Science.gov

Sample records for optical pattern recognition

  1. Fringe patterns generated by micro-optical sensors for pattern recognition.

    PubMed

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

    2015-01-01

    We present a new result of pattern recognition generation scheme using a small-scale optical muscle sensing system, which consisted of an optical add-drop filter incorporating two nonlinear optical side ring resonators. When light from laser source enters into the system, the device is stimulated by an external physical parameter that introduces a change in the phase of light propagation within the sensing device, which can be formed by the interference fringe patterns. Results obtained have shown that the fringe patterns can be used to form the relationship between signal patterns and fringe pattern recognitions. PMID:24450752

  2. Real-valued composite filters for optical pattern recognition

    NASA Technical Reports Server (NTRS)

    Balendra, A.; Rajan, P. K.

    1993-01-01

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

  3. Optical Processing of Speckle Images with Bacteriorhodopsin for Pattern Recognition

    NASA Technical Reports Server (NTRS)

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

    1994-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

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

    PubMed

    Aljada, Muhsen; Alameh, Kamal

    2007-05-28

    Next generation High-Speed optical packet switching networks require components capable of recognising the optical header to enable on-the-fly accurate switching of incoming data packets to their destinations. This paper experimentally demonstrates a comparison between two different optical header recognition structures; A passive structure based on the use of Fiber Bragg Gratings (FBGs), whereas the active structure employs Opto-VLSI processors that synthesise dynamic wavelength profile through digital phase holograms. The structures are experimentally demonstrated at 10Gbps. Performance comparison between the two structures is also discussed. These optical header recognition structures are attractive for multiwavelength optical network and applications. PMID:19547006

  8. 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.

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

    NASA Technical Reports Server (NTRS)

    Juday, Richard D. (editor)

    1988-01-01

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

  10. A Novel Optical/digital Processing System for Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Boone, Bradley G.; Shukla, Oodaye B.

    1993-01-01

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

  11. Three dimensional measurement of micro-optical components using digital holography and pattern recognition

    NASA Astrophysics Data System (ADS)

    Kim, Do-Hyung; Jeon, Sungbin; Cho, Janghyun; Lim, Geon; Park, No-Cheol; Park, Young-Pil

    2015-09-01

    This paper proposes a method for inspecting transparent micro-optical components that combines digital holography and pattern recognition. As many micro-optical components have array structures with numerous elements, the uniformity of each element is important. Consequently, an effective inspection requires simultaneous measurement of these elements. Pattern recognition is used to solve this issue and can be adopted effectively using the unique characteristics of digital holography to obtain both amplitude and phase information on the object. To verify this approach, an experimental demonstration was performed with a micro-lens array using a circle-detection algorithm based on the Hough Transform. As an experimental results 30 micro-lenses are detected and measured simultaneously by using proposed inspection method.

  12. Comparative study of optical-digital vs all-digital techniques in textural pattern recognition

    NASA Astrophysics Data System (ADS)

    Otoole, R. K.; Stark, H.

    1980-08-01

    The application of both optical-digital and all-digital techniques in textural pattern recognition is examined and a comparison of the two approaches is made. The optical-digital scheme makes use of an optical-digital computer to generate textural measurements based on the 2-D irradiance spectrum. The all-digital scheme produces measurements based on gray-tone spatial-dependence matrices. In both cases two feature extraction algorithms were employed: the Hotelling trace method and the Foley-Sammon discriminant vector analysis. Classification was accomplished using the k-nearest neighbor decision rule. The performance of these techniques was evaluated in an experiment involving the classification of four texture patterns. The results show that, for the textures chosen, both approaches give high classification accuracy with the optical-digital method performing somewhat better.

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

    NASA Technical Reports Server (NTRS)

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

    1987-01-01

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

  14. Pattern recognition principles

    NASA Technical Reports Server (NTRS)

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

    1974-01-01

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

  15. Adaptive pattern recognition correlators

    NASA Astrophysics Data System (ADS)

    Shamir, Joseph

    1997-10-01

    Optical pattern recognition is significantly improved by exploiting the attributes of both optics and electronics in architectures containing combinations of optical systems and electronic processors. Essential interfaces for such hybrid systems are spatial light modulators and optoelectronic recording devices. These devices suffer from technological limitations that must be alleviated by suitably designed optical architectures and efficient processing algorithms. We review several correlator architectures and discuss their adaptability to hybrid systems. Due to the computational complexity encountered in such hybrid electro-optical systems, iterative optimization methods can be efficiently employed. Some of the algorithms presented are especially useful for treating real physical systems having properties that cannot be exactly defined or quantified. Algorithms that were successful in these architectures belong to two families, the basically stochastic algorithms and the projection-onto-constraint-sets algorithms. The former is particularly suitable for applications in hybrid electro- optical learning systems, while the latter is extremely powerful for the design of spatial filters with properties that are difficult to achieve by other means. A case study is given of an adaptive correlator for rotation-, scale-, and shift-invariant pattern recognition.

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

    NASA Technical Reports Server (NTRS)

    Rajan, P. K.; Balendra, Anushia

    1992-01-01

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

  17. Study on pattern recognition method based on fiber optic perimeter system

    NASA Astrophysics Data System (ADS)

    Xu, Haiyan; Zhang, Xuewu; Zhang, Zhuo; Li, Min

    2015-10-01

    All-fiber interferometer sensor system is a new type of system, which could be used in long-distance, strong-EMI condition for monitoring and inspection. A fiber optic perimeter detection system based on all-fiber interferometric sensor is proposed, through the back-end analysis, processing and intelligent identification, which can distinguish effects of different intrusion activities. In this paper, the universal steps in triggering pattern recognition is introduced, which includes signal characteristics extracting by accurate endpoint detecting, templates establishing by training, and pattern matching. By training the samples acquired in the laboratory, this paper uses the wavelet transformation to decompose the detection signals of the intrusion activities into sub-signals in different frequency bands with multi-resolution analysis. Then extracts the features of the above mentioned intrusions signals by frequency band energy and wavelet information entropy and the system could recognize the intrusion activities occurred along the perimeter sensors. Experiment results show that the proposed method for the perimeter is able to differentiate intrusion signals from ambient noises such as windy and walk effectively. What's more, the recognition rate of the system is improved while deduced the false alarm rate, the approach is proved by large practical experiment and project.

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

    NASA Technical Reports Server (NTRS)

    Rajan, P. K.; Khan, Ajmal

    1993-01-01

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

  19. A comparison of real-time optical correlators for pattern recognition

    SciTech Connect

    Stalker, K.T.; Molley, P.A.; Hansche, B.D.

    1989-01-01

    Two types of optical correlators have been built to investigate real-time pattern recognition. The first employs one-dimensional devices to perform the two dimensional correlation in real time. This architecture uses an array of light emitting diodes (LED's) to input an electronically stored reference image into the processor in parallel. The input scene data is introduced into the processor one line at a time using an acousto-optic device (AOD). Multichannel time integrating correlations are performed in the row direction using the AOD and in the column direction using a charge coupled device (CCD) operating in the time delay and integrate mode. a processor has been built using this technology which correlates a 64 /times/ 44 pixel binary reference image with a 256 /times/ 232 input scene at video rates. The second correlator is a space integrating Fourier transform based correlator. A magneto optic-device (MOD) is used at the Fourier transform plane to rapidly change filter functions. The binary nature of the MOD device necessitates using either a binary phase or binary amplitude representation of the desired complex filter function. For this reason, several types of Binary Phase-Only Filter (BPOF) representations have been analyzed and experimentally investigated. Experimental correlation results have been obtained using both the Hartley BPOF and a newly developed class of complex binary filters, called Quad-Phase-Only Filters (QPOF). The performance of the two systems will be compared on the basis of processing speed, space bandwidth product, processor size and light efficiency. The inherent differences between incoherent and coherent processing and their implications for filter design will also be discussed. Finally, estimates of future performance will be presented. 10 refs., 8 figs., 2 tabs.

  20. Perception & Pattern Recognition Classic Model of Perception

    E-print Network

    Coulson, Seana

    1 Perception & Pattern Recognition Classic Model of Perception Pattern Recognition · Process of connecting perceptual information w/info in LTM ­ Visual Pattern Recognition ­ Auditory Pattern Recognition Perception Geons · Can help explain recognition of degraded objects Degraded Objects · Disrupt Geon

  1. Pattern recognition in bioinformatics.

    PubMed

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

    2013-09-01

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

  2. Scanning Behaviour and Pattern Recognition 

    E-print Network

    Lishman, John Rowland

    1976-01-01

    Two basic models of human pattern recognition have been advanced: feature analysis and hypothesis testing. These can only be discriminated by looking at behaviour before recognition. This is studied here by having the ...

  3. Pattern Recognition with Hamiltonian Dynamics

    NASA Astrophysics Data System (ADS)

    Potapov, Alexei B.; Ali, M. K.

    We consider pattern recognition schemes that are based upon Hamiltonian dynamical system. Different oscillatory modes are used for storing and encoding patterns, and the effect of resonance is used for determining the most excited mode. We also propose a new technique for pattern orthogonalization resorting to hidden dimensions. Numerical experiments confirm high storage capacity and absence of false memories for the proposed system. Hamiltonian systems may be important as classical analogs of quantum computing systems or quantum neural networks.

  4. 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.

  5. PATTERN RECOGNITION ROBI POLIKAR

    E-print Network

    Polikar, Robi

    specifically, what features we use to solve this classification problem. Of course, real-world pattern on an individ- ual's credit report data, among many others. More re- cently, a growing number of biomedical, automated digital mam- mography analysis for early detection of breast cancer, automated electrocardiogram

  6. Photonic correlator pattern recognition: Application to autonomous docking

    NASA Technical Reports Server (NTRS)

    Sjolander, Gary W.

    1991-01-01

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

  7. Forensic Pattern Recognition Anil K. Jain

    E-print Network

    Forensic Pattern Recognition Anil K. Jain Michigan State University September 1, 2015 http://biometrics.cse.msu.edu/ #12;Examples of Forensic Patterns Fingerprint Face Ballistic image (breech face impression) Scars, Marks, and Tattoos (SMT) #12;Forensic Pattern Recognition Systems Double-loop changed to left loop

  8. Inverse scattering approach to improving pattern recognition

    NASA Astrophysics Data System (ADS)

    Chapline, George; Fu, Chi-Yung

    2005-05-01

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

  9. Inverse Scattering Approach to Improving Pattern Recognition

    SciTech Connect

    Chapline, G; Fu, C

    2005-02-15

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

  10. Spectral feature classification and spatial pattern recognition

    NASA Technical Reports Server (NTRS)

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

    1979-01-01

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

  11. Extending Morphological Signatures for Visual Pattern Recognition

    E-print Network

    Lefèvre, Sébastien

    techniques are very dependent on the accuracy of the image features they rely on. The image analysis if it can be used directly to solve various pattern recognition problems related to image data, the simple Introduction When applied on visual information such as digital images, pattern recognition and data mining

  12. Pattern activation/recognition theory of mind

    PubMed Central

    du Castel, Bertrand

    2015-01-01

    In his 2012 book How to Create a Mind, Ray Kurzweil defines a “Pattern Recognition Theory of Mind” that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call “Pattern Activation/Recognition Theory of Mind.” While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation. PMID:26236228

  13. Pattern activation/recognition theory of mind.

    PubMed

    du Castel, Bertrand

    2015-01-01

    In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation. PMID:26236228

  14. Automatic Recognition of Class Blueprint Patterns Diplomarbeit

    E-print Network

    Lanza, Michele

    automatically in a software system. Our approach is based on the theory of graph pattern recognition, mainly to do something no one has done before, and it was a dream-come-true for me because I always wanted

  15. Generalized Feature Extraction for Structural Pattern Recognition

    E-print Network

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

  16. Generalized Feature Extraction for Structural Pattern Recognition

    E-print Network

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

  17. Pattern recognition, inner products and correlation filters

    SciTech Connect

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

    1991-01-01

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

  18. 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.

  19. Pattern recognition of transillumination images for diagnosis of rheumatoid arthritis

    NASA Astrophysics Data System (ADS)

    Bauer, Joanna; Boerner, Ewa; Podbielska, Halina; Suchwalko, Artur

    2005-09-01

    In this work the statistical pattern recognition methods were applied for evaluation of transillumination images of interphalangeal joints of patients suffering from rheumatoid arthritis. Special portable apparatus was constructed for performing the transillumination examination. It consisted of He-Ne laser with optics for collimated illumination, special holder for placing the finger (perpendicular to optical axis, dorsal site towards camera), and CCD camera with memory stick. 20 ill patients and 20 healthy volunteers were examined. The captured images with 1152x864 resolution were converted into the gray level pictures and analyzed by means of statistical pattern recognition method. Principal Component Analysis (PCA) and cluster analysis by use of 1-NN method (1 Nearest Neighbour) were applied for classification. The recognition system was able to differentiate correctly between healthy and ill subjects with 72.35% accuracy in case the data base composed of 40 persons.

  20. Word recognition using ideal word patterns

    NASA Astrophysics Data System (ADS)

    Zhao, Sheila X.; Srihari, Sargur N.

    1994-03-01

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

  1. Pattern Recognition, Vol. 30, No. 5, pp. 751-768, 1997 1997 Pattern Recognition Society. Published by Elsevier Science Ltd

    E-print Network

    Poggio, Tomaso

    in an attempt to improve filter design by the introduction of nonlinearity. © 1997 Pattern Recognition SocietyPergamon Pattern Recognition, Vol. 30, No. 5, pp. 751-768, 1997 © 1997 Pattern Recognition Society--Template matching by means of cross-correlation is common practice in pattern recognition in spite of its drawbacks

  2. 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.

  3. Associative Pattern Recognition In Analog VLSI Circuits

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1995-01-01

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

  4. 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…

  5. 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…

  6. Simulated Consciousness in Automatic Pattern Recognition

    E-print Network

    van Vliet, Lucas J.

    Simulated Consciousness in Automatic Pattern Recognition Dialogues in Psychology,4.0, June 7, 1999 empiricism in consciousness research) at the Mind­4 conference, Dublin City University, Dublin, Ireland, on August 17, 1999. Abstract Consciousness is discussed in terms of local and global aspects of sensory data

  7. QUANTITATIVE PATTERN RECOGNITION USING NONLINEAR MODELBASED ANALYSIS

    E-print Network

    Abidi, Mongi A.

    A nonlinear model­based approach is taken to quantitatively analyze time series data gener- ated by analyticalQUANTITATIVE PATTERN RECOGNITION USING NONLINEAR MODEL­BASED ANALYSIS A Dissertation Presented program and data interpretation module (DIM) functional leader, for his valuable guidance in the world

  8. 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.

  9. 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.

  10. Pattern recognition techniques in Polarimetry

    NASA Astrophysics Data System (ADS)

    Ariste, Arturo López

    2015-10-01

    Sparsity is a property of data by which it can be represented using a small number of patterns. It is the key concept behind an evergrowing list of mathematical techniques for handling data and recover from it signals or information in conditions previously thought impossible. The application of those techniques to spectropolarimetric data is relatively straightforward. We present three examples of such application: the use of Principal Component Analysis to invert the magnetic field in solar prominences from spectropolarimetry of the He D3 line, the removal of fringes from spectropolarimetric data with Relevance Vector Machines, and the retrieval of high resolution spectra from low resolution data with Compressed Sensing.

  11. 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.

  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. DNA pattern recognition using canonical correlation algorithm.

    PubMed

    Sarkar, B K; Chakraborty, Chiranjib

    2015-10-01

    We performed canonical correlation analysis as an unsupervised statistical tool to describe related views of the same semantic object for identifying patterns. A pattern recognition technique based on canonical correlation analysis (CCA) was proposed for finding required genetic code in the DNA sequence. Two related but different objects were considered: one was a particular pattern, and other was test DNA sequence. CCA found correlations between two observations of the same semantic pattern and test sequence. It is concluded that the relationship possesses maximum value in the position where the pattern exists. As a case study, the potential of CCA was demonstrated on the sequence found from HIV-1 preferred integration sites. The subsequences on the left and right flanking from the integration site were considered as the two views, and statistically significant relationships were established between these two views to elucidate the viral preference as an important factor for the correlation. PMID:26564973

  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. 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).

  16. Pattern Recognition in Time Series

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  17. 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.

  18. 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.

  19. 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...

  20. 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.

  1. Pattern recognition in hyperspectral persistent imaging

    NASA Astrophysics Data System (ADS)

    Rosario, Dalton; Romano, Joao; Borel, Christoph

    2015-05-01

    We give updates on a persistent imaging experiment dataset, being considered for public release in a foreseeable future, and present additional observations analyzing a subset of the dataset. The experiment is a long-term collaborative effort among the Army Research Laboratory, Army Armament RDEC, and Air Force Institute of Technology that focuses on the collection and exploitation of longwave infrared (LWIR) hyperspectral imagery. We emphasize the inherent challenges associated with using remotely sensed LWIR hyperspectral imagery for material recognition, and show that this data type violates key data assumptions conventionally used in the scientific community to develop detection/ID algorithms, i.e., normality, independence, identical distribution. We treat LWIR hyperspectral imagery as Longitudinal Data and aim at proposing a more realistic framework for material recognition as a function of spectral evolution through time, and discuss limitations. The defining characteristic of a longitudinal study is that objects are measured repeatedly through time and, as a result, data are dependent. This is in contrast to cross-sectional studies in which the outcomes of a specific event are observed by randomly sampling from a large population of relevant objects in which data are assumed independent. Researchers in the remote sensing community generally assume the problem of object recognition to be cross-sectional. But through a longitudinal analysis of a fixed site with multiple material types, we quantify and argue that, as data evolve through a full diurnal cycle, pattern recognition problems are longitudinal in nature and that by applying this knowledge may lead to better algorithms.

  2. Pattern recognition receptors in microbial keratitis.

    PubMed

    Taube, M-A; Del Mar Cendra, M; Elsahn, A; Christodoulides, M; Hossain, P

    2015-11-01

    Microbial keratitis is a significant cause of global visual impairment and blindness. Corneal infection can be caused by a wide variety of pathogens, each of which exhibits a range of mechanisms by which the immune system is activated. The complexity of the immune response to corneal infection is only now beginning to be elucidated. Crucial to the cornea's defences are the pattern-recognition receptors: Toll-like and Nod-like receptors and the subsequent activation of inflammatory pathways. These inflammatory pathways include the inflammasome and can lead to significant tissue destruction and corneal damage, with the potential for resultant blindness. Understanding the immune mechanisms behind this tissue destruction may enable improved identification of therapeutic targets to aid development of more specific therapies for reducing corneal damage in infectious keratitis. This review summarises current knowledge of pattern-recognition receptors and their downstream pathways in response to the major keratitis-causing organisms and alludes to potential therapeutic approaches that could alleviate corneal blindness. PMID:26160532

  3. Searching for pulsars using image pattern recognition

    E-print Network

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

    2014-01-01

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

  4. Allergen-Specific Pattern Recognition Receptor Pathways

    PubMed Central

    Wills-Karp, Marsha

    2012-01-01

    Summary Allergic diseases continue to plague modernized societies, underscoring the need to identify the molecular basis for the propensity of a small number of environmental proteins to provoke maladaptive, allergic responses. Recent data suggest that the ability of allergenic proteins to drive allergic responses in susceptible hosts is driven by their unique innate immune activating capabilities. Although the identification of allergen-specific pattern recognition receptors is in its infancy, studies to date have shown that allergens drive Th2-biased immune responses via directly engaging C-type lectin receptors (dectin-2, DC-SIGN, mannose receptor) on dendritic cells and/or mimicking toll-like receptor 4 signaling complex molecules expressed on airway structural cells. Elucidation of the specific innate immune pathways activated by allergens holds great promise in defining new therapeutic targets for the treatment of allergic diseases. PMID:21093238

  5. 28 October 2011 1Ups and Downs in Pattern Recognition Ups and Downs in

    E-print Network

    Duin, Robert P.W.

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

  6. 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

  7. Pattern recognition: A basis for remote sensing data analysis

    NASA Technical Reports Server (NTRS)

    Swain, P. H.

    1973-01-01

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

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

    E-print Network

    Bebis, George

    Gender Recognition from Faces Using Bandlet and Local Binary Patterns Faten A. Alomar, Ghulam-- In this paper, multi-scale bandlet and local binary pattern (LBP) based method for gender recognition from faces database, and the highest accuracy of 99.13% is obtained with the proposed method. Keywords-- Gender

  9. Recognition of On-Line Handwritten Patterns Through Shape Metamorphosis

    E-print Network

    Recognition of On-Line Handwritten Patterns Through Shape Metamorphosis I. Pavlidis R. Singh N pattern recognition through the use of shape metamorphosis. It is based o n the premise that if two shapes are similar they don't have to undergo a substantial metamorphosis process in order for one to assume

  10. Image pattern recognition supporting interactive analysis and graphical visualization

    NASA Technical Reports Server (NTRS)

    Coggins, James M.

    1992-01-01

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

  11. Pattern recognition in the database of a mask layout

    NASA Astrophysics Data System (ADS)

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

    2002-07-01

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

  12. 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.

  13. Patterns, Fishing and Nonlinear Optics

    NASA Astrophysics Data System (ADS)

    Geddes, John Bruce

    Motivated by a conversation with my brother, a deep sea fisherman off the east coast of Scotland, I review the concepts which unify the topic of pattern formation in nonequilibrium systems. As a specific example of a pattern-forming system, I go on to examine pattern formation in nonlinear optics and I discuss two nonlinear optical systems in considerable detail. The first, counterpropagating laser beams in a nonlinear Kerr medium, results in the prediction and numerical observation of hexagonal patterns in a self-focusing medium, and of square patterns in a self-defocusing medium. Furthermore, a novel Hopf bifurcation is observed which destabilises the hexagons and an explanation in terms of a coupled-amplitude model is given. The other system, namely the mean-field model of propagation in a nonlinear cavity, also gives rise to hexagonal patterns in a self-focusing medium. By extending this model to include the vector nature of the electric field, polarisation patterns are predicted and observed for a self-defocusing medium. Roll patterns dominate close to threshold, while farther from threshold labyrinthine patterns are found. By driving the system very hard, a transition to a regime consisting of polarisation domains connected by fronts is also observed. Finally, numerical algorithms appropriate for solving the model equations are discussed and an alternative algorithm is presented which may be of use in pattern-forming systems in general.

  14. Progress in diagnostic pattern recognition (DPR)

    NASA Astrophysics Data System (ADS)

    Moecks, Joachim; Kocherscheidt, Gerrit; Koehler, W.; Petrich, Wolfgang H.

    2004-07-01

    The identification and analysis of disease-specific signatures in mid-infrared spectra of serum forms the basis of a method called "Diagnostic Pattern Recognition (DPR)". A conceivable usage of this method in clinical diagnostics requires that the method be applied in a convenient and robust manner. Thus, automation, room-temperature operation and reproducibility the prerequisite improvements toward routine application. We have investigated the performance two identical, semi-automated DPR systems. In contrast to previous measurements, which required MCT detectors, the use of a DLaTGS detector allowed the systems to be operated without the requirement of liquid nitrogen cooling. A series of measurements showed that automated pipetting improves the reproducibility significantly as compared to manual pipetting. For automated pipetting, the within-day variations are of minor importance. However, day-to-day variations may decrease the reproducibility in some spectral regions by more than a factor of two. Slight dependence of the reproducibility on the protein content of the serum samples has been observed.

  15. 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%.

  16. Pattern recognition issues on anisotropic smoothed particle hydrodynamics

    NASA Astrophysics Data System (ADS)

    Pereira Marinho, Eraldo

    2014-03-01

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

  17. Searching for pulsars using image pattern recognition

    SciTech Connect

    Zhu, W. W.; Berndsen, A.; Madsen, E. C.; Tan, M.; Stairs, I. H.; Brazier, A.; Lazarus, P.; Lynch, R.; Scholz, P.; Stovall, K.; Cohen, S.; Dartez, L. P.; Lunsford, G.; Martinez, J. G.; Mata, A.; Ransom, S. M.; Banaszak, S.; Biwer, C. M.; Flanigan, J.; Rohr, M. E-mail: berndsen@phas.ubc.ca; 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.

  18. 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.

  19. 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

  20. 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.

  1. TEXTAL: A Pattern Recognition System for Interpreting Electron Density Maps

    E-print Network

    Ioerger, Thomas R.

    . The diffraction patterns represent the Fourier transform of the electron density in the unit cell, so, in principle, the inverse Fourier transform of the diffraction pattern could be used to re­ constructTEXTAL: A Pattern Recognition System for Interpreting Electron Density Maps Thomas R. Ioerger 1

  2. 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.

  3. Visual cluster analysis and pattern recognition template and methods

    SciTech Connect

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

    1993-12-31

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

  4. Visual cluster analysis and pattern recognition template and methods

    DOEpatents

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

    1999-01-01

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

  5. 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.

  6. 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.

  7. The Pandora software development kit for pattern recognition

    NASA Astrophysics Data System (ADS)

    Marshall, J. S.; Thomson, M. A.

    2015-09-01

    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. The algorithms request operations to create or modify data structures and the operations are 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.

  8. PATTERN RECOGNITION STUDIES OF COMPLEX CHROMATOGRAPHIC DATA SETS

    EPA Science Inventory

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

  9. The Pandora Software Development Kit for Pattern Recognition

    E-print Network

    J. S. Marshall; M. A. Thomson

    2015-09-18

    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. The algorithms request operations to create or modify data structures and the operations are 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.

  10. Development of Pattern Recognition in Infant Pigtailed Macaques (Macaca nemestrina).

    ERIC Educational Resources Information Center

    Gunderson, Virginia M.; Sackett, Gene P.

    1984-01-01

    Examined the development of pattern recognition in infant pigtailed macaques using the familiarization novelty technique. Results indicate that by at least 200 days postconception subjects show a consistently reliable visual response to novelty. (Author/RH)

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

    NASA Astrophysics Data System (ADS)

    Yevin, Igor; Koblyakov, Alexander

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

  12. Applications of Moment Invariants to Neurocomputing for Pattern Recognition.

    NASA Astrophysics Data System (ADS)

    Li, Yajun

    1990-01-01

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

  13. Postprocessing for character recognition using pattern features and linguistic information

    NASA Astrophysics Data System (ADS)

    Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

    1993-04-01

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

  14. Pattern Recognition Enables Automatic Parallelization of Numerical Codes

    E-print Network

    Kessler, Christoph

    Pattern Recognition Enables Automatic Parallelization of Numerical Codes Christoph W. Ke�ler message­passing multiprocessors (e.g. iPSC/860, CM­5, nCUBE­2, ...). The key idea is a high­level pattern or similar languages for execution on distributed memory MIMD message passing systems (DMS). These DMS

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  16. 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.

  17. Optical music recognition system which learns

    NASA Astrophysics Data System (ADS)

    Fujinaga, Ichiro

    1993-01-01

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

  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. 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.

  20. 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.

  1. 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

  2. Accurate invariant pattern recognition for perspective camera model

    NASA Astrophysics Data System (ADS)

    Serikova, Mariya G.; Pantyushina, Ekaterina N.; Zyuzin, Vadim V.; Korotaev, Valery V.; Rodrigues, Joel J. P. C.

    2015-05-01

    In this work we present a pattern recognition method based on geometry analysis of a flat pattern. The method provides reliable detection of the pattern in the case when significant perspective deformation is present in the image. The method is based on the fact that collinearity of the lines remains unchanged under perspective transformation. So the recognition feature is the presence of two lines, containing four points each. Eight points form two squares for convenience of applying corner detection algorithms. The method is suitable for automatic pattern detection in a dense environment of false objects. In this work we test the proposed method for statistics of detection and algorithm's performance. For estimation of pattern detection quality we performed image simulation process with random size and spatial frequency of background clutter while both translational (range varied from 200 mm to 1500 mm) and rotational (up to 60°) deformations in given pattern position were added. Simulated measuring system included a camera (4000x4000 sensor with 25 mm lens) and a flat pattern. Tests showed that the proposed method demonstrates no more than 1% recognition error when number of false targets is up to 40.

  3. Hypothesis Support Mechanism for Mid-Level Visual Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Amador, Jose J (Inventor)

    2007-01-01

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

  4. Evolving Cellular Automata Model For Pattern Recognition and Classi cation

    E-print Network

    Ganguly, Niloy

    automata - Multiple Attractor Cellular Automata (MACA), introduced in [6]; while that of non-linear model is built around Generalized Multiple Attractor Cellular Automata (GMACA). The detailed analysis of MACA has along with an introduction to MACA. The MACA based pattern recognition algorithm is discussed in Section

  5. Investigation of pattern recognition algorithms to determine depth and

    E-print Network

    Maguire Jr., Gerald Q.

    Investigation of pattern recognition algorithms to determine depth and volume of water inside to determine depth and volume of water inside the sump of a pumping station Authors: Magnus Stjern & Staffan. Xylem water solutions AB 18 August 2014 KTH Royal Institute of Technology School of Information

  6. A note on core research issues for statistical pattern recognition

    E-print Network

    Duin, Robert P.W.

    theory has multi-disciplinary roots, be- cause engineering disciplines aim to bridge the gap between real that: · Statistical decision and estimation theories com- monly used in PR have been almost entirely deA note on core research issues for statistical pattern recognition Robert P.W. Duin a,*, Fabio Roli

  7. Symbolic Dynamic Filtering for Pattern Recognition in Distributed Sensor Networks

    E-print Network

    Ray, Asok

    Recognition of Mobile Robots in a Laboratory Environment .... 12 5.1 Experimental Procedure for Behavior Identification of Mobile Robots .......... 12 5.2 Pattern Analysis for Behavior Identification of Mobile Robots .................. 14 5.3 Experimental Results for Behavior Identification of Mobile Robots ............. 16 6

  8. EE 813.1 (3L) Introduction to Pattern Recognition

    E-print Network

    Saskatchewan, University of

    , discriminant functions, probability density and parameter estimation, maximum likelihood and minimum risk in pattern recognition (e.g., biometric and biomedical systems). A design project is also required Transformation: Feature Generation and Dimensionality Reduction 5. Feature Selection and Data Fusion 6

  9. Driving Pattern Recognition for Control of Hybrid Electric Trucks

    E-print Network

    Peng, Huei

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

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

    ERIC Educational Resources Information Center

    Suresh, Rahul; Mosser, David M.

    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.…

  11. Pattern Recognition 38 (2005) 767772 www.elsevier.com/locate/patcog

    E-print Network

    Plataniotis, Konstantinos N.

    2005-01-01

    Pattern Recognition 38 (2005) 767­772 www.elsevier.com/locate/patcog Rapid and brief communication.N. Plataniotis / Pattern Recognition 38 (2005) 767­772 input. If the input image with the B-bit code word repre

  12. A New Concept of Vertically Integrated Pattern Recognition Associative Memory

    NASA Astrophysics Data System (ADS)

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

    Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing fast pattern recognition for a track trigger, requiring about three orders of magnitude more associative memory patterns than what was used in the original CDF SVT. Scaling of current technologies is unlikely to satisfy the scientific needs of the future, and investments in transformational new technologies need to be made. In this paper, we will discuss a new concept of using the emerging 3D vertical integration technology to significantly advance the state-of-the-art for fast pattern recognition within and outside HEP. A generic R&D proposal [1] based on this new concept, with a few institutions involved, has recently been submitted to DOE with the goal to design and perform the ASIC engineering necessary to realize a prototype device. The progress of this R&D project will be reported in the future. Here we will only focus on the concept of this new approach.

  13. A new concept of vertically integrated pattern recognition associative memory

    SciTech Connect

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

    2011-11-01

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

  14. Quantum Mechanics, Pattern Recognition, and the Mammalian Brain

    NASA Astrophysics Data System (ADS)

    Chapline, George

    2008-10-01

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

  15. Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

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

  16. Phase Oscillatory Network and Visual Pattern Recognition.

    PubMed

    Follmann, Rosangela; Macau, Elbert E N; Rosa, Epaminondas; Piqueira, José 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

  17. Pattern recognition with parallel associative memory

    NASA Technical Reports Server (NTRS)

    Toth, Charles K.; Schenk, Toni

    1990-01-01

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

  18. 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.

  19. Detection and recognition of repeated tones and tonal patterns.

    PubMed

    McKinley, R C; Weber, D L

    1994-05-01

    This study examined the effect of multiple presentations on signal detection and on signal recognition (identifying one signal from a set of four possible signals) for three different sets of signals. One set was four sinusoids (100-ms duration, frequencies of 707, 1000, 1414, and 2000 Hz). Two sets contained tonal patterns each made of a sequence of seven, 100-ms, sinusoidal components. In the first set, the four patterns consisted of the same seven sinusoids in different orders. In the second set, the four patterns had the same order of relative frequencies, but had frequencies from different 1/4 oct bands centered at 707, 1000, 1414, and 2000 Hz. All stimuli were adjusted to be equally detectable in the presence of a continuous white noise (eta 0 = 20 dB SPL). Each trial contained 1, 2, 4, 8, or 16 presentations of a given signal plus noise (probability of a signal was 0.5) or noise alone. Detectability of the sinusoids generally increased as the square root of the number of presentations; detectability for the tonal patterns increased at a slower rate. Recognition was generally poorer than predicted by the recognition theorem [S.J. Starr, C. E. Metz, L.B. Lusted, and D.J. Goodenough, Radiology 116, 533-538 (1975)] and increased with multiple presentations only as much as expected from the increase in signal detectability. PMID:8207137

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

    NASA Astrophysics Data System (ADS)

    Millán, María S.

    2012-10-01

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

  1. The impact of fluctuations on the recognition of ambiguous patterns.

    PubMed

    Ditzinger, T; Haken, H

    1990-01-01

    The recognition of ambiguous patterns by humans is modelled by coupled differential equations which describe the formation of percepts by means of order parameters which in turn are determined by the saturation of attention parameters. We study the impact of fluctuations on the attention parameters and thus indirectly on the recognition of ambiguous patterns. Excellent agreement with psychophysical experimental results by Price on the transient behaviour of switching times and by Borsellino et al. on the distribution function of switching times as function of the size of the visual field is obtained. Our model allows us to deal also with the shift of width and position of the distribution function with respect to slow and fast observers in the sense of Borsellino. PMID:2257283

  2. 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.

  3. Learning pattern recognition and decision making in the insect brain

    NASA Astrophysics Data System (ADS)

    Huerta, R.

    2013-01-01

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

  4. Applications of pattern recognition in aluminum alloy texture characterization

    NASA Astrophysics Data System (ADS)

    Liu, Guizhong; Rehbein, D. K.; Foley, James C.; Thompson, R. B.

    2000-05-01

    This paper presents a methodology to extract texture information in Aluminum alloys using pattern recognition algorithm. The orientation of the samples can be obtained by the orientation Image Microscope (OIM) technique. The ISO DATA pattern recognition algorithm is implemented to classify the OIM data into different clusters. Based on the classification results, the probability density function (pdf) is estimated. Then, the pdf is expanded as a series of Legendre functions with coefficients, i.e., the orientation distribution coefficients (ODC) as texture parameters. Three of these ODC's are of special interests, namely W400, W420, and W440. This paper includes results from ultrasonic NDE and this novel algorithm.—Ames Laboratory is operated for U.S. Department of Energy by Iowa State University under Contract W-7405-ENG-82. This work was supported by the Office of Basic Energy Sciences as a part of the Center of Excellence of the Synthesis and Processing of Advanced Materials.

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

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1975-01-01

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

  7. 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.

  8. Pattern recognition for Space Applications Center director's discretionary fund

    NASA Technical Reports Server (NTRS)

    Singley, M. E.

    1984-01-01

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

  9. Neurocomputing methods for pattern recognition in nuclear physics

    SciTech Connect

    Gyulassy, M.; Dong, D.; Harlander, M.

    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.

  10. A comparison of proportional control methods for pattern recognition control.

    PubMed

    Simon, Ann M; Stern, Ken; Hargrove, Levi J

    2011-01-01

    Few studies have focused on proportional control with multi-channel electromyographic (EMG) pattern recognition systems. In a simple proportional control algorithm, movement speed is often calculated by averaging the mean absolute values of all EMG channels. The aim of our study was to compare the performance of two types of pattern recognition control (simple proportional and binary on/off) to direct proportional control. Six EMG channels were collected from non-targeted forearm muscles of four healthy subjects. Subjects were prompted to perform eight medium force isometric repetitions of the following contractions: wrist flexion/extension, wrist pronation/supination, hand open/close, and no movement (rest). Control performances were measured during a one-dimensional position-tracking task using a custom-made graphical user interface. The results show that a simple proportional control algorithm for the pattern recognition system outperformed binary on/off control and was comparable to the performance achieved with direct proportional control. PMID:22255058

  11. Conditional random fields for pattern recognition applied to structured data

    SciTech Connect

    Burr, Tom; Skurikhin, Alexei

    2015-07-14

    Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building) or “natural” (such as a tree). Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X) is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs) model structured data using the conditional distribution P(Y|X = x), without specifying a model for P(X), and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples.

  12. Conditional random fields for pattern recognition applied to structured data

    DOE PAGESBeta

    Burr, Tom; Skurikhin, Alexei

    2015-07-14

    Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building) or “natural” (such as a tree). Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X) is difficult because features between parts of the modelmore »are often correlated. Therefore, conditional random fields (CRFs) model structured data using the conditional distribution P(Y|X = x), without specifying a model for P(X), and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples.« less

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

    E-print Network

    V. P. Belavkin; V. P. Maslov

    2004-12-03

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

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

    NASA Astrophysics Data System (ADS)

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

    2008-02-01

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

  15. Innate sensing of viruses by pattern recognition receptors in birds

    PubMed Central

    2013-01-01

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

  16. The approach of optical target recognition via compressive sensing theory

    NASA Astrophysics Data System (ADS)

    Chen, Anhong; Yu, Ying; Mu, Yuqiang; Sun, Xiaosong; Tang, Guojian

    2015-10-01

    An approach of optical target recognition via compressive sensing theory is proposed, its feature expressed on the usually suitability and robust on noise, it made a breakthrough on the complex operation which is used in the common recognition algorithm when the characteristic is extracted, it can classify the target accurately when plenty of information is consisted in the observing data and the test sample can be sparse expressed. and the disturbance error caused by noise could be even eliminated under the recognition structure expressed by sparse, the process of the simulation testify the validity of the proposed method.

  17. Two Levels Fusion Decision for Multispectral Image Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Elmannai, H.; Loghmari, M. A.; Naceur, M. S.

    2015-10-01

    Major goal of multispectral data analysis is land cover classification and related applications. The dimension drawback leads to a small ratio of the remote sensing training data compared to the number of features. Therefore robust methods should be associated to overcome the dimensionality curse. The presented work proposed a pattern recognition approach. Source separation, feature extraction and decisional fusion are the main stages to establish an automatic pattern recognizer. The first stage is pre-processing and is based on non linear source separation. The mixing process is considered non linear with gaussians distributions. The second stage performs feature extraction for Gabor, Wavelet and Curvelet transform. Feature information presentation provides an efficient information description for machine vision projects. The third stage is a decisional fusion performed in two steps. The first step assign the best feature to each source/pattern using the accuracy matrix obtained from the learning data set. The second step is a source majority vote. Classification is performed by Support Vector Machine. Experimentation results show that the proposed fusion method enhances the classification accuracy and provide powerful tool for pattern recognition.

  18. 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.

  19. Pattern Recognition 35 (2002) 22792301 www.elsevier.com/locate/patcog

    E-print Network

    van Vliet, Lucas J.

    2002-01-01

    to the application of pattern recognition techniques in image processing and speciÿcally to the application of neuralPattern Recognition 35 (2002) 2279­2301 www.elsevier.com/locate/patcog Image processing with neural by the algorithm: preprocessing, data reduction=feature extraction, segmentation, object recognition, image

  20. Facilities for digital pattern recognition: an ECG detective trick.

    PubMed

    Poll, R; Henssge, R

    1988-01-01

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

  1. 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

  2. Electronic system with memristive synapses for pattern recognition

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

    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.

  3. A star pattern recognition algorithm for autonomous attitude determination

    NASA Astrophysics Data System (ADS)

    van Bezooijen, R. W. H.

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

  4. PREPRINT, published in International Journal of Pattern Recognition and Artificial Intelligence,

    E-print Network

    Würtz, Rolf P.

    PREPRINT, published in International Journal of Pattern Recognition and Artificial Intelligence Recognition and Artificial Intelligence, Vol. 17, No. 7, pages 1279­1302 (2003) 1280 Tino Lourens and Rolf P

  5. Collocation and Pattern Recognition Effects on System Failure Remediation

    NASA Technical Reports Server (NTRS)

    Trujillo, Anna C.; Press, Hayes N.

    2007-01-01

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

  6. 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.

  7. A statistical pattern recognition paradigm for structural health monitoring

    SciTech Connect

    Farrar, C. R.; Sohn, H.; Park, G. H.

    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.

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

    E-print Network

    Pennsylvania, University of

    vision, workload, performance, stress, human face, cortisol, heart rate, astronauts, Markov modelsOptical Computer Recognition of Facial Expressions Associated with Stress Induced by Performance, METAXAS DN. Optical computer recognition of facial expressions associated with stress induced

  9. An Efficient Vein Pattern-based Recognition System

    E-print Network

    Soni, Mohit; Rao, M S; Gupta, Phalguni

    2010-01-01

    This paper presents an efficient human recognition system based on vein pattern from the palma dorsa. A new absorption based technique has been proposed to collect good quality images with the help of a low cost camera and light source. The system automatically detects the region of interest from the image and does the necessary preprocessing to extract features. A Euclidean Distance based matching technique has been used for making the decision. It has been tested on a data set of 1750 image samples collected from 341 individuals. The accuracy of the verification system is found to be 99.26% with false rejection rate (FRR) of 0.03%.

  10. Ubiquitination of pattern recognition receptors in plant innate immunity

    PubMed Central

    Shan, Libo

    2014-01-01

    Lacking an adaptive immune system, plants largely rely on plasma membrane-resident pattern recognition receptors (PRRs) to sense pathogen invasion. Activation of PRRs leads to the profound immune responses that coordinately contribute to the restriction of pathogen multiplication. Protein posttranslational modifications dynamically shape the intensity and duration of the signaling pathways. In this review, we discuss the specific regulation of PRR activation and signaling by protein ubiquitination, endocytosis and degradation, with a particular focus on bacterial flagellin receptor FLS2 (flagellin sensing 2) in Arabidopsis. PMID:25275148

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

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Sayuri; Ohta, Maiko; Kodate, Kashiko

    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 various correlation methods, our face recognition engine revealed the most accurate EER of less than 1%. By using the JAVA interface on this system, we implemented the stable system taking pictures, providing functions to prevent spoofing while transferring images. This recognition system was tested on 300 women students and the results proved this system effective.

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

    SciTech Connect

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

    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.

  13. Adaptive channel selection for improving chromatic discrimination in colour pattern recognition

    NASA Astrophysics Data System (ADS)

    Pérez, E.; Millán, M. S.

    1997-02-01

    A new method to select colour channels in colour pattern recognition systems is presented to improve the chromatic discrimination capability. Instead of using the conventional RGB decomposition, we propose to use other narrow-band channels that can show more variations of the objects of a scene. This method exploits the spectral information of the objects and is useful in discriminating objects with similar colour. Simulated and experimental optical correlation results, for scenes with objects of similar colours, are presented and discussed. Model and natural objects are considered and a variety of ranges are studied.

  14. Simultaneous Electro-Optical Tracking for Nanoparticle Recognition and Counting.

    PubMed

    Angeli, Elena; Volpe, Andrea; Fanzio, Paola; Repetto, Luca; Firpo, Giuseppe; Guida, Patrizia; Lo Savio, Roberto; Wanunu, Meni; Valbusa, Ugo

    2015-09-01

    We present the first detailed experimental observation and analysis of nanoparticle electrophoresis through a nanochannel obtained with synchronous high-bandwidth electrical and camera recordings. Optically determined particle diffusion coefficients agree with values extracted from fitting electrical transport measurements to distributions from 1D Fokker-Planck diffusion-drift theory. This combined tracking strategy enables optical recognition and electrical characterization of nanoparticles in solution, which can have a broad range of applications in biology and materials science. PMID:26225640

  15. 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.

  16. Chinese Optical Character Recognition for Information Extraction from Video Images

    E-print Network

    Lyu, Michael R.

    Chinese Optical Character Recognition for Information Extraction from Video Images Wing Hang Cheung The Chinese University of Hong Kong Shatin, N.T., Hong Kong Abstract A number of research work on text ex- traction from videos is conducted in these few years, but not many focus on the Chinese language. Due to di

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

    ERIC Educational Resources Information Center

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

    2006-01-01

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

  18. Time-series pattern recognition with an immune algorithm

    NASA Astrophysics Data System (ADS)

    Paprocka, I.; Kempa, W. M.; Grabowik, C.; Kalinowski, K.

    2015-11-01

    In this paper, changes in sequences pattern describing damage-sensitive features of an object which undergoes a failure mode are recognized using an immune algorithm. A frequency response change is an effect for various failure modes occurrence. The objective of this paper is to present immune algorithm for pattern recognition which can discover dependencies between failure mode and effect - frequency response change. Changes in the effect are described with noise due to the fact that the object operates in external conditions. In the immune algorithm antibodies encode various changes in the effect after a given mode occurrence by a number of time. A pathogen encodes a noisy effect of the mode occurrence. Antibodies belonging to a given neighbourhood represent effects after a given type of failure mode occurrence. Antibodies from the neighbourhood undergo clonal selection and affinity maturation process. With the best matched antibody the type of failure mode is achieved.

  19. An auditory feature detection circuit for sound pattern recognition

    PubMed Central

    Schöneich, Stefan; Kostarakos, Konstantinos; Hedwig, Berthold

    2015-01-01

    From human language to birdsong and the chirps of insects, acoustic communication is based on amplitude and frequency modulation of sound signals. Whereas frequency processing starts at the level of the hearing organs, temporal features of the sound amplitude such as rhythms or pulse rates require processing by central auditory neurons. Besides several theoretical concepts, brain circuits that detect temporal features of a sound signal are poorly understood. We focused on acoustically communicating field crickets and show how five neurons in the brain of females form an auditory feature detector circuit for the pulse pattern of the male calling song. The processing is based on a coincidence detector mechanism that selectively responds when a direct neural response and an intrinsically delayed response to the sound pulses coincide. This circuit provides the basis for auditory mate recognition in field crickets and reveals a principal mechanism of sensory processing underlying the perception of temporal patterns. PMID:26601259

  20. 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; 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.

  1. Pattern recognition characterizations of micromechanical and morphological materials states via analytical quantitative ultrasonics

    NASA Astrophysics Data System (ADS)

    Williams, J. H., Jr.; Lee, S. S.

    1986-01-01

    One potential approach to the quantitative acquisition of discriminatory information that can isolate a single structural state is pattern recognition. The pattern recognition characterizations of micromechanical and morphological materials states via analytical quantiative ultrasonics are outlined. The concepts, terminology, and techniques of statistical pattern recognition are reviewed. Feature extraction and classification and states of the structure can be determined via a program of ultrasonic data generation.

  2. Wavelet-based moment invariants for pattern recognition

    NASA Astrophysics Data System (ADS)

    Chen, Guangyi; Xie, Wenfang

    2011-07-01

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

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

    E-print Network

    Leandro G. Almeida; Mihailo Backovic; Mathieu Cliche; Seung J. Lee; Maxim Perelstein

    2015-01-23

    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 jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

  4. Pattern recognition of satellite cloud imagery for improved weather prediction

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

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

  5. Playing tag with ANN: boosted top identification with pattern recognition

    NASA Astrophysics Data System (ADS)

    Almeida, Leandro G.; Backovi?, Mihailo; Cliche, Mathieu; Lee, Seung J.; Perelstein, Maxim

    2015-07-01

    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 jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

  6. Pattern Recognition Receptors in Cancer Progression and Metastasis

    PubMed Central

    Pandey, Sanjay; Singh, Saurabh; Anang, Vandana; Bhatt, Anant N; Natarajan, K; Dwarakanath, Bilikere S

    2015-01-01

    The innate immune system is an integral component of the inflammatory response to pathophysiological stimuli. Toll-like receptors (TLRs) and inflammasomes are the major sensors and pattern recognition receptors (PRRs) of the innate immune system that activate stimulus (signal)-specific pro-inflammatory responses. Chronic activation of PRRs has been found to be associated with the aggressiveness of various cancers and poor prognosis. Involvement of PRRs was earlier considered to be limited to infection- and injury-driven carcinogenesis, where they are activated by pathogenic ligands. With the recognition of damage-associated molecular patterns (DAMPs) as ligands of PRRs, the role of PRRs in carcinogenesis has also been implicated in other non-pathogen-driven neoplasms. Dying (apoptotic or necrotic) cells shed a plethora of DAMPs causing persistent activation of PRRs, leading to chronic inflammation and carcinogenesis. Such chronic activation of TLRs promotes tumor cell proliferation and enhances tumor cell invasion and metastasis by regulating pro-inflammatory cytokines, metalloproteinases, and integrins. Due to the decisive role of PRRs in carcinogenesis, targeting PRRs appears to be an effective cancer-preventive strategy. This review provides a brief account on the association of PRRs with various cancers and their role in carcinogenesis. PMID:26279628

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Pattern-Recognition System for Approaching a Known Target

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terrance; Cheng, Yang

    2008-01-01

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

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

    E-print Network

    Chung, Albert C. S.

    Face Recognition by Using Elongated Local Binary Patterns with Average Maximum Distance Gradient@cse.ust.hk, achung@cse.ust.hk Abstract. In this paper, we propose a new face recognition approach based on local images is low, we also conduct additional face recognition experiments on the two databases by reducing

  13. Dense pattern optical multipass cell

    DOEpatents

    Silver, Joel A [Santa Fe, NM

    2009-01-13

    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. 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.

  15. Geometry Of Discrete Sets With Applications To Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Sinha, Divyendu

    1990-03-01

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

  16. A Gesture Recognition System for Detecting Behavioral Patterns of ADHD.

    PubMed

    Bautista, Miguel Angel; Hernandez-Vela, Antonio; Escalera, Sergio; Igual, Laura; Pujol, Oriol; Moya, Josep; Violant, Veronica; Anguera, Maria T

    2016-01-01

    We present an application of gesture recognition using an extension of dynamic time warping (DTW) to recognize behavioral patterns of attention deficit hyperactivity disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either Gaussian mixture models or an approximation of convex hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intraclass gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioral patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multimodal dataset (RGB plus depth) of ADHD children recordings with behavioral patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context. PMID:26684256

  17. Vibrotactile pattern recognition: a portable compact tactile matrix.

    PubMed

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

    2012-02-01

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

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

    PubMed

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

    2014-01-01

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

  19. Pattern recognition by wavelet transforms using macro fibre composites transducers

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  20. Vocal folds disorder detection using pattern recognition methods.

    PubMed

    Wang, Jianglin; Jo, Cheolwoo

    2007-01-01

    Diagnosis of pathological voice is one of the most important issues in biomedical applications of speech technology. This study focuses on the classification of pathological voice using the HMM(Hidden Markov Model), the GMM(Gaussian Mixture Model) and a SVM (Support Vector Machine), and then compares the results to work done previously using an ANN (Artificial Neural Network). Speech data were collected from those without and those with vocal disorders. Normal and pathological speech data were mixed in out experiment. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chosen. Then the pattern recognition methods (HMM, GMM and SVM) were used to distinguish the mixed data into categories of normal and pathological speech. We found that the GMM-based method can give us superior classification rates compared to the other classification methods. PMID:18002689

  1. Carbon Nanotube Synaptic Transistor Network for Pattern Recognition.

    PubMed

    Kim, Sungho; Yoon, Jinsu; Kim, Hee-Dong; Choi, Sung-Jin

    2015-11-18

    Inspired by the human brain, a neuromorphic system combining complementary metal-oxide semiconductor (CMOS) and adjustable synaptic devices may offer new computing paradigms by enabling massive neural-network parallelism. In particular, synaptic devices, which are capable of emulating the functions of biological synapses, are used as the essential building blocks for an information storage and processing system. However, previous synaptic devices based on two-terminal resistive devices remain challenging because of their variability and specific physical mechanisms of resistance change, which lead to a bottleneck in the implementation of a high-density synaptic device network. Here we report that a three-terminal synaptic transistor based on carbon nanotubes can provide reliable synaptic functions that encode relative timing and regulate weight change. In addition, using system-level simulations, the developed synaptic transistor network associated with CMOS circuits can perform unsupervised learning for pattern recognition using a simplified spike-timing-dependent plasticity scheme. PMID:26512729

  2. Online Pattern Recognition for the ALICE High Level Trigger

    NASA Astrophysics Data System (ADS)

    Lindenstruth, V.; Loizides, C.; Rohrich, D.; Skaali, B.; Steinbeck, T.; Stock, R.; Tilsner, H.; Ullaland, K.; Vestbo, A.; Vik, T.

    2004-06-01

    The ALICE High Level Trigger has to process data online, in order to select interesting (sub)events, or to compress data efficiently by modeling techniques. Focusing on the main data source, the Time Projection Chamber (TPC), we present two pattern recognition methods under investigation: a sequential approach (cluster finder and track follower) and an iterative approach (track candidate finder and cluster deconvoluter). We show, that the former is suited for pp and low multiplicity PbPb collisions, whereas the latter might be applicable for high multiplicity PbPb collisions of dN/dy>3000. Based on the developed tracking schemes we show that using modeling techniques a compression factor of around 10 might be achievable.

  3. Pattern recognition of magnetic resonance images with application to atherosclerosis

    SciTech Connect

    Carman, C.S.

    1989-01-01

    Magnetic resonance imaging provides excellent soft tissue contrast enabling the non-invasive visualization of soft tissue diseases. The quantification of tissues visible in MR images would significantly increase the diagnostic information available. While tissue selection methods exist for CT images, those same methods do not work with MR images. This dissertation focuses on the application of image processing and pattern recognition techniques to MR images for the identification and quantification of soft tissues, atherosclerosis in particular. Atherosclerosis is a chronic disease of human arteries responsible for significant mortality and medical expense. Current diagnostic methods are invasive and carry significant risk. Supervised pattern recognition methods were investigated for tissue identification in MR images. The classifiers were trained A Fisher linear classifier successfully identified the tissues of interest from MR images of excised arteries, performing better than a minimum distance to the means classifier. Quantitative measures of the disease state were computed from the results and 3-D displays were generated of the diseased anatomy. For tissue in vivo, adequate histology can be difficult to collect, increasing the difficulty of training the classifiers and making the results less accurate. Cluster analysis was used in this dissertation to generate the training information. A new cluster analysis method was developed. ISODATA was modified to use hierarchical stopping rules. The new method was tested in a Monte Carlo study and with real world data sets. Comparisons were made with published methods using the same data. An information theoretic criterion, the CAIC, was found to be an excellent criteria for hierarchical stopping rules.

  4. Modeling and History Matching Hydrocarbon Production from Marcellus Shale using Data Mining and Pattern Recognition Technologies

    E-print Network

    Mohaghegh, Shahab

    in southwestern Pennsylvania using advanced data mining and pattern recognition technologies. In this new approachSPE 161184 Modeling and History Matching Hydrocarbon Production from Marcellus Shale using Data Mining and Pattern Recognition Technologies S. Esmaili, A. Kalantari-Dahaghi, SPE, West Virginia

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

    E-print Network

    New Mexico, University of

    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

  6. Comparison of rule-building expert systems with pattern recognition for the classification of analytical data

    SciTech Connect

    Derde, M.P.; Buydens, L.; Guns, C.; Massart, D.L.; Hopke, P.K.

    1987-07-15

    Two expert systems of the rule-building type, TIMM and EX-TRAN, are compared with pattern recognition methods for the classification of olive oils of different origins. Both expert systems are more user-friendly than the pattern recognition programs and TIMM yields slightly better results than nearest neighbors classifiers and linear discriminant analysis.

  7. Pattern recognition, steps in science and consciousness. Tutorial, Barcelona, 7 July 2008

    E-print Network

    Duin, Robert P.W.

    Pattern recognition, steps in science and consciousness. Tutorial, Barcelona, 7 July 2008 Robert P, and thereby on his consciousness. In the first part of the presentation the technology of pattern recognition fail- ures and steps in science and consciousness. Slides: 0. Introduction 1. Generalization 2

  8. CSE 455/555 Introduction to Pattern Recognition SUNY at Buffalo

    E-print Network

    Corso, Jason J.

    ) the design and construction and a pattern recognition system and 2) the major approaches in statistical-list@listserv.buffalo.edu for both 455 and 555 students. Student Updates: All updates will be posted to the course website and sent in pattern recognition system design such as the curse of dimensionality. Finally, the student will have

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

    ERIC Educational Resources Information Center

    Evans, John M. , Ed.; And Others

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

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

    PubMed Central

    Loo, Chu Kiong

    2014-01-01

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

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

    PubMed

    Yousefi, Bardia; Loo, Chu Kiong

    2014-01-01

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

  12. Image Reconstruction, Recognition, Using Image Processing, Pattern Recognition and the Hough Transform.

    NASA Astrophysics Data System (ADS)

    Seshadri, M. D.

    1992-01-01

    In this dissertation research, we have demonstrated the need for integration of various imaging methodologies, such as image reconstruction from projections, image processing, pattern and feature recognition using chain codes and the Hough transform. Further an integration of these image processing techniques have been brought about for medical imaging systems. An example of this is, classification and identification of brain scans, into normal, haemorrhaged, and lacunar infarcted brain scans. Low level processing was performed using LOG and a variation of LOG. Intermediate level processing used contour completion and chain encoding. Hough transform was used to detect any analytic shapes in the edge images. All these information were used by the data abstraction routine which also extracted information from the user, in the form of a general query. These were input into a backpropagation, which is a very popular supervised neural network. During learning process an output vector was supplied by the expert to the neural network. While performing the neural network compared the input and with the help of the weight matrix computed the output. This output was compared with the expert's opinion and a percentage deviation was calculated. In the case of brain scans this value was about 95%, when the test input vector did not vary, by more than two pixels with the training or learning input vector. A good classification of the brain scans were performed using the integrated imaging system. Identification of various organs in the abdominal region was also successful, within 90% recognition rate, depending on the noise in the image.

  13. 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.

  14. Pattern recognition and PID procedure with the ALICE-HMPID

    NASA Astrophysics Data System (ADS)

    Volpe, Giacomo

    2014-12-01

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

  15. A pyramidal neural network for visual pattern recognition.

    PubMed

    Phung, Son Lam; Bouzerdoum, Abdesselam

    2007-03-01

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

  16. 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.

  17. 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.

  18. 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.

  19. Pattern Recognition: The Importance of Dispersion in Crystal Collimation

    SciTech Connect

    Peggs,S.; Shiraishi, S.

    2008-09-01

    One aspect of the upcoming CRYSTAL experiment is to study the dynamics of single protons circulating the SPS in the presence of a crystal. Under some circumstances (for example under crystal channeling) a proton may hit the crystal and the neighboring silicon strip position detectors only once, before extraction from the SPS. In general (at most crystal rotation angles) it is expected that single protons will hit the crystal many times, with many accelerator turns between each hit, before escaping. Intermediate regimes are also possible (for example under volume reflection) in which a proton hits the crystal only a few times over many turns before being lost. It is crucial that the data analysis of each single proton data set be able to distinguish between these different dynamical phases, and to be able to convincingly demonstrate that the fundamental processes at play in each phase are well understood. Distinguishing between dynamical phases depends crucially on the ability to perform pattern recognition--at least visually, but preferably quantitatively--on the single proton data sets. This note shows that synchrotron oscillations significantly affect the hit pattern of a proton on the crystal. (By hit pattern we mean either the measurement vector of turn number and penetration depth, for each proton, or a vector that can be directly derived from the measurement vector, such as the vector of inferred synchrotron phase and penetration depth.) The analysis is (deliberately) as rudimentary as possible, using an elementary linear calculation which neither includes any higher order effects in the accelerator, nor any dynamical interactions between the test proton and the crystal or the silicon detectors. Single particle simulation studies need to be carried out for CRYSTAL, exploring realistic effects besides dispersion, such as multiple scattering, dead zones, energy loss, dispersion slope, and linear coupling. Only after analysis software becomes available to interpret the output of such studies will it be possible to predict with any confidence that it will be possible to distinguish all single proton dynamical phases in the CRYSTAL experiment. Then reality will prevail.

  20. Pattern Recognition 39 (2006) 969979 www.elsevier.com/locate/patcog

    E-print Network

    Plataniotis, Konstantinos N.

    2006-01-01

    a variety of image processing and pattern recognition tasks. Specifically, a gait-based Parts of this work extraction process is also proposed for the transformation of human silhouettes into low-dimensional feature interest are techniques which try to tackle the gait recognition problem using only sequences

  1. Italic Font Recognition Using Stroke Pattern Analysis on Wavelet Decomposed Word Images

    E-print Network

    Tan, Chew Lim

    images with a few italic words. These techniques are based on feature analysis on each individual that directly extracts individual word features from document images without the use of OCR[7], font recognitionItalic Font Recognition Using Stroke Pattern Analysis on Wavelet Decomposed Word Images Li Zhang

  2. Pattern Recognition 38 (2005) 607611 www.elsevier.com/locate/patcog

    E-print Network

    Lee, Doheon

    2005-01-01

    technique for pat- tern recognition, image processing, and data mining. The kernel-based classificationPattern Recognition 38 (2005) 607­611 www.elsevier.com/locate/patcog Rapid and brief communication Evaluation of the performance of clustering algorithms in kernel-induced feature space Dae-Won Kima, , Ki

  3. Human actions recognition using bag of optical flow words

    NASA Astrophysics Data System (ADS)

    Zhang, Xu; Miao, Zhenjiang; Wan, Lili

    2012-04-01

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

  4. 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: ...

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

    EPA Science Inventory

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

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

    EPA Science Inventory

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

  7. 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.

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

    E-print Network

    Prieto Orlando, Rodrigo Javier

    1994-01-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  10. Spatial pattern recognition for crop-livestock systems using multispectral data 

    E-print Network

    González, Adrián

    2008-01-01

    Within the field of pattern recognition (PR) a very active area is the clustering and classification of multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance ...

  11. Visual Empirical Region of Influence (VERI) Pattern Recognition Algorithms

    Energy Science and Technology Software Center (ESTSC)

    2002-05-01

    We developed new pattern recognition (PR) algorithms based on a human visual perception model. We named these algorithms Visual Empirical Region of Influence (VERI) algorithms. To compare the new algorithm's effectiveness against othe PR algorithms, we benchmarked their clustering capabilities with a standard set of two-dimensional data that is well known in the PR community. The VERI algorithm succeeded in clustering all the data correctly. No existing algorithm had previously clustered all the pattens inmore »the data set successfully. The commands to execute VERI algorithms are quite difficult to master when executed from a DOS command line. The algorithm requires several parameters to operate correctly. From our own experiences we realized that if we wanted to provide a new data analysis tool to the PR community we would have to provide a new data analysis tool to the PR community we would have to make the tool powerful, yet easy and intuitive to use. That was our motivation for developing graphical user interfaces (GUI's) to the VERI algorithms. We developed GUI's to control the VERI algorithm in a single pass mode and in an optimization mode. We also developed a visualization technique that allows users to graphically animate and visually inspect multi-dimensional data after it has been classified by the VERI algorithms. The visualization technique that allows users to graphically animate and visually inspect multi-dimensional data after it has been classified by the VERI algorithms. The visualization package is integrated into the single pass interface. Both the single pass interface and optimization interface are part of the PR software package we have developed and make available to other users. The single pass mode only finds PR results for the sets of features in the data set that are manually requested by the user. The optimization model uses a brute force method of searching through the cominations 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. The VERI interface tools were written using the Tcl/Tk 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 interfaces run the VERI algorithms in Leave-One-Out mode using the Euclidean metric.« less

  12. Multiresolution pattern recognition of small volcanos in Magellan data

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

    The Magellan data is a treasure-trove for scientific analysis of venusian geology, providing far more detail than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations. However, at this point, planetary scientists are being overwhelmed by the sheer quantities of data collected--data analysis technology has not kept pace with our ability to collect and store it. In particular, 'small-shield' volcanos (less than 20 km in diameter) are the most abundant visible geologic feature on the planet. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanos visible in the Magellan data. Identifying and studying these volcanos is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is very time-consuming. Hence, we have undertaken the development of techniques to partially automate this task. The goal is not the unrealistic one of total automation, but rather the development of a useful tool to aid the project scientists. The primary constraints for this particular problem are as follows: (1) the method must be reasonably robust; and (2) the method must be reasonably fast. Unlike most geological features, the small volcanos of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include the following: (1) a circular planimetric outline; (2) known diameter frequency distribution from preliminary studies; (3) a limited number of basic morphological shapes; and (4) the common occurrence of a single, circular summit pit at the center of the edifice.

  13. 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 ...

  14. 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

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

    PubMed Central

    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

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

    E-print Network

    Hollerbach, John M.

    Finger Force Direction Recognition by Principal Component Analysis of Fingernail Coloration Pattern pattern is presented to infer fingertip force direction during planar contact. Images from 7 sub- jects for all people. But accurate detection of fingertip force direction requires individually calibrated

  17. SINGER IDENTIFICATION IN POLYPHONIC MUSIC USING VOCAL SEPARATION AND PATTERN RECOGNITION METHODS

    E-print Network

    Virtanen, Tuomas

    SINGER IDENTIFICATION IN POLYPHONIC MUSIC USING VOCAL SEPARATION AND PATTERN RECOGNITION METHODS on pattern classification together with an algorithm for vocal separation. Classification stra- tegies Singing voice is the main focus of attention in musical pieces with a vocal part; most people use

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

    E-print Network

    Bachmann, Michael

    Adsorption and Pattern Recognition of Polymers at Complex Surfaces with Attractive Stripelike diagram of polymer adsorption at substrates with attractive stripelike patterns in the parameter space spanned by the adsorption affinity of the stripes and temperature. Results were obtained by extensive

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

    E-print Network

    Behavioral/Systems/Cognitive Temporal-Pattern Recognition by Single Neurons in a Sensory Pathway these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons-cellpatchrecordingsfromELpneuronsinvivorevealedthreepatternsofinterpulseinterval(IPI)tuning:low-passneuronstuned to long intervals, high-pass neurons tuned to short intervals, and bandpass neurons tuned to intermediate

  20. A cortex-like learning machine for temporal hierarchical pattern clustering, detection, and recognition

    E-print Network

    Rathinam, Muruhan

    network Learning machine Unsupervised learning Clustering Pattern recognition Spiking neuron Dendritic,51,5,24], associative memories [36,67,48,1,62,65], support vector machines [66,9,57], spiking neural networks [41A cortex-like learning machine for temporal hierarchical pattern clustering, detection

  1. Role of Delay of Feedback on Subsequent Pattern Recognition Transfer Tasks.

    ERIC Educational Resources Information Center

    Schroth, Marvin L.; Lund, Elissa

    1993-01-01

    Two experiments with 100 undergraduates investigated effects of delay of feedback on immediate and delayed transfer tasks involving different pattern recognition strategies. Delay of feedback resulted in greater retention of the concepts underlying construction of the different patterns in all transfer tasks. Results support the Kulhavy-Anderson…

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

    E-print Network

    Jayaraman, Arthi

    Designing Pattern-Recognition Surfaces for Selective Adsorption of Copolymer Sequences Using; published 24 February 2005) We describe a simulation method to design surfaces for recognizing specific containing two types of sites and allow the simulation to iterate towards an optimal surface pattern that can

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

    SciTech Connect

    Schaller, Gernot; Schuetzhold, Ralf

    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.

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

    NASA Astrophysics Data System (ADS)

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

    1993-03-01

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

  5. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System.

    PubMed

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency. PMID:26346654

  6. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    PubMed Central

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency. PMID:26346654

  7. Probing Atomic Dynamics and Structures Using Optical Patterns

    NASA Astrophysics Data System (ADS)

    Schmittberger, Bonnie L.; Gauthier, Daniel J.

    2015-05-01

    Pattern formation is a widely studied phenomenon that can provide fundamental insights into nonlinear systems. Emergent patterns in cold atoms are of particular interest in condensed matter physics and quantum information science because one can relate optical patterns to spatial structures in the atoms. In our experimental system, we study multimode optical patterns generated from a sample of cold, thermal atoms. We observe this nonlinear optical phenomenon at record low input powers due to the highly nonlinear nature of the spatial bunching of atoms in an optical lattice. We present a detailed study of the dynamics of these bunched atoms during optical pattern formation. We show how small changes in the atomic density distribution affect the symmetry of the generated patterns as well as the nature of the nonlinearity that describes the light-atom interaction. We gratefully acknowledge the financial support of the National Science Foundation through Grant #PHY-1206040.

  8. Differentiation of opium and poppy straw using capillary electrophoresis and pattern recognition techniques.

    PubMed

    Reid, Raymond G; Durham, David G; Boyle, Susanne P; Low, Ann S; Wangboonskul, Jinda

    2007-12-12

    Opium samples from four different locations and poppy straw from different plant varieties have been assayed using micellar capillary electrophoresis incorporating a sweeping technique. Individual alkaloids (morphine, codeine, papaverine, noscapine, thebaine, oripavine, reticuline and narceine) were quantitatively determined in the different samples by a validated capillary electrophoresis method. Unsupervised pattern recognition of the opium samples and the poppy straw samples using hierarchical cluster analysis (HCA) and principal component analysis (PCA), showed distinct clusters. Supervised pattern recognition using soft independent modelling of class analogy (SIMCA) was performed to show individual groupings and allow unknown samples to be classified according to the models built using the CZE assay results. PMID:18022406

  9. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2003-01-01

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

  11. Innate Pattern Recognition and Categorization in a Jumping Spider

    PubMed Central

    Dolev, Yinnon; Nelson, Ximena J.

    2014-01-01

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

  12. 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...

  13. Recognition of distinctive patterns of gallium-67 distribution in sarcoidosis

    SciTech Connect

    Sulavik, S.B.; Spencer, R.P.; Weed, D.A.; Shapiro, H.R.; Shiue, S.T.; Castriotta, R.J. )

    1990-12-01

    Assessment of gallium-67 ({sup 67}Ga) uptake in the salivary and lacrimal glands and intrathoracic lymph nodes was made in 605 consecutive patients including 65 with sarcoidosis. A distinctive intrathoracic lymph node {sup 67}Ga uptake pattern, resembling the Greek letter lambda, was observed only in sarcoidosis (72%). Symmetrical lacrimal gland and parotid gland {sup 67}Ga uptake (panda appearance) was noted in 79% of sarcoidosis patients. A simultaneous lambda and panda pattern (62%) or a panda appearance with radiographic bilateral, symmetrical, hilar lymphadenopathy (6%) was present only in sarcoidosis patients. The presence of either of these patterns was particularly prevalent in roentgen Stages I (80%) or II (74%). We conclude that simultaneous (a) lambda and panda images, or (b) a panda image with bilateral symmetrical hilar lymphadenopathy on chest X-ray represent distinctive patterns which are highly specific for sarcoidosis, and may obviate the need for invasive diagnostic procedures.

  14. 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.

  15. Movement pattern recognition in basketball free-throw shooting.

    PubMed

    Schmidt, Andrea

    2012-04-01

    The purpose of the present study was to analyze the movement patterns of free-throw shooters in basketball at different skill levels. There were two points of interest. First, to explore what information can be drawn from the movement pattern and second, to examine the methodological possibilities of pattern analysis. To this end, several qualitative and quantitative methods were employed. The resulting data were converged in a triangulation. Using a special kind of ANN named Dynamically Controlled Networks (DyCoN), a 'complex feature' consisting of several isolated features (angle displacements and velocities of the articulations of the kinematic chain) was calculated. This 'complex feature' was displayed by a trajectory combining several neurons of the network, reflecting the devolution of the twelve angle measures over the time course of each shooting action. In further network analyses individual characteristics were detected, as well as movement phases. Throwing patterns were successfully classified and the stability and variability of the realized pattern were established. The movement patterns found were clearly individually shaped as well as formed by the skill level. The triangulation confirmed the individual movement organizations. Finally, a high stability of the network methods was documented. PMID:22402277

  16. Optical music recognition on the International Music Score Library Project

    NASA Astrophysics Data System (ADS)

    Raphael, Christopher; Jin, Rong

    2013-12-01

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

  17. Accepted for publication, IEEE Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition. Face Recognition: A Convolutional Neural Network Approach

    E-print Network

    Giles, C. Lee

    , and a convolutional neural network. The self-organizing map provides a quanti- zation of the image samples and Pattern Recognition. Face Recognition: A Convolutional Neural Network Approach Steve Lawrence ¢¡ £¥¤ , C methods. The system combines local image sampling, a self-organizing map neural network

  18. A content-based image retrieval method for optical colonoscopy images based on image recognition techniques

    NASA Astrophysics Data System (ADS)

    Nosato, Hirokazu; Sakanashi, Hidenori; Takahashi, Eiichi; Murakawa, Masahiro

    2015-03-01

    This paper proposes a content-based image retrieval method for optical colonoscopy images that can find images similar to ones being diagnosed. Optical colonoscopy is a method of direct observation for colons and rectums to diagnose bowel diseases. It is the most common procedure for screening, surveillance and treatment. However, diagnostic accuracy for intractable inflammatory bowel diseases, such as ulcerative colitis (UC), is highly dependent on the experience and knowledge of the medical doctor, because there is considerable variety in the appearances of colonic mucosa within inflammations with UC. In order to solve this issue, this paper proposes a content-based image retrieval method based on image recognition techniques. The proposed retrieval method can find similar images from a database of images diagnosed as UC, and can potentially furnish the medical records associated with the retrieved images to assist the UC diagnosis. Within the proposed method, color histogram features and higher order local auto-correlation (HLAC) features are adopted to represent the color information and geometrical information of optical colonoscopy images, respectively. Moreover, considering various characteristics of UC colonoscopy images, such as vascular patterns and the roughness of the colonic mucosa, we also propose an image enhancement method to highlight the appearances of colonic mucosa in UC. In an experiment using 161 UC images from 32 patients, we demonstrate that our method improves the accuracy of retrieving similar UC images.

  19. Foundations for a syntatic pattern recognition system for genomic DNA sequences

    SciTech Connect

    Searles, D.B.

    1993-03-01

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

  20. Development of Pattern Recognition Software for Tracks of Ionizing Radiation In Medipix2-Based

    E-print Network

    Vilalta, Ricardo

    Development of Pattern Recognition Software for Tracks of Ionizing Radiation In Medipix2-Based tool for the automated identification and classification of tracks of ionizing radiation as measured classification of sources of ionizing radiation as captured by the hybrid semiconductor pixel detector Medipix2

  1. Emotion Recognition by Children With Down Syndrome: Investigation of Specific Impairments and Error Patterns

    ERIC Educational Resources Information Center

    Williams, Katie R.; Wishart, Jennifer G.; Pitcairn, Tom K.; Willis, Diane S.

    2005-01-01

    The ability of children with Down syndrome to recognize expressions of emotion was compared to performance in typically developing and nonspecific intellectual disability groups matched on either MA or a performance-related measure. Our goal was to (a) resolve whether specific emotions present recognition difficulties; (b) investigate patterns of…

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

    E-print Network

    Schechner, Yoav Yosef

    2004-01-01

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

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

    EPA Science Inventory

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

  4. 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.

  5. 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…

  6. Detecting Molecular Fingerprints in Single Molecule Force Spectroscopy Using Pattern Recognition

    E-print Network

    , force spectroscopy, AFM, pattern recognition, GFP One of the most fundamental and challenging problems. The folding process of proteins is generally described as diffusion in a high dimensional energy-landscape.1) Recent advances in single molecule force spectroscopy have made it possible to explore the energy

  7. 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…

  8. 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…

  9. Foreword to the Special Issue on Pattern Recognition in Remote Sensing

    E-print Network

    Aksoy, Selim

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

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

    E-print Network

    Freeman, Walter J.

    2007-01-01

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

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

    E-print Network

    McLachlan, Geoff

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

  12. 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 ...

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

    EPA Science Inventory

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

  14. Pattern Recognition Using Genetic Programming for Classification of Diabetes and Modulation

    E-print Network

    Fernandez, Thomas

    Pattern Recognition Using Genetic Programming for Classification of Diabetes and Modulation Data as it not only reduces the dimensions of the data but also increases the classification accuracy. A genetic world classification problems (diabetes detection and modulation classification) are used to evaluate

  15. Pattern Recognition Letters 3 (1985) 191-194 May 1985 North-Holland

    E-print Network

    Masci, Frank

    1985-01-01

    - cluded to demonstrate the efficacy of this technique. Key words: Picture processing, pattern recognition be detected within an image. Common ap- plication of this technique uses feature extraction, e.g. edge detection, to form an image from which an analytical shape may be determined. For a feature F governed

  16. Clustering-based pattern recognition applied to chemical recognition using SAW array signals

    SciTech Connect

    Osbourn, G.C.; Bartholomew, J.W.; Frye, G.C.; Ricco, A.J.

    1994-05-01

    We present a new patter recognition (PR) technique for chemical identification using arrays of microsensors. The technique relies on a new empirical approach to k-dimensional cluster analysis which incorporates measured human visual perceptions of difficult 2- dimensional clusters. The method can handle nonlinear SAW array data, detects both unexpected (outlier) and unreliable array responses, and has no user-adjustable parameters. We use this technique to guide the development of arrays of thin-film-coated SAW (Surface Acoustic Wave) devices that produce optimal PR performance for distinguishing a variety of volatile organic compounds, organophosphonates and water.

  17. An associative memory readout for ESNs with applications to dynamical pattern recognition.

    PubMed

    Ozturk, Mustafa C; Principe, José C

    2007-04-01

    The use of echo state networks (ESN) to find patterns in time (dynamical pattern recognition) has been limited. This paper argues that ESNs are particularly well suited for dynamical pattern recognition and proposes a linear associative memory (LAM) as a novel readout for ESNs. From the class of LAMs, the minimum average correlation energy (MACE) filter is adopted because of its high rejection characteristics that allow its use as a detector in the automatic pattern recognition literature. In the ESN application, the MACE interprets the states of the ESN as a two-dimensional "image", one dimension being time and the other the processing element index (space). An optimal template image for each class, which associates ESN states with the class label, can be analytically computed using training data. During testing, ESN states are correlated with each template image and the class label of the template with the highest correlation is assigned to the input pattern. The ESN-MACE combination leads to a nonlinear template matcher with robust noise performance as needed in non-Gaussian, nonlinear digital communication channels. A real-world data experiment for chemical sensing with an electronic nose is included to demonstrate the validity of this approach. Moreover, the proposed readout can also be used with liquid state machines eliminating the need to convert spike trains into continuous signals by binning or low-pass filtering. PMID:17513087

  18. 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.

  19. The contribution of pattern recognition of seismic and morphostructural data to seismic hazard assessment

    E-print Network

    Peresan, Antonella; Soloviev, Alexander; Panza, Giuliano F

    2014-01-01

    The reliable statistical characterization of the spatial and temporal properties of large earthquakes occurrence is one of the most debated issues in seismic hazard assessment, due to the unavoidably limited observations from past events. We show that pattern recognition techniques, which are designed in a formal and testable way, may provide significant space-time constraints about impending strong earthquakes. This information, when combined with physically sound methods for ground shaking computation, like the neo-deterministic approach (NDSHA), may produce effectively preventive seismic hazard maps. Pattern recognition analysis of morphostructural data provide quantitative and systematic criteria for identifying the areas prone to the largest events, taking into account a wide set of possible geophysical and geological data, whilst the formal identification of precursory seismicity patterns (by means of CN and M8S algorithms), duly validated by prospective testing, provides useful constraints about impend...

  20. A modified multi-channel EMG feature for upper limb motion pattern recognition.

    PubMed

    Tsai, An-Chih; Luh, Jer-Junn; Lin, Ta-Te

    2012-01-01

    The EMG signal is a well-known and useful biomedical signal. Much information related to muscles and human motions is included in EMG signals. Many approaches have proposed various methods that tried to recognize human motion via EMG signals. However, one of the critical problems of motion pattern recognition is that the performance of recognition is easily affected by the normalization procedure and may not work well on different days. In this paper, a modified feature of the multi-channel EMG signal is proposed and the normalization procedure is also simplified by using this modified feature. To recognize motion pattern, we applied the support vector machine (SVM) to build the motion pattern recognition model. In training and validation procedures, we used the 2-DoF exoskeleton robot arm system to do the designed pose, and the multi-channel EMG signals were obtained while the user resisted the robot. Experiment results indicate that the performance of applying the proposed feature (94.9%) is better than that of conventional features. Moreover, the performances of the recognition model, which applies the modified feature to recognize the motions on different days, are more stable than other conventional features. PMID:23366705

  1. Escherichia coli O157:H7 restriction pattern recognition by artificial neural network.

    PubMed Central

    Carson, C A; Keller, J M; McAdoo, K K; Wang, D; Higgins, B; Bailey, C W; Thorne, J G; Payne, B J; Skala, M; Hahn, A W

    1995-01-01

    An artificial neural network model for the recognition of Escherichia coli O157:H7 restriction patterns was designed. In the training phase, images of two classes of E. coli isolates (O157:H7 and non-O157:H7) were digitized and transmitted to the neural network. The system was then tested for recognition of images not included in the training set. Promising results were achieved with the designed network configuration, providing a basis for further study. This application of a new generation of computation technology serves as an example of its usefulness in microbiology. PMID:8576341

  2. Understanding Complexity: Pattern Recognitions, Emergent Phenomena and Causal Coupling

    NASA Astrophysics Data System (ADS)

    Raia, F.

    2010-12-01

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

  3. Solar and space weather phenomenological forecasting using pattern recognition operators

    NASA Astrophysics Data System (ADS)

    Rosa, R.; Ramos, F.; Vijaykumar, N.; Andrade, M.; Fernandes, F.; Cecatto, J.; Sharma, A.; Sawant, H.

    Yohkoh, SOHO and HESSI satellites have shown morphological change of the coronal magnetic structures in several scales. Particularly, the soft X ray images- have revealed the existence of dynamic structures with magnetic field configuration varying from regular to complex patterns. In order to characterize the spatio- temporal evolution of such structures, a methodology is proposed in terms of matrix computational operators to quantify the amount of symmetry breaking along the gradient field evolution of the sequence of images. Characterization of symmetry breaking in the gradient field of the energy envelope has been an useful tool to understand complex plasma regimes. In this paper we introduce the application of the Gradient Pattern Analysis (GPA) technique as a new matrix computational operator for spatio-temporal plasma gradient field analysis. This operator yields a measure of the symmetry breaking and phase disorder parameters responding to the active region plasma regimes. In order to characterize the GPA performance into the context of solar physics, we apply this technique on X-ray emission measurement from solar coronal plasma observed by means of Yohkoh satellite. The preliminary results and interpretations suggest a new phenomenological approach for the spatio- temporal evolution of soft X ray active regions, mainly those whose morphology- goes from a regular to a complex magnetic configuration a companied by thec increase of the dissipated energy. We discuss the importance of this semi-empirical modelling for space weather forecasting into the context of solar-terrestrial relationship.

  4. Application of a pattern recognition technique to the prediction of tire noise

    NASA Astrophysics Data System (ADS)

    Chiu, Jinn-Tong; Tu, Fu-Yuan

    2015-08-01

    Tire treads are one of the main sources of car noise. To meet the EU's tire noise regulation ECE-R117, a new method using a pattern recognition technique is adopted in this paper to predict noise from tire tread patterns, thus facilitating the design of low-noise tires. When tires come into contact with the road surface, air pumping may occur in the grooves of tire tread patterns. Using the image of a tread pattern, a matrix is constructed by setting the patterns of tire grooves and tread blocks. The length and width of the contact patch are multiplied by weight functions. The resulting sound pressure as a function of time is subjected to a Fourier transform to simulate a 1/3-octave-band sound pressure level. A particle swarm algorithm is adopted to optimize the weighting parameters for the sound pressure in the frequency domain so that simulated values approach the measured noise level. Two sets of optimal weighting parameters associated with the length and width of the contact patch are obtained. Finally, the weight function is used to predict the tread pattern noise of tires in the same series. A comparison of the prediction and experimental results reveals that, in the 1/3-octave band of frequency (800-2000 Hz), average errors in sound pressure are within 2.5 dB. The feasibility of the proposed application of the pattern recognition technique in predicting noise from tire treads is verified.

  5. Direct Nano-Patterning With Nano-Optic Devices 

    E-print Network

    Meenashi Sundaram, Vijay

    2011-08-08

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

  6. 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

  7. Computer Analysis of the HIS Bundle Electrocardiogram: Pattern Recognition in a Constrained Environment

    PubMed Central

    Soffer, Stuart B.; Atlee, John L.; Malkinson, Carol

    1977-01-01

    The time required for and the tedious task of manually digitizing analog data in experiments designed to determine drug effects on atrioventricular conduction intervals and refractory periods (His bundle catheter electrocardiography) have indicated the need for an automated data reduction phase. A pattern recognition program designed for use with the Norland 2001 digital, programmable, calculating oscilloscope has been developed for this purpose and is described in this report. This program differs from existing pattern recognition programs for the analysis of surface ECG signals in that: 1) programming tasks were accomplished through an interpretative language operating on a series of keystrokes whose functions are microprogrammed; and, 2) the program analyzes both surface and His bundle ECG signals displayed simultaneously. The program developed for the Norland 2001 successfully detects approximately 70 per cent of the events of interest and thereby achieves a satisfactory level of performance.

  8. 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

  9. Unification of support vector machines and soft computing paradigms for pattern recognition

    NASA Astrophysics Data System (ADS)

    Li, Ying; Jiao, Licheng

    2001-09-01

    This paper analyzes support vector machines (SVMs) and several commonly used soft computing paradigms for pattern recognition including neural and wavelet networks, and fuzzy systems. Bayesian classifiers, fuzzy partitions, etc and tries to outline the similarities and differences among them. Support vector machines provide a new approach to the problem of pattern recognition with clear connections to the underlying statistical learning theory. We try to bring SVMs into the framework of the unification paradigm called the weighted radial basis function paradigm. Unifying different classes of methods has enormous advantages, such as the ability to merge all such techniques within the same system. It is hoped that this paper would provide theoretical guides for the study and applications of support vector machine and soft computing paradigms.

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

    NASA Technical Reports Server (NTRS)

    Hinton, Yolanda L.

    1999-01-01

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

  11. Interfamily transfer of a plant pattern-recognition receptor confers broad-spectrum bacterial resistance.

    PubMed

    Lacombe, Séverine; Rougon-Cardoso, Alejandra; Sherwood, Emma; Peeters, Nemo; Dahlbeck, Douglas; van Esse, H Peter; Smoker, Matthew; Rallapalli, Ghanasyam; Thomma, Bart P H J; Staskawicz, Brian; Jones, Jonathan D G; Zipfel, Cyril

    2010-04-01

    Plant diseases cause massive losses in agriculture. Increasing the natural defenses of plants may reduce the impact of phytopathogens on agricultural productivity. Pattern-recognition receptors (PRRs) detect microbes by recognizing conserved pathogen-associated molecular patterns (PAMPs). Although the overall importance of PAMP-triggered immunity for plant defense is established, it has not been used to confer disease resistance in crops. We report that activity of a PRR is retained after its transfer between two plant families. Expression of EFR (ref. 4), a PRR from the cruciferous plant Arabidopsis thaliana, confers responsiveness to bacterial elongation factor Tu in the solanaceous plants Nicotiana benthamiana and tomato (Solanum lycopersicum), making them more resistant to a range of phytopathogenic bacteria from different genera. Our results in controlled laboratory conditions suggest that heterologous expression of PAMP recognition systems could be used to engineer broad-spectrum disease resistance to important bacterial pathogens, potentially enabling more durable and sustainable resistance in the field. PMID:20231819

  12. 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.

  13. 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.

  14. Development and evaluation of pattern recognition techniques for fluorescence diagnosis of atherosclerosis

    NASA Astrophysics Data System (ADS)

    Papaodysseus, Constantinos N.; Kassis, Kyriakos A.; Gonis, Helen; Yova, Dido M.

    1996-05-01

    A field where fluorescence spectroscopy might be of great interest for diagnosis, is coronary atherosclerosis and therefore spectroscopic characterization of cardiovascular tissues has been extensively studied. Nevertheless there are several limitations in the precise interpretation of the spectroscopic differences, between normal and atherosclerotic arteries since the tissue is a complex and multilayer structure. Therefore the spectra of individual chromophores could overlap and re-absorption phenomena could occur, too. Another major difficulty arises from the necessity of convenient classification algorithms and the assessment of their feasibility to use fluorescence information, for accurate diagnosis. As a result in order to assess the feasibility of utilizing spectral information to discriminate arterial tissue type several classification algorithms were developed and evaluated. In this work the following pattern recognition techniques have been tested and evaluated: (1) Distance measure (or norm, or metric) based pattern recognition techniques. Methodologically speaking, based on the histopathological diagnosis, a training set of spectra has been classified into four different categories (healthy, fibrous, calcified, heavy calcified) and in each of these four training groups a representative spectrum has been recorded. (2) A pattern recognition method based on statistical considerations. Discrimination between either the four aforementioned classes (categories) or pairs of them is achieved since peak intensities in appropriate wavelengths appear to correlate efficiently with tissue type. The difference of each training set member from the corresponding representative has been defined by using various appropriate distance measures and the sample statistical properties for each category of the training group has been found. Appropriate statistical analysis has been performed in order to deduce the distribution of the distance measures and of the coefficients of the whole population for each one of the four categories, with at least 99% confidence interval. A validation set of samples has been used in order to test and compare the aforementioned pattern recognition algorithms. A performance comparison of the aforementioned algorithms has been undertaken.

  15. Real-time composite pattern demodulation using optical correlators

    NASA Astrophysics Data System (ADS)

    Lau, Daniel L.; Hassebrook, Laurence G.; Lu, Thomas; Chao, Tien-Hsin

    2006-05-01

    Structured light illumination refers to a technique of acquiring 3-D surface scans through triangulation between a camera and a projector. Because traditional structured-light systems use multiple patterns projected sequentially in time, SLI is not typically associated with applications involving moving surfaces. To address this problem, the authors have introduced a technique referred to as composite pattern projection which involves the combining of a set of standard SLI patterns into a continuously projected pattern such that depth can be recovered from a single, captured image. As such, composite patterns can be used for tracking moving objects in 3-D space. The problem with composite patterns, though, is the added computational complexity associated with demodulating the captured image and extract the component SLI patterns. So in this paper, we introduce a means of achieving real-time pattern demodulation through the use of optical correlators with demonstrated results achieving a processing rate of over 100 frames per second.

  16. 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.; Shochet, M.; Tang, F.; 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.

  17. 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.

  18. Evaluation Of Binary-Phase-Only-Filters For Distortion-Invariant Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Jared, David A.; Ennis, David J.; Dreskin, Sandy A.

    1988-05-01

    Conventional techniques of designing distortion-invariant filters for optical correlators presuppose the use of complex-valued filters. Synthetic-discriminant-functions (SDFs)," lock-and-tumble filters," and circular harmonic filters5 are examples of these methods. However, since programmable, complex-valued spatial-light-modulators (SLMs) do not exist and appear difficult to fabricate, the utility of these techniques for actual implementation are severely limited. On the other hand, programmable SLMs limited to quantized levels of amplitude and/or phase are presently available. Jared and Ennis6 recently proposed a modification to the conventional SDF approach which includes the filter modulation in the filter synthesis called filter-SDF (fSDF). They demonstrated that it is possible to construct a filter limited to binary modulation or phase modulation that will achieve a specified peak-correlation for a set of training images. The development of the fSDF approach was driven by the practical concern to make present-day SLMs with limited modulation capabilities functional for distortion-invariant pattern recognition. However, initial work on fSDFs did not examine the peak-correlation response for images in the distortion-range that were not members of the training set. This paper considers the performance of fSDF binary-phase-only-filters (BPOFs) for images in the distortion-range that were not members of the training set. This evaluation is essential towards understanding the number of training images necessary to span a distortion-range. As the extent of the distortion-range increases, the number of training images necessary to effectively cover the distortion-range increases. Since filters of limited modulation have an implicit information capacity, a trade-off occurs between the extent of the distortion-range and the performance of the correlator. This paper considers the nature of this trade-off for fSDF-BPOFs, and the likely constraints this trade-off will impose on a realized optical correlator. A brief review of the fSDF method is presented in Section 2. Aspects of the simulation are discussed in Section 3. The results of correlating fSDF-BPOFs with images not in the training set and the effect of increasing the distortion-range are presented in Section 4 for in-plane-rotation and out-of-plane-rotation. A discussion of these results and the conclusions reached are found in Sections 5 and 6, respectively.

  19. Binary phase only reference for invariant pattern recognition with the joint transform correlator

    NASA Astrophysics Data System (ADS)

    Butt, J. A.; Wilkinson, T. D.

    2006-05-01

    The joint transform correlator (JTC) is one of two main optical image processing architectures which provide us with a highly effective way of comparing images in a wide range of applications. Traditionally an optical correlator is used to compare an unknown input scene with a pre-captured reference image library, to detect if the reference occurs within the input. There is a new class of application for the JTC where they are used as image comparators, not having a known reference image, rather frames from a video sequence form both the input and reference. The JTC input plane is formed by combining the current frame with the previous frame in a video sequence and if the frames match, then there will be a correlation peak. If the objects move then the peaks will move (tracking) and if something has changed in the scene, then the correlation between the two frames is lost. This forms the basis of a very powerful application for the JTC in Defense and Security. Any change in the scene can be recorded and with the inherent shift invariance property of the correlator, any movement of the objects in the scene can also be detected. A major limitation of the JTC is its intolerance to rotation and scale changes in input compared to the reference images. The strength of the correlation signal decreases as the input object rotates or varies in scale relative to the reference object. We have designed binary phase only filters using the direct binary search algorithm for rotation invariant pattern recognition for a 1/f JTC. Simulation and experimental results are included. If the relative alignment of the images in the input plane is known then the desirable fringes in the resulting joint power spectrum (JPS) can be selectively enhanced during the binarisation process. This can have a highly beneficial effect on the resulting correlation intensities. For the input plane in which input and reference images are placed side by side we develop the vertical edge enhancement (VEE) technique that concentrate solely on the vertical components of the JPS during the binarisation process. Simulation and experiments proves that VEE enhances the correlation intensities and suppresses the zero order noise.

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

    PubMed

    Li, Youzhi; Rosen, Joseph

    2002-09-01

    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-dimensional function. After proper filtering, the resulting function is coded into a computer-generated hologram. When this hologram is coherently illuminated, a correlation space is reconstructed such that light peaks indicate the existence and locations of true targets in the observed 3-D scene. Experimental results and comparisons with results of another 3-D object recognition technique are presented. PMID:12216869

  1. Hand biometric recognition based on fused hand geometry and vascular patterns.

    PubMed

    Park, GiTae; Kim, Soowon

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-05-01

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

  3. Artificial convolution neural network with wavelet kernels for disease pattern recognition

    NASA Astrophysics Data System (ADS)

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

    1995-05-01

    A two-dimensional convolution neural network (CNN) with wavelet kernels (WK) has been developed for image pattern recognition. The structure of the CNN is a simplified version of the neocognitron. We used only a two-level structure and eliminated all complex-cell layers. Nets between two adjacent layers in the feature selection level of the CNN are selectively interconnected across groups. In this part of the CNN signals processing, each group in the receiving layer receives signals from a group of weights (i.e., kernels). For the forward signal propagation, the product obtained from the kernel convoluting the front layer is collected onto the corresponding matrix element of the receiving layer. In this paper, the convolution kernels of the CNN (CNN/WK) are wavelet based and are trained by a supervised manner. In the development of the CNN/WK, we forced each updated convolution kernel to be orthonormal. Therefore, features (transformed coefficients) selected on the transform domain are linearly independent. Hence, the fully connected layers in the classification level of the CNN can perform more effectively. The applications of the CNN for disease pattern recognition have been very successful. When isolated patterns were further processed by internal filtering and classification layers were built into the neural network structure, the disease patterns were more easily recognized. Although, we did not receive substantial improvement of the ROC performance using the CNN/WK, this method may assist us in the analysis of the trained kernels and eventually lead to the optimization of feature extraction in a course of disease pattern recognition.

  4. 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.

  5. Expanding the universe of cytokines and pattern recognition receptors: galectins and glycans in innate immunity.

    PubMed

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

    2011-02-01

    Effective immunity relies on the recognition of pathogens and tumors by innate immune cells through diverse pattern recognition receptors (PRRs) that lead to initiation of signaling processes and secretion of pro- and anti-inflammatory cytokines. Galectins, a family of endogenous lectins widely expressed in infected and neoplastic tissues have emerged as part of the portfolio of soluble mediators and pattern recognition receptors responsible for eliciting and controlling innate immunity. These highly conserved glycan-binding proteins can control immune cell processes through binding to specific glycan structures on pathogens and tumors or by acting intracellularly via modulation of selective signaling pathways. Recent findings demonstrate that various galectin family members influence the fate and physiology of different innate immune cells including polymorphonuclear neutrophils, mast cells, macrophages, and dendritic cells. Moreover, several pathogens may actually utilize galectins as a mechanism of host invasion. In this review, we aim to highlight and integrate recent discoveries that have led to our current understanding of the role of galectins in host-pathogen interactions and innate immunity. Challenges for the future will embrace the rational manipulation of galectin-glycan interactions to instruct and shape innate immunity during microbial infections, inflammation, and cancer. PMID:21184154

  6. Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms

    PubMed Central

    Sensinger, Jonathon W.; Lock, Blair A.; Kuiken, Todd A.

    2011-01-01

    Pattern Recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without knowledge of the intended class, thus achieving adaptation that is not cumbersome to the user, but at the cost of reduced accuracy. This study reports a novel adaptive experiment on eight subjects that allowed repeated measures post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 26% compared to the nonadapting classifier. Most unsupervised adaptation paradigms provided smaller reductions in error, due to frequent uncertainty of the correct class. One method that selected high-confidence samples showed the most practical implementation, although the other methods warrant future investigation. Supervised adaptation should be considered for incorporation into any clinically viable pattern recognition controller, and unsupervised adaptation should receive renewed interest in order to provide transparent adaptation. PMID:19497834

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

    PubMed

    Mian, Ajmal

    2011-04-11

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

  8. 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.

  9. An acidic microenvironment sets the humoral pattern recognition molecule PTX3 in a tissue repair mode.

    PubMed

    Doni, Andrea; Musso, Tiziana; Morone, Diego; Bastone, Antonio; Zambelli, Vanessa; Sironi, Marina; Castagnoli, Carlotta; Cambieri, Irene; Stravalaci, Matteo; Pasqualini, Fabio; Laface, Ilaria; Valentino, Sonia; Tartari, Silvia; Ponzetta, Andrea; Maina, Virginia; Barbieri, Silvia S; Tremoli, Elena; Catapano, Alberico L; Norata, Giuseppe D; Bottazzi, Barbara; Garlanda, Cecilia; Mantovani, Alberto

    2015-06-01

    Pentraxin 3 (PTX3) is a fluid-phase pattern recognition molecule and a key component of the humoral arm of innate immunity. In four different models of tissue damage in mice, PTX3 deficiency was associated with increased fibrin deposition and persistence, and thicker clots, followed by increased collagen deposition, when compared with controls. Ptx3-deficient macrophages showed defective pericellular fibrinolysis in vitro. PTX3-bound fibrinogen/fibrin and plasminogen at acidic pH and increased plasmin-mediated fibrinolysis. The second exon-encoded N-terminal domain of PTX3 recapitulated the activity of the intact molecule. Thus, a prototypic component of humoral innate immunity, PTX3, plays a nonredundant role in the orchestration of tissue repair and remodeling. Tissue acidification resulting from metabolic adaptation during tissue repair sets PTX3 in a tissue remodeling and repair mode, suggesting that matrix and microbial recognition are common, ancestral features of the humoral arm of innate immunity. PMID:25964372

  10. Restoration of Pattern Recognition Receptor Costimulation to Treat Chromoblastomycosis, a Chronic Fungal Infection of the Skin

    PubMed Central

    da Glória Sousa, Maria; Reid, Delyth M.; Schweighoffer, Edina; Tybulewicz, Victor; Ruland, Jürgen; Langhorne, Jean; Yamasaki, Sho; Taylor, Philip R.; Almeida, Sandro R.; Brown, Gordon D.

    2011-01-01

    Summary Chromoblastomycosis is a chronic skin infection caused by the fungus Fonsecaea pedrosoi. Exploring the reasons underlying the chronic nature of F. pedrosoi infection in a murine model of chromoblastomycosis, we find that chronicity develops due to a lack of pattern recognition receptor (PRR) costimulation. F. pedrosoi was recognized primarily by C-type lectin receptors (CLRs), but not by Toll-like receptors (TLRs), which resulted in the defective induction of proinflammatory cytokines. Inflammatory responses to F. pedrosoi could be reinstated by TLR costimulation, but also required the CLR Mincle and signaling via the Syk/CARD9 pathway. Importantly, exogenously administering TLR ligands helped clear F. pedrosoi infection in vivo. These results demonstrate how a failure in innate recognition can result in chronic infection, highlight the importance of coordinated PRR signaling, and provide proof of the principle that exogenously applied PRR agonists can be used therapeutically. PMID:21575914

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    PubMed Central

    Orr, Selinda; Ferguson, Brian; Symmons, Martyn F.; Boyle, Joseph P.; Monie, Tom P.

    2015-01-01

    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

  13. An acidic microenvironment sets the humoral pattern recognition molecule PTX3 in a tissue repair mode

    PubMed Central

    Doni, Andrea; Musso, Tiziana; Morone, Diego; Bastone, Antonio; Zambelli, Vanessa; Sironi, Marina; Castagnoli, Carlotta; Cambieri, Irene; Stravalaci, Matteo; Pasqualini, Fabio; Laface, Ilaria; Valentino, Sonia; Tartari, Silvia; Ponzetta, Andrea; Maina, Virginia; Barbieri, Silvia S.; Tremoli, Elena; Catapano, Alberico L.; Norata, Giuseppe D.; Bottazzi, Barbara; Garlanda, Cecilia

    2015-01-01

    Pentraxin 3 (PTX3) is a fluid-phase pattern recognition molecule and a key component of the humoral arm of innate immunity. In four different models of tissue damage in mice, PTX3 deficiency was associated with increased fibrin deposition and persistence, and thicker clots, followed by increased collagen deposition, when compared with controls. Ptx3-deficient macrophages showed defective pericellular fibrinolysis in vitro. PTX3-bound fibrinogen/fibrin and plasminogen at acidic pH and increased plasmin-mediated fibrinolysis. The second exon-encoded N-terminal domain of PTX3 recapitulated the activity of the intact molecule. Thus, a prototypic component of humoral innate immunity, PTX3, plays a nonredundant role in the orchestration of tissue repair and remodeling. Tissue acidification resulting from metabolic adaptation during tissue repair sets PTX3 in a tissue remodeling and repair mode, suggesting that matrix and microbial recognition are common, ancestral features of the humoral arm of innate immunity. PMID:25964372

  14. Early innate responses to pathogens: pattern recognition by unconventional human T-cells.

    PubMed

    Liuzzi, Anna Rita; McLaren, James E; Price, David A; Eberl, Matthias

    2015-10-01

    Although typically viewed as a feature of innate immune responses, microbial pattern recognition is increasingly acknowledged as a function of particular cells nominally categorized within the adaptive immune system. Groundbreaking research over the past three years has shown how unconventional human T-cells carrying invariant or semi-invariant TCRs that are not restricted by classical MHC molecules sense microbial compounds via entirely novel antigen presenting pathways. This review will focus on the innate-like recognition of non-self metabolites by V?9/V?2 T-cells, mucosal-associated invariant T (MAIT) cells and germline-encoded mycolyl-reactive (GEM) T-cells, with an emphasis on early immune responses in acute infection. PMID:26182978

  15. Early innate responses to pathogens: pattern recognition by unconventional human T-cells

    PubMed Central

    Price, David A.; Eberl, Matthias

    2015-01-01

    Although typically viewed as a feature of innate immune responses, microbial pattern recognition is increasingly acknowledged as a function of particular cells nominally categorized within the adaptive immune system. Groundbreaking research over the past three years has shown how unconventional human T-cells carrying invariant or semi-invariant TCRs that are not restricted by classical MHC molecules sense microbial compounds via entirely novel antigen presenting pathways. This review will focus on the innate-like recognition of non-self metabolites by V?9/V?2 T-cells, mucosal-associated invariant T (MAIT) cells and germline-encoded mycolyl-reactive (GEM) T-cells, with an emphasis on early immune responses in acute infection. PMID:26182978

  16. OPTICAL NANO-HOLE ARRAYS FOR MOLECULAR RECOGNITION AND DETECTION O. M. PICIU

    E-print Network

    van Vliet, Lucas J.

    OPTICAL NANO-HOLE ARRAYS FOR MOLECULAR RECOGNITION AND DETECTION O. M. PICIU , M. C. VAN DER KROGT of periodical nano-cavities in thin metal layers on glass substrates. Every cavity serves as a reaction chamber- plate, where each hole serves as an individual reaction chamber, is the application of the nano

  17. An Optically Driven Bistable Janus Rotor with Patterned Metal Coatings.

    PubMed

    Zong, Yiwu; Liu, Jing; Liu, Rui; Guo, Honglian; Yang, Mingcheng; Li, Zhiyuan; Chen, Ke

    2015-11-24

    Bistable rotation is realized for a gold-coated Janus colloidal particle in an infrared optical trap. The metal coating on the Janus particles are patterned by sputtering gold on a monolayer of closely packed polystyrene particles. The Janus particle is observed to stably rotate in an optical trap. Both the direction and the rate of rotation can be experimentally controlled. Numerical calculations reveal that the bistable rotation is the result of spontaneous symmetry breaking induced by the uneven curvature of the coating patterns on the Janus sphere. Our results thus provide a simple method to construct large quantities of fully functional rotary motors for nano- or microdevices. PMID:26481901

  18. Laser illuminator and optical system for disk patterning

    DOEpatents

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

    2000-01-01

    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.

  19. 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

  20. 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

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

    PubMed Central

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

    2006-01-01

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

  2. 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

  3. 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.

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

    SciTech Connect

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

    1981-01-01

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

  5. REMOVAL OF SPECTRO-POLARIMETRIC FRINGES BY TWO-DIMENSIONAL PATTERN RECOGNITION

    SciTech Connect

    Casini, R.; Judge, P. G.; Schad, T. A.

    2012-09-10

    We present a pattern-recognition-based approach to the problem of the removal of polarized fringes from spectro-polarimetric data. We demonstrate that two-dimensional principal component analysis can be trained on a given spectro-polarimetric map in order to identify and isolate fringe structures from the spectra. This allows us, in principle, to reconstruct the data without the fringe component, providing an effective and clean solution to the problem. The results presented in this paper point in the direction of revising the way that science and calibration data should be planned for a typical spectro-polarimetric observing run.

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

    PubMed Central

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

    2014-01-01

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

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

    USGS Publications Warehouse

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

    1985-01-01

    A suite of well-characterized humic and fulvic acids of freshwater, soil and plant origin was subjected to pyrolysis-mass spectrometry and the resulting data were analyzed by pattern recognition and factor analysis. A factor analysis plot of the data shows that the humic acids and fulvic acids can be segregated into two distinct classes. Carbohydrate and phenolic components are more pronounced in the pyrolysis products of the fulvic acids, and saturated and unsaturated hydrocarbons contribute more to the humic acid pyrolysis products. A second factor analysis plot shows a separation which appears to be based primarily on whether the samples are of aquatic or soil origin. ?? 1985.

  8. Optical pattern formation with a two-level nonlinearity

    NASA Astrophysics Data System (ADS)

    Camara, A.; Kaiser, R.; Labeyrie, G.; Firth, W. J.; Oppo, G.-L.; Robb, G. R. M.; Arnold, A. S.; Ackemann, T.

    2015-07-01

    We present an experimental and theoretical investigation of spontaneous pattern formation in the transverse section of a single retroreflected laser beam passing through a cloud of cold rubidium atoms. In contrast to previously investigated systems, the nonlinearity at work here is that of a two-level atom, which realizes the paradigmatic situation considered in many theoretical studies of optical pattern formation. In particular, we are able to observe the disappearance of the patterns at high intensity due to the intrinsic saturable character of two-level atomic transitions.

  9. Optical pattern formation with a 2-level nonlinearity

    E-print Network

    Camara, A; Labeyrie, G; Firth, W J; Oppo, G -L; Robb, G R M; Arnold, A S; Ackemann, T

    2015-01-01

    We present an experimental and theoretical investigation of spontaneous pattern formation in the transverse section of a single retro-reflected laser beam passing through a cloud of cold Rubidium atoms. In contrast to previously investigated systems, the nonlinearity at work here is that of a 2-level atom, which realizes the paradigmatic situation considered in many theoretical studies of optical pattern formation. In particular, we are able to observe the disappearance of the patterns at high intensity due to the intrinsic saturable character of 2-level atomic transitions.

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

    NASA Astrophysics Data System (ADS)

    Köhler, Andreas; Ohrnberger, Matthias; Scherbaum, Frank

    2010-09-01

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

  11. Optical and SAR data integration for automatic change pattern detection

    NASA Astrophysics Data System (ADS)

    Mishra, B.; Susaki, J.

    2014-09-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  14. 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%.

  15. All-optical 2-bit header recognition and packet switching using polarization bistable VCSELs.

    PubMed

    Hayashi, Daisuke; Nakao, Kazuya; Katayama, Takeo; Kawaguchi, Hitoshi

    2015-04-01

    We propose and evaluate an all-optical 2-bit header recognition and packet switching method using two 1.55-µm polarization bistable vertical-cavity surface-emitting lasers (VCSELs) and three optical switches. Polarization bistable VCSELs acted as flip-flop devices by using AND-gate operations of the header and set pulses, together with the reset pulses. Optical packets including 40-Gb/s non-return-to-zero pseudo-random bit-sequence payloads were successfully sent to one of four ports according to the state of two bits in the headers with a 4-bit 500-Mb/s return-to-zero format. The input pulse powers were 17.2 to 31.8 dB lower than the VCSEL output power. We also examined an extension of this method to multi-bit header recognition and packet switching. PMID:25968674

  16. Optical Imaging of Flow Pattern and Phantom

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

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

  17. In: Pattern Recognition: Theory and Application Editor: Erwin A. Zoeller, pp. 17-71

    E-print Network

    Ray, Asok

    that is instrumented with ultrasonic flaw detectors and an optical travelling microscope. Time series data of observed condition) may alter the quasi-static behavior patterns of human-engineered complex systems. This chapter Dynamics, Information Theory, and Statistical Signal Processing, where time series data from selected

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

    NASA Astrophysics Data System (ADS)

    Xie, Zhihua; Liu, Guodong

    2011-07-01

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

  19. 40 Optics & Photonics News December 2003 SPATIAL PATTERNS IN NONLINEAR OPTICS

    E-print Network

    Soljaèiæ, Marin

    in (resonant) lasers (see Fig. 1). In fact, despite the incoherence of the light, this cavity threshold40 Optics & Photonics News December 2003 SPATIAL PATTERNS IN NONLINEAR OPTICS terns of this non-resonant system exhibit spatial line narrowing with the increase of feedback, resembling the line narrow- ing

  20. 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

  1. A PATTERN RECOGNITION APPROACH TO THE PATIENT WITH A SUSPECTED MYOPATHY

    PubMed Central

    Barohn, Richard J.; Dimachkie, Mazen M.; Jackson, Carlayne E.

    2014-01-01

    Myopathies are a heterogeneous group of disorders that can be challenging to diagnose. The purpose of this review is to provide a diagnostic approach based predominantly upon the clinical history and neurologic examination. Laboratory testing that can be subsequently used to confirm the suspected diagnosis based upon this pattern recognition approach will also be discussed. Over the past decade, there have been numerous discoveries allowing clinicians to diagnose myopathies with genetic testing. Unfortunately, some of the testing, particularly molecular genetics, is extremely expensive and frequently not covered by insurance. Careful consideration of the distribution of muscle weakness and attention to common patterns of involvement in the context of other aspects of the neurologic examination and laboratory evaluation should assist the clinician in making a timely and accurate diagnosis, and sometimes can minimize the expense of further testing PMID:25037080

  2. Detection of adulteration in acetonitrile using near infrared spectroscopy coupled with pattern recognition techniques.

    PubMed

    Hu, Le-Qian; Yin, Chun-Ling; Zeng, Zhi-Peng

    2015-12-01

    In this paper, near infrared spectroscopy (NIR) in cooperation with the pattern recognition techniques were used to determine the type of neat acetonitrile and the adulteration in acetonitrile. NIR spectra were collected between 400 nm and 2498 nm. The experimental data were first subjected to analysis of principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Then support vector machine (SVM) were applied to develop classification models and the best parameter combination was selected by grid search. Under the best parameter combination, the classification accuracy rates of three types of neat acetonitrile reached 87.5%, and 100% for the adulteration with different concentration levels. The results showed that NIR spectroscopy combined with SVM could be utilized for determining the potential adulterants including water, ethanol, isopropyl alcohol, acrylonitrile, methanol, and by-products associated with the production of acetonitrile. PMID:26123603

  3. Pattern Recognition in Collective Cognitive Systems: Hybrid Human-Machine Learning (HHML) By Heterogeneous Ensembles

    E-print Network

    Dashti, Hesam T; Siahpirani, Alireza F; Tonejc, Jernej; Uilecan, Ioan V; Simas, Tiago; Miranda, Bruno; Ribeiro, Rita; Wang, Liya; Assadi, Amir H

    2010-01-01

    The ubiquitous role of the cyber-infrastructures, such as the WWW, provides myriad opportunities for machine learning and its broad spectrum of application domains taking advantage of digital communication. Pattern classification and feature extraction are among the first applications of machine learning that have received extensive attention. The most remarkable achievements have addressed data sets of moderate-to-large size. The 'data deluge' in the last decade or two has posed new challenges for AI researchers to design new, effective and accurate algorithms for similar tasks using ultra-massive data sets and complex (natural or synthetic) dynamical systems. We propose a novel principled approach to feature extraction in hybrid architectures comprised of humans and machines in networked communication, who collaborate to solve a pre-assigned pattern recognition (feature extraction) task. There are two practical considerations addressed below: (1) Human experts, such as plant biologists or astronomers, often...

  4. The optimization of technological condition in the fermentation process of glutamate by pattern recognition method.

    PubMed

    Xu, C; Chen, C; Wang, H; Sun, J

    1994-01-01

    The technological condition in the fermentation process of fermentation glutamate (such as pH value, temperature, ventilation rate, etc.) were optimized by computerized pattern recognition method. The visible optimum region may be found based on the mapping from the multi-dimensional pattern space into a plane. It is then transformed along the reciprocal direction into the original data space using Monte Carlo simulation, so the orientation of optimization and the best combination of all parameters can be determined. A new mathematical model is being proposed based on the experimental evidence in production. The transfer ratio of glucose to glutamic acid, the production capacity and the glutamic acid concentration increase 2.9%, 1.45% and 2.65% respectively by operating this optimization method. The method has been widely extended to factories and has granted in decreasing the expense of raw materials and that of the production cost. PMID:7803686

  5. Gas chromatographic organic acid profiling analysis of brandies and whiskeys for pattern recognition analysis.

    PubMed

    Park, Y J; Kim, K R; Kim, J H

    1999-06-01

    An efficient gas chromatographic profiling and pattern recognition method is described for brandy and whiskey samples according to their organic acid contents. It involves solid-phase extraction of organic acids using Chromosorb P with subsequent conversion to stable tert-butyldimethylsilyl derivatives for the direct analysis by capillary column gas chromatography and gas chromatography-mass spectrometry. A total of 12 organic acids were reproducibly identified in liquor samples (1 mL). When the GC profiles were simplified to their retention index spectra, characteristic patterns were obtained for each liquor sample as well as for each group average. Stepwise discriminant analysis provided star symbols characteristic for each liquor sample and group average. As expected, canonical discriminant analysis correctly classified 23 liquor samples studied into two groups of either brandy or whiskey. PMID:10794629

  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. 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.

  8. Design and implementation of moment invariants for pattern recognition in VLSI (very large scale integration)

    SciTech Connect

    Armstrong, G.A.; Simpson, M.L. ); Bouldin, D.W. )

    1990-01-01

    This paper describes the design of a very large scale integration (VLSI) application specific integrated circuit (ASIC) for use in pattern recognition. The pattern recognition scheme uses Hu and Maitra's algorithms for moment invariants. A prototype design was generated that resolved the long delay time of the multiplier by custom designing adder cells based on the Manchester carry chain. Use of the Manchester carry chain effectively incorporated the lookahead carry function into the adder cells. The prototype ASIC is currently being fabricated in 2.0-mm compiled simulator for metal oxide semiconductor (CMOS) technology (simulated at 20 MHz). The prototype consisted of a 4 {times} 8 multiplier and a 12-bit accumulator stage. The present ASIC design consists of a 9 {times} 26 multiplier (maximum propagation time of 50 ns) and a 48-bit accumulator stage. The final ASICs will be used in parallel at the board level to achieve the 56 MegaPixels/s (230 million operations per second (MOPs)) necessary to perform the moment invariant algorithms in real time on 512 {times} 512 pixel images with 256 grey scales. 11 refs., 3 figs., 7 tabs.

  9. Pattern recognition applied to infrared images for early alerts in fog

    NASA Astrophysics Data System (ADS)

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

    2014-09-01

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

  10. 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.

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

    PubMed Central

    Shevtsova, Ekaterina; Hansson, Christer

    2011-01-01

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

  12. Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition

    PubMed Central

    Ortiz-Catalan, Max

    2015-01-01

    Accurate descriptors of muscular activity play an important role in clinical practice and rehabilitation research. Such descriptors are features of myoelectric signals extracted from sliding time windows. A wide variety of myoelectric features have been used as inputs to pattern recognition algorithms that aim to decode motor volition. The output of these algorithms can then be used to control limb prostheses, exoskeletons, and rehabilitation therapies. In the present study, cardinality is introduced and compared with traditional time-domain (Hudgins' set) and other recently proposed myoelectric features (for example, rough entropy). Cardinality was found to consistently outperform other features, including those that are more sophisticated and computationally expensive, despite variations in sampling frequency, time window length, contraction dynamics, type, and number of movements (single or simultaneous), and classification algorithms. Provided that the signal resolution is kept between 12 and 14 bits, cardinality improves myoelectric pattern recognition for the prediction of motion volition. This technology is instrumental for the rehabilitation of amputees and patients with motor impairments where myoelectric signals are viable. All code and data used in this work is available online within BioPatRec. PMID:26578873

  13. Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition.

    PubMed

    Ortiz-Catalan, Max

    2015-01-01

    Accurate descriptors of muscular activity play an important role in clinical practice and rehabilitation research. Such descriptors are features of myoelectric signals extracted from sliding time windows. A wide variety of myoelectric features have been used as inputs to pattern recognition algorithms that aim to decode motor volition. The output of these algorithms can then be used to control limb prostheses, exoskeletons, and rehabilitation therapies. In the present study, cardinality is introduced and compared with traditional time-domain (Hudgins' set) and other recently proposed myoelectric features (for example, rough entropy). Cardinality was found to consistently outperform other features, including those that are more sophisticated and computationally expensive, despite variations in sampling frequency, time window length, contraction dynamics, type, and number of movements (single or simultaneous), and classification algorithms. Provided that the signal resolution is kept between 12 and 14 bits, cardinality improves myoelectric pattern recognition for the prediction of motion volition. This technology is instrumental for the rehabilitation of amputees and patients with motor impairments where myoelectric signals are viable. All code and data used in this work is available online within BioPatRec. PMID:26578873

  14. 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.; Crooks, R.M.; Garcia, M.E.; Peez, R.; Spindler, R.; Kaiser, M.E.

    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.

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

    PubMed

    Watanabe, Eriko; Kodate, Kashiko

    2005-02-10

    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 postprocessing times. The binary real-only matched filter is devised for the sake of face recognition, and the system is optimized by the false-rejection rate (FRR) and the false-acceptance rate (FAR), according to 300 samples selected by the biometrics guideline. From trial 1:N identification experiments with the optical parallel correlator, we acquired low error rates of 2.6% FRR and 1.3% FAR. Facial images of people wearing thin glasses or heavy makeup that rendered identification difficult were identified with this system. PMID:15751848

  16. 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

  17. Optically rewritable patterns of nuclear magnetization in gallium arsenide.

    PubMed

    King, Jonathan P; Li, Yunpu; Meriles, Carlos A; Reimer, Jeffrey A

    2012-01-01

    The control of nuclear spin polarization is important to the design of materials and algorithms for spin-based quantum computing and spintronics. Towards that end, it would be convenient to control the sign and magnitude of nuclear polarization as a function of position within the host lattice. Here we show that, by exploiting different mechanisms for electron-nuclear interaction in the optical pumping process, we are able to control and image the sign of the nuclear polarization as a function of distance from an irradiated GaAs surface. This control is achieved using a crafted combination of light helicity, intensity and wavelength, and is further tuned via use of NMR pulse sequences. These results demonstrate all-optical creation of micron scale, rewritable patterns of positive and negative nuclear polarization in a bulk semiconductor without the need for ferromagnets, lithographic patterning techniques, or quantum-confined structures. PMID:22735446

  18. Efficient Spatio-Temporal Local Binary Patterns for Spontaneous Facial Micro-Expression Recognition

    PubMed Central

    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. 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.

  20. 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

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

    E-print Network

    Hasanhodzic, Jasmina, 1979-

    2004-01-01

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

  2. 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/

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

    NASA Technical Reports Server (NTRS)

    Smyth, P.; Mellstrom, J.

    1990-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Solorza, S.; Álvarez-Borrego, J.

    2013-11-01

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

  5. 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.

  6. Specificity of Correlation Pattern Recognition Methods Application in Security Holograms Identity Control Apparatus

    NASA Astrophysics Data System (ADS)

    Zlokazov, E. Yu.; Starikov, R. S.; Odinokov, S. B.; Tsyganov, I. K.; Talalaev, V. E.; Koluchkin, V. V.

    Automatic inspection of security hologram (SH) identity is highly demanded issue due high distribution of SH worldwide to protect documents such as passports, driving licenses, banknotes etc. While most of the known approaches use inspection of SH design features none of these approaches inspect the features of its surface relief that is a direct contribution to original master matrix used for these holograms production. In our previous works we represented the device that was developed to provide SH identification by processing of coherent responses of its surface elements. Most of the algorithms used in this device are based on application of correlation pattern recognition methods. The main issue of the present article is a description of these methods application specificities.

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

    NASA Astrophysics Data System (ADS)

    Xie, Songhua; Li, Dehua; Nie, Hui

    2009-10-01

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

  8. The pattern recognition method for the CsI-RICH detector in ALICE

    NASA Astrophysics Data System (ADS)

    di Bari, Domenico; Alice Collaboration

    2003-04-01

    The High Momentum Particle Identification Detector in the ALICE experiment is based on a Ring Imaging Cherenkov (RICH) detector with a CsI photocathode. The identification of Cherenkov photons, especially in conditions of high occupancy as will be the case in Pb-Pb collisions at the Large Hadron Collider, requires efficient pattern recognition algorithms. The results of an algorithm based on the Hough transform, that maps the pad coordinate space directly to the Cherenkov angle parameter space, will be shown. The performance of the method on real events collected in the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven (USA), where a prototype of the RICH detector developed in the ALICE framework has been successfully operated, will also be described.

  9. Offline accuracy: A potentially misleading metric in myoelectric pattern recognition for prosthetic control.

    PubMed

    Ortiz-Catalan, Max; Rouhani, Faezeh; Branemark, Rickard; Hakansson, Bo

    2015-08-01

    Offline accuracy has been the preferred performance measure in myoelectric pattern recognition (MPR) for the prediction of motion volition. In this study, different metrics relating the fundamental binary prediction outcomes were analyzed. Our results indicate that global accuracy is biased by 1) the unbalanced number of possible true positive and negative outcomes, and 2) the almost perfect specificity and negative predicted value, which were consistently found across algorithms, topologies, and movements (individual and simultaneous). Therefore, class-specific accuracy is advisable instead. Additionally, we propose the use of precision (positive predictive value) and sensitivity (recall) as a complement to accuracy to better describe the discrimination capabilities of MPR algorithms, as these consider the effect of false predictions. However, all the studied offline metrics failed to predict real-time decoding, and therefore real-time testing continue to be necessary to truly evaluate the clinical usability of MPR. PMID:26736467

  10. Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database

    E-print Network

    Proctor, D D

    2006-01-01

    Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This report presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low resolution data. 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. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the R...

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

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

    PubMed

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

    2015-01-01

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

  13. Pattern Recognition Pergamon Press 1973. Vol. 5, pp. 199-211. Printed in Great Britain The "Rubber-Mask" Technique II.

    E-print Network

    Widrow, Bernard

    Pattern Recognition Pergamon Press 1973. Vol. 5, pp. 199-211. Printed in Great Britain The "Rubber to date, and relates the rubber mask technique to previous work. A scheme for incorporating flexible by flexible matching, is also presented. Flexible templates Rubber masks Pattern recognition and memory system

  14. 7-10 October, 2015 Aachen Germany GCPR 2015 is the 37th annual symposium of the German Association for Pattern Recognition

    E-print Network

    Kobbelt, Leif

    topics Image/video processing, analysis, and computer vision Machine learning and pattern recognition, and applications of pattern recognition, machine learning and computer vision. http://gcpr2015.rwth-aachen.de GCPR photos: Robert Menzel Call for papers Authors are invited to submit high-quality papers presenting

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

    E-print Network

    Vese, Luminita A.

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

  16. 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

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

    SciTech Connect

    Searles, D.B.

    1993-03-01

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

  18. British Machine Vision Association, Applied Vision Association, and Society for Pattern Recognition Image Features & Statistics Symposium 2005

    E-print Network

    Rajashekar, Umesh

    British Machine Vision Association, Applied Vision Association, and Society for Pattern Recognition on a given input image. This has applications for machine vision, auto-foveated video compression properties are of particular interest due to their applicability to biologically motivated artificial vision

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

    NASA Technical Reports Server (NTRS)

    Heydorn, R. D.

    1984-01-01

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

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

    EPA Science Inventory

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

  1. Robust Optical Recognition of Cursive Pashto Script Using Scale, Rotation and Location Invariant Approach.

    PubMed

    Ahmad, Riaz; Naz, Saeeda; Afzal, Muhammad Zeshan; Amin, Sayed Hassan; Breuel, Thomas

    2015-01-01

    The presence of a large number of unique shapes called ligatures in cursive languages, along with variations due to scaling, orientation and location provides one of the most challenging pattern recognition problems. Recognition of the large number of ligatures is often a complicated task in oriental languages such as Pashto, Urdu, Persian and Arabic. Research on cursive script recognition often ignores the fact that scaling, orientation, location and font variations are common in printed cursive text. Therefore, these variations are not included in image databases and in experimental evaluations. This research uncovers challenges faced by Arabic cursive script recognition in a holistic framework by considering Pashto as a test case, because Pashto language has larger alphabet set than Arabic, Persian and Urdu. A database containing 8000 images of 1000 unique ligatures having scaling, orientation and location variations is introduced. In this article, a feature space based on scale invariant feature transform (SIFT) along with a segmentation framework has been proposed for overcoming the above mentioned challenges. The experimental results show a significantly improved performance of proposed scheme over traditional feature extraction techniques such as principal component analysis (PCA). PMID:26368566

  2. Robust Optical Recognition of Cursive Pashto Script Using Scale, Rotation and Location Invariant Approach

    PubMed Central

    Ahmad, Riaz; Naz, Saeeda; Afzal, Muhammad Zeshan; Amin, Sayed Hassan; Breuel, Thomas

    2015-01-01

    The presence of a large number of unique shapes called ligatures in cursive languages, along with variations due to scaling, orientation and location provides one of the most challenging pattern recognition problems. Recognition of the large number of ligatures is often a complicated task in oriental languages such as Pashto, Urdu, Persian and Arabic. Research on cursive script recognition often ignores the fact that scaling, orientation, location and font variations are common in printed cursive text. Therefore, these variations are not included in image databases and in experimental evaluations. This research uncovers challenges faced by Arabic cursive script recognition in a holistic framework by considering Pashto as a test case, because Pashto language has larger alphabet set than Arabic, Persian and Urdu. A database containing 8000 images of 1000 unique ligatures having scaling, orientation and location variations is introduced. In this article, a feature space based on scale invariant feature transform (SIFT) along with a segmentation framework has been proposed for overcoming the above mentioned challenges. The experimental results show a significantly improved performance of proposed scheme over traditional feature extraction techniques such as principal component analysis (PCA). PMID:26368566

  3. Improved local ternary patterns for automatic target recognition in infrared imagery.

    PubMed

    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

  4. 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

  5. Inhibition of pattern recognition receptor-mediated inflammation by bioactive phytochemicals.

    PubMed

    Zhao, Ling; Lee, Joo Y; Hwang, Daniel H

    2011-06-01

    Emerging evidence reveals that pattern-recognition receptors (PRRs), Toll-like receptors (TLRs), and nucleotide-binding oligomerization domain proteins (NODs) mediate both infection-induced and sterile inflammation by recognizing pathogen-associated molecular patterns and endogenous molecules, respectively. PRR-mediated chronic inflammation is a determinant for the development and progression of chronic diseases including cancer, atherosclerosis, and insulin resistance. Recent studies demonstrated that certain phytochemicals inhibit PRR-mediated pro-inflammation. Curcumin, helenalin, and cinnamaldehyde with ?, ?-unsaturated carbonyl groups, or sulforaphane with an isothiocyanate group, inhibit TLR4 activation by interfering with cysteine residue-mediated receptor dimerization, while resveratrol, with no unsaturated carbonyl group, did not. Similarly, curcumin, parthenolide, and helenalin, but not resveratrol and (-)-epigallocatechin-3-gallate (EGCG), also inhibit NOD2 activation by interfering with NOD2 dimerization. In contrast, resveratrol, EGCG, luteolin, and structural analogs of luteolin specifically inhibit TLR3 and TLR4 signaling by targeting TANK binding kinase 1 (TBK1) and receptor interacting protein 1 (RIP1) in Toll/IL-1 receptor domain-containing adaptor inducing IFN-? (TRIF) complex. Together, these results suggest that PRRs and downstream signaling components are molecular targets for dietary strategies to reduce PRR-mediated chronic inflammation and consequent risks of chronic diseases. PMID:21631512

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

    PubMed Central

    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. Characterization of edible seaweed harvested on the Galician coast (northwestern Spain) using pattern recognition techniques and major and trace element data.

    PubMed

    Romarís-Hortas, Vanessa; García-Sartal, Cristina; Barciela-Alonso, María Carmen; Moreda-Piñeiro, Antonio; Bermejo-Barrera, Pilar

    2010-02-10

    Major and trace elements in North Atlantic seaweed originating from Galicia (northwestern Spain) were determined by using inductively coupled plasma-optical emission spectrometry (ICP-OES) (Ba, Ca, Cu, K, Mg, Mn, Na, Sr, and Zn), inductively coupled plasma-mass spectrometry (ICP-MS) (Br and I) and hydride generation-atomic fluorescence spectrometry (HG-AFS) (As). Pattern recognition techniques were then used to classify the edible seaweed according to their type (red, brown, and green seaweed) and also their variety (Wakame, Fucus, Sea Spaghetti, Kombu, Dulse, Nori, and Sea Lettuce). Principal component analysis (PCA) and cluster analysis (CA) were used as exploratory techniques, and linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA) were used as classification procedures. In total, t12 elements were determined in a range of 35 edible seaweed samples (20 brown seaweed, 10 red seaweed, 4 green seaweed, and 1 canned seaweed). Natural groupings of the samples (brown, red, and green types) were observed using PCA and CA (squared Euclidean distance between objects and Ward method as clustering procedure). The application of LDA gave correct assignation percentages of 100% for brown, red, and green types at a significance level of 5%. However, a satisfactory classification (recognition and prediction) using SIMCA was obtained only for red seaweed (100% of cases correctly classified), whereas percentages of 89 and 80% were obtained for brown seaweed for recognition (training set) and prediction (testing set), respectively. PMID:20063888

  8. Intelligent word-based text recognition

    NASA Astrophysics Data System (ADS)

    Hoenes, Frank; Bleisinger, Rainer; Dengel, Andreas R.

    1991-02-01

    STRACT The need for making information within paper documents available for computers increases steadily. In this paper we present a system which is capable to read and to simply understand address blocks of business letters. It is based on optical word recognition (OWR) techniques uses feature recognition methods based on word shapes and is largly independent from different fonts and sizes. Even uncertainly recognized words can be identified using a dictionary and a specific verification algorithm. Additionally recognition accuracy is improved considering different knowledge layers like address syntax and logical dictionaries. Keywords: Text recognition document layout classification text analysis pattern recognition intelligent interfaces

  9. 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?

  10. Pattern recognition analysis of the turbulent flow past a backward facing step

    NASA Astrophysics Data System (ADS)

    Scarano, F.; Benocci, C.; Riethmuller, M. L.

    1999-12-01

    A pattern recognition technique for the investigation of large-scale coherent structures, is applied to analyze the turbulent separated flow over a backward facing step (BFS) at a Reynolds number Reh=5.0×103. The instantaneous two-dimensional velocity distribution is obtained by means of digital particle image velocimetry (D-PIV) measurements. High spatial resolution (?r/h=1/25) is achieved with the application of an iterative window refinement image processing algorithm. The measurement plane is oriented in order to investigate spanwise aligned vortices footprints. The detection algorithm is based on velocity pattern spatial cross correlation. An additional isotropy condition is imposed to improve the detection of vortices and shear layer. The structure of the shear layer emanating from the step edge is examined emphasizing the role of coherent fluctuations with a length scale d ranging from 0.12 h to 0.44 h. A characteristic statistical spatial occurrence is found for the educed spanwise-aligned rollers: a quasi-linear spreading region extends from x/h=0.8 up to x/h=3.5. Within the same region the production of turbulent kinetic energy exhibits a maximum. At smaller scale, the vortices show a significant presence of counter-rotating structures inside the free shear layer suggesting that the spanwise rollers undergo early three dimensional instability and breakdown within a few step units. Conditional data averaging is also applied to the results and structural properties (coherent velocity, vorticity and turbulence production) are highlighted: close to the step edge the coherent vorticity distribution is strongly distorted showing an intense interaction between the rollers and the shear layer. A roughly circular pattern is recovered downstream x/h=4.

  11. Modeling Tsi Variations Using Automated Pattern Recognition Software On Mount Wilson Data

    NASA Astrophysics Data System (ADS)

    Parker, D. G.; Ulrich, R. K.; Bertello, L.; Boyden, J. E.; Pap, J. M.

    2008-12-01

    This poster presents the results of using the AutoClass software, a Bayesian finite mixture model based pattern recognition program developed by Cheeseman and Stutz (1996), on Mount Wilson Solar Observatory (MWO) intensity and magnetogram images to identify spatially resolved areas on the solar surface associated with TSI emissions. Using indices based on the resolved patterns identified by AutoClass from MWO images, and a linear regression fit of those indices to satellite observations of TSI, we were able to model the satellite observations from the MWO data with a correlation of better than 0.96 for the period 1996 to 2007. The association of the spatial surface regional patterns identified by AutoClass with the indices developed from them also allows construction of spatially resolved images of the Sun as it would be "seen" by TSI measuring instruments like Virgo if they were able to capture resolved images. This approach holds out the possibility of creating an on-going, accurate, independent estimate of TSI variations from ground based observations which could be used to compare, and identify the sources of disagreement among, TSI observations from the various satellite instruments and to fill in gaps in the satellite record. Further, the spatial resolution of these "images" should assist in identifying with greater accuracy the particular solar surface regions associated with TSI variations. Also, since the particular set of MWO data on which this analysis is based is available on a daily basis back to at least 1985, and on an intermittent basis before then, it may be possible to construct an independent estimate of TSI emission at several solar minima to ascertain if there has been any significant increase or decrease, a topic of significance to determining what part, if any, solar TSI variations play in global warming. Cheeseman, P. & Stutz, J.,1996, in Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamny (Eds.). (AAAI Press), p.61

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

    NASA Technical Reports Server (NTRS)

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

    2002-01-01

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

  13. [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

  14. 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.

  15. What can neuromorphic event-driven precise timing add to spike-based pattern recognition?

    PubMed

    Akolkar, Himanshu; Meyer, Cedric; Clady, Zavier; Marre, Olivier; Bartolozzi, Chiara; Panzeri, Stefano; Benosman, Ryad

    2015-03-01

    This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray levels into spike timings is currently at the basis of almost every spike-based modeling of biological visual systems. The use of images naturally leads to generating incorrect artificial and redundant spike timings and, more important, also contradicts biological findings indicating that visual processing is massively parallel, asynchronous with high temporal resolution. A new concept for acquiring visual information through pixel-individual asynchronous level-crossing sampling has been proposed in a recent generation of asynchronous neuromorphic visual sensors. Unlike conventional cameras, these sensors acquire data not at fixed points in time for the entire array but at fixed amplitude changes of their input, resulting optimally sparse in space and time-pixel individually and precisely timed only if new, (previously unknown) information is available (event based). This letter uses the high temporal resolution spiking output of neuromorphic event-based visual sensors to show that lowering time precision degrades performance on several recognition tasks specifically when reaching the conventional range of machine vision acquisition frequencies (30-60 Hz). The use of information theory to characterize separability between classes for each temporal resolution shows that high temporal acquisition provides up to 70% more information that conventional spikes generated from frame-based acquisition as used in standard artificial vision, thus drastically increasing the separability between classes of objects. Experiments on real data show that the amount of information loss is correlated with temporal precision. Our information-theoretic study highlights the potentials of neuromorphic asynchronous visual sensors for both practical applications and theoretical investigations. Moreover, it suggests that representing visual information as a precise sequence of spike times as reported in the retina offers considerable advantages for neuro-inspired visual computations. PMID:25602775

  16. Calibration of zoom lens with virtual optical pattern

    NASA Astrophysics Data System (ADS)

    Arfaoui, Aymen; Thibault, Simon; Desaulniers, Pierre

    2015-05-01

    The accuracy of calibration patterns, their fabrication, and their setup are some of the important challenges in a zoom-lens camera calibration process. We address these problems by using a cross-diffractive optical element, which generates a virtual, dense, and robust calibration grid. We show that a 33×33 calibration grid provides enough control points to fill the entire field of view for 10 to 20× zoom lenses. We show that the calibration of a zoom camera at infinity can be done using this method. A polynomial function has been used to model the variation of the intrinsic calibration parameters over the zoom range. The obtained calibration model has also been validated using a well-known target pattern.

  17. Pattern Recognition 35 (2002) 28012821 www.elsevier.com/locate/patcog

    E-print Network

    Ramachandran, Ravi

    2002-01-01

    October 2001 Abstract Speaker recognition refers to the concept of recognizing a speaker by his=her voice to the concept of recogniz- ing a speaker by his=her voice or speech samples. In auto- matic speech recognition. In automatic speaker recognition, an algorithm generates a hypothesis concerning the speaker's identity

  18. Monitoring of facial stress during space flight: Optical computer recognition combining discriminative and generative methods

    NASA Astrophysics Data System (ADS)

    Dinges, David F.; Venkataraman, Sundara; McGlinchey, Eleanor L.; Metaxas, Dimitris N.

    2007-02-01

    Astronauts are required to perform mission-critical tasks at a high level of functional capability throughout spaceflight. Stressors can compromise their ability to do so, making early objective detection of neurobehavioral problems in spaceflight a priority. Computer optical approaches offer a completely unobtrusive way to detect distress during critical operations in space flight. A methodology was developed and a study completed to determine whether optical computer recognition algorithms could be used to discriminate facial expressions during stress induced by performance demands. Stress recognition from a facial image sequence is a subject that has not received much attention although it is an important problem for many applications beyond space flight (security, human-computer interaction, etc.). This paper proposes a comprehensive method to detect stress from facial image sequences by using a model-based tracker. The image sequences were captured as subjects underwent a battery of psychological tests under high- and low-stress conditions. A cue integration-based tracking system accurately captured the rigid and non-rigid parameters of different parts of the face (eyebrows, lips). The labeled sequences were used to train the recognition system, which consisted of generative (hidden Markov model) and discriminative (support vector machine) parts that yield results superior to using either approach individually. The current optical algorithm methods performed at a 68% accuracy rate in an experimental study of 60 healthy adults undergoing periods of high-stress versus low-stress performance demands. Accuracy and practical feasibility of the technique is being improved further with automatic multi-resolution selection for the discretization of the mask, and automated face detection and mask initialization algorithms.

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

    NASA Technical Reports Server (NTRS)

    Smith, William L.; Ebert, Elizabeth

    1990-01-01

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

  20. All-optical recognition method of double two-dimensional optical orthogonal codes-based labels using four-wave mixing.

    PubMed

    Zhang, Chongfu; Wang, Leyang; Perumal, Sathishkumar; Qiu, Kun; Zhou, Heng

    2011-08-01

    A novel all-optical label recognition method is proposed and demonstrated experimentally which is based on fiber Bragg gratings (FBGs)-based encoder/decoder and semiconductor optical amplifier (SOA). In this scheme, the optical label is firstly decoded properly, the decoded signal then generates the 1st and the 2nd order four-wave mixing (FWM) effect in different SOA, any of the frequencies achieved by the 2nd order FWM is extracted to recognize the optical label. The proposed solution can favor hardware simplicity over bandwidth efficiency in order to achieve the double two-dimensional optical orthogonal codes (2D-OOCs)-based optical label recognition in an optical packet switching (OPS) system where the bandwidth efficiency can be improved by FWM effect in SOA to achieve optical label processing and reasonable spacing of wavelengths for the payloads and optical label. The feasibility of the proposed method is validated by two experiments of the double 2D-OOCs-based optical label generation and recognition, the effect of the optical label on the payloads is also considered. These results show that the proposed method can (1) reduce effectively the code auto-correlation /cross-correlation requirements of the optical label identification and remove the cross-correlation pulses after optical decoding, (2) increase greatly the coding capacity and the number of the available optical labels, (3) improve the reliability and bandwidth efficiency of the optical label identification. The experimental results also show that the optical label has a high extinction ratio and can be operated easily. PMID:21934855

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

    NASA Astrophysics Data System (ADS)

    Intriligator, M.

    2011-12-01

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

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

    PubMed Central

    Deretic, Vojo

    2011-01-01

    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 alarming 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 viruses, 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

  3. 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.

  4. 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

  5. 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.

  6. Computer-assisted studies of molecular structure and genotoxic activity by pattern recognition techniques.

    PubMed Central

    Stouch, T R; Jurs, P C

    1985-01-01

    Often a compound's biological activity is determined by complex relationships between its structural components. Such a relationship often can only be adequately described and exploited by multivariate structure-activity relationship (SAR) studies that can deal with many variables simultaneously. Pattern recognition (PR) is a multivariate technique that is well suited for the qualitative, active-inactive, data that is often supplied by biological assays. PR studies of compounds of known activity can yield information that will allow the prediction of the activity of untested compounds. ADAPT is a computerized system that was developed for such PR-SAR studies. A general introduction to this field is presented and the methodology used for such a study is described in the context of an actual study of mutagenic compounds. The data requirements, descriptor generation, and the details of a PR study are discussed. In addition, the example study was chosen to highlight the problems that may occur if a study is not well formulated and carefully executed. Current work and future plans for computerized mutagen screening are discussed. PMID:3905380

  7. 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.

  8. 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.

  9. A pattern recognition approach to spasmodic dysphonia and muscle tension dysphonia automatic classification.

    PubMed

    Schlotthauer, Gastón; Torres, María Eugenia; Jackson-Menaldi, María Cristina

    2010-05-01

    Spasmodic dysphonia (SD) and muscle tension dysphonia (MTD) are two voice disorders that present similar characteristics. Usually, they can be differentiated only by experienced voice clinicians. There are many reasons that support the idea that SD is a neurological disease, requiring surgical treatments or, more usually, laryngeal botulinum toxin A injections as a therapeutic option. On the other hand, MTD is a functional disorder correctable with voice therapy. The importance of a correct diagnosis of these two disorders is critical at the treatment-selection moment. In this article, we present and compare the results of neural network and support vector machine-based methods that can help the clinicians to confirm their diagnosis. As a preliminary approach to the problem, we used only a sustained vowel /a/ to extract eight acoustic parameters. Then, a pattern recognition algorithm classifies the voice as normal, SD, or MTD. For comparison with previous works, we also separated the voices into normal and pathological (SD and MTD) voices with the methods proposed here. The results overcome the best classification rates between normal and pathological voices that have been previously reported, and demonstrate that our methods are very effective in distinguishing between MTD and SD. PMID:20346617

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

    PubMed

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

    2015-02-01

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

  11. 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.

  12. 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.

  13. Telehealth streams reduction based on pattern recognition techniques for events detection and efficient storage in EHR.

    PubMed

    Henriques, J; Rocha, T; Paredes, S; de Carvalho, P

    2013-01-01

    This work proposes a framework for telehealth streams analysis, founded on a pattern recognition technique that evaluates the similarity between multi-sensorial biosignals. The strategy combines the Haar wavelet with the Karhunen-Loève transforms to describe biosignals by means of a reduced set of parameters. These, that reflect the dynamic behavior of the biosignals, can support the detection of relevant clinical conditions. Moreover, the simplicity and fast execution of the proposed approach allow its application in real-time operation, as well as provide a practical way to manage historical electronic health records: i) common and uncommon behaviors can be distinguished; ii) the creation of different models, tailored to specific conditions can be efficiently stored. The efficiency of the methodology is assessed through its performance analysis, namely by computing the required number of operations and the compression rate. Its effectiveness is evaluated in the prediction of decompensation episodes using biosignals daily collected in the myHeart study (blood pressure, weight, respiration and heart rates). PMID:24111477

  14. Use of a pattern recognition technique to control a multifunctional prosthesis.

    PubMed

    Aghili, F; Haghpanahi, M

    1995-05-01

    Various kinds of command source can be used to control an above-elbow prosthesis. But none of them can be used to perform a specified task easily. The research is devoted to investigation of the potential effectiveness of applying the kinematic data of the shoulder joint to control an upper-limb prosthesis. Using these data as input signals, an appropriate signal processing technique, pattern recognition, is utilised to derive control commands. The purpose of the investigation is to use these commands to control a multifunctional prosthesis so that an amputee can perform a few tasks. For testing performance accuracy, a goniometer is worn by a subject with intact arm. It is also interfaced to a digital computer. Next, he is asked to do one of the predefined tasks for which the joint angle trajectories have already been derived and stored. Almost as soon as the shoulder joint angles are sampled and sent to the computer, the program calculates the elbow and wrist angles. These values are compared with actual elbow and wrist angles, which are monitored by the goniometer. PMID:7666702

  15. A Fundamental Study on Spectrum Center Estimation of Solar Spectral Irradiation by the Statistical Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Iijima, Aya; Suzuki, Kazumi; Wakao, Shinji; Kawasaki, Norihiro; Usami, Akira

    With a background of environmental problems and energy issues, it is expected that PV systems will be introduced rapidly and connected with power grids on a large scale in the future. For this reason, the concern to which PV power generation will affect supply and demand adjustment in electric power in the future arises and the technique of correctly grasping the PV power generation becomes increasingly important. The PV power generation depends on solar irradiance, temperature of a module and solar spectral irradiance. Solar spectral irradiance is distribution of the strength of the light for every wavelength. As the spectrum sensitivity of solar cell depends on kind of solar cell, it becomes important for exact grasp of PV power generation. Especially the preparation of solar spectral irradiance is, however, not easy because the observational instrument of solar spectral irradiance is expensive. With this background, in this paper, we propose a new method based on statistical pattern recognition for estimating the spectrum center which is representative index of solar spectral irradiance. Some numerical examples obtained by the proposed method are also presented.

  16. 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.

  17. Differential expression of pattern recognition receptors in the three pathological forms of sheep paratuberculosis.

    PubMed

    Nalubamba, King; Smeed, Jennifer; Gossner, Anton; Watkins, Craig; Dalziel, Robert; Hopkins, John

    2008-05-01

    Paratuberculosis is a chronic inflammatory disease of the gut caused by Mycobacterium avium subspecies paratuberculosis. Three forms have been described in sheep--paucibacillary, multibacillary and asymptomatic. The pauci- and multibacillary forms are characterized by type 1 and type 2 immune responses respectively; asymptomatic animals have no clinical signs or pathology. What determines this polarization is unknown, although pattern recognition receptors (PRR) have been implicated in other mycobacterial diseases. To investigate this in sheep paratuberculosis we used real-time RT-PCR to quantify the expression of fifteen PRR and adaptor genes from forty infected and nine control animals. These data show that there is a relationship between the different pathological forms and PRR transcript profiles. Nine PRRs were up-regulated in asymptomatic animals; with TLR9 being significantly raised in relation to the other three groups. Comparison of the three infected groups showed increases in many PRRs, with CARD15 and Dectin-2 being particularly high in both diseased groups. Significant differences between the pauci- and multibacillary animals included TLR2, CD14 and Dectin-1. Sequence analysis of TLR2 exon 2 and CARD15 exon 11 in the forty animals failed to identify any relationship between SNPs and pathological form. PMID:18457974

  18. Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database

    E-print Network

    D. D. Proctor

    2006-05-03

    Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This report presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low resolution data. 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. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. 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.

  19. 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

  20. Comparison Of Eigenvector-Based Statistical Pattern Recognition Algorithms For Hybrid Processing

    NASA Astrophysics Data System (ADS)

    Tian, Q.; Fainman, Y.; Lee, Sing H.

    1989-02-01

    The pattern recognition algorithms based on eigenvector analysis (group 2) are theoretically and experimentally compared in this part of the paper. Group 2 consists of Foley-Sammon (F-S) transform, Hotelling trace criterion (HTC), Fukunaga-Koontz (F-K) transform, linear discriminant function (LDF) and generalized matched filter (GMF). It is shown that all eigenvector-based algorithms can be represented in a generalized eigenvector form. However, the calculations of the discriminant vectors are different for different algorithms. Summaries on how to calculate the discriminant functions for the F-S, HTC and F-K transforms are provided. Especially for the more practical, underdetermined case, where the number of training images is less than the number of pixels in each image, the calculations usually require the inversion of a large, singular, pixel correlation (or covariance) matrix. We suggest solving this problem by finding its pseudo-inverse, which requires inverting only the smaller, non-singular image correlation (or covariance) matrix plus multiplying several non-singular matrices. We also compare theoretically the effectiveness for classification with the discriminant functions from F-S, HTC and F-K with LDF and GMF, and between the linear-mapping-based algorithms and the eigenvector-based algorithms. Experimentally, we compare the eigenvector-based algorithms using a set of image data bases each image consisting of 64 x 64 pixels.

  1. Dynamic Assessment of Water Quality Based on a Variable Fuzzy Pattern Recognition Model

    PubMed Central

    Xu, Shiguo; Wang, Tianxiang; Hu, Suduan

    2015-01-01

    Water quality assessment is an important foundation of water resource protection and is affected by many indicators. The dynamic and fuzzy changes of water quality lead to problems for proper assessment. This paper explores a method which is in accordance with the water quality changes. The proposed method is based on the variable fuzzy pattern recognition (VFPR) model and combines the analytic hierarchy process (AHP) model with the entropy weight (EW) method. The proposed method was applied to dynamically assess the water quality of Biliuhe Reservoir (Dailan, China). The results show that the water quality level is between levels 2 and 3 and worse in August or September, caused by the increasing water temperature and rainfall. Weights and methods are compared and random errors of the values of indicators are analyzed. It is concluded that the proposed method has advantages of dynamism, fuzzification and stability by considering the interval influence of multiple indicators and using the average level characteristic values of four models as results. PMID:25689998

  2. Evaluation of the use of fiber optic sensors in identification of fresco fracturing patterns

    NASA Astrophysics Data System (ADS)

    Glisic, Branko; Sigurdardottir, Dorotea; Dobkin, David P.

    2015-04-01

    Ageing of materials and extreme events tend to damage structures, and ancient historical monuments are particularly vulnerable due to their age and long-term exposure to adverse events and influences. As an example, the wall paintings (frescoes) from the seventeenth century BCE found at the archaeological site of Akrotiri (Santorini, Greece) were recovered from volcanic ash in fragments with dimensions ranging from a few centimeters to a few decimeters. Identification of the fracturing patterns is helpful to the process of piecing together the fragments of frescos. Previous work has involved looking at fracturing patterns in frescos that have been reassembled. Recent work has looked at the process by which fractures develop. Current identification techniques involve experimental study of fracture development on plaster molds using a high-speed camera combined with sophisticated algorithms for pattern recognition. However, the use of a high-speed camera is challenging due to very demanding data processing and analysis and some inaccuracies in identification of fracture initialization generated by light conditions. This paper aims to evaluate whether or not short-gauge fiber optic sensors (FOS) based on Fiber Brag-Gratings (FBG), can be used to help identify the fracturing patterns of falling frescoes as a complement to high-speed cameras. In total four tests were performed using surface and embedded sensors on various plaster molds. The data taken by sensors installed on the surface of the mold were more complex to analyze and interpret than the data taken by embedded sensors, since the former reflected combined influence from fracture and bending. While their practicality is challenged by cost, moderately dense arrays of embedded FOS are found to be a plausible complement to the high speed-camera in the experiments.

  3. Monitoring mental work and pattern recognition of a human brain with a functional near-infrared imager

    NASA Astrophysics Data System (ADS)

    Chen, Weiguo; Zeng, Shaoqun; Luo, Qingming; Gong, Hui; Yang, Zhongzhong; Guan, Lingchu; Chance, Britton

    1999-03-01

    A NIRS imager is used as a real time monitor in psychological test to record the response in blood oxyhemoglobin state and blood flow of the frontal gyri of human subject. The imager has 9 lamps and 4 dual detector pairs and an area of 9*4 cm. In mental work and pattern recognition test, we recorded oxygen consumption and blood flow changes of the volunteer's frontal gyri. The psychological results showed that down part of the left frontal gyri has intensive relation with pattern recognition and has definite boundaries. However, the mental work involved more zones of frontal gyri and it may be a more complicated think model. The results also suggested that brain have an exquisite and complicated adjust ability. As a result, the oxygen supplement in excited area increased as the neuron excited.

  4. Oxidative burst and nitric oxide responses in carp macrophages induced by zymosan, MacroGard(®) and selective dectin-1 agonists suggest recognition by multiple pattern recognition receptors.

    PubMed

    Pietretti, D; Vera-Jimenez, N I; Hoole, D; Wiegertjes, G F

    2013-09-01

    ?-Glucans are glucose polymers that are found in the cell walls of plants, bacteria, certain fungi, mushrooms and the cell wall of baker's yeast. In mammals, myeloid cells express several receptors capable of recognizing ?-glucans, with the C-type lectin receptor dectin-1 in conjunction with Toll-like receptor 2 (TLR2), considered key receptors for recognition of ?-glucan. In our studies to determine the possible involvement of these receptors on carp macrophages a range of sources of ?-glucans were utilized including particulate ?-glucan preparations of baker's yeast such as zymosan, which is composed of insoluble ?-glucan and mannan, and MacroGard(®), a ?-glucan-based feed ingredient for farmed animals including several fish species. Both preparations were confirmed TLR2 ligands by measuring activation of HEK293 cells transfected with human TLR2 and CD14, co-transfected with a secreted embryonic alkaline phosphatase (SEAP) reporter gene. In addition, dectin-1-specific ligands in mammals i.e. zymosan treated to deplete the TLR-stimulating properties and curdlan, were monitored for their effects on carp macrophages by measuring reactive oxygen and nitrogen radicals production, as well as cytokine gene expression by real-time PCR. Results clearly show the ability of carp macrophages to strongly react to particulate ?-glucans with an increase in the production of reactive oxygen and nitrogen radicals and an increase in cytokine gene expression, in particular il-1?, il-6 and il-11. We identified carp il-6, that was previously unknown. In addition, carp macrophages are less, but not unresponsive to selective dectin-1 agonists, suggesting recognition of ?-glucans by multiple pattern recognition receptors that could include TLR but also non-TLR receptors. Candidate receptors for recognition of ?-glucans are discussed. PMID:23831551

  5. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics.

    PubMed

    Wolfers, Thomas; Buitelaar, Jan K; Beckmann, Christian F; Franke, Barbara; Marquand, Andre F

    2015-10-01

    Psychiatric disorders are increasingly being recognised as having a biological basis, but their diagnosis is made exclusively behaviourally. A promising approach for 'biomarker' discovery has been based on pattern recognition methods applied to neuroimaging data, which could yield clinical utility in future. In this review we survey the literature on pattern recognition for making diagnostic predictions in psychiatric disorders, and evaluate progress made in translating such findings towards clinical application. We evaluate studies on many criteria, including data modalities used, the types of features extracted and algorithm applied. We identify problems common to many studies, such as a relatively small sample size and a primary focus on estimating generalisability within a single study. Furthermore, we highlight challenges that are not widely acknowledged in the field including the importance of accommodating disease prevalence, the necessity of more extensive validation using large carefully acquired samples, the need for methodological innovations to improve accuracy and to discriminate between multiple disorders simultaneously. Finally, we identify specific clinical contexts in which pattern recognition can add value in the short to medium term. PMID:26254595

  6. Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping.

    PubMed

    Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

    2015-08-01

    Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work. PMID:26225994

  7. Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping

    PubMed Central

    Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

    2015-01-01

    Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work. PMID:26225994

  8. Fast optical monitoring of microscopic excitation patterns in cardiac muscle.

    PubMed Central

    Müller, W; Windisch, H; Tritthart, H A

    1989-01-01

    Many vital processes depend on the generation, changes, and conduction of cellular transmembrane potentials. Optical monitoring systems are well suited to detect such cellular electrical activities in networks of excitable cells and also tissues simultaneously at multiple sites. Here, an exceptionally fast array system (16 x 16 photodiodes, up to 4,000,000 samples per second, 12-bit resolution) for imaging voltage-sensitive dye fluorescence, permitted real time measurements of excitation patterns at a microscopic size scale (256 pixels within an area of 1.8-8 mm2), in rat cardiac muscle in vitro. Results emphasize a recent hypothesis for cardiac impulse conduction, based on cardiac structural complexities, that is contradictory to all continuous cable theory models. Images FIGURE 2 PMID:2790142

  9. Emotion Recognition Pattern in Adolescent Boys with Attention-Deficit/Hyperactivity Disorder

    PubMed Central

    Bozsik, Csilla; Gadoros, Julia; Inantsy-Pap, Judit

    2014-01-01

    Background. Social and emotional deficits were recently considered as inherent features of individuals with attention-deficit hyperactivity disorder (ADHD), but only sporadic literature data exist on emotion recognition in adolescents with ADHD. The aim of the present study was to establish emotion recognition profile in adolescent boys with ADHD in comparison with control adolescents. Methods. Forty-four adolescent boys (13–16 years) participated in the study after informed consent; 22 boys had a clinical diagnosis of ADHD, while data were also assessed from 22 adolescent control boys matched for age and Raven IQ. Parent- and self-reported behavioral characteristics were assessed by the means of the Strengths and Difficulties Questionnaire. The recognition of six basic emotions was evaluated by the “Facial Expressions of Emotion-Stimuli and Tests.” Results. Compared to controls, adolescents with ADHD were more sensitive in the recognition of disgust and, worse in the recognition of fear and showed a tendency for impaired recognition of sadness. Hyperactivity measures showed an inverse correlation with fear recognition. Conclusion. Our data suggest that adolescent boys with ADHD have alterations in the recognition of specific emotions. PMID:25110694

  10. Pattern Recognition 39 (2006) 12151229 www.elsevier.com/locate/patcog

    E-print Network

    2006-01-01

    recognition, defect detection in manufacturing, obstacle avoidance in robotics and others. The recognition easily recognize faces, spoken words, hand- written or printed digits, images and many other things such as banking, multimedia, forensic science, computer vision, remote sensing, image recogni- tion, speech

  11. Pattern Recognition 38 (2005) 449452 www.elsevier.com/locate/patcog

    E-print Network

    2005-01-01

    on the framework of canonical correlation analysis (CCA) applying image recognition. Apart from canonical vectors (CPV); Generalized CPV (GCPV); Feature fusion; Handwritten character recognition 1. Introduction Canonical correlation analysis (CCA) is an important multiple data processing technique [1­4]. In Ref. [4

  12. Hadron Optics: Diffraction Patterns in Deeply Virtual Compton Scattering

    E-print Network

    S. J. Brodsky; D. Chakrabarti; A. Harindranath; A. Mukherjee; J. P. Vary

    2006-08-10

    We show that the Fourier transform of the Deeply Virtual Compton Scattering (DVCS) amplitude with respect to the skewness variable $\\zeta$ at fixed invariant momentum transfer squared $t$ provides a unique way to visualize the structure of the target hadron in the boost-invariant longitudinal coordinate space. The results are analogous to the diffractive scattering of a wave in optics. As a specific example, we utilize the quantum fluctuations of a fermion state at one loop in QED to obtain the behavior of the DVCS amplitude for electron-photon scattering. We then simulate the wavefunctions for a hadron by differentiating the above LFWFs with respect to $M^2$ and study the corresponding DVCS amplitudes in light-front longitudinal space. In both cases we observe that the diffractive patterns in the longitudinal variable conjugate to $\\zeta$ sharpen and the positions of the first minima move in with increasing momentum transfer. For fixed $t$, higher minima appear at positions which are integral multiples of the lowest minimum. Both these observations strongly support the analogy with diffraction in optics.

  13. 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.

  14. 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

  15. WC1 is a hybrid ?? TCR coreceptor and pattern recognition receptor for pathogenic bacteria.

    PubMed

    Hsu, Haoting; Chen, Chuang; Nenninger, Ariel; Holz, Lauren; Baldwin, Cynthia L; Telfer, Janice C

    2015-03-01

    WC1 proteins are uniquely expressed on ?? T cells and belong to the scavenger receptor cysteine-rich (SRCR) superfamily. While present in variable, and sometimes high, numbers in the genomes of mammals and birds, in cattle there are 13 distinct genes (WC1-1 to WC1-13). All bovine WC1 proteins can serve as coreceptors for the TCR in a tyrosine phosphorylation dependent manner, and some are required for the ?? T cell response to Leptospira. We hypothesized that individual WC1 receptors encode Ag specificity via coligation of bacteria with the ?? TCR. SRCR domain binding was directly correlated with ?? T cell response, as WC1-3 SRCR domains from Leptospira-responsive cells, but not WC1-4 SRCR domains from Leptospira-nonresponsive cells, bound to multiple serovars of two Leptospira species, L. borgpetersenii, and L. interrogans. Three to five of eleven WC1-3 SRCR domains, but none of the eleven WC1-4 SRCR domains, interacted with Leptospira spp. and Borrelia burgdorferi, but not with Escherichia coli or Staphylococcus aureus. Mutational analysis indicated that the active site for bacterial binding in one of the SRCR domains is composed of amino acids in three discontinuous regions. Recombinant WC1 SRCR domains with the ability to bind leptospires inhibited Leptospira growth. Our data suggest that WC1 gene arrays play a multifaceted role in the ?? T cell response to bacteria, including acting as hybrid pattern recognition receptors and TCR coreceptors, and they may function as antimicrobials. PMID:25632007

  16. Transcriptional modulation of pattern recognition receptors in acute colitis in mice.

    PubMed

    Zheng, Bin; Morgan, Mary E; van de Kant, Hendrik J G; Garssen, Johan; Folkerts, Gert; Kraneveld, Aletta D

    2013-12-01

    Pattern recognition receptors (PRRs), such as Toll-like receptors (TLRs), contribute to the development of intestinal inflammatory diseases, like inflammatory bowel disease (IBD). Supporting investigations of the underlying mechanisms of IBD, this study provides an extensive PRR expression survey together with T-cell associated factors along the murine colon during experimental colitis. 8-12 week-old C57BL/6 mice were treated with dextran sodium sulfate (DSS) to induce colitis. The mRNA expression levels of Tlr1-9, Nod1, Nod2, T cell subset-associated master transcription factors and cytokines were determined using qPCR. The expression of TLR2, 4, 5 and 6 was determined with immunohistochemistry. Th1 and Th17 associated responses were quantified in the mesenteric lymph nodes (mLNs) using flow cytometry. In DSS treated mice, the mRNA expression of the majority of PRRs was increased relative to healthy controls and correlated with the degree of inflammation. The exceptions were Tlr1 and Tlr5, which displayed unchanged and down-regulated transcription, respectively. Furthermore, in healthy animals, there was increased transcription of Tlr2, 3 and 5 near the caecum as opposed the region near the rectum. Within the inflamed regions, the mRNA expression of Th1-, Th17- and regulatory T-cell associated cytokines was enhanced, while there was no change for Th2-associated cytokines. In agreement with the mRNA expression, enhanced IFN? and IL-17 producing cells were observed in stimulated mLNs. This study provides an extensive expression survey of PRRs along the colon during the acute colitis and shows that the induced inflammation is characterized by a Th1- and IL-17 mediated cytokine response. PMID:23851050

  17. 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

  18. Supervised pattern recognition procedures for discrimination of whiskeys from gas chromatography/mass spectrometry congener analysis.

    PubMed

    González-Arjona, Domingo; López-Pérez, Germán; González-Gallero, Víctor; González, A Gustavo

    2006-03-22

    The volatile congener analysis of 52 commercialized whiskeys (24 samples of single malt Scotch whiskey, 18 samples of bourbon whiskey, and 10 samples of Irish whiskey) was carried out by gas chromatography/mass spectrometry after liquid-liquid extraction with dichloromethane. Pattern recognition procedures were applied for discrimination of different whiskey categories. Multivariate data analysis includes linear discriminant analysis (LDA), k nearest neighbors (KNN), soft independent modeling of class analogy (SIMCA), procrustes discriminant analysis (PDA), and artificial neural networks techniques involving multilayer perceptrons (MLP) and probabilistic neural networks (PNN). Classification rules were validated by considering the number of false positives (FPs) and false negatives (FNs) of each class associated to the prediction set. Artificial neural networks led to the best results because of their intrinsic nonlinear features. Both techniques, MLP and PNN, gave zero FPs and zero FNs for all of the categories. KNN is a nonparametric method that also provides zero FPs and FNs for every class but only when selecting K = 3 neighbors. PDA produced good results also (zero FPs and FNs always) but only by selecting nine principal components for class modeling. LDA shows a lesser classification performance, because of the building of linear frontiers between classes that does not apply in many real situations. LDA led to one FP for bourbons and one FN for scotches. The worse results were obtained with SIMCA, which gave a higher number of FPs (five for both scotches and bourbons) and FNs (six for scotchs and two for bourbons). The possible cause of these findings is the strong influence of class inhomogeneities on the SIMCA performance. It is remarkable that in any case, all of the methodologies lead to zero FPs and FNs for the Irish whiskeys. PMID:16536565

  19. An LRR-only protein representing a new type of pattern recognition receptor in Chlamys farreri.

    PubMed

    Wang, Mengqiang; Wang, Lingling; Guo, Ying; Yi, Qilin; Song, Linsheng

    2016-01-01

    Accumulating evidence has demonstrated that leucine-rich repeat (LRR)-only proteins could mediate protein-ligand and protein-protein interactions and were involved in the immune response. In the present study, an LRR-only protein (designed as CfLRRop-1) was cloned from Zhikong scallop Chlamys farreri. The complete cDNA sequence of CfLRRop-1 contained an open reading frame (ORF) of 1377 bp, which encoded a protein of 458 amino acids. An LRRNT motif, an LRR_7 motif and seven LRR motifs were found in the deduced amino acid sequence of CfLRRop-1. And these seven LRR motifs contained a conserved signature sequence LxxLxLxxNxL. The mRNA transcripts of CfLRRop-1 were constitutively expressed in all the tested tissues, including haemocytes, muscle, mantle, gill, hepatopancreas and gonad, with the highest expression level in hepatopancreas. After the stimulation of lipopolysaccharide (LPS), peptidoglycan (PGN), glucan (GLU) and polyinosinic-polycytidylic acid (poly I:C), the mRNA transcripts of CfLRRop-1 in haemocytes all increased firstly within the first 6 h and secondly during 12-24 h post stimulation. The mRNA expression level of CfLRRop-1 was continuously up-regulated, after the expression of CfTLR (previously identified Toll-like receptor in C. farreri) was suppressed via RNA interference (RNAi). The recombinant CfLRRop-1 protein could directly bind LPS, PGN, GLU and poly I:C, and induce the release of TNF-? in mixed primary cultured scallop haemocytes. These results collectively indicated that CfLRRop-1 would function as a powerful pattern recognition receptor (PRR) and play a pivotal role in the immune response of scallops. PMID:26385592

  20. Pattern recognition in the GEM central tracker at luminosity of 10{sup 33} cm{sup {minus}2}s{sup {minus}1}

    SciTech Connect

    Brooks, M.L.; Kinnison, W.W.

    1994-01-01

    A GEANT based pattern recognition algorithm has been developed for simulations of the GEM central tracker. We describe the pattern recognition algorithm and present the results of studies of the track finding efficiency for single isolated tracks and for all tracks present in a Higgs event with minimum bias background at luminosity 10{sup 33} cm{sup {minus}2}s{sup {minus}l}.

  1. 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

  2. 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.

  3. A comparative study of pattern recognition classifiers to predict physical activities using smartphones and wearable body sensors.

    PubMed

    Kouris, Ioannis; Koutsouris, Dimitris

    2012-01-01

    This paper presents a wireless body area network platform that performs physical activities recognition using accelerometers, biosignals and smartphones. Multiple classifiers and sensor combinations were examined to identify the classifier with the best recognition performance for the static and dynamic activities. The Functional Trees classifier proved to provide the best results among the classifiers evaluated (Naive Bayes, Bayesian Networks, Support Vector Machines and Decision Trees [C4.5, Random Forest]) and was used to train the model which was implemented for the real time activity recognition on the smartphone. The identified patterns of daily physical activities were used to examine conformance with medical advice, regarding physical activity guidelines. An algorithm based on Skip Chain Conditional Random Fields, received as inputs the recognized activities and data retrieved from the GPS receiver of the smartphone to develop dynamic daily patterns that enhance prediction results. The presented platform can be extended to be used in the prevention of short-term complications of metabolic diseases such as diabetes. PMID:23000559

  4. Numerical simulation of a speckle pattern formed by radiation of optical vortices in a multimode optical fibre

    SciTech Connect

    Kiesewetter, D V

    2008-02-28

    The formation of optical vortices in optical fibres is studied. Speckle patterns of optical fibres produced due to the interference of guided modes and optical vortices are simulated. The spatial statistical characteristics of speckle structures are obtained. It is shown that speckle structures, formed by the radiation of optical vortices rotating in the same direction, have the characteristic azimuthal angular dimensions of spots that are considerably larger than their axial size, whereas speckle patterns, formed by vortices rotating in the opposite directions, do not virtually differ from speckles produced due to the interference of usual guided modes. A method is proposed to distinguish speckles produced by optical vortices rotating in the same direction from speckles formed by usual guided modes with the predominant excitation of modes with comparatively small azimuthal indices. A comparison with experimental data is performed. (optical fibres)

  5. Shorter version appears in IEEE Conf. on Computer Vision and Pattern Recognition, Washington DC, 2004. Scalable Discriminant Feature Selection for Image Retrieval and Recognition

    E-print Network

    Vasconcelos, Nuno M.

    , 2004. Scalable Discriminant Feature Selection for Image Retrieval and Recognition Nuno Vasconcelos such as object recognition or image retrieval require feature selection (FS) algorithms that scale well enough, including visual recognition, image retrieval, or the recognition of people and events, can be formalized

  6. A population-competition model for analyzing transverse optical patterns including optical control and structural anisotropy

    NASA Astrophysics Data System (ADS)

    Tse, Y. C.; Chan, Chris K. P.; Luk, M. H.; Kwong, N. H.; Leung, P. T.; Binder, R.; Schumacher, Stefan

    2015-08-01

    We present a detailed study of a low-dimensional population-competition (PC) model suitable for analysis of the dynamics of certain modulational instability patterns in extended systems. The model is applied to analyze the transverse optical exciton-polariton patterns in semiconductor quantum well microcavities. It is shown that, despite its simplicity, the PC model describes quite well the competitions among various two-spot and hexagonal patterns when four physical parameters, representing density saturation, hexagon stabilization, anisotropy, and switching beam intensity, are varied. The combined effects of the last three parameters are given detailed considerations here. Although the model is developed in the context of semiconductor polariton patterns, its equations have more general applicability, and the results obtained here may benefit the investigation of other pattern-forming systems. The simplicity of the PC model allows us to organize all steady state solutions in a parameter space ‘phase diagram’. Each region in the phase diagram is characterized by the number and type of solutions. The main numerical task is to compute inter-region boundary surfaces, where some steady states either appear, disappear, or change their stability status. The singularity types of the boundary points, given by Catastrophe theory, are shown to provide a simple geometric overview of the boundary surfaces. With all stable and unstable steady states and the phase boundaries delimited and characterized, we have attained a comprehensive understanding of the structure of the four-parameter phase diagram. We analyze this rich structure in detail and show that it provides a transparent and organized interpretation of competitions among various patterns built on the hexagonal state space.

  7. 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

  8. 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.

  9. 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.

  10. Comparison of optical characteristics according to shape change based on micro prism pattern

    NASA Astrophysics Data System (ADS)

    Je, Tae-Jin; Kim, Chang-Eui; Choi, Hwan-Jin; Kang, Myoung-Chang; Jeon, Eun-chae; Park, Min-gyu; Jo, Byeong-Muk; Lee, Bong-Jae

    2015-07-01

    For high-functional optical films composed of micro patterns, the optical properties, such as the diffraction, reflection and diffusion, depend on the pattern size, shape, and arrangement. For this reason, a high precision machining process and the technology of pattern design were studied in order to increase function and efficiency. The basic shapes of micro patterns are often prisms, square pyramids and triangular pyramids. Generally, a prism pattern on a flat surface can be continuously grooved by a diamond tool same as a shape angle of the pattern. The square pyramid shape is perpendicularly machined on the prism pattern. The triangular pyramid is made with a bisection of the square pyramid along the diagonal direction. Thus, optical properties can be changed according to prism patterns produced by mechanical machining. In this paper, prism, square pyramid and triangular pyramid pattern molds were machined, and optical properties of the respective shapes were compared. The machining experiment employed an ultra-precision 4-axis planer, V-shape diamond tools, and Cu-plating molds. The machined micro patterns were replicated using UV-resin; then light-transmission measurements were performed to confirm the optical properties of the mold pattern.

  11. 2040 OPTICS LETTERS / Vol. 29, No. 17 / September 1, 2004 Geometric depolarization in patterns formed by

    E-print Network

    Lacoste, David

    2040 OPTICS LETTERS / Vol. 29, No. 17 / September 1, 2004 Geometric depolarization in patterns optical activity in a helically wound optical fiber.5 Berry's geometric phase in these references of the geometric Berry's phases in the medium leads to a loss of the polarization degree of the light, i

  12. A System for Automated Extraction of Metadata from Scanned Documents using Layout Recognition and String Pattern Search Models

    PubMed Central

    Misra, Dharitri; Chen, Siyuan; Thoma, George R.

    2010-01-01

    One of the most expensive aspects of archiving digital documents is the manual acquisition of context-sensitive metadata useful for the subsequent discovery of, and access to, the archived items. For certain types of textual documents, such as journal articles, pamphlets, official government records, etc., where the metadata is contained within the body of the documents, a cost effective method is to identify and extract the metadata in an automated way, applying machine learning and string pattern search techniques. At the U. S. National Library of Medicine (NLM) we have developed an automated metadata extraction (AME) system that employs layout classification and recognition models with a metadata pattern search model for a text corpus with structured or semi-structured information. A combination of Support Vector Machine and Hidden Markov Model is used to create the layout recognition models from a training set of the corpus, following which a rule-based metadata search model is used to extract the embedded metadata by analyzing the string patterns within and surrounding each field in the recognized layouts. In this paper, we describe the design of our AME system, with focus on the metadata search model. We present the extraction results for a historic collection from the Food and Drug Administration, and outline how the system may be adapted for similar collections. Finally, we discuss some ongoing enhancements to our AME system. PMID:21179386

  13. User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control

    NASA Astrophysics Data System (ADS)

    He, Jiayuan; Zhang, Dingguo; Jiang, Ning; Sheng, Xinjun; Farina, Dario; Zhu, Xiangyang

    2015-08-01

    Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. Significance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.

  14. 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.

  15. 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.

  16. Fundamental remote science research program. Part 2: Status report of the mathematical pattern recognition and image analysis project

    NASA Technical Reports Server (NTRS)

    Heydorn, R. P.

    1984-01-01

    The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of he Earth from remotely sensed measurements of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inferences about the Earth. This report summarizes the progress that has been made toward this program goal by each of the principal investigators in the MPRIA Program.

  17. Refinement of pattern recognition of coherent structures in turbulent shear flows and a comparison between detection techniques

    NASA Astrophysics Data System (ADS)

    Brodkey, Robert S.; Aouad, Yousef G.; Valizadeh-Alavi, Hedayatollah; Eckelmann, Helmut

    The present account summarizes changes recently incorporated into the pattern recognition (PR) program used by Wallace, Brodkey, and Eckelmann (1977). These changes were instituted so that detailed comparisons could be made between the PR technique and other detection schemes for coherent structures currently in vogue [e.g., VITA and quadrant splitting (QS)]. The X-film data obtained in the Göttingen oil channel were analyzed by the new program to extract additional information to provide a detailed comparison between techniques. The comparison is presented as a composite picture of the key elements of the sequence of events.

  18. 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''.

  19. Fingerprints of Learned Object Recognition Seen in the fMRI Activation Patterns of Lateral Occipital Complex.

    PubMed

    Roth, Zvi N; Zohary, Ehud

    2015-09-01

    One feature of visual processing in the ventral stream is that cortical responses gradually depart from the physical aspects of the visual stimulus and become correlated with perceptual experience. Thus, unlike early retinotopic areas, the responses in the object-related lateral occipital complex (LOC) are typically immune to parameter changes (e.g., contrast, location, etc.) when these do not affect recognition. Here, we use a complementary approach to highlight changes in brain activity following a shift in the perceptual state (in the absence of any alteration in the physical image). Specifically, we focus on LOC and early visual cortex (EVC) and compare their functional magnetic resonance imaging (fMRI) responses to degraded object images, before and after fast perceptual learning that renders initially unrecognized objects identifiable. Using 3 complementary analyses, we find that, in LOC, unlike EVC, learned recognition is associated with a change in the multivoxel response pattern to degraded object images, such that the response becomes significantly more correlated with that evoked by the intact version of the same image. This provides further evidence that the coding in LOC reflects the recognition of visual objects. PMID:24692511

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

    NASA Astrophysics Data System (ADS)

    Grassberger, P.

    2004-10-01

    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 satisfaction in breaking out of the limitations set by the traditional classification of sciences. Indeed, this classification had never been strict, and physicists in particular had always ventured into other fields. Helmholtz, in the middle of the 19th century, had considered himself a physicist when working on physiology, stressing that the physics of animate nature is as much a legitimate field of activity as the physics of inanimate nature. Later, Max Delbrück and Francis Crick did for experimental biology what Schrödinger did for its theoretical foundation. And many of the experimental techniques used in chemistry, biology, and medicine were developed by a steady stream of talented physicists who left their proper discipline to venture out into the wider world of science. The development we have witnessed over the last thirty years or so is different. It started with neural networks where methods could be applied which had been developed for spin glasses, but todays list includes vehicular traffic (driven lattice gases), geology (self-organized criticality), economy (fractal stochastic processes and large scale simulations), engineering (dynamical chaos), and many others. By staying in the physics departments, these activities have transformed the physics curriculum and the view physicists have of themselves. In many departments there are now courses on econophysics or on biological physics, and some universities offer degrees in the physics of traffic or in econophysics. In order to document this change of attitude, many former Institutes for Statistical Physics are now renamed as Institutes for Complex Systems Science, manifesting thereby the claim that studying the complexity of the world surrounding us is a legitimate branch of physics: after the science of the infinitely large and the science of the infinitely small, it is now the science of the infinitely complex. The present book tries to give an overview of these developments. No volume of 360 pages can of course give a complete and balanced account. Therefore it is necessary to pick out representative problems, and to illustrate with them how concepts and methods from statistical physics can be made useful in circumstances which their creators never had in mind. This is essentially the goal that the book tries to attain, as also stated on its back cover: `This book provides a unique insight into the latest breakthroughs in a consistent manner, at a level accessible to undergraduates, yet with enough attention to the theory and computation to satisfy the professional researcher.' Measured against these high goals, the book has failed. The articles are of very uneven quality. The only paper written manifestly for undergraduates is the one on first passage problems by Ding and Rangarajan. Others, like the articles on protein folding by Hansmann, on clustering by Steinbach et al, and on thermal convection by Rogers et al should still be very useful for students, but, for example, the excellent article by Y-K Yu on sequence alignment is written mainly for specialists. While the above articles (and several other ones) are indeed well written and of sufficiently broad interest to be included in such a volume, I cannot say this of all the papers. Some seem more the outcome of a PhD thesis (Thomakos on predicting the direction of a time series, Jirsa on variability of timing), rather than a review of a more substantial piece of work. Others (for example Aspnes et al on the variability of stock markets) are extremely technical, and some (Stanley et al on economy) are just sloppy and superficial. The article on competition in b

  1. Image Analysis And Pattern Recognition For Porosity Estimation From Thin Sections

    NASA Astrophysics Data System (ADS)

    Richa, R.; Mukerji, T.; Keehm, Y.; Mavko, G.

    2005-12-01

    Estimating porosity from thin sections is one of the key steps in many different rock physics and petrologic analyses. The estimated porosity is a critical input for computing transport properties of rocks from thin sections. The porosity estimate and its uncertainty depend, amongst other things, on the image analyses techniques used. In this poster, we present the results of exploring different image analysis algorithms for estimating porosity from thin section. The general methodology for calculating porosity from thin section involves conversion of a colored image to a binary image. The average of the binary image gives us the porosity. As most thin sections use blue epoxy impregnation, the conversion to binary image requires computationally identifying pixels that are blue. One of the challenges is to capture the variability of the color value, all of which are nominally blue. We compared two different color spaces, RGB and HSV color space, which can be used to specify the blue color. Two different approaches were tried for converting the colored image in different color spaces to a binary image. The first approach involved using thresholds for conversion. A single dimension threshold based on the intensity histogram as well as a multiple dimension threshold based on (RGB) and (HSV) pixel values were explored. In general, multiple dimension thresholding in HSV space gave better results but the choice of threshold is subjective. The second approach involved statistical pattern recognition and classifying of grains and pores. We tested both discriminant analysis and neural network classification. A training data was defined using different groups of pixels from selected pore and grain regions of the thin section. The trivariate training data consists of the range of HSV values for each group (grain or pore). A misclassification error was calculated for the different classification algorithms as the fraction of the observations in the training data that are misclassified. The quadratic discriminant method seems to give the best results and least error. The misclassification error was about 12.6%. The neural network classification depends upon the residual error to be achieved. Different instantiations of the neural network give slightly different porosities for a specified residual. The variance of the estimated porosities decreases with decrease in residual error, but the trade-off is an increased bias in the estimate. This is the expected bias-variance trade-off behavior. In general, the HSV color space gave better results in specifying the blue color than the RGB color space. The multiple dimensional threshold works better than the single threshold. It also proved to be a simpler, though subjective, method than statistical discriminant analysis. Though discriminant analysis gave good results, it involved preparation of training data from the thin section, which adds to processing time. Nevertheless, it can be useful for identifying different types of grains and hence may be useful for computational methods that require not only classification of pore space but also different grain types.

  2. High-accuracy and robust face recognition system based on optical parallel correlator using a temporal image sequence

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Mami; Ohta, Maiko; Kodate, Kashiko

    2005-09-01

    Face recognition is used in a wide range of security systems, such as monitoring credit card use, searching for individuals with street cameras via Internet and maintaining immigration control. There are still many technical subjects under study. For instance, the number of images that can be stored is limited under the current system, and the rate of recognition must be improved to account for photo shots taken at different angles under various conditions. We implemented a fully automatic Fast Face Recognition Optical Correlator (FARCO) system by using a 1000 frame/s optical parallel correlator designed and assembled by us. Operational speed for the 1: N (i.e. matching a pair of images among N, where N refers to the number of images in the database) identification experiment (4000 face images) amounts to less than 1.5 seconds, including the pre/post processing. From trial 1: N identification experiments using FARCO, we acquired low error rates of 2.6% False Reject Rate and 1.3% False Accept Rate. By making the most of the high-speed data-processing capability of this system, much more robustness can be achieved for various recognition conditions when large-category data are registered for a single person. We propose a face recognition algorithm for the FARCO while employing a temporal image sequence of moving images. Applying this algorithm to a natural posture, a two times higher recognition rate scored compared with our conventional system. The system has high potential for future use in a variety of purposes such as search for criminal suspects by use of street and airport video cameras, registration of babies at hospitals or handling of an immeasurable number of images in a database.

  3. TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

    SciTech Connect

    Chen, H; Tan, J; Kavanaugh, J; Dolly, S; Gay, H; Thorstad, W; Anastasio, M; Altman, M; Mutic, S; Li, H

    2014-06-15

    Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-time and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand-alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.

  4. A Hough Transform Procedure Applied to Occult-2 Data for Pattern Recognition in SDO/AIA Images

    NASA Astrophysics Data System (ADS)

    Csillaghy, A.; Massone, A. M.; Piana, M.; Perasso, A.

    2014-12-01

    The exploitation of solar data provided by the NASA mission Atmospheric Imaging Assembly in the Solar Dynamics Observatory (SDO/AIA) requires the availability of computational methods able to detect, trace and analyze numerous phenomena like flares, filaments, coronal mass ejections and active regions. OCCULT-2, an automated pattern recognition code for the extraction of one-dimensional curvilinear features from two-dimensional images, has proved a notable effectiveness in the identification of loop structures in SDO/AIA data. We now present a novel approach for the automatic determination of the mathematical equations describing these structures that relies on the results provided by OCCULT-2. Specifically, our approach implements a generalization of the Hough transform procedure to the recognition of patterns described by algebraic curves. In this method the points determined by OCCULT-2 are transformed into either curves or surfaces in the parameter space and an accumulator procedure is used to optimize the parameter values that recognize the curve in the image space. We validate this procedure against synthetic but realistically simulated data and show its effectiveness in the analysis of a number of SDO/AIA images.

  5. Applying pattern recognition methods to analyze the molecular properties of a homologous series of nitrogen mustard agents.

    PubMed

    Bartzatt, Ronald; Donigan, Laura

    2006-01-01

    The purpose of this research was to analyze the pharmacological properties of a homologous series of nitrogen mustard (N-mustard) agents formed after inserting 1 to 9 methylene groups (-CH2-) between 2 -N(CH2CH2Cl)2 groups. These compounds were shown to have significant correlations and associations in their properties after analysis by pattern recognition methods including hierarchical classification, cluster analysis, nonmetric multi-dimensional scaling (MDS), detrended correspondence analysis, K-means cluster analysis, discriminant analysis, and self-organizing tree algorithm (SOTA) analysis. Detrended correspondence analysis showed a linear-like association of the 9 homologs, and hierarchical classification showed that each homolog had great similarity to at least one other member of the series-as did cluster analysis using paired-group distance measure. Nonmetric multi-dimensional scaling was able to discriminate homologs 2 and 3 (by number of methylene groups) from homologs 4, 5, and 6 as a group, and from homologs 7, 8, and 9 as a group. Discriminant analysis, K-means cluster analysis, and hierarchical classification distinguished the high molecular weight homologs from low molecular weight homologs. As the number of methylene groups increased the aqueous solubility decreased, dermal permeation coefficient increased, Log P increased, molar volume increased, parachor increased, and index of refraction decreased. Application of pattern recognition methods discerned useful interrelationships within the homologous series that will determine specific and beneficial clinical applications for each homolog and methods of administration. PMID:16796353

  6. A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network.

    PubMed

    Xu, Jing; Wang, Zhongbin; Tan, Chao; Si, Lei; Liu, Xinhua

    2015-01-01

    In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD) and Probabilistic Neural Network (PNN) is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF) components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method. PMID:26528985

  7. 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

  8. RangerMaster{trademark}: Real-time pattern recognition software for in-field analysis of radiation sources

    SciTech Connect

    Murray, W.S.; Ziemba, F.; Szluk, N.

    1998-12-31

    RangerMaster{trademark} is the embedded firmware for Quantrad Sensor`s integrated nuclear instrument package, the Ranger{trademark}. The Ranger{trademark}, which is both a gamma-ray and neutron detection system, was originally developed at Los Alamos National Laboratory for in situ surveys at the Plutonium Facility to confirm the presence of nuclear materials. The new RangerMaster{trademark} software expands the library of isotopes and simplifies the operation of the instrument by providing an easy mode suitable for untrained operators. The expanded library of the Ranger{trademark} now includes medical isotopes {sup 99}Tc, {sup 201}Tl, {sup 111}In, {sup 67}Ga, {sup 133}Xe, {sup 103}Pa, and {sup 131}I; industrial isotopes {sup 241}Am, {sup 57}Co, {sup 133}Ba, {sup 137}Cs, {sup 40}K, {sup 60}Co, {sup 232}Th, {sup 226}Ra, and {sup 207}Bi; and nuclear materials {sup 235}U, {sup 238}U, {sup 233}U, and {sup 239}Pu. To accomplish isotopic identification, a simulated spectrum for each of the isotopes was generated using SYNTH. The SYNTH spectra formed the basis for the knowledge-based expert system and selection of the regions of interest that are used in the pattern recognition system. The knowledge-based pattern recognition system was tested against actual spectra under field conditions.

  9. 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.

  10. Quantitative measurement and control of optical Moiré pattern in an autostereoscopic liquid crystal display system.

    PubMed

    Zhou, Yangui; Krebs, Peter; Fan, Hang; Liang, Haowen; Su, Jianbang; Wang, Jiahui; Zhou, Jianying

    2015-02-20

    A quantitative description of an optical moiré pattern produced in an autostereoscopic liquid crystal display system is proposed using a contrast sensitivity function. The numerical simulation, carried out in the spatial frequency domain, is applied to a directional backlit, spatially and temporally hybrid controlled display system. The moiré pattern produced from the superimposed binary optical components is examined systematically, and the results show that the visibility of the moiré pattern can be manipulated with proper grating settings. Good agreement between experiment and simulation demonstrates that the proposed theory can be applied as a design guideline to remove the moiré patterns occurring in an autostereoscopic display system. PMID:25968221

  11. Structural characteristics of the recognition site for cholinergic ligands in the nicotinic acetylcholine receptor from squid optical ganglia

    SciTech Connect

    Plyashkevich, Yu.G.; Demushkin, V.P.

    1986-01-20

    The influence of chemical modification on the parameters of the binding of cholinergic ligands by the nicotinic acetylcholine receptor of squid optical ganglia was investigated. The presence of two subpopulations of recognition sites, differing in the composition of the groups contained in them, was detected. It was established with high probability that subpopulation I contains arginine and tyrosine residues and a carboxyl group while subpopulation II contains an amino group, a thyrosine residue, and a carboxyl group. Moreover, in both subpopulations there is an amino group important only for the binding of tubocurarin. On the basis of the results obtained, a model of the recognition sites for cholinergic ligands of the nicotinic acetylcholine receptor of squid optical ganglia is proposed.

  12. Inverse opal spheres based on polyionic liquids as functional microspheres with tunable optical properties and molecular recognition capabilities.

    PubMed

    Cui, Jiecheng; Zhu, Wei; Gao, Ning; Li, Jian; Yang, Haowei; Jiang, Yin; Seidel, Philipp; Ravoo, Bart Jan; Li, Guangtao

    2014-04-01

    Based on the combination of the unique features of both polyionic liquids and spherical colloidal crystals, a new class of inverse opaline spheres with a series of distinct properties was fabricated. It was found that such photonic spheres could not only be used as stimuli-responsive photonic microgels, but also serve as multifunctional microspheres that mimic the main characteristics of conventional molecules, including intrinsic optical properties, specific molecular recognition, reactivity and derivatization, and anisotropy. PMID:24596228

  13. Photonics: From target recognition to lesion detection

    NASA Technical Reports Server (NTRS)

    Henry, E. Michael

    1994-01-01

    Since 1989, Martin Marietta has invested in the development of an innovative concept for robust real-time pattern recognition for any two-dimensioanal sensor. This concept has been tested in simulation, and in laboratory and field hardware, for a number of DOD and commercial uses from automatic target recognition to manufacturing inspection. We have now joined Rose Health Care Systems in developing its use for medical diagnostics. The concept is based on determining regions of interest by using optical Fourier bandpassing as a scene segmentation technique, enhancing those regions using wavelet filters, passing the enhanced regions to a neural network for analysis and initial pattern identification, and following this initial identification with confirmation by optical correlation. The optical scene segmentation and pattern confirmation are performed by the same optical module. The neural network is a recursive error minimization network with a small number of connections and nodes that rapidly converges to a global minimum.

  14. Selective growth of silica nanowires using an Au catalyst for optical recognition of interleukin-10

    NASA Astrophysics Data System (ADS)

    Sekhar, Praveen K.; Ramgir, Niranjan S.; Joshi, Rakesh K.; Bhansali, Shekhar

    2008-06-01

    The vapor-liquid-solid (VLS) growth procedure has been extended for the selective growth of silica nanowires on SiO2 layer by using Au as a catalyst. The nanowires were grown in an open tube furnace at 1100 °C for 60 min using Ar as a carrier gas. The average diameter of these bottom-up nucleated wires was found to be 200 nm. Transmission electron microscopy analysis indicates the amorphous nature of these nanoscale wires and suggests an Si-silica heterostructure. The localized silica nanowires have been used as an immunoassay template in the detection of interleukin-10 which is a lung cancer biomarker. Such a nanostructured platform offered a tenfold enhancement in the optical response, aiding the recognition of IL-10 in comparison to a bare silica substrate. The role of nanowires in the immunoassay was verified through the quenching behavior in the photoluminescence (PL) spectra. Two orders of reduction in PL intensity have been observed after completion of the immunoassay with significant quenching after executing every step of the protocol. The potential of this site-specific growth of silica nanowires on SiO2 as a multi-modal biosensing platform has been discussed.

  15. A new technique of recognition for coded targets in optical 3D measurement

    NASA Astrophysics Data System (ADS)

    Guo, Changye; Cheng, Xiaosheng; Cui, Haihua; Dai, Ning; Weng, Jinping

    2014-11-01

    A new technique for coded targets recognition in optical 3D-measurement application is proposed in this paper. Traditionally, point cloud registration is based on homologous features, such as the curvature, which is time-consuming and not reliable. For this, we paste some coded targets onto the surface of the object to be measured to improve the optimum target location and accurate correspondence among multi-source images. Circular coded targets are used, and an algorithm to automatically detecting them is proposed. This algorithm extracts targets with intensive bimodal histogram features from complex background, and filters noise according to their size, shape and intensity. In addition, the coded targets' identification is conducted out by their ring codes. We affine them around the circle inversely, set foreground and background respectively as 1 and 0 to constitute a binary number, and finally shift one bit every time to calculate a decimal one of the binary number to determine a minimum decimal number as its code. In this 3Dmeasurement application, we build a mutual relationship between different viewpoints containing three or more coded targets with different codes. Experiments show that it is of efficiency to obtain global surface data of an object to be measured and is robust to the projection angles and noise.

  16. Architecture of optical sensor for recognition of multiple toxic metal ions from water.

    PubMed

    Shenashen, M A; El-Safty, S A; Elshehy, E A

    2013-09-15

    Here, we designed novel optical sensor based on the wormhole hexagonal mesoporous core/multi-shell silica nanoparticles that enabled the selective recognition and removal of these extremely toxic metals from drinking water. The surface-coating process of a mesoporous core/double-shell silica platforms by several consequence decorations using a cationic surfactant with double alkyl tails (CS-DAT) and then a synthesized dicarboxylate 1,5-diphenyl-3-thiocarbazone (III) signaling probe enabled us to create a unique hierarchical multi-shell sensor. In this design, the high loading capacity and wrapping of the CS-DAT and III organic moieties could be achieved, leading to the formation of silica core with multi-shells that formed from double-silica, CS-DAT, and III dressing layers. In this sensing system, notable changes in color and reflectance intensity of the multi-shelled sensor for Cu(2+), Co(2+), Cd(2+), and Hg(2+) ions, were observed at pH 2, 8, 9.5 and 11.5, respectively. The multi-shelled sensor is added to enable accessibility for continuous monitoring of several different toxic metal ions and efficient multi-ion sensing and removal capabilities with respect to reversibility, selectivity, and signal stability. PMID:23856314

  17. Pattern recognition of the targets with help of polarization properties of the signal

    NASA Astrophysics Data System (ADS)

    Ponomaryov, Volodymyr I.; de Rivera, Luis N.; Castellanos, Aldo B.; Popov, Anatoly V.

    1999-10-01

    We proposed to use the possibility of recognition of the targets on background of the scattering from the surface, weather objects with the help of polarimetric 3-cm radar. It has been investigated such polarization characteristics: the amplitudes of the polarization matrix elements; an anisotropy coefficient; depolarization coefficient; asymmetry coefficient; the energy section was less than 1 dB at ranges up to 15 km and less than 1.5 dB at ranges up to 100 km. During the experiments urban objects and 6 various ships of small displacement having the closest values of the backscattering cross-section were used. The analysis has shown: the factor of the polarization selection for anisotropy objects and weather objects had the values about 0.02-0.08 Isotropy had the values of polarimetric correlation factor for hydrometers about 0.7-0.8, for earth surface about 0.8-0.9, for sea surface - from 0.33 to 0.7. The results of the work of recognition algorithm of a class 'concrete objects', and 'metal objects' are submitted as example in the paper. The result of experiments have shown that the probability of correct recognition of the identified objects was in the limits from 0.93 to 0.97.

  18. Fabrication of two-dimensional micro patterns for adaptive optics by using laser interference lithography

    NASA Astrophysics Data System (ADS)

    Li, Xinghui; Cai, Yindi; Aihara, Ryo; Shimizu, Yuki; Ito, So; Gao, Wei

    2015-07-01

    This paper presents a fabrication method of two-dimensional micro patterns for adaptive optics with a micrometric or sub-micrometric period to be used for fabrication of micro lens array or two-dimensional diffraction gratings. A multibeam two-axis Lloyd's mirror interferometer is employed to carry out laser interference lithography for the fabrication of two-dimensional grating structures. In the proposed instrument, the optical setup consists of a light source providing a laser beam, a multi-beam generator, two plane mirrors to generate a two-dimensional XY interference pattern and a substrate on which the XY interference pattern is to be exposed. In this paper, pattern exposure tests are carried out by the developed optical configuration optimized by computer simulations. Some experimental results of the XY pattern fabrication will be reported.

  19. A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

    PubMed Central

    Kaplan, Bernhard A.; Lansner, Anders

    2014-01-01

    Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID:24570657

  20. Constructing a safety and security system by medical applications of a fast face recognition optical parallel correlator

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Mami; Ohta, Maiko; Murakami, Yasuo; Kodate, Kashiko

    2006-01-01

    Medical errors and patient safety have always received a great deal of attention, as they can be critically life-threatening and significant matters. Hospitals and medical personnel are trying their utmost to avoid these errors. Currently in the medical field, patients' record is identified through their PIN numbers and ID cards. However, for patients who cannot speak or move, or who suffer from memory disturbances, alternative methods would be more desirable, and necessary in some cases. The authors previously proposed and fabricated a specially-designed correlator called FARCO (Fast Face Recognition Optical Correlator) based on the Vanderlugt Correlator1, which operates at the speed of 1000 faces/s 2,3,4. Combined with high-speed display devices, the four-channel processing could achieve such high operational speed as 4000 faces/s. Running trial experiments on a 1-to-N identification basis using the optical parallel correlator, we succeeded in acquiring low error rates of 1 % FMR and 2.3 % FNMR. In this paper, we propose a robust face recognition system using the FARCO for focusing on the safety and security of the medical field. We apply our face recognition system to registration of inpatients, in particular children and infants, before and after medical treatments or operations. The proposed system has recorded a higher recognition rate by multiplexing both input and database facial images from moving images. The system was also tested and evaluated for further practical use, leaving excellent results. Hence, our face recognition system could function effectively as an integral part of medical system, meeting these essential requirements of safety, security and privacy.

  1. Normal Patterns of Deja Experience in a Healthy, Blind Male: Challenging Optical Pathway Delay Theory

    ERIC Educational Resources Information Center

    O'Connor, Akira R.; Moulin, Christopher J. A.

    2006-01-01

    We report the case of a 25-year-old healthy, blind male, MT, who experiences normal patterns of deja vu. The optical pathway delay theory of deja vu formation assumes that neuronal input from the optical pathways is necessary for the formation of the experience. Surprisingly, although the sensation of deja vu is known to be experienced by blind…

  2. A model of the neural mechanisms responsible for pattern recognition and stimulus specific habituation in toads.

    PubMed

    Lara, R; Arbib, M A

    1985-01-01

    A neural model of the mechanisms possibly responsible for stimulus-specific habituation in toads is proposed. The model follows the hypothesis that prey-predator recognition is performed by command units as a result of retina-tectum-pretectum interaction. The model allow us to study the possible coding that the nervous system of toads uses for different prey stimuli, the neural mechanisms of habituation and dishabituation, and the dynamic changes that the command units may have during these processes. The model proposes specific hypothesis and experiments to clarify the nature of these processes and to test the validity of the command unit hypothesis. PMID:3970983

  3. Two-photon patterning of optical waveguides in flexible polymers

    NASA Astrophysics Data System (ADS)

    Bichler, Sabine; Feldbacher, Sonja; Woods, Rachel; Satzinger, Valentin; Schmidt, Volker; Jakopic, Georg; Langer, Gregor; Kern, Wolfgang

    2009-08-01

    Over the last few years two-photon based photo-processes have become an important method to generate 3D microstructures in organic materials without the use of masks and molds. The present work deals with the fabrication of optical waveguides in a flexible polysiloxane matrix for data transmission on printed circuit boards (PCB). In the developed system the waveguide core is formed by two-photon induced photo polymerization (TPIP) of selected monomers, which are dissolved in a silicone matrix. Through the photo-induced polymerization an interpenetrating network is generated, resulting in a refractive index change between the illuminated waveguide cladding and the illuminated core material. Because of the optical transparency, flexibility, chemical and thermal stability polysiloxanes were chosen as optical matrix material. Different types of phenyl methacrylates with a high refractive index were used as monomers. In order to obtain a high contrast in refractive index, the monomers were removed from non-illuminated regions in a vacuum process after laser exposure. The written optical waveguides were evidenced by phase contrast microscopy, revealing an excellent structuring behaviour of the developed material. Optical techniques e.g. cut-back measurements and light extraction tests were applied to characterize the inscribed waveguide structures and to detect the resulting optical loss. To determine the refractive index change upon UV-irradiation spectroscopic ellipsometry was applied. Thus, a difference of ?n=0.02 between the non-illuminated cladding and the illuminated core material was detected. Further, prototypes of optical interconnects on PCBs were fabricated by inscription of a waveguide bundle between a mounted laser and photo diode, resulting in the desired increase of the transmitted photocurrent after TPA structuring. In conclusion, the obtained results demonstrate that fully flexible optical interconnects are accessible by the developed process.

  4. Self-organizing discovery, recognition, and prediction of hemodynamic patterns in the intensive care unit 

    E-print Network

    Spencer, Ronald Glen

    1994-01-01

    In order to properly care for critically ill patients in the intensive care unit (ICU), clinicians must be aware of hemodynamic patterns. In a typical ICU a variety of physiologic measurements are made continuously and intermittently in an attempt...

  5. Rate of growth pattern of yeast cells studied under optical tweezers

    NASA Astrophysics Data System (ADS)

    Charrunchon, Sookpichaya; Limtrakul, Jumras; Chattham, Nattaporn

    2013-06-01

    Cell growth and division has been of scientists' interest for over generations. Several mathematical models have been reported derived from conventional method of cell culture. Here we applied optical tweezers to guide cell division directionally. The patterns of Saccharonmyces bayanus yeast growth was studied under 1064 nm line optical tweezers generated by time-shared multiple optical traps. Yeast growth was found following the path of the generated laser patterns in linear, circular, square and L shapes, speculatively as a result of localized heating effect due to absorption at the focal point.

  6. Development of gas chromatographic/mass spectrometry-pattern recognition method for the quality control of Korean Angelica.

    PubMed

    Piao, Xiang-Lan; Park, Jeong Hill; Cui, Jian; Kim, Dong-Hyun; Yoo, Hye Hyun

    2007-09-01

    This paper describes gas chromatographic/mass spectrometry (GC/MS)-pattern recognition methods for the quality control of Korean Angelica. A total of 57 Angelicae radix samples, including Angelica gigas (Korean origin), A. sinensis (Chinese origin) and A. acutiloba (Japanese origin), were analyzed by GC/MS, with a principal component analysis (PCA) subsequently applied to 10 common peaks selected from each chromatogram. As a result, the samples were clustered according to their origins on the PC score plot. The loading plot revealed that decursin and decursinol angelate were the most contributive principles distinguishing Korean samples from Chinese and Japanese samples, In addition, a discriminant model was developed for classification of the Angelicae radix, using a discriminant analysis (DA), and validated with a training set (three from A. gigas, four from A. sinensis, and three from A. acutiloba). All samples tested were successfully classified according to their species origin. PMID:17537609

  7. Real Time Pattern Recognition And Feature Analysis From Video Signals Applied To Eye Movement And Pupillary Reflex Monitoring

    NASA Astrophysics Data System (ADS)

    Charlier, Jacques R.; Bariseau, Jean-Luc; Chuffart, Vincent; Marsy, Frangoise; Hache, Jean-Claude

    1984-06-01

    Original techniques for real time pattern recognition and feature analysis from standard video signals have been developed. These techniques have been applied to the monitoring of eye movements and pupillary size during visual field and electrophysio-logical examinations in routine ophtalmological practice. The basic features of the resulting instrument are : 1- the use of low-cost hardware, i.e. standard video equipment and LSI circuitry. 2- the measurement of eye, orientation from the position of the bright pupil relative to the corneal reflection. 3- "real time" processing and high data throughout of 50 samples per second, allowing pupillary and oculomotor reflex analysis. 4- specialized hardware and software permitting an adjustment free feature identifica-tion and analysis directly from video signals. Severe perturbations of the ocular video images can be handled by the system, including partial occlusions of the pupil with eye lids or eye lashes, fluctuations of amplitude levels and parasite light reflections.

  8. Classification of rapeseed and soybean oils by use of unsupervised pattern-recognition methods and neural networks.

    PubMed

    Weso?owski, M; Suchacz, B

    2001-10-01

    Unsupervised pattern-recognition methods and Kohonen neural networks have been applied to the classification of rapeseed and soybean oil samples according to their type and quality by use of chemical and physical properties (density, refractive index, saponification value, and iodine and acid numbers) and thermal properties (thermal decomposition temperatures) as variables. A multilayer feed-forward (MLF) neural network (NN) has been used to select the most important variables for accurate classification of edible oils. To accomplish this task different neural networks architectures trained by back propagation of error method, using chemical, physical, and thermal properties as inputs, were employed. The network with the best performance and the smallest root mean squared (RMS) error was chosen. The results of MLF network sensitivity analysis enabled the identification of key properties, which were again used as variables in principal components analysis (PCA), cluster analysis (CA), and in Kohonen self-organizing feature maps (SOFM) to prove their reliability. PMID:11688644

  9. Modeling excitation-emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety.

    PubMed

    Azcarate, Silvana M; de Araújo Gomes, Adriano; Alcaraz, Mirta R; Ugulino de Araújo, Mário C; Camiña, José M; Goicoechea, Héctor C

    2015-10-01

    This paper reports the modeling of excitation-emission matrices for classification of Argentinean white wines according to the grape variety employing chemometric tools for pattern recognition. The discriminative power of the data was first investigated using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The score plots showed strong overlapping between classes. A forty-one samples set was partitioned into training and test sets by the Kennard-Stone algorithm. The algorithms evaluated were SIMCA, N- and U-PLS-DA and SPA-LDA. The fit of the implemented models was assessed by mean of accuracy, sensitivity and specificity. These models were then used to assign the type of grape of the wines corresponding to the twenty samples test set. The best results were obtained for U-PLS-DA and SPA-LDA with 76% and 80% accuracy. PMID:25872447

  10. Self-regulation and cross-regulation of pattern-recognition receptor signalling in health and disease.

    PubMed

    Cao, Xuetao

    2015-12-29

    In the initiation of innate immune responses against pathogens, pattern-recognition receptors (PRRs) have an essential role in recognizing specific components of microorganisms and triggering responses that eliminate the invading microorganisms. However, inappropriate activation of PRRs can lead to prolonged inflammation and even to autoimmune and inflammatory diseases. Thus, PRR-triggered responses are regulated through the degradation or translocation of the innate receptors themselves and through the involvement of intracellular regulators or amplifiers. In addition, a complex interplay between PRRs and/or other immune pathways finely tunes the outcome of host immune defence responses. In this Review, I describe many of the numerous distinct mechanisms for the self-regulation and cross-regulation of innate immune receptor signalling. PMID:26711677

  11. Buggy Creek virus (Togaviridae: Alphavirus) upregulates expression of pattern recognition receptors and interferons in House Sparrows (Passer domesticus).

    PubMed

    Fassbinder-Orth, Carol A; Barak, Virginia A; Rainwater, Ellecia L; Altrichter, Ashley M

    2014-06-01

    Birds serve as reservoirs for at least 10 arthropod-borne viruses, yet specific immune responses of birds to arboviral infections are relatively unknown. Here, adult House Sparrows were inoculated with an arboviral alphavirus, Buggy Creek virus (BCRV), or saline, and euthanized between 1 and 3 days postinoculation. Virological dynamics and gene expression dynamics were investigated. Birds did not develop viremia postinoculation, but cytopathic virus was found in the skeletal muscle and spleen of birds 1 and 3 days postinoculation (DPI). Viral RNA was detected in the blood of BCRV-infected birds 1 and 2 DPI, in oral swabs 1-3 DPI, and in brain, heart, skeletal muscle, and spleen 1-3 DPI. Multiple genes were significantly upregulated following BCRV infection, including pattern recognition receptors (TLR7, TLR15, RIG-1), type I interferon (IFN-?), and type II interferon (IFN-?). This is the first study to report avian immunological gene expression profiles following an arboviral infection. PMID:24866749

  12. Rotationally invariant pattern recognition in a photorefractive joint transform correlator using circular harmonic filters and the wavelet transform

    NASA Astrophysics Data System (ADS)

    Zhang, H.; Cartwright, C. M.; Ding, M. S.; Wang, Z. Q.; Liang, B. L.; Gillespie, A.

    2001-08-01

    A photorefractive joint transform correlator using circular harmonic filters and the wavelet transform for high discriminant and rotationally invariant pattern recognition is reported. In the experimental implementation, a photorefractive crystal, BSO, is used as the dynamic holographic medium and a bank of wavelet functions, derived from a mother Mexican-hat wavelet have been selected to achieve a band-pass operation in the Fourier domain. The zero-order and second-order circular harmonic components are used in the investigation. It is shown that, with the proper dilation factors, there is a trade-off between peak correlation intensity and signal-to-noise ratio (SNR) with the wavelet transform filters. With the second-order circular harmonic, an improvement of 63% in the SNR is achieved with only a 4% loss in the peak correlation intensity.

  13. Human serum albumin supported lipid patterns for the targeted recognition of microspheres coated by membrane based on ss-DNA hybridization

    SciTech Connect

    Zhang Xiaoming; He Qiang; Duan Li; Li Junbai . E-mail: jbli@iccas.ac.cn

    2006-10-27

    Human serum albumin (HSA) patterns have been successfully fabricated for the deposition of lipid bilayer, 1,2-dimyristoyl-sglycerophosphate (DMPA), by making use of the micro-contact printing ({mu}CP) technique and liposome fusion. Confocal laser scanning microscopy (CLSM) results indicate that lipid bilayer has been assembled in HSA patterns with a good stability. Such well-defined lipid patterns formed on HSA surface create possibility to incorporate specific components like channels or receptors for specific recognition. In view of this, microspheres coated with lipid membranes were immobilized in HSA-supported lipid patterns via the hybridization of complementary ss-DNAs. This procedure enables to transfer solid materials to a soft surface through a specific recognition.

  14. April 20, 2012 16:2 FaceRecognitionFromVideoDraft17 International Journal of Pattern Recognition and Artificial Intelligence

    E-print Network

    Bowyer, Kevin W.

    on face recognition from video sources has intensified in recent years. The ensuing results have understanding 1. Introduction Research on face recognition from video has intensified throughout the last decade degraded viewing conditions.1,2 In traditional face image ac- quisition settings, such as passport agencies

  15. Proceedings of IEEE conference on "Computer Vision and Pattern Recognition" (CVPR), 1999 vol. II, p.517 A New Bayesian Framework for Object Recognition

    E-print Network

    Boykov, Yuri

    for the MRF model that we employ. Our approach contrasts with most feature-based object recognition techniques introduce an approach to feature-based object recognition, using maximum a posteriori (MAP) es- timation. The SCM method operates directly on the image feature map, rather than rely- ing on the graph

  16. Recognition of Grouping Patterns in Trademarks Based on the Gestalt Psychology

    NASA Astrophysics Data System (ADS)

    Iguchi, Hiromasa; Abe, Koji; Misawa, Tadanobu; Kimura, Haruhiko; Daido, Yoshimasa

    According to the Gestalt principals, in this paper, we model features for measuring the attraction degree between couples of image components, and grouping areas in trademark images are recognized. This investigation would be used for content-based image retrieval from the view of mirroring human perception for images. The features of proximity, shape similarity, closure, and good continuation are extracted from every combination of two components in an image. After that, according to results of the judgments, a grouping pattern for the query is fixed. Besides, changing combination of the features, the proposed method can output plural grouping patterns. In the experiments, we have evaluated the proposed method on 74 test images comparing between outputs by the proposed method and grouping patterns for the test images obtained from results of questionnaires by 104 participants.

  17. The role of eigenvalues in linear feature selection theory. [of pattern recognition

    NASA Technical Reports Server (NTRS)

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

    1977-01-01

    The analysis concerns the role of eigenvalues in determining a particular measure of pattern class distinction called the divergence, which is the pairwise average of the expected interclass divergence derived from Hajek's two-class divergence. Decel and Quirein (1973) showed that there always exists a k x n real matrix B such that the transformation determined by B maximizes divergence in k-dimensional space, and that B can be written as a product involving an orthogonal n x n matrix U. In the present paper it is shown that divergence measure of pattern class distinction does not depend on the eigenvalues of U.

  18. Pattern Recognition Pergamon Press 1973. Vol, 5, pp. 175-197. Printed in Great Britain The "Rubber-Mask" Technique I.

    E-print Network

    Widrow, Bernard

    Pattern Recognition Pergamon Press 1973. Vol, 5, pp. 175-197. Printed in Great Britain The "Rubber in size, fuzzy, rotated, translated, observed at an unusual perspective, etc. Flexible templates (rubber, and electroencephalogram waveforms are illustrated. The rubber-mask technique will probably be usable in a wide variety

  19. Accepted to Proceedings of International Conference on Pattern Recognition (ICPR'06), Hong Kong, China, 20-24 August 2006 LDV Remote Voice Acquisition and Enhancement

    E-print Network

    Zhu, Zhigang

    algorithms are needed in order to improve the performance of recognizing a noisy voice detected by the LDVAccepted to Proceedings of International Conference on Pattern Recognition (ICPR'06), Hong Kong, China, 20-24 August 2006 1 LDV Remote Voice Acquisition and Enhancement Weihong Li1* , Ming Liu2

  20. In Energy Minimization Methods in Computer Vision and Pattern Recognition at Nice, September, 2001. Figueiredo, Zerubia, Jain, Eds., Lecture Notes in Computer Science, Springer.

    E-print Network

    August, Jonas

    In Energy Minimization Methods in Computer Vision and Pattern Recognition at Nice, September, 2001 Curvature for Filtering Curve Images Jonas August and Steven W. Zucker Center for Computational Vision. Figueiredo, Zerubia, Jain, Eds., Lecture Notes in Computer Science, Springer. A Markov Process Using

  1. To appear in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007 Riemannian Analysis of Probability Density Functions with

    E-print Network

    Srivastava, Anuj

    To appear in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007 University joshi@eng.fsu.edu Abstract Applications in computer vision involve statisti- cally analyzing shape classification. 1. Introduction Several applications in computer vision, such as tex- ture

  2. Near re-entrant dense pattern optical multipass cell

    DOEpatents

    Silver, Joel A. (Santa Fe, NM)

    2007-12-11

    A multiple pass optical cell and method comprising providing a pair of opposed mirrors, one cylindrical and one spherical, introducing light into the cell via an entrance mechanism, and extracting light from the cell via an exit mechanism, wherein the entrance mechanism and exit mechanism are coextensive or non-coextensive.

  3. Near re-entrant dense pattern optical multipass cell

    NASA Technical Reports Server (NTRS)

    Silver, Joel A. (Inventor)

    2007-01-01

    A multiple pass optical cell and method comprising providing a pair of opposed mirrors, one cylindrical and one spherical, introducing light into the cell via an entrance mechanism, and extracting light from the cell via an exit mechanism, wherein the entrance mechanism and exit mechanism are coextensive or non-coextensive.

  4. Circadian Patterns Recognition in Ecosystems by Wavelet Filtering and Fuzzy Clustering

    E-print Network

    . They are the consequence of the daily variations of the solar radiation and follow the general pattern of Figure 1 forecasting and management. A previous study of the circadian cycles (Marsili- Libelli, 2004) used a fuzzy Management". International Environmental Modelling and Software Society, Osnabrueck, Germany, June 2004. #12

  5. Information Theory, Pattern Recognition and Neural Networks Sketch of expected answers

    E-print Network

    MacKay, David J.C.

    function, so the dynamics are known to be stable. The Hebb rule for storing patterns in a Hop#12;eld of memories, to avoid collisions. Speed-up o#11;ered by hash functions compared with, say, alphabetical these methods, diÃ?cult to detect convergence. Exact sampling method o#11;ers an answer to this question in some

  6. Fully Exploiting The Potential Of The Periodic Table Through Pattern Recognition.

    ERIC Educational Resources Information Center

    Schultz, Emeric

    2005-01-01

    An approach to learning chemical facts that starts with the periodic table and depends primarily on recognizing and completing patterns and following a few simple rules is described. This approach exploits the exceptions that arise and uses them as opportunities for further concept development.

  7. Feature Reduction for Improved Recognition of Subcellular Location Patterns in Fluorescence Microscope Images

    E-print Network

    Murphy, Robert F.

    classification accuracy for all major subcellular patterns in HeLa cells. Keywords: proteomics, subcellular The central goal of proteomics is to clarify the mechanism by which each protein in a given cell type carries probe or an antibody, is introduced into a cell and fluorescence microscope images are taken

  8. Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

    NASA Astrophysics Data System (ADS)

    Srimani, P. K.; Parimala, Y. G.

    2011-12-01

    A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

  9. Pattern recognition and data mining techniques to identify factors in wafer processing and control determining overlay error

    NASA Astrophysics Data System (ADS)

    Lam, Auguste; Ypma, Alexander; Gatefait, Maxime; Deckers, David; Koopman, Arne; van Haren, Richard; Beltman, Jan

    2015-03-01

    On-product overlay can be improved through the use of context data from the fab and the scanner. Continuous improvements in lithography and processing performance over the past years have resulted in consequent overlay performance improvement for critical layers. Identification of the remaining factors causing systematic disturbances and inefficiencies will further reduce overlay. By building a context database, mappings between context, fingerprints and alignment & overlay metrology can be learned through techniques from pattern recognition and data mining. We relate structure (`patterns') in the metrology data to relevant contextual factors. Once understood, these factors could be moved to the known effects (e.g. the presence of systematic fingerprints from reticle writing error or lens and reticle heating). Hence, we build up a knowledge base of known effects based on data. Outcomes from such an integral (`holistic') approach to lithography data analysis may be exploited in a model-based predictive overlay controller that combines feedback and feedforward control [1]. Hence, the available measurements from scanner, fab and metrology equipment are combined to reveal opportunities for further overlay improvement which would otherwise go unnoticed.

  10. Broad and direct interaction between TLR and Siglec families of pattern recognition receptors and its regulation by Neu1

    PubMed Central

    Chen, Guo-Yun; Brown, Nicholas K; Wu, Wei; Khedri, Zahra; Yu, Hai; Chen, Xi; van de Vlekkert, Diantha; D'Azzo, Alessandra; Zheng, Pan; Liu, Yang

    2014-01-01

    Both pathogen- and tissue damage-associated molecular patterns induce inflammation through toll-like receptors (TLRs), while sialic acid-binding immunoglobulin superfamily lectin receptors (Siglecs) provide negative regulation. Here we report extensive and direct interactions between these pattern recognition receptors. The promiscuous TLR binders were human SIGLEC-5/9 and mouse Siglec-3/E/F. Mouse Siglec-G did not show appreciable binding to any TLRs tested. Correspondingly, Siglece deletion enhanced dendritic cell responses to all microbial TLR ligands tested, while Siglecg deletion did not affect the responses to these ligands. TLR4 activation triggers Neu1 translocation to cell surface to disrupt TLR4:Siglec-E interaction. Conversely, sialidase inhibitor Neu5Gc2en prevented TLR4 ligand-induced disruption of TLR4:Siglec E/F interactions. Absence of Neu1 in hematopoietic cells or systematic treatment with sialidase inhibitor Neu5Gc2en protected mice against endotoxemia. Our data raised an intriguing possibility of a broad repression of TLR function by Siglecs and a sialidase-mediated de-repression that allows positive feedback of TLR activation during infection. DOI: http://dx.doi.org/10.7554/eLife.04066.001 PMID:25187624

  11. Automated pattern recognition to support geological mapping and exploration target generation - A case study from southern Namibia

    NASA Astrophysics Data System (ADS)

    Eberle, Detlef; Hutchins, David; Das, Sonali; Majumdar, Anandamayee; Paasche, Hendrik

    2015-06-01

    This paper demonstrates a methodology for the automatic joint interpretation of high resolution airborne geophysical and space-borne remote sensing data to support geological mapping in a largely automated, fast and objective manner. At the request of the Geological Survey of Namibia (GSN), part of the Gordonia Subprovince of the Namaqua Metamorphic Belt situated in southern Namibia was selected for this study. All data - covering an area of 120 km by 100 km in size - were gridded, with a spacing of adjacent data points of only 200 m. The data points were coincident for all data sets. Published criteria were used to characterize the airborne magnetic data and to establish a set of attributes suitable for the recognition of linear features and their pattern within the study area. This multi-attribute analysis of the airborne magnetic data provided the magnetic lineament pattern of the study area. To obtain a (pseudo-) lithology map of the area, the high resolution airborne gamma-ray data were integrated with selected Landsat band data using unsupervised fuzzy partitioning clustering. The outcome of this unsupervised clustering is a classified (zonal) map which in terms of the power of spatial resolution is superior to any regional geological mapping. The classified zones are then assigned geological/geophysical parameters and attributes known from the study area, e.g. lithology, physical rock properties, age, chemical composition, geophysical field characteristics, etc. This information is obtained from the examination of archived geological reports, borehole logs, any kind of existing geological/geophysical data and maps as well as ground truth controls where deemed necessary. To obtain a confidence measure validating the unsupervised fuzzy clustering results and receive a quality criterion of the classified zones, stepwise linear discriminant analysis was chosen. Only a small percentage (8%) of the samples was misclassified by discriminant analysis when compared to the result obtained from unsupervised fuzzy clustering. Furthermore, a comparison of the aposterior probability of class assignment with the trustworthiness values provided by fuzzy clustering also indicates only slight differences. These observed differences can be explained by the exponential class probability term which tends to deliver either fairly high or low probability values. The methodology and results presented here demonstrate that automated objective pattern recognition can essentially contribute to geological mapping of large study areas and mineral exploration target generation. This methodology is considered well suited to a number of African countries whose large territories have recently been covered by high resolution airborne geophysical data, but where existing geological mapping is poor, incomplete or outdated.

  12. Gas recognition using a neural network approach to plasma optical emission spectroscopy

    NASA Astrophysics Data System (ADS)

    Hyland, Mark; Mariotti, Davide; Dubitzky, Werner; McLaughlin, James A.; Maguire, Paul

    2000-10-01

    A system has been developed which enables the detection and recognition of various gases. Plasma emission spectroscopy has been used to record spectra from volatile species of acetone, vinegar, and coffee beans, along with air and nitrogen spectra. The spectra have been uniquely processed and fed into an artificial neural network program for training and recognition of unknown gases. The system as a whole can be grouped into the emerging and diverse area of artificial nose technology. The sy stem has shown to provide a solution to the recognition of simple gases and odours (air, nitrogen, acetone) and could also satisfactorily recognise more complex samples (vinegar and coffee beans). Recognition is performed in seconds; this being a positive aspect for many artificial nose applications.

  13. Novel optical super-resolution pattern with upright edges diffracted by a tiny thin aperture.

    PubMed

    Wu, Jiu Hui; Zhou, Kejiang

    2015-08-24

    In the past decade numerous efforts have been concentrated to achieve optical imaging resolution beyond the diffraction limit. In this letter a thin microcavity theory of near-field optics is proposed by using the power flow theorem firstly. According to this theory, the near-field optical diffraction from a tiny aperture whose diameter is less than one-tenth incident wavelength embedded in a thin conducting film is investigated by considering this tiny aperture as a thin nanocavity. It is very surprising that there exists a kind of novel super-resolution diffraction patterns showing resolution better than ?/80 (? is the incident wavelength), which is revealed for the first time to our knowledge in this letter. The mechanism that has allowed the imaging with this kind of super-resolution patterns is due to the interaction between the incident wave and the thin nanocavity with a complex wavenumber. More precisely, these super-resolution patterns with discontinuous upright peaks are formed by one or three items of the integration series about the cylindrical waves according to our simulation results. This novel optical super-resolution with upright edges by using the thin microcavity theory presented in the study could have potential applications in the future semiconductor lithography process, nano-size laser-drilling technology, microscopy, optical storage, optical switch, and optical information processing. PMID:26368194

  14. Patterning via optical-saturable transformations: A review and simple simulation model

    SciTech Connect

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

    2014-11-10

    Most of the nanoscale fabrication in the semiconductor industry is based on patterning with scanning-electron beam lithography (SEBL). Although this approach is very versatile and has very high resolution, it is intrinsically a serial writing process, and therefore, relatively slow. Our group has been investigating alternative nano-fabrication techniques, adapted from ideas of saturating optical transitions such as those used in stimulated emission-depletion microscopy and related methods, and optical interference lithography. Linewidths and resolutions on the scale of a few tens of nanometers and below are highly desirable for various applications in nanotechnology. However, the spatial resolution of optical lithography is restricted by diffraction. In the past, we developed absorbance modulation to overcome this limit. This approach utilizes photochromic molecules that can be optically switched between two thermally stable states, one opaque and the other transparent. However, absorbance modulation is limited to surface (2-D) patterning. Here, we report on an alternative approach that exploits unique combinations of spectrally selective reversible and irreversible photochemical transitions to achieve deep subwavelength resolution with potential extension to 3-dimensions. This approach, which we refer to as patterning via optical-saturable transformations have the potential for massive parallelism, enabling the creation of nanostructures and devices at a speed far surpassing what is possible with SEBL. The aim of our research is to translate the success in circumventing Abbe's diffraction limit in optical microscopy to optical lithography.

  15. Direct-patterned optical waveguides on amorphous silicon films

    DOEpatents

    Vernon, Steve; Bond, Tiziana C.; Bond, Steven W.; Pocha, Michael D.; Hau-Riege, Stefan

    2005-08-02

    An optical waveguide structure is formed by embedding a core material within a medium of lower refractive index, i.e. the cladding. The optical index of refraction of amorphous silicon (a-Si) and polycrystalline silicon (p-Si), in the wavelength range between about 1.2 and about 1.6 micrometers, differ by up to about 20%, with the amorphous phase having the larger index. Spatially selective laser crystallization of amorphous silicon provides a mechanism for controlling the spatial variation of the refractive index and for surrounding the amorphous regions with crystalline material. In cases where an amorphous silicon film is interposed between layers of low refractive index, for example, a structure comprised of a SiO.sub.2 substrate, a Si film and an SiO.sub.2 film, the formation of guided wave structures is particularly simple.

  16. The vision in "blind" justice: expert perception, judgment, and visual cognition in forensic pattern recognition.

    PubMed

    Dror, Itiel E; Cole, Simon A

    2010-04-01

    Many forensic disciplines require experts to judge whether two complex patterns are sufficiently similar to conclude that both originate from the same source. Studies in this area have revealed that there are a number of factors that affect perception and judgment and that decisions are subjective and susceptible to extraneous influences (such as emotional context, expectation, and motivation). Some studies have shown that the same expert examiner, examining the same prints but within different contexts, may reach different and contradictory decisions. However, such effects are not always present; some examiners seem more susceptible to such influences than do others--especially when the pattern matching is "hard to call" and when the forensic experts are not aware that they are being observed in an experimental study. Studying forensic examiners can contribute to our understanding of expertise and decision making, as well as have implications for forensic science and other areas of expertise. PMID:20382914

  17. An approach toward an analysis of the pattern recognition involved in the stellar orientation of birds

    NASA Technical Reports Server (NTRS)

    Wallraff, H. G.

    1972-01-01

    A conditioning method was used to investigate the orientational responses of ducks as affected by manipulations of the stellar patterns in a planetarium. Under simulated natural skies it was possible to train a bird to a particular direction successively under all positions of the rotating sphere at a constant latitude. The responses were independent of the phase relationships between local time, season, and appearance of the sky provided the bird had been trained under the particular sector of the sphere some time before.

  18. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

    PubMed

    Han, Bing; Taha, Tarek M

    2010-04-01

    There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain. PMID:20357844

  19. Automatic classification of sulcal regions of the human brain cortex using pattern recognition

    NASA Astrophysics Data System (ADS)

    Behnke, Kirsten J.; Rettmann, Maryam E.; Pham, Dzung L.; Shen, Dinggang; Resnick, Susan M.; Davatzikos, Christos; Prince, Jerry L.

    2003-05-01

    Parcellation of the cortex has received a great deal of attention in magnetic resonance (MR) image analysis, but its usefulness has been limited by time-consuming algorithms that require manual labeling. An automatic labeling scheme is necessary to accurately and consistently parcellate a large number of brains. The large variation of cortical folding patterns makes automatic labeling a challenging problem, which cannot be solved by deformable atlas registration alone. In this work, an automated classification scheme that consists of a mix of both atlas driven and data driven methods is proposed to label the sulcal regions, which are defined as the gray matter regions of the cortical surface surrounding each sulcus. The premise for this algorithm is that sulcal regions can be classified according to the pattern of anatomical features (e.g. supramarginal gyrus, cuneus, etc.) associated with each region. Using a nearest-neighbor approach, a sulcal region is classified as being in the same class as the sulcus from a set of training data which has the nearest pattern of anatomical features. Using just one subject as training data, the algorithm correctly labeled 83% of the regions that make up the main sulci of the cortex.

  20. Influence of graphene coating on speckle-pattern rotation of light in gyrotropic optical fiber.

    PubMed

    Kuzmin, Dmitry A; Bychkov, Igor V; Shavrov, Vladimir G

    2015-03-15

    In the present work, change in speckle-pattern of linearly polarized light passed through graphene-covered optical fiber placed in external magnetic field is investigated. The possibility of magnetic speckle-pattern rotation suppression and inverse speckle-pattern rotation effect is shown. This effect can be controlled by a chemical potential of graphene layer, which can be changed easily by a gate voltage, for example. For quartz optical fiber at wavelength 0.633 ?m, core diameter 9 ?m, and fiber length 5 cm, an inverse rotation value of 17° is reached at chemical potential of graphene layer about 1 eV and magnetic field strength 30 kOe. Results of the work may be useful for different magneto-optics, opto-electronics, and photonics applications. PMID:25768139