash; Bottazzi, Barbara; Garlanda, Cecilia; Mantovani, Alberto

    2012-01-01

    Pentraxins are a family of evolutionarily conserved pattern-recognition proteins that are made up of five identical subunits. Based on the primary structure of the subunit, the pentraxins are divided into two groups: short pentraxins and long pentraxins. C-reactive protein (CRP) and serum amyloid P-component (SAP) are the two short pentraxins. The prototype protein of the long pentraxin group is pentraxin 3 (PTX3). CRP and SAP are produced primarily in the liver while PTX3 is produced in a variety of tissues during inflammation. The main functions of short pentraxins are to recognize a variety of pathogenic agents and then to either eliminate them or neutralize their harmful effects by utilizing the complement pathways and macrophages in the host. CRP binds to modified low-density lipoproteins, bacterial polysaccharides, apoptotic cells, and nuclear materials. By virtue of these recognition functions, CRP participates in the resolution of cardiovascular, infectious, and autoimmune diseases. SAP recognizes carbohydrates, nuclear substances, and amyloid fibrils and thus participates in the resolution of infectious diseases, autoimmunity, and amyloidosis. PTX3 interacts with several ligands, including growth factors, extracellular matrix component and selected pathogens, playing a role in complement activation and facilitating pathogen recognition by phagocytes. In addition, data in gene-targeted mice show that PTX3 is essential in female fertility, participating in the assembly of the cumulus oophorus extra-cellular matrix. PTX3 is therefore a nonredundant component of the humoral arm of innate immunity as well as a tuner of inflammation. Thus, in conjunction with the other components of innate immunity, the pentraxins use their pattern-recognition property for the benefit of the host. PMID:19799114

  15. Pattern recognition and neural networks

    Microsoft Academic Search

    Brian D. Ripley

    1996-01-01

    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

  16. Crosswind Measurements through Pattern Recognition Techniques

    Microsoft Academic Search

    Fredrick Taylor; Jack Smith; Thomas Pries

    1975-01-01

    Optical devices currently used for crosswind measurements are calibrated and results interpreted on the basis of theoretical predictions. However, under strong turbulence conditions, the experimental observations do not compare well with the theoretical predictions. This deficiency can be overcome by use of a learning machine which utilizes a pattern recognition technique. Basically this approach substitutes observed experience for a detailed

  17. Pattern Recognition for Earthquake Detection

    Microsoft Academic Search

    Manfred Joswig

    1987-01-01

    The detector algorithms in use at date rely on negative decision logic: based on a model of the ambient noise process they detect all deviations, but many of them are false alarms. The principal alternative to this approach is pattern recognition, which tests on positive correlation with some known signal patterns. The Sonogram-detector realizes this scheme for single seismogram traces.

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

    DOEpatents

    Molley, Perry A. (Albuquerque, NM)

    1991-01-01

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

  19. ARTICLE IN PRESS Pattern Recognition ( )

    E-print Network

    Kirzhner Valery

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

  20. Neural Networks for Pattern Recognition

    Microsoft Academic Search

    Christopher M. Bishop

    1995-01-01

    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,

  1. Information input in an optical pattern recognition system using a relay tube based on the pockels effect

    Microsoft Academic Search

    G. Groh; G. Marie

    1970-01-01

    This communication describes an experimental arrangement for pattern recognition by means of holography, which uses an electron beam-addressed crystal of deuterated KDP (KD2PO4) as an input device. This enables an incoherently illuminated image to be investigated almost in real time. Moreover, by suitably operating the KD2PO4 crystal the disturbing zero order term of the Fourier spectrum of the object can

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

    E-print Network

    Dupont, Stéphane

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

  3. The use of context in pattern recognition

    Microsoft Academic Search

    Godfried T. Toussaint

    1978-01-01

    The importance of contextual information, at various different levels, for the satisfactory solution of pattern recognition problems is illustrated by examples. A tutorial survey of techniques for using contextual information in pattern recognition is presented. Emphasis is placed on the problems of image classification and text recognition, where the text is in the form of machine and handprinted characters, cursive

  4. Character and pattern recognition based on moire images

    NASA Astrophysics Data System (ADS)

    Chatterjee, Chanchal; Bieman, Leonard H.

    1995-08-01

    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.

  5. Adaptive pattern recognition and neural networks

    Microsoft Academic Search

    Yoh-Han Pao; Yohhan

    1989-01-01

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

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

    PubMed

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

    2014-01-01

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

  7. Discriminant-function-based minimum recognition error rate pattern-recognition approach to speech recognition

    Microsoft Academic Search

    Wu Chou

    2000-01-01

    A discriminant function-based minimum recognition error rate pattern recognition approach is described and studied for various applications in speech processing. This approach departs from the conventional paradigm, which links a classification\\/recognition task to the problem of distribution estimation. Instead, it takes a discriminant function based statistical pattern recognition approach. The suitability of this approach for classification error rate minimization is

  8. A Pattern Recognition System Using Evolvable Hardware

    Microsoft Academic Search

    Masaya Iwata; Isamu Kajitani; Hitoshi Yamada; Hitoshi Iba; Tetsuya Higuchi

    1996-01-01

    We describe a high-speed pattern recognition system using Evolvable Hardware (EHW), which can change its own hardware structure by genetic learning in order to adapt best to the environment. The purpose of the system is to show that EHW can work as a recognition device with such robustness for the noise as seen in the recognition systems based on neural

  9. Quantum Pattern Recognition of Classical Signal

    E-print Network

    Chao-Yang Pang; Cong-Bao Ding; Ben-Qiong Hu

    2007-09-04

    It's the key research topic of signal processing that recognizing genuine targets real time from the disturbed signal which has giant amount of data. A quantum algorithm for pattern recognition of classical signal which has time complexity O(sqrt(N)) is presented in this paper. Key Words: Pattern recognition, Grover's algorithm, Rotation on subspace

  10. Learning Decision Trees and Tree Automata for a~Syntactic Pattern Recognition Task

    Microsoft Academic Search

    José M. Sempere; Damián López

    2003-01-01

    Decision trees have been widely used for different tasks in artificial intelligence and data mining. Tree automata have been used in pattern recognition tasks to represent some features of objects to be classified. Here we propose a method that combines both approaches to solve a classical problem in pattern recognition such as Optical Charac- ter Recognition. We propose a method

  11. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, June 1997, pages 457--462 EgoMotion Estimation Using Optical Flow Fields Observed from

    E-print Network

    Hung, Yi-Ping

    In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, June 1997, pages 457--462 Ego­Motion Estimation Using Optical Flow Fields Observed from Multiple Cameras An, Taiwan Email: hung@iis.sinica.edu.tw Abstract In this paper, we consider a multi­camera vision system

  12. Pattern recognition with magnonic holographic memory device

    NASA Astrophysics Data System (ADS)

    Kozhevnikov, A.; Gertz, F.; Dudko, G.; Filimonov, Y.; Khitun, A.

    2015-04-01

    In this work, we present experimental data demonstrating the possibility of using magnonic holographic devices for pattern recognition. The prototype eight-terminal device consists of a magnetic matrix with micro-antennas placed on the periphery of the matrix to excite and detect spin waves. The principle of operation is based on the effect of spin wave interference, which is similar to the operation of optical holographic devices. Input information is encoded in the phases of the spin waves generated on the edges of the magnonic matrix, while the output corresponds to the amplitude of the inductive voltage produced by the interfering spin waves on the other side of the matrix. The level of the output voltage depends on the combination of the input phases as well as on the internal structure of the magnonic matrix. Experimental data collected for several magnonic matrixes show the unique output signatures in which maxima and minima correspond to specific input phase patterns. Potentially, magnonic holographic devices may provide a higher storage density compare to optical counterparts due to a shorter wavelength and compatibility with conventional electronic devices. The challenges and shortcoming of the magnonic holographic devices are also discussed.

  13. Status of pattern recognition with wavelet analysis

    Microsoft Academic Search

    Yuanyan Tang

    2008-01-01

    Pattern recognition has become one of the fastest growing research topics in the fields of computer science and electrical\\u000a and electronic engineering in the recent years. Advanced research and development in pattern recognition have found numerous\\u000a applications in such areas as artificial intelligence, information security, biometrics, military science and technology,\\u000a finance and economics, weather forecast, image processing, communication, biomedical engineering,

  14. Large-memory real-time multichannel multiplexed pattern recognition

    NASA Technical Reports Server (NTRS)

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

    1984-01-01

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

  15. Face recognition based on fringe pattern analysis

    NASA Astrophysics Data System (ADS)

    Guo, Hong; Huang, Peisen

    2010-03-01

    Two-dimensional face-recognition techniques suffer from facial texture and illumination variations. Although 3-D techniques can overcome these limitations, the reconstruction and storage expenses of 3-D information are extremely high. We present a novel face-recognition method that directly utilizes 3-D information encoded in face fringe patterns without having to reconstruct 3-D geometry. In the proposed method, a digital video projector is employed to sequentially project three phase-shifted sinusoidal fringe patterns onto the subject's face. Meanwhile, a camera is used to capture the distorted fringe patterns from an offset angle. Afterward, the face fringe images are analyzed by the phase-shifting method and the Fourier transform method to obtain a spectral representation of the 3-D face. Finally, the eigenface algorithm is applied to the face-spectrum images to perform face recognition. Simulation and experimental results demonstrate that the proposed method achieved satisfactory recognition rates with reduced computational complexity and storage expenses.

  16. Pattern recognition using asymmetric attractor neural networks

    SciTech Connect

    Jin Tao; Zhao Hong [Physics Department of Lanzhou University, Lanzhou 730000 (China); Physics Department of Xiamen University, Xiamen 361005 (China)

    2005-12-15

    The asymmetric attractor neural networks designed by the Monte Carlo- (MC-) adaptation rule are shown to be promising candidates for pattern recognition. In such a neural network with relatively low symmetry, when the members of a set of template patterns are stored as fixed-point attractors, their attraction basins are shown to be isolated islands embedded in a ''chaotic sea.'' The sizes of these islands can be controlled by a single parameter. We show that these properties can be used for effective pattern recognition and rejection. In our method, the pattern to be identified is attracted to a template pattern or a chaotic attractor. If the difference between the pattern to be identified and the template pattern is smaller than a predescribed threshold, the pattern is attracted to the template pattern automatically and thus is identified as belonging to this template pattern. Otherwise, it wanders in a chaotic attractor for ever and thus is rejected as an unknown pattern. The maximum sizes of these islands allowed by this kind of neural networks are determined by a modified MC-adaptation rule which are shown to be able to dramatically enlarge the sizes of the islands. We illustrate the use of our method for pattern recognition and rejection with an example of recognizing a set of Chinese characters.

  17. Public domain optical character recognition

    NASA Astrophysics Data System (ADS)

    Garris, Michael D.; Blue, James L.; Candela, Gerald T.; Dimmick, Darrin L.; Geist, Jon C.; Grother, Patrick J.; Janet, Stanley A.; Wilson, Charles L.

    1995-03-01

    A public domain document processing system has been developed by the National Institute of Standards and Technology (NIST). The system is a standard reference form-based handprint recognition system for evaluating optical character recognition (OCR), and it is intended to provide a baseline of performance on an open application. The system's source code, training data, performance assessment tools, and type of forms processed are all publicly available. The system recognizes the handprint entered on handwriting sample forms like the ones distributed with NIST Special Database 1. From these forms, the system reads hand-printed numeric fields, upper and lowercase alphabetic fields, and unconstrained text paragraphs comprised of words from a limited-size dictionary. The modular design of the system makes it useful for component evaluation and comparison, training and testing set validation, and multiple system voting schemes. The system contains a number of significant contributions to OCR technology, including an optimized probabilistic neural network (PNN) classifier that operates a factor of 20 times faster than traditional software implementations of the algorithm. The source code for the recognition system is written in C and is organized into 11 libraries. In all, there are approximately 19,000 lines of code supporting more than 550 subroutines. Source code is provided for form registration, form removal, field isolation, field segmentation, character normalization, feature extraction, character classification, and dictionary-based postprocessing. The recognition system has been successfully compiled and tested on a host of UNIX workstations. This paper gives an overview of the recognition system's software architecture, including descriptions of the various system components along with timing and accuracy statistics.

  18. Pattern-Recognition Processor Using Holographic Photopolymer

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Cammack, Kevin

    2006-01-01

    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.

  19. Quantum Pattern Recognition With Liquid State NMR

    E-print Network

    Neigovzen, Rodion; Sollacher, Rudolf; Glaser, Steffen J

    2008-01-01

    A novel quantum pattern recognition scheme is presented, which combines the idea of a classic Hopfield neural network with quantum adiabatic 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 simultaneously return multiple recognized patterns. The approach also promises extension of classic memory capacity. A proof of principle for the algorithm for two qubits is provided using a liquid state NMR quantum computer.

  20. Invariant Pattern Recognition Based on Centroids

    NASA Astrophysics Data System (ADS)

    Chang, Shoude; Arsenault, Henri H.; Garcia-Martinez, Pascuala; Grover, Chander P.

    2000-12-01

    A new method for pattern recognition that is invariant under changes of position, orientation, intensity, and scale is presented. The centroids of objects provide unique points that are related to the energy distribution. For obtaining more such unique points a conformal transform can be used to rearrange the energy distribution of the object. By means of the conformal transform many different centroids can be produced from the same object. A useful pattern-recognition and object-registration method that yields a position-, rotation-, intensity-, and scale-invariant feature vector based on these centroids can be created.

  1. Fast Star Pattern Recognition Using Spherical Triangles

    E-print Network

    Crassidis, John L.

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

  2. Fast Star Pattern Recognition Using Planar Triangles

    E-print Network

    Crassidis, John L.

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

  3. Pattern Recognition Letters North-Holland

    E-print Network

    Aladjem, Mayer

    is considered to be an impor- tant problem in pattern recognition. The mapping methods oriented to classifier design (Siedlecki et al. (1988a)) are special cases of feature extraction methods. The main difference concerns the dimen- sions of the feature space. Thus, the mapping methods are restricted to dimension two

  4. PATTERN RECOGNITION APPLIED IN FINE ART AUTHENTICATION

    Microsoft Academic Search

    Guilherme N. Teixeira; Raul Q. Feitosa

    This work investigates pattern recognition tech- niques that can be used to identify the author of f ine art paintings based on the content of their digitized i mages. The methodology proposed consists of four sequential phases: image acquisition, segmentation, feature ex trac- tion, and classification. For segmenting brushstrok es it is assumed that the hue is quite uniform in

  5. Conformal Predictions in Multimedia Pattern Recognition

    ERIC Educational Resources Information Center

    Nallure Balasubramanian, Vineeth

    2010-01-01

    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…

  6. Improved seismic discrimination using pattern recognition

    Microsoft Academic Search

    Dag Tjøstheim

    1978-01-01

    The problem of discriminating between earthquakes and underground nuclear explosions is formulated as a problem in pattern recognition. As such it may be separated into two stages, feature extraction and classification. The short-period (SP) features consist of mb and autoregressive parameters characterising the preceding noise, signal and coda. The long-period (LP) features consist of LP power spectral estimates taken within

  7. ISO ground attitude determination using pattern recognition

    Microsoft Academic Search

    A. J. Batten

    1993-01-01

    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

  8. Pattern Recognition by Retina-Like Devices.

    ERIC Educational Resources Information Center

    Weiman, Carl F. R.; Rothstein, Jerome

    This study has investigated some pattern recognition capabilities of devices consisting of arrays of cooperating elements acting in parallel. The problem of recognizing straight lines in general position on the quadratic lattice has been completely solved by applying parallel acting algorithms to a special code for lines on the lattice. The…

  9. Gender Recognition from Faces Using Bandlet and Local Binary Patterns

    E-print Network

    Bebis, George

    . The latter algorithms refer to "category specific" recognition systems, which are subsystems of a face recognition system [1]. Many research show that performing gender recognition prior face recognitionGender Recognition from Faces Using Bandlet and Local Binary Patterns Faten A. Alomar, Ghulam

  10. Pattern recognition monitoring of PEM fuel cell

    DOEpatents

    Meltser, Mark Alexander (Pittsford, NY)

    1999-01-01

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

  11. Pattern recognition monitoring of PEM fuel cell

    DOEpatents

    Meltser, M.A.

    1999-08-31

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

  12. Artificial Immune Systems: A Novel Paradigm to Pattern Recognition

    E-print Network

    Kent, University of

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

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

    NASA Astrophysics Data System (ADS)

    Yan, Yumei; Wu, Jian; Lin, Jintong

    2005-04-01

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

  14. Pattern recognition receptors in antifungal immunity.

    PubMed

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

    2015-03-01

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

  15. Transductive Confidence Machines for Pattern Recognition

    Microsoft Academic Search

    Kostas Proedrou; Ilia Nouretdinov; Volodya Vovk; Alexander Gammerman

    2002-01-01

    We propose a new algorithm for pattern recognition that outputs some measures of “reliability” for every prediction made,\\u000a in contrast to the current algorithms that output “bare” predictions only. Our method uses a rule similar to that of nearest\\u000a neighbours to infer predictions; thus its predictive performance is close to that of nearest neighbours, while the measures\\u000a of confidence it

  16. VLSI Microsystem for Rapid Bioinformatic Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Fang, Wai-Chi; Lue, Jaw-Chyng

    2009-01-01

    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).

  17. Opto-VLSI-based correlator architecture for multiwavelength optical header recognition

    Microsoft Academic Search

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

    2006-01-01

    A novel optical correlator employing an opto-very-large-scale-integration (VLSI) processor to construct the routing lookup table, in conjunction with an array of fiber Bragg gratings (FBGs) for multiwavelength optical header recognition is demonstrated. The FBG array provides wavelength-dependent time delays, whereas the opto-VLSI processor generates wavelength intensity profiles that match arbitrary bit patterns. The recognition of 4-b optical patterns is experimentally

  18. Developing Signal-Pattern-Recognition Programs

    NASA Technical Reports Server (NTRS)

    Shelton, Robert O.; Hammen, David

    2006-01-01

    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.

  19. Pattern recognition and control in manipulation

    NASA Technical Reports Server (NTRS)

    Bejczy, A. K.; Tomovic, R.

    1976-01-01

    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.

  20. Pigment Melanin: Pattern for Iris Recognition

    E-print Network

    Hosseini, Mahdi S; Soltanian-Zadeh, Hamid

    2009-01-01

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

  1. Statistical pattern recognition algorithms for autofluorescence imaging

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

  2. Assisted peptide folding by surface pattern recognition.

    PubMed

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

    2011-03-01

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

  3. Intrusion detection using pattern recognition methods

    NASA Astrophysics Data System (ADS)

    Jiang, Nan; Yu, Li

    2007-09-01

    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.

  4. A biologically inspired model for pattern recognition*

    PubMed Central

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

    2010-01-01

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

  5. SPARSE REPRESENTATION FOR COMPUTER VISION AND PATTERN RECOGNITION

    E-print Network

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

  6. Automated Loaded Transmission-Line Testing Using Pattern Recognition Techniques

    Microsoft Academic Search

    William T. Bisignani

    1975-01-01

    A pattern recognition approach to the automatic testing of loaded transmission lines is investigated. The pattern recognition technique selected allows automatic testing of a loaded line requiring, as data, only the magnitude of the input impedance of the line over the frequencies of interest. For standard voice quality lines, these frequencies lie between 200-4000 Hz. Thirty-two features are automatically extracted

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

    E-print Network

    Ünay, Devrim

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

  8. PARALLEL SELF-ORGANIZING FEATURE MAPS FOR UNSUPERVISED PATTERN RECOGNITION

    Microsoft Academic Search

    TERRANCE L. HUNTSBERGER; PONGSAK AJJIMARANGSEE

    1990-01-01

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

  9. Pattern-recognition receptors in human eosinophils

    PubMed Central

    Kvarnhammar, Anne Månsson; Cardell, Lars Olaf

    2012-01-01

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

  10. Optical sensing: recognition elements and devices

    NASA Astrophysics Data System (ADS)

    Gauglitz, Guenter G.

    2012-09-01

    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.

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

    DOEpatents

    Zheng, Yufeng

    2014-12-23

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

  12. Searching for pulsars using image pattern recognition

    SciTech Connect

    Zhu, W. W.; Berndsen, A.; Madsen, E. C.; Tan, M.; Stairs, I. H. [Department of Physics and Astronomy, 6224 Agricultural Road, University of British Columbia, Vancouver, BC, V6T 1Z1 (Canada); Brazier, A. [Astronomy Department, Cornell University, Ithaca, NY 14853 (United States); Lazarus, P. [Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, D-53121 Bonn (Germany); Lynch, R.; Scholz, P. [Department of Physics, McGill University, Montreal, QC H3A 2T8 (Canada); Stovall, K.; Cohen, S.; Dartez, L. P.; Lunsford, G.; Martinez, J. G.; Mata, A. [Center for Advanced Radio Astronomy, University of Texas at Brownsville, Brownsville, TX 78520 (United States); Ransom, S. M. [NRAO, Charlottesville, VA 22903 (United States); Banaszak, S.; Biwer, C. M.; Flanigan, J.; Rohr, M., E-mail: zhuww@phas.ubc.ca, E-mail: berndsen@phas.ubc.ca [Center for Gravitation, Cosmology and Astrophysics. University of Wisconsin Milwaukee, Milwaukee, WI 53211 (United States); and others

    2014-02-01

    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.

  13. Searching for Pulsars Using Image Pattern Recognition

    NASA Astrophysics Data System (ADS)

    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

    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.

  14. Pattern-Recognition Receptors and Gastric Cancer

    PubMed Central

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

    2014-01-01

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

  15. Human-Computer Interaction for Complex Pattern Recognition Problems

    E-print Network

    Salama, Khaled

    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

  16. DTWRadon-based Shape Descriptor for Pattern Recognition

    E-print Network

    Paris-Sud XI, Université de

    such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon, the method proves its generic behaviour by providing better recognition performance. Overall, we validate

  17. Proceedings of the eighth international conference on pattern recognition

    SciTech Connect

    Not Available

    1986-01-01

    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.

  18. Proceedings of the NASA/MPRIA Workshop: Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Guseman, L. F., Jr.

    1983-01-01

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

  19. Visual cluster analysis and pattern recognition template and methods

    DOEpatents

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

    1999-05-04

    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.

  20. The Pandora Software Development Kit for Pattern Recognition

    E-print Network

    Marshall, J S

    2015-01-01

    The development of automated solutions to pattern recognition problems is important in many areas of scientific research and human endeavour. This paper describes the implementation of the Pandora Software Development Kit, which aids the process of designing, implementing and running pattern recognition algorithms. The Pandora Application Programming Interfaces ensure simple specification of the building-blocks defining a pattern recognition problem. The logic required to solve the problem is implemented in algorithms, with all operations to create or modify event data structures requested by algorithms and performed by the Pandora framework. This design promotes an approach using many decoupled algorithms, each addressing specific topologies. Details of algorithms addressing two pattern recognition problems in High Energy Physics are presented: reconstruction of events at a high-energy e+e- linear collider and reconstruction of cosmic ray or neutrino events in a liquid argon time projection chamber.

  1. Apply lightweight recognition algorithms in optical music recognition

    NASA Astrophysics Data System (ADS)

    Pham, Viet-Khoi; Nguyen, Hai-Dang; Nguyen-Khac, Tung-Anh; Tran, Minh-Triet

    2015-02-01

    The problems of digitalization and transformation of musical scores into machine-readable format are necessary to be solved since they help people to enjoy music, to learn music, to conserve music sheets, and even to assist music composers. However, the results of existing methods still require improvements for higher accuracy. Therefore, the authors propose lightweight algorithms for Optical Music Recognition to help people to recognize and automatically play musical scores. In our proposal, after removing staff lines and extracting symbols, each music symbol is represented as a grid of identical M ? N cells, and the features are extracted and classified with multiple lightweight SVM classifiers. Through experiments, the authors find that the size of 10 ? 12 cells yields the highest precision value. Experimental results on the dataset consisting of 4929 music symbols taken from 18 modern music sheets in the Synthetic Score Database show that our proposed method is able to classify printed musical scores with accuracy up to 99.56%.

  2. Toxicity modeling and prediction with pattern recognition.

    PubMed Central

    Wold, S; Dunn, W J; Hellberg, S

    1985-01-01

    Empirical models can be constructed relating the change in toxicity to the change in chemical structure for series of similar compounds or mixtures. The first step is to translate the variation in structure to quantitative numbers. This gives a data table, a data matrix denoted by X, which then is analyzed. The same type of the models can be used to relate the variation of in vivo data to the variation of a battery of in vitro tests. A single data analytical model cannot be applied to a set of compounds of diverse chemical structure. For such data sets, separate models must be developed for each subgroup of compounds. The data analytical problem then partly is one of classification, pattern recognition (PARC). The assumption of structural and biological similarity within each subset of modeled compounds is then essential for empirical models to apply. PARC is often used to classify compounds as active (toxic) or inactive. The data structure is then often asymmetric which puts special demands on the data analysis, making the traditional PARC methods inapplicable. Depending on the desired information from the data analysis and on the type of available data, four levels of PARC can be distinguished: (I) the data X are used to develop rules for classifying future compounds into one of the classes represented in X; (II) same as I, but the possibility of future compounds belonging to "unknown" classes not represented in X is taken into account; (III) same as II, plus the quantitative prediction of one activity variable (here toxicity) in some classes; (IV) same as III, but several quantitative activity (toxicity) variables are predicted. PMID:3905377

  3. Markov logic networks for optical chemical structure recognition.

    PubMed

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

    2014-08-25

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

  4. Robust facial expression recognition using local binary patterns

    Microsoft Academic Search

    Caifeng Shan; Shaogang Gong; Peter W. Mcowan

    2005-01-01

    A novel low-computation discriminative feature space is in- troduced for facial expression recognition capable of ro- bust performance over a rang of image resolutions. Our ap- proach is based on the simple Local Binary Patterns (LBP) for representing salient micro-patterns of face images. Com- pared to Gabor wavelets, the LBP features can be extracted faster in a single scan through

  5. Interpreting complex data from a three-sensor multipoint optical fibre ethanol concentration sensor system using artificial neural network pattern recognition

    Microsoft Academic Search

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

    2004-01-01

    A three-sensor element multipoint optical fibre sensor system capable of detecting varying ethanol concentrations in water for use in industrial process water systems is reported. The sensor system utilizes a U-bend configuration for each sensor element in order to maximize the sensitivity of each of the sensing regions along the optical fibre cable. The sensor system is interrogated using a

  6. Syntactic pattern recognition for HRR signatures

    NASA Astrophysics Data System (ADS)

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

    2000-08-01

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

  7. Detection and recognition of angular frequency patterns.

    PubMed

    Wilson, Hugh R; Propp, Roni

    2015-05-01

    Previous research has extensively explored visual encoding of smoothly curved, closed contours described by sinusoidal variation of pattern radius as a function of polar angle (RF patterns). Although the contours of many biologically significant objects are curved, we also confront shapes with a more jagged and angular appearance. To study these, we introduce here a novel class of visual stimuli that deform smoothly from a circle to an equilateral polygon with N sides (AF patterns). Threshold measurements reveal that both AF and RF patterns can be discriminated from circles at the same deformation amplitude, approximately 18.0arcsec, which is in the hyperacuity range. Thresholds were slightly higher for patterns with 3.0 cycles than for those with 5.0 cycles. Discrimination between AF and RF patterns was 75% correct at an amplitude that was approximately 3.0 times the threshold amplitude, which implies that AF and RF patterns activate different neural populations. Experiments with jittered patterns in which the contour was broken into several pieces and shifted inward or outward had much less effect on AF patterns than on RF patterns. Similarly, thresholds for single angles of AF patterns showed no significant difference from thresholds for the entire AF pattern. Taken together, these results imply that the visual system incorporates angles explicitly in the representation of closed object contours, but it suggests that angular contours are represented more locally than are curved contours. PMID:25782363

  8. Face Recognition Using Local Quantized Patterns and Gabor Filters

    NASA Astrophysics Data System (ADS)

    Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

    2015-05-01

    The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

  9. Pigment Melanin: Pattern for Iris Recognition

    Microsoft Academic Search

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

    2010-01-01

    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

  10. Complements to 'Pattern Recognition and Neural Networks

    Microsoft Academic Search

    B. d. Ripley

    1996-01-01

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

  11. Quantum pattern recognition with liquid-state nuclear magnetic resonance

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

    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.

  12. Quantum pattern recognition with liquid-state nuclear magnetic resonance

    E-print Network

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

    2009-04-20

    A novel 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.

  13. Pattern Recognition Methods for Querying and Browsing Technical Documentation

    Microsoft Academic Search

    Karl Tombre; Bart Lamiroy

    2008-01-01

    Graphics recognition deals with the specific pattern recognition problems found in graphics-rich documents, typical technical\\u000a documentation of all kinds. In this paper, we propose a short journey through 20 years of involvement and contributions within\\u000a this scientific community, and explore more precisely a few interesting issues found when the problem is to browse, query\\u000a and navigate in a large and

  14. An Application of Syntactic Pattern Recognition to Seismic Discrimination

    Microsoft Academic Search

    Hsi-Ho Liu; King-Sun Fu

    1983-01-01

    Two syntactic methods for the recognition of seismic waveforms are presented in this paper. The seismic waveforms are represented by strings of primitives. Primitive extraction is based on cluster analysis. Finite-state grammars are inferred from the training samples. The nearest-neighbor decision rule and error-correcting finite-state parsers are used for pattern classification. While both show equal recognition performance, the nearest-neighbor rule

  15. An application of syntactic pattern recognition to seismic discrimination

    Microsoft Academic Search

    H. H. Liu; K. S. Fu

    1981-01-01

    Two syntactic methods for the recognition of seismic waveforms are presented in this paper. The seismic waveforms are represented by sentences (strings of primitives). Primitive extraction is based on a cluster analysis. Finite-state grammars are inferred from the training samples. The nearest-neighbor decision rule and error-correcting finite-state parsers are used for pattern classification. While both show equal recognition performance, the

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

    E-print Network

    Bowden, Richard

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

  17. Identification of biomolecules by terahertz spectroscopy and fuzzy pattern recognition

    NASA Astrophysics Data System (ADS)

    Chen, Tao; Li, Zhi; Mo, Wei

    2013-04-01

    An approach for automatic identification of terahertz (THz) spectra of biomolecules is proposed based on principal component analysis (PCA) and fuzzy pattern recognition in this paper, and THz transmittance spectra of some typical amino acid and saccharide biomolecular samples are investigated to prove its feasibility. Firstly, PCA is applied to reduce the dimensionality of the original spectrum data and extract features of the data. Secondly, instead of the original spectrum variables, the selected principal component scores matrix is fed into the model of fuzzy pattern recognition, where a principle of fuzzy closeness based optimization is employed to identify those samples. Results demonstrate that THz spectroscopy combined with PCA and fuzzy pattern recognition can be efficiently utilized for automatic identification of biomolecules. The proposed approach provides a new effective method in the detection and identification of biomolecules using THz spectroscopy.

  18. Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terry

    2011-01-01

    The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

  19. A feature extraction and pattern recognition receiver employing wavelet analysis and artificial intelligence for signal detection in diffuse optical wireless communications

    Microsoft Academic Search

    R. J. Dickenson; Z. Ghassemlooy

    2003-01-01

    Optical wireless diffuse indoor infrared communication systems have as yet large unrealized bandwidths that are not subject to the same regulatory control as radio frequency systems. Usually, well established RF techniques are used to combat channel imperfections for IR implementations. Here, we introduce a novel receiver system based on the multiresolution time-frequency feature extraction capabilities of wavelet analysis, coupled with

  20. On deformable models for visual pattern recognition

    Microsoft Academic Search

    Kwok-wai Cheung; Dit-yan Yeung; Roland T. Chin

    2002-01-01

    This paper reviews model-based methods for non-rigid shape recognition. These methods model, match and classify non-rigid shapes, which are generally problematic for conventational algorithms using rigid models. Issues including model representation, optimization criteria formulation, model matching, and classi3cation are examined in detail with the objective to provide interested researchers a roadmap for exploring the 3eld. This paper emphasizes on 2D

  1. Ultrasonography of ovarian masses using a pattern recognition approach

    PubMed Central

    Jung, Sung Il

    2015-01-01

    As a primary imaging modality, ultrasonography (US) can provide diagnostic information for evaluating ovarian masses. Using a pattern recognition approach through gray-scale transvaginal US, ovarian masses can be diagnosed with high specificity and sensitivity. Doppler US may allow ovarian masses to be diagnosed as benign or malignant with even greater confidence. In order to differentiate benign and malignant ovarian masses, it is necessary to categorize ovarian masses into unilocular cyst, unilocular solid cyst, multilocular cyst, multilocular solid cyst, and solid tumor, and then to detect typical US features that demonstrate malignancy based on pattern recognition approach. PMID:25797108

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

    PubMed Central

    Suresh, Rahul

    2013-01-01

    Infection by pathogenic microbes initiates a set of complex interactions between the pathogen and the host mediated by pattern recognition receptors. Innate immune responses play direct roles in host defense during the early stages of infection, and they also exert a profound influence on the generation of the adaptive immune responses that ensue. An improved understanding of the pattern recognition receptors that mediate innate responses and their downstream effects after receptor ligation has the potential to lead to new ways to improve vaccines and prevent autoimmunity. This review focuses on the control of innate immune activation and the role that innate immune receptors play in helping to maintain tissue homeostasis. PMID:24292903

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

    PubMed

    Suresh, Rahul; Mosser, David M

    2013-12-01

    Infection by pathogenic microbes initiates a set of complex interactions between the pathogen and the host mediated by pattern recognition receptors. Innate immune responses play direct roles in host defense during the early stages of infection, and they also exert a profound influence on the generation of the adaptive immune responses that ensue. An improved understanding of the pattern recognition receptors that mediate innate responses and their downstream effects after receptor ligation has the potential to lead to new ways to improve vaccines and prevent autoimmunity. This review focuses on the control of innate immune activation and the role that innate immune receptors play in helping to maintain tissue homeostasis. PMID:24292903

  4. Ultrasonography of ovarian masses using a pattern recognition approach.

    PubMed

    Jung, Sung Il

    2015-07-01

    As a primary imaging modality, ultrasonography (US) can provide diagnostic information for evaluating ovarian masses. Using a pattern recognition approach through gray-scale transvaginal US, ovarian masses can be diagnosed with high specificity and sensitivity. Doppler US may allow ovarian masses to be diagnosed as benign or malignant with even greater confidence. In order to differentiate benign and malignant ovarian masses, it is necessary to categorize ovarian masses into unilocular cyst, unilocular solid cyst, multilocular cyst, multilocular solid cyst, and solid tumor, and then to detect typical US features that demonstrate malignancy based on pattern recognition approach. PMID:25797108

  5. Detection and recognition of analytes based on their crystallization patterns

    DOEpatents

    Morozov, Victor (Manassas, VA); Bailey, Charles L. (Cross Junction, VA); Vsevolodov, Nikolai N. (Kensington, MD); Elliott, Adam (Manassas, VA)

    2008-05-06

    The invention contemplates a method for recognition of proteins and other biological molecules by imaging morphology, size and distribution of crystalline and amorphous dry residues in droplets (further referred to as "crystallization pattern") containing predetermined amount of certain crystal-forming organic compounds (reporters) to which protein to be analyzed is added. It has been shown that changes in the crystallization patterns of a number of amino-acids can be used as a "signature" of a protein added. It was also found that both the character of changer in the crystallization patter and the fact of such changes can be used as recognition elements in analysis of protein molecules.

  6. Analog parallel processor hardware for high speed pattern recognition

    NASA Technical Reports Server (NTRS)

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

    1990-01-01

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

  7. Auditory orientation in crickets: Pattern recognition controls reactive steering

    NASA Astrophysics Data System (ADS)

    Poulet, James F. A.; Hedwig, Berthold

    2005-10-01

    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

  8. Mathematical Pattern Recognition Spring Semester 2011

    E-print Network

    Southern California, University of

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

  9. Pattern Recognition Approach to Neuropathy and Neuronopathy

    PubMed Central

    Barohn, Richard J; Amato, Anthony A.

    2014-01-01

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

  10. Neural substrates for visual pattern recognition learning in Igo

    Microsoft Academic Search

    Kosuke Itoh; Hideaki Kitamura; Yukihiko Fujii; Tsutomu Nakada

    2008-01-01

    Different contexts require different visual pattern recognitions even for identical retinal inputs, and acquiring expertise in various visual-cognitive skills requires long-term training to become capable of recognizing relevant visual patterns in otherwise ambiguous stimuli. This 3-Tesla fMRI experiment exploited shikatsu-mondai (life-or-death problems) in the Oriental board game of Igo (Go) to identify the neural substrates supporting this gradual and adaptive

  11. Comparative effects of optical-correlator signal-dependent and signal-independent noise on pattern-recognition performance with the phase-only filter

    NASA Astrophysics Data System (ADS)

    Terrillon, Jean-Christophe

    1995-11-01

    The comparative effects of optical-correlator signal-dependent and additive signal-independent noise on correlation-filter performance are analyzed by three different performance measures. For an identical value of the signal-to-noise ratio imposed on each type of noise in a binary input image, computer simulations performed with the phase-only filter show (i) that additive Gaussian signal-independent noise yields a much larger correlation-performance degradation than signal-dependent noise and (ii) that the different types of signal-dependent noise lead to similar correlation results because of similar effects on the input image that are inherent to the nature of the noise.

  12. Infrared target simulation environment for pattern recognition applications

    Microsoft Academic Search

    Andreas E. Savakis; Nicholas George

    1994-01-01

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

  13. Shape variability and spatial relationships modeling in statistical pattern recognition

    Microsoft Academic Search

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

    2004-01-01

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

  14. The Illinois Pattern Recognition Computer-ILLIAC III

    Microsoft Academic Search

    BRUCE H. McCORMICKt

    1963-01-01

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

  15. FACIAL EXPRESSION RECOGNITION WITH LOCAL BINARY PATTERNS AND LINEAR PROGRAMMING

    Microsoft Academic Search

    Xiaoyi Feng; Abdenour Hadid

    2005-01-01

    In this work, we propose a novel approach to recognize facial expressions from static images. First, the Local Binary Patterns (LBP) are used to efficiently represent the facial images and then the Linear Programming (LP) technique is adopted to classify the seven facial expressions? anger, disgust, fear, happiness, sadness, surprise and neutral. Experimental results demonstrate an average recognition accuracy of

  16. Facial Emotion Recognition in Schizophrenia: Intensity Effects and Error Pattern

    Microsoft Academic Search

    Christian G. Kohler; Travis H. Turner; B. S. Warren; B. Bilker; Colleen M. Brensinger; Steven J. Siegel; Stephen J. Kanes; Raquel E. Gur; Ruben C. Gur

    2003-01-01

    Objective: The authors used color photo- graphs of emotional and neutral expres- sions to investigate recognition patterns of five universal emotions in schizophrenia. Method: Twenty-eight stable outpatients with schizophrenia (19 men and nine women) and 61 healthy subjects (29 men and 32 women) completed an emotion discrimination test that presented mild and extreme intensities of happy, sad, an- gry, fearful,

  17. Fuzzy Techniques of Pattern Recognition in Risk and Claim Classification

    Microsoft Academic Search

    Richard A. Derrig; Krzysztof M. Ostaszewski

    1995-01-01

    Summary Applications of fuzzy set theory (FST) to property casualty and life insurance have emerged in the last few years through the work of Lemaire (1990), Cummins and Derrig (1991, 1993) and Ostaszewski (1993). This paper continues that line of research by providing an overview of fuzzy pattern recognition techniques. We utilize them in clustering for risk and claim classification.

  18. Application of pattern recognition to the discrimination of roasted coffees

    Microsoft Academic Search

    M. J. Martín; F. Pablos; A. G. González

    1996-01-01

    Pattern recognition procedures have been applied to samples of roasted coffee. Some of them are torrefacto samples, that is coffee roasted with addition of sugar. Principal component analysis, cluster analysis, linear discriminant analysis, soft independent modelling of class analogies, and Spearman correlation studies have been carried out. Caffeine, aqueous extract, amino acids, polyphenols, 5-(hydroxymethyl)furfural, potassium, sodium, calcium, iron, manganese and

  19. R Corresponding author. Pattern Recognition 32 (1999) 339--355

    E-print Network

    Yetisgen-Yildiz, Meliha

    1999-01-01

    R Corresponding author. Pattern Recognition 32 (1999) 339--355 3D object identification with color and curvature signatures Adnan A.Y. Mustafa *, Linda G. Shapiro , Mark A. Ganter Department of Mechanical; curvature signatures and spectral (i.e. color) signatures. Furthermore, the system employs an inexpensive

  20. International Journal of Pattern Recognition and Artificial Intelligence

    E-print Network

    Fuchs, Henry

    International Journal of Pattern Recognition and Artificial Intelligence Vol. 19, No. 4 (2005) 533 ILIE Computer Science Department University of North Carolina at Chapel Hill Sitterson Hall, CB# 3175, Chapel Hill, NC 27599, USA adyilie@cs.unc.edu RAMESH RASKAR Mitsubishi Electric Research Labs 201

  1. Neural field model for the recognition of biological motion patterns

    E-print Network

    Poggio, Tomaso

    ­206 Cambridge, MA 02139, USA Tel.: 617 253 0549 FAX: 617 253 2964 E­mail: giese@mit.edu Keywords: biological Center for Biological and Computational Learning, M.I.T., Cambridge, 45, Carletonstreet, MA 02139 Email for the recognition of complex motion patterns, like biological motion and actions. This paper investi­ gates

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

    Microsoft Academic Search

    P Grassberger

    2004-01-01

    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

  3. Statistical pattern recognition in remote sensing

    Microsoft Academic Search

    Chi Hau Chen; Pei-gee Peter Ho

    2008-01-01

    Remote sensing with sensors mounted on satellites or aircrafts is much needed for resource management, environmental monitoring, disaster response, and homeland defense. Remote sensing data considered include those from multispectral, hyperspectral, radar, optical, and infrared sensors. Classification is often one of the major tasks in information processing. For example, we need to identify vegetations, waterways, and man-made structures from remote

  4. PATTERN RECOGNITION IN INTENSIVE CARE ONLINE MONITORING

    Microsoft Academic Search

    R. Fried; U. Gather; M. Imhoff

    Clinical information systems can record numerous variables describing the patient's state at high sampling frequencies. Intelligent alarm systems and suitable bedside decision support are needed to cope with this flood of information. A basic task here is the fast and correct detection of important patterns of change such as level shifts and trends in the data. We present approaches for

  5. 3D face database for human pattern recognition

    NASA Astrophysics Data System (ADS)

    Song, LiMei; Lu, Lu

    2008-10-01

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

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

    PubMed

    Love, Ryan J; Jones, Kim S

    2013-09-01

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

  7. Cellular Phone Face Recognition System Based on Optical Phase Correlation

    Microsoft Academic Search

    Eriko Watanabe; Sayuri Ishikawa; Maiko Ohta; Kashiko Kodate

    2007-01-01

    We propose a high security facial recognition system using a cellular phone on the mobile network. This system is composed of a face recognition engine based on optical phase correlation which uses phase information with emphasis on a Fourier domain, a control sever and the cellular phone with a compact camera for taking pictures, as a portable terminal. Compared with

  8. Optical character recognition of handwritten Arabic using hidden Markov models

    Microsoft Academic Search

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

    2011-01-01

    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

  9. An application of syntactic pattern recognition to seismic discrimination

    NASA Astrophysics Data System (ADS)

    Liu, H. H.; Fu, K. S.

    1981-08-01

    Two syntactic methods for the recognition of seismic waveforms are presented in this paper. The seismic waveforms are represented by sentences (strings of primitives). Primitive extraction is based on a cluster analysis. Finite-state grammars are inferred from the training samples. The nearest-neighbor decision rule and error-correcting finite-state parsers are used for pattern classification. While both show equal recognition performance, the nearest-neighbor rule is much faster in computation speed. The classification of real earthquake/explosion data is presented as an application example.

  10. Phase Oscillatory Network and Visual Pattern Recognition.

    PubMed

    Follmann, Rosangela; Macau, Elbert E N; Rosa, Epaminondas; Piqueira, Jose R C

    2015-07-01

    We explore a properly interconnected set of Kuramoto type oscillators that results in a new associative-memory network configuration, which includes second- and third-order additional terms in the Fourier expansion of the network's coupling. Investigation of the response of the network to different external stimuli indicates an increase in the network capability for coding and information retrieval. Comparison of the network output with that of an equivalent experiment with subjects, for recognizing perturbed binary patterns, shows comparable results between the two approaches. We also discuss the enhanced storage capacity of the network. PMID:25137734

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

    Microsoft Academic Search

    Robert A. Baron; Michael D. Ensley

    2006-01-01

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

  12. High speed optical object recognition processor with massive holographic memory

    NASA Technical Reports Server (NTRS)

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

    2002-01-01

    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.

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

    E-print Network

    Bhunia, Swarup

    Wavelet-Based Neural Pattern Analyzer for Behaviorally Significant Burst Pattern Recognition-bandwidth link. We present a novel wavelet- based approach for detecting spikes, grouping them as bursts-resolution wavelet analysis [4] of recorded signals to de-noise the data, detect and sort spikes and then determine

  14. Pattern Recognition Software and Techniques for Biological Image Analysis

    PubMed Central

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

    2010-01-01

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

  15. Object detection by optical correlator and intelligence recognition surveillance systems

    NASA Astrophysics Data System (ADS)

    Sheng, Yunlong

    2013-09-01

    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.

  16. Pattern recognition in correlated and uncorrelated noise

    PubMed Central

    Conrey, Brianna; Gold, Jason M.

    2009-01-01

    This study examined how correlated, or filtered, noise affected efficiency for recognizing two types of signal patterns, Gabor patches and three-dimensional objects. In general, compared with the ideal observer, human observers were most efficient at performing tasks in low-pass noise, followed by white noise; they were least efficient in high-pass noise. Simulations demonstrated that contrast-dependent internal noise was likely to have limited human performance in the high-pass conditions for both signal types. Classification images showed that observers were likely adopting different strategies in the presence of low-pass versus white noise. However, efficiencies were underpredicted by the linear classification images and asymmetries were present in the classification subimages, indicating the influence of nonlinear processes. Response consistency analyses indicated that lower contrast-dependent internal noise contributed somewhat to higher efficiencies in low-pass noise for Gabor patches but not objects. Taken together, the results of these experiments suggest a complex interaction among signals, external noise spectra, and internal noise in determining efficiency in correlated and uncorrelated noise. PMID:19884919

  17. Pattern recognition for selective odor detection with gas sensor arrays.

    PubMed

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

    2012-01-01

    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

  18. Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays

    PubMed Central

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

    2012-01-01

    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

  19. Markov sequential pattern recognition : dependency and the unknown class.

    SciTech Connect

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

    2004-10-01

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

  20. Face Recognition with Patterns of Oriented Edge Magnitudes

    Microsoft Academic Search

    Ngoc-Son Vu; Alice Caplier

    2010-01-01

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

  1. Neural networks and pattern recognition in human-computer interaction

    Microsoft Academic Search

    Janet Finlay; Russell Beale

    1993-01-01

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

  2. A Novel Driving Pattern Recognition and Status Monitoring System

    Microsoft Academic Search

    Jiann-der Lee; Jiann-der Li; Li-chang Liu; Chi-ming Chen

    2006-01-01

    \\u000a This paper describes a novel driving pattern recognition and status monitoring system based on the orientation information.\\u000a Two fixed cameras are used to capture the driver’s image and the front-road image. The driver’s sight line and the driving\\u000a lane path are found from these 2 captured images and are mapped into a global coordinate. Two correlation coefficients among\\u000a the driver’s

  3. Pattern recognition used to investigate multivariate data in analytical chemistry

    SciTech Connect

    Jurs, P.C.

    1986-06-06

    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.

  4. A Paper Checking Instrument with Fuzzy Pattern Recognition

    Microsoft Academic Search

    Yuezong Wang; Desheng Li; Jinghui Liu

    2006-01-01

    In this paper, a page-checking instrument via MCU Atmega128 and an algorithm of fuzzy pattern recognition is developed for automatic bookbinding machines. This instrument detects dynamically wrong pages locating in many word pages, graphic pages and word-graphic pages, and at the same time, sends messages to automatic bookbinding machine that lets wrong pages pass as soon as receiving messages. This

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

    Microsoft Academic Search

    M. Steinlin; S. Blaser; E. Boltshauser

    1998-01-01

    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

  6. Neurocomputing methods for pattern recognition in nuclear physics

    SciTech Connect

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

    1991-12-31

    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.

  7. A pattern recognition application framework for biomedical datasets

    Microsoft Academic Search

    Rodrigo Vivanco; ALEKSANDER B. DEMKO; Mark Jarmasz; RAY L. SOMORJAI; NICK J. PIZZI

    2007-01-01

    Pattern recognition techniques are widely used in the biomedical domain, solving problems ranging from the prediction of cancers to the detection of neural activations in the human brain. Modern biomedical techniques, such as magnetic resonance spectroscopy (MRS) or imaging (MRI), produce voluminous, high-dimensional datasets, whose reliable analysis by medical practitioners requires high-performance, user-friendly programs. Furthermore, researchers who develop such programs

  8. Pattern recognition of earthquake prone area in North China

    E-print Network

    Gu, Ji-Min

    1989-01-01

    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...

  9. An optical processor for object recognition and tracking

    NASA Technical Reports Server (NTRS)

    Sloan, J.; Udomkesmalee, S.

    1987-01-01

    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.

  10. Mixed pattern matching-based traffic abnormal behavior recognition.

    PubMed

    Wu, Jian; Cui, Zhiming; Sheng, Victor S; Shi, Yujie; Zhao, Pengpeng

    2014-01-01

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

  11. Singlemode optical fiber electronic speckle pattern interferometry

    Microsoft Academic Search

    H. M. Shang

    1996-01-01

    This paper describes two electronic speckle pattern interferometric (ESPI) systems using single mode optical fibers, namely, conventional ESPI and phase shifting ESPI (PSESPI), and their applications to the inspection of unbonds in carbon-epoxy honeycomb composite structures. It is observed that phase shifting ESPI produces fringe patterns of higher contrast than conventional ESPI. With the remarkably good quality fringe patterns obtained

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

    E-print Network

    Kim, Tae-Kyun

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

  13. Issues in evolving GP based classifiers for a pattern recognition task

    Microsoft Academic Search

    Ankur M. Teredesai; Venu Govindaraju

    2004-01-01

    This paper discusses issues when evolving genetic programming (GP) classifiers for a pattern recognition task such as handwritten digit recognition. Developing elegant solutions for handwritten digit classification is a challenging task. Similarly, design and training of classifiers using genetic programming is a relatively new approach in pattern recognition as compared to other traditional techniques. Several strategies for GP training are

  14. Electronic system with memristive synapses for pattern recognition

    PubMed Central

    Park, Sangsu; Chu, Myonglae; Kim, Jongin; Noh, Jinwoo; Jeon, Moongu; Hun Lee, Byoung; Hwang, Hyunsang; Lee, Boreom; Lee, Byung-geun

    2015-01-01

    Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction. PMID:25941950

  15. Electronic system with memristive synapses for pattern recognition.

    PubMed

    Park, Sangsu; Chu, Myonglae; Kim, Jongin; Noh, Jinwoo; Jeon, Moongu; Hun Lee, Byoung; Hwang, Hyunsang; Lee, Boreom; Lee, Byung-Geun

    2015-01-01

    Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction. PMID:25941950

  16. Recognition as a challenging label-free optical sensing system

    NASA Astrophysics Data System (ADS)

    Gauglitz, Günter

    2013-05-01

    Optical biosensors are increasingly used in application areas of environmental analysis, healthcare and food safety. The quality of the biosensor's results depends on the interaction layer, the detection principles, and evaluation strategies, not only on the biopolymer layer but also especially on recognition elements. Using label-free optical sensing, non-specific interaction between sample and transducer has to be reduced, and the selectivity of recognition elements has to be improved. For this reason, strategies to avoid non-specific interaction even in blood and milk are discussed, a variety of upcoming recognition is given. Based on the classification of direct optical detection methods, some examples for the above mentioned applications are reviewed. Trends as well as advantages of parallel multisport detection for kinetic evaluation are also part of the lecture.

  17. Pattern Recognition for a Flight Dynamics Monte Carlo Simulation

    NASA Technical Reports Server (NTRS)

    Restrepo, Carolina; Hurtado, John E.

    2011-01-01

    The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.

  18. Compact Binary Patterns (CBP) with Multiple Patch Classifiers for Fast and Accurate Face Recognition

    Microsoft Academic Search

    Hieu V. Nguyen; Li Bai

    2010-01-01

    \\u000a Face recognition is one of the most active research areas in pattern recognition for the last decades because of its potential\\u000a applications as well as scientific challenges. Although numerous methods for face recognition have been developed, recognition\\u000a accuracy and speed still remain a problem. In this paper, we propose a novel method for fast and accurate face recognition.\\u000a The contribution

  19. A statistical pattern recognition paradigm for structural health monitoring

    SciTech Connect

    Farrar, C. R. (Charles R.); Sohn, H. (Hoon); Park, G. H. (Gyu Hae)

    2004-01-01

    The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system's current or future performance. Our approach is to address the SHM problem in the context of a statistical pattern recognition paradigm (Farrar, Nix and Doebling, 2001). In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition, (3) Feature Extraction, and (4) Statistical Model Development for Feature Discrimination. When one attempts to apply this paradigm to data from 'real-world' structures, it quickly becomes apparent that data cleansing, normalization, fusion and compression, which can be implemented with either hardware or software, are inherent in Parts 2-4 of this paradigm. The authors believe that all approaches to SHM, as well as all traditional non-destructive evaluation procedures (e.g. ultrasonic inspection, acoustic emissions, active thermography) can be cast in the context of this statistical pattern recognition paradigm. It should be noted that the statistical modeling portion of the structural health monitoring process has received the least attention in the technical literature. The algorithms used in statistical model development usually fall into the three categories of group classification, regression analysis or outlier detection. The ability to use a particular statistical procedure from one of these categories will depend on the availability of data from both an undamaged and damaged structure. This paper will discuss each portion of the SHM statistical pattern recognition paradigm.

  20. Recognition of Japanese devoiced vowels using array of plastic optical fiber moisture sensors

    NASA Astrophysics Data System (ADS)

    Morisawa, Masayuki; Taki, Tomohito; Natori, Yoichi; Muto, Shinzo

    2008-04-01

    A novel plastic-optical-fiber (POF) microphone for discerning devoiced breath, which is based on the detection of moisture pattern depending on the shape of mouth, have been studied. In the experiment coupled with this microphone and Dynamic Programming (DP) matching method, recognition rate over 90% was obtained against five devoiced vowels in Japanese. Therefore, using this system, verbally handicapped people will create sounds with a small effort.

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

    SciTech Connect

    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

    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.

  2. Comparison of eye imaging pattern recognition using neural network

    NASA Astrophysics Data System (ADS)

    Bukhari, W. M.; Syed A., M.; Nasir, M. N. M.; Sulaima, M. F.; Yahaya, M. S.

    2015-05-01

    The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

  3. A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation.

    PubMed

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

    2014-03-28

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

  4. Memristor-MOS analog correlator for pattern recognition system.

    PubMed

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

    2013-05-01

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

  5. Control chart pattern recognition using a back propagation neural network

    NASA Astrophysics Data System (ADS)

    Spoerre, Julie K.; Perry, Marcus B.

    2000-10-01

    In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). On-line automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. A back propagation artificial neural network is used to model X-bar quality control charts and identify process instability situations as specified by the Western Electric Statistical Quality Control handbook. Results indicate that the performance of the back propagation neural network is very accurate in identifying these control chart patterns. This work is significant in that the neural network output can serve as a link to process parameters in a closed-loop control system. In this way, adjustments to the process can be made on-line and quality problems averted.

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

    SciTech Connect

    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

    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.

  7. Optical character recognition of handwritten Arabic using hidden Markov models

    SciTech Connect

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

    2011-01-01

    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.

  8. Gene prediction by pattern recognition and homology search

    SciTech Connect

    Xu, Y.; Uberbacher, E.C.

    1996-05-01

    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.

  9. Playing Tag with ANN: Boosted Top Identification with Pattern Recognition

    E-print Network

    Almeida, Leandro G; Cliche, Mathieu; Lee, Seung J; Perelstein, Maxim

    2015-01-01

    Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a "digital image" of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p_T in the 1100-1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the ...

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

    NASA Technical Reports Server (NTRS)

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

    1984-01-01

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

  11. Survey and bibliography of Arabic optical text recognition

    Microsoft Academic Search

    Badr Al-Badr; Sabri A. Mahmoud

    1995-01-01

    Research work on Arabic optical text recognition (AOTR), although lagging that of other languages, is becoming more intensive than before and commercial systems for AOTR are becoming available. This paper presents a comprehensive survey and bibliography of research on AOTR, by covering all the research publications on AOTR to which the authors had access. This paper introduces the general topic

  12. Optical Character Recognition of Heavily Distorted Text Segments

    E-print Network

    be generated, which are recognizable by humans but not by artificial intelligence, shows that research in the field of artificial intelligence in the range of optical character recognition is not as advanced ­ Height and Minimal Area . . . . . . . . . . . . . . . 28 5.2.4 Necessary Condition ­ Open Counters

  13. Image Description with Local Patterns: An Application to Face Recognition

    NASA Astrophysics Data System (ADS)

    Zhou, Wei; Ahrary, Alireza; Kamata, Sei-Ichiro

    In this paper, we propose a novel approach for presenting the local features of digital image using 1D Local Patterns by Multi-Scans (1DLPMS). We also consider the extentions and simplifications of the proposed approach into facial images analysis. The proposed approach consists of three steps. At the first step, the gray values of pixels in image are represented as a vector giving the local neighborhood intensity distrubutions of the pixels. Then, multi-scans are applied to capture different spatial information on the image with advantage of less computation than other traditional ways, such as Local Binary Patterns (LBP). The second step is encoding the local features based on different encoding rules using 1D local patterns. This transformation is expected to be less sensitive to illumination variations besides preserving the appearance of images embedded in the original gray scale. At the final step, Grouped 1D Local Patterns by Multi-Scans (G1DLPMS) is applied to make the proposed approach computationally simpler and easy to extend. Next, we further formulate boosted algorithm to extract the most discriminant local features. The evaluated results demonstrate that the proposed approach outperforms the conventional approaches in terms of accuracy in applications of face recognition, gender estimation and facial expression.

  14. Automatic target recognition using a feature-based optical neural network

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin

    1992-01-01

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

  15. FACIAL EXPRESSION RECOGNITION USING ADVANCED LOCAL BINARY PATTERNS, TSALLIS ENTROPIES AND GLOBAL APPEARANCE FEATURES

    E-print Network

    Yeung, Dit-Yan

    FACIAL EXPRESSION RECOGNITION USING ADVANCED LOCAL BINARY PATTERNS, TSALLIS ENTROPIES AND GLOBAL,fwkevin,achung,dyyeung}@cse.ust.hk ABSTRACT This paper proposes a novel facial expression recognition approach based on two sets of features recognition, Machine vision, 1. INTRODUCTION Within the last couple of years, automatic facial expression

  16. FACIAL EXPRESSION RECOGNITION USING ADVANCED LOCAL BINARY PATTERNS, TSALLIS ENTROPIES AND GLOBAL APPEARANCE FEATURES

    E-print Network

    Chung, Albert C. S.

    FACIAL EXPRESSION RECOGNITION USING ADVANCED LOCAL BINARY PATTERNS, TSALLIS ENTROPIES AND GLOBAL proposes a novel facial expression recognition approach based on two sets of features extracted from on the JAFFE database, and compared with two widely used facial expres- sion recognition approaches

  17. Automatic Facial Expression Recognition Based on Local Binary Patterns of Local Areas

    Microsoft Academic Search

    Wei-feng Liu; Shu-juan Li; Yan-jiang Wang

    2009-01-01

    Automatic facial expression recognition is one of the great challenges in facial expression recognition field. An algrithom of automatic facial expression recognition is proposed based on Local Binary Patterns of local areas (LLBP)in this work. First, the position of eye balls is fixed by projection method. Then the local areas of the eyes and mouthpsilas neighbourhood could be determined through

  18. A Renaissance of Elicitors: Perception of Microbe-Associated Molecular Patterns and Danger Signals by Pattern-Recognition Receptors

    Microsoft Academic Search

    Thomas Boller; Georg Felix

    2009-01-01

    Microbe-associated molecular patterns (MAMPs) are molecular signa- tures typical of whole classes of microbes, and their recognition plays a key role in innate immunity. Endogenous elicitors are similarly recog- nized as damage-associated molecular patterns (DAMPs). This review focuses on the diversity of MAMPs\\/DAMPs and on progress to iden- tify the corresponding pattern recognition receptors (PRRs) in plants. The two best-characterized

  19. An In Vitro DNA Hypernetwork for Digit Pattern Recognition Kyung-Ae Yang1,2

    E-print Network

    application of DNA computing in other pattern recognition such as direct gene expression pattern or image is supported in part by the Ministry of Knowledge Economy (MEC project) and the Korea Science Foundation

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

    E-print Network

    Fields, Matthew James

    2008-10-10

    An experimental approach to traffic flow analysis is presented in which methodology from pattern recognition is applied to a specific dataset to examine its utility in determining traffic patterns. The selected dataset ...

  1. Interaction of Pattern Recognition Receptors with Mycobacterium Tuberculosis.

    PubMed

    Mortaz, Esmaeil; Adcock, Ian M; Tabarsi, Payam; Masjedi, Mohammad Reza; Mansouri, Davood; Velayati, Ali Akbar; Casanova, Jean-Laurent; Barnes, Peter J

    2014-10-14

    Tuberculosis (TB) is considered a major worldwide health problem with 10 million new cases diagnosed each year. Our understanding of TB immunology has become greater and more refined since the identification of Mycobacterium tuberculosis (MTB) as an etiologic agent and the recognition of new signaling pathways modulating infection. Understanding the mechanisms through which the cells of the immune system recognize MTB can be an important step in designing novel therapeutic approaches, as well as improving the limited success of current vaccination strategies. A great challenge in chronic disease is to understand the complexities, mechanisms, and consequences of host interactions with pathogens. Innate immune responses along with the involvement of distinct inflammatory mediators and cells play an important role in the host defense against the MTB. Several classes of pattern recognition receptors (PRRs) are involved in the recognition of MTB including Toll-Like Receptors (TLRs), C-type lectin receptors (CLRs) and Nod-like receptors (NLRs) linked to inflammasome activation. Among the TLR family, TLR1, TLR2, TLR4, and TLR9 and their down-stream signaling proteins play critical roles in the initiation of the immune response in the pathogenesis of TB. The inflammasome pathway is associated with the coordinated release of cytokines such as IL-1? and IL-18 which also play a role in the pathogenesis of TB. Understanding the cross-talk between these signaling pathways will impact on the design of novel therapeutic strategies and in the development of vaccines and immunotherapy regimes. Abnormalities in PRR signaling pathways regulated by TB will affect disease pathogenesis and need to be elucidated. In this review we provide an update on PRR signaling during M. tuberculosis infection and indicate how greater knowledge of these pathways may lead to new therapeutic opportunities. PMID:25312698

  2. A novel thermal face recognition approach using face pattern words

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng

    2010-04-01

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

  3. Using Decision Trees for Comparing Pattern Recognition Feature Sets

    SciTech Connect

    Proctor, D D

    2005-08-18

    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.

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

    PubMed

    Pei, Tiefan; Jin, Changiie

    2005-09-01

    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

  5. Recognition of lipopolysaccharide pattern by TLR4 complexes

    PubMed Central

    Park, Beom Seok; Lee, Jie-Oh

    2013-01-01

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

  6. Using Genetic Algorithms to Explore Pattern Recognition in the Immune System

    E-print Network

    Forrest, Stephanie

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

  7. Using Genetic Algorithms to Explore Pattern Recognition in the Immune System

    Microsoft Academic Search

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

    1993-01-01

    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

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

    Microsoft Academic Search

    Gail A. Carpenter; Stephen Grossberg

    1988-01-01

    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

  9. Teaching image processing and pattern recognition with the Intel OpenCV library

    Microsoft Academic Search

    Adam Kozlowski; Aleksandra Królak

    2009-01-01

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

  10. Fuzzy pattern recognition method for assessing groundwater vulnerability to pollution in the Zhangji area

    Microsoft Academic Search

    MAO Yuan-yuan; ZHANG Xue-gang; WANG Lian-sheng

    Based on the widely used DRASTIC method, a fuzzy pattern recognition and optimization method was proposed and applied to the fissured-karstic aquifer of Zhangji area for assessing groundwater vulnerability to pollution. The result is compared with DRASTIC method. It is shown that by taking the fuzziness into consideration, the fuzzy pattern recognition and optimization method reflects more efficiently the fuzzy

  11. Research on fault pattern recognition for aircraft fuel system with its performance simulation

    Microsoft Academic Search

    Haifeng Wang; Bifeng Song; Fangyi Wan

    2011-01-01

    This paper presents application research on fault pattern recognition for aircraft fuel system based on its performance simulation. Fault pattern recognition method is used to perform fault detection, fault isolation, fault prognostics and so on in complicated system, which is basic research contents of prognostics and health management (PHM). Since the hardware of fuel system for an aircraft is too

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

    E-print Network

    Ferreira, Márcia M. C.

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

  13. Dense Pattern Optical Multipass Cell

    NASA Technical Reports Server (NTRS)

    Silver, Joel A. (Inventor)

    2009-01-01

    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.

  14. Optical Pattern Generator Using Excimer Laser

    NASA Astrophysics Data System (ADS)

    Hafner, Bernhard F.

    1988-01-01

    Reticles (masks on enlarged scale) are needed for optical pattern transfer in the production of integrated semiconductor circuits. In order to meet present requirements for 5X reticles only a direct writing technique is feasible. This means direct exposing of photoresist either with light or an electron beam. Many of todays highly dense reticles require some 10 5 to 10 6 discrete exposures when generated with an optical pattern generator. Optical pattern generators normally use mercury arc lamps to expose positive photoresist, which in turn need 200 milliseconds for each of these discrete exposures, thus requiring to stop the table at every exposure position ("stop and go" mode). This results in running times of several days per reticle. Therefore most reticles are nowadays being manufactured with very expensive e-beam machines. In the early 80's we started the first experiments to expose photoresist with an excimer laser. In order to obtain the maximum gain in speed, the goal was to operate with only one excimer laser pulse per exposure, so that a fast "flash on the fly" operation with an optical pattern generator became true. Equipping a conventional optical pattern generator with an excimer laser as the light source, it has become possible to expose substrates coated with standard photoresist in the "flash on the fly" mode with only 13 nanoseconds per flash. So the thruput could be increased up to 25 times in comparison to a pattern generator equipped with a mercury lamp. A comparison of both operation modes will show that an immense increase of speed is possible, even when a ten years old M3600 pattern generator is used. This system is in function now with very high reliability since more than three years in our IC development line.

  15. Workshop on Pattern Recognition in Information Systems, pp. 124-133, Miami, FL, 2005 Activity Identification and Visualization1

    E-print Network

    Hoff, William A.

    Workshop on Pattern Recognition in Information Systems, pp. 124-133, Miami, FL, 2005 Activity of this project. #12;Workshop on Pattern Recognition in Information Systems, pp. 124-133, Miami, FL, 2005 any

  16. Pattern formation in optical parametric oscillators

    Microsoft Academic Search

    S. Ducci; N. Treps; A. Maître; C. Fabre

    2001-01-01

    We have observed transverse pattern formation in type-II continuous-wave triply resonant optical parametric oscillators (OPOs). Different kinds of cavities with degenerate transverse modes have been studied: confocal, concentric, semiconcentric, and planar. We have investigated the OPO oscillation threshold and the transverse intensity distribution of the emitted fields as a function of the distance from the degeneracy position. The experimental configurations

  17. Pattern recognition using a device substituting audition for vision in blindfolded sighted subjects.

    PubMed

    Poirier, C; De Volder, A; Tranduy, D; Scheiber, C

    2007-03-14

    A major question in the field of sensory substitution concerns the nature of the perception generated by sensory substitution devices. In the present fMRI study, we investigated the neural substrates of pattern recognition through a device substituting audition for vision in blindfolded sighted subjects, before and after a short training period. Before training, pattern recognition recruited dorsal and ventral extra-striate areas. After training, the recruitment of these visual areas was found to have increased. These results suggest that visual imagery processes could be involved in pattern recognition and that perception through the substitution device could be visual-like. PMID:17116311

  18. Algorithms for pattern recognition in images of cell cultures

    NASA Astrophysics Data System (ADS)

    Mendes, Joyce M.; Peixoto, Nathalia L.; Ramirez-Fernandez, Francisco J.

    2001-06-01

    Several applications of silicon microstructures in areas such as neurobiology and electrophysiology have been stimulating the development of microsystems with the objective of mechanical support to monitor and control several parameters in cell cultures. In this work a multi-microelectrode arrays was fabricated over a glass plate to obtain the growth of neuronal cell monitoring their behavior during cell development. To identify the neuron core and axon an approach for implementation of edge detectors algorithms associated to images is described. The necessity of efficient and reliable algorithms for image processing and interpretation is justified by its large field of applications in several areas as well as medicine, robotics, cellular biology, computational vision and pattern recognition. In this work, it is investigated the adequacy of some edge detectors algorithms such as Canny, Marr-Hildreth. Some alterations in those methods are propose to improve the identification of both cell core and axonal growth measure. We compare the operator to edge detector proposed by Canny, Marr-Hildreth operator and application of Hough Transform. For evaluation of algorithms adaptations, we developed a method for automatic cell segmentation and measurement. Our goal is to find a set of parameters defining the location of the objects to compare the original and processed images.

  19. Imbalanced learning for pattern recognition: an empirical study

    NASA Astrophysics Data System (ADS)

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

    2010-10-01

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

  20. Scalable pattern recognition for large-scale scientific data mining

    SciTech Connect

    Kamath, C.; Musick, R.

    1998-03-23

    Our ability to generate data far outstrips our ability to explore and understand it. The true value of this data lies not in its final size or complexity, but rather in our ability to exploit the data to achieve scientific goals. The data generated by programs such as ASCI have such a large scale that it is impractical to manually analyze, explore, and understand it. As a result, useful information is overlooked, and the potential benefits of increased computational and data gathering capabilities are only partially realized. The difficulties that will be faced by ASCI applications in the near future are foreshadowed by the challenges currently facing astrophysicists in making full use of the data they have collected over the years. For example, among other difficulties, astrophysicists have expressed concern that the sheer size of their data restricts them to looking at very small, narrow portions at any one time. This narrow focus has resulted in the loss of ``serendipitous`` discoveries which have been so vital to progress in the area in the past. To solve this problem, a new generation of computational tools and techniques is needed to help automate the exploration and management of large scientific data. This whitepaper proposes applying and extending ideas from the area of data mining, in particular pattern recognition, to improve the way in which scientists interact with large, multi-dimensional, time-varying data.

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

    NASA Astrophysics Data System (ADS)

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

    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.

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

    E-print Network

    Jaffe, Jules

    for Recognition Under Variable Lighting: Faces Athinodoros S. Georghiades David J. Kriegman Peter N. Belhumeur di erent even when viewed in xed pose. To handle this variability, an object recognition system must and in accuracy. Yet these methods su er from an important drawback: recognition of an object (or face) under

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

    NASA Astrophysics Data System (ADS)

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

    2013-01-01

    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.

  4. Optical Fourier diffractometry applied to degraded bone structure recognition

    NASA Astrophysics Data System (ADS)

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

    1993-09-01

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

  5. Introduction to Pattern Recognition1 Anil K. Jain, Robert P.W. Duin

    E-print Network

    Duin, Robert P.W.

    .g., a dog, a mammal, an animal). 1. Introduction. Since our early childhood, we have been observing patterns). Thus, a pattern could be a finger- print image, a handwritten cursive word, a human face, a speech sig recognition remains an elusive goal. Humans are the best pattern recognizers in most scenarios, yet we do

  6. Geometric proto-symbol manipulation towards language-based motion pattern synthesis and recognition

    Microsoft Academic Search

    Tetsunari Inamura; Tomohiro Shibata

    2008-01-01

    In this paper, we propose an improved mimesis method for interpolation and extrapolation of motion patterns in the proto-symbol space towards an ultimate goal that motion pattern synthesis and recognition of humanoid robots are achieved by means of natural language. The proto-symbol space is a topological space which abstracts motion patterns by utilizing continuous hidden Markov models. An interpolation algorithm

  7. Computation Strategies for Volume Local Binary Patterns applied to Action Recognition

    E-print Network

    Computation Strategies for Volume Local Binary Patterns applied to Action Recognition F. Baumann, A.R. China ljtale@gmail.com Abstract Volume Local Binary Patterns are a well-known fea- ture type to describe object characteristics in the spatio- temporal domain. Apart from the computation of a binary pattern

  8. Nonadiabatic pattern formation in optical parametric oscillators.

    PubMed

    Longhi, S

    2000-06-19

    A nonadiabatic mechanism for pattern formation in parametrically forced systems subjected to a slow periodic modulation of the excitation frequency is proposed and discussed in detail for the case of an optical parametric oscillator. It is demonstrated that nonautonomous dynamics may induce nonadiabatic off-axis emission of down-converted photons even when the signal field is blueshifted from the nearby cavity resonance. PMID:10991047

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

    NASA Technical Reports Server (NTRS)

    Guseman, L. F., Jr.

    1983-01-01

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

  10. Application of pattern recognition methods in electropuncture diagnostics for assessment of neuropsychic state

    Microsoft Academic Search

    O. V. Sen’ko; A. V. Kuznetsova; A. N. Strel’nikov; V. B. Mamaev

    2006-01-01

    The logical and statistical methods of pattern recognition and data analysis are applied for studying the capability of electropuncture\\u000a diagnostics for the estimation of the neuropsychic states (stress) of school children during an examination period.

  11. 9.913-C Pattern Recognition for Machine Vision, Spring 2002

    E-print Network

    Poggio, Tomaso

    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: ...

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

    E-print Network

    Massachusetts Institute of Technology. Media Laboratory.

    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 ...

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

    E-print Network

    Houang, Anne-Benedicte

    1992-01-01

    A HIERARCHY OF PATTERN RECOGNITION ALGORITHMS FOR THE DIAGNOSIS OF SUCKER ROD PUMPED WELLS A Thesis by ANNE-BENEDICTE HOUANG Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... for the degree of MASTER OF SCIENCE December 1992 Major Subject: Petroleum Engineering A HIERARCHY OF PATTERN RECOGNITION ALGORITHMS FOR THE DIAGNOSIS OF SUCKER ROD PUMPED WELLS A Thesis by ANNE-BENEDICTE HOUANG Approved as to style and content by...

  14. Flat foot functional evaluation using pattern recognition of ground reaction data

    Microsoft Academic Search

    A. Bertani; A. Cappello; M. G. Benedetti; L. Simoncini; F. Catani

    1999-01-01

    Objective. Main purpose of this study was to apply quantitative gait analysis and statistical pattern recognition as clinical decision-making aids in flat foot diagnosis and post-surgery monitoring.Design. Statistical pattern recognition techniques were applied to discriminate between normal and flat foot populations through ground reaction force measurements; ground reaction forces time course was assumed as a sensible index of the foot

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

    Microsoft Academic Search

    Pavle Stepanic; Ilija V. Latinovic; Zeljko Djurovic

    2009-01-01

    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

  16. Fuzzy pattern recognition method for assessing groundwater vulnerability to pollution in the Zhangji area

    Microsoft Academic Search

    Yuan-yuan Mao; Xue-gang Zhang; Lian-sheng Wang

    2006-01-01

    Based on the widely used DRASTIC method, a fuzzy pattern recognition and optimization method was proposed and applied to the\\u000a fissured-karstic aquifer of Zhangji area for assessing groundwater vulnerability to pollution. The result is compared with\\u000a DRASTIC method. It is shown that by taking the fuzziness into consideration, the fuzzy pattern recognition and optimization\\u000a method reflects more efficiently the fuzzy

  17. Applications of pattern recognition techniques to online fault detection

    SciTech Connect

    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

    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.

  18. Hand posture recognition using jointly optical flow and dimensionality reduction

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  19. Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 338345, 1998. 1 Boundary Finding with Correspondence Using

    E-print Network

    Duncan, James S.

    step in a number of computer vision applications such as stereo disparity, object recognition, motionProc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 338­345, 1998. 1 Boundary Finding and computer vision applications including robot vision, pattern recognition and biomedical image pro­ cessing

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

    PubMed Central

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

    2015-01-01

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

  1. Cross-kingdom patterns of alternative splicing and splice recognition

    E-print Network

    McGuire, Abigail Manson

    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 ...

  2. Cross-kingdom patterns of alternative splicing and splice recognition

    Microsoft Academic Search

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

    2008-01-01

    Background  Variations in transcript splicing can reveal how eukaryotes recognize intronic splice sites. Retained introns (RIs) commonly\\u000a appear when the intron definition (ID) mechanism of splice site recognition inconsistently identifies intron-exon boundaries,\\u000a and cassette exons (CEs) are often caused by variable recognition of splice junctions by the exon definition (ED) mechanism.\\u000a We have performed a comprehensive survey of alternative splicing across

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

    PubMed Central

    Swartz, R. Andrew

    2013-01-01

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

  4. Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

    PubMed

    Ming, Yue; Wang, Guangchao; Fan, Chunxiao

    2015-01-01

    With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition. PMID:25942404

  5. 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...

  6. INDUCTIVE INFERENCE THEORY — A UNIFIED APPROACH TO PROBLEMS IN PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

    Microsoft Academic Search

    Ray J. Solomonoff

    1975-01-01

    Recent results in induction theory are reviewed that demonstrate the general adequacy of the induction system of Solomonoff and Willis. Sev- eral problems in pattern recognition and A.I. are investigated through these methods. The theory is used to obtain the a priori probabilities that are necessary in the application of stochastic languages to pattern recog- nition. A simple, quantitative solution

  7. Enhanced optical inspectability of patterned EUVL mask

    NASA Astrophysics Data System (ADS)

    Liang, Ted; Stivers, Alan R.; Yan, Pei-yang; Tejnil, Edita; Zhang, Guojing

    2002-03-01

    For optical inspection of Extreme Ultraviolet Lithography (EUVL) masks using Deep Ultraviolet (DUV) light, contrast from reflected light is used to form the image of the mask and detect the defects. The inspectability of a patterned mask depends on the optical properties, surface conditions and thickness of absorber and buffer layer. The issue in EUVL mask inspection is the relatively low image contrast in the inspection tool because both the EUV-reflective and EUV-absorbing regions reflect DUV light. The need of a buffer layer to protect the multilayer (ML) reflector during mask processing and defect repair necessitates two inspections for a patterned mask: one with the buffer layer on to find the defect for repair and one with the buffer layer removed to qualify a final mask. Since the ML appears bright at DUV inspection wavelengths, the buffer layer is also chosen to give high reflectivity. Therefore, the absorber reflectivity must be low enough to provide high image contrast and to avoid the edge interference effect. Recently, we have developed a surface treatment process to reduce the reflectivity of absorber layer and result in a DUV contrast approaching 90 percent. This greatly improves the optical inspectability of EUVL mask to a level similar to conventional transmission mask. In this paper, we describe the overall EUVL mask inspection strategy and present a comprehensive discussion on mask optimization in materials selection and modification for high inspectability. We report the reflectivity of Mo-Si multilayer, buffer layers using SiO2 and Ru, and absorber layers of Cr and TaN. We will demonstrate with DUV inspection images of the optimized EUVL masks that the image contrast and quality from reflected light are close to those of conventional photo-masks with transmitted light. This greatly enhanced EUVL mask inspectability will increase defect detectability for inspection tools and simplify image rendering in die-to-database inspection.

  8. Principal patterns of fractional-order differential gradients for face recognition

    NASA Astrophysics Data System (ADS)

    Yu, Lei; Cao, Qi; Zhao, Anping

    2015-01-01

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

  9. An Auditory Feature Detection Circuit for Sound Pattern Recognition

    E-print Network

    Schöneich, Stefan; Kostarakos, Konstantinos; Hedwig, Berthold

    2015-06-17

    neurons in the brain of a sound-producing fish. J. Comp. Physiol. A 180, 439-450 (1997). 9. B. Ronacher, R. M. Hennig, J. Clemens, Computational principles underlying recognition of acoustic signals in grasshoppers and crickets. J. Comp. Physiol. A 201...

  10. HotSpotter -Patterned Species Instance Recognition Jonathan P. Crall

    E-print Network

    Bystroff, Chris

    . Images of animals may be taken by anyone who has a camera -- scientists and their assistants, ecotourists -- and a ranked list of matching animal im- ages from the database is returned. Here, the correct match, an image for animal population analysis depends on locating and recognizing the animals in each image. The recognition

  11. Quantum algorithm for optical-template recognition with noise filtering

    SciTech Connect

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

    2006-07-15

    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.

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

    NASA Astrophysics Data System (ADS)

    Wang, Huafeng; Han, Yong; Zhang, Zhaoxiang

    2011-12-01

    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.

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

    NASA Astrophysics Data System (ADS)

    Wang, Huafeng; Han, Yong; Zhang, Zhaoxiang

    2012-01-01

    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.

  14. Direct Nano-Patterning With Nano-Optic Devices 

    E-print Network

    Meenashi Sundaram, Vijay

    2011-08-08

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

  15. Feature competition and feature extraction in a noniterative neural network pattern recognition scheme

    NASA Astrophysics Data System (ADS)

    Hu, Chia-Lun J.

    1998-03-01

    As we published in the last few years, when the given input- output training vector pairs satisfy a PLI (positive-linear- independency) condition, the training and the application of a hard-limited neural network can be achieved non-iteratively with very short training time and very robust recognition when it is applied to recognize any untrained patterns. The key feature in this novel pattern recognition system is the use of slack constants in solving the connection matrix when the PLI condition is satisfied. Generally there are infinitely many ways of selecting the slack constants for meeting the training-recognition goal, but there is only one way to select them if an optimal robustness is sought in the recognition of the untrained patterns. This particular way of selecting the slack constants carries some special physical properties of the system -- the automatic feature extraction in the learning mode and the automatic feature competition in the recognition mode. Physical significance as well as mathematical analysis of these novel properties are to be explained in detail in this article. Real-time experiments are to be presented in an unedited movie. It is seen that in the system, the training of 4 hand-written characters is close to real time (less than 0.1 sec.) and the recognition of the untrained hand-written characters is greater than 90% accurate.

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

    NASA Technical Reports Server (NTRS)

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

    1989-01-01

    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.

  17. Phylogenetic analysis and expression profiling of the pattern recognition receptors: Insights into molecular recognition of invading pathogens in Manduca sexta.

    PubMed

    Zhang, Xiufeng; He, Yan; Cao, Xiaolong; Gunaratna, Ramesh T; Chen, Yun-Ru; Blissard, Gary; Kanost, Michael R; Jiang, Haobo

    2015-07-01

    Pattern recognition receptors (PRRs) detect microbial pathogens and trigger innate immune responses. Previous biochemical studies have elucidated the physiological functions of eleven PRRs in Manduca sexta but our understanding of the recognition process is still limited, lacking genomic perspectives. While 34 C-type lectin-domain proteins and 16 Toll-like receptors are reported in the companion papers, we present here 120 other putative PRRs identified through the genome annotation. These include 76 leucine-rich repeat (LRR) proteins, 14 peptidoglycan recognition proteins, 6 EGF/Nim-domain proteins, 5 ?-1,3-glucanase-related proteins, 4 galectins, 4 fibrinogen-related proteins, 3 thioester proteins, 5 immunoglobulin-domain proteins, 2 hemocytins, and 1 Reeler. Sequence alignment and phylogenetic analysis reveal the evolution history of a diverse repertoire of proteins for pathogen recognition. While functions of insect LRR proteins are mostly unknown, their structure diversification is phenomenal: In addition to the Toll homologs, 22 LRR proteins with a signal peptide are expected to be secreted; 18 LRR proteins lacking signal peptides may be cytoplasmic; 36 LRRs with a signal peptide and a transmembrane segment may be non-Toll receptors on the surface of cells. Expression profiles of the 120 genes in 52 tissue samples reflect complex regulation in various developmental stages and physiological states, including some likely by Rel family transcription factors via ?B motifs in the promoter regions. This collection of information is expected to facilitate future biochemical studies detailing their respective roles in this model insect. PMID:25701384

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

    E-print Network

    Kim, Tae-Kyun

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

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

    SciTech Connect

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

    1995-12-01

    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.

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

    NASA Astrophysics Data System (ADS)

    Sick, J. N.

    2003-12-01

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

  1. Laser Opto-Electronic Correlator for Robotic Vision Automated Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Marzwell, Neville

    1995-01-01

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

  2. Artificial Neural Network Circuit for Spectral Pattern Recognition 

    E-print Network

    Rasheed, Farah

    2013-09-04

    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...

  3. Artificial Neural Network Circuit for Spectral Pattern Recognition

    E-print Network

    Rasheed, Farah

    2013-09-04

    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...

  4. Cross-kingdom patterns of alternative splicing and splice recognition

    PubMed Central

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

    2008-01-01

    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

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

    PubMed

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

    2009-09-01

    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

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

    PubMed Central

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

    2010-01-01

    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

  7. Representing pattern recognition-embedded systems through object-process diagrams: the case of the machine drawing understanding system

    Microsoft Academic Search

    Dov Dori

    1995-01-01

    Pattern recognition involves a host of problems, algorithms and techniques dealing with all aspects of detection and classification of objects in scenes and their conversion into meaningful interpretations. Frequently, such tasks are embedded within vision systems that are themselves embedded within more complex systems. Concentrating on minute, albeit important, details of some pattern recognition algorithm, may result in blurring of

  8. Analog design of a new neural network for optical character recognition

    Microsoft Academic Search

    Ian P. Morns; Satnam Singh Dlay

    1999-01-01

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

  9. Crowding by a single bar: probing pattern recognition mechanisms in the visual periphery.

    PubMed

    Põder, Endel

    2014-01-01

    Whereas visual crowding does not greatly affect the detection of the presence of simple visual features, it heavily inhibits combining them into recognizable objects. Still, crowding effects have rarely been directly related to general pattern recognition mechanisms. In this study, pattern recognition mechanisms in visual periphery were probed using a single crowding feature. Observers had to identify the orientation of a rotated T presented briefly in a peripheral location. Adjacent to the target, a single bar was presented. The bar was either horizontal or vertical and located in a random direction from the target. It appears that such a crowding bar has very strong and regular effects on the identification of the target orientation. The observer's responses are determined by approximate relative positions of basic visual features; exact image-based similarity to the target is not important. A version of the "standard model" of object recognition with second-order features explains the main regularities of the data. PMID:25378369

  10. Determination of neural-network topology for partial discharge pulse pattern recognition

    Microsoft Academic Search

    M. M. A. Salama; R. Bartnikas

    2002-01-01

    A time-series approach has been employed to devise neural-network topologies for time dependent partial discharge pulse pattern recognition applications. The cascaded output neural-network structure was found to provide the highest success rate in differentiating between two different partial discharge patterns. This was accomplished by utilizing the indexed feature of the first stage output as one of the inputs into the

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

    ERIC Educational Resources Information Center

    Geary, Leo C.

    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…

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

    SciTech Connect

    Ma, H.

    1994-12-31

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

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

    ERIC Educational Resources Information Center

    Welk, Dorette Sugg

    2002-01-01

    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…

  14. Z .Pattern Recognition Letters 18 1997 11671178 Bayesian YingYang machine, clustering and number of

    E-print Network

    Xu, Lei

    Z .Pattern Recognition Letters 18 1997 1167­1178 Bayesian Ying­Yang machine, clustering and number, NT, Hong Kong, China Abstract It is shown that a particular case of the Bayesian Ying­Yang learning to be more robust in learning. Finally, experimental results are provided. q 1997 Elsevier Science B

  15. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 3D Face Recognition Under Expressions,

    E-print Network

    Boyer, Edmond

    in the scanning technology. Additionally, variations in face scans due to changes in facial expressions can alsoIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 3D Face Recognition Under goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial

  16. Pattern recognition of PD in large turbine generators with a neural network system

    Microsoft Academic Search

    Guangning Wu; Hengkun Xie; Hui Ma; Xiongwei Jiang; Zhiqing Chen; Delin Sun

    1997-01-01

    In this paper, a neural network system used for pattern recognition of partial discharge (PD) is described. The neural network is a three-layer artificial neural system with feed forward connections, and its learning method is back propagation algorithm incorporating with an external teacher signal. Digital PD pulse signal can be obtained by a PD pulse digitized record system. Combination of

  17. Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks

    E-print Network

    Perl, Jürgen

    Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks Jürgen Perl, Peter to store these data but to transform them into useful information. Artificial Neural Networks turn out the data. This is the point where Artificial Neural Networks can become extremely helpful: They are able

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

    Microsoft Academic Search

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

    2011-01-01

    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

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

    Microsoft Academic Search

    Masayuki Hisada; Seiichi Ozawa; Kau Zhang; Nikola Kasabov

    2010-01-01

    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

  20. 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 ...

  1. Critical cues for auditory pattern recognition in speech: Implications for cochlear implant speech processor design

    E-print Network

    Allen, Jont

    Critical cues for auditory pattern recognition in speech: Implications for cochlear implant speech in conditions with full spectral cues. Cochlear implant (CI) patients and hearing-impaired (HI) listeners have Implants and Perception, House Ear Institute 2100 W. Third St., Los Angeles, CA 90057, USA shannon

  2. CUDA Implementation of Deformable Pattern Recognition and its Application to MNIST Handwritten Digit Database

    Microsoft Academic Search

    Yoshiki Mizukami; Katsumi Tadamura; Jonathan Warrell; Peng Li; Simon Prince

    2010-01-01

    In this study we propose a deformable pattern recognition method with CUDA implementation. In order to achieve the proper correspondence between foreground pixels of input and prototype images, a pair of distance maps are generated from input and prototype images, whose pixel values are given based on the distance to the nearest foreground pixel. Then a regularization technique computes the

  3. GPU implementation of deformable pattern recognition using prototype-parallel displacement computation

    Microsoft Academic Search

    Yoshiki Mizukami; Katsumi Tadamura

    In this paper, for the reduction of the computation time of a deformable ap- proach to pattern recognition, prototype-parallel displacement computation on GPUs (PPDC-GPU) is proposed. The displacement computation used in this study has the virtue of simplicity and consists of locally parallel process- ing, therefore it is suitable for the implementation on graphical processing units (GPUs). In the proposed

  4. A strip chart recorder pattern recognition tool kit for Shuttle operations

    NASA Technical Reports Server (NTRS)

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

    1993-01-01

    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.

  5. Application of Wavelet Packet Transform in Pattern Recognition of Near-IR Data

    E-print Network

    Guerrini, Carla

    Application of Wavelet Packet Transform in Pattern Recognition of Near-IR Data Beata Walczak, Bas, Laarbeeklaan 103, B-1090 Brussel, Belgium The wavelet packet transform is studied as a tool for improving does not improve the results. In this paper we study the application of the wavelet packet transform

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

    ERIC Educational Resources Information Center

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

    2010-01-01

    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…

  7. Color component marking and convolution-based encoding for polychromatic pattern recognition

    Microsoft Academic Search

    Xiaopeng Deng; Daomu Zhao

    2011-01-01

    A novel method for polychromatic pattern recognition is proposed based on color component marking and convolution-based encoding. Three random phase functions are first chosen as marks to multiply with the three color components of the input images. Then the three marked color components are combined into one image using convolution-based encoding. Finally, the combined images are served as the input

  8. Fast and accurate pitch detection using pattern recognition and adaptive time-domain analysis

    Microsoft Academic Search

    D. Prezas; J. Picone; D. Thomson

    1986-01-01

    A method of determining pitch and voicing information from speech signals is presented. The algorithm, which employs time-domain analysis and pattern recognition techniques, is fast and yields accurate pitch and voicing estimates. A search routine is employed to find periodicity in each of four signals derived from the speech waveform and the results are combined to form a pitch estimate.

  9. Pattern recognition of partial discharge in XLPE cables using a neural network

    Microsoft Academic Search

    H. Suzuki; T. Endoh

    1992-01-01

    An experimental study of pattern recognition of partial discharge (PD) in a crosslinked polyethylene (XLPE) cable by using a neural network (NN) system is described. The NN system was a three-layer artificial neural network system with feedforward connections, and its learning method was a backpropagation algorithm incorporating an external teacher signal. Input information for the NN was a combination of

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

    ERIC Educational Resources Information Center

    Silberstang, Joyce; London, Manuel

    2009-01-01

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

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

    ERIC Educational Resources Information Center

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

    2012-01-01

    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…

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

    E-print Network

    Wang, Xiaogang

    2012-01-01

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

  13. Pattern recognition-a technique for induction machines rotor broken bar detection

    Microsoft Academic Search

    Masoud Haji; Hamid A. Toliyat

    2001-01-01

    A pattern recognition technique based on Bayes minimum error classifier is developed to detect broken rotor bar faults in induction motors at the steady state. The proposed algorithm uses only stator currents as input without the need for any other variables. Initially, rotor speed is estimated from the stator currents, then appropriate features are extracted. The produced feature vector is

  14. Combined blur and affine moment invariants and their use in pattern recognition

    Microsoft Academic Search

    Tomás Suk; Jan Flusser

    2003-01-01

    The paper is devoted to the recognition of objects and patterns deformed by imaging geometry as well as by unknown blurring. We introduce a new class of features invariant simultaneously to blurring with a centrosymmetric PSF and to a#ne transformation. As we prove in the paper, they can be constructed by combining a#ne moment invariants and blur invariants derived earlier.

  15. Pattern Recognition for Ecological Science and Environmental Monitoring: An Initial Report

    Microsoft Academic Search

    Eric N. Mortensen; Enrique L. Delgado; Hongli Deng; David Lytle; Andrew Moldenke; Robert Paasch; Linda Shapiro; Pengcheng Wu; Wei Zhang; Thomas G. Dietterich

    Many ecological science and environmental monitoring problems can benefit from inexpensive, automated methods of counting insect and mesofaunal populations. Existing methods for obtaining population counts require expensive and tedious manual identification by human experts. This chapter describes the development of general-purpose pattern-recognition algorithms for identification and classification of insects and mesofauna and the design and construction of mechanical devices for

  16. J. Marti et al. (Eds.): Pattern Recognition and Image Analysis, LNCS 4477, 386393, 2007.

    E-print Network

    J. Mart´i et al. (Eds.): Pattern Recognition and Image Analysis, LNCS 4477, 386­393, 2007. c Springer-Verlag Berlin Heidelberg 2007 Variational Deconvolution of Multi-Channel Images with Inequality Constraints Martin Welk1 and James G. Nagy2 1 Mathematical Image Analysis Group Faculty of Mathematics

  17. Information resonance and pattern recognition in classical and quantum systems: toward a `language

    E-print Network

    @ix.netcom.com. Affiliations are for identification only. quantum neural networks, renormalization, state space algebraInformation resonance and pattern recognition in classical and quantum systems: toward a `language model' of hierarchical neural structure and process Rodrick Wallace, Ph.D. The New York State

  18. Pattern Recognition Based Software for Oil Spills Identification by Gas-Chromatography and IR Spectrophotometry

    Microsoft Academic Search

    Dumitru Staniloae; Bogdan Petrescu; Constantin Patroescu

    2001-01-01

    For environmental control purposes, floating oil spills in harbours, off shore areas and their sources must often be identified. Pattern recognition, applied to JR spectrophotometric data (600-2000 cm m 1 range), and to chromatographic data ( n -alkanes) for the spill and various suspected sources such as oil and fuels from ships bunkers and harbour installations, can lead to definite

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

    SciTech Connect

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

    1993-08-01

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

  20. A DRASTIC-based fuzzy pattern recognition methodology for groundwater vulnerability evaluation

    Microsoft Academic Search

    CHEN SHOUYU; FU GUANGTAO

    2003-01-01

    The evaluation of groundwater vulnerability based on DRASTIC is a process that recognizes the level type of a sample by comparing it with the standard values from the data in the DRASTIC index. However, it is difficult to find the same value as those standard values in practice. As the above mentioned procedure is a typical fuzzy pattern recognition problem,

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

    Microsoft Academic Search

    L. T. Wille

    2004-01-01

    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

  2. Financial time series forecasts using fuzzy and long memory pattern recognition systems

    Microsoft Academic Search

    Sameer Singh; Jonathan Fieldsend

    2000-01-01

    In this paper, the concept of long memory systems for forecasting is developed. The pattern modelling and recognition system and fuzzy single nearest neighbour methods are introduced as local approximation tools for forecasting. Such systems are used for matching the current state of the time-series with past states to make a forecast. In the past, the PMRS system has been

  3. A Long Memory Pattern Modelling and Recognition System for Financial Time-Series Forecasting

    Microsoft Academic Search

    Sameer Singh

    1999-01-01

    In this paper, the concept of a long memory system for forecasting is developed. Pattern modelling and recognition systems are introduced as local approximation tools for forecasting. Such systems are used for matching the current state of the time-series with past states to make a forecast. In the past, this system has been successfully used for forecasting the Santa Fe

  4. Shift-, rotation-, and scale-invariant shape recognition system using an optical Hough transform

    NASA Astrophysics Data System (ADS)

    Schmid, Volker R.; Bader, Gerhard; Lueder, Ernst H.

    1998-02-01

    We present a hybrid shape recognition system with an optical Hough transform processor. The features of the Hough space offer a separate cancellation of distortions caused by translations and rotations. Scale invariance is also provided by suitable normalization. The proposed system extends the capabilities of Hough transform based detection from only straight lines to areas bounded by edges. A very compact optical design is achieved by a microlens array processor accepting incoherent light as direct optical input and realizing the computationally expensive connections massively parallel. Our newly developed algorithm extracts rotation and translation invariant normalized patterns of bright spots on a 2D grid. A neural network classifier maps the 2D features via a nonlinear hidden layer onto the classification output vector. We propose initialization of the connection weights according to regions of activity specifically assigned to each neuron in the hidden layer using a competitive network. The presented system is designed for industry inspection applications. Presently we have demonstrated detection of six different machined parts in real-time. Our method yields very promising detection results of more than 96% correctly classified parts.

  5. Pattern recognition computation using action potential timing for stimulus representation

    Microsoft Academic Search

    J. J. Hopfield

    1995-01-01

    A computational model is described in which the sizes of variables are represented by the explicit times at which action potentials occur, rather than by the more usual 'firing rate' of neurons. The comparison of patterns over sets of analogue variables is done by a network using different delays for different information paths. This mode of computation explains how one

  6. A survey of fuzzy clustering algorithms for pattern recognition. I

    Microsoft Academic Search

    Andrea Baraldi; Palma Blonda

    1999-01-01

    Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where the expressive power of clustering systems can be compared on the basis of a meaningful set of common functional features. Part I of this paper reviews the following issues related to clustering approaches found in the literature: relative (probabilistic) and

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

    PubMed Central

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

    2012-01-01

    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

  8. Recognition of haptic interaction patterns in dyadic joint object manipulation.

    PubMed

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

    2015-01-01

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

  9. Emotional Faces in Context: Age Differences in Recognition Accuracy and Scanning Patterns

    PubMed Central

    Noh, Soo Rim; Isaacowitz, Derek M.

    2014-01-01

    While age-related declines in facial expression recognition are well documented, previous research relied mostly on isolated faces devoid of context. We investigated the effects of context on age differences in recognition of facial emotions and in visual scanning patterns of emotional faces. While their eye movements were monitored, younger and older participants viewed facial expressions (i.e., anger, disgust) in contexts that were emotionally congruent, incongruent, or neutral to the facial expression to be identified. Both age groups had highest recognition rates of facial expressions in the congruent context, followed by the neutral context, and recognition rates in the incongruent context were worst. These context effects were more pronounced for older adults. Compared to younger adults, older adults exhibited a greater benefit from congruent contextual information, regardless of facial expression. Context also influenced the pattern of visual scanning characteristics of emotional faces in a similar manner across age groups. In addition, older adults initially attended more to context overall. Our data highlight the importance of considering the role of context in understanding emotion recognition in adulthood. PMID:23163713

  10. Pattern Recognition of the Household Water Consumption through Signal Analysis

    Microsoft Academic Search

    Giovana Almeida; José Vieira; José Marques; Alberto Cardoso

    2011-01-01

    \\u000a This paper presents the initial results of a research project that aims to develop a method for losses\\/leakage detection and\\u000a household water consumption characterization through the detailed patterns analysis of signals generated by water meters.\\u000a The Department of Civil Engineering (University of Coimbra) supports the research as part of a PhD Project. An experimental\\u000a facility is used for signals acquisition

  11. Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

    NASA Astrophysics Data System (ADS)

    Yao, Ruigen; Pakzad, Shamim N.

    2012-08-01

    Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

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

    NASA Astrophysics Data System (ADS)

    Essa, Almabrok E.; Asari, Vijayan K.

    2014-04-01

    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.

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

    NASA Astrophysics Data System (ADS)

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

    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.

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

    NASA Technical Reports Server (NTRS)

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

    1989-01-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2015-01-01

    In response to bacterial and fungal infections in insects and mammals, distinct families of innate immune pattern recognition receptors (PRRs) initiate highly complex intracellular signaling cascades. Those cascades induce a variety of immune functions that restrain the spread of microbes in the host. Insect and mammalian innate immune receptors include molecules that recognize conserved microbial molecular patterns. Innate immune recognition leads to the recruitment of adaptor molecules forming multi-protein complexes that include kinases, transcription factors, and other regulatory molecules. Innate immune signaling cascades induce the expression of genes encoding antimicrobial peptides and other key factors that mount and regulate the immune response against microbial challenge. In this review, we summarize our current understanding of the bacterial and fungal PRRs for homologous innate signaling pathways of insects and mammals in an effort to provide a framework for future studies. PMID:25674081

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

    NASA Astrophysics Data System (ADS)

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

    2011-04-01

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

  18. Optical fiber digital speckle pattern correlation method for displacement measurement

    Microsoft Academic Search

    Rongxun Liu; Xinwei Liu

    1995-01-01

    This paper briefly reviews present situation on optical fiber-speckle method used for measuring displacements or deformations of an object, and the optical fiber-digital white light speckle patterns correlation method for whole field displacement measurement on a remote surface of a trial object is revealed. The principle of correlation method is demonstrated. A special program for image correlation analysis by computer

  19. Single-mode fiber optic in electronic speckle pattern interferometry

    Microsoft Academic Search

    Wei Liu; Yushan Tan; Xianglin Wang; Haitao Wang; Lijun Jiang

    1993-01-01

    A new technique has been developed for applying fiber optics to electronic speckle pattern interferometry (ESPI) to measure unbonded faults in the carbon\\/epoxy honeycomb composite. The fiberized ESPI system we designed is introduced in detail. This paper also develops the theoretical and experimental approach of singlemode fiber optics phase-step ESPI. This approach produces a significant improvement in fringe contrast and

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

    Microsoft Academic Search

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

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

  1. Pattern Classification and Recognition of Invertebrate Functional Groups Using Self-Organizing Neural Networks

    Microsoft Academic Search

    WenJun Zhang

    2007-01-01

    Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern\\u000a classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional\\u000a groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing\\u000a competitive learning neural networks. Comparisons between neural network models,

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

    Microsoft Academic Search

    Caifeng Shan; Shaogang Gong; Peter W. Mcowan

    2009-01-01

    Automatic facial expression analysis is an interesting and challenging problem, and impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. In this paper, we empirically evaluate facial representation based on statistical local features, Local Binary Patterns, for person-independent

  3. Stochastic Error-Correcting Syntax Analysis for Recognition of Noisy Patterns

    Microsoft Academic Search

    Shin-yee Lu; King-sun Fu

    1977-01-01

    In this paper, a probabilistic model for error-correcting parsing with substitution, insertion, and deletion errors is introduced. The formulation of maximum-likelihood error-correcting parser (MLECP) by incorporating the noise model into stochastic grammars is also presented. The use of stochastic error-correcting parsers for recognition of noisy and\\/or distorted patterns results in a process of high accuracy, but with low efficiency. In

  4. Minimum description length principle in the field of image analysis and pattern recognition

    Microsoft Academic Search

    A. S. Potapov

    2011-01-01

    Problems of decision criterion in the tasks of image analysis and pattern recognition are considered. Overlearning as a practical\\u000a consequence of fundamental paradoxes in inductive inference is illustrated with examples. Theoretical (on the base of algorithmic\\u000a complexity) and practical formulations of the minimum description length (MDL) principle are given. Decrease of the overlearning\\u000a effect is shown in the examples of

  5. Pattern recognition of porphyry copper deposits in New Mexico and Texas 

    E-print Network

    Piatt, David Allan

    1984-01-01

    . They predicted the occurrence of uranium deposits near the Colorado-Utah border using feature questions in Table 2. The ore bodies in that region are located in penecordant bodies in the Jurrasic Morrison formation (sandstone and interbedded mudstone). Briggs...) Andre gash, (M ber) Ear R. os ins (Head of Department) May 1984 ABSTRACT Pattern Recognition of Porphyry Copper Deposits in New Mexico and Texas. (Nay 1984) David Allan Piatt, B. S. , Colorado State University Chairman of Advisory Committee: Dr...

  6. Applications of matrix derivatives to optimization problems in statistical pattern recognition

    NASA Technical Reports Server (NTRS)

    Morrell, J. S.

    1975-01-01

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

  7. Unsupervised Structure Damage Classification Based on the Data Clustering and Artificial Immune Pattern Recognition

    Microsoft Academic Search

    Bo Chen; Chuanzhi Zang

    2009-01-01

    This paper presents an unsupervised structure damage classification algorithm based on the data clustering technique and the\\u000a artificial immune pattern recognition. The presented method uses time series measurement of a structure’s dynamic response\\u000a to extract damage-sensitive features for the structure damage classification. The Data Clustering (DC) technique is employed\\u000a to cluster training data to a specified number of clusters and

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

    Microsoft Academic Search

    Trine H. Mogensen; Søren R. Paludan

    2005-01-01

    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

  9. Design of a Page-Checking Instrument Based on Fuzzy Pattern Recognition

    Microsoft Academic Search

    Yuezong Wang; Desheng Li

    2006-01-01

    In this paper, a page-checking instrument via MCU Atmega128 and fuzzy pattern recognition theory is developed for automatic bookbinding machines. This instrument detects dynamically wrong pages locating in many word pages, graphic pages and word-graphic pages, and at the same time, takes a message to automatic bookbinding machine that lets wrong pages pass as soon as receiving the message. This

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

    E-print Network

    Prieto Orlando, Rodrigo Javier

    1994-01-01

    . The features selected by these systems should identify the intrinsic characteristics of the objects being classified (Bongard 1970). For example, a pattern recognition system classifying bananas from lemons will not use color as a feature, it will however... are put together again through the use of disjuntions (logical or's), they form a description of the class itself (Bongard 1970). In the example of the lemon and the banana, the banana features would be given by statements such as: bananas are solid...

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

    SciTech Connect

    Kurt Beran; John Christenson; Dragos Nica; Kenny Gross

    2002-12-15

    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.

  12. Fast and adaptive network of spiking neurons for multi-view visual pattern recognition

    Microsoft Academic Search

    Simei Gomes Wysoski; Lubica Benuskova; Nikola Kasabov

    2008-01-01

    In this paper, we describe and evaluate a new spiking neural network (SNN) architecture and its corresponding learning procedure to perform fast and adaptive multi-view visual pattern recognition. The network is composed of a simplified type of integrate-and-fire neurons arranged hierarchically in four layers of two-dimensional neuronal maps. Using a Hebbian-based training, the network adaptively changes its structure in order

  13. Macroscopic quantum fluctuations in noise-sustained optical patterns

    Microsoft Academic Search

    Roberta Zambrini; Stephen M. Barnett; Pere Colet; Maxi San Miguel

    2002-01-01

    We investigate quantum effects in pattern formation for a degenerate optical parametric oscillator with walk-off. This device has a convective regime in which macroscopic patterns are both initiated and sustained by quantum noise. Familiar methods based on linearization about a pseudoclassical field fail in this regime and new approaches are required. We employ a method in which the pump field

  14. Quantum correlations in noise-sustained optical patterns

    Microsoft Academic Search

    S. Barnett; R. Zambrini; G. Izus; M. San Miguel; P. Colet

    2000-01-01

    Noise-sustained patterns have been recently predicted in several systems including a Kerr cavity and an optical parametric oscillator. In these systems a regime of convective instability can appear, in which a transverse pattern arises as a macroscopic manifestation of amplified and spatially structured quantum noise, with magnification factors of several order of magnitudes. We study quantum correlations in the convective

  15. Three-dimensional visualization for evaluating automated, geomorphic pattern-recognition analyses of crustal structures

    NASA Astrophysics Data System (ADS)

    Foley, M. G.

    1991-02-01

    We are developing and applying a suite of automated remote geologic analysis (RGA) methods at Pacific Northwest Laboratory (PNL) for extracting structural and tectonic patterns from digital models of topography and other spatially registered geophysical data. In analyzing a map area, the geologist employs a variety of spatial representations (e.g., topographic maps; oblique, vertical and vertical stereographic aerial photographs; satellite-sensor images) in addition to actual field observations to provide a basis for recognizing features (patterns) diagnostic or suggestive of various geologic and geomorphic features. We intend that our automated analyses of digital models of elevation use the same photogeologic pattern-recognition methods as the geologist's; otherwise there is no direct basis for manually evaluating results of the automated analysis. Any system for automating geologic analysis should extend the geologist's pattern-recognition abilities and quantify them, rather than replace them. This requirement means that results of automated structural pattern-recognition analyses must be evaluated by geologists using the same method that would be employed in manual field checking: visual examination of the three-dimensional relationships among rocks, erosional patterns, and identifiable structures. Interactive computer-graphics in quantitative (i.e., spatially registered), simulated three-dimensional perspective and stereo are thus critical to the integration and interpretation of topography, imagery, point data, RGA-identified fracture/fault planes, stratigraphy, contoured geophysical data, nonplanar surfaces, boreholes, and three-dimensional zones (e.g., crush zones at fracture intersections). This graphical interaction presents the megabytes of digital geologic and geophysical data to the geologist in the same spatial format that field observations would take, permitting direct evaluation of RGA methods and results.

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

    E-print Network

    Raftery, Adrian

    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

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

    SciTech Connect

    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

    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.

  18. Patterns of diabetic macular edema with optical coherence tomography

    Microsoft Academic Search

    Tomohiro Otani; Shoji Kishi; Yasuhiro Maruyama

    1999-01-01

    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

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

    PubMed Central

    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

    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

  20. Teaching image processing and pattern recognition with the Intel OpenCV library

    NASA Astrophysics Data System (ADS)

    Koz?owski, Adam; Królak, Aleksandra

    2009-06-01

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

  1. NOISENOISE--SUSTAINED PATTERNS INSUSTAINED PATTERNS IN NONLINEAR OPTICSNONLINEAR OPTICS

    E-print Network

    Colet, Pere

    -dimensional (hexagonal pattern) Type I (one polarization) Type II (two polarizations) #12;|E0|2 |Ast|2 =1 1d self-focusing Absolutely unstable Convectively unstable )(q )/( ik [ ] [ ] 0)(Re0,)(Re >> s kq #12;1d self-focusing Kerr Absolute instability threshold: 2224 0 )2(12)( ak||A||Akk stst +--+-= #12;1d self-focusing Kerr cavities

  2. High reflectivity superstructured FBG for coherent optical code generation and recognition

    Microsoft Academic Search

    Xu Wang; Koji Matsushima; Akihiko Nishiki; Naoya Wada; Ken-Ichi Kitayama

    2004-01-01

    The performance of the phase-shifted superstructured fiber Bragg grating (SSFBG) for optical code (OC) recognition was investigated with different reflectivity as well as input pulse width. The auto-correlation peak (PA) and the ratios of PA to the maximum wing level (P\\/W) and cross-correlation level (P\\/C) were used to quantitatively evaluate the OC recognition performance. There is a conflict between obtaining

  3. Structural Pattern Recognition Techniques for Data Retrieval in Massive Fusion Databases

    SciTech Connect

    Vega, J.; Ratta, G. A.; Castro, P.; Pereira, A.; Portas, A. [Asociacion EURATOM/CIEMAT para Fusion. Avda. Complutense, 22. 28040 Madrid (Spain); Murari, A. [Consorzio RFX-Associazione EURATOM ENEA per la Fusione. I-35127 Padua (Italy)

    2008-03-12

    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.

  4. Pattern recognition method applied to the forecast of strong earthquakes in South American seismic prone areas

    SciTech Connect

    Benavidez, A.

    1986-01-01

    The pattern recognition method is applied to the Andean seismic region that extends from southern latitudes 2 to 27 in the South American continent, to set a criterion for the prediction of the potential sites of strong earthquakes epicenters in the zone. It is assumed that two hypothesis hold. First, the strong earthquake epicenters typically cluster around the intersection of morphostructural lineaments. Second, the rules of recognition obtained for neighboring zones which exhibit distinctive neotectonic evolution, state of stress, spatial earthquake distribution and geological development, may be different in spite of the fact that the morphostructural zoning does not reflect a separation between them. Hence, the region is divided into two broad-scale tectonic segments located above slabs of similar scale in the Nazca plate in which subduction takes place almost subhorizontally (dipping at an angle of about 10) between latitudes 2S and 15S, and at a steeper angle (of approximately 30) within latitudes 15S to 27S. The morphostructural zoning is carried out for both zones with the determination of the lineaments and the corresponding disjunctive knots which are defined as the objects of recognition when applying the pattern recognition method. The Cora-3 algorithm is used as the computational procedure for the search of the rule of recognition of dangerous and non-dangerous sites for each zone. The set criteria contain in each case several characteristic features that represent the topography, geology and tectonics of each region. Also, it is shown that they have a physical meaning that mostly reflects the style of tectonic deformation in the related regions.

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

    Microsoft Academic Search

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

    2009-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Ogiela, Marek R.; Tadeusiewicz, Ryszard

    2000-04-01

    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.

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

    PubMed Central

    Narayanan, Arun; Wang, DeLiang

    2013-01-01

    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

  8. Stimulation of innate immune responses by malarial glycosylphosphatidylinositol via pattern recognition receptors.

    PubMed

    Nebl, T; De Veer, M J; Schofield, L

    2005-01-01

    The glycosylphosphatidylinositol (GPI) anchor of Plasmodium falciparum is thought to function as a critical toxin that contributes to severe malarial pathogenesis by eliciting the production of proinflammatory responses by the innate immune system of mammalian hosts. Analysis of the fine structure of P. falciparum GPI suggests a requirement for the presence of both core glycan and lipid moieties in the recognition and signalling of parasite glycolipids by host immune cells. It has been demonstrated that GPI anchors of various parasitic protozoa can mediate cellular immune responses via members of the Toll-like family of pattern recognition receptors (TLRs). Recent studies indicate that GPI anchors of P. falciparum and other protozoa are preferentially recognized by TLR-2, involving the MyD88-dependent activation of specific signalling pathways that mediate the production of proinflammatory cytokines and nitric oxide from host macrophages in vitro. However, the contribution of malaria GPI toxin to severe disease syndromes and the role of specific TLRs or other pattern recognition receptors in innate immunity in vivo is only just beginning to be characterized. A better understanding of the molecular mechanisms underlying severe malarial pathogenesis may yet lead to substantial new insights with important implications for the development of novel therapeutics for malaria treatment. PMID:16281992

  9. Assessment of laser-dazzling effects on TV cameras by means of pattern recognition algorithms

    NASA Astrophysics Data System (ADS)

    Durécu, Anne; Vasseur, Olivier; Bourdon, Pierre; Eberle, Bernd; Bürsing, Helge; Dellinger, Jean; Duchateau, Nicolas

    2007-10-01

    Imaging systems are widespread observation tools used to fulfil various functions such as detection, recognition, identification and video-tracking. These devices can be dazzled by using intensive light sources, e.g. lasers. In order to avoid such a disturbance, dazzling effects in TV-cameras must be better understood. In this paper we studied the influence of laser-dazzling on the performance of pattern recognition algorithms. The experiments were performed using a black and white TV-CCD-camera, dazzled by a nanosecond frequency doubled Nd:YAG laser. The camera observed a scene comprising different geometrical forms which had to be recognized by the algorithm. Different dazzling conditions were studied by varying the laser repetition rate, the pulse energy and the position of the geometrical forms relative to the laser spot. The algorithm is based on edge detection and locates areas with forms similar to a reference symbol. As a measure of correspondence it computes the degree of correlation of the different areas. The experiments show that dazzling can highly affect the performance of the used pattern recognition algorithms by generating lots of spurious edges which mimic the reference symbol. As a consequence dazzling results in detrimental effects, since it not only prevents the recognizing of well defined symbols, but it also creates many false alarms.

  10. Object recognition using three-dimensional optical quasi-correlation

    E-print Network

    Rosen, Joseph

    , 2002 A novel method of three-dimensional (3-D) object recognition is proposed. Several projections of a 3-D target are recorded under white-light illumination and fused into a single complex two the existence and locations of true targets in the observed 3-D scene. Experimental results and comparisons

  11. Wavelength detection using optical fiber speckle patterns

    Microsoft Academic Search

    Byungchoon Yang; Il-Min Lee; Byoungho Lee

    2000-01-01

    If the wavelength of injected light changes, the phase of the field also changes. Then the intensity distribution also changes. This property has been introduced to the application of holographic recording to reduce crosstalk and enhance hologram density. We can use this property in the sensing of wavelength change in an optical fiber sensing system. In the experiment, the laser

  12. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies.

    PubMed

    Kambeitz, Joseph; Kambeitz-Ilankovic, Lana; Leucht, Stefan; Wood, Stephen; Davatzikos, Christos; Malchow, Berend; Falkai, Peter; Koutsouleris, Nikolaos

    2015-06-01

    Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7-83.5%) and a specificity of 80.3% (95% CI: 76.9-83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9-88.2%) and similar specificity (76.9%, 95% CI: 71.3-81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9-80.4%, specificity of 79.0%, 95% CI: 74.6-82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity. PMID:25601228

  13. Pattern Recognition

    NSDL National Science Digital Library

    Dr Gene Tagliarini

    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

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

    PubMed Central

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

    2013-01-01

    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

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

    PubMed Central

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

    2013-01-01

    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

  16. International Union of Basic and Clinical Pharmacology. XCVI. Pattern recognition receptors in health and disease.

    PubMed

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

    2015-04-01

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

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

    PubMed

    Hao, Liangwang; Hong, Wenxue

    2013-10-01

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

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

    NASA Astrophysics Data System (ADS)

    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

    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.

  19. Optical measurement of the IC photomask pattern area.

    PubMed

    Karatsu, O

    1979-07-01

    Optical measurement of the integrated-circuit (IC) photomask pattern area is performed. The relationship between aperture dimension and diverging angle of diffracted light is numerically estimated after scalar electromagnetic theory, and the expected measurement error is derived. The experimental measurement of the pattern area is made using typical IC photomasks. In the experiment, converging optics and an integrating sphere are suitably arranged so as to minimize the error introduced by omitting the higher-order diffraction. Some of the measured data are compared with values which are calculated from the IC layout design data, and they are found to be in good agreement. PMID:20212628

  20. Optical character recognition: an illustrated guide to the frontier

    NASA Astrophysics Data System (ADS)

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

    1999-12-01

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

  1. User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

    PubMed

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-01-01

    This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. PMID:26007738

  2. User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm

    PubMed Central

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-01-01

    This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. PMID:26007738

  3. Enhanced optical magnetoelectric effect in a patterned polar ferrimagnet

    Microsoft Academic Search

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

    2006-01-01

    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 mum on a surface of GaFeO3 crystal and used the diffracted light as a probe. Optical ME modulation signal for Bragg spot

  4. Macroscopic quantum fluctuations in noise-sustained optical patterns

    Microsoft Academic Search

    Roberta Zambrini; Stephen M. Barnett; Pere Colet; Maxi San Miguel

    2002-01-01

    We investigate quantum effects in pattern-formation for a degenerate optical\\u000aparametric oscillator with walk-off. This device has a convective regime in\\u000awhich macroscopic patterns are both initiated and sustained by quantum noise.\\u000aFamiliar methods based linearization about a pseudo-classical field fail in\\u000athis regime and new approaches are required. We employ a method in which the\\u000apump field is treated

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

    PubMed Central

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

    2013-01-01

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

  6. Pattern recognition techniques for horizontal and vertically upward multiphase flow measurement

    NASA Astrophysics Data System (ADS)

    Arubi, Tesi I. M.; Yeung, Hoi

    2012-03-01

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

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

    PubMed Central

    2013-01-01

    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

  8. Optical Music Recognition System within a Large-Scale Digitization Project

    Microsoft Academic Search

    G. Sayeed Choudhury; M. Droetboom; Tim Dilauro; Ichiro Fujinaga; Brian Harrington

    2000-01-01

    An adaptive optical music recognition system is being de- veloped as part of an experiment in creating a comprehen- sive framework of tools to manage the workflow of large- scale digitization projects. This framework will support the path from physical object and\\/or digitized material into a digital library repository, and offer effective tools for incorporating metadata and perusing the content

  9. Facial expression recognition in video sequence images by using optical flow

    Microsoft Academic Search

    Behnam Kabirian Dehkordi; Javad Haddadnia

    2010-01-01

    In this paper a new method for facial expression recognition is presented. According to this algorithm, an appropriate mask is designed using Gabor filters, and it is convolved with first frame of video sequence images. Then oval part of face is specified and its main components are characterized. By using Lucas Kanade method for optical flow analysis to determine the

  10. Laser illuminator and optical system for disk patterning

    SciTech Connect

    Hackel, L.A.; Dane, C.B.; Dixit, S.N.; Everett, M.; Honig, J.

    2000-03-14

    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.

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

    Microsoft Academic Search

    Kunihiko Fukushima

    1980-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

    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.

  13. Neutron-gamma discrimination employing pattern recognition of the signal from liquid scintillator

    NASA Astrophysics Data System (ADS)

    Kamada, Kohji; Enokido, Uhji; Ogawa, Seiji

    1999-05-01

    A pattern recognition method was applied to the neutron-gamma discrimination of the pulses from the liquid scintillator, NE-213. The circuit for the discrimination is composed of A/D converter, fast SCA, memory control circuit, two digital delay lines and two buffer memories. All components are packed on a small circuit board and are installed into a personal computer. Experiments using a weak 252Cf n-? source were undertaken to test the feasibility of the circuit. The circuit is of very easy adjustment and, at the same time, of very economical price when compared with usual discrimination circuits, such as the TAC system.

  14. Results in the application of pattern recognition methods to nuclear reactor core component surveillance

    SciTech Connect

    Gonzalez, R.C.; Fry, D.N.; Kryter, R.C.

    1973-01-01

    From nuclear science symposium; San Francisco, California, USA (14 Nov 1973). Pattern recognition methods were applied to analyze and interpret neutron noise data from the High Flux Isotope Reactor (HFIR) at ORNL. The results show that it is feasible to detect some core component failures by means of machine- discernible differences in the time-dependent noise power spectra. These neutron spectra (signatures) were analyzed by using a clusterseeking algorithm to derive a set of templates for automatic computer evaluation of the reactor's mechanical integrity and soundness. (auth)

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

    PubMed

    Hossain, Md Murad; Norazmi, Mohd-Nor

    2013-01-01

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

  16. Artwork diagnostics with fiber-optic digital speckle pattern interferometry

    Microsoft Academic Search

    D. Paoletti; G. Schirripa Spagnolo; M. Facchini; P. Zanetta

    1993-01-01

    A mobile interferometer, combining the properties of digital speckle pattern interferometry testing with flexibility of fiber optic illumination is reported. Experimental results for in- and out-of-plane displacement measurements in frescoe supports and for detection of cracks or detachments in wooden pannel painting models are presented.

  17. Application of hierarchical clustering technique for numerical tectonic regionalization of the Zagros (Iran): an unsupervised pattern recognition

    NASA Astrophysics Data System (ADS)

    Zadeh, Rezvan Mehdi; Naser Hashemi, Seyed

    2010-05-01

    Hierarchical clustering technique is fundamentally a method data exploration and identifying the patterns inherent in a data set. This numerical pattern recognition technique involves the recognition of pattern and grouping structure in data where no a priori classification exists. This paper attempts to present a general quantitative pattern based on hierarchical clustering technique for tectonic regionalization of the Zagros in order to presenting and demonstrating the ability of this clustering method as an unsupervised pattern recognition technique to perform a numerical tectonic zoning study. For this purpose, after preparation the multivariate data matrix containing 137 sub-areas and 18 quantitative variables, the relationships among the sub-areas were obtained through cluster analysis using Ward's method, and Euclidean distance as similarity measure. The results, synthesized in a dendrogram, were used for providing a series of automated tectonic zoning maps of the region produced in different levels of similarities that show trends in tectonic evolution of it. In general, the automated tectonic zoning maps presented in this study show good agreement with the overall geological and quantitative works. However, in the present study some new findings about the tectonic nature of the region have been obtained. This study simply presents the necessity and usefulness of hierarchical cluster analysis, as an appropriate statistical pattern recognition technique, for increasing the degree of the objectivity of the regionalization researches in the Earth sciences. Also, it introduces this analytical method a very powerful technique for a meaningful data reduction and interpreting the data and indicate many hidden patterns.

  18. Automatic modulation format recognition for the next generation optical communication networks using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Guesmi, Latifa; Hraghi, Abir; Menif, Mourad

    2015-03-01

    A new technique for Automatic Modulation Format Recognition (AMFR) in next generation optical communication networks is presented. This technique uses the Artificial Neural Network (ANN) in conjunction with the features of Linear Optical Sampling (LOS) of the detected signal at high bit rates using direct detection or coherent detection. The use of LOS method for this purpose mainly driven by the increase of bit rates which enables the measurement of eye diagrams. The efficiency of this technique is demonstrated under different transmission impairments such as chromatic dispersion (CD) in the range of -500 to 500 ps/nm, differential group delay (DGD) in the range of 0-15 ps and the optical signal tonoise ratio (OSNR) in the range of 10-30 dB. The results of numerical simulation for various modulation formats demonstrate successful recognition from a known bit rates with a higher estimation accuracy, which exceeds 99.8%.

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

    NASA Astrophysics Data System (ADS)

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

    2014-08-01

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

  20. Discriminant analysis of milk adulteration based on near-infrared spectroscopy and pattern recognition

    NASA Astrophysics Data System (ADS)

    Liu, Rong; Lv, Guorong; He, Bin; Xu, Kexin

    2011-03-01

    Since the beginning of the 21st century, the issue of food safety is becoming a global concern. It is very important to develop a rapid, cost-effective, and widely available method for food adulteration detection. In this paper, near-infrared spectroscopy techniques and pattern recognition were applied to study the qualitative discriminant analysis method. The samples were prepared and adulterated with one of the three adulterants, urea, glucose and melamine with different concentrations. First, the spectral characteristics of milk and adulterant samples were analyzed. Then, pattern recognition methods were used for qualitative discriminant analysis of milk adulteration. Soft independent modeling of class analogy and partial least squares discriminant analysis (PLSDA) were used to construct discriminant models, respectively. Furthermore, the optimization method of the model was studied. The best spectral pretreatment methods and the optimal band were determined. In the optimal conditions, PLSDA models were constructed respectively for each type of adulterated sample sets (urea, melamine and glucose) and all the three types of adulterated sample sets. Results showed that, the discrimination accuracy of model achieved 93.2% in the classification of different adulterated and unadulterated milk samples. Thus, it can be concluded that near-infrared spectroscopy and PLSDA can be used to identify whether the milk has been adulterated or not and the type of adulterant used.

  1. Analysis of Polar Clouds from Satellite Imagery Using Pattern Recognition and a Statistical Cloud Analysis Scheme.

    NASA Astrophysics Data System (ADS)

    Ebert, Elizabeth E.

    1989-05-01

    The analysis of cloud cover in the polar regions from satellite data is more difficult than at other latitudes because the visible and thermal contrasts between the cloud cover and the underlying surface are frequently quite small. Pattern recognition has proven to be a useful tool in detecting and identifying several cloud types over snow and ice. Here a pattern recognition algorithm in combined with a hybrid histogram-spatial coherence (HHSC) scheme to derive cloud classification and fractional coverage, surface and cloud visible albedos and infrared brightness temperatures from multispectral AVHRR satellite imagery. The accuracy of the cloud fraction estimates were between 0.05 and 0.26, based on the mean absolute difference between the automated and manual nephanalyses of nearly 1000 training samples. The HHSC demonstrated greater accuracy at estimating cloud friction than three different threshold. methods. An important result is that the prior classification of a sample may significantly improve the accuracy of the analysis of cloud fraction, albedos and brightness temperatures over that of an unclassified sample.The algorithm is demonstrated for a set of AVHRR imagery from the summertime Arctic. The automated classification and analysis are in good agreement with manual interpretation of the satellite imagery and with surface observations.

  2. SAW arrays using dendrimers and pattern recognition to detect volatile organics

    SciTech Connect

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

    1998-08-01

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

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

    PubMed Central

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

    2013-01-01

    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

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

    NASA Astrophysics Data System (ADS)

    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

    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.

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

    PubMed Central

    Horton, Nathan C.; Mathew, Porunelloor A.

    2015-01-01

    Natural killer (NK) cells are a key constituent of the innate immune system, protecting against bacteria, virally infected cells, and cancer. Recognition and protective function against such cells are dictated by activating and inhibitory receptors on the surface of the NK cell, which bind to specific ligands on the surface of target cells. Among the activating receptors is a small class of specialized receptors termed the natural cytotoxicity receptors (NCRs) comprised of NKp30, NKp46, and NKp44. The NCRs are key receptors in the recognition and termination of virally infected and tumor cells. Since their discovery over 10?years ago, ligands corresponding to the NCRs have largely remained elusive. Recent identification of the cellular ligands for NKp44 and NKp30 as exosomal proliferating cell nuclear antigen (PCNA) and HLA-B-associated transcript 3 (BAT3), respectively, implicate that NCRs may function as receptors for damage-associated molecular pattern (DAMP) molecules. In this review, we focus on NKp44, which surprisingly recognizes two distinct ligands resulting in either activation or inhibition of NK cell effector responses in response to tumor cells. The inhibitory function of NKp44 requires further study as it may play a pivotal role in placentation in addition to being exploited by tumors as a mechanism to escape NK cell killing. Finally, we suggest that the NCRs are a class of pattern recognition receptors, which recognize signals of genomic instability and cellular stress via interaction with the c-terminus of DAMP molecules localized to the surface of target cells by various co-ligands. PMID:25699048

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

    SciTech Connect

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

    2006-04-21

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

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

    PubMed Central

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

    2011-01-01

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

  8. Real-time intelligent pattern recognition algorithm for surface EMG signals

    PubMed Central

    Khezri, Mahdi; Jahed, Mehran

    2007-01-01

    Background Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided a user assessment routine to evaluate the correctness of executed movements. Methods We propose to use an intelligent approach based on adaptive neuro-fuzzy inference system (ANFIS) integrated with a real-time learning scheme to identify hand motion commands. For this purpose and to consider the effect of user evaluation on recognizing hand movements, vision feedback is applied to increase the capability of our system. By using this scheme the user may assess the correctness of the performed hand movement. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a subtractive clustering algorithm has been developed. To design an effective system, we consider a conventional scheme of EMG pattern recognition system. To design this system we propose to use two different sets of EMG features, namely time domain (TD) and time-frequency representation (TFR). Also in order to decrease the undesirable effects of the dimension of these feature sets, principle component analysis (PCA) is utilized. Results In this study, the myoelectric signals considered for classification consists of six unique hand movements. Features chosen for EMG signal are time and time-frequency domain. In this work we demonstrate the capability of an EMG pattern recognition system using ANFIS as classifier with a real-time learning method. Our results reveal that the utilized real-time ANFIS approach along with the user evaluation provides a 96.7% average accuracy. This rate is superior to the previously reported result utilizing artificial neural networks (ANN) real-time method [1]. Conclusion This study shows that ANFIS real-time learning method coupled with mixed time and time-frequency features as EMG features can provide acceptable results for designing sEMG pattern recognition system suitable for hand prosthesis control. PMID:18053184

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

    PubMed

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

    2014-05-01

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

  10. Overexpression of a pattern-recognition receptor, peptidoglycan-recognition protein-LE, activates imd/relish-mediated antibacterial defense and the prophenoloxidase cascade in Drosophila larvae

    PubMed Central

    Takehana, Aya; Katsuyama, Tomonori; Yano, Tamaki; Oshima, Yoshiteru; Takada, Haruhiko; Aigaki, Toshiro; Kurata, Shoichiro

    2002-01-01

    In Drosophila, microbial infection activates an antimicrobial defense system involving the activation of proteolytic cascades in the hemolymph and intracellular signaling pathways, the immune deficiency (imd) and Toll pathways, in immune-responsive tissues. The mechanisms for microbial recognition are largely unknown. We report that, in larvae, the imd-mediated antibacterial defense is activated by peptidoglycan-recognition protein (PGRP)-LE, a PGRP-family member in Drosophila. Consistent with this, PGRP-LE binds to the diaminopimelic acid-type peptidoglycan, a cell-wall component of the bacteria capable of activating the imd pathway, but not to the lysine-type peptidoglycan. Moreover, PGRP-LE activates the prophenoloxidase cascade, a proteolytic cascade in the hemolymph. Therefore, PGRP-LE acts as a pattern-recognition receptor to the diaminopimelic acid-type peptidoglycan and activates both the proteolytic cascade and intracellular signaling in Drosophila immunity. PMID:12359879

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

    PubMed

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

    2014-07-01

    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

  12. A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements.

    PubMed

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

    2014-01-01

    Myoelectric control has been used for decades to control powered upper limb prostheses. Conventional, amplitude-based control has been employed to control a single prosthesis degree of freedom (DOF) such as closing and opening of the hand. Within the last decade, new and advanced arm and hand prostheses have been constructed that are capable of actuating numerous DOFs. Pattern recognition control has been proposed to control a greater number of DOFs than conventional control, but has traditionally been limited to sequentially controlling DOFs one at a time. However, able-bodied individuals use multiple DOFs simultaneously, and it may be beneficial to provide amputees the ability to perform simultaneous movements. In this study, four amputees who had undergone targeted motor reinnervation (TMR) surgery with previous training using myoelectric prostheses were configured to use three control strategies: 1) conventional amplitude-based myoelectric control, 2) sequential (one-DOF) pattern recognition control, 3) simultaneous pattern recognition control. Simultaneous pattern recognition was enabled by having amputees train each simultaneous movement as a separate motion class. For tasks that required control over just one DOF, sequential pattern recognition based control performed the best with the lowest average completion times, completion rates and length error. For tasks that required control over 2 DOFs, the simultaneous pattern recognition controller performed the best with the lowest average completion times, completion rates and length error compared to the other control strategies. In the two strategies in which users could employ simultaneous movements (conventional and simultaneous pattern recognition), amputees chose to use simultaneous movements 78% of the time with simultaneous pattern recognition and 64% of the time with conventional control for tasks that required two DOF motions to reach the target. These results suggest that when amputees are given the ability to control multiple DOFs simultaneously, they choose to perform tasks that utilize multiple DOFs with simultaneous movements. Additionally, they were able to perform these tasks with higher performance (faster speed, lower length error and higher completion rates) without losing substantial performance in 1 DOF tasks. PMID:24410948

  13. Histone deacetylase expression patterns in developing murine optic nerve

    PubMed Central

    2014-01-01

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

  14. Wavelet-modified binary phase-only morphological correlation for color pattern recognition

    NASA Astrophysics Data System (ADS)

    Aran, Amit; Munshi, Soumika; Beri, Vinod K.; Gupta, Arun K.

    2009-06-01

    This paper reports a morphological phase-only correlation technique based on bit-map representation for recognition of color as well as grey images in a hybrid digital-optical correlation architecture. The color image is decomposed into its R, G and B components, and each component is further decomposed into eight disjoint elementary images depending upon the bit-map representation of the color value at each pixel. Bit-map representation of the pixel values of an image reduces the required computational time. A set of twenty-four disjoint wavelet-modified binary phase-only filters (WBPOFs) are generated from these bit-map decomposed images. The target image is similarly decomposed into eight disjoint images each of R, G and B and their digital Fourier transforms multiplied with the corresponding WBPOFs. The product functions thus obtained are added up to form a single resultant product function, whose optical Fourier transformation gives the correlation peaks for the presence of R, G and B components in the image. The single product function overcomes the necessity of obtaining the final optical Fourier transformation of the R, G and B components separately. The novelty of this approach lies in the fact that the WBPOFs synthesized by this procedure are thus able to identify both colored as well as gray images and can tolerate salt-and-pepper noise to a considerable extent.

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

    NASA Astrophysics Data System (ADS)

    Watanabe, Mutsumi; Nishi, Natsuko

    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.

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

    NASA Astrophysics Data System (ADS)

    Ozheredov, V. A.

    2012-12-01

    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.

  17. Performance of an optimum receiver designed for pattern recognition with nonoverlapping target and scene noise

    NASA Astrophysics Data System (ADS)

    Javidi, Bahram; Fazlollahi, Amir; Willett, Peter; Réfrégier, Philippe

    1995-07-01

    The design of an optimum receiver for pattern recognition is based on multiple-alternative hypothesis testing with unknown parameters for detecting and locating a noisy target or a noise-free target in scene noise that is spatially nonoverlapping with this target. The optimum receiver designed for a noise-free target has the interesting property of detecting, without error, a noise-free target that has unknown illumination by using operations that are independent of the scene-noise statistics. We investigate the performance of the optimum receiver designed for nonoverlapping target and scene noise in terms of rotation and scale sensitivity of the input targets and discrimination against similar objects. Because it is not possible in practical systems to have a completely noise-free target, we examine how the performance of the optimum receiver designed for a noise-free target is affected when there is some overlapping noise on the target. The application of the optimum receiver to binary character recognition is described. Computer simulation results are provided.

  18. Efficient spatio-temporal local binary patterns for spontaneous facial micro-expression recognition.

    PubMed

    Wang, Yandan; See, John; Phan, Raphael C-W; Oh, Yee-Hui

    2015-01-01

    Micro-expression recognition is still in the preliminary stage, owing much to the numerous difficulties faced in the development of datasets. Since micro-expression is an important affective clue for clinical diagnosis and deceit analysis, much effort has gone into the creation of these datasets for research purposes. There are currently two publicly available spontaneous micro-expression datasets-SMIC and CASME II, both with baseline results released using the widely used dynamic texture descriptor LBP-TOP for feature extraction. Although LBP-TOP is popular and widely used, it is still not compact enough. In this paper, we draw further inspiration from the concept of LBP-TOP that considers three orthogonal planes by proposing two efficient approaches for feature extraction. The compact robust form described by the proposed LBP-Six Intersection Points (SIP) and a super-compact LBP-Three Mean Orthogonal Planes (MOP) not only preserves the essential patterns, but also reduces the redundancy that affects the discriminality of the encoded features. Through a comprehensive set of experiments, we demonstrate the strengths of our approaches in terms of recognition accuracy and efficiency. PMID:25993498

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

    PubMed Central

    2012-01-01

    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

  20. Implementation of a high-speed face recognition system that uses an optical parallel correlator

    Microsoft Academic Search

    Eriko Watanabe; Kashiko Kodate

    2005-01-01

    We implement a fully automatic fast face recognition system by using a 1000 frame\\/s optical parallel correlator designed and assembled by us. The operational speed for the 1:N (i.e., matching one image against N, where N refers to the number of images in the database) identification experiment (4000 face images) amounts to less than 1.5 s, including the preprocessing and

  1. Selection of Unstable Patterns and Control of Optical Turbulence by Fourier Plane Filtering

    Microsoft Academic Search

    A. V. Mamaev; M. Saffman

    1998-01-01

    We report on selection and stabilization of transverse optical patterns in a feedback mirror experiment. Amplitude filtering in the Fourier plane is used to select otherwise unstable spatial patterns. Optical turbulence observed for nonlinearities far above the pattern formation threshold is stabilized by low-pass filtering of the optical power spectrum. Experimental observations obtained with a photorefractive nonlinearity are consistent with

  2. Novel algorithms for improved pattern recognition using the US FDA Adverse Event Network Analyzer.

    PubMed

    Botsis, Taxiarchis; Scott, John; Goud, Ravi; Toman, Pamela; Sutherland, Andrea; Ball, Robert

    2014-01-01

    The medical review of adverse event reports for medical products requires the processing of "big data" stored in spontaneous reporting systems, such as the US Vaccine Adverse Event Reporting System (VAERS). VAERS data are not well suited to traditional statistical analyses so we developed the FDA Adverse Event Network Analyzer (AENA) and three novel network analysis approaches to extract information from these data. Our new approaches include a weighting scheme based on co-occurring triplets in reports, a visualization layout inspired by the islands algorithm, and a network growth methodology for the detection of outliers. We explored and verified these approaches by analysing the historical signal of Intussusception (IS) after the administration of RotaShield vaccine (RV) in 1999. We believe that our study supports the use of AENA for pattern recognition in medical product safety and other clinical data. PMID:25160375

  3. Automated real-time structure health monitoring via signature pattern recognition

    NASA Astrophysics Data System (ADS)

    Sun, Fanping P.; Chaudhry, Zaffir A.; Rogers, Craig A.; Majmundar, M.; Liang, Chen

    1995-05-01

    Described in this paper are the details of an automated real-time structure health monitoring system. The system is based on structural signature pattern recognition. It uses an array of piezoceramic patches bonded to the structure as integrated sensor-actuators, an electric impedance analyzer for structural frequency response function acquisition and a PC for control and graphic display. An assembled 3-bay truss structure is employed as a test bed. Two issues, the localization of sensing area and the sensor temperature drift, which are critical for the success of this technique are addressed and a novel approach of providing temperature compensation using probability correlation function is presented. Due to the negligible weight and size of the solid-state sensor array and its ability to sense incipient-type damage, the system can eventually be implemented on many types of structures such as aircraft, spacecraft, large-span dome roof and steel bridges requiring multilocation and real-time health monitoring.

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

    NASA Astrophysics Data System (ADS)

    Koblesky, Theodore

    2012-03-01

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

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

    NASA Astrophysics Data System (ADS)

    Cornale, P.; Barbera, S.

    2009-05-01

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

  6. Direct ubiquitination of pattern recognition receptor FLS2 attenuates plant innate immunity

    PubMed Central

    Lu, Dongping; Lin, Wenwei; Gao, Xiquan; Wu, Shujing; Cheng, Cheng; Avila, Julian; Heese, Antje; Devarenne, Timothy P.; He, Ping; Shan, Libo

    2011-01-01

    Innate immune responses are triggered by the activation of pattern-recognition receptors (PRRs). The Arabidopsis PRR FLS2 senses bacterial flagellin and initiates immune signaling by association with BAK1. The molecular mechanisms underlying the attenuation of FLS2 activation are largely unknown. We report that flagellin induces recruitment of two closely related U-box E3 ubiquitin ligases PUB12 and PUB13 to FLS2 receptor complex in Arabidopsis. BAK1 phosphorylates PUB12/13 and is required for FLS2-PUB12/13 association. PUB12/13 polyubiquitinate FLS2 and promote flagellin-induced FLS2 degradation, and the pub12 and pub13 mutants displayed elevated immune responses to flagellin treatment. Our study has revealed a unique regulatory circuit of direct ubiquitination and turnover of FLS2 by BAK1-mediated phosphorylation and recruitment of specific E3 ligases for attenuation of immune signaling. PMID:21680842

  7. Direct ubiquitination of pattern recognition receptor FLS2 attenuates plant innate immunity.

    PubMed

    Lu, Dongping; Lin, Wenwei; Gao, Xiquan; Wu, Shujing; Cheng, Cheng; Avila, Julian; Heese, Antje; Devarenne, Timothy P; He, Ping; Shan, Libo

    2011-06-17

    Innate immune responses are triggered by the activation of pattern-recognition receptors (PRRs). The Arabidopsis PRR FLAGELLIN-SENSING 2 (FLS2) senses bacterial flagellin and initiates immune signaling through association with BAK1. The molecular mechanisms underlying the attenuation of FLS2 activation are largely unknown. We report that flagellin induces recruitment of two closely related U-box E3 ubiquitin ligases, PUB12 and PUB13, to FLS2 receptor complex in Arabidopsis. BAK1 phosphorylates PUB12 and PUB13 and is required for FLS2-PUB12/13 association. PUB12 and PUB13 polyubiquitinate FLS2 and promote flagellin-induced FLS2 degradation, and the pub12 and pub13 mutants displayed elevated immune responses to flagellin treatment. Our study has revealed a unique regulatory circuit of direct ubiquitination and turnover of FLS2 by BAK1-mediated phosphorylation and recruitment of specific E3 ligases for attenuation of immune signaling. PMID:21680842

  8. Pattern Recognition of Cancer Cells Using Aptamer-Conjugated Magnetic Nanoparticles

    PubMed Central

    Bamrungsap, Suwussa; Chen, Tao; Shukoor, Mohammed Ibrahim; Chen, Zhuo; Sefah, Kwame; Chen, Yan; Tan, Weihong

    2012-01-01

    Biocompatible magnetic nanosensors based on reversible self-assembly of dispersed magnetic nanoparticles into stable nanoassemblies have been used as effective magnetic relaxation switches (MRSw) for the detection of molecular interactions. We report, for the first time, the design of MRSw based on aptamer-conjugated magnetic nanoparticles (ACMNPs). The ACMNPs capitalize on the ability of aptamers to specifically bind target cancer cells, as well as the large surface area of MNPs to accommodate multiple aptamer binding events. The ACMNPs can detect as few as 10 cancer cells in 250 ?L of sample. The ACMNPs’ specificity and sensitivity are also demonstrated by detection in cell mixtures and complex biological media, including fetal bovine serum (FBS), human plasma, and whole blood. Furthermore, by using an array of ACMNPs, various cell types can be differentiated through pattern recognition, thus creating a cellular molecular profile which will allow clinicians to accurately identify cancer cells at the molecular and single cell level. PMID:22424140

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

    NASA Astrophysics Data System (ADS)

    Coronel-Beltrán, Angel

    2014-10-01

    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%.

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

    SciTech Connect

    MARTINEZ, RUBEL F.

    2002-06-01

    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.

  11. Studies of the pattern recognition molecule H-ficolin: specificity and purification.

    PubMed

    Zacho, Rikke M; Jensen, Lisbeth; Terp, Randi; Jensenius, Jens C; Thiel, Steffen

    2012-03-01

    Ficolins are pattern recognition molecules of the innate immune system. H-ficolin is found in plasma associated with mannan-binding lectin-associated serine proteases (MASPs). When H-ficolin binds to microorganisms the MASPs are activated, which in turn activate the complement system. H-ficolin is the most abundant ficolin in humans, yet its ligand binding characteristics and biological role remain obscure. We examined the binding of H-ficolin to Aerococcus viridans as well as to a more defined artificial target, i.e. acetylated bovine serum albumin. A strict dependence for calcium ions and inhibition at high NaCl concentration was found. The binding to acetylated bovine serum albumin was inhibited by acetylsalicylic acid and sodium acetate as well as by N-acetylated glucosamine and galactosamine (GlcNAc and GalNAc) and glycine (GlyNAc). The binding to A. viridans was sensitive to the same compounds, but, importantly, higher concentrations were needed for inhibition. N-Acetylated cysteine was also inhibitory, but this inhibition was parallel with reduction in the oligomerization of H-ficolin and thus represents structural changes of the molecule. Based on our findings, we developed a procedure for the purification of H-ficolin from serum, involving PEG precipitation, affinity chromatography on Sepharose derivatized with acetylated serum albumin, ion exchange chromatography, and gel permeation chromatography. The purified H-ficolin was observed to elute at 700 kDa, similar to what we find for H-ficolin in whole serum. MASP-2 was co-purified with H-ficolin, and the purified H-ficolin·MASP-2 complex could activate complement as measured by cleavage of complement factor C4. This study extends our knowledge of the specificity of this pattern recognition molecule, and the purified product will enable further studies. PMID:22238349

  12. The serum mannose-binding protein and the macrophage mannose receptor are pattern recognition molecules that link innate and adaptive immunity

    Microsoft Academic Search

    Iain P Fraser; Henry Koziel; R. Alan B Ezekowitz

    1998-01-01

    The innate immune system evolved to protect the host in the early phases of an infectious challenge. The soluble mannose binding protein, and the cell surface mannose receptor are two key pattern recognition molecules of innate immunity. The ligand binding specificity of these molecules enables them to differentiate ‘self’ from ‘non-self’. These pattern recognition capabilities are coupled to effector functions,

  13. Patterning surfaces with colloidal particles using optical tweezers

    NASA Astrophysics Data System (ADS)

    Hoogenboom, J. P.; Vossen, D. L. J.; Faivre-Moskalenko, C.; Dogterom, M.; van Blaaderen, A.

    2002-06-01

    A method for positioning colloidal particles on surfaces in any designed pattern is described. Optical tweezers are used to bring particles from a reservoir to the substrate where opposite surface charges are used to immobilize particles on the surface. Both chemical surface modification and polyelectrolyte coating of either substrate or colloids make the method generally applicable. We show that using this technique large, two-dimensional patterns can be created that can be dried without distortions by critical point drying. As an example we show the positioning of 79 nm radius metallodielectric particles and we show how two-dimensional patterns can be used to direct three-dimensional epitaxial crystal growth. The method is inexpensive, relatively fast, and can be fully automated.

  14. Perception of pathogenic or beneficial bacteria and their evasion of host immunity: pattern recognition receptors in the frontline

    PubMed Central

    Trdá, Lucie; Boutrot, Freddy; Claverie, Justine; Brulé, Daphnée; Dorey, Stephan; Poinssot, Benoit

    2015-01-01

    Plants are continuously monitoring the presence of microorganisms to establish an adapted response. Plants commonly use pattern recognition receptors (PRRs) to perceive microbe- or pathogen-associated molecular patterns (MAMPs/PAMPs) which are microorganism molecular signatures. Located at the plant plasma membrane, the PRRs are generally receptor-like kinases (RLKs) or receptor-like proteins (RLPs). MAMP detection will lead to the establishment of a plant defense program called MAMP-triggered immunity (MTI). In this review, we overview the RLKs and RLPs that assure early recognition and control of pathogenic or beneficial bacteria. We also highlight the crucial function of PRRs during plant-microbe interactions, with a special emphasis on the receptors of the bacterial flagellin and peptidoglycan. In addition, we discuss the multiple strategies used by bacteria to evade PRR-mediated recognition. PMID:25904927

  15. 1 Introduction In the early days of computer vision and pattern recognition, writing a program to recognize an object meant

    E-print Network

    Learned-Miller, Erik

    certainly benefit from teachers, but they are not nearly so needy as current computers in learning new tasks1 Introduction In the early days of computer vision and pattern recognition, writing a program. We had learned how to teach the computer. As the excitement grew about learning methods, a new

  16. 734 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 11, NO. 3, MAY 2000 Pattern Recognition Via Synchronization in

    E-print Network

    Izhikevich, Eugene

    734 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 11, NO. 3, MAY 2000 Pattern Recognition Via Synchronization in Phase-Locked Loop Neural Networks Frank C. Hoppensteadt, Member, IEEE, and Eugene M. Izhikevich Abstract--We propose a novel architecture of an oscillatory neural network that consists of phase

  17. The broad pattern recognition spectrum of the Toll-like receptor in mollusk Zhikong scallop Chlamys farreri.

    PubMed

    Wang, Mengqiang; Wang, Lingling; Guo, Ying; Sun, Rui; Yue, Feng; Yi, Qilin; Song, Linsheng

    2015-10-01

    Toll-like receptors (TLRs) are among the most studied pattern recognition receptors (PRRs) playing essential roles in innate immune defenses. In the present study, the basic features of CfTLR in mollusk Zhikong scallop Chlamys farreri, including sequence homology, tissue distribution, subcellular localization and ligands spectrum, were investigated to elucidate its pattern recognition. The elements of extracellular domains (ECD) in CfTLR displayed high homology to the corresponding parts of the ECDs in TLRs from Homo sapiens. CfTLR protein was detected in hemocytes, mantle, gills, hepatopancreas, kidney and gonad of the scallops, and it was localized in both the plasma membranes and the lysosomes in HEK293T cells. CfTLR could activate NF?B in response to multiple HsTLR ligands including Pam3CSK4, glucan (GLU), peptidoglycan (PGN), polyriboinosinic:polyribocytidylic acid (poly I:C), Imiquimod and three types of CpG. Additionally, the scallop serum could enhance the induction of NF?B in the CfTLR expressing cells elicited by most PAMPs, including GLU, PGN, Imiquimod and four types of CpG. It could be concluded that this primitive mollusk TLR shared a hybrid function in pattern recognition and could recognize broader ligands than mammalian TLRs, and its mosaic capability of pathogen associated molecular pattern (PAMP) recognition might be based on the basic features of its structure, ligand properties and the assistance of some components in scallop serum. PMID:26026245

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

    Microsoft Academic Search

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

    2004-01-01

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

  19. Leak Detection of Municipal Water Supply Network Based on the Cluster-Analysis and Fuzzy Pattern Recognition

    Microsoft Academic Search

    Li Xia; Li Guo-jin

    2010-01-01

    Considering a Supervisory Control And Data Acquisition (SCADA) system has been installed in many municipal water supply networks, the water network scale mostly is big and the district meter area (DMA) have not been realized in China, a leak detection model based on the cluster-analysis and fuzzy pattern recognition theorem is introduced. First, computer simulation of pipe leak is realized,

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

    Microsoft Academic Search

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

    1982-01-01

    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

  1. Recognition and tracking of impulse patterns with delay adaptation in biology-inspired pulse processing neural net (BPN) hardware

    Microsoft Academic Search

    Hellmut Napp-Zinn; Michael Jansen; Rolf Eckmiller

    1996-01-01

    The application of an electronic real time emulator for biology-inspired pulse processing neural networks (BPN) to recognition and temporal tracking of discrete impulse patterns via delay adaptation is demonstrated. The electronic emulation includes biologically plausible features, such as asynchronous impulses, membrane potentials and adaptive weights, as well as a mechanism to modify signal delays. The rule for the adaptation of

  2. 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...

  3. Photopolymer diffractive optical elements in electronic speckle pattern shearing interferometry

    NASA Astrophysics Data System (ADS)

    Mihaylova, Emilia; Naydenova, Izabela; Duignan, Barry; Martin, Suzanne; Toal, Vincent

    2006-09-01

    In this paper we present an electronic speckle pattern shearing interferometer using a photopolymer diffractive optical element in the form of a holographic grating, in combination with a ground glass to shear the images. The sheared images on the ground glass are further imaged onto a CCD camera. The distance between the grating and the ground glass can be used to control the shear and to vary the sensitivity of the system. The direction of sensitivity is easily controlled by rotation of the diffraction grating around its normal. Introducing photopolymer holographic gratings in ESPSI gives the advantage of using high aperture optical elements at relatively low cost. The fact that the diffractive optical element is a photopolymer layer on glass substrate with thickness of 2 mm makes for a compact optical system. The system was successfully used for detection of the resonant frequencies of a vibrating object. Most of the published work on vibration analysis is analytical. Very few experimental results are available in the literature. The well known laser Doppler vibrometers (LDV) and accelerometers used for modal analysis are pointwise measurement techniques, although multipoint LDV is available at significant cost. Electronic speckle pattern techniques suitable for experimental detection of the resonant frequencies of vibrating objects are very promising for vibration analysis because they are whole field and non-contact. A finite element model is developed for prediction of the vibration modes of the object under test. Detection of vibrational modes of aluminium diaphragm is demonstrated and compared with the theoretical model. The results obtained are very promising for future application of ESPSI systems with HOEs, for modal analysis. A significant advantage of shearography over electronic speckle pattern interferometry is that ESPSI is relatively insensitive to external disturbances. Another advantage of the proposed system is that it could be easily converted to a phase-shifting electronic speckle shearing interferometer.

  4. Desynchronizing a chaotic pattern recognition neural network to model inaccurate perception.

    PubMed

    Calitoiu, Dragos; Oommen, B John; Nussbaum, Doron

    2007-06-01

    The usual goal of modeling natural and artificial perception involves determining how a system can extract the object that it perceives from an image that is noisy. The "inverse" of this problem is one of modeling how even a clear image can be perceived to be blurred in certain contexts. To our knowledge, there is no solution to this in the literature other than for an oversimplified model in which the true image is garbled with noise by the perceiver himself. In this paper, we propose a chaotic model of pattern recognition (PR) for the theory of "blurring." This paper, which is an extension to a companion paper demonstrates how one can model blurring from the view point of a chaotic PR system. Unlike the companion paper in which a chaotic PR system extracts the pattern from the input, in this case, we show that even without the inclusion of additional noise, perception of an object can be "blurred" if the dynamics of the chaotic system are modified. We thus propose a formal model and present an analysis using the Lyapunov exponents and the Routh-Hurwitz criterion. We also demonstrate experimentally the validity of our model by using a numeral data set. A byproduct of this model is the theoretical possibility of desynchronization of the periodic behavior of the brain (as a chaotic system), rendering us the possibility of predicting, controlling, and annulling epileptic behavior. PMID:17550122

  5. Improved Local Ternary Patterns for Automatic Target Recognition in Infrared Imagery

    PubMed Central

    Wu, Xiaosheng; Sun, Junding; Fan, Guoliang; Wang, Zhiheng

    2015-01-01

    This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme. Referring to the orthogonal combination of local binary patterns (OC_LBP), the orthogonal combination of LTP (OC_LTP) is adopted to reduce the dimensionality of the LTP histogram. Further, a novel operator, called the soft concave-convex orthogonal combination of robust LTP (SCC_OC_RLTP), is proposed by combing RLTP, SCCP and OC_LTP Finally, the new operator is used for ATR along with a blocking schedule to improve its discriminability and a feature selection technique to enhance its efficiency Experimental results on infrared imagery show that the proposed features can achieve competitive ATR results compared with the state-of-the-art methods. PMID:25785311

  6. A multi-modal face recognition method using complete local derivative patterns and depth maps.

    PubMed

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

    2014-01-01

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

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

    PubMed Central

    2014-01-01

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

  8. Automated Detection of Selective Logging in Amazon Forests Using Airborne Lidar Data and Pattern Recognition Algorithms

    NASA Astrophysics Data System (ADS)

    Keller, M. M.; d'Oliveira, M. N.; Takemura, C. M.; Vitoria, D.; Araujo, L. S.; Morton, D. C.

    2012-12-01

    Selective logging, the removal of several valuable timber trees per hectare, is an important land use in the Brazilian Amazon and may degrade forests through long term changes in structure, loss of forest carbon and species diversity. Similar to deforestation, the annual area affected by selected logging has declined significantly in the past decade. Nonetheless, this land use affects several thousand km2 per year in Brazil. We studied a 1000 ha area of the Antimary State Forest (FEA) in the State of Acre, Brazil (9.304 ?S, 68.281 ?W) that has a basal area of 22.5 m2 ha-1 and an above-ground biomass of 231 Mg ha-1. Logging intensity was low, approximately 10 to 15 m3 ha-1. We collected small-footprint airborne lidar data using an Optech ALTM 3100EA over the study area once each in 2010 and 2011. The study area contained both recent and older logging that used both conventional and technologically advanced logging techniques. Lidar return density averaged over 20 m-2 for both collection periods with estimated horizontal and vertical precision of 0.30 and 0.15 m. A relative density model comparing returns from 0 to 1 m elevation to returns in 1-5 m elevation range revealed the pattern of roads and skid trails. These patterns were confirmed by ground-based GPS survey. A GIS model of the road and skid network was built using lidar and ground data. We tested and compared two pattern recognition approaches used to automate logging detection. Both segmentation using commercial eCognition segmentation and a Frangi filter algorithm identified the road and skid trail network compared to the GIS model. We report on the effectiveness of these two techniques.

  9. Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Maas, Christian; Schmalzl, Jörg

    2013-08-01

    Ground Penetrating Radar (GPR) is used for the localization of supply lines, land mines, pipes and many other buried objects. These objects can be recognized in the recorded data as reflection hyperbolas with a typical shape depending on depth and material of the object and the surrounding material. To obtain the parameters, the shape of the hyperbola has to be fitted. In the last years several methods were developed to automate this task during post-processing. In this paper we show another approach for the automated localization of reflection hyperbolas in GPR data by solving a pattern recognition problem in grayscale images. In contrast to other methods our detection program is also able to immediately mark potential objects in real-time. For this task we use a version of the Viola-Jones learning algorithm, which is part of the open source library "OpenCV". This algorithm was initially developed for face recognition, but can be adapted to any other simple shape. In our program it is used to narrow down the location of reflection hyperbolas to certain areas in the GPR data. In order to extract the exact location and the velocity of the hyperbolas we apply a simple Hough Transform for hyperbolas. Because the Viola-Jones Algorithm reduces the input for the computational expensive Hough Transform dramatically the detection system can also be implemented on normal field computers, so on-site application is possible. The developed detection system shows promising results and detection rates in unprocessed radargrams. In order to improve the detection results and apply the program to noisy radar images more data of different GPR systems as input for the learning algorithm is necessary.

  10. [Raman spectroscopy combined with pattern recognition methods for rapid identification of crude soybean oil adulteration].

    PubMed

    Li, Bing-Ning; Wu, Yan-Wen; Wang, Yu; Zu, Wen-Chuan; Chen, Shun-Cong

    2014-10-01

    In the present paper, a non-destructive, simple and rapid analytical method was proposed based on Raman spectroscopy (Raman) combined with principal component analysis (PCA) and support vector machine (SVM) as pattern recognition methods for adulteration of crude soybean oil (CSO). Based on fingerprint characteristics of Raman, the spectra of 28 CSOs, 46 refined edible oils (REOs) and 110 adulterated oil samples were analyzed and used for discrimination model establishment. The preprocessing methods include choosing spectral band of 780-1,800 cm(-1), Y-axis intensity correction, baseline correction and normalization in succession. After those series of spectral pretreatment, PCA was usually employed for extracting characteristic variables of all Raman spectral data and 7 principal components which were the highest contributions of all data were used as var- iables for SVM model. The SVM discrimination model was established by randomly picking 20 CSOs and 95 adulterated oils as calibration set, and 8 CSOs and 35 adulterated oils as validation set. There were 4 kinds of kernel function algorithm (linear, polynomial, RBF, sigmoid) respectively used for establishing SVM models and grid-search for optimization of parameters of all the SVM models. The classification results of 4 models were compared by their discrimination performances and the optimal SVM model was based on linear kernel classification algorithm with 100% accuracy rate of calibration set recognition, a zero misjudgment rate and the lowest detection limit of 2.5%. The above results showed that Raman combined PCA-SVM could discriminate CSO adulteration with refined edible oils. Since Raman spectroscopy is simple, rapid, non-destructive, environment friendly, and suitable for field testing, it will provide an alternative method for edible oil adulteration analysis. PMID:25739210

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

    PubMed Central

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

    2014-01-01

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

  12. Face Recognition by Using Elongated Local Binary Patterns with Average Maximum

    E-print Network

    Chung, Albert C. S.

    method is evaluated by performing facial expression recognition experiments on two databases: ORL Introduction Automatic facial recognition (AFR) has been the topic of extensive research in the past several approach to face recognition from static images by using new features to effectively represent facial

  13. Portable Electronic Nose System for Identification of Synthesized Gasoline Using Metal Oxide Gas Sensor and Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Kim, Young Wung; Park, Hong Bae; Lee, In Soo; Cho, Jung Hwan

    2011-09-01

    This paper describes a portable electronic nose (e-nose) system for use in the identification of synthesized gasoline, comprised of a single semiconductor type of gas sensor and pattern recognition neural networks. The designed e-nose system consists of a one-chip microcontroller, a pre-concentrator, and a gas sensor. Two different neural networks, a multilayer perceptron (MLP) neural network and a fuzzy ARTMAP neural network were applied to discriminate synthesized gasoline from normal gasoline. The results of the classification showed 100% and 85% recognition rates for the training data set and testing data set, respectively.

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

    PubMed

    Jatobá, Luciana C; Grossmann, Ulrich; Kunze, Chistophe; Ottenbacher, Jörg; Stork, Wilhelm

    2008-01-01

    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 movement is being performed. This work presents a comparison of different methods for recognition of daily-life activities, which will serve as basis for the development of an online activity monitoring system. PMID:19163901

  15. An Application of Pattern Recognition techniques for the Analysis of Geoelectrical Signals in relation to the earthquake activity of Western Greece

    Microsoft Academic Search

    APOSTOLOS IFANTIS; VASILIS NIKOLAIDIS; GEORGE ECONOMOU

    2006-01-01

    A pattern recognition based approach is used in this study to examine single-channel Long Term Geoelectric Potential difference (LTGP) data recorded during the 1998-2003 period in Western Greece. Seeking a schema that automatically discovers data features containing information possibly related to seismic activity, patterns in LTGP data recorded during small (72-hour) consecutive time segments are examined, and pattern recognition methods

  16. Macroscopic quantum fluctuations in noise-sustained optical patterns

    E-print Network

    Zambrini, R; Colet, P; Miguel, M S; Zambrini, Roberta; Barnett, Stephen M.; Colet, Pere; Miguel, Maxi San

    2002-01-01

    We investigate quantum effects in pattern-formation for a degenerate optical parametric oscillator with walk-off. This device has a convective regime in which macroscopic patterns are both initiated and sustained by quantum noise. Familiar methods based linearization about a pseudo-classical field fail in this regime and new approaches are required. We employ a method in which the pump field is treated as a $c$-number variable but is driven by the $c$-number representation of the quantum sub-harmonic signal field. This allows us to include the effects of the fluctuations in the signal on the pump, which in turn act back on the signal. We find that the non-classical effects, in the form of squeezing, survive just above the threshold of the convective regime. Further above threshold the macroscopic quantum noise suppresses these effects.

  17. Macroscopic quantum fluctuations in noise-sustained optical patterns

    E-print Network

    Roberta Zambrini; Stephen M. Barnett; Pere Colet; Maxi San Miguel

    2002-04-29

    We investigate quantum effects in pattern-formation for a degenerate optical parametric oscillator with walk-off. This device has a convective regime in which macroscopic patterns are both initiated and sustained by quantum noise. Familiar methods based linearization about a pseudo-classical field fail in this regime and new approaches are required. We employ a method in which the pump field is treated as a $c$-number variable but is driven by the $c$-number representation of the quantum sub-harmonic signal field. This allows us to include the effects of the fluctuations in the signal on the pump, which in turn act back on the signal. We find that the non-classical effects, in the form of squeezing, survive just above the threshold of the convective regime. Further above threshold the macroscopic quantum noise suppresses these effects.

  18. Single-pixel optical imaging with compressed reference intensity patterns

    NASA Astrophysics Data System (ADS)

    Chen, Wen; Chen, Xudong

    2015-03-01

    Ghost imaging with single-pixel bucket detector has attracted more and more current attention due to its marked physical characteristics. However, in ghost imaging, a large number of reference intensity patterns are usually required for object reconstruction, hence many applications based on ghost imaging (such as tomography and optical security) may be tedious since heavy storage or transmission is requested. In this paper, we report that the compressed reference intensity patterns can be used for object recovery in computational ghost imaging (with single-pixel bucket detector), and object verification can be further conducted. Only a small portion (such as 2.0% pixels) of each reference intensity pattern is used for object reconstruction, and the recovered object is verified by using nonlinear correlation algorithm. Since statistical characteristic and speckle averaging property are inherent in ghost imaging, sidelobes or multiple peaks can be effectively suppressed or eliminated in the nonlinear correlation outputs when random pixel positions are selected from each reference intensity pattern. Since pixel positions can be randomly selected from each 2D reference intensity pattern (such as total measurements of 20000), a large key space and high flexibility can be generated when the proposed method is applied for authenticationbased cryptography. When compressive sensing is used to recover the object with a small number of measurements, the proposed strategy could still be feasible through further compressing the recorded data (i.e., reference intensity patterns) followed by object verification. It is expected that the proposed method not only compresses the recorded data and facilitates the storage or transmission, but also can build up novel capability (i.e., classical or quantum information verification) for ghost imaging.

  19. Optical properties of micro-patterned silver nanoparticle substrates.

    PubMed

    Stranik, Ondrej; Iacopino, Daniela; Nooney, Robert; McDonagh, Colette; Maccraith, Brian D

    2010-01-01

    In this paper, we describe a novel technique for depositing metal nanoparticles (NPs) on a planar substrate whereby the NPs are micro-patterned on the surface by a simple stamp-printing procedure. The method exploits the attractive force between negatively charged colloidal metal NPs and positively-charged polyelectrolyte layers which have been selectively deposited on the surface. Using this technique, large uniform areas of patterned metal NPs, with different plasmonic properties, were achieved by optimisation of the stamping process. We report the observation of unusual fluorescence emission from these structures. The emission was measured using epifluorescence microscopy. Fluorescence lifetime behaviour was also measured. Furthermore, the mu-patterned NPs exhibited blinking behaviour under 469 nm excitation and the fluorescence spectrum was multi-peaked. It has been established that the fluorescence is independent of the plasmon resonance properties of the NPs. As well as optimising the novel NP mu-patterning technique, this work discusses the origin and characteristics of the anomalous fluorescence behaviour in order to characterise and minimise this unwanted background contribution in the use of metal NPs for plasmonic enhancement of fluorescence for optical biochip applications. PMID:19821014

  20. Model of an axially strained weakly guiding optical fiber modal pattern

    NASA Technical Reports Server (NTRS)

    Egalon, Claudio O.; Rogowski, Robert S.

    1992-01-01

    Axial strain can be determined by monitoring the modal pattern variation of an optical fiber. The results of a numerical model developed to calculate the modal pattern variation at the end of a weakly guiding optical fiber under axial strain is presented. Whenever an optical fiber is under stress, the optical path length, the index of refraction, and the propagation constants of each fiber mode change. In consequence, the modal phase term for the fields and the fiber output pattern are also modified. For multimode fibers, very complicated patterns result. The predicted patterns are presented, and an expression for the phase variation with strain is derived.

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

    PubMed Central

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

    2011-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1987-01-01

    The invention relates to an apparatus and associated methods for the optical recognition and tracking of multiple objects in real time. Multiple point spatial filters are employed that pre-define the objects to be recognized at run-time. The system takes the basic technology of a Vander Lugt filter and adds a hololens. The technique replaces time, space and cost-intensive digital techniques. In place of multiple objects, the system can also recognize multiple orientations of a single object. This later capability has potential for space applications where space and weight are at a premium.

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

    NASA Technical Reports Server (NTRS)

    Kiang, Richard K.

    1992-01-01

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

  4. Autophagy as an innate immunity paradigm: expanding the scope and repertoire of pattern recognition receptors.

    PubMed

    Deretic, Vojo

    2012-02-01

    Autophagy is rapidly developing into a new immunological paradigm. The latest links now include overlaps between autophagy and innate immune signaling via TBK-1 and IKK?/?, and the role of autophagy in inflammation directed by the inflammasome. Autophagy's innate immunity connections include responses to pathogen and damage-associated molecular patterns including alarmins such as HMGB1 and IL-1?, Toll-like receptors, Nod-like receptors including NLRC4, NLRP3 and NLRP4, and RIG-I-like receptors. Autophagic adaptors referred to as SLRs (sequestosome 1/p62-like receptors) are themselves a category of pattern recognition receptors. SLRs empower autophagy to eliminate intracellular microbes by direct capture and by facilitating generation and delivery of antimicrobial peptides, and also serve as inflammatory signaling platforms. SLRs contribute to autophagic control of intracellular microbes, including Mycobacterium tuberculosis, Salmonella, Listeria, Shigella, HIV-1 and Sindbis virus, but act as double-edged sword and contribute to inflammation and cell death. Autophagy roles in innate immunity continue to expand vertically and laterally, and now include antimicrobial function downstream of vitamin D3 action in tuberculosis and AIDS. Recent data expand the connections between immunity-related GTPases and autophagy to include not only IRGM but also several members of the Gbp (guanlyate-binding proteins) family. The efficacy with which autophagy handles microbes, microbial products and sterile endogenous irritants governs whether the outcome will be with suppression of or with excess inflammation, the latter reflected in human diseases that have strong inflammatory components including tuberculosis and Crohn's disease. PMID:22118953

  5. Structure of the F-Spondin Domain of Mindin, an Integrin Ligand and Pattern Recognition Molecule

    SciTech Connect

    Li, Y.; Cao, C; Jia, W; Yu, L; Mo, M; Wang, Q; Huang, Y; Lim, J; Ishihara, M; et. al.

    2009-01-01

    Mindin (spondin-2) is an extracellular matrix protein of unknown structure that is required for efficient T-cell priming by dendritic cells. Additionally, mindin functions as a pattern recognition molecule for initiating innate immune responses. These dual functions are mediated by interactions with integrins and microbial pathogens, respectively. Mindin comprises an N-terminal F-spondin (FS) domain and C-terminal thrombospondin type 1 repeat (TSR). We determined the structure of the FS domain at 1.8-A resolution. The structure revealed an eight-stranded antiparallel ?-sandwich motif resembling that of membrane-targeting C2 domains, including a bound calcium ion. We demonstrated that the FS domain mediates integrin binding and identified the binding site by mutagenesis. The mindin FS domain therefore represents a new integrin ligand. We further showed that mindin recognizes lipopolysaccharide (LPS) through its TSR domain, and obtained evidence that C-mannosylation of the TSR influences LPS binding. Through these dual interactions, the FS and TSR domains of mindin promote activation of both adaptive and innate immune responses.

  6. Extending applicability of cluster based pattern recognition with efficient approximation techniques

    SciTech Connect

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

    1997-03-01

    The fundamental goal of this research has been to improve computational efficiency of the Visually Empirical Region of Influence (VERI) based clustering and pattern recognition (PR) algorithms we developed in previous work. The original clustering algorithm, when applied to data sets with N points, ran in time proportional to N{sup 3} (denoted with the notation O (N{sup 3})), which limited the size of data sets it could find solutions for. Results generated from our original clustering algorithm were superior to commercial clustering packages. These results warranted our efforts to improve the runtimes of our algorithms. This report describes the new algorithms, advances and obstacles met in their development. The report gives qualitative and quantitative analysis of the improved algorithms performances. With the information in this report, an interested user can determine which algorithm is best for a given problem in clustering (2-D) or PR (K-D), and can estimate how long it will run using the runtime plots of the algorithms before using any software.

  7. E3 ubiquitin ligases Pellinos as regulators of pattern recognition receptor signaling and immune responses.

    PubMed

    Medvedev, Andrei E; Murphy, Michael; Zhou, Hao; Li, Xiaoxia

    2015-07-01

    Pellinos are a family of E3 ubiquitin ligases discovered for their role in catalyzing K63-linked polyubiquitination of Pelle, an interleukin-1 (IL-1) receptor-associated kinase homolog in the Drosophila Toll pathway. Subsequent studies have revealed the central and non-redundant roles of mammalian Pellino-1, Pellino-2, and Pelino-3 in signaling pathways emanating from IL-1 receptors, Toll-like receptors, NOD-like receptors, T- and B-cell receptors. While Pellinos ability to interact with many signaling intermediates suggested their scaffolding roles, recent findings in mice expressing ligase-inactive Pellinos demonstrated the importance of Pellino ubiquitin ligase activity. Cell-specific functions of Pellinos have emerged, e.g. Pellino-1 being a negative regulator in T lymphocytes and a positive regulator in myeloid cells, and details of molecular regulation of receptor signaling by various members of the Pellino family have been revealed. In this review, we summarize current information about Pellino-mediated regulation of signaling by pattern recognition receptors, T-cell and B-cell receptors and tumor necrosis factor receptors, and discuss Pellinos roles in sepsis and infectious diseases, as well as in autoimmune, inflammatory, and allergic disorders. We also provide our perspective on the potential of targeting Pellinos with peptide- or small molecule-based drug compounds as a new therapeutic approach for septic shock and autoimmune pathologies. PMID:26085210

  8. Real-time and simultaneous control of artificial limbs based on pattern recognition algorithms.

    PubMed

    Ortiz-Catalan, Max; Håkansson, Bo; Brånemark, Rickard

    2014-07-01

    The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigate different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. On the other hand, classifiers inherently capable of simultaneous predictions, such as the multi-layer perceptron (MLP), were found to be more cost effective, as they can be successfully employed in their simplest form. In the prediction of individual movements, the one-vs-one (OVO) topology was found to improve classification accuracy across different classifiers and it was therefore used to benchmark the benefits of simultaneous control. As opposed to previous work reporting only offline accuracy, the classification performance and the resulting controllability are evaluated in real time using the motion test and target achievement control (TAC) test, respectively. We propose a simultaneous classification strategy based on MLP that outperformed a top classifier for individual movements (LDA-OVO), thus improving the state-of-the-art classification approach. Furthermore, all the presented classification strategies and data collected in this study are freely available in BioPatRec, an open source platform for the development of advanced prosthetic control strategies. PMID:24710833

  9. Pattern recognition techniques for failure trend detection in SSME ground tests

    NASA Technical Reports Server (NTRS)

    Choudry, A.

    1987-01-01

    The Space Shuttle Main Engine (SSME) is a complex power plant. To evaluate its performance 1200 hot-wire ground tests have been conducted, varying in duration from 0 to 500 secs. During the test some 500 sensors are sampled every 20 ms. The sensors are generally bounded by red lines so that an excursion beyond could lead to premature shutdown. In 27 tests it was not possible to effect an orderly premature shutdown, resulting in major incidents with serious damage to the SSME and test stand. The application of pattern recognition are investigated to detect SSME performance trends that may lead to major incidents. Based on the sensor data a set of (n) features is defined. At any time during the test, the state of the SSME is given by a point in the n-dimensional feature space. The history of a test can now be represented as a trajectory in the n-dimensional feature space. Portions of the normal trajectories and failed test trajectories would lie in different regions of the n-dimensional feature space. The latter can now be partitioned into regions of normal and failed tests. Thus, it is possible to examine the trajectory of a test in progress and predict if it is going into the normal or failure region.

  10. Structure of the F-spondin Domain of Mindin an Integrin Ligand and Pattern Recognition Molecule

    SciTech Connect

    Y Li; C Cao; W Jia; L Yu; M Mo; Q Wang; Y Huang; J Lim; M Ishihara; et. al.

    2011-12-31

    Mindin (spondin-2) is an extracellular matrix protein of unknown structure that is required for efficient T-cell priming by dendritic cells. Additionally, mindin functions as a pattern recognition molecule for initiating innate immune responses. These dual functions are mediated by interactions with integrins and microbial pathogens, respectively. Mindin comprises an N-terminal F-spondin (FS) domain and C-terminal thrombospondin type 1 repeat (TSR). We determined the structure of the FS domain at 1.8-A resolution. The structure revealed an eight-stranded antiparallel beta-sandwich motif resembling that of membrane-targeting C2 domains, including a bound calcium ion. We demonstrated that the FS domain mediates integrin binding and identified the binding site by mutagenesis. The mindin FS domain therefore represents a new integrin ligand. We further showed that mindin recognizes lipopolysaccharide (LPS) through its TSR domain, and obtained evidence that C-mannosylation of the TSR influences LPS binding. Through these dual interactions, the FS and TSR domains of mindin promote activation of both adaptive and innate immune responses.

  11. Enter at your own risk: how enteroviruses navigate the dangerous world of pattern recognition receptor signaling.

    PubMed

    Harris, Katharine G; Coyne, Carolyn B

    2013-09-01

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

  12. Fast hand recognition method using limited area of IR projection pattern

    NASA Astrophysics Data System (ADS)

    Yamamoto, Shoji; Kamimigaki, Sayuri; Tsumura, Norimichi; Nakaguchi, Toshiya; Miyake, Yoichi

    2009-02-01

    We have been developing a rapid proto-typing display system which can verify an appearance of final product in finishing and painting industry. In this system, it is necessary to measure detail information of hand position and shape to recognize the worker's instruction. Therefore, we apply a rapid hand measurement which combine a roughly detecting of hand position and shape by spatial encoding method with IR projection. For detecting of hand position, non-linearity interval strips are used for detecting objects that are lower than constant height. The interval of strips is devised in relation to an angle of camera axis to make equal the height in detecting. For detecting of hand shape, the temporal and spatial encoding pattern is projected only an area of hand position. This measurement is enough rough because our prototyping display system need only to classify the shape of tracing, touching, pushing, and picking. Therefore, the limited process with limited area is possible to reconstruct the shape of hand very fast. A practical result shows that the position and shape recognition is performed about one second; and operator comment that such the time delay doesn't become a stress as for actual hand operation.

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

    PubMed Central

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

    2014-01-01

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

  14. Feature correlation for particle image velocimetry: An application of pattern recognition

    SciTech Connect

    Zhang, X.; Cox, C.S. [Univ. of California, San Diego, La Jolla, CA (United States). Scripps Institution of Oceanography

    1995-12-31

    Particle Image Velocimetry (PIV) has been used successfully for measuring instantaneous two dimensional velocity fields. Analyzing PIV images involves matching particle images captured sequentially. A feature-recognition method is proposed here for analyzing PIV images. It first extracts structural features of the particle pattern after their locations have been isolated from images. A preliminary process is to replace the particle images by the Cartesian coordinates of particle centers. In this way the brightness of particle images plays no further part, and the point positions are used to establish structural features: topological relations between each point and its neighbors. The interrogation area is defined by a limited number of neighboring points. The size and shape of each interrogation area varies with the distribution of neighbors. A fit to motion, rotation and distortion among the neighbors is then carried out in the space of topological relations of successive images. In this way changes of structural features define fluid spatial translation, rotation, and deformations within each interrogation region. Measurement of feature space in two successive images demands knowledge of the locations of corresponding points derived from individual particles in the two images. Classification of point correspondences, despite confusingly discordant displacements from one image to the next, can be made by taking advantage of physical limitations on the possible movement of particles between the two images. It is found that feature space search and correlation is a much more efficient procedure than correlation operations in the two dimensional image domain.

  15. On damage diagnosis for a wind turbine blade using pattern recognition

    NASA Astrophysics Data System (ADS)

    Dervilis, N.; Choi, M.; Taylor, S. G.; Barthorpe, R. J.; Park, G.; Farrar, C. R.; Worden, K.

    2014-03-01

    With the increased interest in implementation of wind turbine power plants in remote areas, structural health monitoring (SHM) will be one of the key cards in the efficient establishment of wind turbines in the energy arena. Detection of blade damage at an early stage is a critical problem, as blade failure can lead to a catastrophic outcome for the entire wind turbine system. Experimental measurements from vibration analysis were extracted from a 9 m CX-100 blade by researchers at Los Alamos National Laboratory (LANL) throughout a full-scale fatigue test conducted at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC). The blade was harmonically excited at its first natural frequency using a Universal Resonant EXcitation (UREX) system. In the current study, machine learning algorithms based on Artificial Neural Networks (ANNs), including an Auto-Associative Neural Network (AANN) based on a standard ANN form and a novel approach to auto-association with Radial Basis Functions (RBFs) networks are used, which are optimised for fast and efficient runs. This paper introduces such pattern recognition methods into the wind energy field and attempts to address the effectiveness of such methods by combining vibration response data with novelty detection techniques.

  16. Implementation of a new pattern recognition adaptive controller developed through optimization

    SciTech Connect

    Seem, J.E. [Johnson Controls, Inc., Milwaukee, WI (United States)

    1997-12-31

    Optimization methods were used to develop a novel pattern recognition adaptive controller (PRAC) that automatically adjusts the gain and integral time of proportional-integral controllers while under closed-loop control. PRAC will improve the performance of P1 control systems that are exhibiting either oscillatory or sluggish behavior. The new controller is easy to use and provides near-optimal performance for a range of systems and noise levels. Also, the algorithm is computationally efficient and does not have large memory requirements. Thus, the algorithm can be implemented in today`s digital control systems for heating, ventilating, and air-conditioning (HVAC) equipment. PRAC has successfully tuned control systems for heating, ventilating, and air-conditioning (HVAC) equipment. PRAC has successfully tuned control systems for HVAC equipment in office buildings, high schools, universities, national laboratories, department stores, hospitals, clinics, and large sports stadiums. This paper describes methods for implementing the new PRAC. The paper also contains simulation and field test results.

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

    PubMed Central

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

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

  19. Pattern recognition in hyperspectral imagery using one dimensional maximum average correlation height filter and Mahalanobis distance

    NASA Astrophysics Data System (ADS)

    Islam, M. F.; Alam, M. S.; Elbakary, M. I.

    2007-04-01

    Pattern recognition in hyperspectral imagery is a challenging task as the objects occupy only a few pixels or less. The presence of noise can make detection more complicated as spectral signature of pixels can change due to noise. In this paper a technique is proposed for detection in hyperspectral imagery using one dimensional maximum average correlation height (MACH) filter. MACH filter is a type of matched spatial training filter which is widely used for spatial aperture radar (SAR), laser radar (LADAR), forward looking infrared (FLIR) and other class of two-dimensional imageries to train and detect objects. For hyperspectral case a modified one-dimensional MACH filter is proposed which uses likely variations of a given ideal spectral signature for training. Each pixel vector of the data cube is then compared with the detection filter using Mahalanobis distance. Based on Mahalanobis distance between the trained filter and the pixels of the imagery, two classes are formed called the background class which does not contain a desired object and the object class which does contain the desired object. By applying threshold boundary, a decision is then made whether a given pixel belongs to the background class or object class. The simulation results using real life hyperspectral imagery show that the proposed technique can detect and classify the desired objects with a higher rate of efficiency even for very small and scattered objects.

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

    NASA Astrophysics Data System (ADS)

    Kostopoulos, Spiros; Glotsos, Dimitris; Sidiropoulos, Konstantinos; Asvestas, Pantelis; Cavouras, Dionisis; Kalatzis, Ioannis

    2014-03-01

    The aim of the present study was to implement a pattern recognition system for the discrimination of healthy from malignant prostate tumors from proteomic Mass Spectroscopy (MS) samples and to identify m/z intervals of potential biomarkers associated with prostate cancer. One hundred and six MS-spectra were studied in total. Sixty three spectra corresponded to healthy cases (PSA < 1) and forty three spectra were cancerous (PSA > 10). The MS-spectra are publicly available from the NCI Clinical Proteomics Database. The pre-processing comprised the steps: denoising, normalization, peak extraction and peak alignment. Due to the enormous number of features that rose from MS-spectra as informative peaks, and in order to secure optimum system design, the classification task was performed by programming in parallel the multiprocessors of an nVIDIA GPU card, using the CUDA framework. The proposed system achieved 98.1% accuracy. The identified m/z intervals displayed significant statistical differences between the two classes and were found to possess adequate discriminatory power in characterizing prostate samples, when employed in the design of the classification system. Those intervals should be further investigated since they might lead to the identification of potential new biomarkers for prostate cancer.

  1. Optical Patterning of Three-Dimensional Carbon Nanotube Microstructures

    NASA Astrophysics Data System (ADS)

    Hung, Wei-Husuan; Kumar, Rajay; Bushmaker, Adam; Bronikowski, Michael J.; Cronin, Stephen B.

    2008-03-01

    We present an optical, non-contact method for patterning three-dimensional carbon nanotube microstructures. In this method, a 1?m diameter focused laser spot is used to burn patterns in dense arrays of vertically grown multiwalled carbon nanotubes. The threshold for laser burnout and the depth of burnout are determined by Raman spectroscopy and scanning electron microscopy. Using a high precision translation stage to control the position of the laser spot on the sample, we create several 3D patterns to illustrate this method's potential use for the rapid prototyping of carbon nanotube microstructures [1]. After laser surface treatment, we observe undercut profiles, changes in nanotube density, and nanoparticle formation, which provide insight into the unique evolution of the nanotube microstructures during the burnout process. This non-lithographic method provides new opportunities for chemically sensitive applications of nanotubes and expands their possible applications into new areas. [1] Hung, Wei Hsuan, Kumar, Rajay, Bushmaker, Adam, Cronin, Stephen B., and Bronikowski, Michael J. Rapid prototyping of three-dimensional microstructures from multiwalled carbon nanotubes. Applied Physics Letters 91, 093121 (2007).

  2. [Effect of baicalin on pattern recognition receptor TLR2/4-NOD2 and its significance of druggability].

    PubMed

    Chai, Yu-Shuang; Lei, Fan; Xing, Dong-Ming; Ding, Yi; Du, Li-Jun

    2013-08-01

    Activation pattern recognition receptors can cause the startup of downstream signaling pathways, the expression of inflammatory factors, and finally immunological inflammatory reaction. Either exogenous pathogenic microorganisms or endogenous tissue components can activate these pattern recognition receptors as ligands at varying degrees, and then cause the immunological inflammatory reaction. Therefore, it is of great significance to inhibit relevant receptors, as well as the immunological inflammatory reaction, in order to avoid tissue injury during the course of disease. Baicalin is able to specifically inhibit the expression of TLR2/4-NOD2, inhibit the expression of inflammatory factors IL-1beta, IL-6 and TNF-alpha, and thereby reducing the injury of the tissue cells during the course of disease. This effect is non-specific with tissues, which is of great theoretical and practical significance in druggability. In addition, the drug metabolism and toxicity of baicalin are also discussed for its druggability in this article. PMID:24228579

  3. The Relationship Between Kernel and Classifier Fusion in Kernel-Based Multi-Modal Pattern Recognition: An Experimental Study

    Microsoft Academic Search

    DAVID WINDRIDGE; VADIM MOTTL; ALEXANDER TATARCHUK; ANDREY ELISEYEV

    2007-01-01

    Two distinct principles of multi-modal kernel-based pattern recognition, kernel and classifier fusion, are demonstrated to share common underlying characteristics via the use of a novel kernel-based technique for combining modalities under fully general conditions, namely, the neutral-point method. This method presents a conservative kernel-based strategy for dealing with missing and disjoint training data in independent measurement modalities that can be

  4. Using of FPGA Coprocessor for Improving the Execution Speed of the Pattern Recognition Algorithm for ATLAS - High Energy Physics Experiment

    Microsoft Academic Search

    Christian Hinkelbein; Andrei Khomich; Andreas Kugel; Reinhard Männer; Matthias Müller

    2004-01-01

    \\u000a Pattern recognition algorithms are used in experimental High Energy physics for getting parameters (features) of particles\\u000a tracks in detectors. It is particularly important to have fast algorithms in trigger system. This paper investigates the suitability\\u000a of using FPGA coprocessor for speedup of the TRT-LUT algorithm – one of the feature extraction algorithms for second level\\u000a trigger for ATLAS experiment (CERN). Two

  5. Quality Control of Wood-Pulp Chips Using A 3D Laser Scanner and Functional Pattern Recognition

    Microsoft Academic Search

    M. Lopez; J. A. Vilan; J. M. Matias; J. Taboada

    2007-01-01

    We describe a real-time quality control system for wood chips using a 3D laser scanner. The work evaluates the appropriateness of applying a functional rather than the typical vectorial approach to a pattern recognition problem. The problem to be resolved was to construct an online system for controlling wood-pulp chip granulometry quality for implementation in a wood-pulp factory. A functional

  6. Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data

    Microsoft Academic Search

    Qingda Zang; David A. Keire; Lucinda F. Buhse; Richard D. Wood; Dinesh P. Mital; Syed Haque; Shankar Srinivasan; Christine M. V. Moore; Moheb Nasr; Ali Al-Hakim; Michael L. Trehy; William J. Welsh

    Chemometric analysis of a set of one-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectral data for heparin sodium active pharmaceutical ingredient (API) samples was employed\\u000a to distinguish USP-grade heparin samples from those containing oversulfated chondroitin sulfate (OSCS) contaminant and\\/or\\u000a unacceptable levels of dermatan sulfate (DS) impurity. Three chemometric pattern recognition approaches were implemented:\\u000a classification and regression tree (CART), artificial neural

  7. EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury.

    PubMed

    Liu, Jie; Li, Xiaoyan; Li, Guanglin; Zhou, Ping

    2014-07-01

    Myoelectric pattern recognition with a large number of electromyogram (EMG) channels provides an approach to assessing motor control information available from the recorded muscles. In order to develop a practical myoelectric control system, a feature dependent channel reduction method was developed in this study to determine a small number of EMG channels for myoelectric pattern recognition analysis. The method selects appropriate raw EMG features for classification of different movements, using the minimum Redundancy Maximum Relevance (mRMR) and the Markov random field (MRF) methods to rank a large number of EMG features, respectively. A k-nearest neighbor (KNN) classifier was used to evaluate the performance of the selected features in terms of classification accuracy. The method was tested using 57 channels' surface EMG signals recorded from forearm and hand muscles of individuals with incomplete spinal cord injury (SCI). Our results demonstrate that appropriate selection of a small number of raw EMG features from different recording channels resulted in similar high classification accuracies as achieved by using all the EMG channels or features. Compared with the conventional sequential forward selection (SFS) method, the feature dependent method does not require repeated classifier implementation. It can effectively reduce redundant information not only cross different channels, but also cross different features in the same channel. Such hybrid feature-channel selection from a large number of EMG recording channels can reduce computational cost for implementation of a myoelectric pattern recognition based control system. PMID:24844608

  8. Comparison of two exploratory data analysis methods for classification of Phyllanthus chemical fingerprint: unsupervised vs. supervised pattern recognition technologies.

    PubMed

    Guo, Jianru; Chen, QianQian; Wang, Caiyun; Qiu, Hongcong; Liu, Buming; Jiang, Zhi-Hong; Zhang, Wei

    2015-02-01

    In this study, unsupervised and supervised classification methods were compared for comprehensive analysis of the fingerprints of 26 Phyllanthus samples from different geographical regions and species. A total of 63 compounds were identified and tentatively assigned structures for the establishment of fingerprints using high-performance liquid chromatography time-of-flight mass spectrometry (HPLC/TOFMS). Unsupervised and supervised pattern recognition technologies including principal component analysis (PCA), nearest neighbors algorithm (NN), partial least squares discriminant analysis (PLS-DA), and artificial neural network (ANN) were employed. Results showed that Phyllanthus could be correctly classified according to their geographical locations and species through ANN and PLS-DA. Important variables for clusters discrimination were also identified by PCA. Although unsupervised and supervised pattern recognitions have their own disadvantage and application scope, they are effective and reliable for studying fingerprints of traditional Chinese medicines (TCM). These two technologies are complementary and can be superimposed. Our study is the first holistic comparison of supervised and unsupervised pattern recognition technologies in the TCM chemical fingerprinting. They showed advantages in sample classification and data mining, respectively. PMID:25504091

  9. Pattern recognition control of multifunction myoelectric prostheses by patients with congenital transradial limb defects: A preliminary study

    PubMed Central

    Kryger, Michael; Schultz, Aimee E; Kuiken, Todd A

    2015-01-01

    Background Electromyography (EMG) pattern recognition offers the potential for improved control of multifunction myoelectric prostheses. However, it is unclear whether this technology can be successfully used by congenital amputees. Objective The purpose of this investigation was to assess the ability of congenital transradial amputees to control a virtual multifunction prosthesis using EMG pattern recognition and compare their performance to that of acquired amputees from a previous study. Study Design Preliminary cross-sectional study. Methods Four congenital transradial amputees trained and tested a linear discriminant analysis (LDA) classifier with four wrist movements, five hand movements, and a no movement class. Subjects then tested the classifier in real time using a virtual arm. Results Performance metrics for the residual limb were poorer than those with the intact limb (classification accuracy: 52.1% ± 15.0% vs. 93.2% ± 15.8%; motion-completion rate: 49.0% ± 23.0% vs. 84.0% ± 9.4%; motion-completion time: 2.05 ± 0.75 s vs. 1.13 ± 0.05 s, respectively). On average, performance with the residual limb by congenital amputees was reduced compared to that reported for acquired transradial amputees. However, one subject performed similarly to acquired amputees. Conclusions Pattern recognition control may be a viable option for some congenital amputees. Further study is warranted to determine success factors. PMID:21960053

  10. Open set text-independent speaker recognition based on set-score pattern classification

    Microsoft Academic Search

    Jiuqing Deng; Qixiu Hu

    2003-01-01

    We propose a two-stage recognition schema for open set text-independent speaker recognition tasks. First we try to find a best matched model (which gets the best score) for the unknown speaker like many other systems. But then unlike other classical threshold selecting methods that make decisions based on the best score, we use the scores over a reference speakers set

  11. Symmetry properties and exact patterns in birefringent optical fibers

    NASA Astrophysics Data System (ADS)

    Alfinito, E.; Leo, M.; Leo, R. A.; Soliani, G.; Solombrino, L.

    1995-09-01

    A pair of nonlinear Schrödinger equations, describing the propagation of waves in birefringent optical fibers, is studied by means of a Lie group technique. The symmetry algebra and the symmetry group associated with the equations are exploited to provide exact configurations. These are the soliton profile, which corresponds to a linear combination of the coordinate translations and the constant change of phase, a solution expressed in terms of the sinus elliptic function, a solution related to the Galilean boost, and other solutions which may be used as a guide for the creation of different experimental patterns. Among them, of special interest is a configuration involving the loss coefficient of the fiber, whose ``mass density'' is time independent and behaves as a screened Coulomb potential in the space variable.

  12. Optical Magnetoelectric Effect of Patterned Oxide Superlattices with Ferromagnetic Interfaces

    NASA Astrophysics Data System (ADS)

    Kida, N.; Yamada, H.; Sato, H.; Arima, T.; Kawasaki, M.; Akoh, H.; Tokura, Y.

    2007-11-01

    Nonreciprocal directional dichroism, termed the optical magnetoelectric (OME) effect, has been observed in patterned superlattice (SL) composed of perovskite oxides, LaMnO3, SrMnO3, and LaAlO3. Such a tricolor SL with ferromagnetic interfaces is expected to artificially break both space-inversion and time-reversal symmetries and hence to show the OME effect. The Bragg diffraction from the grating structure with a period of 4?m fabricated on the SL was employed to sensitively detect the OME effect, yielding the relative change of the diffracted light intensity (˜0.2% 0.5%) upon a reversal of either the in-plane magnetization or the propagation vector of the diffracted light.

  13. Focused ion beam 3D nano-patterned optical fiber tips for advanced beam profile engineering

    NASA Astrophysics Data System (ADS)

    Janeiro, Ricardo; Flores, Raquel; Ribeiro, Ana R.; Jorge, Pedro; Viegas, Jaime

    2015-03-01

    Focused ion beam (FIB) patterning of 3D topography on optical fiber tips for application in stand-alone, rugged and simplified setups for optical tweezers cell sorters, optical near-field lithography and optical beam profile engineering are reported. We demonstrate various configurations based on single-step FIB patterning, multiple-step FIB processing and hybrid approaches based on optical fiber pre- and post-FIB treatment with either etching, fusion splicing, photopolymerization or electroplating steps for optical fiber texture, topography and composition engineering. Different conductive coatings for minimal charge accumulation and beam drift are studied with the relative merits compared. Furthermore optimal beam parameters for accurate pattern replication and positioning are also presented. Measured experimental field profiles are compared with numerical simulations of fabricated optical fiber tips for fabrication accuracy evaluation. Applications employing these engineered fiber tips in the field of optical tweezers, optical vortex generation, photolithography, photo-polymerization and beam forming are presented.

  14. Seismic hazard assessment and pattern recognition of earthquake prone areas in the Po Plain (Italy)

    NASA Astrophysics Data System (ADS)

    Gorshkov, Alexander; Peresan, Antonella; Soloviev, Alexander; Panza, Giuliano F.

    2014-05-01

    A systematic and quantitative assessment, capable of providing first-order consistent information about the sites where large earthquakes may occur, is crucial for the knowledgeable seismic hazard evaluation. The methodology for the pattern recognition of areas prone to large earthquakes is based on the morphostructural zoning method (MSZ), which employs topographic data and present-day tectonic structures for the mapping of earthquake-controlling structures (i.e. the nodes formed around lineaments intersections) and does not require the knowledge about past seismicity. The nodes are assumed to be characterized by a uniform set of topographic, geologic, and geophysical parameters; on the basis of such parameters the pattern recognition algorithm defines a classification rule to discriminate seismogenic and non-seismogenic nodes. This methodology has been successfully applied since the early 1970s in a number of regions worldwide, including California, where it permitted the identification of areas that have been subsequently struck by strong events and that previously were not considered prone to strong earthquakes. Recent studies on the Iberian Peninsula and the Rhone Valley, have demonstrated the applicability of MSZ to flat basins, with a relatively flat topography. In this study, the analysis is applied to the Po Plain (Northern Italy), an area characterized by a flat topography, to allow for the systematic identification of the nodes prone to earthquakes with magnitude larger or equal to M=5.0. The MSZ method differs from the standard morphostructural analysis where the term "lineament" is used to define the complex of alignments detectable on topographic maps or on satellite images. According to that definition the lineament is locally defined and the existence of the lineament does not depend on the surrounding areas. In MSZ, the primary element is the block - a relatively homogeneous area - while the lineament is a secondary element of the morphostructure. The identified earthquake prone areas provide first-order systematic information that may significantly contribute to seismic hazard assessment in the Italian territory. The information about the possible location of strong earthquakes provided by the morphostructural analysis, in fact, can be naturally incorporated in the neo-deterministic procedure for seismic hazard assessment (NDSHA), so as to fill in possible gaps in known seismicity. Moreover, the space information about earthquake prone areas can be fruitfully combined with the space-time information provided by the quantitative analysis of the seismic flow, so as to identify the priority areas (with linear dimensions of few tens kilometers), where the probability of a strong earthquake is relatively high, for detailed local scale studies. The new indications about the seismogenic potential obtained from this study, although less accurate than detailed fault studies, have the advantage of being independent on past seismicity information, since they rely on the systematic and quantitative analysis of the available geological and morphostructural data. Thus, this analysis appears particularly useful in areas where historical information is scarce; special attention should be paid to seismogenic nodes that are not related with known active faults or past earthquakes.

  15. Mapping Tumor Hypoxia In Vivo Using Pattern Recognition of Dynamic Contrast-enhanced MRI Data12

    PubMed Central

    Stoyanova, Radka; Huang, Kris; Sandler, Kiri; Cho, HyungJoon; Carlin, Sean; Zanzonico, Pat B; Koutcher, Jason A; Ackerstaff, Ellen

    2012-01-01

    In solid tumors, hypoxia contributes significantly to radiation and chemotherapy resistance and to poor outcomes. The “gold standard” pO2 electrode measurements of hypoxia in vivo are unsatisfactory because they are invasive and have limited spatial coverage. Here, we present an approach to identify areas of tumor hypoxia using the signal versus time curves of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data as a surrogate marker of hypoxia. We apply an unsupervised pattern recognition (PR) technique to determine the differential signal versus time curves associated with different tumor microenvironmental characteristics in DCE-MRI data of a preclinical cancer model. Well-perfused tumor areas are identified by rapid contrast uptake followed by rapid washout; hypoxic areas, which are regions of reduced vascularization, are identified by delayed contrast signal buildup and washout; and necrotic areas exhibit slow or no contrast uptake and no discernible washout over the experimental observation. The strength of the PR concept is that it captures the pixel-enhancing behavior in its entirety—during both contrast agent uptake and washout—and thus, subtleties in the temporal behavior of contrast enhancement related to features of the tumor microenvironment (driven by vascular changes) may be detected. The assignment of the tumor compartments/microenvironment to well vascularized, hypoxic, and necrotic is validated by comparison to data previously obtained using complementary imaging modalities. The proposed novel analysis approach has the advantage that it can be readily translated to the clinic, as DCE-MRI is used routinely for the identification of tumors in patients, is widely available, and easily implemented on any clinical magnet. PMID:23326621

  16. Sequence variability of the pattern recognition receptor Mermaid mediates specificity of marine nematode symbioses

    PubMed Central

    Bulgheresi, Silvia; Gruber-Vodicka, Harald R; Heindl, Niels R; Dirks, Ulrich; Kostadinova, Maria; Breiteneder, Heimo; Ott, Joerg A

    2011-01-01

    Selection of a specific microbial partner by the host is an all-important process. It guarantees the persistence of highly specific symbioses throughout host generations. The cuticle of the marine nematode Laxus oneistus is covered by a single phylotype of sulfur-oxidizing bacteria. They are embedded in a layer of host-secreted mucus containing the mannose-binding protein Mermaid. This Ca2+-dependent lectin mediates symbiont aggregation and attachment to the nematode. Here, we show that Stilbonema majum—a symbiotic nematode co-occurring with L. oneistus in shallow water sediment—is covered by bacteria phylogenetically distinct to those covering L. oneistus. Mermaid cDNA analysis revealed extensive protein sequence variability in both the nematode species. We expressed three recombinant Mermaid isoforms, which based on the structural predictions display the most different carbohydrate recognition domains (CRDs). We show that the three CRDs (DNT, DDA and GDA types) possess different affinities for L. oneistus and S. majum symbionts. In particular, the GDA type, exclusively expressed by S. majum, displays highest agglutination activity towards its symbionts and lowest towards its L. oneistus symbionts. Moreover, incubation of L. oneistus in the GDA type does not result in complete symbiont detachment, whereas incubation in the other types does. This indicates that the presence of particular Mermaid isoforms on the nematode surface has a role in the attachment of specific symbionts. This is the first report of the functional role of sequence variability in a microbe-associated molecular patterns receptor in a beneficial association. PMID:21228893

  17. Recognition and analysis of aircraft targets by radar, using structural pattern representations derived from polarimetric signatures

    NASA Astrophysics Data System (ADS)

    Chamberlain, Neil Frederick

    Automatic recognition of aircraft by means of radar signals is examined. In particular, the problem of utilizing polarimetric information from the monostatic, wideband, scattering matrix to target classification and analysis is considered. The approach taken is to effect a decomposition of the target in terms of its predominant scattering centers and their polarimetric properties. This polarimetric target model is based on a new formalism for the description of the vector behavior of wideband electromagnetic planewaves, called transient polarization. The transient polarization response of a radar object is a 3-dimensional, time-dependent electric field locus. Conceptually, this signature can be envisioned as the result of transmitting a short circularly polarized pulse toward the object. Transient polarization states are expressed as a 3-tuple of time-dependent polarimetric parameters: amplitude, ellipticity, and tilt. Scattering centers are identified by the peaks in the amplitude. The polarimetric parameters of canonical targets, simplified aircraft target combinations, and scaled model commercial airliners are analyzed. The performance of radar target identification systems employing polarimetric features is evaluated by means of Monte-Carlo simulation studies. Both decision-theoretic and language-theoretic techniques are used for this purpose. A target is represented as a string of polarimetrically-related symbols, and is classified using syntactic methods. Target classification simulations show that polarimetric pattern representations extracted from transient polarization responses may be used for reliable and flexible target classification. In addition, the polarimetric techniques developed may be used as a tool for concise characterization and analysis of time-domain electromagnetic scattering.

  18. Intestinal microbiota diversity and expression of pattern recognition receptors in newly weaned piglets.

    PubMed

    Tao, Xin; Xu, Ziwei; Wan, Jing

    2015-04-01

    This study evaluated the gastrointestinal microbial diversity and the expression of pattern recognition receptors (PRRs) of the small intestine during the first week post-weaning in newly weaned piglets. Sixteen piglets were sacrificed on days 0, 1, 4, and 7 post-weaning. Luminal contents from the stomach, ileum, and colon were collected to determine the microbiota diversity; intestinal mucosa from the ileum was collected to assess mRNA expression of PRRs, including toll-like receptors (TLRs) and NOD-like receptors (NLRs); sections of ileum were examined immunohistochemically to assess the immunoglobulin-secreting cells. The results showed that the number of denaturing gradient gel electrophoresis (DGGE) bands from the ileum and colon contents were significantly reduced in the d 4 post-weaning group. Biodiversity indexes (Shannon-Wiener index, richness index, and evenness index) were significantly decreased in the ileum of weaning groups. These indexes decreased in the colon of the d 4 post-weaning group. No significant differences were obtained in the stomach. With the exception of TLR5, the mRNA expressions of TLR2, TLR4, and TLR7 increased post-weaning. The mRNA expressions of NOD1 and NOD2 were significantly affected in the d 4 post-weaning group, and there were no significant differences in the d 1 or d 7 post-weaning groups. Analysis of the immunoglobulin-secreting (IgA, IgG, and IgM) cells showed that the ratio of each immunoglobulin was significantly higher on d 7 than d 0. The results revealed that microbial diversity was lower in the ileum and on d 4 post-weaning. Weaning significantly affected the expression of intestinal PRRs mainly on d 1 and d 4 post-weaning. The expression of specific PRRs was triggered by weaning to recognize distinctive microbiota and promote the development and maturation of the intestinal mucosal immunity. PMID:25528290

  19. Hybrid neural network and rule-based pattern recognition system capable of self-modification

    SciTech Connect

    Glover, C.W.; Silliman, M.; Walker, M.; Spelt, P.F. (Oak Ridge National Lab., TN (USA)); Rao, N.S.V. (Old Dominion Coll., Norfolk, VA (USA). Dept. of Computer Science)

    1990-01-01

    This paper describes a hybrid neural network and rule-based pattern recognition system architecture which is capable of self-modification or learning. The central research issue to be addressed for a multiclassifier hybrid system is whether such a system can perform better than the two classifiers taken by themselves. The hybrid system employs a hierarchical architecture, and it can be interfaced with one or more sensors. Feature extraction routines operating on raw sensor data produce feature vectors which serve as inputs to neural network classifiers at the next level in the hierarchy. These low-level neural networks are trained to provide further discrimination of the sensor data. A set of feature vectors is formed from a concatenation of information from the feature extraction routines and the low-level neural network results. A rule-based classifier system uses this feature set to determine if certain expected environmental states, conditions, or objects are present in the sensors' current data stream. The rule-based system has been given an a priori set of models of the expected environmental states, conditions, or objects which it is expected to identify. The rule-based system forms many candidate directed graphs of various combinations of incoming features vectors, and it uses a suitably chosen metric to measure the similarity between candidate and model directed graphs. The rule-based system must decide if there is a match between one of the candidate graphs and a model graph. If a match is found, then the rule-based system invokes a routine to create and train a new high-level neural network from the appropriate feature vector data to recognize when this model state is present in future sensor data streams. 12 refs., 3 figs.

  20. Type I interferon and pattern recognition receptor signaling following particulate matter inhalation

    PubMed Central

    2012-01-01

    Background Welding, a process that generates an aerosol containing gases and metal-rich particulates, induces adverse physiological effects including inflammation, immunosuppression and cardiovascular dysfunction. This study utilized microarray technology and subsequent pathway analysis as an exploratory search for markers/mechanisms of in vivo systemic effects following inhalation. Mice were exposed by inhalation to gas metal arc – stainless steel (GMA-SS) welding fume at 40?mg/m3 for 3?hr/d for 10?d and sacrificed 4?hr, 14?d and 28?d post-exposure. Whole blood cells, aorta and lung were harvested for global gene expression analysis with subsequent Ingenuity Pathway Analysis and confirmatory qRT-PCR. Serum was collected for protein profiling. Results The novel finding was a dominant type I interferon signaling network with the transcription factor Irf7 as a central component maintained through 28?d. Remarkably, these effects showed consistency across all tissues indicating a systemic type I interferon response that was complemented by changes in serum proteins (decreased MMP-9, CRP and increased VCAM1, oncostatin M, IP-10). In addition, pulmonary expression of interferon ? and ? and Irf7 specific pattern recognition receptors (PRR) and signaling molecules (Ddx58, Ifih1, Dhx58, ISGF3) were induced, an effect that showed specificity when compared to other inflammatory exposures. Also, a canonical pathway indicated a coordinated response of multiple PRR and associated signaling molecules (Tlr7, Tlr2, Clec7a, Nlrp3, Myd88) to inhalation of GMA-SS. Conclusion This methodological approach has the potential to identify consistent, prominent and/or novel pathways and provides insight into mechanisms that contribute to pulmonary and systemic effects following toxicant exposure. PMID:22776377

  1. Functional analysis of pattern recognition receptors in miniature dachshunds with inflammatory colorectal polyps

    PubMed Central

    IGARASHI, Hirotaka; OHNO, Koichi; FUJIWARA-IGARASHI, Aki; KANEMOTO, Hideyuki; FUKUSHIMA, Kenjiro; GOTO-KOSHINO, Yuko; UCHIDA, Kazuyuki; TSUJIMOTO, Hajime

    2014-01-01

    Inflammatory colorectal polyps (ICRPs) frequently occur in miniature dachshunds (MDs) in Japan. MDs with ICRPs develop multiple polyps with severe neutrophil infiltration that respond to immunosuppressive therapy. Therefore, ICRPs are thought to constitute a novel, breed-specific form of canine inflammatory bowel disease (IBD). Pattern recognition receptors (PRRs) play a key role in the distinction of pathogens from commensal bacteria and food antigens. Dysfunction resulting from genetic disorders of PRRs have been linked to human and canine IBD. Therefore, we analyzed the reactivity of PRRs in MDs with ICRPs. Twenty-six MDs with ICRPs and 16 control MDs were recruited. Peripheral blood-derived monocytes were obtained from each dog and then stimulated with PRR ligands for 6 and 24 hr; subsequently, messenger RNA (mRNA) expression levels and protein secretion of IL-1? were quantified using quantitative real-time PCR and ELISA, respectively. The levels of IL-1? mRNA and protein secretion after stimulation with a nucleotide-binding oligomerization domain 2 (NOD2) ligand were significantly greater in monocytes from ICRP-affected MDs than in those from control MDs. In addition, IL-1? protein secretion induced by toll-like receptor (TLR) 1/2, TLR2 and TLR2/6 stimulation was also significantly greater in ICRP-affected MDs. These results suggest that reactivity against NOD2, TLR1/2, TLR2 and TLR2/6 signals is enhanced in ICRP-affected MDs and may play a role in the pathogenesis of ICRPs in MDs. Additional studies of the genetic background of these PRRs should be performed. PMID:25650150

  2. The importance of the temporal pattern of syllables and the syllable structure of display calls for individual recognition in the genus aptenodytes.

    PubMed

    Robisson, P

    1991-01-01

    Aptenodytes penguins have a recognition call consisting of a series of repeated syllables. Play-back experiments were conducted to determine the respective importance of the temporal pattern of syllables and the syllable structure. Two hybrid signals were created by switching the temporal pattern of the familiar call (partner or parent) with that of an alien call, then played back to relevant partners. The hybrid signal with the familiar temporal pattern of syllables and an alien syllable structure induced no response of individual recognition, while the hybrid signal with an alien temporal pattern of syllables and the familiar syllable structure elicited some positive responses. PMID:24897176

  3. Optical generation of a circular harmonic filter for rotation and translation invariant optical pattern recognition

    NASA Technical Reports Server (NTRS)

    Cheng, Yeou-Yen

    1987-01-01

    A new method to generate a circular harmonic filter with both rotation and translation invariance is described, which is based on a phase-shifted double-exposure (PSDE) technique. An expression for the peak correlation intensity at the origin for the correlation plane as a function of target orientation for the PSDE filter is derived. Experimental results confirming the theoretical predictions are provided.

  4. [Research on the application of grey system theory in the pattern recognition for chromatographic fingerprints of traditional Chinese medicine].

    PubMed

    Wei, Hang; Lin, Li; Zhang, Yuan; Wang, Lianjing; Chen, Qinqun

    2013-02-01

    A model based on grey system theory was proposed for pattern recognition in chromatographic fingerprints (CF) of traditional Chinese medicine (TCM). The grey relational grade among the data series of each testing CF and the ideal CF was obtained by entropy and norm respectively, then the principle of "maximal matching degree" was introduced to make judgments, so as to achieve the purpose of variety identification and quality evaluation. A satisfactory result in the high performance liquid chromatographic (HPLC) analysis of 56 batches of different varieties of Exocarpium Citrus Grandis was achieved with this model. The errors in the chromatographic fingerprint analysis caused by traditional similarity method or grey correlation method were overcome, as the samples of Citrus grandis 'Tomentosa' and Citrus grandis (L.) Osbeck were correctly distinguished in the experiment. Furthermore in the study on the variety identification of Citrus grandis 'Tomentosa', the recognition rates were up to 92.85%, although the types and the contents of the chemical compositions of the samples were very close. At the same time, the model had the merits of low computation complexity and easy operation by computer programming. The research indicated that the grey system theory has good applicability to pattern recognition in the chromatographic fingerprints of TCM. PMID:23697176

  5. Application of pattern recognition techniques to problems in advanced pollution monitoring. Final report, November 1989-December 1991

    SciTech Connect

    Lavine, B.K.; Stine, A.B.; Qin, X.H.

    1995-05-01

    This technical report details the development and implementation of a pattern recognition technique, termed the Fuzzy-c Varieties (FCV) technique. This technique is intended to classify patterns which exhibit membership in only a single class, or data patterns which may partially represent multiple classes. The ability for the patterns to represent multiple classes permits the technique to be of potential value for classifying environmental analysis patterns of mixed samples, such as samples of mixed fuels. The technique was developed and applied to data patterns representing classification measurements on iris flowers, the Fisher iris data set. It was tested further with data patterns representing gas chromatograms of pure and mixed samples of jet fuels. The FCV classification algorithm was implemented as a computer program, written in the FORTRAN computer programming language, and using data display capabilities provided by the X-Windows standard graphical user interface. The technical report describes the classification results obtained by the FCV system from the Fisher iris data set and from the jet fuel data sets. The technical report also provides a user`s guide to the FCV computer software.

  6. COGNITNE PSYCHOLOGY 3, 382-407 (19%`) Pattern Recognition and Categorization1

    E-print Network

    Cottrell, Garrison W.

    for classifying objects as equivalent. The rule must specify the critical properties of the stimuli, the manner of dimensions. Figure 1 shows the stimulus patterns used. The patterns consisted of Brunswik faces which stimulus patterns

  7. Proc. IEEE Computer Vision & Pattern Recognition (CVPR), 2012 1 Example-based Cross-Modal Denoising

    E-print Network

    Schechner, Yoav Yosef

    experiments. 1. Introduction Smartphones, tablets and a range of other devices inte- grate cameras for human and machine hear- ing. The aim is not computational word recognition [11]. #12;D´ej`a Vu + D´

  8. TreeRipper web application: towards a fully automated optical tree recognition software

    PubMed Central

    2011-01-01

    Background Relationships between species, genes and genomes have been printed as trees for over a century. Whilst this may have been the best format for exchanging and sharing phylogenetic hypotheses during the 20th century, the worldwide web now provides faster and automated ways of transferring and sharing phylogenetic knowledge. However, novel software is needed to defrost these published phylogenies for the 21st century. Results TreeRipper is a simple website for the fully-automated recognition of multifurcating phylogenetic trees (http://linnaeus.zoology.gla.ac.uk/~jhughes/treeripper/). The program accepts a range of input image formats (PNG, JPG/JPEG or GIF). The underlying command line c++ program follows a number of cleaning steps to detect lines, remove node labels, patch-up broken lines and corners and detect line edges. The edge contour is then determined to detect the branch length, tip label positions and the topology of the tree. Optical Character Recognition (OCR) is used to convert the tip labels into text with the freely available tesseract-ocr software. 32% of images meeting the prerequisites for TreeRipper were successfully recognised, the largest tree had 115 leaves. Conclusions Despite the diversity of ways phylogenies have been illustrated making the design of a fully automated tree recognition software difficult, TreeRipper is a step towards automating the digitization of past phylogenies. We also provide a dataset of 100 tree images and associated tree files for training and/or benchmarking future software. TreeRipper is an open source project licensed under the GNU General Public Licence v3. PMID:21599881

  9. Criteria for pathology recognition in optical coherence tomography of fallopian tubes

    NASA Astrophysics Data System (ADS)

    Kirillin, Mikhail; Panteleeva, Olga; Yunusova, Ekaterina; Donchenko, Ekaterina; Shakhova, Natalia

    2012-08-01

    An increase of infertility and chronic pelvic pains syndrome, a growing level of latent diseases of this group, as well as a stably high percentage (up to 25% for infertility and up to 60% for the chronic pelvic pains syndrome) of undetermined origin raises the requirement for novel introscopic diagnostic techniques. We demonstrate abilities of optical coherence tomography (OCT) as a complementary technique to laparoscopy in diagnostics of fallopian tubes pathologies. We have acquired OCT images of different parts of fallopian tubes in norm and with morphologically proven pathology. Based on comparative analysis of the OCT data and the results of histological studies, we have worked out the subjective OCT criteria for distinguishing between unaltered and pathologic tissues. The developed criteria are verified in blind recognition tests. Diagnostic efficacy of OCT diagnostics in the case ofpelvic inflammatory diseases has been statistically evaluated, and high diagnostic accuracy (88%) is shown. Basing of the subjective criteria, an attempt to develop independent criteria aimed for automated recognition of pathological states in fallopian tubes is undertaken. Enhanced diagnostic accuracy (96%) of the developed independent criteria is demonstrated.

  10. Jellyfish prediction of occurrence from remote sensing data and a non-linear pattern recognition approach

    NASA Astrophysics Data System (ADS)

    Albajes-Eizagirre, Anton; Romero, Laia; Soria-Frisch, Aureli; Vanhellemont, Quinten

    2011-11-01

    Impact of jellyfish in human activities has been increasingly reported worldwide in recent years. Segments such as tourism, water sports and leisure, fisheries and aquaculture are commonly damaged when facing blooms of gelatinous zooplankton. Hence the prediction of the appearance and disappearance of jellyfish in our coasts, which is not fully understood from its biological point of view, has been approached as a pattern recognition problem in the paper presented herein, where a set of potential ecological cues was selected to test their usefulness for prediction. Remote sensing data was used to describe environmental conditions that could support the occurrence of jellyfish blooms with the aim of capturing physical-biological interactions: forcing, coastal morphology, food availability, and water mass characteristics are some of the variables that seem to exert an effect on jellyfish accumulation on the shoreline, under specific spatial and temporal windows. A data-driven model based on computational intelligence techniques has been designed and implemented to predict jellyfish events on the beach area as a function of environmental conditions. Data from 2009 over the NW Mediterranean continental shelf have been used to train and test this prediction protocol. Standard level 2 products are used from MODIS (NASA OceanColor) and MERIS (ESA - FRS data). The procedure for designing the analysis system can be described as following. The aforementioned satellite data has been used as feature set for the performance evaluation. Ground truth has been extracted from visual observations by human agents on different beach sites along the Catalan area. After collecting the evaluation data set, the performance between different computational intelligence approaches have been compared. The outperforming one in terms of its generalization capability has been selected for prediction recall. Different tests have been conducted in order to assess the prediction capability of the resulting system in operational conditions. This includes taking into account several types of features with different distances in both the spatial and temporal domains with respect to prediction time and site. Moreover the generalization capability has been measured via cross-fold validation. The implementation and performance evaluation results are detailed in the present communication together with the feature extraction from satellite data. To the best of our knowledge the developed application constitutes the first implementation of an automate system for the prediction of jellyfish appearance founded on remote sensing technologies.

  11. Surface EMG pattern recognition for real-time control of a wrist exoskeleton

    PubMed Central

    2010-01-01

    Background Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work. Methods Both sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme. Results The data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms. Conclusions The results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist. PMID:20796304

  12. Chances and limits of single-station seismic event clustering by unsupervised pattern recognition

    NASA Astrophysics Data System (ADS)

    Sick, Benjamin; Guggenmos, Matthias; Joswig, Manfred

    2015-06-01

    Automatic classification of local seismic events which are only recorded at single stations poses great challenges because of weak hypocentre constraints. This study investigates how single-station event clusters relate to geographic hypocentre regions and common source processes. Typical applications arise in local seismic networks where reliable ground truth by a dense temporal network precedes or follows a sparse (permanent) installation. The seismic signals for this study comprise a 3-month subset from a field campaign to map subduction below northern Chile (PISCO '94). Due to favourable ground noise conditions in the Atacama desert, the data set contains an abundance of shallow and deeper earthquakes, and many quarry explosions. Often event signatures overlap, posing a challenge to any signal processing scheme. Pattern recognition must work on reduced seismograms to restrict parameter dimensionality. Continuous parameter extraction based on noise-adapted spectrograms was chosen instead of discrete representation by, for example, amplitudes, onset times or spectral ratios to ensure consideration of potentially hidden features. Visualization of the derived feature vectors for human inspection and template matching algorithms was hereby possible. Because event classes shall comprise earthquake regions regardless of magnitude, clustering based on amplitudes is prevented by proper normalization of feature vectors. Principal component analysis is applied to further reduce the number of features used to train a self-organizing map (SOM). The SOM will topologically arrange prototypes of each event class in a 2-D map. Overcoming the restrictions of this black-box approach, the arranged prototypes could be transformed back to spectrograms to allow for visualization and interpretation of event classes. The final step relates prototypes to ground-truth information, confirming the potential of automated, coarse-grain hypocentre clustering based on single-station seismograms. The approach was tested by a twofold cross-validation whereby multiple sets of feature vectors from half the events are compared by a one-nearest neighbour classifier in combination with an Euclidean distance measure resulting in an overall correct geographic separation rate of 80.5 per cent.

  13. Dendritic morphology predicts pattern recognition performance in multi-compartmental model neurons with and without active conductances.

    PubMed

    de Sousa, Giseli; Maex, Reinoud; Adams, Rod; Davey, Neil; Steuber, Volker

    2015-04-01

    In this paper we examine how a neuron's dendritic morphology can affect its pattern recognition performance. We use two different algorithms to systematically explore the space of dendritic morphologies: an algorithm that generates all possible dendritic trees with 22 terminal points, and one that creates representative samples of trees with 128 terminal points. Based on these trees, we construct multi-compartmental models. To assess the performance of the resulting neuronal models, we quantify their ability to discriminate learnt and novel input patterns. We find that the dendritic morphology does have a considerable effect on pattern recognition performance and that the neuronal performance is inversely correlated with the mean depth of the dendritic tree. The results also reveal that the asymmetry index of the dendritic tree does not correlate with the performance for the full range of tree morphologies. The performance of neurons with dendritic tapering is best predicted by the mean and variance of the electrotonic distance of their synapses to the soma. All relationships found for passive neuron models also hold, even in more accentuated form, for neurons with active membranes. PMID:25380637

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

    PubMed

    Fukushima, K

    1980-01-01

    A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by "learning without a teacher", and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname "neocognitron". After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consists of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of "S-cells", which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of "C-cells" similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any "teacher" during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cells of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern. PMID:7370364

  15. Pigeon pattern electroretinogram: A response unaffected by chronic section of the optic nerve

    Microsoft Academic Search

    P. Bagnoli; V. Porciatti; W. Francesconi; R. Barsellotti

    1984-01-01

    Retinal evoked responses to sinusoidal gratings modulated in counterphase (pattern ERG) have been recorded from the pigeon eye. The pattern ERG amplitude depends upon the temporal frequency of the modulation, the contrast, the spatial frequency and the area of the stimulus. In 8 pigeons the pattern ERG has been recorded at different times after the unilateral section of the optic

  16. An improved version of the table look-up algorithm for pattern recognition. [for MSS data processing

    NASA Technical Reports Server (NTRS)

    Eppler, W. G.

    1974-01-01

    The table look-up approach to pattern recognition has been used for 3 years at several research centers in a variety of applications. A new version has been developed which is faster, requires significantly less core memory, and retains full precision of the input data. The new version can be used on low-cost minicomputers having 32K words (16 bits each) of core memory and fixed-point arithmetic; no special-purpose hardware is required. An initial FORTRAN version of this system can classify an ERTS computer-compatible tape into 24 classes in less than 15 minutes.

  17. Detection of ``single-leg separated`` heart valves using statistical pattern recognition with the nearest neighbor classifier

    SciTech Connect

    Buhl, M.R.; Clark, G.A.; Candy, J.V.; Thomas, G.H.

    1993-07-16

    The goal of this work was to detect ``single-leg separated`` Bjoerk-Shiley Convexo-Concave heart valves which had been implanted in sheep. A ``single-leg separated`` heart valve contains a fracture in the outlet strut resulting in an increased risk of mechanical failure. The approach presented in this report detects such fractures by applying statistical pattern recognition with the nearest neighbor classifier to the acoustic signatures of the valve opening. This approach is discussed and results of applying it to real data are given.

  18. Detection of ``single-leg separated`` heart valves using statistical pattern recognition with the nearest neighbor classifier. Revision 1

    SciTech Connect

    Buhl, M.R.; Clark, G.A.; Candy, J.V.; Thomas, G.H.

    1993-12-01

    The goal of this work was to detect ``single-leg separated`` Bjoerk-Shiley Convexo-Concave heart valves which had been implanted in sheep. A ``single-leg separated`` heart valve contains a fracture in the outlet strut resulting in an increased risk of mechanical failure. The approach presented in this report detects such fractures by applying statistical pattern recognition with the nearest neighbor classifier to the acoustic signatures of the valve opening. This approach is discussed and results of applying it to real data are given.

  19. Wide-threat detection: recognition of adversarial missions and activity patterns in Empire Challenge 2009

    NASA Astrophysics Data System (ADS)

    Levchuk, Georgiy; Shabarekh, Charlotte; Furjanic, Caitlin

    2011-06-01

    In this paper, we present results of adversarial activity recognition using data collected in the Empire Challenge (EC 09) exercise. The EC09 experiment provided an opportunity to evaluate our probabilistic spatiotemporal mission recognition algorithms using the data from live air-born and ground sensors. Using ambiguous and noisy data about locations of entities and motion events on the ground, the algorithms inferred the types and locations of OPFOR activities, including reconnaissance, cache runs, IED emplacements, logistics, and planning meetings. In this paper, we present detailed summary of the validation study and recognition accuracy results. Our algorithms were able to detect locations and types of over 75% of hostile activities in EC09 while producing 25% false alarms.

  20. A pattern recognition scheme for large curvature circular tracks and an FPGA implementation using hash sorter

    SciTech Connect

    Wu, Jin-Yuan; Shi, Z.; /Fermilab

    2004-12-01

    Strong magnetic field in today's colliding detectors causes track recognition more difficult due to large track curvatures. In this document, we present a global track recognition scheme based on track angle measurements for circular tracks passing the collision point. It uses no approximations in the track equation and therefore is suitable for both large and small curvature tracks. The scheme can be implemented both in hardware for lower-level trigger or in software for higher-level trigger or offline analysis codes. We will discuss an example of FPGA implementations using ''hash sorter''.

  1. Theoretical analysis of pattern effect suppression in semiconductor optical amplifier utilizing optical delay interferometer

    NASA Astrophysics Data System (ADS)

    Zoiros, K. E.; Siarkos, T.; Koukourlis, C. S.

    2008-07-01

    The ability of an optical delay interferometer (ODI) to suppress the pattern effect that is inherently present in a straightforward, solitary semiconductor optical amplifier (SOA) whose dynamic response is slower than the period of its driving high-speed return-to-zero (RZ) data signal is theoretically investigated. For this purpose an existing comprehensive model that simulates and links the operation of these two elements is methodically applied to their concatenated configuration. In this manner an extensive set of curves is numerically obtained, which allow to analyze and assess the impact of the input pulse energy and width as well as of the SOA carrier lifetime, linewidth enhancement factor and small signal gain on the amplitude modulation of the transmitted sequence at the output of each one of these block units. Their thorough study and interpretation reveals that the employment of the ODI can significantly reduce the value of this quality metric resulting from a single SOA only. The main offered benefit, however, is that any technical restrictions regarding the involved critical parameters can be considerably relaxed while at the same time their useful operational range can be extended. These important findings highlight the necessity of placing this passive device after the SOA and exploiting it in order to effectively alleviate the detrimental pattern-dependent degradation. This fact in conjunction with its overall practicality renders it a promising candidate for enhancing, within the frame of the proposed scheme, the performance of SOAs that are employed as pure amplification elements in fiber-optic communication systems and networking applications.

  2. Early pattern recognition in severe perinatal asphyxia: a prospective MRI study

    Microsoft Academic Search

    O. Baenziger; E. Martin; M. Steinlin; M. Good; R. Largo; R. Burger; S. Fanconi; G. Duc; R. Buchli; H. Rumpel; E. Boltshauser

    1993-01-01

    On the basis of MRI examination in 88 neonates and infants with perinatal asphyxia, we defined 6 different patterns on T2-weighted images: pattern A-scattered hyperintensity of both hemispheres of the telencephalon with blurred border zones between cortex and white matter, indicating diffuse brain injury; pattern B-parasagittal hyperintensity extending into the corona radiata, corresponding to the watershed zones; pattern C-hyper-and hypointense

  3. CONTROL PARAMETER ESTIMATION FOR A PHYSICAL MODEL OF A TRUMPET USING PATTERN RECOGNITION

    Microsoft Academic Search

    Xavier Rodet; Dirk van Dyck

    In this paper we address the problem of automatically deter- mining the control parameters of a physical model in order to simulate a given sound. Since the mathematical inver- sion of the model is very difficult, techniques from the pat- tern recognition field are proposed which can be applied to a very large class of systems. Although this approach is

  4. Visual Recognition Based on Temporal Cortex Cells: Viewer-Centred Processing of Pattern Configuration

    Microsoft Academic Search

    David I. Perrett; Mike W. Oram

    1998-01-01

    Abstract A model of recognition is described based on cell properties in the ventral cortical stream of visual processing in the primate brain. At a critical intermediate stage in this system, ‘Elaborate’ feature sensitive cells respond selectively to visual features in a way that depends on size (+\\/- 1 octave), orientation (+\\/-45°) but does not depend on position within central

  5. Patterns of entry and correction in large vocabulary continuous speech recognition systems

    Microsoft Academic Search

    Clare-Marie Karat; Christine Halverson; Daniel Horn; John Karat

    1999-01-01

    A study was conducted to evaluate user performance andsatisfaction in completion of a set of text creation tasks usingthree commercially available continuous speech recognition systems.The study also compared user performance on similar tasks usingkeyboard input. One part of the study (Initial Use) involved 24users who enrolled, received training and carried out practicetasks, and then completed a set of transcription and

  6. Patterns of Entry and Correction in Large Vocabulary Continuous Speech Recognition Systems

    Microsoft Academic Search

    Clare-Marie Karat; Christine Halverson; Daniel Horn; John Karat

    2002-01-01

    A study was conducted to evaluate user performance and satisfaction in completion of a set of text creation tasks using three commercially available continuous speech recognition systems. The study also compared user performance on similar tasks using keyboard input. One part of the study (Initial Use) involved 24 users who enrolled, received training and carried out practice tasks, and then

  7. The Kernel Common Vector Method: A Novel Nonlinear Subspace Classifier for Pattern Recognition

    Microsoft Academic Search

    Hakan Cevikalp; Marian Neamtu; Atalay Barkana

    2007-01-01

    The common vector (CV) method is a linear sub- space classifier method which allows one to discriminate between classes of data sets, such as those arising in image and word recognition. This method utilizes subspaces that represent classes during classification. Each subspace is modeled such that common features of all samples in the corresponding class are extracted. To accomplish this

  8. Development of Materials for the Clinical Assessment of Speech Recognition: The Speech Sound Pattern Discrimination Test.

    ERIC Educational Resources Information Center

    Bochner, Joseph H.; Garrison, Wayne M.; Sussman, Joan E.; Burkard, Robert F.

    2003-01-01

    This study evaluated an assessment procedure designed to assess speech recognition ability in individuals with mild-to-moderate hearing losses. Sets of phonetic contrasts were presented within sentence contexts to 53 listeners (31 hearing impaired) in four listening conditions. The procedure distinguished between normal and hearing impaired…

  9. Accepted for 26. DAGM-Symposium "Pattern Recognition", Tubingen, Aug. 2004 Invariants for Discrete Structures -An

    E-print Network

    functions and we applied it successfully for the classification of scanned pollen in 3D. In this paper we the problem of e.g. space invariant recognition. The idea is to find a mapping T which is able to extract/or orientation changes. Such a transformation T necessarily maps all images of an equivalence class of objects

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

    E-print Network

    Paris-Sud XI, Université de

    recognition, image processing, non-linear normalization, feature extraction. 1. Introduction A "mokkan to read mokkans. Section 3 presents its image processing libraries. Section 4 describes user of Agriculture and Technology National Research Institute for Cultural Properties nakagawa@cc.tuat.ac.jp, {kei

  11. Pattern Recognition 39 (2006) 969979 www.elsevier.com/locate/patcog

    E-print Network

    Plataniotis, Konstantinos N.

    2006-01-01

    . Published by Elsevier Ltd. All rights reserved. Keywords: Gait; Angular analysis; Time normalization; Recognition; Verification 1. Introduction The analysis of the human way of walking, termed gait, can be used. In this paper, we propose a methodology for gait analysis using silhou- ette sequences and then focus our

  12. Pattern Recognition 42 (2009) 871 --885 Contents lists available at ScienceDirect

    E-print Network

    Slatton, Clint

    2009-01-01

    target detection and recognition (ATR) [1] for synthetic aperture radar (SAR) image [2­4] and biometric filters for signal detection under linear channel and white noise conditions [9,10]. For image detection Accepted 23 September 2008 Keywords: Minimum average correlation energy (MACE) filter Correntropy

  13. Speechreading: an overview of image processing, feature extraction, sensory integration and pattern recognition techniques

    Microsoft Academic Search

    David G. Stork; Marcus E. Hennecke

    1996-01-01

    We give an overview of speechreading systems from the perspective of the face and gesture recognition community, paying particular attention to approaches to key design decisions and the benefits and drawbacks. We discuss the central issue of sensory integration how much processing of the acoustic and the visual information should go on before integration how should it be integrated. We

  14. Tandem-pulsed acousto-optics: an analytical framework of modulated high-contrast speckle patterns

    NASA Astrophysics Data System (ADS)

    Resink, S. G.; Steenbergen, W.

    2015-06-01

    Recently we presented acousto-optic (AO) probing of scattering media using addition or subtraction of speckle patterns due to tandem nanosecond pulses. Here we present a theoretical framework for ideal (polarized, noise-free) speckle patterns with unity contrast that links ultrasound-induced optical phase modulation, the fraction of light that is tagged by ultrasound, speckle contrast, mean square difference of speckle patterns and the contrast of the summation of speckle patterns acquired at different ultrasound phases. We derive the important relations from basic assumptions and definitions, and then validate them with simulations. For ultrasound-generated phase modulation angles below 0.7?rad (assuming uniform modulation), we are now able to relate speckle pattern statistics to the acousto-optic phase modulation. Hence our theory allows quantifying speckle observations in terms of ultrasonically tagged fractions of light for near-unity-contrast speckle patterns.

  15. Tandem-pulsed acousto-optics: an analytical framework of modulated high-contrast speckle patterns.

    PubMed

    Resink, S G; Steenbergen, W

    2015-06-01

    Recently we presented acousto-optic (AO) probing of scattering media using addition or subtraction of speckle patterns due to tandem nanosecond pulses. Here we present a theoretical framework for ideal (polarized, noise-free) speckle patterns with unity contrast that links ultrasound-induced optical phase modulation, the fraction of light that is tagged by ultrasound, speckle contrast, mean square difference of speckle patterns and the contrast of the summation of speckle patterns acquired at different ultrasound phases. We derive the important relations from basic assumptions and definitions, and then validate them with simulations. For ultrasound-generated phase modulation angles below 0.7?rad (assuming uniform modulation), we are now able to relate speckle pattern statistics to the acousto-optic phase modulation. Hence our theory allows quantifying speckle observations in terms of ultrasonically tagged fractions of light for near-unity-contrast speckle patterns. PMID:25985079

  16. Comparison of several supervised pattern recognition techniques for detecting additive methamidophos in rotenone preparation by near-infrared spectroscopy.

    PubMed

    Tang, Guo; Tian, Kuangda; Song, Xiangzhong; Xiong, Yanmei; Min, Shungeng

    2014-01-01

    In this paper, different supervised pattern recognition methods have been applied to detect the manually additive methamidophos in rotenone preparation. The aim of this paper was to examine the performances of different supervised pattern recognition techniques: soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), artificial neutral networks (ANN), and support vector machine (SVM). The results obtained show that SVM is the most effective techniques with 100.0% classification accuracy followed by ANN, PLS-DA and with the accuracy of 97.5% and 93.3% respectively while SIMCA yields the poorest result of 85.8%. We hope that the results obtained in this study will help both further chemometric investigations and investigations in the sphere of applied vibrational spectroscopy of sophisticated multicomponent systems. Furthermore, the use of portable instrument and satisfactory classification also indicated the possibility of detecting illicit-addition at scene by near-infrared (NIR) spectroscopy which makes a great sense in pesticide quality control. PMID:24368288

  17. Activation and Regulation of the Pattern Recognition Receptors in Obesity-Induced Adipose Tissue Inflammation and Insulin Resistance

    PubMed Central

    Watanabe, Yasuharu; Nagai, Yoshinori; Takatsu, Kiyoshi

    2013-01-01

    Obesity-associated chronic tissue inflammation is a key contributing factor to type 2 diabetes mellitus, and a number of studies have clearly demonstrated that the immune system and metabolism are highly integrated. Recent advances in deciphering the various immune cells and signaling networks that link the immune and metabolic systems have contributed to our understanding of the pathogenesis of obesity-associated inflammation. Other recent studies have suggested that pattern recognition receptors in the innate immune system recognize various kinds of endogenous and exogenous ligands, and have a crucial role in initiating or promoting obesity-associated chronic inflammation. Importantly, these mediators act on insulin target cells or on insulin-producing cells impairing insulin sensitivity and its secretion. Here, we discuss how various pattern recognition receptors in the immune system underlie the etiology of obesity-associated inflammation and insulin resistance, with a particular focus on the TLR (Toll-like receptor) family protein Radioprotective 105 (RP105)/myeloid differentiation protein-1 (MD-1). PMID:24064574

  18. Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition.

    PubMed

    Sinues, Pablo Martínez-Lozano; Alonso-Salces, Rosa M; Zingaro, Lorenzo; Finiguerra, Alessandro; Holland, Margaret V; Guillou, Claude; Cristoni, Simone

    2012-11-28

    A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool. PMID:23146391

  19. Quality Evaluation and Pattern Recognition Analyses of Bioactive Marker Compounds from Farfarae Flos Using HPLC/PDA.

    PubMed

    Seo, U Min; Zhao, Bing Tian; Kim, Won Il; Seo, Eun Kyoung; Lee, Jae Hyun; Min, Byung Sun; Shin, Beom Soo; Son, Jong Keun; Woo, Mi Hee

    2015-07-01

    The flower bud of Tussilago farfara L., called Farfarae Flos, has traditionally been used in Oriental medicine for the treatment of bronchitis and asthma. To establish a standard for quality control as well as the reliable identification of Farfarae Flos, the contents of five standards, rutin (1), isoquercetin (2), 3,5-dicaffeoylquinic acid (3), tussilagone (4), and tussilagonone (5), were determined by quantitative high-performance liquid chromatography (HPLC)/photodiode array (PDA) analysis. The five standards were separated on a YoungJinBioChrom Aegispak C18-L (250-mm×4.6-mm, 5-µm) column by gradient elution using 0.03% trifluoroacetic acid in water (A), with acetonitrile (B) as the mobile phase. The flow rate was 1.0?mL/min, and the UV detector wavelength was set at 220?nm. The method was successfully used in the analysis of Farfarae Flos from different geographic origins with relatively simple conditions and procedures, and the results demonstrated satisfactory linearity, recovery, precision, accuracy, stability, and robustness. The HPLC analytical method for pattern recognition analysis was validated by repeated analysis of 62 Farfarae Flos samples. This result indicated that the established HPLC/PDA method is suitable for quantitation and pattern recognition analyses for the quality evaluation of Farfarae Flos. PMID:25971744

  20. Automated classification of single airborne particles from two-dimensional angle-resolved optical scattering (TAOS) patterns by non-linear filtering

    NASA Astrophysics Data System (ADS)

    Crosta, Giovanni Franco; Pan, Yong-Le; Aptowicz, Kevin B.; Casati, Caterina; Pinnick, Ronald G.; Chang, Richard K.; Videen, Gorden W.

    2013-12-01

    Measurement of two-dimensional angle-resolved optical scattering (TAOS) patterns is an attractive technique for detecting and characterizing micron-sized airborne particles. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. By reformulating the problem in statistical learning terms, a solution is proposed herewith: rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified through a learning machine, where feature extraction interacts with multivariate statistical analysis. Feature extraction relies on spectrum enhancement, which includes the discrete cosine FOURIER transform and non-linear operations. Multivariate statistical analysis includes computation of the principal components and supervised training, based on the maximization of a suitable figure of merit. All algorithms have been combined together to analyze TAOS patterns, organize feature vectors, design classification experiments, carry out supervised training, assign unknown patterns to classes, and fuse information from different training and recognition experiments. The algorithms have been tested on a data set with more than 3000 TAOS patterns. The parameters that control the algorithms at different stages have been allowed to vary within suitable bounds and are optimized to some extent. Classification has been targeted at discriminating aerosolized Bacillus subtilis particles, a simulant of anthrax, from atmospheric aerosol particles and interfering particles, like diesel soot. By assuming that all training and recognition patterns come from the respective reference materials only, the most satisfactory classification result corresponds to 20% false negatives from B. subtilis particles and <11% false positives from all other aerosol particles. The most effective operations have consisted of thresholding TAOS patterns in order to reject defective ones, and forming training sets from three or four pattern classes. The presented automated classification method may be adapted into a real-time operation technique, capable of detecting and characterizing micron-sized airborne particles.