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Sample records for optical pattern recognition

  1. Optical Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Yu, Francis T. S.; Jutamulia, Suganda

    2008-10-01

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

  2. Optical Pattern Recognition With Self-Amplification

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang

    1994-01-01

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

  3. Multiple degree of freedom optical pattern recognition

    NASA Technical Reports Server (NTRS)

    Casasent, D.

    1987-01-01

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

  4. Optical recognition of statistical patterns

    NASA Astrophysics Data System (ADS)

    Lee, S. H.

    1981-12-01

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

  5. Optical pattern recognition in cuneiform inscription analysis

    NASA Astrophysics Data System (ADS)

    Gruber, Hartmut; Wernicke, Guenther K.; Demoli, Nazif; Dahms, Uwe

    1995-03-01

    The application of a multifunctional extended optoelectronic correlator (MEOC) system in the field of pattern recognition is presented. The MEOC device is based on the extended optical correlator (EOC) architecture in conjunction with a digital image processing system. The EOC system is a three-lens coherent correlator with three separate planes usable for in- line spatial filtering of signals. Combining amplitude spatial filtering in both the frequency and image planes with complex filtering in the matched spatial filter (MSF) plane, the MEOC system was used for performing various complex procedures in the pattern recognition area. Furthermore, the MEOC device was advanced by inserting an analogue coherent optical averaging (ACOA) setup. Subjects of interest were cuneiform inscriptions on an original Babylonian cuneiform tablet. The investigations using the MEOC system were carried out in the following steps: feature extraction, average pattern mask production, average matched spatial filter production, and finally the correlation experiment. The results show that classical MSFs of averaged objects combine a low in-class sensitivity with a high discrimination ability for out-of-class objects, if suitable preprocessing steps have preceded.

  6. Bifurcating optical pattern recognition in photorefractive crystals

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang

    1993-01-01

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

  7. Bifurcating optical pattern recognition in photorefractive crystals

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang

    1993-01-01

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

  8. Real time large memory optical pattern recognition

    NASA Astrophysics Data System (ADS)

    Gregory, D. A.

    1984-06-01

    A large memory optical recognition system has been developed and tested. The memory consists of an array of stored holographic Fourier transform matched filters in a VanderLugt type correlator. The filters are stored (on high resolution Kodak plates) and addressed using a novel holographic multi-focus lens. This element acts as a diffraction grating, splitting the input beam into 25 elements and as a lens, producing the Fourier transform of each of the 25 elements. The filters are created and addressed using a HeNe laser and a Hughes liquid crystal light valve (LCLV) to produce a coherent image from a television monitor. The LCLV also allows the filters to be addressed in real time thereby making the use of a transparency as an input scene unnecessary. This provides for real time recognition. Thus far, more than 70 matched filters have been stored on a single holographic plate and addressed in parallel and in real time with good correlations resulting. This research and the results obtained should contribute to allowing serious consideration to be given to the use of optical recognition systems for a wide variety of civilian and military applications.

  9. Self-amplified optical pattern recognition system

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Inventor)

    1994-01-01

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

  10. A Compact Prototype of an Optical Pattern Recognition System

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

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

  11. Self-amplified optical pattern-recognition technique

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang

    1992-01-01

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

  12. Achromatic optical correlator for white light pattern recognition

    NASA Technical Reports Server (NTRS)

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

    1987-01-01

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

  13. Optical Implementation Of High-Speed Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Arsenault, Henri H.; April, Gilbert V.; Bergeron, Alain; Francois, Veronique; Leclerc, Luc; Sheng, Yunlong

    1989-10-01

    The capability of Optics to carry out operations in parallel on large masses of data has fueled the current interest in many areas of Optics. However many of the Optical methods proposed have so far only been implemented in digital simulations. Some new or recently implemented methods for invariant pattern recognition will be reviewed, and the problems associated with implementing them with Optics will be discussed. Some of the methods discussed will include pattern recognition with advanced invariant matched filters designed to enhance classification performance and reduce sidelobes and the effects of noise and clutter; neural network content-addressable memories and symbolic substitution systems; and neural logic modules and their combinations with other neural networks to carry out fuzzy logic operations. The optical implementation of the above exemples and of some others will be discussed. In particular, a new representation of 2-D objects that represents any object by a small number of coefficients with invariant (rotation and scale) properties will be introduced, a method to realize it optically will be described, and its combination with a neural network to accomplish invariant pattern classification will be proposed.

  14. 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.

  15. 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.

  16. 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.

  17. Pattern Recognition in Optical Remote Sensing Data Processing

    NASA Astrophysics Data System (ADS)

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

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

  18. 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.

  19. Real-Time Large Memory, Optical Pattern Recognition.

    NASA Astrophysics Data System (ADS)

    Gregory, Don Allen

    A large memory optical recognition system has been developed and tested. The memory consists of an array of stored holographic Fourier transform matched filters in a Vander Lugt type correlator. The filters are stored (on high resolution Kodak plates) and addressed using a novel holographic multi-focus lens. This element acts as a diffraction grating, splitting the input beam into 25 elements and as a lens, producing the Fourier transform of each of the 25 elements. the filters are created and addressed using a HeNe laser and a Hughes liquid crystal light valve (LCLV) to produce a coherent image from a television monitor. The LCLV also allows the filters to be addressed in "real-time" thereby making the use of a transparency as an input scene unnecessary. This provides for real -time recognition. Thus far, more than seventy matched filters have been stored on a single holographic plate and addressed in parallel and in real time with good correlations resulting. This research and the results obtained should contribute to allowing serious consideration to be given to the use of optical recognition systems for a wide variety of civilian and military applications.

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

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Editor)

    1989-01-01

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

  1. 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.

  2. Optical pattern recognition in the analysis of ancient Babylonian cuneiform inspection

    NASA Astrophysics Data System (ADS)

    Gruber, Hartmut; Demoli, Nazif; Wernicke, Guenther K.; Dahms, Uwe

    1995-11-01

    The subject of interest is ancient Babylonian cuneiform inscriptions in clay tablets representing three-dimensional carriers of information. Investigations have been carried out on original cuneiform signs as well as on models of signs. For the characterization of inscriptions by means of optical pattern recognition techniques an algorithm is presented that includes the following steps: data reduction, feature extraction, average pattern production, power spectrum mask production, average matched spatial filter production, and finally the correlation experiment.

  3. 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.

  4. 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.

  5. 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.

  6. Muscle Sensor Model Using Small Scale Optical Device for Pattern Recognitions

    PubMed Central

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

    2013-01-01

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

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

    SciTech Connect

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

    1988-01-01

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

  8. 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.

  9. 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.

  10. Modeling optical pattern recognition algorithms for object tracking based on nonlinear equivalent models and subtraction of frames

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Nikolskyy, Aleksandr I.; Lazarev, Alexander A.

    2015-12-01

    We have proposed and discussed optical pattern recognition algorithms for object tracking based on nonlinear equivalent models and subtraction of frames. Experimental results of suggested algorithms in Mathcad and LabVIEW are shown. Application of equivalent functions and difference of frames gives good results for recognition and tracking moving objects.

  11. 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.

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

    NASA Astrophysics Data System (ADS)

    Freeman, Mark Olmsted

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

  13. 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.

  14. 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.

  15. Smart pattern recognition

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  16. 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.

  17. Design and simulation of a multiport neural network heteroassociative memory for optical pattern recognitions

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir; Lazarev, Alexander; Grabovlyak, Sveta

    2012-04-01

    The modified matrix equivalently models (MMEMs) of multiport neural network heteroassociative memory (MP_NN_HAM) with double adaptive - equivalently weighing (DAEW) for recognition of 1D and 2D-patterns (images) are offered. It is shown, that computing process in MP_NN_HAM under using the proposed MMEMs, is reduced to two-step and multi-step algorithms and step-by-step matrix-matrix (tensor-tensor) procedures. The base operations and structural components for construction of MP_NN_HAM are matrix-matrix multipliers and matrixes of nonlinear converters, including threshold transformations. Advantages of such MMEMs for MP_NN_HAM were shown and confirmed by computer simulation results. The aim of paper is research of improved models and MP_NN_HAM for input 1D and 2D signals with unipolar coding and their capacity determination. The given results of computer simulations confirmed the perspective of such models. Results were also received for case of a MP_NN_HAM on base of MMEMs capacity exceeded a neurons amount. This memory is intended to recognize parallel and refresh P input distorted images (N-element vector). Such MP_NN_HAM is a kind of combination consisting of P independently functioning NN_HAM with common memory. Variants of optical realization of MP_NN_HAM architectures are considered in paper. A whole system is consists of two matrix-matrix (for 1D patterns) or two tensortensor (for 2D patterns) equivalentors (E) (or nonequivalentors (NE)) (MME and MMNE or TTE and TTNE).The proposed E (or NE) architecture with temporary integration has more large dimension of HAM and more simple design.

  18. 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

  19. Image Recognition Based on Biometric Pattern Recognition

    NASA Astrophysics Data System (ADS)

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

    2011-09-01

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

  20. Optical Character Recognition.

    ERIC Educational Resources Information Center

    Converso, L.; Hocek, S.

    1990-01-01

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

  1. Pattern Recognition in Photoacoustic Dataset

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  2. Design of double refractive pattern recognition system for optical low pass filter

    NASA Astrophysics Data System (ADS)

    Li, Xiaoyan; Jin, Shangzhou; Wang, Le

    2008-03-01

    Photo-electronic imaging system is a discrete imaging system, according to Nyquist sampling theorem, if the maximum spatial frequency is higher than Nyquist frequency, there is aliasing, and Morie fringe appears on image. The quality of image is receded and the trueness of color depressed. An optical low pass filter (OLPF) used in front of photo-electronic imaging sensor, can effectively limit the frequency spectrum width and critically satisfy Nyquist sampling condition. Thereby, the aliasing will be eliminated and the quality of the image will be improved. This paper analyzes the characteristics of frequency response of the OLPF and designs a novel system to measure the optical characteristic of the OLPF. According to the characteristic of birefringent crystal, a light spot will be separated by the OLPF into several light spots which will be processed by the computer. For the size of light point determined the limit of measurement accuracy of OLPF's thickness, laser source, which can obtain light point with 2um diameter is used here as a target light point. Magnified lens are used to improve the precision of the system. Other system used long working distance (WD) microscope objective. Instead, this novel system uses the standard 100x optical microscope objective (WD<0.2mm) as magnifying system. In this way, the cost of the system will be reduced in a great deal. The software of the system is also very powerful, in addition to the basic function image caption and scanning, it can automatically detect the number of light spots, distance and angles between light spots. The system can accurately measure the distance of point light at a high resolution of 0.1um, and the measurable thickness of OLPF is from 0.5 to 5mm.

  3. 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.

  4. Massive parallel optical pattern recognition and retrieval via a two-stage high-capacity multichannel holographic random access memory system

    SciTech Connect

    Cai, Luzhong; Liu, Hua-Kuang

    2000-02-01

    The multistage holographic optical random access memory (HORAM) system reported recently by Liu et al. provides a new degree of freedom for improving storage capacity. We further present a theoretical and practical analysis of the HORAM system with experimental results. Our discussions include the system design and geometrical requirements, its applications for multichannel pattern recognition and associative memory, the 2-D and 3-D information storage capacity, and multichannel image storage and retrieval via VanderLugt correlator (VLC) filters and joint transform holograms. A series of experiments are performed to demonstrate the feasibility of the multichannel pattern recognition and image retrieval with both the VLC and joint transform correlator (JTC) architectures. The experimental results with as many as 2025 channels show good agreement with the theoretical analysis. (c) 2000 Society of Photo-Optical Instrumentation Engineers.

  5. Fuzzy models for pattern recognition

    SciTech Connect

    Bezdek, James C.; Pal, Sankar K.

    1994-01-01

    FUZZY sets were introduced in 1965 by Lotfi Zadeh as a new way to represent vagueness in everyday life. They are a generalization of conventional set theory, one of the basic structures underlying computational mathematics and models. Computational pattern recognition has played a central role in the development of fuzzy models because fuzzy interpretations of data structures are a very natural and intuitively plausible way to formulate and solve various problems. Fuzzy control theory has also provided a wide variety of real, fielded system applications of fuzzy technology. We shall have little more to say about the growth of fuzzy models in control, except to the extent that pattern recognition algorithms and methods described in this book impact control systems. Collected here are many of the seminal papers in the field. There will be, of course, omissions that are neither by intent nor ignorance; we cannot reproduce all of the important papers that have helped in the evolution of fuzzy pattern recognition (there may be as many as five hundred) even in this narrow application domain. We will attempt, in each chapter introduction, to comment on some of the important papers that not been included and we ask both readers and authors to understand that a book such as this simply cannot {open_quotes}contain everything.{close_quotes} Our objective in Chapter 1 is to describe the basic structure of fuzzy sets theory as it applies to the major problems encountered in the design of a pattern recognition system.

  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. Optical pattern recognition algorithms on neural-logic equivalent models and demonstration of their prospects and possible implementations

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Zaitsev, Alexandr V.; Voloshin, Victor M.

    2001-03-01

    Historic information regarding the appearance and creation of fundamentals of algebra-logical apparatus-`equivalental algebra' for description of neuro-nets paradigms and algorithms is considered which is unification of theory of neuron nets (NN), linear algebra and the most generalized neuro-biology extended for matrix case. A survey is given of `equivalental models' of neuron nets and associative memory is suggested new, modified matrix-tenzor neurological equivalental models (MTNLEMS) are offered with double adaptive-equivalental weighing (DAEW) for spatial-non- invariant recognition (SNIR) and space-invariant recognition (SIR) of 2D images (patterns). It is shown, that MTNLEMS DAEW are the most generalized, they can describe the processes in NN both within the frames of known paradigms and within new `equivalental' paradigm of non-interaction type, and the computing process in NN under using the offered MTNLEMs DAEW is reduced to two-step and multi-step algorithms and step-by-step matrix-tenzor procedures (for SNIR) and procedures of defining of space-dependent equivalental functions from two images (for SIR).

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

    PubMed

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

    2014-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  10. Postprocessing algorithm for the optical recognition of degraded characters

    NASA Astrophysics Data System (ADS)

    Liu, Haisong; Wu, Minxian; Jin, Guofan; Yan, Yingbai

    1999-01-01

    The researches of document recognition and retrieval have grown rapidly in recent years, especially the processing of real-world documents, photocopies, faxes, and microfiches, in which the characters may be degraded. Optical character recognition has some advantages dealing with the degraded cases because it makes use of the whole information included in the degraded characters. In this paper, we use a simple postprocessing algorithm, which is based on the similarity measure techniques, on the optical correlation output plane to improve the discrimination ability of the pattern recognition procedures, especially for the optical recognition of degraded characters. The study is divided into two parts: one recounts a computer simulated example corresponding to pattern recognition by the use of input images that may be blurred, rotated, or corrupted by additive Gaussian noise, and the second part describes an incoherent optical correlator based optoelectronic processor for the character recognition.

  11. 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.

  12. 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.

  13. Optical character recognition using a real-time correlator

    NASA Astrophysics Data System (ADS)

    Alam, Mohammad S.; Gu, Y.

    1995-08-01

    A new technique for real-time optical character recognition using a joint transform correlator is proposed. This technique employs a special feature-extracted reference image for detecting a wide range of characters in one step. The proposed system also shows feasibility for feature- extracted pattern recognition.

  14. Strategies for the color character recognition by optical multichannel correlation

    NASA Astrophysics Data System (ADS)

    Millan Garcia-Verela, Maria S.; Yzuel, Maria J.; Campos Rubio, Juan C.; Ferreira, Carlos

    1991-09-01

    Several strategies in optical pattern recognition are applied to recognize objects which differ only on their color distribution. The two problems studied are the discrimination of a given object among other objects and the recognition of a character contained in an object. High pass matched filters and phase only filters are used to carry out the strategies.

  15. Public domain optical character recognition

    NASA Astrophysics Data System (ADS)

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

    1995-03-01

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

  16. Optical character recognition based on nonredundant correlation measurements.

    PubMed

    Braunecker, B; Hauck, R; Lohmann, A W

    1979-08-15

    The essence of character recognition is a comparison between the unknown character and a set of reference patterns. Usually, these reference patterns are all possible characters themselves, the whole alphabet in the case of letter characters. Obviously, N analog measurements are highly redundant, since only K = log(2)N binary decisions are enough to identify one out of N characters. Therefore, we devised K reference patterns accordingly. These patterns, called principal components, are found by digital image processing, but used in an optical analog computer. We will explain the concept of principal components, and we will describe experiments with several optical character recognition systems, based on this concept. PMID:20212746

  17. 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

  18. 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

  19. Pattern recognition with magnonic holographic memory device

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  20. Pattern recognition with magnonic holographic memory device

    SciTech Connect

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

    2015-04-06

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

  1. Distortion invariant pattern recognition with phase filters

    NASA Technical Reports Server (NTRS)

    Rosen, Joseph; Shamir, Joseph

    1987-01-01

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

  2. An Exercise in Critical Thinking: Pattern Recognition.

    ERIC Educational Resources Information Center

    Postiglione, Ralph

    1988-01-01

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

  3. Visual cluster analysis and pattern recognition methods

    DOEpatents

    Osbourn, Gordon Cecil; Martinez, Rubel Francisco

    2001-01-01

    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.

  4. Adaptive pattern recognition and neural networks

    SciTech Connect

    Pao, Yohhan.

    1989-01-01

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

  5. 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.

  6. Feature-preserving thinning algorithm for optical character recognition

    NASA Astrophysics Data System (ADS)

    Fu, Hsin-Chia; Cheng, Ting-Shan; Chiang, Cheng-Chin; Roan, Shing-Ming

    1995-07-01

    We propose an improved thinning algorithm. Basically, the algorithm uses a 3 X 3 window to accommodate an eight-neighbor skeleton in each thinning iteration. This algorithm overcomes many thinning problems, such as Y-shaped distortions, spiky skeletons, and skeleton shortening, and thus preserves precise features of digital character patterns. By using this thinning algorithm, better structural features of a character pattern can be provided to an optical character recognition system, such that accurate recognition results can be achieved.

  7. Optical correlation recognition based on LCOS

    NASA Astrophysics Data System (ADS)

    Tang, Mingchuan; Wu, Jianhong

    2013-08-01

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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Learned pattern recognition using synthetic-discriminant-functions

    NASA Technical Reports Server (NTRS)

    Jared, David A.; Ennis, David J.

    1986-01-01

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

  13. [Recognition and categorization of butterfly patterns].

    PubMed

    Hakoda, Y; Ando, M; Nakamizo, S

    1993-04-01

    Two experiments are reported which deal with the nature of categorization of visual patterns. Stimulus materials were schematic butterfly patterns. Systematic transformations of five physical features (fore wing, hind wing, size, body length and color) were applied to a prototype to generate a set of instances. In Experiment 1, subjects were asked to judge the visual similarity between each instance and the prototype. Similarity ratings were found to be related to an each instance's transformational distance from the prototype. In Experiment 2, subjects were exposed to a subset of instances of the pattern which varied in their transformational distance from the prototype, and then given a recognition test with confidence ratings. The recognition item consisted of the old and new instances including the prototype. Recognition ratings were found to be related to each instance's family resemblance score rather than its transformational distance and subjective similarity to the prototype. These results support Rosch and Mervis's family resemblance model of categorization. PMID:8355434

  14. Sequence Pattern Recognition in Genome Analysis

    NASA Astrophysics Data System (ADS)

    Luo, Liaofu; Lu, Jun

    2007-12-01

    The problem of pattern recognition in genome analysis is studied. How the sequence information is extracted and integrated in the approach to sequence pattern recognition is discussed in detail. We propose two methods for calculation and prediction. The first is the Information Deviation Measure with Quadratic Discriminant (IDQD) and the second is the Information Deviation Measure with U-transformation Discriminant (IDUD). The former is applicable in case of sequence information obeying Gaussian-type distribution and the latter can be used in more general statistical distributions of sequence information.

  15. 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.

  16. 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.

  17. 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…

  18. 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…

  19. 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…

  20. Pattern Recognition in Pharmacokinetic Data Analysis.

    PubMed

    Gabrielsson, Johan; Meibohm, Bernd; Weiner, Daniel

    2016-01-01

    Pattern recognition is a key element in pharmacokinetic data analyses when first selecting a model to be regressed to data. We call this process going from data to insight and it is an important aspect of exploratory data analysis (EDA). But there are very few formal ways or strategies that scientists typically use when the experiment has been done and data collected. This report deals with identifying the properties of a kinetic model by dissecting the pattern that concentration-time data reveal. Pattern recognition is a pivotal activity when modeling kinetic data, because a rigorous strategy is essential for dissecting the determinants behind concentration-time courses. First, we extend a commonly used relationship for calculation of the number of potential model parameters by simultaneously utilizing all concentration-time courses. Then, a set of points to consider are proposed that specifically addresses exploratory data analyses, number of phases in the concentration-time course, baseline behavior, time delays, peak shifts with increasing doses, flip-flop phenomena, saturation, and other potential nonlinearities that an experienced eye catches in the data. Finally, we set up a series of equations related to the patterns. In other words, we look at what causes the shapes that make up the concentration-time course and propose a strategy to construct a model. By practicing pattern recognition, one can significantly improve the quality and timeliness of data analysis and model building. A consequence of this is a better understanding of the complete concentration-time profile. PMID:26338231

  1. Explicit pattern recognition models for speech perception

    NASA Astrophysics Data System (ADS)

    Nearey, Terrance M.

    2003-10-01

    Optimal statistical classification of arbitrary input signals can be obtained, in principle, via a Bayesian classifier, given (perfect) knowledge of the distributions of signal properties for the set of target categories. At least for certain constrained problems, such as the perception of isolated vowels, simple (imperfect) statistical pattern recognition techniques can accurately predict human listeners' performance. This paper sketches several relatively successful case studies of the application of static pattern recognition techniques to speech perception. (Static techniques require inputs of a fixed length, e.g., F1 and F2 for isolated vowels.) Real speech clearly requires dynamic pattern recognition, allowing inputs of arbitrary length. Certain such methods, such as dynamic programming and hidden Markov models, have been widely exploited in automatic speech recognition. The present paper will describe initial attempts to apply variants of such methods to the data from a perception experiment [T. Nearey and R. Smits, J. Acoust. Soc. Am. 111 (2002)] involving the perception of three (VCV) or four (VCCV) segment strings. Practical and conceptual problems in the application of such techniques to human perception will be discussed. [Work supported by SSHRC.

  2. Applications of chaotic neurodynamics in pattern recognition

    NASA Astrophysics Data System (ADS)

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

    1991-08-01

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

  3. 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.

  4. Pattern recognition monitoring of PEM fuel cell

    DOEpatents

    Meltser, Mark Alexander

    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.

  5. 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.

  6. A pattern recognition account of decision making.

    PubMed

    Massaro, D W

    1994-09-01

    In the domain of pattern recognition, experiments have shown that perceivers integrate multiple sources of information in an optimal manner. In contrast, other research has been interpreted to mean that decision making is nonoptimal. As an example, Tversky and Kahneman (1983) have shown that subjects commit a conjunction fallacy because they judge it more likely that a fictitious person named Linda is a bank teller and a feminist than just a bank teller. This judgment supposedly violates probability theory, because the probability of two events can never be greater than the probability of either event alone. The present research tests the hypothesis that subjects interpret this judgment task as a pattern recognition task. If this hypothesis is correct, subjects' judgments should be described accurately by the fuzzy logical model of perception (FLMP)--a successful model of pattern recognition. In the first experiment, the Linda task was extended to an expanded factorial design with five vocations and five avocations. The probability ratings were described well by the FLMP and described poorly by a simple probability model. The second experiment included (1) two fictitious people, Linda and Joan, as response alternatives and (2) both ratings and categorization judgments. Although the ratings were accurately described by both the FLMP and an averaging of the sources of information, the categorization judgments were described better by the FLMP. These results reveal important similarities in recognizing patterns and in decision making. Given that the FLMP is an optimal method for combining multiple sources of information, the probability judgments appear to be optimal in the same manner as pattern-recognition judgments. PMID:7968557

  7. 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

  8. 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).

  9. 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.

  10. 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.

  11. Blurred image restoration based on synergetic pattern recognition

    NASA Astrophysics Data System (ADS)

    Chen, Dingguo; Gao, Jun; Pan, Menxian; Liang, Dong

    2001-09-01

    The POCS method was original developed in 1960's. It is applied in many fields such as: image processing, signal recovery and optics. The POCS method allows us to incorporate into iteration scheme available information about the experimental data and the measurement error as well as priori constraints based on physical reasoning. It is important to note that the POCS-method doesn't lead to a unique `optimum' solution. The next step to projection is to find a optimal method within a `solution space'. Based on synergetic theory founded by Haken in 1970's, this optimal problem can be resolved by synergetic pattern recognition procedure. In our paper, we propose a synergetic pattern recognition approach to accomplish the optimal processing.

  12. Automated optical recognition of degraded handwritten characters

    NASA Astrophysics Data System (ADS)

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

    1992-08-01

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

  13. Pattern-recognition receptors in pulp defense.

    PubMed

    Staquet, M-J; Carrouel, F; Keller, J-F; Baudouin, C; Msika, P; Bleicher, F; Kufer, T A; Farges, J-C

    2011-07-01

    Initial sensing of infection is mediated by germline-encoded pattern-recognition receptors (PRRs), the activation of which leads to the expression of inflammatory mediators responsible for the elimination of pathogens and infected cells. PRRs act as immune sensors that provide immediate cell responses to pathogen invasion or tissue injury. Here, we review the expression of PRRs in human dental pulp cells, namely, receptors from the Toll-like (TLR) and Nod-like NLR families, by which cells recognize bacteria. Particular attention is given to odontoblasts, which are the first cells encountered by pathogens and represent, in the tooth, the first line of defense for the host. Understanding cellular and molecular mechanisms associated with the recognition of bacterial pathogens by odontoblasts is critical for the development of therapeutic strategies that aim at preventing excessive pulp inflammation and related deleterious effects. PMID:21677082

  14. 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.

  15. Pattern Recognition in Pharmacodynamic Data Analysis.

    PubMed

    Gabrielsson, Johan; Hjorth, Stephan

    2016-01-01

    Pattern recognition is a key element in pharmacodynamic analyses as a first step to identify drug action and selection of a pharmacodynamic model. The essence of this process is going from data to insight through exploratory data analysis. There are few formal strategies that scientists typically use when the experiment has been done and data collected. This report attempts to ameliorate this deficit by identifying the properties of a pharmacodynamic model via dissection of the pattern revealed in response-time data. Pattern recognition in pharmacodynamic analyses contrasts with pharmacokinetic analyses with respect to time course. Thus, the time course of drug in plasma usually differs markedly from the time course of the biomarker response, as a consequence of a myriad of interactions (transport to biophase, binding to target, activation of target and downstream mediators, physiological response, cascade and amplification of biosignals, homeostatic feedback) between the events of exposure to test compound and the occurrence of the biomarker response. Homing in on this important-but less often addressed-element, 20 datasets of varying complexity were analyzed, and from this, we summarize a set of points to consider, specifically addressing baseline behavior, number of phases in the response-time course, time delays between concentration- and response-time courses, peak shifts in response with increasing doses, saturation, and other potential nonlinearities. These strategies will hopefully give a better understanding of the complete pharmacodynamic response-time profile. PMID:26542613

  16. Syntactic Pattern Recognition Approach To Scene Matching

    NASA Astrophysics Data System (ADS)

    Gilmore, John F.

    1983-03-01

    This paper describes a technique for matching two images containing natural terrain and tactical objects using syntactic pattern recognition. A preprocessor analyzes each image to identify potential areas of interest. Points of interest in an image are classified and a graph possessing properties of invariance is created based on these points. Classification derived grammar strings are generated for each classified graph structure. A local match analysis is performed and the best global match is constructed. A probability-of-match metric is computed in order to evaluate the global match. Examples demonstrating these steps are provided and actual FLIR image results are shown.

  17. 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.

  18. An optoelectronic hybrid system proposed for iris pattern recognition

    NASA Astrophysics Data System (ADS)

    Cai, De; Tan, Qiaofeng; Yan, Yingbai; Jin, Guofan; He, Qingsheng

    2004-10-01

    The biometric feature, iris, has advantages in person identification, such as complex texture, almost unchanged throughout the lifespan. Compared with the famous methods propose by Daugman and Boles, the system of Yong Zhu, et al., not only takes good use of the 2D texture, but also is more robust for using statistic values of the wavelet transformed images as features for recognition. Because wavelet transform is time consuming, a volume holography opto-electronic hybrid system with high parallelism is constructed in this paper. Li Ding, et al., introduced wavelet packet transform into an optical recognition system based on volume holography to reduce the number of images stored in the photo-refractive crystal. By joint best basis selection, eigen-images corresponding to the best wavelet packet bases are generated and stored to replace the reference images. This replacement results in high compression. Theoretical analysis and experimental results both show their scheme achieves significant compression and accurate recognition at the same time. Wavelet packet compression is also utilized in our system. But the best basis selection algorithm is modified. For iris identification, we use the recognition capacity of each wavelet packet basis instead of the entropy because the latter is not for recognition. Furthermore, in the post-processing stage, we use statistic features, like Yong Zhu, to represent each iris pattern which makes the system more robust to the errors caused by optical system. So our system combines the advantages of optics parallelism, high image compression and accuracy of digital processing. Simulation results show a high identification rate is obtained.

  19. Success potential of automated star pattern recognition

    NASA Technical Reports Server (NTRS)

    Van Bezooijen, R. W. H.

    1986-01-01

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

  20. Urdu character recognition using fourier descriptors for optical networks

    NASA Astrophysics Data System (ADS)

    Lodhi, S. M.; Matin, M. A.

    2005-08-01

    This work deals with the problem of recognition of Urdu characters using Fourier descriptors for optical networks. In particular, the scope of this work has been to develop a robust Urdu characters pattern classification, representation, and recognition system, which can classify patterns even if they are deformed by transformations like rotation, scaling, and translation or any combination of these, in the presence of noise. Fourier descriptors are used to uniquely describe, classify, and recognize Urdu characters within one sub-class, that provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. Although current information transmission media i.e. cable, Ethernet etc. may still be used for communications but we proposed new technology i.e. WDM (Wavelength Division Multiplexing) due to its high speed and low loss transmission. Finally experimental results are presented to show the power and robustness of the proposed technique for Urdu character recognition.

  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. Guideline for Optical Character Recognition Forms.

    ERIC Educational Resources Information Center

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

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

  3. Intrusion detection using pattern recognition methods

    NASA Astrophysics Data System (ADS)

    Jiang, Nan; Yu, Li

    2007-09-01

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

  4. Teaching Prereading Skills through Training in Pattern Recognition

    ERIC Educational Resources Information Center

    Montgomery, Diane

    1977-01-01

    This study, using the Visual Pattern Recognition Test for Prereading Skills, found that beginning readers improve in word recognition if they are given training in identifying essential components of letters. (HOD)

  5. Optical character recognition with feature extraction and associative memory matrix

    NASA Astrophysics Data System (ADS)

    Sasaki, Osami; Shibahara, Akihito; Suzuki, Takamasa

    1998-06-01

    A method is proposed in which handwritten characters are recognized using feature extraction and an associative memory matrix. In feature extraction, simple processes such as shifting and superimposing patterns are executed. A memory matrix is generated with singular value decomposition and by modifying small singular values. The method is optically implemented with two liquid crystal displays. Experimental results for the recognition of 25 handwritten alphabet characters clearly shows the effectiveness of the method.

  6. Comparison of computer-based and optical face recognition paradigms

    NASA Astrophysics Data System (ADS)

    Alorf, Abdulaziz A.

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

  7. A neural network for visual pattern recognition

    SciTech Connect

    Fukushima, K.

    1988-03-01

    A modeling approach, which is a synthetic approach using neural network models, continues to gain importance. In the modeling approach, the authors study how to interconnect neurons to synthesize a brain model, which is a network with the same functions and abilities as the brain. The relationship between modeling neutral networks and neurophysiology resembles that between theoretical physics and experimental physics. Modeling takes synthetic approach, while neurophysiology or psychology takes an analytical approach. Modeling neural networks is useful in explaining the brain and also in engineering applications. It brings the results of neurophysiological and psychological research to engineering applications in the most direct way possible. This article discusses a neural network model thus obtained, a model with selective attention in visual pattern recognition.

  8. Low-Cost Optical Character Recognition System

    NASA Astrophysics Data System (ADS)

    Cheng, Charles C. K.

    1980-02-01

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

  9. Fermentation database mining by pattern recognition.

    PubMed

    Stephanopoulos, G; Locher, G; Duff, M J; Kamimura, R; Stephanopoulos, G

    1997-03-01

    A large volume of data is routinely collected during the course of typical fermentation and other processes. Such data provide the required basis for process documentation and occasionally are also used for process analysis and improvement. The information density of these data is often low, and automatic condensing, analysis, and interpretation ("database mining") are highly desirable. In this article we present a methodology whereby process variables are processed to create a database of derivative process quantities representative of the global patterns, intermediate trends, and local characteristics of the process. A powerful search algorithm subsequently attempts to extract the specific process variables and their particular attributes that uniquely characterize a class of process outcomes such as high- or low-yield fermentations.The basic components of our pattern recognition methodology are described along with applications to the analysis of two sets of data from industrial fermentations. Results indicate that truly discriminating variables do exist in typical fermentation data and they can be useful in identifying the causes or symptoms of different process outcomes. The methodology has been implemented in a user-friendly software, named db-miner, which facilitates the application of the methodology for efficient and speedy analysis of fermentation process data. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 53: 443-452, 1997. PMID:18634039

  10. Optical sensing: recognition elements and devices

    NASA Astrophysics Data System (ADS)

    Gauglitz, Guenter G.

    2012-09-01

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

  11. Pattern-Recognition Algorithm for Locking Laser Frequency

    NASA Technical Reports Server (NTRS)

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

    2006-01-01

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

  12. Optical matched filtering for character recognition

    NASA Astrophysics Data System (ADS)

    Lohmann, A. W.

    1983-01-01

    It is proposed that the concept of a matched filter be modified in such a way that the output pattern consists of vertical fringes. It is noted that these fringes are quite suitable as a 'recognition signature' because the vertical position of the input character becomes irrelevant; that is, when the input is moving horizontally, the output fringes move correspondingly. A slit detector will thus produce an electronic single-frequency signal. Even better performance can be expected from a detector with a fringe mask in front of it. A detector system of this type is tuned resonantly to a particular moving fringe pattern. This modified matched filtering concept may encompass such properties as multiplexing, principal component recognition, pupil replication, and incoherent inputs.

  13. Corn leaf disease spot recognition comparative study of Bayesian classification and fuzzy pattern recognition

    NASA Astrophysics Data System (ADS)

    Zhu, JingFu; Zhang, BaiYi

    Crop diseases occurrence have a great impact on Agricultural Production. Using the technology based on machine recognition to identify crop diseases automatically has important significance on agricultural production. The principles of the Bayesian Classification and the Fuzzy Pattern Recognition are introduced in this paper. Classification on 5 kinds of corn leaf diseases spot respectively are implemented based these two methods. The results show that the average recognition rate of Fuzzy Pattern Recognition is higher than Bayesian Classification's on corn leaf disease spot. Average recognition rate of the 5 kinds of corn leaf disease spot is more than 93%.

  14. 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.

  15. 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.

  16. Pattern-recognition receptors in human eosinophils.

    PubMed

    Kvarnhammar, Anne Månsson; Cardell, Lars Olaf

    2012-05-01

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

  17. Polynomial expansion for shift- and one- or two-dimensional scale-invariant pattern recognition.

    PubMed

    Zalevsky, Z; Mendlovic, D

    1995-08-10

    A polynomial expansion is suggested for achieving optical invariant pattern recognition. The expansion results in a real function and thus is theoretically able to be implemented under both coherent and spatially incoherent illumination. One obtains the expansion after applying the Gram-Schmidt algorithm on the Laurent's series in order to achieve orthonormality. The initial Laurent term with which we apply the Gram-Schmidt procedure is chosen according to the desired expansion order. The use of the polynomial expansion is demonstrated for shift- and one-dimensional scale-invariant pattern recognition as well as for shift-and two-dimensional scale-invariant recognition. PMID:21052361

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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%.

  3. 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.

  4. Pattern recognition in the satellite temperature retrieval problem

    NASA Technical Reports Server (NTRS)

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

    1985-01-01

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

  5. Spontaneous optical fractal pattern formation.

    PubMed

    Huang, J G; McDonald, G S

    2005-05-01

    We report, for the first time, spontaneous nonlinear optical spatial fractals. The proposed generic mechanism employs intrinsic nonlinear dynamics both to generate an initial pattern seed and to fill out structure across decades of spatial scale. We demonstrate this in one of the simplest of nonlinear optical systems, composed of a Kerr slice and a single-feedback mirror. In this case, the smallest pattern scales are limited by either the optical wavelength or the diffusion length of the medium photoexcitation. The dimension characteristics of these particular fractals are also derived. PMID:15904294

  6. Recognition of affect based on gait patterns.

    PubMed

    Karg, Michelle; Kühnlenz, Kolja; Buss, Martin

    2010-08-01

    To provide a means for recognition of affect from a distance, this paper analyzes the capability of gait to reveal a person's affective state. We address interindividual versus person-dependent recognition, recognition based on discrete affective states versus recognition based on affective dimensions, and efficient feature extraction with respect to affect. Principal component analysis (PCA), kernel PCA, linear discriminant analysis, and general discriminant analysis are compared to either reduce temporal information in gait or extract relevant features for classification. Although expression of affect in gait is covered by the primary task of locomotion, person-dependent recognition of motion capture data reaches 95% accuracy based on the observation of a single stride. In particular, different levels of arousal and dominance are suitable for being recognized in gait. It is concluded that gait can be used as an additional modality for the recognition of affect. Application scenarios include monitoring in high-security areas, human-robot interaction, and cognitive home environments. PMID:20350859

  7. Classification and machine recognition of severe weather patterns

    NASA Technical Reports Server (NTRS)

    Wang, P. P.; Burns, R. C.

    1976-01-01

    Forecasting and warning of severe weather conditions are treated from the vantage point of pattern recognition by machine. Pictorial patterns and waveform patterns are distinguished. Time series data on sferics are dealt with by considering waveform patterns. A severe storm patterns recognition machine is described, along with schemes for detection via cross-correlation of time series (same channel or different channels). Syntactic and decision-theoretic approaches to feature extraction are discussed. Active and decayed tornados and thunderstorms, lightning discharges, and funnels and their related time series data are studied.

  8. 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.

  9. 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.

  10. 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.

  11. 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)

  12. 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 ...

  13. Visual cluster analysis and pattern recognition template and methods

    DOEpatents

    Osbourn, Gordon Cecil; Martinez, Rubel Francisco

    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.

  14. 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.

  15. 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.

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

    SciTech Connect

    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.

  17. A Feature Extraction Toolbox for Pattern Recognition Application

    SciTech Connect

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

    1998-11-23

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

  18. Pattern Recognition Using Artificial Neural Network: A Review

    NASA Astrophysics Data System (ADS)

    Kim, Tai-Hoon

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

  19. 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.

  20. Applying SIMD to optical character recognition (OCR)

    NASA Astrophysics Data System (ADS)

    Yu, Guan; Lafruit, Gauthier; Stahl, Richard; Corporaal, Henk; Schelkens, Peter

    2008-04-01

    Optical Character Recognition (OCR) techniques are widely used in data/text entry, process automation. Decades of research efforts have made the accurate recognition of typewritten text largely accepted as a solved problem. Driven by practical usage demands, the low complexity and high performance implementation techniques of OCR systems are studied. Recent research shows that it may not be possible even for a simple OCR to run on a portable device without a specialized digital signal processor. In this paper, we present a highly data-parallelized implementation of OCR for typewritten text onto the linear processor array of the Xetal chip. Besides the preprocessing stage, the most computation intensive part of OCR recognizing individual characters is highly parallelized onto the Single Instruction Multiple Data (SIMD) engine of the Xetal chip, which can process a VGA-resolution text frame within one tenth of a second. In addition, different parallelization schemes are explored to make trade-off between the degree of parallelism and the costs of preprocessing to reorganize data to feed the SIMD engine and post-processing to assemble and collect results. The exploration of parallelized OCR application brings additional performance gain when mapped onto the linear processor array of the Xetal chip.

  1. Wavelet preprocessing in optical character recognition

    NASA Astrophysics Data System (ADS)

    Lopez-Sanchez, Jose M.; Dorronsoro, Jose R.

    1995-04-01

    Using wavelet decompositions to obtain compressed input data for optical character classifiers is a natural idea. However, extensive character preprocessing, maybe by other, different, techniques is usually necessary to achieve adequate recognition rates. We show how, prior to that compression, wavelet techniques can also be used in tasks such as character localization, filtering and scaling that are essential to obtain good recognition performances. We propose a fast two pass bottom-up procedure to perform these tasks over the Haar multiresolution decomposition of single black and white characters. Starting at the lowest resolution, filtering is performed first, yielding as a by-product a first set of character localization and scaling parameters. Carrying those steps upwards in the resolution decomposition allows finer grain filtering and more precise localization and scaling values. Once the desired decomposition level has been reached, actual scaling is performed transforming the resulting wavelet coefficients in terms of a mapping of the new scale basis functions upon those of the original one. Our algorithms have essentially the same order complexity as multiresolution decompositions and can be carried out in integer arithmetic.

  2. Detection and recognition of angular frequency patterns.

    PubMed

    Wilson, Hugh R; Propp, Roni

    2015-05-01

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

  3. 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).

  4. 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...

  5. 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.

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

    NASA Astrophysics Data System (ADS)

    Terrillon, Jean-Christophe

    1995-11-01

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

  7. 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.

  8. The role of pattern recognition receptors in the innate recognition of Candida albicans

    PubMed Central

    Zheng, Nan-Xin; Wang, Yan; Hu, Dan-Dan; Yan, Lan; Jiang, Yuan-Ying

    2015-01-01

    Candida albicans is both a commensal microorganism in healthy individuals and a major fungal pathogen causing high mortality in immunocompromised patients. Yeast-hypha morphological transition is a well known virulence trait of C. albicans. Host innate immunity to C. albicans critically requires pattern recognition receptors (PRRs). In this review, we summarize the PRRs involved in the recognition of C. albicans in epithelial cells, endothelial cells, and phagocytic cells separately. We figure out the differential recognition of yeasts and hyphae, the findings on PRR-deficient mice, and the discoveries on human PRR-related single nucleotide polymorphisms (SNPs). PMID:25714264

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

    PubMed Central

    Suresh, Rahul

    2013-01-01

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

  10. Ultrasonography of ovarian masses using a pattern recognition approach

    PubMed Central

    Jung, Sung Il

    2015-01-01

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

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

    DOEpatents

    Morozov, Victor; Bailey, Charles L.; Vsevolodov, Nikolai N.; Elliott, Adam

    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.

  12. AN OPTICAL CHARACTER RECOGNITION RESEARCH AND DEMONSTRATION PROJECT.

    ERIC Educational Resources Information Center

    1968

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

  13. Optical character recognition (OCR) in uncontrolled environments using optical correlators

    NASA Astrophysics Data System (ADS)

    Morin, Andre; Bergeron, Alain; Prevost, Donald; Radloff, Ernst A.

    1999-03-01

    With the emergence of a global economy, companies are more than ever pressured for improved efficiency. Int he transportation industry there is a growing need for better tracking of the status of containers in transit. This would lead to improved handling operation, reduce the number of errors, increase the throughput and enable the use of electronic data interchange (EDI). As electronic tags are not generalized in this industry, containers identification must rely on optical character recognition of the codes printed on the containers. OCR has been one of the first applications envisaged for optical correlation technologies as a result of their high-speed direct detection and identification capabilities. Until now though, most of the work in this area had been performed on computer-generated symbols. Field applications however, must cope with varying symbol fonts and sizes, colors and backgrounds, illumination levels, etc. Environmental variables such as dust, dirt and rust must also be accounted for. Together, these variables lead to a hard-to- solve problem. This paper presents INO's optical correlator and discusses the methods used to generate the identification vectors from which the OCR classification is achieved. It is shown that good results can be obtained on gray-scale real- life images when a multiple composite-filters strategy combined to an innovative classification method.

  14. Electronic tongue generating continuous recognition patterns for protein analysis.

    PubMed

    Hou, Yanxia; Genua, Maria; Garçon, Laurie-Amandine; Buhot, Arnaud; Calemczuk, Roberto; Bonnaffé, David; Lortat-Jacob, Hugues; Livache, Thierry

    2014-01-01

    In current protocol, a combinatorial approach has been developed to simplify the design and production of sensing materials for the construction of electronic tongues (eT) for protein analysis. By mixing a small number of simple and easily accessible molecules with different physicochemical properties, used as building blocks (BBs), in varying and controlled proportions and allowing the mixtures to self-assemble on the gold surface of a prism, an array of combinatorial surfaces featuring appropriate properties for protein sensing was created. In this way, a great number of cross-reactive receptors can be rapidly and efficiently obtained. By combining such an array of combinatorial cross-reactive receptors (CoCRRs) with an optical detection system such as surface plasmon resonance imaging (SPRi), the obtained eT can monitor the binding events in real-time and generate continuous recognition patterns including 2D continuous evolution profile (CEP) and 3D continuous evolution landscape (CEL) for samples in liquid. Such an eT system is efficient for discrimination of common purified proteins. PMID:25286325

  15. An Evaluation of PC-Based Optical Character Recognition Systems.

    ERIC Educational Resources Information Center

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

    1991-01-01

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

  16. Control of antiviral immunity by pattern recognition and the microbiome

    PubMed Central

    Pang, Iris K.; Iwasaki, Akiko

    2013-01-01

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

  17. 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.

  18. 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

  19. Processing Chinese form-document by using optical character recognition

    NASA Astrophysics Data System (ADS)

    Chang, Ming-Wen; Jeng, Bor-Shenn; Chien, Bing-Shan; Lu, Sheng-Hua; Lan, Yu-Pin

    1990-07-01

    We propose a document analysis system to separate the graphic image and text and optical Chinese character recognition to recognize characters for processing Chinese form-document. It is a useful and efficient tool. 1.

  20. 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.

  1. Biometric verification based on grip-pattern recognition

    NASA Astrophysics Data System (ADS)

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

    2004-06-01

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

  2. 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.

  3. 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.…

  4. 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.…

  5. Hybrid optical correlator for character recognition

    NASA Astrophysics Data System (ADS)

    Chen, Yansong; Li, Dehuan

    1994-06-01

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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. An optical processor for object recognition and tracking

    NASA Technical Reports Server (NTRS)

    Sloan, J.; Udomkesmalee, S.

    1987-01-01

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

  11. Polynomial expansion for shift-and one-or two-dimensional scale-invariant pattern recognition

    NASA Astrophysics Data System (ADS)

    Zalevsky, Zeev; Mendlovic, David

    1995-08-01

    A polynomial expansion is suggested for achieving optical invariant pattern recognition. The expansion results in a real function and thus is theoretically able to be implemented under both coherent and spatially incoherent illumination. One obtains the expansion after applying the Gram-Schmidt algorithm on the Laurent's series in order to achieve orthonormality. The initial Laurent term with which we apply the Gram-Schmidt procedure is chosen according to the desired expansion order. The use of the polynomial expansion is demonstrated for shift-and one-dimensional scale-invariant pattern recognition as well as for shift-and two-dimensional scale-invariant recognition.

  12. Feature-based neural wavelet optical character recognition system

    NASA Astrophysics Data System (ADS)

    Iftekharuddin, Khan M.; Schechinger, Terry D.; Jemili, Kanaan; Karim, Mohammad A.

    1995-11-01

    A hybrid character recognition system that uses a feature-extraction method is proposed. The features are extracted using a wavelet transform, preclassified using a k-nearest-neighbor- based neural net and subsequently postprocessed using an optical correlator. This feature- based neural wavelet optical architecture is then tested on blurred character images.

  13. [Recognition of corn seeds based on pattern recognition and near infrared spectroscopy technology].

    PubMed

    Liu, Tian-Ling; Su, Qi-Ya; Sun, Qun; Yang, Li-Ming

    2012-06-01

    Pattern recognition technology and data mining methods have become a hot topic in chemometrics. Near infrared (NIR) spectroscopic analysis has been widely used in spectrum signal processing and modeling due to its advantages of quickness, simplicity and nondestructiveness. Based on five different methods of pattern recognition, namely the locally linear embedding (LLE), wavelet transform (WT), principal component analysis (PCA), partial least squares (PLS) and support vector machine (SVM), the pattern recognition system for corn seeds is proposed using NIR technology, and applied to classification of 108 hybrid samples and 178 female samples for corn seeds. Firstly, we get rid of noise or reduce the dimension using LLE, WT, PCA and PLS, and then use SVM to identify two-class samples. In the meantime, 1-norm SVM is the method of direct classification and identification. Experimental results for three different spectral regions show that the performances of three methods, i. e. PCA+SVM, LLE+SVM, PLS+SVM, are superior to WT+SVM and 1-norm SVM methods, and obtain a high classification accuracy, which indicates the feasibility and effectiveness of the proposed methods. Moreover, this investigation provides the theoretical support and practical method for recognition of corn seeds utilizing near infrared spectral data. PMID:22870637

  14. [Recognition of corn seeds based on pattern recognition and near infrared spectroscopy technology].

    PubMed

    Liu, Tian-ling; Su, Qi-ya; Sun, Qun; Yang, Li-ming

    2012-05-01

    Pattern recognition technology and data mining methods have become a hot topic in chemometrics. Near infrared (NIR) spectroscopic analysis has been widely used in spectrum signal processing and modeling since it has advantages of quickness, simplicity and nondestructiveness. Based on five different methods of pattern recognition, namely the locally linear embedding (LLE), wavelet transform (WT), principal component analysis (PCA), partial least squares (PLS) and support vector machine (SVM), the pattern recognition system for corn seeds was proposed using NIR technology, and applied to classification of 108 hybrid samples and 178 female samples for corn seeds. Firstly, we get rid of noise or reduce the dimension using LLE, WT, PCA, PLS, and then use SVM to identify two-class samples. In the meantime, 1-norm SVM is the method of direct classification and identification. Experimental results of three different spectral regions show that the performances of three methods: PCA+SVM, LLE+SVM, PLS+SVM are superior to WT+SVM and 1-norm SVM methods, and obtain a high classification accuracy, which indicates the feasibility and effectiveness of the proposed methods. Moreover, this investigation provides the theoretical support and practical method for recognition of corn seeds utilizing near infrared spectral data. PMID:22827055

  15. Do pattern recognition skills transfer across sports? A preliminary analysis.

    PubMed

    Smeeton, Nicholas J; Ward, Paul; Williams, A Mark

    2004-02-01

    The ability to recognize patterns of play is fundamental to performance in team sports. While typically assumed to be domain-specific, pattern recognition skills may transfer from one sport to another if similarities exist in the perceptual features and their relations and/or the strategies used to encode and retrieve relevant information. A transfer paradigm was employed to compare skilled and less skilled soccer, field hockey and volleyball players' pattern recognition skills. Participants viewed structured and unstructured action sequences from each sport, half of which were randomly represented with clips not previously seen. The task was to identify previously viewed action sequences quickly and accurately. Transfer of pattern recognition skill was dependent on the participant's skill, sport practised, nature of the task and degree of structure. The skilled soccer and hockey players were quicker than the skilled volleyball players at recognizing structured soccer and hockey action sequences. Performance differences were not observed on the structured volleyball trials between the skilled soccer, field hockey and volleyball players. The skilled field hockey and soccer players were able to transfer perceptual information or strategies between their respective sports. The less skilled participants' results were less clear. Implications for domain-specific expertise, transfer and diversity across domains are discussed. PMID:14998098

  16. Optical font recognition of single Chinese character

    NASA Astrophysics Data System (ADS)

    Chen, Li; Ding, Xiaoqing

    2003-01-01

    Printed character image contains not only the information of characters, but also the information of fonts. Font information is essential in layout analysis and reconstruction, and is helpful to improve the performance of character recognition system. An algorithm for font recognition of single Chinese character is proposed in this paper. The aim is to analyze a single Chinese character and to identify the font. No priori knowledge of characters is required for font recognition. The new algorithm can recognize the font of a single Chinese character while existing methods are all based on a block of text. Stroke property features and stroke distribution features are extracted from a single Chinese character and two classifiers are employed to classify different fonts. We combine these two classifiers by logistic regression method to get the final result. Experiment shows that our method can recognize the font of a single Chinese character effectively.

  17. 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

  18. Real-Time Pattern Recognition - An Industrial Example

    NASA Astrophysics Data System (ADS)

    Fitton, Gary M.

    1981-11-01

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

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

    SciTech Connect

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

    2004-10-01

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

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

    PubMed Central

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

    2012-01-01

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

  1. 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.

  2. 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.

  3. 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.

  4. 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,

  5. 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.

  6. Classification of Simultaneous Movements using Surface EMG Pattern Recognition

    PubMed Central

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

    2014-01-01

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

  7. 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.

  8. Adaptive myoelectric pattern recognition toward improved multifunctional prosthesis control.

    PubMed

    Liu, Jie

    2015-04-01

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

  9. 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

  10. Method of synthesized phase objects for pattern recognition with rotation invariance

    NASA Astrophysics Data System (ADS)

    Ostroukh, Alexander P.; Butok, Alexander M.; Shvets, Rostislav A.; Yezhov, Pavel V.; Kim, Jin-Tae; Kuzmenko, Alexander V.

    2015-11-01

    We present a development of the method of synthesized phase objects (SPO-method) [1] for the rotation-invariant pattern recognition. For the standard method of recognition and the SPO-method, the comparison of the parameters of correlation signals for a number of amplitude objects is executed at the realization of a rotation in an optical-digital correlator with the joint Fourier transformation. It is shown that not only the invariance relative to a rotation at a realization of the joint correlation for synthesized phase objects (SP-objects) but also the main advantage of the method of SP-objects over the reference one such as the unified ?-like recognition signal with the largest possible signal-to-noise ratio independent of the type of an object are attained.

  11. 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.

  12. 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.

  13. Recognition of Human Oncogenic Viruses by Host Pattern-Recognition Receptors

    PubMed Central

    Di Paolo, Nelson C.

    2014-01-01

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

  14. Pattern Recognition Using The Ring-Wedge Detector And Neural-Network Software

    NASA Astrophysics Data System (ADS)

    George, Nicholas; Wang, Shen-Ge; Venable, Dennis L.

    1989-10-01

    In pattern recognition and in optical metrology, optical transform systems have been widely applied. Their use is particularly appropriate when the object is detailed and the recognition depends upon features that can be coarsely sampled in the transform space. Now with the advent of neural-network software, it is shown that the major difficulty in applying this optoelectronic hybrid is overcome. Using the ring-wedge photodetector and neural-network software, we illustrate the classification technique using thumbprints. This is a problem of known difficulty to us. Instead of a 4 person-month effort to devise software for its solution, we find that a 4-hour effort is all that is required. Other experiments also discussed are the sorting of photographs of cats and dogs, particulate suspensions, and image quality of digital halftones. All of these are shown to be promising examples for the application of the ring-wedge detector and neural-network software.

  15. 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.

  16. 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.

  17. 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

  18. A star pattern recognition algorithm for autonomous attitude determination

    NASA Technical Reports Server (NTRS)

    Van Bezooijen, R. W. H.

    1990-01-01

    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.

  19. 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.

  20. 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

  1. Segmentation-free approach to optical character recognition

    NASA Astrophysics Data System (ADS)

    Chen, Chien-Huei; DeCurtins, Jeff L.

    1993-04-01

    This paper presents a segmentation-free approach to optical character recognition (OCR) based on the concept of occluded object recognition, in which objects are recognized and then segmented out from the image. In applying the concept of occluded object recognition to the problem of OCR, we treat characters as touching or occluded objects that are subject to special constraints on their poses, i.e., they are juxtaposed with little or no freedom in rotation. Based on these characteristics, we combine two very powerful techniques used in occluded object recognition -- indexing and voting (pose clustering) -- and tailor them to the problem of OCR. This results in a segmentation-free OCR approach that is both highly efficient and robust. We note that recently some techniques have been proposed for handwritten OCR that conceptually are also segmentation-free, although these techniques are quite different from ours.

  2. 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.

  3. 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

  4. Character Recognition And Optical Characteristics Of Image Scanners

    NASA Astrophysics Data System (ADS)

    Sziranyi, Tamas; Boroczki, Agoston; Kovacs, Tamas

    1990-01-01

    A method is shown in this paper to calculate the possible character recognition error rate, which is originated from the optical transfer functions and sampling performance of the scanner. The increase of the possible reading error is the result of the image degradation through the optical transfer, and can be measured by the so-called similarity error. Theoretical and experimental results are in a good accordance. The different character types, scanners and printer outputs can be characterized by this method, as well.

  5. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2011-04-01

    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.

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

    ERIC Educational Resources Information Center

    Marsden, Jim

    1993-01-01

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

  8. Effectiveness of certain features for optical character recognition

    NASA Astrophysics Data System (ADS)

    Kovacs, Emoke; Marosi, Istvan

    1991-02-01

    In this paper we focus on some features frequently employed in Optical Character Recognition (OCR) especially in algorithms based on contour analysis. These features are of a topological and/or geometric kind somehow describing the characters. The results of an experiment to examine the identification and separation power of some features are summarized.

  9. 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.

  10. 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.

  11. 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.

  12. Neural substrates for visual pattern recognition learning in Igo.

    PubMed

    Itoh, Kosuke; Kitamura, Hideaki; Fujii, Yukihiko; Nakada, Tsutomu

    2008-08-28

    Different contexts require different visual pattern recognitions even for identical retinal inputs, and acquiring expertise in various visual-cognitive skills requires long-term training to become capable of recognizing relevant visual patterns in otherwise ambiguous stimuli. This 3-Tesla fMRI experiment exploited shikatsu-mondai (life-or-death problems) in the Oriental board game of Igo (Go) to identify the neural substrates supporting this gradual and adaptive learning. In shikatsu-mondai, the player adds stones to the board with the objective of making, or preventing the opponent from making nigan (two eyes), or the topology of figure of eight, with these stones. Without learning the game, passive viewing of shikatsu-mondai activated the occipito-temporal cortices, reflecting visual processing without the recognition of nigan. Several days after two-hour training, passive viewing of the same stimuli additionally activated the premotor area, intraparietal sulcus, and a visual area near the junction of the (left) intraparietal and transverse occipital sulci, demonstrating plastic changes in neuronal responsivity to the stimuli that contained indications of nigan. Behavioral tests confirmed that the participants had successfully learned to recognize nigan and solve the problems. In the newly activated regions, the level of neural activity while viewing the problems correlated positively with the level of achievement in learning. These results conformed to the hypothesis that recognition of a newly learned visual pattern is supported by the activities of fronto-parietal and visual cortical neurons that interact via newly formed functional connections among these regions. These connections would provide the medium by which the fronto-parietal system modulates visual cortical activity to attain behaviorally relevant perceptions. PMID:18621033

  13. Pattern recognition descriptor using the Z-Fisher transform

    NASA Astrophysics Data System (ADS)

    Barajas-García, Carolina; Solorza-Calderón, Selene; Álvarez-Borrego, Josué

    2015-09-01

    In this work is presented a pattern recognition image descriptor invariant to rotation, scale and translation (RST), which classify images using the Z-Fisher transform. A binary rings mask is generated using the Fourier transform. The normalized analytic Fourier-Mellin amplitude spectrum is filtered with that mask to build 1D signature. The signatures comparison of the problem image and the target are done by the Pearson correlation coefficient (PCC). In general, those PCC values do not satisfy a normal distribution, hence the Fisher's Z distribution is employed to determine the confidence level of the RST invariant descriptor. The descriptor presents a confidence level of 95%.

  14. Application Of Pattern Recognition Techniques To Process Cartographic Data

    NASA Astrophysics Data System (ADS)

    Morean, Orlando A.; Kasturi, Rangachar

    1984-06-01

    Graphical representation of cartocraphic data consists of symbols assigned to physical entities, interconnections to denote spatial and structural relationships among such symbols, and text to describe and identify these symbols and interconnections. An image understanding system to extract useful information from geographical maps is proposed. Pattern recognition techniques are applied to identify geometrical forms of various symbols. For each valid symbol identified, a set of files is created to store pertinent structural information extracted from the image. These files, in conjunction with a knowledge base of geographic data, are used to answer simple queries related to the spatial organization of the objects in the map.

  15. Adaptive optical biocompact disk for molecular recognition

    NASA Astrophysics Data System (ADS)

    Peng, Leilei; Varma, Manoj M.; Regnier, Fred E.; Nolte, David D.

    2005-05-01

    We report the use of adaptive interferometry to detect a monolayer of protein immobilized in a periodic pattern on a spinning glass disk. A photorefractive quantum-well device acting as an adaptive beam mixer in a two-wave mixing geometry stabilizes the interferometric quadrature in the far field. Phase modulation generated by the spinning biolayer pattern in the probe beam is detected as a homodyne signal free of amplitude modulation. Binding between antibodies and immobilized antigens in a two-analyte immunoassay was tested with high specificity and without observable cross reactivity.

  16. 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.

  17. PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

    NASA Astrophysics Data System (ADS)

    Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

    2004-11-01

    Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

  18. Patterns of myoinvasion in endometrial adenocarcinoma: recognition and implications.

    PubMed

    Cole, Adam J; Quick, Charles M

    2013-05-01

    Endometrial endometrioid adenocarcinoma (EEC) is the most common malignancy of the female genital tract, partly attributable to chronic estrogen exposure secondary to increased obesity rates. Tumor stage, which in most cases is based on depth of invasion (DOI), is of critical importance in determining if additional treatment is needed. However, the array of invasive morphologies within the spectrum of ECC can make the determination of DOI difficult. Several morphologic patterns of invasion have been described, including diffusely infiltrating irregular glands, "broad front" (or pushing border), adenoma malignum, adenomyosis-like, and microcystic, elongated, and fragmented glands. EEC may often contain a mixture of invasive patterns, which can further complicate evaluation of these common tumors. Recognition of these patterns may lead to more accurate staging, but perhaps more importantly, some patterns may be associated with adverse prognostic features. The purpose of this review is to highlight the various invasive patterns of EEC and note their unique pitfalls and prognostic implications in an effort to improve staging accuracy and treatment and follow-up for the thousands of women affected by this disease each year. PMID:23574770

  19. 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.

  20. 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

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

    NASA Astrophysics Data System (ADS)

    Spoerre, Julie K.; Perry, Marcus B.

    2000-10-01

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

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

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

    SciTech Connect

    Stinson, M.C.; Lee, O.W.; Steckenrider, J.S.; Ellingson, W.A.

    1994-09-01

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

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

    NASA Astrophysics Data System (ADS)

    Baird, Bill

    1986-10-01

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

  11. 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.

  12. 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…

  13. Sheep-pelt grading using laser scanning and pattern recognition

    NASA Astrophysics Data System (ADS)

    Bowman, Chris C.; Hilton, Peter J.; Power, P. Wayne; Hayes, Michael P.; Gabric, Richard P.

    1996-10-01

    This paper presents an overview of work underway at Industrial Research Limited directed ultimately at developing an automated grading system for pickled sheep pelts. The wide variety of defects and indistinct nature of some of them illustrate the difficulties associated with the automatic inspection of natural and varying products which poser significant technical challenges. A novel imaging approach has been taken to highlight the features of interest and thus simplify the inspection task. A laser scanner has been developed which provides simultaneous acquisition of three image types representing transmission, reflectance and fluorescent properties of the pelts. Of particular interest is the fluorescence image which highlights pelt defects not normally apparent with either the naked eye or a camera. The transmission and reflection images can be used in conjunction with the fluorescence image for defect detection as well as for calculating pelt area and for recognition of pelt identification marks. Various types of pattern recognition algorithms are under investigation to assess their potential for automating the grading process using as inputs the three image types. The approach taken is based on supervised learning using feature vectors derived in various ways for the pelt images.

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

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng

    2010-04-01

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

  15. Structural pattern recognition using genetic algorithms with specialized operators.

    PubMed

    Khoo, K G; Suganthan, P N

    2003-01-01

    This paper presents a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. The objective of our work is to improve the GA-based ARG matching procedures leading to a faster convergence rate and better quality mapping between a scene ARG and a set of given model ARGs. In this study, potential solutions are represented by integer strings indicating the mapping between scene and model vertices. The fitness of each solution string is computed by accumulating the similarity between the unary and binary attributes of the matched vertex pairs. We propose novel crossover and mutation operators, specifically for this problem. With these specialized genetic operators, the proposed algorithm converges to better quality solutions at a faster rate than the standard genetic algorithm (SGA). In addition, the proposed algorithm is also capable of recognizing multiple instances of any model object. An efficient pose-clustering algorithm is used to eliminate occasional wrong mappings and to determine the presence/pose of the model in the scene. We demonstrate the superior performance of our proposed algorithm using extensive experimental results. PMID:18238167

  16. Face recognition using local gradient binary count pattern

    NASA Astrophysics Data System (ADS)

    Zhao, Xiaochao; Lin, Yaping; Ou, Bo; Yang, Junfeng; Wu, Zhelun

    2015-11-01

    A local feature descriptor, the local gradient binary count pattern (LGBCP), is proposed for face recognition. Unlike some current methods that extract features directly from a face image in the spatial domain, LGBCP encodes the local gradient information of the face's texture in an effective way and provides a more discriminative code than other methods. We compute the gradient information of a face image through convolutions with compass masks. The gradient information is encoded using the local binary count operator. We divide a face into several subregions and extract the distribution of the LGBCP codes from each subregion. Then all the histograms are concatenated into a vector, which is used for face description. For recognition, the chi-square statistic is used to measure the similarity of different feature vectors. Besides directly calculating the similarity of two feature vectors, we provide a weighted matching scheme in which different weights are assigned to different subregions. The nearest-neighborhood classifier is exploited for classification. Experiments are conducted on the FERET, CAS-PEAL, and AR face databases. LGBCP achieves 96.15% on the Fb set of FERET. For CAS-PEAL, LGBCP gets 96.97%, 98.91%, and 90.89% on the aging, distance, and expression sets, respectively.

  17. The Role of Pattern Recognition Receptors in Intestinal Inflammation

    PubMed Central

    Fukata, Masayuki; Arditi, Moshe

    2013-01-01

    Recognition of microorganisms by pattern recognition receptors (PRRs) is the primary component of innate immunity that is responsible for the maintenance of host-microbial interactions in intestinal mucosa. Disregulation in host-commensal interactions has been implicated as the central pathogenesis of inflammatory bowel disease (IBD), which predisposes to developing colorectal cancer. Recent animal studies have begun to outline some unique physiology and pathology involving each PRR signaling in the intestine. The major roles played by PRRs in the gut appear to be regulation of the number and the composition of commensal bacteria, epithelial proliferation and mucosal permiability in response to epithelial injury. In addition, PRR signaling in lamina propria immune cells may be involved in induction of inflammation in response to invasion of pathogens. Because some PRR-deficient mice have shown variable susceptibility to colitis, the outcome of intestinal inflammation may be modified depending on PRR signaling in epithelial cells, immune cells, and the composition of commensal flora. Through recent findings in animal models of IBD, this review will discuss how abnormal PRR signaling may contribute to the pathogenesis of inflammation and inflammation-associated tumorigenesis in the intestine. PMID:23515136

  18. Flexible Segmentation and Matching for Optical Character Recognition

    NASA Astrophysics Data System (ADS)

    Sun, San-Wei; Kung, Sun-Yuan

    1989-11-01

    This paper presents a flexible image segmentation and feature matching method based on dynamic programming techniques to resolve the spatial deformation of Optical Character Recognition (OCR) problems. A 2-subcycle thinning algorithm is presented to extract a character skeleton which is 8-connected. In addition, two feature extraction methods are devised, which will extract the projected 1-D profiles of stroke distributions and 2-D background distribution respectively. The performance of the scheme is superior to that of an equally divided segmentation scheme.

  19. 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

  20. 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

  1. 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.

  2. Lung surfactant proteins A and D as pattern recognition proteins.

    PubMed

    Waters, Patrick; Vaid, Mudit; Kishore, Uday; Madan, Taruna

    2009-01-01

    Lung surfactant proteins A and D belong to a group of soluble humoral pattern recognition receptors, called collectins, which modulate the immune response to microorganisms. They bind essential carbohydrate and lipid antigens found on the surface of microorganisms via low affinity C-type lectin domains and regulate the host's response by binding to immune cell surface receptors. They form multimeric structures that bind, agglutinate, opsonise and neutralize many different pathogenic microorganisms including bacteria, yeast, fungi and viruses. They modulate the uptake of these microorganisms by phagocytic cells as well as both the inflammatory and the adaptive immune responses. Recent data have also highlighted their involvement in clearance of apoptotic cells, hypersensitivity and a number of lung diseases. PMID:19799113

  3. 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.

  4. New Digital Architecture of CNN for Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Raschman, Emil; Záluský, Roman; ?ura?ková, Daniela

    2010-07-01

    The paper deals with the design of a new digital CNN (Cellular Neural Network) architecture for pattern recognition. The main parameters of the new design were the area consumption of the chip and the speed of calculation in one iteration. The CNN was designed as a digital synchronous circuit. The largest area of the chip belongs to the multiplication unit. In the new architecture we replaced the parallel multiplication unit by a simple AND gate performing serial multiplication. The natural property of this method of multiplication is rounding. We verified some basic properties of the proposed CNN such as edge detection, filling of the edges and noise removing. At the end we compared the designed network with other two CNNs. The new architecture allows to save till 86% gates in comparison with CNN with parallel multipliers.

  5. Biological agent detection and identification using pattern recognition

    NASA Astrophysics Data System (ADS)

    Braun, Jerome J.; Glina, Yan; Judson, Nicholas; Transue, Kevin D.

    2005-05-01

    This paper discusses a novel approach for the automatic identification of biological agents. The essence of the approach is a combination of gene expression, microarray-based sensing, information fusion, machine learning and pattern recognition. Integration of these elements is a distinguishing aspect of the approach, leading to a number of significant advantages. Amongst them are the applicability to various agent types including bacteria, viruses, toxins, and other, ability to operate without the knowledge of a pathogen's genome sequence and without the need for bioagent-speciific materials or reagents, and a high level of extensibility. Furthermore, the approach allows detection of uncatalogued agents, including emerging pathogens. The approach offers a promising avenue for automatic identification of biological agents for applications such as medical diagnostics, bioforensics, and biodefense.

  6. Pattern Recognition in Gamma-Gamma Coincidence Data sets

    NASA Astrophysics Data System (ADS)

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

    1991-10-01

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

  7. [New immunology--immunology of pattern recognition receptors].

    PubMed

    Lebedev, K A; Poniakina, I D

    2006-01-01

    Pattern recognition receptors (PRRs) have been found on all cells of the body--cells of the innate and adaptive immune systems, epithelial and endothelial cells, keratinocytes, etc. PRRs can recognize specific molecular structures of microorganisms as well as allergens and other substances. The interaction with ligands of foreign microorganisms activates PRRs, after which host cells start to produce cytokines to both specifically activate innate immunity and to control adaptive immune reactions. On the other hand, no immune response develops against microorganisms of the normal microflora. Practically, the development of all immune responses is controlled by PRRs. These responses start in epithelial cells, skin cells, and vascular epithelial cells, which meet alien first. The immune system uses these cells to control the composition of normal microflora. Accordingly, the definition of immune system functions should be complemented by the regulation of body's microflora in addition to the protection from alien and altered self. PMID:17086961

  8. Pattern recognition receptors and control of adaptive immunity.

    PubMed

    Palm, Noah W; Medzhitov, Ruslan

    2009-01-01

    The mammalian immune system effectively fights infection through the cooperation of two connected systems, innate and adaptive immunity. Germ-line encoded pattern recognition receptors (PRRs) of the innate immune system sense the presence of infection and activate innate immunity. Some PRRs also induce signals that lead to the activation of adaptive immunity. Adaptive immunity is controlled by PRR-induced signals at multiple checkpoints dictating the initiation of a response, the type of response, the magnitude and duration of the response, and the production of long-term memory. PRRs thus instruct the adaptive immune system on when and how to best respond to a particular infection. In this review, we discuss the roles of various PRRs in control of adaptive immunity. PMID:19120487

  9. 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

  10. Pattern recognition analysis of polar clouds during summer and winter

    NASA Technical Reports Server (NTRS)

    Ebert, Elizabeth E.

    1992-01-01

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

  11. Orthogonal combination of local binary patterns for dynamic texture recognition

    NASA Astrophysics Data System (ADS)

    Chen, Yin; Guo, Xuejun; Klein, Dominik

    2015-12-01

    Dynamic texture (DT) is an extension of texture to the temporal domain. Recognizing DTs has received increasing attention. Volume local binary pattern (VLBP) is the most widely used descriptor for DTs. However, it is time consuming to recognize DTs using VLBP due to the large scale of data and the high dimensionality of the descriptor itself. In this paper, we propose a new operator called orthogonal combination of VLBP (OC-VLBP) for DT recognition. The original VLBP is decomposed both longitudinally and latitudinally, and then combined to constitute the OC-VLBP operator, so that the dimensionality of the original VLBP descriptor is lowered. The experimental results show that the proposed operator significantly reduces the computational costs of recognizing DTs without much loss in recognizing accuracy.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. Automatic identification of oculomotor behavior using pattern recognition techniques.

    PubMed

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

    2015-05-01

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

  19. High Speed Striation Pattern Recognition In Contracting Cardiac Myocytes

    NASA Astrophysics Data System (ADS)

    Roos, Kenneth P.; Bliton, A. Christyne; Lubell, Bradford A.; Parker, John M.; Patton, Mark J.; Taylor, Stuart R.

    1989-06-01

    The understanding of muscle contraction and relaxation requires the quantitation of movement at the sub-micron level in living cells. Two complementary non-RS-170 imaging systems used for authentic real time measurement of contractile dynamics are described and compared. Images from isolated skeletal or cardiac muscle cells are projected by an optical microscope onto single line or area charge-coupled device (CCD) photodiode arrays. These data are digitized and stored for subsequent image processing and analysis. The inherently low contrast muscle striation patterns are enhanced and their rapid movement measured with an accuracy at least an order of magnitude greater than traditional limits of optical resolution. The features of each image format are complementary and when combined provide the maximum overall information in time and space.

  20. Programmable accelerating chip for optical Chinese character recognition

    NASA Astrophysics Data System (ADS)

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

    1994-09-01

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

  1. Optical patterns with different wavelengths

    NASA Astrophysics Data System (ADS)

    Kozyreff, G.; Tlidi, M.

    2004-06-01

    The semiconductor resonator is an example of an optical system where two modulational instabilities with different wave numbers coexist. In the limit of nascent bistability, the dynamics is generically described by a nonvariational real order parameter equation, of which we give a detailed derivation. This considerably simplifies the linear and weakly nonlinear stability analyses. When the two instabilities are close together, we derive normal form equations and put special emphasis on “envelope” branches of solutions. These particular solutions may connect the two instability points or form an isola. On the basis of these rigorous results, we finally discuss the case of distant modulational instabilities, in both one and two transverse dimensions.

  2. Analysis of the hand vein pattern for people recognition

    NASA Astrophysics Data System (ADS)

    Castro-Ortega, R.; Toxqui-Quitl, C.; Cristóbal, G.; Marcos, J. Victor; Padilla-Vivanco, A.; Hurtado Pérez, R.

    2015-09-01

    The shape of the hand vascular pattern contains useful and unique features that can be used for identifying and authenticating people, with applications in access control, medicine and financial services. In this work, an optical system for the image acquisition of the hand vascular pattern is implemented. It consists of a CCD camera with sensitivity in the IR and a light source with emission in the 880 nm. The IR radiation interacts with the desoxyhemoglobin, hemoglobin and water present in the blood of the veins, making possible to see the vein pattern underneath skin. The segmentation of the Region Of Interest (ROI) is achieved using geometrical moments locating the centroid of an image. For enhancement of the vein pattern we use the technique of Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE). In order to remove unnecessary information such as body hair and skinfolds, a low pass filter is implemented. A method based on geometric moments is used to obtain the invariant descriptors of the input images. The classification task is achieved using Artificial Neural Networks (ANN) and K-Nearest Neighbors (K-nn) algorithms. Experimental results using our database show a percentage of correct classification, higher of 86.36% with ANN for 912 images of 38 people with 12 versions each one.

  3. 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

  4. Optical Chinese Character Recognition Using Accumulated Stroke Features

    NASA Astrophysics Data System (ADS)

    Jeng, Bor-Shenn

    1989-07-01

    An intelligent optical Chinese character recognition system using accumulated stroke features has been developed to solve the input problem of Chinese characters. The hardware architecture of the system is built on an IBM PC-AT with three extension boards: the preprocessor board, the feature extraction board, and the matching recognition board. The system can recognize, at the same time in the same program, either printed or handwritten Chinese characters of different styles and sizes. At present, a total of 5401 commonly used Chinese characters can be recognized. Results show that 99% of printed characters and 90% of constrained handwritten characters can be correctly recognized, at a speed of about 300 characters per minute.

  5. 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.

  6. 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…

  7. Shift- and deformation-robust optical character recognition based on parallel extraction of simple features

    NASA Astrophysics Data System (ADS)

    Jang, Ju-Seog; Shin, Dong-Hak

    1997-03-01

    For a flexible pattern recognition system that is robust to the input variations, a feature extraction approach is investigated. Two types of features are extracted: one is line orientations, and the other is the eigenvectors of the covariance matrix of the patterns that cannot be distinguished with the line orientation features alone. For the feature extraction, the Vander Lugt-type filters are used, which are recorded in a small spot of holographic recording medium by use of multiplexing techniques. A multilayer perceptron implemented in a computer is trained with a set of optically extracted features, so that it can recognize the input patterns that are not used in the training. Through preliminary experiments, where English character patterns composed of only straight line segments were tested, the feasibility of our approach is demonstrated.

  8. Comparison of neural network classifiers for optical character recognition

    NASA Astrophysics Data System (ADS)

    Baker, Thomas E.; McCartor, Hal

    1992-08-01

    The recognition of handwritten characters is an important technology for document processing and for advanced user interfaces. Recent advances in artificial neural network (ANN) classifiers have shown impressive pattern recognition results when using noisy data. One advantage of ANN algorithms is that they are parallel by design, which allows a natural implementation on high-speed parallel architectures. The availability of standard databases of handwritten characters permits a fair comparison between different OCR classifiers. This paper compares the classification performance of two popular ANN algorithms: Back Propagation and Learning Vector Quantization. A set of digits from the National Institute of Standards and Technology''s Handwritten Database is used to test the two classifiers. Each algorithm''s execution time and memory efficiency is also compared, based on an implementation for Adaptive Solutions'' highly parallel CNAPS architecture. We also show that a fair comparison cannot be made between OCR research that does not use the same set of characters for testing.

  9. 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.

  10. Imbalanced learning for pattern recognition: an empirical study

    NASA Astrophysics Data System (ADS)

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

    2010-10-01

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

  11. Algorithms for pattern recognition in images of cell cultures

    NASA Astrophysics Data System (ADS)

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

    2001-06-01

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

  12. 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.

  13. A time domain based classifier for ECG pattern recognition.

    PubMed

    Shorten, G P; Burke, M J

    2011-01-01

    Pattern recognition, and in particular dynamic time warping has been applied to the ECG for many different purposes over the last decade. Significant research on creating adaptive, feature based, and more complex forms of the algorithm in order to increase its ability to classify an ECG signal accurately has been performed. Despite this increase in complexity and in the number of variations of the dynamic time warping algorithm there has been less focus on actually using the results of dynamic time warping to relate the reference and test signals to each other as accurately as possible. The majority of dynamic time warping algorithms published in the literature, even the most complex of them, classify the most accurate match to a reference signal based only on resulting Euclidean distance or slope difference between samples of the known reference and unknown query signal. This article demonstrates how a combination of measurements including heart-rate, amplitude and required warping time alignment can be used to reduce the resulting error in the classification of a query signal after the query and reference signals have been warped together. Its benefits are verified with significant testing. PMID:22255456

  14. Ground pattern analysis in the Great Plains. [pattern recognition and mapping of areal geology in Kansas

    NASA Technical Reports Server (NTRS)

    Davis, J. C.; Ulaby, F. T. (principal investigators); Mcnaughton, J. L.

    1974-01-01

    The author has identified the following significant results. Spatial frequency analysis of ERTS-1 images appears to be useful in discriminating between large scale ground patterns in Kansas. Using parameters derived from the optical data processing of ERTS-1 images, sample areas from large physiographic categories have been accurately identified.

  15. 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.

  16. Simple online recognition of optical data strings based on conservative optical logic

    NASA Astrophysics Data System (ADS)

    Caulfield, H. John; Shamir, Joseph; Zavalin, Andrey I.; Silberman, Enrique; Qian, Lei; Vikram, Chandra S.

    2006-06-01

    Optical packet switching relies on the ability of a system to recognize header information on an optical signal. Unless the headers are very short with large Hamming distances, optical correlation fails and optical logic becomes attractive because it can handle long headers with Hamming distances as low as 1. Unfortunately, the only optical logic gates fast enough to keep up with current communication speeds involve semiconductor optical amplifiers and do not lend themselves to the incorporation of large numbers of elements for header recognition and would consume a lot of power as well. The ideal system would operate at any bandwidth with no power consumption. We describe how to design and build such a system by using passive optical logic. This too leads to practical problems that we discuss. We show theoretically various ways to use optical interferometric logic for reliable recognition of long data streams such as headers in optical communication. In addition, we demonstrate one particularly simple experimental approach using interferometric coinc gates.

  17. 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

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

    SciTech Connect

    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 in 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.

  19. 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.

  20. Applications of pattern recognition techniques to online fault detection

    SciTech Connect

    Singer, R.M.; Gross, K.C.; King, R.W.

    1993-11-01

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

  1. CD14: a soluble pattern recognition receptor in milk.

    PubMed

    Vidal, Karine; Donnet-Hughes, Anne

    2008-01-01

    An innate immune system capable of distinguishing among self, non-self, and danger is a prerequisite for health. Upon antigenic challenge, pattern recognition receptors (PRRs), such as the Toll-like receptor (TLR) family of proteins, enable this system to recognize and interact with a number of microbial components and endogenous host proteins. In the healthy host, such interactions culminate in tolerance to self-antigen, dietary antigen, and commensal microorganisms but in protection against pathogenic attack. This duality implies tightly regulated control mechanisms that are not expected of the inexperienced neonatal immune system. Indeed, the increased susceptibility of newborn infants to infection and to certain allergens suggests that the capacity to handle certain antigenic challenges is not inherent. The observation that breast-fed infants experience a lower incidence of infections, inflammation, and allergies than formula-fed infants suggests that exogenous factors in milk may play a regulatory role. There is increasing evidence to suggest that upon exposure to antigen, breast milk educates the neonatal immune system in the decision-making processes underlying the immune response to microbes. Breast milk contains a multitude of factors such as immunoglobulins, glycoproteins, glycolipids, and antimicrobial peptides that, qualitatively or quantitatively, may modulate how neonatal cells perceive and respond to microbial components. The specific role of several of these factors is highlighted in other chapters in this book. However, an emerging concept is that breast milk influences the neonatal immune system's perception of "danger." Here we discuss how CD14, a soluble PRR in milk, may contribute to this education. PMID:18183930

  2. Submicrometer Pattern Correction For Optical Lithography

    NASA Astrophysics Data System (ADS)

    Ito, Tetsuo; Kadota, Kazuya; Fukui, Hiroshi; Nagao, Masaki; Sugimoto, Aritoshi; Nozaki, Masahiro; Kato, Takeshi

    1988-01-01

    To realize higher CD controls of submicrometer devices, the submicrometer pattern corrections were investigated in optical reduction steppers considering the primary residual aberrations. The optical pattern fidelities on the reduction pattern transfer were estimated at first using the three-dimensional photoresist image simulator RESPROT (Resist Process Three-Dimensional Simulator), which is examined the Seidel's primary aberrations, i.e. spherical aberration, astigmatism, field curvature, distortion and coma. From RESPROT calculations it was known that astigmatism affects pattern shape depending on image height, coma and distortion make position shifts in exposure field, and image contrast is influenced by field curvature. These results were reflected to device design rules, process latitude enhancements and lens manufacturings. To use premature high NA g-line lenses and minimize the diffraction limit for submicrometer area, reticle pattern corrections are very useful for sub-space patterns writing in contact holes, "tailoring" of W/L for MOS-gate patterns, and sub-field position control for distortion correction on ER writing. For almost of this investigation, 5-10nm order corrections were required from original design on wafer. In order to make good use of higher NA lenses, focus latitude enhancement are required because field curvature control is very severe. For these demand, multi-step superpositions of focus and exposure, FLEX -method, is useful to enhance the depth of focus effectively. These simulated and examined results are covenient to realize submicrometer devices on advantageous optical lithography using more shorter wavelength, i.e. i-line or excirner laser steppers.

  3. Pattern recognition applied to mineral characterization of Brazilian coffees and sugar-cane spirits

    NASA Astrophysics Data System (ADS)

    Fernandes, Andréa P.; Santos, Mirian C.; Lemos, Sherlan G.; Ferreira, Márcia M. C.; Nogueira, Ana Rita A.; Nóbrega, Joaquim A.

    2005-06-01

    Aluminium, Ca, Cu, Fe, K, Mg, Mn, Na, Pb, S, Se, Si, Sn, Sr, and Zn were determined in coffee and sugar-cane spirit (cachaça) samples by axial viewing inductively coupled plasma optical emission spectrometry (ICP OES). Pattern recognition techniques such as principal component analysis and cluster analysis were applied to data sets in order to characterize samples with relation to their geographical origin and production mode (industrial or homemade and organically or conventionally produced). Attempts to correlate metal ion content with the geographical origin of coffee and the production mode (organic or conventional) of cachaça were not successful. Some differentiation was suggested for the geographical origin of cachaça of three regions (Northeast, Central, and South), and for coffee samples, related to the production mode. Clear separations were only obtained for differentiation between industrial and homemade cachaças, and between instant soluble and roasted coffees.

  4. 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.

  5. 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.

  6. Pattern Recognition Analysis of Ascitic Fluid Chromatograms for Patients with Liver Disorders

    PubMed Central

    Cohen, M.E.; Hudson, D.L.; Gitlin, N.; Mann, L.T.; Van den Bogaerde, J.

    1985-01-01

    Pattern recognition techniques have been applied in a number of medical applications. In the method described here, new orthogonal polynomials developed by Cohen are used as potential functions in a supervised learning approach to pattern recognition. This method is applied to the analysis of chromatograms obtained from ascitic fluid taken from patients who have spontaneous bacterial peritonitis, as well as from a control group. The chromatograms show the presence of organic acids in the ascitic fluid. The objective of the pattern recognition analysis is to obtain a method of identifying patients with spontaneous bacterial peritonitis by pattern analysis of the chromatogram. Results obtained show that as high as ninety-seven percent of the cases may be classified correctly using this method. The pattern recognition method is also compared to standard statistical discriminant analysis techniques, and is shown to be superior.

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

    PubMed

    Vasta, Gerardo R

    2012-01-01

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

  8. The software peculiarities of pattern recognition in track detectors

    NASA Astrophysics Data System (ADS)

    Starkov, N.

    2015-12-01

    The different kinds of nuclear track recognition algorithms are represented. Several complicated samples of use them in physical experiments are considered. The some processing methods of complicated images are described.

  9. The role of binocular disparity in rapid scene and pattern recognition

    PubMed Central

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

    2013-01-01

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

  10. Improving optical character recognition accuracy using adaptive image restoration

    NASA Astrophysics Data System (ADS)

    Stubberud, Peter A.; Kanai, Junichi; Kalluri, Venugopal

    1996-07-01

    A technique that can improve the optical character recognition (OCR) accuracy of text images is presented. By using the output from an OCR system and a distorted text image, this technique trains an adaptive restoration filter and then applies the filter to the distorted text that the OCR system could not recognize. The restored text image is then reprocessed by the OCR system, and the restored characters are recognized with a higher accuracy than the distorted text. A series of experiments were performed to determine a feasible adaptive restoration filter architecture, to establish this technique's ability to restore uniformly distorted text, and to demonstrate this technique's ability to improve the OCR accuracy of real world text documents. The results of these experiments show that this technique can improve both pixel and OCR accuracy of distorted text images.

  11. Songbirds use spectral shape, not pitch, for sound pattern recognition.

    PubMed

    Bregman, Micah R; Patel, Aniruddh D; Gentner, Timothy Q

    2016-02-01

    Humans easily recognize "transposed" musical melodies shifted up or down in log frequency. Surprisingly, songbirds seem to lack this capacity, although they can learn to recognize human melodies and use complex acoustic sequences for communication. Decades of research have led to the widespread belief that songbirds, unlike humans, are strongly biased to use absolute pitch (AP) in melody recognition. This work relies almost exclusively on acoustically simple stimuli that may belie sensitivities to more complex spectral features. Here, we investigate melody recognition in a species of songbird, the European Starling (Sturnus vulgaris), using tone sequences that vary in both pitch and timbre. We find that small manipulations altering either pitch or timbre independently can drive melody recognition to chance, suggesting that both percepts are poor descriptors of the perceptual cues used by birds for this task. Instead we show that melody recognition can generalize even in the absence of pitch, as long as the spectral shapes of the constituent tones are preserved. These results challenge conventional views regarding the use of pitch cues in nonhuman auditory sequence recognition. PMID:26811447

  12. Pattern recognition experiments in the mandala/cosine domain.

    PubMed

    Hsu, Y S; Prum, S; Kagel, J H; Andrews, H C

    1983-05-01

    The problem of recognition of objects in images is investigated from the simultaneous viewpoints of image bandwidth compression and automatic target recognition. A scenario is suggested in which recognition is implemented on features in the block cosine transform domain which is useful for data compression as well. While most image frames would be processed by the automatic recognition algorithms in the compressed domain without need for image reconstruction, this still allows for visual image classification of targets with poor recognition rates (by human viewing at the receiving terminal). It has been found that the Mandala sorting of the block cosine domain results in a more effective domain for selecting target identification parameters. Useful features from this Mandala/cosine domain are developed based upon correlation parameters and homogeneity measures which appear to successfully discriminate between natural and man-made objects. The Bhattacharyya feature discriminator is used to provide a 10:1 compression of the feature space for implementation of simple statistical decision surfaces (Gaussian and minimum distance classification). Imagery sensed in the visible spectra with a resolution of approximately 5-10 ft is used to illustrate the success of the technique on targets such as ships to be separated from clouds. A data set of 38 images is used for experimental verification with typical classification results ranging from the high 80's to low 90 percentile regions depending on the options choosen. PMID:21869136

  13. 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.

  14. 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...

  15. 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...

  16. 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...

  17. A dynamical pattern recognition model of γ activity in auditory cortex.

    PubMed

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

    2012-04-01

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

  18. 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

  19. Computerized literature reference system: use of an optical scanner and optical character recognition software.

    PubMed

    Lossef, S V; Schwartz, L H

    1990-09-01

    A computerized reference system for radiology journal articles was developed by using an IBM-compatible personal computer with a hand-held optical scanner and optical character recognition software. This allows direct entry of scanned text from printed material into word processing or data-base files. Additionally, line diagrams and photographs of radiographs can be incorporated into these files. A text search and retrieval software program enables rapid searching for keywords in scanned documents. The hand scanner and software programs are commercially available, relatively inexpensive, and easily used. This permits construction of a personalized radiology literature file of readily accessible text and images requiring minimal typing or keystroke entry. PMID:2132300

  20. 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

  1. 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

  2. PTX3, a humoral pattern recognition molecule at the interface between microbe and matrix recognition.

    PubMed

    Garlanda, Cecilia; Jaillon, Sebastien; Doni, Andrea; Bottazzi, Barbara; Mantovani, Alberto

    2016-02-01

    Innate immunity consists of a cellular and a humoral arm. PTX3 is a fluid patter recognition molecule (PRM) with antibody-like properties. Gene targeted mice and genetic associations in humans suggest that PTX3 plays a non-redundant role in resistance against selected pathogens (e.g. Aspergillus fumigatus, Pseudomonas aeruginosa, uropathogenic Escherichia coli) and in the regulation of inflammation. PTX3 acts as an extrinsic oncosuppressor by taming complement elicited tumor-promoting inflammation. Recent results indicate that, by interacting with provisional matrix components, PTX3 contributes to the orchestration of tissue repair. An acidic pH sets PTX3 in a tissue repair mode, while retaining anti-microbial recognition. Based on these data and scattered information on humoral PRM and matrix components, we surmise that matrix and microbial recognition are related functions in evolution. PMID:26650391

  3. Complex spatial filtering for parallel recognition of color pattern using liquid crystal panels

    NASA Astrophysics Data System (ADS)

    Kakuta, Mitsugu; Yamaguchi, Masahiro; Ohyama, Nagaaki

    1994-09-01

    We present a technique for the optical implementation of a color image correlation using a liquid crystal spatial light modulator (LC-SLM). A color image composed of 3D vectors of RGB primaries, is mapped onto a 2D plane to extract chromatic information from the image. Then, the 2D vector image is defined as a complex function, which is displayed on the LC- SLM and employed in a complex correlation system. Results from computer simulation and an experiment are also demonstrated. Optical parallel recognition processor for color image recognition is realized by the coherent optical system of single wavelength.

  4. Automatic recognition of coded-pattern sequence by using image cross-correlation

    NASA Astrophysics Data System (ADS)

    Zhong, Sidong; Gao, Zhi

    2005-12-01

    In order to solve the problem of automatic target recognition in photogrammetry, a method of recognition for coded-pattern sequence by using image cross-correlation is presented. Coded-pattern sequences are a series of patterns that posses unique identification information, which will be extracted to realize recognition. The concrete operation is to do cross-correlation of the real pattern image and the fictitious templet image of every possible pattern. The basis of this method is the theory of signal processing that the operation of cross-correlation can detect the resemblance of signals with different offset. The result of experiment shows that this method is applicable in many situations and also has the characteristics of high accuracy and high speed.

  5. A Training Strategy for Learning Pattern Recognition Control for Myoelectric Prostheses

    PubMed Central

    Powell, Michael A.; Thakor, Nitish V.

    2012-01-01

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

  6. Feature-preserving thinning algorithm for optical character recognition

    NASA Astrophysics Data System (ADS)

    Cheng, Ting-Shan; Chiang, Cheng-Chin; Roan, Shing-Ming; Fu, Hsin-Chia

    1993-04-01

    Thinning is usually regarded as a process of deleting boundary pixels of a character pattern until all strokes are of one pixel in width without deforming the original stroke configuration and connection. Suppose an OCR system uses deformed skeletons as recognition features, we may see that a `T' may erroneously be recognized as a `Y' or `r.' In order to preserve original stroke features, we propose that global attributes should be considered in the thinning procedure. In this paper, we present a new 3 X 3 window-based binary thinning method that considers both local and global attributes in each thinning iteration. We have designed and implemented a fast thinning algorithm to incorporate these two attributes. This algorithm can (1) prevent any excessive removing of pixels at the junction of two strokes or at the end of a stroke, which causes Y-shaped or shortened skeletons, and (2) can detect and remove any spooky type noise (one or two pixels standing on the surface of a stroke) which usually produces spiky skeletons in most of the previously proposed thinning algorithms. Experiment results show that our thinning method can preserve precise skeleton features of the original character patterns.

  7. Dentate gyrus supports slope recognition memory, shades of grey-context pattern separation and recognition memory, and CA3 supports pattern completion for object memory.

    PubMed

    Kesner, Raymond P; Kirk, Ryan A; Yu, Zhenghui; Polansky, Caitlin; Musso, Nick D

    2016-03-01

    In order to examine the role of the dorsal dentate gyrus (dDG) in slope (vertical space) recognition and possible pattern separation, various slope (vertical space) degrees were used in a novel exploratory paradigm to measure novelty detection for changes in slope (vertical space) recognition memory and slope memory pattern separation in Experiment 1. The results of the experiment indicate that control rats displayed a slope recognition memory function with a pattern separation process for slope memory that is dependent upon the magnitude of change in slope between study and test phases. In contrast, the dDG lesioned rats displayed an impairment in slope recognition memory, though because there was no significant interaction between the two groups and slope memory, a reliable pattern separation impairment for slope could not be firmly established in the DG lesioned rats. In Experiment 2, in order to determine whether, the dDG plays a role in shades of grey spatial context recognition and possible pattern separation, shades of grey were used in a novel exploratory paradigm to measure novelty detection for changes in the shades of grey context environment. The results of the experiment indicate that control rats displayed a shades of grey-context pattern separation effect across levels of separation of context (shades of grey). In contrast, the DG lesioned rats displayed a significant interaction between the two groups and levels of shades of grey suggesting impairment in a pattern separation function for levels of shades of grey. In Experiment 3 in order to determine whether the dorsal CA3 (dCA3) plays a role in object pattern completion, a new task requiring less training and using a choice that was based on choosing the correct set of objects on a two-choice discrimination task was used. The results indicated that control rats displayed a pattern completion function based on the availability of one, two, three or four cues. In contrast, the dCA3 lesioned rats displayed a significant interaction between the two groups and the number of available objects suggesting impairment in a pattern completion function for object cues. PMID:26318932

  8. A biologically based model for recognition of 2-D occluded patterns.

    PubMed

    Saifullah, Mohammad; Balkenius, Christian; Jönsson, Arne

    2014-02-01

    In this work, we present a biologically inspired model for recognition of occluded patterns. The general architecture of the model is based on the two visual information processing pathways of the human visual system, i.e. the ventral and the dorsal pathways. The proposed hierarchically structured model consists of three parallel processing channels. The main channel learns invariant representations of the input patterns and is responsible for pattern recognition task. But, it is limited to process one pattern at a time. The direct channel represents the biologically based direct connection from the lower to the higher processing level in the human visual cortex. It computes rapid top-down pattern-specific cues to modulate processing in the other two channels. The spatial channel mimics the dorsal pathway of the visual cortex. It generates a combined saliency map of the input patterns and, later, segments the part of the map representing the occluded pattern. This segmentation process is based on our hypothesis that the dorsal pathway, in addition to encoding spatial properties, encodes the shape representations of the patterns as well. The lateral interaction between the main and the spatial channels at appropriate processing levels and top-down, pattern-specific modulation of the these two channels by the direct channel strengthen the locations and features representing the occluded pattern. Consequently, occluded patterns become focus of attention in the ventral channel and also the pattern selected for further processing along this channel for final recognition. PMID:24122414

  9. 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

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

    PubMed

    Ming, Yue; Wang, Guangchao; Fan, Chunxiao

    2015-01-01

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

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

    PubMed

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

    2013-01-01

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

  12. Recognition of tire tread pattern on its trace by computer videotechnology

    NASA Astrophysics Data System (ADS)

    Kalinkin, Mikhael Y.; Usanov, Dmitry A.; Skripal, Anatoli V.

    2005-06-01

    The algorithm of analysis of videoirnage of tread pattern trace has been presented. The videoimage comparison is carried out with the help of image subtraction method. Realization of method of recognition of tire tread pattern has been described. The experimental results are given.

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

    EPA Science Inventory

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

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

    NASA Technical Reports Server (NTRS)

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

    1988-01-01

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

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

    ERIC Educational Resources Information Center

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

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

  16. Position Saliency in Children's Short Term Recognition Memory for Graphic Patterns.

    ERIC Educational Resources Information Center

    Leslie, Ronald Carl

    This study presents an account of position saliency in terms of children's ability to utilize graphic information, and in particular the serial encoding of information from letters in a graphic pattern. By varying the number and position of the letters distinguishing graphic patterns (positive condition) in a short-term recognition memory (STRM)…

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  18. 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

  19. Innate pattern recognition and categorization in a jumping spider.

    PubMed

    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

  20. 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.

  1. Validation of document image defect models for optical character recognition

    SciTech Connect

    Li, Y.; Lopresti, D.; Tomkins, A.

    1994-12-31

    In this paper we consider the problem of evaluating models for physical defects affecting the optical character recognition (OCR) process. While a number of such models have been proposed, the contention that they produce the desired result is typically argued in an ad hoc and informal way. We introduce a rigorous and more pragmatic definition of when a model is accurate: we say a defect model is validated if the OCR errors induced by the model are effectively indistinguishable from the errors encountered when using real scanned documents. We present two measures to quantify this similarity: the Vector Space method and the Coin Bias method. The former adapts an approach used in information retrieval, the latter simulates an observer attempting to do better than a {open_quotes}random{close_quotes} guesser. We compare and contrast the two techniques based on experimental data; both seem to work well, suggesting this is an appropriate formalism for the development and evaluation of document image defect models.

  2. Demonstration of optical header recognition for BPSK data using novel design of logic gates

    NASA Astrophysics Data System (ADS)

    Kakarla, Ravikiran; Venkitesh, Deepa

    2016-03-01

    We demonstrate the experimental implementation of an all-optical header recognition system for phase modulated data using logic gates, realized with the least number of active elements compared to conventional demonstrations. We experimentally implement the individual optical AND, XNOR/XOR logic gates and optimize their performances. We integrate these logic gates to build an all-optical header recognition system. We verify the working of the header recognition system for different combinations of header and local address bits. We also discuss the implementation challenges of the demonstrated system.

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

    PubMed Central

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

    2008-01-01

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

  4. Finger vein recognition using local line binary pattern.

    PubMed

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

    2011-01-01

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

  5. Finger Vein Recognition Using Local Line Binary Pattern

    PubMed Central

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

    2011-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Yu, Lei; Cao, Qi; Zhao, Anping

    2015-01-01

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

  7. Platform-independent optical character and curve recognition system

    NASA Astrophysics Data System (ADS)

    Goldberg, Robert R.; Robinson, Jonathan

    1998-10-01

    Alphanumerics and other characters can be decomposed into a minimal number of components, namely, line segments and, circular and elliptical arcs. The combination and relative location of these components (i.e. the character signature) uniquely determine the character identity. We are developing a pattern recognition engine, Software Engineering Engine (SEE), which computes the set of all line segments, circular and elliptical arcs that a given digital curve represents. From this obtained set, the original line segment or geometric arc that best fits the digital curve is extracted. Thus, the underlying shape of the digital curve can be determined with subpixel accuracy. SEE computes all this in linear time in the number of pixels in the digital curve. To further recognize characters, SEE will determine the linear, circular and elliptical components that comprise each character. SEE will then compare this character signature from the image with signatures in a character-signature database to secure the best fit. This approach has applications to the interpretation of engineering symbols and will be extended to interpret dimensionality associated with geometric objects indicated in an engineering sketch.

  8. 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.

  9. 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.

  10. 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

  11. Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns

    NASA Astrophysics Data System (ADS)

    Carpenter, Gail A.; Grossberg, Stephen

    1988-02-01

    Adaptive resonance architectures are neural networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequences of input patterns. This article introduces ART 2, a class of adaptive resonance architectures which rapidly self-organize pattern recognition categories in response to arbitrary sequences of either analog of binary input patterns. In order to cope with arbitrary sequences of analog input patterns, ART 2 architectures embody solutions to a number of design principles, such as the stability-plasticity tradeoff, the search-direct access tradeoff, and the match-reset tradeoff. In these architectures, top-down learned expectation and matching mechanisms are critical in self-stabilizing the code learning process. A parallel search scheme updates itself adaptively as the learning process unfolds, and realizes a form of real-time hypothesis discovery, testing, learning, and recognition. After learning self-stabilizes, the search process is automatically disengaged. Thereafter input patterns directly access their recognition codes without any search. Thus recognition time for familiar inputs does not increase with the complexity of the learned code. A novel input pattern can directly access a category if it shares invariant properties with the set of familiar exemplars of that category. A parameter called the attentional vigilance parameter determines how fine the categories will be. If vigilance increases (decreases) due to environmental feedback, then the system automatically searches for and learns finer (coarser) recognition categories. Gain control parameters enable the architecture to suppress noise up to a prescribed level. The architecture's global design enables it to learn effectively despite the high degree of nonlinearity of such mechanisms.

  12. 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.

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

    ERIC Educational Resources Information Center

    Perez, Ernest

    1990-01-01

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

  14. Bioacoustic systems: insights for acoustical imaging and pattern recognition (Invited Paper)

    NASA Astrophysics Data System (ADS)

    Altes, Richard A.

    1987-09-01

    Standard performance measures and statistical tests must be altered for research on animal sonar. The narrowband range-Doppler ambiguity function must be redefined to analyze wideband signals. A new range, cross-range ambiguity function is needed to represent angle estimation and spatial resolution properties of animal sonar systems. Echoes are transformed into time-frequency (spectrogram-like) representations by the peripheral auditory system. Detection, estimation, and pattern recognition capabilities of animals should thus be analyzed in terms of operations on spectrograms. The methods developed for bioacoustic research yield new insights into the design of man-made imaging and pattern recognition systems. The range, cross-range ambiguity function can be used to improve imaging performance. Important features for echo pattern recognition are illustrated by time-frequency plots showing (i) principal components for spectrograms and (ii) templates for optimum discrimination between data classes.

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

    PubMed

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

    2015-04-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1989-01-01

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

  17. Pattern recognition receptors as potential therapeutic targets in inflammatory rheumatic disease.

    PubMed

    Mullen, Lisa M; Chamberlain, Giselle; Sacre, Sandra

    2015-01-01

    The pattern recognition receptors of the innate immune system are part of the first line of defence against pathogens. However, they also have the ability to respond to danger signals that are frequently elevated during tissue damage and at sites of inflammation. Inadvertent activation of pattern recognition receptors has been proposed to contribute to the pathogenesis of many conditions including inflammatory rheumatic diseases. Prolonged inflammation most often results in pain and damage to tissues. In particular, the Toll-like receptors and nucleotide-binding oligomerisation domain-like receptors that form inflammasomes have been postulated as key contributors to the inflammation observed in rheumatoid arthritis, osteoarthritis, gout and systemic lupus erythematosus. As such, there is increasing interest in targeting these receptors for therapeutic treatment in the clinic. Here the role of pattern recognition receptors in the pathogenesis of these diseases is discussed, with an update on the development of interventions to modulate the activity of these potential therapeutic targets. PMID:25975607

  18. 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.

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

    SciTech Connect

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

    1995-12-01

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

  20. 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

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

    PubMed

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

    2015-01-01

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

  2. Oxidized LDL: Diversity, Patterns of Recognition, and Pathophysiology

    PubMed Central

    Volkov, Suncica; Subbaiah, Papasani V.

    2010-01-01

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

  3. 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.

  4. 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.

  5. Thrombelastographic Pattern Recognition in Renal Disease and Trauma

    PubMed Central

    Chapman, Michael P.; Moore, Ernest E.; Burneikis, Dominykas; Moore, Hunter B.; Gonzalez, Eduardo; Anderson, Kelsey C.; Ramos, Christopher R.; Banerjee, Anirban

    2015-01-01

    Introduction Thrombelastography (TEG) is a viscoelastic hemostatic assay. We have observed that end stage renal disease (ESRD) and trauma induced coagulopathy (TIC) produce distinctive TEG tracings. We hypothesized that rigorously definable TEG patterns could discriminate between healthy controls and patients with ESRD and TIC. Methods TEG was performed on blood from ESRD patients (n=54) and blood from trauma patients requiring a massive blood transfusion (n=16). Plots of independent TEG parameters were analyzed for patterns coupled to disease state, compared to controls. Decision trees for taxonomic classification were then built using the “R-Project” statistical software. Results Minimally overlapping clusters of TEG results were observed for the three patient groups when coordinate pairs of maximum amplitude (MA) and TEG activated clotting time (ACT) were plotted on orthogonal axes. Based upon these groupings, a taxonomical classification tree was constructed using MA and TEG ACT. Branch points were set at an ACT of 103 seconds, and these branches subdivided for MA at 60.8 mm for the high ACT branch and 72.6 mm for the low ACT branch, providing a correct classification rate of 93.4%. Conclusions ESRD and TIC demonstrate distinct TEG patterns. The coagulopathy of ESRD is typified by a prolonged enzymatic phase of clot formation, with normal-to-elevated final clot strength. Conversely, TIC is typified by prolonged clot formation and weakened clot strength. Our taxonomic categorization constitutes a rigorous system for the algorithmic interpretation of TEG based upon cluster analysis. This will form the basis for clinical decision support software for viscoelastic hemostatic assays. PMID:25577141

  6. Critical Song Features for Auditory Pattern Recognition in Crickets

    PubMed Central

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Ding, Li; Zhou, Runjing; Liu, Guiying

    2010-08-01

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

  8. Study on local Gabor binary patterns for face representation and recognition

    NASA Astrophysics Data System (ADS)

    Ge, Wei; Han, Chunling; Quan, Wei

    2015-12-01

    More recently, Local Binary Patterns(LBP) has received much attention in face representation and recognition. The original LBP operator could describe the spatial structure information, which are the variety edge or variety angle features of local facial images essentially, they are important factors of classify different faces. But the scale and orientation of the edge features include more detail information which could be used to classify different persons efficiently, while original LBP operator could not to extract the information. In this paper, based on the introduction of original LBP-based facial representation and recognition, the histogram sequences of local Gabor binary patterns are used to representation facial image. Principal Component Analysis (PCA) method is used to classification the histogram sequences, which have been converted to vectors. Recognition experimental results show that the method we used in this paper increases nearly 6% than the classification performance of original LBP operator.

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

    SciTech Connect

    Carpenter, G.A.; Grossberg, S.

    1988-03-01

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

  10. 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.

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

    SciTech Connect

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

    1993-08-01

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

  12. 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…

  13. 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...

  14. 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...

  15. 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 ...

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

    SciTech Connect

    Not Available

    1986-01-01

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

  17. Proceedings of the IEEE conference on computer vision and pattern recognition

    SciTech Connect

    Not Available

    1985-01-01

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

  18. 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…

  19. 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.

  20. 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…

  1. 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…

  2. 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…

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

    ERIC Educational Resources Information Center

    Silberstang, Joyce; London, Manuel

    2009-01-01

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

  4. 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…

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

    NASA Technical Reports Server (NTRS)

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

    1993-01-01

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

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

    EPA Science Inventory

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

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

    EPA Science Inventory

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

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

    EPA Science Inventory

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

  9. 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.

  10. Recognition of pathogenic microbes by the Drosophila phagocytic pattern recognition receptor Eater.

    PubMed

    Chung, Yoon-Suk Alexander; Kocks, Christine

    2011-07-29

    Non-opsonic phagocytosis is a primordial form of pathogen recognition that is mediated by the direct interaction of phagocytic receptors with microbial surfaces. In the fruit fly Drosophila melanogaster, the EGF-like repeat containing scavenger receptor Eater is expressed by phagocytes and is required to survive infections with gram-positive and gram-negative bacteria. However, the mechanisms by which this receptor recognizes different types of bacteria are poorly understood. To address this problem, we generated a soluble, Fc-tagged receptor variant of Eater comprising the N-terminal 199 amino acids including four EGF-like repeats. We first established that Eater-Fc displayed specific binding to broad yet distinct classes of heat- or ethanol-inactivated microbes and behaved similarly to the membrane-bound, full-length Eater receptor. We then used Eater-Fc as a tool to probe Eater binding to the surface of live bacteria. Eater-Fc bound equally well to naive or inactivated Staphylococcus aureus or Enterococcus faecalis, suggesting that in vivo, Eater directly targets live gram-positive bacteria, enabling their phagocytic clearance and destruction. By contrast, Eater-Fc was unable to interact with live, naive gram-negative bacteria (Escherichia coli, Serratia marcescens, and Pseudomonas aeruginosa). For these bacteria, Eater-Fc binding required membrane-disrupting treatments. Furthermore, we found that cecropin A, a cationic, membrane-disrupting antimicrobial peptide, could promote Eater-Fc binding to live E. coli, even at sublethal concentrations. These results suggest a previously unrecognized mechanism by which antimicrobial peptides cooperate with phagocytic receptors to extend the range of microbes that can be targeted by a single, germline-encoded receptor. PMID:21613218

  11. [Tumorigenesis: interplay of pattern recognition receptors and autophagy].

    PubMed

    Mûzes, Györgyi; Sipos, Ferenc

    2016-03-01

    According to recent data, the involvement of autophagy in tumor development is unquestionable. Nevertheless, cell-derived pathogen/danger-associated molecular pattern (PAMP/DAMP)-sensing Toll-like receptors (TLRs) are also able to contribute to tumorigenesis and immune escape of malignantly transformed cells. Besides immunocompetent cells, several types of tumors also exhibit TLRs. TLR- and autophagy-related signaling pathways, on the other hand, may evolve anti-tumor effects in a context dependent cell- and microenvironment-specific mode. Nowadays, the autophagy machinery has been considered as a crucial homeostatic process of eukaryotic cells, and as essential constituent of the immune system influencing antimicrobial and inflammation-related immune responses. Accumulating evidence indicates that TLRs and autophagy are interdependent in response to PAMPs and DAMPs, in addition there is a bi-directional controling cross-modulation between them. Regarding personalized medicine, theoretically, it is reasonable that manipulation of the TLR-autophagy regulatory loop might be adaptable for anti-cancer therapy. PMID:26934352

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

    PubMed Central

    Noh, Soo Rim; Isaacowitz, Derek M.

    2014-01-01

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

  13. Superresolution Imaging of Optical Vortices in a Speckle Pattern.

    PubMed

    Pascucci, Marco; Tessier, Gilles; Emiliani, Valentina; Guillon, Marc

    2016-03-01

    We characterize, experimentally, the intensity minima of a polarized high numerical aperture optical speckle pattern and the topological charges of the associated optical vortices. The negative of a speckle pattern is imprinted in a uniform fluorescent sample by photobleaching. The remaining fluorescence is imaged with superresolution stimulated emission depletion microscopy, which reveals subdiffraction fluorescence confinement at the center of optical vortices. The intensity statistics of saturated negative speckle patterns are predicted and measured. The charge of optical vortices is determined by controlling the handedness of circular polarization, and the creation or annihilation of a vortex pair along propagation is shown. PMID:26991179

  14. Superresolution Imaging of Optical Vortices in a Speckle Pattern

    NASA Astrophysics Data System (ADS)

    Pascucci, Marco; Tessier, Gilles; Emiliani, Valentina; Guillon, Marc

    2016-03-01

    We characterize, experimentally, the intensity minima of a polarized high numerical aperture optical speckle pattern and the topological charges of the associated optical vortices. The negative of a speckle pattern is imprinted in a uniform fluorescent sample by photobleaching. The remaining fluorescence is imaged with superresolution stimulated emission depletion microscopy, which reveals subdiffraction fluorescence confinement at the center of optical vortices. The intensity statistics of saturated negative speckle patterns are predicted and measured. The charge of optical vortices is determined by controlling the handedness of circular polarization, and the creation or annihilation of a vortex pair along propagation is shown.

  15. 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

  16. 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

  17. Heuristic algorithm for optical character recognition of Arabic script

    NASA Astrophysics Data System (ADS)

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

    1996-02-01

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

  18. Developing A General Purpose Optical Character Recognition System

    NASA Astrophysics Data System (ADS)

    Marosi, I.; Kovacs, E.

    1989-07-01

    The most important points in the development of an OCR system are the font independence and the ability to read free layout text. The feature extraction algorithm based on contour tracing generates size invariant geometrical and topological features which make the recognition as font independent as possible. In our OCR system (Recognita) these features are arranged in a tree structure which enables fast classification to be done. The character and line finding algorithm is designed to meet the second requirement including the recognition of proportional spacing, ligatures, kerning and automatic separation of graphics and text.

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

    NASA Technical Reports Server (NTRS)

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

    1989-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

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

  1. Recording holograms with diagonal coding on binary spatial light modulators for pattern recognition.

    PubMed

    Bergeron, A; April, G V; Arsenault, H H

    1990-04-10

    The performance of computer-generated holographic matched filters recorded on 128 x 128 pixel supports was studied experimentally, with the objective of recording holograms on spatial light modulators such as the Semetex SIGHT-MOD for rotation-invariant pattern recognition. A new method of diagonal coding is introduced. This method yields good results, as it allows a more accurate phase quantization. Experimental results from computer simulations show that high speed rotation-invariant recognition of simple shapes is possible with such holograms. PMID:20563057

  2. Modal Control in Semiconductor Optical Waveguides With Uniaxially Patterned Layers

    NASA Astrophysics Data System (ADS)

    Subashiev, Arsen V.; Luryi, Serge

    2006-03-01

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

  3. Optical character recognition of camera-captured images based on phase features

    NASA Astrophysics Data System (ADS)

    Diaz-Escobar, Julia; Kober, Vitaly

    2015-09-01

    Nowadays most of digital information is obtained using mobile devices specially smartphones. In particular, it brings the opportunity for optical character recognition in camera-captured images. For this reason many recognition applications have been recently developed such as recognition of license plates, business cards, receipts and street signal; document classification, augmented reality, language translator and so on. Camera-captured images are usually affected by geometric distortions, nonuniform illumination, shadow, noise, which make difficult the recognition task with existing systems. It is well known that the Fourier phase contains a lot of important information regardless of the Fourier magnitude. So, in this work we propose a phase-based recognition system exploiting phase-congruency features for illumination/scale invariance. The performance of the proposed system is tested in terms of miss classifications and false alarms with the help of computer simulation.

  4. One-pass manufacturing of multimaterial colloidal particles using optical recognition-enhanced laser direct imaging lithography

    NASA Astrophysics Data System (ADS)

    Kavčič, Blaž; Kokot, Gašper; Poberaj, Igor; Babić, Dušan; Osterman, Natan

    2016-02-01

    We report on a maskless lithography rapid prototyping system for the fabrication of multimaterial hybrid structures in standard i-line negative photoresists enriched by the addition of functionalization particles. The system uses a combination of image recognition methods to detect particle positions in the photoresist and laser direct imaging to illuminate it with a focused ultraviolet laser. A set of acousto-optic deflectors, used to steer the laser, enables precise high-speed illumination of complex patterns. As a result, hybrid micron-sized structures composed of a base particle embedded in a photoresist frame can be manufactured using a one-pass process.

  5. 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.

  6. Optical character recognition using a memory matrix generated from singular value decomposition

    NASA Astrophysics Data System (ADS)

    Sasaki, Osami; Sakata, Kenichi; Shibahara, Akihito; Suzuki, Takamasa

    1998-09-01

    A memory matrix provides output vectors that are specified by the corresponding input vectors. If the input vectors represent input characters, the memory matrix performs character recognition. When the input characters contain noise, it is difficult to recognize the characters. To overcome this difficulty, we generate a new memory matrix by using singular value decomposition and manipulating the singular values. The effectiveness of the memory matrix is made clear by recognized 26 alphabets characters containing noise with an optical recognition system.

  7. 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.

  8. Basic research projects in the field of optical character recognition

    NASA Astrophysics Data System (ADS)

    Schuermann, J.; Held, H. J.; Hildebrandt, V.

    1980-12-01

    A classifier concept and classifier adaptation procedure are presented. The indirect polynomial approach is explained. The control of computational effort in the calculation of the classifier is discussed. The multiplication of a set of samples by means of specific stochastic changes in the character features is proposed. The effective organization of the adaptation process and the improvement of the organization of the classification process itself are outlined. In some cases, results lead to a direct improvement in recognition performance.

  9. Comparison study of feature extraction methods in structural damage pattern recognition

    NASA Astrophysics Data System (ADS)

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

    2011-04-01

    This paper compares the performance of various feature extraction methods applied to structural sensor measurements acquired in-situ, from a decommissioned bridge under realistic damage scenarios. Three feature extraction methods are applied to sensor data to generate feature vectors for normal and damaged structure data patterns. The investigated feature extraction methods include identification of both time domain methods as well as frequency domain methods. The evaluation of the feature extraction methods is performed by examining distance values among different patterns, distance values among feature vectors in the same pattern, and pattern recognition success rate. The test data used in the comparison study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case data sets, including undamaged cases and pier settlement cases (different depths), are used to test the separation of feature vectors among different patterns and the pattern recognition success rate for different feature extraction methods is reported.

  10. 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.

  11. 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

  12. Should I stay or should I go? Traffic control for plant pattern recognition receptors.

    PubMed

    Frescatada-Rosa, Márcia; Robatzek, Silke; Kuhn, Hannah

    2015-12-01

    Plants employ cell surface-localised receptors to recognise potential invaders via perception of microbe-derived molecules. This is mediated by pattern recognition receptors (PRRs) that bind microbe-associated or damage-associated molecular patterns or perceive apoplastic effector proteins secreted by microorganisms. In either case, effective recognition and initiation of appropriate defence responses rely on a signalling competent pool of receptors at the cell surface. Maintenance of this pool of receptors at the plasma membrane is guaranteed by sorting of properly folded ligand-unbound and ligand-bound receptors via the secretory-endosomal network in an activation-dependent manner. Recent findings highlight that ligand-induced endocytosis is found across members of distinct PRR families suggesting a conserved mechanism by which PRRs and immunity is regulated. PMID:26344487

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

    NASA Technical Reports Server (NTRS)

    Melhorn, W. N.; Sinnock, S.

    1973-01-01

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

  14. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-09-01

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

  16. 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.

  17. Pattern recognition model for aerosol classification with atmospheric backscatter lidars: principles and simulations

    NASA Astrophysics Data System (ADS)

    Liu, Dong; Yang, Yongying; Zhang, Yupeng; Cheng, Zhongtao; Wang, Zhifei; Luo, Jing; Su, Lin; Yang, Liming; Shen, Yibing; Bai, Jian; Wang, Kaiwei

    2015-01-01

    A pattern recognition model for aerosol classification with atmospheric backscatter lidars is proposed and studied in detail. The theoretical framework and the implementation process of the proposed model are presented. Computer simulations have been carried out to verify the practicability and robustness of this model. The k-fold cross-validation method is employed in the process of classifier designing to choose the proper decision rule, which is mainly based on statistical pattern recognition theory. At the same time, the validity of the model is evaluated. The generalized self-validation is also carried out in the computer simulations to verify the stability of the model. The analysis of the performances in reduced status, especially the instance of application to Cloud-Aerosol Lidar with Orthogonal Polarization, demonstrates the generalization ability and performance of this model.

  18. Comparing Shape and Texture Features for Pattern Recognition in Simulation Data

    SciTech Connect

    Newsam, S; Kamath, C

    2004-12-10

    Shape and texture features have been used for some time for pattern recognition in datasets such as remote sensed imagery, medical imagery, photographs, etc. In this paper, we investigate shape and texture features for pattern recognition in simulation data. In particular, we explore which features are suitable for characterizing regions of interest in images resulting from fluid mixing simulations. Three texture features--gray level co-occurrence matrices, wavelets, and Gabor filters--and two shape features--geometric moments and the angular radial transform--are compared. The features are evaluated using a similarity retrieval framework. Our preliminary results indicate that Gabor filters perform the best among the texture features and the angular radial transform performs the best among the shape features. The feature which performs the best overall is dependent on how the groundtruth dataset is created.

  19. 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.

  20. 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.

  1. 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.

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

    NASA Technical Reports Server (NTRS)

    Morrell, J. S.

    1975-01-01

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

  3. 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.

  4. Metrics of algebraic closures in pattern recognition problems with two nonoverlapping classes

    NASA Astrophysics Data System (ADS)

    D'Yakonov, A. G.

    2008-05-01

    It is shown that, in the pattern recognition problem with two nonoverlapping classes, the matrices of estimates of the object closeness are described by a metric. The transition to the algebraic closure of the model of recognizing operators of finite degree corresponds to the application of a special transformation of this metric. It is proved that the minimal degree correct algorithm can be found as a polynomial of a special form. A simple criterion for testing classification implementations is obtained.

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

    PubMed Central

    2013-01-01

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

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

    SciTech Connect

    Foley, M.G.

    1991-02-01

    We are developing and applying a suite of automated remote geologic analysis (RGA) methods at Pacific Northwest Laboratory (PNL) for extracting structural and tectonic patterns from digital models of topography and other spatially registered geophysical data. In analyzing a map area, the geologist employs a variety of spatial representations (e.g., topographic maps; oblique, vertical and vertical stereographic aerial photographs; satellite-sensor images) in addition to actual field observations to provide a basis for recognizing features (patterns) diagnostic or suggestive of various geologic and geomorphic features. We intend that our automated analyses of digital models of elevation use the same photogeologic pattern-recognition methods as the geologist's; otherwise there is no direct basis for manually evaluating results of the automated analysis. Any system for automating geologic analysis should extend the geologist's pattern-recognition abilities and quantify them, rather than replace them. This requirement means that results of automated structural pattern-recognition analyses must be evaluated by geologists using the same method that would be employed in manual field checking: visual examination of the three-dimensional relationships among rocks, erosional patterns, and identifiable structures. Interactive computer-graphics in quantitative (i.e., spatially registered), simulated three-dimensional perspective and stereo are thus critical to the integration and interpretation of topography, imagery, point data, RGA-identified fracture/fault planes, stratigraphy, contoured geophysical data, nonplanar surfaces, boreholes, and three-dimensional zones (e.g., crush zones at fracture intersections). This graphical interaction presents the megabytes of digital geologic and geophysical data to the geologist in the same spatial format that field observations would take, permitting direct evaluation of RGA methods and results. 5 refs., 2 figs.

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

    PubMed Central

    Lee, Sean; Nitin, Mantri

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Kozłowski, 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.

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

    SciTech Connect

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

    1996-03-01

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

  10. 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.

  11. Optical music recognition using skeleton structure and neural network

    NASA Astrophysics Data System (ADS)

    Chang, Seok C.; Soak, Sang M.; Shin, Taehwan; Ahn, Byung-Ha

    2002-03-01

    In this paper, our experiment consists of three steps to recognize printed music. The first step is the pre-processing stage: finding threshold for binary images, identifying staff-line parts, and removing them. The second step is the recognition stage. We first classify notes and other symbols by their sizes and characteristics. The skeleton structure analysis is used for recognizing music notes due to their complex combination of piano scores and the back-propagation and projection profile method are used for other symbols after their normalizing. The last step is the review stage. In which we investigate their syntactic validity and correct unrecognized or misconceived symbols.

  12. The DSFPN, a new neural network for optical character recognition.

    PubMed

    Morns, L P; Dlay, S S

    1999-01-01

    A new type of neural network for recognition tasks is presented in this paper. The network, called the dynamic supervised forward-propagation network (DSFPN), is based on the forward only version of the counterpropagation network (CPN). The DSFPN, trains using a supervised algorithm and can grow dynamically during training, allowing subclasses in the training data to be learnt in an unsupervised manner. It is shown to train in times comparable to the CPN while giving better classification accuracies than the popular backpropagation network. Both Fourier descriptors and wavelet descriptors are used for image preprocessing and the wavelets are proven to give a far better performance. PMID:18252647

  13. Probabilistic Prediction Of Heavy Rainfall Using Pattern Recognition Technique Based On Self-Organizing Map (SOM)

    NASA Astrophysics Data System (ADS)

    Nishiyama, K.; Jinno, K.; Wakimizu, K.

    2007-12-01

    Most of the heavy rainfall systems are closely related to spatially-extended meteorological information, in other words, multi-dimensional information. Therefore, in this study, pattern recognition technique based on Self- Organizing Map (SOM) was applied to the prediction of heavy rainfall in the rainy season in Japan in combination with Back Propagation (BP: supervised ANN). The SOM is an unsupervised ANN-based pattern recognition technique, projecting high-dimensional input variables onto two-dimensional regularly-arranged units for visualization. Here, the patterns of meteorological field characterizing the rainy season (BAIU) in Japan were classified using the SOM, and related to rainfall data using the BP. From the results, the rainfall prediction technique succeeded in constructing meteorologically-significant relationships between complicated meteorological field patterns and rainfall by focusing on the inherent properties of the SOM. Particularly, it was clearly shown that high probability of heavy rainfall corresponds to a meteorological field pattern characterized by Low-Level Jet (LLJ) and ample water vapor, which was closely associated with disastrous rainfall events in Japan.

  14. Search and retrieval of plasma wave forms: Structural pattern recognition approach

    NASA Astrophysics Data System (ADS)

    Dormido-Canto, S.; Farias, G.; Vega, J.; Dormido, R.; Sánchez, J.; Duro, N.; Santos, M.; Martin, J. A.; Pajares, G.

    2006-10-01

    Databases for fusion experiments are designed to store several million wave forms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques can allow identification of similar plasma behaviors. Further developments in this area must be focused on four aspects: large databases, feature extraction, similarity function, and search/retrieval efficiency. This article describes an approach for pattern searching within wave forms. The technique is performed in three stages. Firstly, the signals are filtered. Secondly, signals are encoded according to a discrete set of values (code alphabet). Finally, pattern recognition is carried out via string comparisons. The definition of code alphabets enables the description of wave forms as strings, instead of representing the signals in terms of multidimensional data vectors. An alphabet of just five letters can be enough to describe any signal. In this way, signals can be stored as a sequence of characters in a relational database, thereby allowing the use of powerful structured query languages to search for patterns and also ensuring quick data access.

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

    SciTech Connect

    Benavidez, A.

    1986-01-01

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

  16. The Immune System as a Model for Pattern Recognition and Classification

    PubMed Central

    Carter, Jerome H.

    2000-01-01

    Objective: To design a pattern recognition engine based on concepts derived from mammalian immune systems. Design: A supervised learning system (Immunos-81) was created using software abstractions of T cells, B cells, antibodies, and their interactions. Artificial T cells control the creation of B-cell populations (clones), which compete for recognition of “unknowns.” The B-cell clone with the “simple highest avidity” (SHA) or “relative highest avidity” (RHA) is considered to have successfully classified the unknown. Measurement: Two standard machine learning data sets, consisting of eight nominal and six continuous variables, were used to test the recognition capabilities of Immunos-81. The first set (Cleveland), consisting of 303 cases of patients with suspected coronary artery disease, was used to perform a ten-way cross-validation. After completing the validation runs, the Cleveland data set was used as a training set prior to presentation of the second data set, consisting of 200 unknown cases. Results: For cross-validation runs, correct recognition using SHA ranged from a high of 96 percent to a low of 63.2 percent. The average correct classification for all runs was 83.2 percent. Using the RHA metric, 11.2 percent were labeled “too close to determine” and no further attempt was made to classify them. Of the remaining cases, 85.5 percent were correctly classified. When the second data set was presented, correct classification occurred in 73.5 percent of cases when SHA was used and in 80.3 percent of cases when RHA was used. Conclusions: The immune system offers a viable paradigm for the design of pattern recognition systems. Additional research is required to fully exploit the nuances of immune computation. PMID:10641961

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

    SciTech Connect

    Vega, J.; Ratta, G. A.; Castro, P.; Pereira, A.; Portas, A.

    2008-03-12

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

  18. Hand Biometric Recognition Based on Fused Hand Geometry and Vascular Patterns

    PubMed Central

    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

  19. Flower algorithm for star pattern recognition in space surveillance with star trackers

    NASA Astrophysics Data System (ADS)

    Gong, Jiaqi; Wu, Lin; Gong, Junbin; Ma, Jie; Tian, Jinwen

    2009-12-01

    Using star tracker to perform space surveillance is a focal point of research in aerospace engineering. However, autonomous attitude determination with star trackers in missions is a challenging task, because of spacecraft attitude dynamics and false stars. We present a novel star pattern recognition algorithm to resolve these problems. The algorithm defines a star pattern, called a flower code, composed of angular distances and circular angles. Then, a three-step strategy is adopted to find the correspondence of the sensor pattern and the catalog pattern, including initial lookup table match, cyclic dynamic match, and validation. A number of experiments are carried out on simulated and real star images. The simulation results show that the proposed method provides improved performance, especially on robustness against false stars. Also, the results for real star images demonstrate the reliability of the method for ground-based measurements.

  20. 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.

  1. Optical Chinese character recognition system using a new pipelined matching and sorting very large scale integration

    NASA Astrophysics Data System (ADS)

    Miou, Char-Shin; Shieh, Dung-Ming; Chang, Gan-How; Chien, Bing-Shan; Chang, Ming-Wen; Jeng, Bor-Shenn

    1993-07-01

    A VLSI implementation of the optical Chinese character recognition (OCCR) system with pipelined and parallel structure is presented. We also propose an efficient method for performing block classification and character segmentation as well as an effective and adaptive feature extraction algorithm for recognizing multifront printed Chinese characters. With the complex and huge amount of data involved in Chinese characters, their recognition requires numerous complex computations. Therefore, to improve the recognition efficiency for practical applications, a VLSI chip is designed and fabricated. To preserve a certain degree of flexibility so that various recognition algorithms can be implemented with the system, only the most time-consuming parts are implemented into the VLSI circuit. By combining the VLSI technology and the effective Chinese character recognition algorithm, a practical OCCR system with high speed, high-recognition rate, and accumulated learning capability is developed. Based on the experimental results, the VLSI chip can process up to 200 characters/s, which is one hundred times faster than the original software algorithm. The recognition rates of three different test conditions are also given.

  2. Optical illumination optimization for patterned defect inspection

    NASA Astrophysics Data System (ADS)

    Barnes, Bryan M.; Quinthanilha, Richard; Sohn, Yeung-Joon; Zhou, Hui; Silver, Richard M.

    2011-03-01

    Rapidly decreasing critical dimensions (CD) for semiconductor devices drive the study of improved methods for the detection of defects within patterned areas. As reduced CDs are being achieved through directional patterning, additional constraints and opportunities present themselves in defect metrology. This simulation and experimental study assesses potential improvements in patterned defect inspection that may be achieved by engineering the light incident to the sample within a high-magnification imaging platform. Simulation variables include the incident angle, polarization, and wavelength for defect types common to directional device layouts. Detectability is determined through differential images between no-defect- and defect-containing images. Alternative metrologies such as interference microscopy are also investigated through modeling. The measurement of a 20 nm defect is demonstrated experimentally using 193 nm light. The complex interplay of unidirectional patterning and highly directional defects is explored using structured off-axis illumination and polarization.

  3. 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

  4. 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

  5. New pattern recognition system in the e-nose for Chinese spirit identification

    NASA Astrophysics Data System (ADS)

    Hui, Zeng; Qiang, Li; Yu, Gu

    2016-02-01

    This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance (QCM) principle, and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an 8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value (A), root-mean-square value (RMS), shape factor value (Sf), crest factor value (Cf), impulse factor value (If), clearance factor value (CLf), kurtosis factor value (Kv) are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis (PCA) method. Finally the back propagation (BP) neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively. Project supported by the National High Technology Research and Development Program of China (Grant No. 2013AA030901) and the Fundamental Research Funds for the Central Universities, China (Grant No. FRF-TP-14-120A2).

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

    NASA Astrophysics Data System (ADS)

    Dybała, Jacek

    2013-07-01

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

  7. Calculating Optical-Transmitter Radiation Pattern s

    NASA Technical Reports Server (NTRS)

    Marshall, William K.; Burk, Brian D.

    1988-01-01

    New formula gives more-accurate gains and pointing losses. Set of approximate formulas predicts angular dependence of far radiation field coherent optical transmitter, telescope having central obscuring disk. Formulas derived without recourse to simplifying assumption of uniform plane-wave illumination used to derive less-accurate traditional formulas.

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

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

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

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

    PubMed

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

    2015-06-01

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

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

    PubMed Central

    2010-01-01

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

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

    PubMed Central

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

    2013-01-01

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

  12. 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

  13. 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.

  14. 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

  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. 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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

    2012-01-01

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

  19. 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.

  20. Using optical wavelet packet transform to improve the performance of an optoelectronic iris recognition system

    NASA Astrophysics Data System (ADS)

    Cai, De; Tan, Qiaofeng; Yan, Yingbai; Jin, Guofan; He, Qingsheng

    2005-01-01

    Iris, one important biometric feature, has unique advantages: it has complex texture and is almost unchanged for the lifespan. So iris recognition has been widely studied for intelligent personal identification. Most of researchers use wavelets as iris feature extractor. And their systems obtain high accuracy. But wavelet transform is time consuming, so the problem is to enhance the useful information but still keep high processing speed. This is the reason we propose an opto-electronic system for iris recognition because of high parallelism of optics. In this system, we use eigen-images generated corresponding to optimally chosen wavelet packets to compress the iris image bank. After optical correlation between eigen-images and input, the statistic features are extracted. Simulation shows that wavelet packets preprocessing of the input images results in higher identification rate. And this preprocessing can be fulfilled by optical wavelet packet transform (OWPT), a new optical transform introduced by us. To generate the approximations of 2-D wavelet packet basis functions for implementing OWPT, mother wavelet, which has scaling functions, is utilized. Using the cascade algorithm and 2-D separable wavelet transform scheme, an optical wavelet packet filter is constructed based on the selected best bases. Inserting this filter makes the recognition performance better.

  1. Optical correlation recognition of infrared target based on wavelet multi-scale product

    NASA Astrophysics Data System (ADS)

    Chen, Fang-han; Wang, Wen-sheng

    2011-06-01

    As one of the most successful optical correlation recognizers, hybrid optoelectronic joint transform correlator (HOJTC) has received more and more attraction than the purely electronic way in the field of target detection and recognition. It primarily because that HOJTC has the advantages of optics as well as those of electronics. This kind of combination determines that the performance of HOJTC is closely related to optical configuration of system and digital image processing technology. For the stability of optical part, a lot of efforts concerning image processing methods have been made in recent years for improving the power of recognition of HOJTC. Edge contours play a decisive role in target detection. In order to obtain adequate contour feature of target, the solution of edge extraction based on wavelet multi-scale product is proposed. Normalized maximum and argument of each point could be defined utilizing wavelet coefficient of image. Both of them contain the relation of coefficient product between each scale. Edge points synthesized the information of multi-scale are extracted by searching local maxima along the direction of gradient. The way adopted fully exploited the character of multi-resolution of wavelet. Simulation experiments and optical experiments indicate that the energy of correlation peaks is obviously enhanced after the original image is processed by wavelet multi-scale product, and it successfully realizes detection and recognition of infrared target.

  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. 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%.

  4. Software for optical recognition of micro- and nano-objects in solids and colloidal solutions

    NASA Astrophysics Data System (ADS)

    Val', O.; Diachenko, L.; Minov, E.; Ostapov, S.; Fochuk, P.; Khalavka, Yu.; Kopach, O.

    2015-11-01

    This paper deals with the development of algorithms and software for optical recognition of growing defects in the semiconductor crystals and metal nanoparticles in colloidal solutions. Input information is a set of photographs from a microscope, as well as a short video-file with nanoparticle's tracks. We used the wavelet technology to filtering and image transformations. As a result of recognition the 3D image is formed with the point, linear and planar growing defects. Defects are sorted by size; different statistical characteristics are computed such as the defect's distribution in layers and in the whole crystal. The system supports arbitrary rotations of the "crystal"; "cutting" by different planes and so on. The software allows you to track the movement of nanoparticles in colloidal solutions; to determine the local temperature and density of the solution. We proposed a new method for quantitative estimation of recognition quality. This method based on the "virtual crystal" model, which has predetermined parameters of the defect subsystem. The software generates a set of photographs, which used as the input information of recognition system. Comparing the statistical parameters of the input data with the recognition results, we can estimate the quality of recognition systems from different manufacturers.

  5. 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

  6. 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

  7. 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

  8. 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

  9. Adsorption of multiblock copolymers onto a chemically heterogeneous surface: A model of pattern recognition

    NASA Astrophysics Data System (ADS)

    Kriksin, Yury A.; Khalatur, Pavel G.; Khokhlov, Alexei R.

    2005-03-01

    We present a statistical mechanical model, which is used to investigate the adsorption behavior of two-letter (AB) copolymers on chemically heterogeneous surfaces. The surfaces with regularly distributed stripes of two types (A and B) and periodic multiblock copolymers (A?B?)x are studied. It is assumed that A(B)-type segments selectively adsorb onto A(B)-type stripes. It is shown that the adsorption strongly depends on the copolymer sequence distribution and the arrangement of selectively adsorbing regions on the surface. The polymer-surface binding proceeds as a two-step process. At the first step, the copolymer having short blocks adsorbs onto the surface as an effective homopolymer, which does not feel chemical pattern. At the second step, when the polymer-surface attraction is sufficiently strong, the adsorbed chain adjusts its equilibrium conformation to reach the perfect bound state, thereby demonstrating ability for pattern recognition. The key element of this mechanism is the redistribution of strongly adsorbed copolymer diblocks A?B?, which behave as surfactants, between multiple AB interfaces separating A and B stripes on the adsorbing surface. Such redistribution is accompanied by a well-pronounced decrease in the system entropy. We have found that marked pattern recognition is possible for copolymers with relatively short blocks at high polymer/surface affinities, beyond the adsorption threshold.

  10. Reliability of fatigue life predictions for tubular joints with load pattern recognition

    SciTech Connect

    Kanegaonkar, H.B.

    1994-12-31

    All parametric formulae for Stress Concentration Factors (SCFs) classify the tubular joints according to the geometry and standard load patterns. Thus the SCFs are purely dependent on geometry, and loadings on the joint braces are ignored which keeps the uncertainty in the SCFs at the same level as the geometric uncertainty. It is shown that the SCFs with load pattern recognition if associated with standard formats for fatigue analysis of offshore platform connection would create theoretical anomalies and practical unsuitability. Particularly, it is shown that these SCFs if associated with deterministic fatigue analysis would violate the basic assumption viz. one-to-one correspondence between the wave height and hotspot stress range. It is shown that multiple values of wave heights may give rise to same stress range. This non compliance of the basic tenet of deterministic fatigue analysis can have significant effect on predicted fatigue life of joints in splash zone. The reliability analysis of fatigue life calculations using SCFs according to load pattern recognition is performed using First Order Second Moment technique. It is shown that fatigue life reliability is less in this case by orders of magnitude compared to one with standard SCFs.

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

    NASA Astrophysics Data System (ADS)

    Baird, Bill

    1986-08-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2013-01-01

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

  14. 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

  15. 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.

  16. Document Form and Character Recognition using SVM

    NASA Astrophysics Data System (ADS)

    Park, Sang-Sung; Shin, Young-Geun; Jung, Won-Kyo; Ahn, Dong-Kyu; Jang, Dong-Sik

    2009-08-01

    Because of development of computer and information communication, EDI (Electronic Data Interchange) has been developing. There is OCR (Optical Character Recognition) of Pattern recognition technology for EDI. OCR contributed to changing many manual in the past into automation. But for the more perfect database of document, much manual is needed for excluding unnecessary recognition. To resolve this problem, we propose document form based character recognition method in this study. Proposed method is divided into document form recognition part and character recognition part. Especially, in character recognition, change character into binarization by using SVM algorithm and extract more correct feature value.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. DUV inspection tool application for beyond optical resolution limit pattern

    NASA Astrophysics Data System (ADS)

    Inoue, Hiromu; Kikuiri, Nobutaka; Tsuchiya, Hideo; Ogawa, Riki; Isomura, Ikunao; Hirano, Takashi; Yoshikawa, Ryoji

    2015-10-01

    Mask inspection tool with DUV laser source has been used for Photo-mask production in many years due to its high sensitivity, high throughput, and good CoO. Due to the advance of NGL technology such as EUVL and Nano-imprint lithography (NIL), there is a demand for extending inspection capability for DUV mask inspection tool for the minute pattern such as hp4xnm or less. But current DUV inspection tool has sensitivity constrain for the minute pattern since inspection optics has the resolution limit determined by the inspection wavelength and optics NA. Based on the unresolved pattern inspection capability study using DUV mask inspection tool NPI-7000 for 14nm/10nm technology nodes, we developed a new optical imaging method and tested its inspection capability for the minute pattern smaller than the optical resolution. We confirmed the excellent defect detection capability and the expendability of DUV optics inspection using the new inspection method. Here, the inspection result of unresolved hp26/20nm pattern obtained by NPI-7000 with the new inspection method is descried.

  3. 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.

  4. Pattern Recognition Receptors and Cytokines in Mycobacterium tuberculosis Infection—The Double-Edged Sword?

    PubMed Central

    Hossain, Md. Murad; Norazmi, Mohd-Nor

    2013-01-01

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

  5. Illumination-invariant pattern recognition using fringe-adjusted joint transform correlator and monogenic signal

    NASA Astrophysics Data System (ADS)

    Sidike, Paheding; Asari, Vijayan K.; Alam, Mohammad S.

    2014-03-01

    The joint transform correlator (JTC) technique has shown attractive performance for real-time pattern recognition applications. Among the various JTC techniques proposed in the literature, the fringe-adjusted JTC (FJTC) yields remarkable promise for object recognition, and it has been shown that the FJTC produces a better correlation output than alternate JTCs under varying illumination conditions of the input scene; however, it has been found that the FJTC is not illumination invariant. Therefore, to alleviate this drawback of the FJTC, an illumination-invariant FJTC, based on combination of the fringe-adjusted filter (FAF) and the monogenic signal, is presented. The performance of the FJTC and the proposed local phase based FJTC technique in unknown input-image with varying illumination is investigated and compared. The proposed detection algorithm makes use of the monogenic signal from a two dimensional object region to extract the local phase information for assisting the FJTC robust to illumination effects. Experimental results show that by utilizing the monogenic phase information enables the FAF-based JTC to produce sharper correlation peaks and higher peak-to-clutter ratio compared to alternate JTCs. The proposed technique may be used as a real-time region-ofinterest identifier in wide-area surveillance for automatic object recognition when the target under very dark or bright condition that beyond human vision.

  6. Evaluating structural pattern recognition for handwritten math via primitive label graphs

    NASA Astrophysics Data System (ADS)

    Zanibbi, Richard; Mouchère, Harold; Viard-Gaudin, Christian

    2013-01-01

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

  7. Weather maps classification over Greek domain based on isobaric line patterns. A pattern recognition approach

    NASA Astrophysics Data System (ADS)

    Zagouras, Athanassios; Argiriou, Athanassios A.; Economou, George; Fotopoulos, Spiros; Flocas, Helena A.

    2013-11-01

    The paper presents a semi-supervised weather classification method based on 850-hPa isobaric level maps. A preprocessing step is employed, where isolines of geopotential height are extracted from weather map images via an image processing procedure. Α feature extraction stage follows where two techniques are applied. The first technique implements phase space reconstruction, and yields multidimensional delay distributions. The second technique is based on chain code representation of signals, from which histogram features are derived. Similarity measures are used to compare multidimensional data and the k-means algorithm is applied in the final stage. The method is applied over the area of Greece, and the resulting catalogues are compared to a subjective classification for this area. Numerical experiments with datasets derived from the European Meteorological Bulletin archives exhibit an up to 91 % accurate agreement with the subjective weather patterns.

  8. A new tree-like fuzzy binary support vector machine for optical character recognition

    NASA Astrophysics Data System (ADS)

    Zhang, Guo-yun; Zhang, Jing

    2005-02-01

    This paper proposes a new tree-like fuzzy binary support vector machines multi-class classifier (FBSVM) for the optical character recognition task. We construct this tree-like classifier by fusing of fuzzy clustering technique and support vector machine (SVM). In k-class task, the new classifier contains k-1 SVM sub-classifiers, but the "one-against-one" method which is usually used contains k(k-1)/2 sub-classifiers. This method also overcomes the drawback such as unclassifiable region that the "one-against-one" method has, and has a good classification performance. Furthermore, it needs less memory. By applying the new classifier to the real mail zipcode digits recognition task, the experimental results indicate that the FBSVM has a better recognition performance.

  9. 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

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

    NASA Astrophysics Data System (ADS)

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

    2011-05-01

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

  11. 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

  12. 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

  13. Investigation of partial discharge signal propagation, attenuation and pattern recognition in a stator winding

    SciTech Connect

    Hudon, C.; Guuinic, P.; Audoli, A.

    1996-12-31

    Off-line partial discharge measurements have been carried out on a 3.5 MVA, hydro-generator. Specific types of defects have been simulated at different locations of a phase winding. The resulting signal was monitored. A modified bar including a defect was either connected immediately at the winding terminal or on one of the coils end. The position of the connection was gradually moved away from the terminal, while monitoring the discharge activity from the external coupler. It was found that slot discharges and other simulated defects generated characteristic Phase Resolved Partial Discharge Patterns (PRPD) easily distinguishable from internal discharge patterns. Results showed that discharge signal attenuation was significant even below 1 MHz. The severity of the defects created was not evaluated but it was found that PRPD recognition was a useful identification tool.

  14. Identification of fuel samples from the Prestige wreckage by pattern recognition methods.

    PubMed

    Fernández-Varela, R; Andrade, J M; Muniategui, S; Prada, D; Ramírez-Villalobos, F

    2008-02-01

    A set of 34 worldwide crude oils, 12 distilled products (kerosene, gas oils, and fuel oils) and 45 oil samples taken from several Galician beaches (NW Spain) after the wreckage of the Prestige tanker off the Galician coast was studied. Gas chromatography with flame ionization detection was combined with chemometric multivariate pattern recognition methods (principal components analysis, cluster analysis and Kohonen neural networks) to differentiate and characterize the Prestige fuel oil. All multivariate studies differentiated between several groups of crude oils, fuel oils, distilled products, and samples belonging to the Prestige's wreck and samples from other illegal discharges. In addition, a reduced set of 13 n-alkanes out of 36, were statistically selected by Procrustes Rotation to cope with the main patterns in the datasets. These variables retained the most important characteristics of the data set and lead to a fast and cheap analytical screening methodology. PMID:18054966

  15. 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.

  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. Analysis of Electrode Shift Effects on Wavelet Features Embedded in a Myoelectric Pattern Recognition System

    PubMed Central

    Fontana, Juan M.; Chiu, Alan W.L.

    2014-01-01

    Myoelectric pattern recognition systems can translate muscle contractions into prosthesis commands; however, the lack of long-term robustness of such systems has resulted in low acceptability. Specifically, socket misalignment may cause disturbances related to electrodes shifting from their original recording location, which affects the myoelectric signals (MES) and produce degradation of the classification performance. In this work, the impact of such disturbances on wavelet features extracted from MES was evaluated in terms of classification accuracy. Additionally, two principal component analysis frameworks were studied to reduce the wavelet feature set. MES from seven able-body subjects and one subject with congenital transradial limb loss were studied. The electrode shifts were artificially introduced by recording signals during six sessions for each subject. A small drop in classification accuracy from 93.8% (no disturbances) to 88.3% (with disturbances) indicated that wavelet features were able to adapt to the variability introduced by electrode shift disturbances. The classification performance of the reduced feature set was significantly lower than the performance of the full wavelet feature set. The results observed in this study suggest that the effect of electrode shift disturbances on the MES can potentially be mitigated by using wavelet features embedded in a pattern recognition system. PMID:25112051

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

    NASA Astrophysics Data System (ADS)

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

    2000-05-01

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

  19. An LLNL perspective on ASCI data mining and pattern recognition requirements

    SciTech Connect

    Baldwin, C; Kamath, C; Musick, R

    1999-01-01

    The working document has been put together by the members of the Sapphire project at LLNL. The goal of Sapphire is to apply and extend techniques from data mining and pattern recognition in order to detect automatically the areas of interest in very large data sets. The intent is to help scientists address the problem of data overload by providing them effective and efficient ways of exploring and analyzing massive data sets. One of the key areas where they expect this technology to be used is in the analysis of the output from ASCI simulations. It is expected that a simulation running on the 100 Tflop ASCI machine in the year 2004 will produce data at the rate of 12TB/hour. Given the difficulties they currently have in analyzing and visualizing a terabyte of data, it is imperative that they start planning now for ways that will make the analysis of petabyte data sets feasible. This document focuses on the relevance of data mining and pattern recognition to ASCI, discusses potential applications of these techniques in ASCI, and identifies research issues that arise as they apply the algorithms in these areas to massive data sets.

  20. 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.

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

    PubMed Central

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

    2013-01-01

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

  2. Applying evidence-based medicine in telehealth: an interactive pattern recognition approximation.

    PubMed

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

    2013-11-01

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

  3. 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

  4. 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.

  5. Early-Onset Aicardi-Goutières Syndrome: Magnetic Resonance Imaging (MRI) Pattern Recognition.

    PubMed

    Vanderver, Adeline; Prust, Morgan; Kadom, Nadja; Demarest, Scott; Crow, Yanick J; Helman, Guy; Orcesi, Simona; La Piana, Roberta; Uggetti, Carla; Wang, Jichuan; Gordisch-Dressman, Heather; van der Knaap, Marjo S; Livingston, John H

    2015-09-01

    Aicardi-Goutières syndrome is an inherited leukodystrophy with calcifying microangiopathy and abnormal central nervous system myelination. As fewer diagnostic computed tomographic (CT) scans are being performed due to increased availability of magnetic resonance imaging (MRI), there is a potential for missed diagnoses on the basis of calcifications. We review a series of patients with MRIs selected from IRB-approved leukodystrophy biorepositories to identify MRI patterns for recognition of early-onset Aicardi-Goutières syndrome and scored for a panel of radiologic predictors. Each individual predictor was tested against disease status using exact logistic regression. Features for pattern recognition of Aicardi-Goutières syndrome are temporal lobe swelling followed by atrophy with temporal horn dilatation, early global cerebral atrophy and visible calcifications, as evidenced by 94.44% of cases of Aicardi-Goutières syndrome correctly classified with a sensitivity of 90.9% and specificity of 96.9%. We identify a panel of MRI features predictive of Aicardi-Goutières syndrome in young patients that would differentiate it from other leukoencephalopathies. PMID:25535058

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

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

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

  7. Pattern recognition of industrial defects by multiresolution analysis with wavelet decomposition

    NASA Astrophysics Data System (ADS)

    Deguillemont, Denis; Lecoeuche, Stephane; Dubus, Jean-Paul

    1998-10-01

    The purpose of this paper is to present a method of pattern recognition applied to detect discrimination in objects manufactured in plastic, metal, glass... This discrimination is needed to avoid problems during the recycling process. Nowadays, the controls are realized by an operator who checks visually these objects. As in texture segmentation, a way to limit the data which much be analyzed, is to use orthogonal transformations. In an industrial background, one of the most interesting transformations is the orthogonal wavelet decomposition. Remaining in the image vector space, this decomposition allows a multi resolution analysis and keeps quite all the original information in the subimages. Applied to industrial objects presenting a complex textured aspect, all the wavelets (Haar, bi-orthogonal...) need post- processing to display the defects. As these defects are seen like texture breakdowns, they can be located in high frequency spatial domain. This has led us to choose Daubechies wavelets that concentrate correctly the useful information in the detail subimages. We show that the defect is more clearly apparent at a given resolution level than in the original image. We give criteria that allow the determination of this optimal resolution level. We present a method that allows the reconstruction of the defect, using the subimages. The defect, appearing on a black background, is then discriminated by an adapted classical pattern recognition method.

  8. 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

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

    PubMed

    Shevtsova, Ekaterina; Hansson, Christer

    2011-01-01

    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

  10. Toxicological evaluation of complex mixtures by pattern recognition: correlating chemical fingerprints to mutagenicity.

    PubMed Central

    Eide, Ingvar; Neverdal, Gunhild; Thorvaldsen, Bodil; Grung, Bjørn; Kvalheim, Olav M

    2002-01-01

    We describe the use of pattern recognition and multivariate regression in the assessment of complex mixtures by correlating chemical fingerprints to the mutagenicity of the mixtures. Mixtures were 20 organic extracts of exhaust particles, each containing 102-170 individual compounds such as polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs, oxy-PAHs, and saturated hydrocarbons. Mixtures were characterized by full-scan GC-MS (gas chromatography-mass spectrometry). Data were resolved into peaks and spectra for individual compounds by an automated curve resolution procedure. Resolved chromatograms were integrated, resulting in a predictor matrix that was used as input to a principal component analysis to evaluate similarities between mixtures (i.e., classification). Furthermore, partial least-squares projections to latent structures were used to correlate the GC-MS data to mutagenicity, as measured in the Ames Salmonella assay (i.e., calibration). The best model (high r2 and Q2) identifies the variables that co-vary with the observed mutagenicity. These variables may subsequently be identified in more detail. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of other organic extracts. We emphasize that both chemical fingerprints as well as detailed data on composition can be used in pattern recognition. PMID:12634129

  11. Toxicological evaluation of complex mixtures by pattern recognition: correlating chemical fingerprints to mutagenicity.

    PubMed

    Eide, Ingvar; Neverdal, Gunhild; Thorvaldsen, Bodil; Grung, Bjørn; Kvalheim, Olav M

    2002-12-01

    We describe the use of pattern recognition and multivariate regression in the assessment of complex mixtures by correlating chemical fingerprints to the mutagenicity of the mixtures. Mixtures were 20 organic extracts of exhaust particles, each containing 102-170 individual compounds such as polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs, oxy-PAHs, and saturated hydrocarbons. Mixtures were characterized by full-scan GC-MS (gas chromatography-mass spectrometry). Data were resolved into peaks and spectra for individual compounds by an automated curve resolution procedure. Resolved chromatograms were integrated, resulting in a predictor matrix that was used as input to a principal component analysis to evaluate similarities between mixtures (i.e., classification). Furthermore, partial least-squares projections to latent structures were used to correlate the GC-MS data to mutagenicity, as measured in the Ames Salmonella assay (i.e., calibration). The best model (high r2 and Q2) identifies the variables that co-vary with the observed mutagenicity. These variables may subsequently be identified in more detail. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of other organic extracts. We emphasize that both chemical fingerprints as well as detailed data on composition can be used in pattern recognition. PMID:12634129

  12. 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.

  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. The spatial vision tree: a generic pattern recognition engine: scientific foundations, design principles, and preliminary tree design

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

    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.

  15. 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.

  16. 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...

  17. 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

  18. Simulation of information processing algorithms in optical neuron networks in training and recognition modes

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Bogukhvalsky, Anatoly K.; Volosovich, Anatoly E.

    1995-11-01

    Simulation results for information processing models and algorithms in optical neural networks (ONN) in the teaching and recognition modes are presented. They are based on equivalent operations of continuous logic and Boolean operations of coincidence, as well as vector-matrix procedures with normalization and threshold operation. It is proved that models based on these operations are united and common for various methods of coding.

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

    PubMed Central

    Khezri, Mahdi; Jahed, Mehran

    2007-01-01

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

  20. 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.