Note: This page contains sample records for the topic optical pattern recognition from Science.gov.
While these samples are representative of the content of Science.gov,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of Science.gov
to obtain the most current and comprehensive results.
Last update: November 12, 2013.
1

Correlation, functional analysis and optical pattern recognition  

SciTech Connect

Correlation integrals have played a central role in optical pattern recognition. The success of correlation, however, has been limited. What is needed is a mathematical operation more complex than correlation. Suitably complex operations are the functionals defined on the Hilbert space of Lebesgue square integrable functions. Correlation is a linear functional of a parameter. In this paper, we develop a representation of functionals in terms of inner products or equivalently correlation functions. We also discuss the role of functionals in neutral networks. Having established a broad relation of correlation to pattern recognition, we discuss the computation of correlation functions using acousto-optics.

Dickey, F.M.; Lee, M.L.; Stalker, K.T.

1994-03-01

2

Optical recognition of statistical patterns  

NASA Astrophysics Data System (ADS)

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.

Lee, S. H.

1981-12-01

3

Optical pattern recognition in cuneiform inscription analysis  

NASA Astrophysics Data System (ADS)

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.

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

1995-03-01

4

Applications of Holography to Pattern Recognition and Optical Memories,  

National Technical Information Service (NTIS)

Holography and its applications to optical associative memories, pattern recognition and three dimensional imaging are discussed. Optical holographic analogue of Hopfield's neural network model is also presented. Using Fourier transform holograms, limited...

S. U. Rehman

1988-01-01

5

Triple invariant optical pattern recognition using circular harmonic synthetic filters  

NASA Astrophysics Data System (ADS)

This article, on the foundation of circular harmonic filter algorithms, combines synthetic discrimination functions to put forward circular harmonic synthetic filters. It solves traditional problems of matched space filters with input target geometrical distortions. On synthetic filters, computer simulations and optical correlation experiments were carried out. Results clearly showed that circular harmonic synthetic filters possess relatively strong triple invariant optical pattern recognition capabilities.

Baotang, Yang; Yu, Cheng

1995-04-01

6

Existence problem of optical correlation based pattern recognition  

Microsoft Academic Search

The existence problem of optical correlation based pattern recognition, namely its range of validity and its limitation, is\\u000a discussed in this paper conjointly with the function approximation theory of neural networks. The conclusion is that only\\u000a if the sets to be recognized are linearly separable (which is rare) or the subsets, in which a segmental sample of the targets\\u000a is

Yanxin Zhang; Sumei Li

2003-01-01

7

Application of moving gratings in BSO to optical pattern recognition  

NASA Astrophysics Data System (ADS)

Moving grating technique is applied to dynamic holographic recording to overcome the difficulties of the fluctuation of the diffraction efficiency and the funning effect in photorefractive crystal BSO. Various nonlinear effects caused by moving gratings at large fringe modulations are experimentally investigated. It is shown in the application of optical pattern recognition that the probability of an error detection is reduced and the signal-to-noise ratio is considerably enhanced. Experimental results are presented.

Mu, Guoguang; Wang, Zhao-Qi

1996-10-01

8

Application of semiconductor optical amplifier logic gates in high-speed all-optical pattern recognition  

Microsoft Academic Search

In this paper, we propose and demonstrate a novel all-optical pattern recognition system. The system is able to detect and locate a specified target pattern within an input data sequence at high line-rate. The key elements of the system are an all-optical XNOR logic gate and an all-optical AND gate, the latter forming part of a recirculating loop. Both these

Xuelin Yang; Roderick P. Webb; Robert J. Manning; David Cotter; Graeme D. Maxwell; Alistair J. Poustie; Sebastien Lardenois

2008-01-01

9

Pattern recognition with the optic nerve visual prosthesis.  

PubMed

A volunteer with retinitis pigmentosa and no residual vision was chronically implanted with an optic nerve electrode connected to an implanted neurostimulator and antenna. An external controller with telemetry was used for electrical activation of the nerve which resulted in phosphene perception. Open-loop stimulation allowed the collection of phosphene attributes and the ability to elicit perception of simple geometrical patterns. Low perception thresholds allowed for large current intensity range within safety limits. In a closed-loop paradigm, the volunteer was using a head-worn video camera to explore a projection screen. The volunteer underwent performance evaluation during the course of a training program with 45 simple patterns. After learning, the volunteer reached a recognition score of 63% with a processing time of 60 s. Mean performance in orientation discrimination reached 100% with a processing time of 8 s. PMID:14616518

Veraart, Claude; Wanet-Defalque, Marie-Chantal; Gérard, Benoît; Vanlierde, Annick; Delbeke, Jean

2003-11-01

10

An investigation of optical composite filters for pattern recognition  

NASA Astrophysics Data System (ADS)

With the technological advancements in high speed CCD cameras and high resolution spatial light modulators (SLMs), joint transform correlators (JTCs) have obtained more attention in optical pattern recognition, for two main reasons: they are robust to environmental perturbation and they do not require a prefabricated Fourier domain filter, as does the VanderLugt correlator (VLC). JTCs suffer from poor detection efficiency, however, particularly in multi-target and high background noise environments. This study presents several efforts to improve the JTC's performance for pattern recognition. An optimum training process using the simulated annealing algorithm for construction of multilevel composite function (MCF) filters in the input domain was studied. MCFs are suitable for current SLMs because of their limited dynamic range. We investigated the performance of MCFs to observe the impact of number of levels on distortion invariance and discriminability. A JTC system with position encoding technique, which allows a real- valued or complex-valued function to be displayed on an amplitude-modulated SLM, is also provided for the optical implementation of MCF filters. Due to the existence of the zero-order spectra, JTCs suffer from poor detection efficiency. To alleviate this problem, a simple method of removing the zero-order spectra in a joint transform power spectrum (JTPS) was investigated. We have shown that the nonzero-order JTC (NOJTC) performs better, compared to the conventional JTC (CJTC), in terms of detection and defraction efficiency, pixel utilization and avoidance of false alarms due to inter-object modulation. Simulated and experimental demonstrations has been provided. A practical method for identifying the Synthetic-Aperture Radar (SAR) image with a 360-degree rotation-invariant based on a set of MCF filters was also investigated. The MCFs are synthesized by the simulated annealing algorithm, which are suitable for current SLMs because of the limited dynamic range. The simulated results show that high discrimination can be achieved among very similar tank SAR images with 360-degree rotation- invariance, by using six 9-level MCF filters. With these features and excellent performance, we anticipate that the MCF filters will play an important role in optical pattern recognition.

Li, Chun-Te

11

Pattern Recognition  

Microsoft Academic Search

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

Barry K. Lavine

2006-01-01

12

Fuzzy neural network for invariant optical pattern recognition  

Microsoft Academic Search

A novel fuzzy neural network (FNN) model for invariant pattern recognition is presented that combines fuzzy set reasoning and artificial neural network techniques. The presented FNN consists of three blocks: fuzzifier, fuzzy perceptron, and defuzzifier. It fuzzifies the input patterns and trains the interconnection weights according to membership functions instead of traditional binary values. The proposed FNN has been applied

Zhiqing Wen; Pochi Yeh; Xiangyang Yang

1996-01-01

13

Optical processing of speckle images with bacteriorhodopsin for pattern recognition  

Microsoft Academic Search

Logarithmic processing of images with multiplicative noise characteristics can be utilized to transform an 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

John D. Downie

1995-01-01

14

Projection-slice synthetic discriminant functions for optical pattern recognition  

Microsoft Academic Search

The projection-slice synthetic discriminant function (PSDF) filter is introduced and proposed for distortion-invariant pattern-recognition applications. The projection-slice theorem, often used in tomographic applications for medical imaging, is utilized to implement a distortion-invariant filter. Taking M projections from one training image and combining them with ( N 1) M projections taken from another N 1 training image accomplishes this. With the

Vahid R. Riasati; Mustafa A. G. Abushagur

1997-01-01

15

All-optical correlation-based bit-pattern recognition with reduced wavelength sensitivity using wavelength conversion  

Microsoft Academic Search

We demonstrate a method of minimizing the wavelength sensitivity of the all-optical bit-pattern recognition relying on a passive optical correlator. We accomplish all-optical 40-Gb\\/s, 8-bit pattern recognition with much reduced wavelength sensitivity by combining a wavelength converter front-end and a passive optical correlator.

I. Kang; M. Rasras; M. Dinu; L. Buhl; S. Cabot; L. Zhang; M. Cappuzzo; L. T. Gomez; Y. F. Chen; S. S. Patel; D. T. Neilson; C. R. Giles; N. Dutta; A. Piccirilli; J. Jaques

2009-01-01

16

Optical Pattern Recognition System Based on Parallel Spatial Filtering Using Synthetic Discriminant Function  

NASA Astrophysics Data System (ADS)

In this paper, we propose an optical pattern recognition system based on spatial filtering using a synthetic discriminant function (SDF). In this system, the input image is multiplexed by a microlens array under incoherent illumination, and then the optical inner product of input images and SDF filters is optically conducted. The image is recognized by thresholding the optical inner product values. The present SDF filter was constructed with pseudogradation transparent filters fabricated by electron-beam lithography. We performed an experiment that involved the recognition of ten Arabic numerals and evaluated the discriminating characteristics of parallel pattern recognition. We confirm that the proposed system can function as a parallel recognition system without the need to scan images.

Takahashi, Takanori; Katayama, Takeo; Iga, Kenichi

2000-03-01

17

Optical time-domain analog pattern correlator for high-speed real-time image recognition.  

PubMed

The speed of image processing is limited by image acquisition circuitry. While optical pattern recognition techniques can reduce the computational burden on digital image processing, their image correlation rates are typically low due to the use of spatial optical elements. Here we report a method that overcomes this limitation and enables fast real-time analog image recognition at a record correlation rate of 36.7 MHz--1000 times higher rates than conventional methods. This technique seamlessly performs image acquisition, correlation, and signal integration all optically in the time domain before analog-to-digital conversion by virtue of optical space-to-time mapping. PMID:21263506

Kim, Sang Hyup; Goda, Keisuke; Fard, Ali; Jalali, Bahram

2011-01-15

18

Projection-slice synthetic discriminant functions for optical pattern recognition.  

PubMed

The projection-slice synthetic discriminant function (PSDF) filter is introduced and proposed for distortion-invariant pattern-recognition applications. The projection-slice theorem, often used in tomographic applications for medical imaging, is utilized to implement a distortion-invariant filter. Taking M projections from one training image and combining them with (N - 1)M projections taken from another N - 1 training image accomplishes this. With the projection-slice theorem, each set of these M projections can be represented as M one-dimensional slices of the two-dimensional Fourier transform of the particular training image. Therefore, the PSDF filter has the advantage of matching each of the training images with at least M slices of their respective Fourier transforms. This filter is theoretically analyzed, numerically simulated, and experimentally implemented and tested to verify the simulation results. These tests show that the PSDF filter significantly outperforms the matched-filter and the basic synthetic discriminant function technique for the particular images used. PMID:18253307

Riasati, V R; Abushagur, M A

1997-05-10

19

Projection-slice synthetic discriminant functions for optical pattern recognition  

NASA Astrophysics Data System (ADS)

The projection-slice synthetic discriminant function (PSDF) filter is introduced and proposed for distortion-invariant pattern-recognition applications. The projection-slice theorem, often used in tomographic applications for medical imaging, is utilized to implement a distortion-invariant filter. Taking M projections from one training image and combining them with ( N 1) M projections taken from another N 1 training image accomplishes this. With the projection-slice theorem, each set of these M projections can be represented as M one-dimensional slices of the two-dimensional Fourier transform of the particular training image. Therefore, the PSDF filter has the advantage of matching each of the training images with at least M slices of their respective Fourier transforms. This filter is theoretically analyzed, numerically simulated, and experimentally implemented and tested to verify the simulation results. These tests show that the PSDF filter significantly outperforms the matched-filter and the basic synthetic discriminant function technique for the particular images used.

Riasati, Vahid R.; Abushagur, Mustafa A. G.

1997-05-01

20

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

SciTech Connect

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

Cline, J.D.

1989-12-01

21

Optical Learning Neural Network Using a Selfoc Microlens Array for Pattern Recognition  

Microsoft Academic Search

An optical system for learning neural networks with a 2-D architecture was constructed using a Selfoc microlens array. Using this system, we achieved pattern recognition of typed alphabet characters detected directly with a CCD camera. The system learned 4 characters according to a random search algorithm in order to avoid the difficulties and the costs of calculations of learning signals,

Masahiko Mori; Yoshio Hayasaki; Ichiroh Tohyama; Toyohiko Yatagai

1994-01-01

22

Optical pattern recognition in the analysis of ancient Babylonian cuneiform inspection  

NASA Astrophysics Data System (ADS)

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.

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

1995-11-01

23

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

NASA Astrophysics Data System (ADS)

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

Lhamon, Michael Earl

24

Distortion-invariant optical pattern recognition by correlative matrix feature criterion  

NASA Astrophysics Data System (ADS)

In this paper, we used correlative matrix as a feature criterion in K-L transformation for feature compression, and then the synthetic matched filter (SMF) was prepared by means of the synthetic discriminant function (SDF). This method can effectively reduce the size of feature image. By this fiber, the shift, scale and rotation in variant optical pattern recognition was realized with a high signal noise ratio of correlation output.

Baotang, Yang; Zhengping, Lan

1994-12-01

25

Latency-free all-optical recognition of 32-bit optical bit patterns at 40 Gb\\/s using a passive optical correlator  

Microsoft Academic Search

We report all-optical detection of 32-bit patterns embedded in 40-Gb\\/s phase-shift keyed data. The bit-pattern recognition is achieved using matched filtering implemented with a reconfigurable silica waveguide tapped delay-line filter.

I. Kang; M. Rasras; M. Dinu; L. Buhl; S. Cabot; M. Cappuzzo; L. T. Gomez; Y. F. Chen; S. S. Patel; C. R. Giles; N. Dutta; A. Piccirilli; J. Jaques

2008-01-01

26

Biological photochrome bacteriorhodopsin and its genetic variant Asp96-Asn as media for optical pattern recognition  

NASA Astrophysics Data System (ADS)

The use of the purple membrane (PM) films that contain wild-type bacteriorhodopsin (BR) and BR variant Asp96-Asn as a real-time recording medium in optical pattern recognition is demonstrated. PM films are characterized by high reversibility (much greater than 10 exp 5 record/erase cycles), the fast time scale of its photoconversions (femtosec to millisec), and the large photochromic shift (about 160 nm) occurring during its photocycle. PM films with high spatial resolution of more than 5000 lines/mm are applied to a dual-axis joint-Fourier-transform (DA-JFT) correlator for holographic pattern recognition. Due to polarization recording properties of PM films, a significant (21-fold) improvement of the signal-to-noise ratio is obtained in the DA-JFT correlator. A real-time correlator system based on PM films operating at a TV frame rate is considered to be feasible, and mutated BRs are considered to be promising media for holographic pattern recognition.

Hampp, Norbert; Thoma, Ralph; Oesterhelt, Dieter; Braeuchle, Christoph

1992-04-01

27

Syntactic Pattern Recognition.  

National Technical Information Service (NTIS)

The document is primarily a review and evaluation of the state of the art of syntactic pattern recognition, a subject of considerable current interest due to the increasing need for powerful new approaches to pattern recognition problems of great complexi...

K. S. Fu P. H. Swain

1970-01-01

28

Optical Pattern Recognition Experiments of Walsh Spatial Frequency Domain Filtering Method  

NASA Astrophysics Data System (ADS)

An optical parallel processor for an image recognition system using microoptical devices has been developed. This system expands input images to coefficients of a two-dimensional Walsh spatial frequency domain and discriminates them by performing the inner product to the reference filter. In this paper, we demonstrate parallel optical discrimination of Arabic numerals using this method. First, the mechanism of this recognition method is presented. Next, the reference filter is designed. In order to implement the reference filter by using the microoptical devices, the fabrication problems were solved. Finally, the recognition system is constructed and effects of deformation on the input image are experimentally evaluated.

Katayama, Takeo; Takahashi, Takanori; Iga, Kenichi

2000-03-01

29

Optical parallel pattern recognition of multiple stored images in a persistent spectral holeburning memory  

NASA Astrophysics Data System (ADS)

We propose and demonstrate a novel parallel pattern recognition technique (pulse-correlation technique) to extract a specific pattern directly from multiple stored images in a persistent spectral holeburning (PHB) memory. Multiple recording channels which exist in the same spatial area but in different frequency regions can be accessed simultaneously with a pulsed laser whose frequency linewidth is comparable with an inhomogeneous width of the PHB material. Using the pulsed laser for pattern recognition, we have succeeded in extracting a specific image instantly from multiple stored images in a PHB material.

Sasaki, H.; Karaki, K.

1998-07-01

30

Patterns in Pattern Recognition: 1968-1974.  

National Technical Information Service (NTIS)

This paper selectively surveys contributions to major topics in pattern recognition since 1968. Representative books and surveys on pattern recognition published during this period are listed. Theoretical models for automatic pattern recognition are contr...

L. Kanal

1974-01-01

31

Smart pattern recognition  

NASA Astrophysics Data System (ADS)

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

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

2013-03-01

32

Use of laser radar imagery in optical pattern recognition: the Optical Processor Enhanced Ladar (OPEL) Program  

Microsoft Academic Search

The Optical Processor Enhanced Ladar (OPEL) program is designed to evaluate the capabilities of a seeker obtained by integrating two state-of-the-art technologies, laser radar, or ladar, and optical correlation. The program is a thirty-two month effort to build, optimize, and test a breadboard seeker system (the OPEL System) that incorporates these two promising technologies. Laser radars produce both range and

Dennis H. Goldstein; Stuart A. Mills; Robert B. Dydyk

1998-01-01

33

Pattern recognition using reduced information content filters  

Microsoft Academic Search

Pattern recognition by optical spatial filtering procedures is discussed using general considerations with the objective of reducing the information content in the spatial filter. The achievement of this objective is very useful toward the wide application of spatial light modulators and also for facilitating distortion invariant recognition. The proposed novel approach is demonstrated by an example employing bipolar spatial filters

Joseph Shamir; H. John Caulfield; Joseph Rosen

1987-01-01

34

Pattern recognition by pentraxins.  

PubMed

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

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

2009-01-01

35

Learning, Self-Learning, and Pattern Recognition.  

National Technical Information Service (NTIS)

Contents: Pattern recognition and the method of potential functions; The methods of stochastic approximations and pattern recognition; Features (measures) of patterns and pattern recognition; Perceptron-type recognition systems and their applications; Set...

L. Kacinskas

1968-01-01

36

Achievable Rates for Pattern Recognition  

Microsoft Academic Search

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

M. Brandon Westover; Joseph A. O'sullivan

2008-01-01

37

Image Recognition Based on Biometric Pattern Recognition  

NASA Astrophysics Data System (ADS)

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.

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

2011-09-01

38

Optical Character Recognition.  

ERIC Educational Resources Information Center

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

Converso, L.; Hocek, S.

1990-01-01

39

Optical Character Recognition.  

ERIC Educational Resources Information Center

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

Converso, L.; Hocek, S.

1990-01-01

40

Pattern Recognition in Photoacoustic Dataset  

NASA Astrophysics Data System (ADS)

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.

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

2013-05-01

41

Achievable Rates for Pattern Recognition  

Microsoft Academic Search

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

M. Brandon Westover; Joseph A. O'sullivan

2005-01-01

42

Synthesized images for pattern recognition  

Microsoft Academic Search

Since there is no generic procedure for machine pattern recognition due to its complexity, ad hoc computer algorithms have been developed for each class of problems. In visual pattern recognition, depending on the area of investigation, it is difficult to obtain test images with the desired characteristics. Capturing the original images in the first place may require special and\\/or expensive

Mario Miyojim; Heng-da Cheng

1995-01-01

43

Image normalization for pattern recognition  

Microsoft Academic Search

In general, there are four basic forms of distortion in the recognition of planar patterns: translation, rotation, scaling and skew. In this paper, a normalization algorithm has been developed which transforms pattern into its normal form such that it is invariant to translation, rotation, scaling and skew. After normalization, the recognition can be performed by a simple matching method. In

Soo-chang Pei; Chao-nan Lin

1995-01-01

44

Pattern Recognition System of Optical Fiber Fusion Defect Based on Fuzzy Neural Network in EPON  

Microsoft Academic Search

Because of having many advantages, optical fiber network is applied widely in high-tech fields. But the existence of optical fiber fusion defects will debase the quality of message transmission. A set of defect recognized system is established based on the compensatory fuzzy neural network of using wavelet and with fast algorithm in this paper. The dasiaenergy- defectpsila method to extract

Zhen Zhang; Rong-xing Guo

2009-01-01

45

Pattern recognition proteins in Manduca sexta plasma  

Microsoft Academic Search

Recognition of nonself is the first step in mounting immune responses. In the innate immune systems of both vertebrates and arthropods, such recognition, termed pattern recognition, is mediated by a group of proteins, known as pattern recognition proteins or receptors. Different pattern recognition proteins recognize and bind to molecules (molecular patterns) present on the surface of microorganisms but absent from

X.-Q. Yu; Y.-F. Zhu; C. Ma; J. A. Fabrick; M. R. Kanost

2002-01-01

46

Multivariant technique for multiclass pattern recognition.  

PubMed

A technique for multiclass optical pattern recognition of different perspective views of an object is described. Each multiclass representation of an object is described as an orthonormal basis function expansion, and a single averaged matched spatial filter is then produced from a weighted linear combination of these functions. The technique is demonstrated for a terminal missile guidance application using IR tank imagery. PMID:20221120

Hester, C F; Casasent, D

1980-06-01

47

Fuzzy Algorithms for Pattern Recognition.  

National Technical Information Service (NTIS)

This paper contains, first, a tutorial of the basic axiomatic structure underlying many fuzzy algorithms for pattern recognition; and second, a summary of the theory and applications connected with several currently available fuzzy techniques. In particul...

J. C. Bezdek

1983-01-01

48

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

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

49

Pattern Recognition from Satellite Altitudes  

Microsoft Academic Search

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

Eugene Darling; R. D. Joseph

1968-01-01

50

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

NASA Astrophysics Data System (ADS)

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 2D and 3D 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 architectures. The experimental results with as many as 2025 channels show good agreement with the theoretical analysis.

Cai, Luzhong; Liu, Hua-Kuang

2000-02-01

51

Fuzzy-neural pattern recognition  

Microsoft Academic Search

The common techniques of neural networks seem to apply perfectly to the classification and to the pattern recognition, but the learning can sometime be long and the complexity of the final network very big. An association between the neural networks and the fuzzy may seem feasible and make significantly easier the learning period and to allow also to simplify its

M. Engel; M. Leclercq; C. Pradels

1994-01-01

52

Pattern recognition proteins in Manduca sexta plasma.  

PubMed

Recognition of nonself is the first step in mounting immune responses. In the innate immune systems of both vertebrates and arthropods, such recognition, termed pattern recognition, is mediated by a group of proteins, known as pattern recognition proteins or receptors. Different pattern recognition proteins recognize and bind to molecules (molecular patterns) present on the surface of microorganisms but absent from animals. These molecular patterns include microbial cell wall components such as bacterial lipopolysaccharide, lipoteichoic acid and peptidoglycan, and fungal beta-1,3-glucans. Binding of pattern recognition proteins to these molecular patterns triggers responses such as phagocytosis, nodule formation, encapsulation, activation of proteinase cascades, and synthesis of antimicrobial peptides. In this article, we describe four classes of pattern recognition proteins, hemolin, peptidoglycan recognition protein, beta-1,3-glucan recognition proteins, and immulectins (C-type lectins) involved in immune responses of the tobacco hornworm, Manduca sexta. PMID:12225919

Yu, X-Q; Zhu, Y-F; Ma, C; Fabrick, J A; Kanost, M R

2002-10-01

53

Pattern recognition by cooperating neural networks  

NASA Astrophysics Data System (ADS)

The study of connectionist models for pattern recognition is mainly motivated by their presumed simultaneous feature selection and classification. Character recognition is a common test case to illustrate the feature extraction and classification characteristics of neural networks. Most of the variability concerning size and rotation can be handled easily, while acquisition conditions are usually controlled. Many examples of neural character recognition applications were presented where the most successful results for optical character recognition (OCR) with image inputs were reported on a layered network (LeCun et al., 1990) integrating feature selection and invariance notions introduced earlier in neocognitron networks. Previously, we have presented a supervised learning algorithm, based on Kohonen's self- organizing feature maps, and its applications to image and speech processing (Midenet et al., 1991). From pattern recognition point of view, the first network performs local feature extraction while the second does a global statistical template matching. We describe these models and their comparative results when applied to a common French handwritten zip-code database. We discuss possible cooperation schemes and show that the performances obtained by these networks working in parallel exceed those of the networks working separately. We conclude by the possible extensions of this work for automatic document processing systems.

Idan, Yizhak; Auger, Jean-Marie

1992-12-01

54

Comparison of Two Modern Pattern Recognition Methods  

Microsoft Academic Search

Two methods of pattern recognition are introduced in this paper: Unsupervised learning algorithm - fuzzy clustering method and supervised learning algorithm - neural network. The pattern recognition becomes failure pattern recognition if it is used in the fault diagnosis of the machine. Both merits and shortages of these two methods are discussed through a specific example in the mechanical faults

Xiaochun Shi

2008-01-01

55

Hybrid modeling in pattern recognition and control  

Microsoft Academic Search

Four technologies are discussed: fuzzy pattern recognition (numerical and syntactic), computational neural networks, and fuzzy control. The paper assesses the maturation of these disciplines by giving two examples of crossfertilization between control and pattern recognition. First, the use of pattern recognition (fuzzy clustering and feed forward neural networks) to help develop and represent fuzzy controllers is illustrated. Second, an example

James C. Bezdek

1995-01-01

56

Adaptive pattern recognition and neural networks  

Microsoft Academic Search

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

Yoh-Han Pao; Yohhan

1989-01-01

57

Pattern recognition with mixed and incomplete data  

Microsoft Academic Search

In this paper, an increasingly active line of study in pattern recognition (PR) called “logical combinatorial pattern recognition”\\u000a (LCPR) is reviewed. Briefly, this refers to pattern recognition problems with mixed and incomplete object descriptions using\\u000a similarity functions less restricted than a distance, i.e., objects described simultaneously in terms of numerical and non-numerical\\u000a features with missing values. The similarity function is

J. Ruiz-Shulcloper

2008-01-01

58

Inverse scattering approach to improving pattern recognition  

NASA Astrophysics Data System (ADS)

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

Chapline, George; Fu, Chi-Yung

2005-05-01

59

Syntactic Pattern Recognition of the ECG  

Microsoft Academic Search

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

Panagiotis Trahanias; Emmanuel Skordalakis

1990-01-01

60

Pattern Recognition Model Based on Cortical Anatomy.  

National Technical Information Service (NTIS)

The report presents material supporting a model for pattern recognition that closely approximates the connectivity evident in the neurons of the cerebral cortex. A computer simulation of the model was tested on three sets of patterns (letters and geometri...

W. O. Mahaffey

1971-01-01

61

Public domain optical character recognition  

NASA Astrophysics Data System (ADS)

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.

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

62

Pattern recognition for statistical process control charts  

Microsoft Academic Search

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

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

1998-01-01

63

Fuzzy Neural Model for Flatness Pattern Recognition  

Microsoft Academic Search

For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three-output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model

Chun-yu JIA; Xiu-ying SHAN; Hong-min LIU; Zhao-ping NIU

2008-01-01

64

Fuzzy Logic-Based Audio Pattern Recognition  

NASA Astrophysics Data System (ADS)

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Malcangi, M.

2008-11-01

65

On Feature Selection in Multiclass Pattern Recognition.  

National Technical Information Service (NTIS)

Several possible solutions are presented to the problem of feature selection in multiclass pattern recognition. The principal objective of research reported in this thesis is to develop a generalized mathematical formulation of feature selection technique...

K. S. Fu P. J. Min

1968-01-01

66

Adaptive pattern recognition and neural networks  

SciTech Connect

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

Pao, Yohhan.

1989-01-01

67

Pattern recognition, inner products and correlation filters  

SciTech Connect

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.

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

1991-01-01

68

Pattern recognition of transillumination images for diagnosis of rheumatoid arthritis  

NASA Astrophysics Data System (ADS)

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.

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

2005-09-01

69

Pattern recognition with weighted complex networks  

NASA Astrophysics Data System (ADS)

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

Cheh, Jigger; Zhao, Hong

2008-11-01

70

Word recognition using ideal word patterns  

NASA Astrophysics Data System (ADS)

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

Zhao, Sheila X.; Srihari, Sargur N.

1994-03-01

71

Pattern recognition using asymmetric attractor neural networks  

NASA Astrophysics Data System (ADS)

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

Jin, Tao; Zhao, Hong

2005-12-01

72

Pattern Recognition with Slow Feature Analysis  

Microsoft Academic Search

Slow feature analysis (SFA) is a new unsupervised algorithm to learn nonlinear functions that ex- tract slowly varying signals out of the input data. In this paper we describe its application to pattern recognition. In this context in order to be slowly varying the functions learned by SFA need to respond similarly to the patterns belonging to the same class.

Pietro Berkes

2005-01-01

73

A new approach to iris pattern recognition  

Microsoft Academic Search

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

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

2004-01-01

74

Neural network techniques in managerial pattern recognition  

Microsoft Academic Search

The management area includes a large class of pattern recognition (classification) problems. Traditionally, these problems have been solved by using statistical methods or expert systems. In practice however, statistical assumptions about the probability distributions of the pattern variables are often not verifiable, and expertise concerning the correct classification is often not explicitly available. These obstacles may make statistical methods and

Shouhong Wang

1990-01-01

75

Neural Network Techniques in Managerial Pattern Recognition  

Microsoft Academic Search

The management area includes a large class of pattern recognition (classification) problems. Traditionally, these problems have been solved by using statistical methods or expert systems. In practice, however, statistical assumptions about the probability distributions of the pattern variables are often not verifiable, and expertise concerning the correct classification is often not explicitly available. These obstacles may make statistical methods and

Shouhong Wang

1990-01-01

76

Interpolating vectors for robust pattern recognition.  

PubMed

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

Fukushima, Kunihiko

2007-07-24

77

Automated pattern recognition system for noise analysis  

SciTech Connect

A pattern recognition system was developed at ORNL for on-line monitoring of noise signals from sensors in a nuclear power plant. The system continuousy measures the power spectral density (PSD) values of the signals and the statistical characteristics of the PSDs in unattended operation. Through statistical comparison of current with past PSDs (pattern recognition), the system detects changes in the noise signals. Because the noise signals contain information about the current operational condition of the plant, a change in these signals could indicate a change, either normal or abnormal, in the operational condition.

Sides, W.H. Jr.; Piety, K.R.

1980-01-01

78

Method of synthesized phase objects for pattern recognition: matched filtering.  

PubMed

To solve the pattern recognition problem, a method of synthesized phase objects is suggested. The essence of the suggested method is that synthesized phase objects are used instead of real amplitude objects. The former is object-dependent phase distributions calculated using the iterative Fourier-transform (IFT) algorithm. The method is experimentally studied with a Vander Lugt optical-digital 4F-correlator. We present the comparative analysis of recognition results using conventional and proposed methods, estimate the sensitivity of the latter to distortions of the structure of objects, and determine the applicability limits. It is demonstrated that the proposed method allows one: (?) to simplify the procedure of choice of recognition signs (criteria); (b) to obtain one-type ?-like recognition signals irrespective of the type of objects; (?) to improve signal-to-noise ratio (SNR) for correlation signals by 20 - 30 dB on average. The spatial separation of the Fourier-spectra of objects and optical noises of the correlator by means of the superposition of the phase grating on recognition objects at the recording of holographic filters and at the matched filtering has additionally improved SNR (>10 dB) for correlation signals. To introduce recognition objects in the correlator, we use a SLM LC-R 2500 device. Matched filters are recorded on a self-developing photopolymer. PMID:23388812

Yezhov, Pavel V; Kuzmenko, Alexander V; Kim, Jin-Tae; Smirnova, Tatiana N

2012-12-31

79

Grip-Pattern Recognition for Smart Guns  

Microsoft Academic Search

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 x 44 piezoresistive elements has been used. An interface has been developed to acquire pressure images from the sensor. The values of the pixels in the pressure-pattern images

J. A. Kauffman; A. M. Bazen; S. H. Gerez; R. N. J. Veldhuis

2003-01-01

80

ISO ground attitude determination using pattern recognition  

Microsoft Academic Search

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

A. J. Batten

1993-01-01

81

Pattern Recognition of the Universal Electronic Nose  

Microsoft Academic Search

An electronic nose is the intelligent instrument that identifies the chemical odors mimicking a human. Now the majority of electronic noses could only identify the specific species, however the human olfactory system is able to characterize and classify many different odors. The problem has prevented their use in wider commercial applications. The pattern recognition methods based on the probabilistic neural

Zhou Tao; Wang Lei; Jionghua Teng

2008-01-01

82

Classification of Potato Chips Using Pattern Recognition  

Microsoft Academic Search

F. P EDRESCHI, D. MERY, F. MENDOZA, AND J.M. AGUILERA ABSTRACT: An approach to classify potato chips using pattern recognition from color digital images consists of 5 steps: (1) image acquisition, (2) preprocessing, (3) segmentation, (4) feature extraction, and (5) classification. Ten chips prepared for each of the following 6 conditions were examined: 2 pretreatments (blanched and unblanched) at 3

83

Image processing and pattern recognition in textiles  

Microsoft Academic Search

Image processing and pattern recognition have been successfully applied in many textile related areas. For example, they have been used in defect detection of cotton fibers and various fabrics. In this work, the application of image processing into animal fiber classification is discussed. Integrated into\\/with artificial neural networks, the image processing technique has provided a useful tool to solve complex

Lingxue Kong; F. H. She

2001-01-01

84

Pattern-recognition Receptors in Pulp Defense  

Microsoft Academic Search

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,

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

2011-01-01

85

Introduction to Pattern Recognition with Intelligent Systems  

Microsoft Academic Search

We describe in this book, new methods for intelligent pattern recognition using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to create powerful hybrid intelligent systems. Combining SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers.

Patricia Melin; Oscar Castillo

86

Genetic algorithms for spectral pattern recognition  

Microsoft Academic Search

The development of a genetic algorithm (GA) for pattern recognition analysis of spectral data is reported. The GA identifies a set of features (wavelengths) that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected features

B. K. Lavine; C. E. Davidson; A. J. Moores

2002-01-01

87

Accuracy effects in pattern recognition neural nets  

Microsoft Academic Search

Various errors, including analog accuracy, nonlinearities, and noise, are present in all neural networks. The author considers their effects in training and testing on two different pattern recognition neural nets. He shows that the neural nets considered allow some such effects to be included inherently in the neural net synthesis algorithm and that the effect of the other error sources

David Casasent

1992-01-01

88

Hierarchical testing designs for pattern recognition  

Microsoft Academic Search

We explore the theoretical foundations of a ``twenty questions'' approach to pattern recognition. The object of the analysis is the computational process itself rather than probability distributions (Bayesian inference) or decision boundaries (statistical learning). Our formulation is motivated by applications to scene interpretation in which there are a great many possible explanations for the data, one (``background'') is statistically dominant,

Gilles Blanchard; Donald Geman

2005-01-01

89

Supervised fuzzy inference network for invariant pattern recognition  

Microsoft Academic Search

A supervised fuzzy inference network (FIN) model and its learning algorithm for invariant pattern recognition are presented in this paper. This fuzzy inference network is suitable for 2-D visual pattern recognition problems and has been tested with letter patterns of black and white pixel values. In contrast to most of the conventional pattern recognition systems, the proposed fuzzy inference network

H. K. Kwan; L. Y. Cai

2000-01-01

90

Applications of chaotic neurodynamics in pattern recognition  

NASA Astrophysics Data System (ADS)

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.

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

1991-08-01

91

Pattern Recognition in Time Series  

NASA Astrophysics Data System (ADS)

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

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

2012-03-01

92

A pattern recognition account of decision making.  

PubMed

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

Massaro, D W

1994-09-01

93

On Linguistic, Statistical, and Mixed Models for Pattern Recognition.  

National Technical Information Service (NTIS)

The report presents a selective discussion of some aspects of linguistic, statistical and mixed approaches to pattern recognition, and considers the potential of transformational grammars, a proposed formalism for pattern analysis and recognition, and heu...

B. Chandrasekaran L. Kanal

1971-01-01

94

Data Reduction for Pattern Recognition and Data Analysis  

Microsoft Academic Search

Pattern recognition involves various human activities of great practical significance, such as data-based bankruptcy prediction,\\u000a speech\\/image recognition, machine fault detection and cancer diagnosis. Clearly, it would be immensely useful to build machines\\u000a to fulfill pattern recognition tasks in a reliable and efficient way. The most general and most natural pattern recognition\\u000a frameworks mainly rely on statistical characterizations of patterns with

Tommy W. S. Chow; Di Huang

2008-01-01

95

Rotation, scale, and translation invariant pattern recognition using feature extraction  

NASA Astrophysics Data System (ADS)

A rotation, scale and translation invariant pattern recognition technique is proposed.It is based on Fourier- Mellin Descriptors (FMD). Each FMD is taken as an independent feature of the object, and a set of those features forms a signature. FMDs are naturally rotation invariant. Translation invariance is achieved through pre- processing. A proper normalization of the FMDs gives the scale invariance property. This approach offers the double advantage of providing invariant signatures of the objects, and a dramatic reduction of the amount of data to process. The compressed invariant feature signature is next presented to a multi-layered perceptron neural network. This final step provides some robustness to the classification of the signatures, enabling good recognition behavior under anamorphically scaled distortion. We also present an original feature extraction technique, adapted to optical calculation of the FMDs. A prototype optical set-up was built, and experimental results are presented.

Prevost, Donald; Doucet, Michel; Bergeron, Alain; Veilleux, Luc; Chevrette, Paul C.; Gingras, Denis J.

1997-03-01

96

A pattern recognition account of decision making  

Microsoft Academic Search

In the domain of pattern recognition, experiments have shown that perceivers integrate multiple sources of information in\\u000a an optimal manner. In contrast, other research has been interpreted to mean that decision making is nonoptimal. As an example,\\u000a Tversky and Kahneman (1983) have shown that subjects commit a conjunction fallacy because they judge it more likely that a\\u000a fictitious person named

Dominic W. Massaro

1994-01-01

97

Extended Cascade-Correlation for Syntactic and Structural Pattern Recognition  

Microsoft Academic Search

Automatic inference is one of the main problems that syntactic and structural pattern recognition must solve for successful applications. Neural networks are artificial intelligence tools which already support automatic inference for successful applications of statistical pattern recognition. In this paper, we suggest that neural networks, and specifically Cascade-Correlation, can be used for automatic inference in syntactic and structural pattern recognition,

Alessandro Sperduti; Darya Majidi; Antonina Starita

1996-01-01

98

A Feature Extraction Toolbox for Pattern Recognition Application  

Microsoft Academic Search

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

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

1998-01-01

99

KOHONEN SELF-ORGANIZING FEATURE MAP IN PATTERN RECOGNITION  

Microsoft Academic Search

Traditional statistical pattern recognition models have been studied quite a long time. Due to the difficulty of pattern recognition task (for example classification) there are many different models for that task. The need for a general model which could be successful in most pattern recognition tasks has led to study new approaches like neural networks and fuzzy sets. This paper

Markus Törmä

100

Patterns, Fishing and Nonlinear Optics  

Microsoft Academic Search

Motivated by a conversation with my brother, a deep sea fisherman off the east coast of Scotland, I review the concepts which unify the topic of pattern formation in nonequilibrium systems. As a specific example of a pattern-forming system, I go on to examine pattern formation in nonlinear optics and I discuss two nonlinear optical systems in considerable detail. The

John Bruce Geddes

1994-01-01

101

Urdu character recognition using fourier descriptors for optical networks  

NASA Astrophysics Data System (ADS)

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.

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

2005-08-01

102

Statistical pattern recognition algorithms for autofluorescence imaging  

NASA Astrophysics Data System (ADS)

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.

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

2009-02-01

103

Contributions in scale and projection invariant pattern recognition  

NASA Astrophysics Data System (ADS)

The aim of this work is the analysis of scale invariant matched filters, scaling the object both in one dimension and in two dimensions. In this line, several contributions have been proposed improving, in one side, the behavior of the known filters, in the other side, introducing new filters showing an improvement in the invariance rank. In this manner, three classical matched filters have been proposed in second chapter, symmetrical one of them, anamorphics the rest, for pattern recognition of signals having different scale in the input image and in the target image. Making use of spherical illumination, and working in the object plane, we obtain lineal movements of the movable part in the correlator, in function of the size ratio between the images in the object plane and in the input plane. So, automatic detection of the objects can be realized. At last, optical results of the behavior of the different correlators is shown. In third chapter, the Mellin Radial Harmonics (MRH) and its scale invariance properties when the filter is matched to a single MRH of the object are deeply studied. In this line, an algorithm of automatic selection of the filter definition parameters has been proposed, to improve its behavior obtaining the highest discrimination capability of the filter. Later, a real filter based on MRHs has been proposed, showing the same invariance properties than the complex filters of MRH. Finally, simulation results about the algorithm behavior are presented, and simulation and optical results concerning the real filter are shown. In fourth chapter, an hybrid optical-digital method for scale and contrast invariant pattern recognition has been proposed, based on the MRHs. This method is based on making a characteristics vector associated to every object of the input image, obtaining scale detection for any scale value. This result is achieved because the comportment of the correlations with the scale factor and the contrast rank in not dependent on the filter parameters, obtaining, furthermore, the recognition of any signal in the input scene, for its different scaled versions, or different contrast, even though the filter is not matched to these signals. Later, the logarithmic radial harmonics (LRH) for scale invariant pattern recognition has been studied, and a new filter as a modification of the LRH is introduced, obtaining better results than the original one for invariant pattern recognition of any contour object. At the end of the chapter, simulation results for the characteristics vectors method, and experimental and simulation results for the modified filter of LRH are shown. Finally, in fifth chapter, the logarithmic harmonics (LH) for projection invariant pattern recognition have been studied, making clear its limitations for high values of the scale. In this line, a new filter based on the LH is presented, obtaining better results than the precedent filter for projection invariant pattern recognition. The chapter ends with simulation and optical results.

Moya Anson, Antonio

104

Success potential of automated star pattern recognition  

NASA Astrophysics Data System (ADS)

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

van Bezooijen, R. W. H.

1986-01-01

105

A Structural Pattern Analysis Approach to Iris Recognition  

Microsoft Academic Search

\\u000a Continuous efforts have been made in searching for robust and effective iris coding methods, since Daugman’s pioneering work\\u000a on iris recognition was published. Proposed algorithms follow the statistical pattern recognition paradigm and encode the\\u000a iris texture information through phase, zero-crossing or texture-analysis based methods. In this paper we propose an iris\\u000a recognition algorithm that follows the structural (syntactic) pattern recognition

Hugo Proença

2008-01-01

106

Intrusion detection using pattern recognition methods  

NASA Astrophysics Data System (ADS)

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

Jiang, Nan; Yu, Li

2007-09-01

107

A biologically inspired model for pattern recognition*  

PubMed Central

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

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

2010-01-01

108

Application of pattern recognition theory to systems for rapid operational diagnosis in microsurgery  

Microsoft Academic Search

The goal of the present work was to apply a pattern recognition theory to the processing of the information acquired by an MOC. A multifiber optical catheter contains a bundle of optical fibers. One fiber or a group of them are used for delivering laser radiation to the studied area (organ or cavity), while the other fibers are used for

E. V. Kazimir

1993-01-01

109

Optical Image Recognition Using Acoustooptic and Charge Coupled Devices.  

National Technical Information Service (NTIS)

Optical image recognition systems based on optical correlators and distributed holographic interconnections are described and experimentally demonstrated. Correlators which rely on acoustooptic devices, optical disks, photorefractive crystals, and charge ...

D. Psaltis

1989-01-01

110

Primary immunodeficiencies of pattern recognition receptors.  

PubMed

Primary immunodeficiencies (PIDs) are severe defects in the capacity of the host to mount a proper immune response, and are characterized by an increased susceptibility to infections. Although classical immunodeficiencies have been characterized based on broad defects in cell populations (e.g. T/B cells or polymorphonuclear leukocytes) or humoral factors (e.g. antibodies or complement), specific immune defects based on well-defined molecular targets have been described more recently. Among these, genetic defects in pattern recognition receptors (PRRs), leading to impaired recognition of invading pathogens by the innate immune system, play an important role in specific defects against human pathogens. Defects have been described in three of the major families of PRRs: the Toll-like receptors, the C-type lectin receptors and the nucleotide-binding domain leucine-rich repeat-containing receptors. By contrast, no defects in the intracellular viral receptors of the RigI helicase family have been described to date. Defects in the PRRs show a broad variation in severity, have a narrow specificity for certain classes of pathogens, and often decrease in severity with age; these characteristics distinguish them from other forms of PIDs. Their discovery has led to important insights into the pathophysiology of infections, and may offer potential novel therapeutic targets for immunotherapy. PMID:22891878

Netea, M G; van de Veerdonk, F L; van der Meer, J W M

2012-09-12

111

A neural network for visual pattern recognition  

SciTech Connect

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.

Fukushima, K.

1988-03-01

112

Dynamic pattern recognition and data storage using localized holographic recording  

NASA Astrophysics Data System (ADS)

A new technique for optical pattern recognition with two-center recording of persistent holograms in doubly doped LiNbO3:Fe:Mn crystal is presented, by which the holograms are localized in separate slices along the recording medium. The localized recording method has the distinctive advantage of selective recording and erasure of the individual holograms without affecting the entire holographic recording medium. This capability enables dynamic content modification of the optical pattern recognition systems. Also, the diffraction efficiency of localized holograms is much larger than that of the normal volume multiplexed holograms. It is theoretically shown that the localized holographic correlator (LHC) outperforms the conventional volume holographic correlators in terms of crosstalk, shift invariance, and capacity. The LHC is experimentally demonstrated. Several persistent holograms are localized within separate slices as close as 33 mum apart along the crystal. The excessive diffraction efficiency of the localized holograms is employed to enhance the LHC robustness through multiplexing several holograms per pattern within individual slices of the recording medium. A holographic data storage system based on two-center holographic recording in a doubly doped LiNbO3:Fe:Mn crystal is developed with angular multiplexing capability. The associated imaging system has been optimized for the pixel matching of pixelated bit patterns generated by a spatial light modulator (SLM) through the recording medium onto a camera. The initial multiplexed holograms show promising contrast of dark and bright pixels. With the optimized imaging system of the developed holographic memory, the implementation of a dynamic read/write data storage system with localized recording is envisioned. The large diffraction efficiency of the localized holograms enables multilevel (M-ary) data coding to improve the storage density of the system.

Karbaschi, Arash

113

A comparison of proportional control methods for pattern recognition control  

Microsoft Academic Search

Few studies have focused on proportional control with multi-channel electromyographic (EMG) pattern recognition systems. In a simple proportional control algorithm, movement speed is often calculated by averaging the mean absolute values of all EMG channels. The aim of our study was to compare the performance of two types of pattern recognition control (simple proportional and binary on\\/off) to direct proportional

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

2011-01-01

114

Pattern Recognition for Ship Based on Bayesian Networks  

Microsoft Academic Search

Bayesian networks (BNs) are a powerful tool for pattern recognition. A BNs has two parts: parameters and structure composed of a directed acyclic graph (DAG) with some nodes. Then, according to the target feature, an approach based on BNs for pattern recognition is presented and the step of the approach is presented: constructing its nodes, modifying the node's states and

Qingjiang Wang; Xiaoguang Gao; Daqing Chen

2007-01-01

115

EMG pattern recognition based on artificial intelligence techniques  

Microsoft Academic Search

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

Sang-Hui Park; Seok-Pil Lee

1998-01-01

116

A robust-invariant pattern recognition model using Fuzzy ART  

Microsoft Academic Search

The purpose of this paper is to present a pattern recognition model that possesses both robust and invariant properties. A ‘robust and invariant’ concept is defined as follows: first, the pattern recognition model can recognize the objects that are translated, scaled, and rotated. Second, the system must have strong resistance to noise. Finally, the completely learned system can recognize new

Mun-hwa Kim; Dong-sik Jang; Young-kyu Yang

2001-01-01

117

A New Pattern Recognition Algorithm Based on Fuzzy Association Degree  

Microsoft Academic Search

Based on fuzzy association degree, a new pattern recognition algorithm is set up. First, some new concepts of fuzzy association coefficient (FAC), fuzzy association degree (FAD) and fuzzy relative weight (FRW) have been proposed for surveying data information. Second, on the basis of the concepts proposed here, a new pattern recognition algorithm has been set up. At last, the algorithm

Shifei Ding; Zhongzhi Shi

2005-01-01

118

Prosthetic Controlled System Based on Signal Pattern Recognition of Electroencephalogram  

Microsoft Academic Search

This study introduced the producing theory and producing region of electroencephalogram (EEG) signal as well as containing physiological information and analyzed the purpose, method and procedure of EEG signal pattern recognition, as well as the latest development and related medical theory of EEG signal acquisition. The procedure of EEG signal pattern recognition consisted of information acquisition, preprocessing, feature extraction and

Zhang Zhen; Fan Hong-liang

2008-01-01

119

Vehicle detection at night using image processing and pattern recognition  

Microsoft Academic Search

We present a methodology to detect vehicles at night using image processing and pattern recognition. The using of mathematical morphology, the techniques of pattern recognition and the studying of perspective influences are the major innovations of our method. First, we present an initialisation phase that involves a road modelling. Afterwards, we present methodologies to detect vehicles on highways. We also

R. Taktak; M. Dufaut; R. Husson

1994-01-01

120

PRISM-A novel framework for pattern recognition  

Microsoft Academic Search

In this paper, we introduce a new model of solving pattern recognition tasks called PRISM (Pattern Recognition using Information Slicing Method). The main concept behind PRISM is the slicing of information through multiple planes across different feature axes to generate a number of cells. The number of cells created and their volume depends upon the number partitions per axes. In

Sameer Singh

2003-01-01

121

Pattern-recognition receptors in human eosinophils  

PubMed Central

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.

Kvarnhammar, Anne Mansson; Cardell, Lars Olaf

2012-01-01

122

PRTSM: Pattern recognition-based time series modeler  

Microsoft Academic Search

In this paper, a new approach using pattern recognition techniques is suggested for time series modeling which means identification of a time series into one of autoregressive moving-average models. Its main recipe is that pattern is derived from a time series and classified into a suitable model via a notion of pattern matching. The pattern is obtained from extended sample

Kun Chang Lee; Sung Joo Park

1989-01-01

123

Patterns, Fishing and Nonlinear Optics  

NASA Astrophysics Data System (ADS)

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

Geddes, John Bruce

124

PARALLEL SELF-ORGANIZING FEATURE MAPS FOR UNSUPERVISED PATTERN RECOGNITION  

Microsoft Academic Search

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

TERRANCE L. HUNTSBERGER; PONGSAK AJJIMARANGSEE

1990-01-01

125

Reseaux de Neurones Artificiels: Application a la Reconnaissance Optique de Partitions Musicales (Artificial Neural Networks: Application to the Optical Recognition of Music Partitions).  

National Technical Information Service (NTIS)

The development and use of artificial neural networks to solve an unusual pattern recognition problem, those of optical musical score recognition, are discussed. A bibliographical study of the main connectionist models, the most frequently used in the pat...

P. Martin

1992-01-01

126

A new optical orthogonal code label and all-optical recognition technology for optical packet switching  

Microsoft Academic Search

An all-optical label recognition method is proposed, which is based on the fiber Bragg grating encoder\\/decoder and a semiconductor optical amplifier, employing multiple two-dimensional optical orthogonal codes for optical label in optical packet switching system. The program effectively reduces the code autocorrelation\\/cross-correlation requirements of label identification and removes the cross-correlation pulses after decoding, greatly increases the coding capacity and number

Leyang Wang; Kun Qiu; Chongfu Zhang; Heng Zhou; Xin Lu

2010-01-01

127

Proceedings of the eighth international conference on pattern recognition  

SciTech Connect

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

Not Available

1986-01-01

128

Pattern Recognition as Rule-Guided Inductive Inference  

Microsoft Academic Search

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

Ryszard S. Michalski

1980-01-01

129

An Overview of Statistical Pattern Recognition Techniques for Speaker Verification  

Microsoft Academic Search

Even though the subject of speaker verification has been investigated for several decades, numerous challenges and new opportunities in robust recognition techniques are still being explored. In this overview paper we first provide a brief introduction to statistical pattern recognition techniques that are commonly used for speaker verification. The second part of the paper presents traditional and modern techniques which

Amin Fazel; Shantanu Chakrabartty

2011-01-01

130

Genetic algorithms for pattern recognition analysis and fusion of sensor data  

Microsoft Academic Search

A genetic algorithm (GA) for pattern recognition analysis of optical sensor data has been developed. The GA selects feature sets based on their principal component (PC) plots. A good PC plot can only be generated using features whose variance or information is primarily about class differences. Hence, the principal component analysis routine in the fitness function of the GA acts

Barry K. Lavine; Anthony J. Moores

1999-01-01

131

Fuzzy pattern recognition of tungsten inert gas weld quality  

Microsoft Academic Search

In this paper, a fuzzy pattern recognition technique is applied to classifying aluminium weld quality in tungsten inert gas (TIG) welding. The pattern vector includes three components, that is, the front height, the back height, and the front width of weld. Based on the values of the pattern vector, good, fair, and poor weld qualities can be automatically classified by

Y. S. Tarng; S. S. Yeh; S. C. Juang

1997-01-01

132

Development of pattern recognition in infant pigtailed macaques (Macaca nemestrina)  

Microsoft Academic Search

Examined the development of pattern recognition in 31 infant pigtailed macaques using the familiarization–novelty technique. Ss were familiarized with 2 identical black and white patterns and tested on the familiar pattern paired with a novel one. Cross-sectional data revealed that a novelty preference occurred with increasing age. Younger Ss (mean age 178 days postconception or 1 postnatal week) did not

Virginia M. Gunderson; Gene P. Sackett

1984-01-01

133

The functional link net in structural pattern recognition  

Microsoft Academic Search

The functional link net is a higher-order generalized delta rule net. With it, input patterns are enhanced by means of functional transformations. In the simulations presented, the capabilities of the functional link net are explored by applying it to the structural pattern recognition, where the structural relationship between parts of a pattern that are of interest and the relevant parts

M. Klaseen; Y.-H. Pao

1990-01-01

134

Grip-pattern recognition: applied to a smart gun  

Microsoft Academic Search

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

Xiaoxin Shang

2008-01-01

135

The Science of Pattern Recognition. Achievements and Perspectives  

Microsoft Academic Search

Summary. Automatic pattern recognition is usually considered as an engineering area study- ing the development and evaluation of systems that imitate or assist the human ability of recognizing patterns. It may, however, also be considered as a science that studies the natural phenomenon that human beings (and possibly other biological systems) are able to discover, distinguish and characterize patterns in

Robert P. W. Duin; Elzbieta Pekalska

2007-01-01

136

Gene prediction by pattern recognition and homology search.  

National Technical Information Service (NTIS)

This paper presents an algorithm for combining pattern recognition-based exon prediction and database homology search in gene model construction. The goal is to use homologous genes or partial genes existing in the database as reference models while const...

Y. Xu E. C. Uberbacher

1996-01-01

137

A Computer Software Package for Statistical Pattern Recognition.  

National Technical Information Service (NTIS)

This report documents important computer programs, with illustrative examples, on pattern recognition techniques in feature selection, classification, orthogonal transforms, and nonlinear mapping. The basic principles of these techniques are described in ...

C. Chen M. Fan

1974-01-01

138

Fourier Shape Analysis. A Multivariate Pattern Recognition Approach.  

National Technical Information Service (NTIS)

This article describes the design of a simple image analysis procedure generating edge points of particles, and a set of pattern recognition algorithms accomplishing end member (source) characterization and mixing proportions.

R. Ehrlich W. E. Full

1983-01-01

139

On the Linear and Quadratic Discriminators for Pattern Recognition.  

National Technical Information Service (NTIS)

In this report, two problems in pattern recognition are considered: (1) when the likelihood ratio criterion results in a quadratic discriminant function, it may be possible to factor the quadratic form into two linear discriminant functions. It is shown t...

P. S. R. S. Rao

1967-01-01

140

Towards a Generalized Template Matching Algorithm for Pictorial Pattern Recognition.  

National Technical Information Service (NTIS)

The paper is concerned with the use of nonlinear regression analysis as a means for achieving pictorial pattern recognition. The object to be identified is presumed to be represented by a set of image plane coordinates representing points associated with ...

H. Hemami R. B. McGhee S. R. Gardner

1970-01-01

141

Theory and Design of a Hybrid Pattern Recognition System.  

National Technical Information Service (NTIS)

Pattern recognition methods can be divided into four different categories: statistical or probabilistic structural, possibilistic or fuzzy, and neural methods. A formal analysis shows that there is a computational complexity versus representational power ...

J. A. Drakopoulos

1995-01-01

142

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 ...

143

Pattern Recognition of EEG to Determine Level of Alertness.  

National Technical Information Service (NTIS)

This report documents the work accomplished during the second reporting period in applying the principles of pattern recognition technology to the analysis of EEG. Using EEG recordings, two sleep state classification systems, based on inputs derived from ...

W. B. Martin

1969-01-01

144

Pattern Recognition of EEG to Determine Level of Alertness.  

National Technical Information Service (NTIS)

The report documents the work accomplished during the fourth reporting period in applying the principles of pattern recognition technology to the analysis of EEG. Several additional sleep scoring decision systems were investigated. Most classifications ar...

W. B. Martin

1970-01-01

145

High-Density Myoelectric Pattern Recognition Toward Improved Stroke Rehabilitation  

Microsoft Academic Search

Myoelectric pattern-recognition techniques have been developed to infer user's intention of performing different functional movements. Thus electromyogram (EMG) can be used as control signals of assisted devices for people with disabilities. Pattern-recognition-based myoelectric control systems have rarely been designed for stroke survivors. Aiming at developing such a system for improved stroke rehabilitation, this study assessed detection of the affected limb's

Xu Zhang; Ping Zhou

2012-01-01

146

Assessment of bioinspired models for pattern recognition in biomimetic systems  

Microsoft Academic Search

The increasing complexity of the artificial implementations of biological systems, such as the so-called electronic noses (e-noses) and tongues (e-tongues), poses issues in sensory feature extraction and fusion, drift compensation and pattern recognition, especially when high reliability is required. In particular, in order to achieve effective results, the pattern recognition system must be carefully designed. In order to investigate a

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

2008-01-01

147

Pattern Recognition Using Artificial Neural Network: A Review  

NASA Astrophysics Data System (ADS)

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

Kim, Tai-Hoon

148

A Feature Extraction Toolbox for Pattern Recognition Application  

SciTech Connect

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

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

1998-11-23

149

Syntactic pattern recognition for HRR signatures  

NASA Astrophysics Data System (ADS)

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

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

2000-08-01

150

DNA sequence pattern recognition methods in GRAIL  

SciTech Connect

The goal of the GRAIL project has been to create a comprehensive analysis environment where a host of questions about genes and genome structure can be answered as quickly and accurately as possible. Constructing this system has entailed solving a number of significant technical challenges including: (a) making coding recognition in sequence more sensitive and accurate, (b) compensating for isochore base compositional effects in coding prediction, (c) developing methods to determine which parts of each strand of a long genomic DNA are the coding strand, (d) improving the accuracy of splice site prediction and recognizing non-consensus sites, and (e) recognizing variable regulatory structures such as polymerase II promoters. An additional challenge has been to construct algorithms which compensate for the deleterious effects of insertion or deletion (indel) errors in the coding region recognition process. This paper addresses progress on these technical issues and the current state of sequence feature recognition methods.

Uberbacher, E.C.; Xu, Ying; Shah, M.; Matis, S.; Guan, X.; Mural, R.J.

1995-12-31

151

Improved recognition of control chart patterns using artificial neural networks  

Microsoft Academic Search

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

Susanta Kumar Gauri; Shankar Chakraborty

2008-01-01

152

Low-light level recognition using COTS optical correlator  

NASA Astrophysics Data System (ADS)

Optical correlation offers high speed processing capabilities of images mainly for filtering and pattern recognition applications. For a long time it has been kept in the laboratories at the state of prototype. The apparition of commercially available source of optical correlator opens the way to a wider spread of this technology. In real-time systems, illumination is an important issue, and has a strong impact on the final result of the correlation. In real-life applications, the level of illumination can vary depending of the environment, time of the day, etc. Two consequences result from the change of the illumination level, the first one is the reduction of the energy signature provided by the object, the second one is the relative augmentation of noise in the input scene. These two facts combined together can directly affect the value of the correlation peak. In off-line process these variations can be partly compensated by normalization of the input image. In live applications compensation has to be made in real-time. In this paper, results of live experiments performed with a COTS correlator are shown. The results indicate that live optical correlation can support a large amount of ambient light reduction in the input image provided that some kind of real-time feedback is provided to artificially increase the dynamical range of input gray level. The results shows that optical correlation, basically an integration operator, can identify object otherwise difficult to analyse conveniently by human being.

Bergeron, Alain

2002-03-01

153

Do pattern recognition skills transfer across sports? A preliminary analysis  

Microsoft Academic Search

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

Nicholas J Smeeton; Paul Ward; A Mark Williams

2004-01-01

154

A VLSI BAM neural network chip for pattern recognition applications  

Microsoft Academic Search

Bi-directional associative memory (BAM) is a two-level nonlinear neural network suitable for pattern recognition applications. One important performance attribute of the discrete BAM is its ability to recall stored pattern pairs, particularly in the presence of noise. In this paper the VLSI implementation of BAM is presented. A modular VLSI processor chip implementing BAM was designed. By using 2 micron

S. M. R. Hasan; Ng Kang Siong

1995-01-01

155

Learning templates from fuzzy examples in structural pattern recognition  

Microsoft Academic Search

A fuzzy-attribute graph (FAG) has been proposed to handle fuzziness in the pattern primitives in structural pattern recognition. FAG has the advantage that one can combine several possible definitions into a single template. However, the template requires human expert definition. In this paper, the author proposes an algorithm that can, from a number of fuzzy instances, find a template that

Kwok-Ping Chan

1994-01-01

156

Learning templates from fuzzy examples in structural pattern recognition  

Microsoft Academic Search

Fuzzy-Attribute Graph (FAG) was proposed to handle fuzziness in the pattern primitives in structural pattern recognition. FAG has the advantage that we can combine several possible definitions into a single template, and hence only one matching is required instead of one for each definition. Also, each vertex or edge of the graph can contain fuzzy attributes to model real-life situations.

Kwok-Ping Chan

1996-01-01

157

Biometric verification based on grip-pattern recognition  

Microsoft Academic Search

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 piezo- resistive elements is used to measure the grip pattern. An interface has been developed to acquire pressure images from the sensor. The values of the

Raymond N. J. Veldhuis; Asker M. Bazen; Joost A. Kauffman; Pieter H. Hartel; Edward J. Delp; Ping W. Wong

2004-01-01

158

Biometric verification based on grip-pattern recognition  

Microsoft Academic Search

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

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

2004-01-01

159

Topology constraint free fuzzy gated neural networks for pattern recognition  

Microsoft Academic Search

A novel topology constraint free neural network architecture using a generalized fuzzy gated neuron model is presented for a pattern recognition task. The main feature is that the network does not require weight adaptation at its input and the weights are initialized directly from the training pattern set. The elimination of the need for iterative weight adaptation schemes facilitates quick

V. Chandrasekaran; Zhi-qiang Liu

1998-01-01

160

On deformable models for visual pattern recognition  

Microsoft Academic Search

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

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

2002-01-01

161

An Evaluation of PC-Based Optical Character Recognition Systems.  

ERIC Educational Resources Information Center

|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)|

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

1991-01-01

162

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

Microsoft Academic Search

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

John Daugman

2003-01-01

163

A Bayesian Framework for Deformable Pattern Recognition With Application to Handwritten Character Recognition  

Microsoft Academic Search

Deformable models have recently been proposed for many pattern recognition applications due to their ability to handle large shape variations. These proposed approaches represent patterns or shapes as deformable models, which deform themselves to match with the input image, and subsequently feed the extracted information into a classifier. The three components—modeling, matching, and classification—are often treated as independent tasks. In

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

1998-01-01

164

Computer Vision and Pattern Recognition in Homeland Security Applications  

Microsoft Academic Search

The tutorial will summarize the status of research and innovation in the field of Security of Computer Vision and Pattern\\u000a Recognition Technology. Two main research areas are considered: intelligent scene analysis in video-surveillance, and mobile\\u000a Automatic Number Plate recognition ANPR, for investigation and crime prevention. The lecture will refer the most recent advances\\u000a of mobile ANPR solutions on board of

Giovanni B. Garibotto

2007-01-01

165

A genetic fuzzy neural network for pattern recognition  

Microsoft Academic Search

In this paper, a genetic fuzzy neural network for pattern recognition is proposed by applying genetic algorithms to the Kwan-Cai fuzzy neural network (1990). A genetic-guided self-organizing learning algorithm is capable of reducing the number of fuzzy neurons and increasing recognition rates for the fixed number of output neurons. The simulations have indicated that the genetic fuzzy neural network can

Abraham Kandel; Yan-Qing Zhang; H. Bunke

1997-01-01

166

Pattern Recognition Methods for Querying and Browsing Technical Documentation  

Microsoft Academic Search

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

Karl Tombre; Bart Lamiroy

2008-01-01

167

Bidirectional plasticity of cortical pattern recognition and behavioral sensory acuity.  

PubMed

Learning to adapt to a complex and fluctuating environment requires the ability to adjust neural representations of sensory stimuli. Through pattern completion processes, cortical networks can reconstruct familiar patterns from degraded input patterns, whereas pattern separation processes allow discrimination of even highly overlapping inputs. Here we show that the balance between pattern separation and completion is experience dependent. Rats given extensive training with overlapping complex odorant mixtures showed improved behavioral discrimination ability and enhanced piriform cortical ensemble pattern separation. In contrast, behavioral training to disregard normally detectable differences between overlapping mixtures resulted in impaired piriform cortical ensemble pattern separation (enhanced pattern completion) and impaired discrimination. This bidirectional effect was not found in the olfactory bulb; it may be due to plasticity within olfactory cortex itself. Thus pattern recognition, and the balance between pattern separation and completion, is highly malleable on the basis of task demands and occurs in concert with changes in perceptual performance. PMID:22101640

Chapuis, Julie; Wilson, Donald A

2011-11-20

168

Quantum pattern recognition with liquid-state nuclear magnetic resonance  

NASA Astrophysics Data System (ADS)

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

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

2009-04-01

169

A robust and biologically plausible spike pattern recognition network  

PubMed Central

The neural mechanisms that enable recognition of spiking patterns in the brain are currently unknown. This is especially relevant in sensory systems, where the brain has to detect such patterns and recognize relevant stimuli by processing peripheral inputs; in particular, it is unclear how sensory systems can recognize time-varying stimuli by processing spiking activity. Because auditory stimuli are represented by time-varying fluctuations in frequency content, it is useful to consider how such stimuli can be recognized by neural processing. Previous models for sound recognition have used pre-processed or low-level auditory signals as input, but complex natural sounds like speech are thought to be processed in auditory cortex, and brain regions involved in object recognition in general must deal with the natural variability present in spike trains. Thus we used neural recordings to investigate how a spike pattern recognition system could deal with the intrinsic variability and diverse response properties of cortical spike trains. We propose a biologically plausible computational spike pattern recognition model that uses an excitatory chain of neurons to spatially preserve the temporal representation of the spike pattern. Using a single neural recording as input, the model can be trained using a spike timing dependent plasticity-based learning rule to recognize neural responses to 20 different bird songs with over 98% accuracy, and can be stimulated to evoke reverse spike pattern playback. Although we test spike train recognition performance in an auditory task, this model can be applied to recognize sufficiently reliable spike patterns from any neuronal system.

Larson, Eric; Perrone, Ben P.; Sen, Kamal; Billimoria, Cyrus P.

2010-01-01

170

Automatic detection and recognition of faint artificial earth satellites using ground based electro-optical sensors  

Microsoft Academic Search

The design of an automatic electro-optic satellite surveillance system, which places special emphasis on accurate sky modeling, pattern recognition, and system tradeoffs. The system consists of a ground based telescope using a mosaic of photoconductive sensors at the focal plane, together with a central processor system. The system purports to be totally automatic in its main search mode and thus

A. J. Verderese

1975-01-01

171

Identification of biomolecules by terahertz spectroscopy and fuzzy pattern recognition  

NASA Astrophysics Data System (ADS)

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

Chen, Tao; Li, Zhi; Mo, Wei

2013-04-01

172

Identification of biomolecules by terahertz spectroscopy and fuzzy pattern recognition.  

PubMed

An approach for automatic identification of terahertz (THz) spectra of biomolecules is proposed based on principal component analysis (PCA) and fuzzy pattern recognition in this paper, and THz transmittance spectra of some typical amino acid and saccharide biomolecular samples are investigated to prove its feasibility. Firstly, PCA is applied to reduce the dimensionality of the original spectrum data and extract features of the data. Secondly, instead of the original spectrum variables, the selected principal component scores matrix is fed into the model of fuzzy pattern recognition, where a principle of fuzzy closeness based optimization is employed to identify those samples. Results demonstrate that THz spectroscopy combined with PCA and fuzzy pattern recognition can be efficiently utilized for automatic identification of biomolecules. The proposed approach provides a new effective method in the detection and identification of biomolecules using THz spectroscopy. PMID:23357678

Chen, Tao; Li, Zhi; Mo, Wei

2013-01-10

173

Optical Font Recognition Using Typographical Features  

Microsoft Academic Search

A new statistical approach based on global typographical features is proposed to the widely neglected problem of font recognition. It aims at the identification of the typeface, weight, slope and size of the text from an image block without any knowledge of the content of that text. The recognition is based on a multivariate Bayesian classifier and operates on a

Abdel Wahab Zramdini; Rolf Ingold

1998-01-01

174

Stochastic resonance in pattern recognition by a holographic neuron model  

NASA Astrophysics Data System (ADS)

The recognition rate of holographic neural synapses, performing a pattern recognition task, is significantly higher when applied to natural, rather than artificial, images. This shortcoming of artificial images can be largely compensated for, if noise is added to the input pattern. The effect is the result of a trade-off between optimal representation of the stimulus (for which noise is favorable) and keeping as much as possible of the stimulus-specific information (for which noise is detrimental). The observed mechanism may play a prominent role for simple biological sensors.

Stoop, R.; Buchli, J.; Keller, G.; Steeb, W.-H.

2003-06-01

175

Object detection by optical correlator and intelligence recognition surveillance systems  

NASA Astrophysics Data System (ADS)

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

Sheng, Yunlong

2013-09-01

176

Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data.  

National Technical Information Service (NTIS)

Pattern recognition encompasses two fundamental tasks: description and classification. Given an object to analyze, a pattern recognition system first generates a description of it (i.e., the pattern) and then classifies the object based on that descriptio...

R. T. Olszewski

2001-01-01

177

Auditory orientation in crickets: Pattern recognition controls reactive steering  

NASA Astrophysics Data System (ADS)

Many groups of insects are specialists in exploiting sensory cues to locate food resources or conspecifics. To achieve orientation, bees and ants analyze the polarization pattern of the sky, male moths orient along the females' odor plume, and cicadas, grasshoppers, and crickets use acoustic signals to locate singing conspecifics. In comparison with olfactory and visual orientation, where learning is involved, auditory processing underlying orientation in insects appears to be more hardwired and genetically determined. In each of these examples, however, orientation requires a recognition process identifying the crucial sensory pattern to interact with a localization process directing the animal's locomotor activity. Here, we characterize this interaction. Using a sensitive trackball system, we show that, during cricket auditory behavior, the recognition process that is tuned toward the species-specific song pattern controls the amplitude of auditory evoked steering responses. Females perform small reactive steering movements toward any sound patterns. Hearing the male's calling song increases the gain of auditory steering within 2-5 s, and the animals even steer toward nonattractive sound patterns inserted into the speciesspecific pattern. This gain control mechanism in the auditory-to-motor pathway allows crickets to pursue species-specific sound patterns temporarily corrupted by environmental factors and may reflect the organization of recognition and localization networks in insects. localization | phonotaxis

Poulet, James F. A.; Hedwig, Berthold

2005-10-01

178

Biometric verification based on grip-pattern recognition  

NASA Astrophysics Data System (ADS)

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

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

2004-06-01

179

An improved MSD-based method for PD pattern recognition  

Microsoft Academic Search

In this paper a new method for multi source partial discharge (PD) pattern recognition by discrete wavelet transform, is presented. The proposed method is based on the application of multi-resolution signal decomposition (MSD) technique applied to a 3D PD pattern image. The multi-resolution technique has shown that some detail images (horizontal, vertical and diagonal) at different decomposition levels have frequencial

R. Candela; P. Romano

2007-01-01

180

Pattern recognition as a caring partnership in families with cancer.  

PubMed

Pattern recognition as a caring partnership in families with cancer The purpose of this study was to address the process of a caring partnership by elaborating pattern recognition as nursing intervention with families with cancer. It is based on Newman's theory of health as expanding consciousness within the unitary-transformative paradigm and is an extension of a previous study of Japanese women with ovarian cancer. A hermeneutic, dialectic method was used to engage 10 Japanese families in which the wife-mothers were hospitalized because of cancer diagnosis. The family included at least the woman with cancer and her primary caregiver. Each of four nurse-researchers entered into partnership with a different family and conducted three interviews with each family. The participants were asked to describe the meaningful persons and events in their family history. The family's story was transmuted into a diagram of sequential patterns of interactional configurations and shared with the family at the second meeting. Evidence of pattern recognition and insight into the meaning of the family pattern were identified further in the remaining meetings. The data revealed five dimensions of a transformative process. Most families found meaning in their patterns and made a shift from separated individuals within the family to trustful caring relationships. One-third of them went through this process within two interviews. The families showed increasing openness, connectedness and trustfulness in caring relationships. In partnership with the family, each nurse-researcher grasped the pattern of the family as a whole and experienced the meaning of caring. Pattern recognition as nursing intervention was a meaning-making transforming process in the family-nurse partnership. PMID:11012802

Endo, E; Nitta, N; Inayoshi, M; Saito, R; Takemura, K; Minegishi, H; Kubo, S; Kondo, M

2000-09-01

181

Recall, recognition, and confidence patterns in eyewitness testimony  

Microsoft Academic Search

SUMMARY The diversity of methods, contents and tests used in the study of eyewitness memory may have contributed to discrepancies in results in this field. In this experiment, using incidental or intentional learning, we examine the recall and recognition of actions and details concerning the central and peripheral information of a kidnapping. A similar pattern emerges in free recall, hits

Malen Migueles; Elvira Garcia-Bajos

1999-01-01

182

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

Microsoft Academic Search

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

P Grassberger

2004-01-01

183

A discrete sequential bidirectional associative memory for multistep pattern recognition  

Microsoft Academic Search

The Discrete Chainable Bidirectional Associative Memory (DCBAM) has been proposed for multistep pattern recognition. However, it is found by our experiment that the state (environment output) of the DCBAM may converge to limited cycles. Hence, the application of the DCBAM is limited. We show in this letter that it is possible to realize multistep retrieval by using conventional Discrete Bidirectional

Donq-liang Lee

1998-01-01

184

Adaptive pattern recognition in the analysis of cardiotocographic records  

Microsoft Academic Search

The recognition of accelerative and decelerative patterns in the fetal heart rate (FHR) is one of the tasks carried out manually by obstetricians when they analyze cardiotocograms for information respecting the fetal state. An approach based on artificial neural networks formed by a multilayer perceptron (MLP) is developed. However, since the system utilizes the FHR signal as direct input, an

Oscar Fontenla-Romero; Amparo Alonso-Betanzos; Bertha Guijarro-Berdiñas

2001-01-01

185

New Digital Architecture of CNN for Pattern Recognition  

Microsoft Academic Search

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.

Emil Raschman; Roman Záluský; Daniela Duracková

2010-01-01

186

Improving myoelectric pattern recognition positional robustness using advanced training protocols  

Microsoft Academic Search

The control of powered upper limb prostheses using the surface electromyogram (EMG) is an important clinical option for amputees. There have been considerable recent improvements in prosthetic hands, but these currently lack a control scheme that can decode movement intent from the EMG to exploit their mechanical dexterity. Pattern recognition based control has the potential to decode many classes of

K. Biron; K. Englehart

2011-01-01

187

The Illinois Pattern Recognition Computer-ILLIAC III  

Microsoft Academic Search

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

BRUCE H. McCORMICKt

1963-01-01

188

High-density myoelectric pattern recognition toward improved stroke rehabilitation.  

PubMed

Myoelectric pattern-recognition techniques have been developed to infer user's intention of performing different functional movements. Thus electromyogram (EMG) can be used as control signals of assisted devices for people with disabilities. Pattern-recognition-based myoelectric control systems have rarely been designed for stroke survivors. Aiming at developing such a system for improved stroke rehabilitation, this study assessed detection of the affected limb's movement intention using high-density surface EMG recording and pattern-recognition techniques. Surface EMG signals comprised of 89 channels were recorded from 12 hemiparetic stroke subjects while they tried to perform 20 different arm, hand, and finger/thumb movements involving the affected limb. A series of pattern-recognition algorithms were implemented to identify the intended tasks of each stroke subject. High classification accuracies (96.1% ± 4.3%) were achieved, indicating that substantial motor control information can be extracted from paretic muscles of stroke survivors. Such information may potentially facilitate improved stroke rehabilitation. PMID:22453603

Zhang, Xu; Zhou, Ping

2012-03-21

189

Statistical pattern recognition with neural networks: benchmarking studies  

Microsoft Academic Search

Three basic types of neural-like networks (backpropagation network, Boltzmann machine, and learning vector quantization), were applied to two representative artificial statistical pattern recognition tasks, each with varying dimensionality. The performance of each network's approach to solving the tasks was evaluated and compared, both to the performance of the other two networks and to the theoretical limit. The learning vector quantization

Teuvo Kohonen; Gyorgy Barna; Ronald Chrisley

1988-01-01

190

Mechanisms of tacit knowing: pattern recognition and synthesis  

Microsoft Academic Search

Purpose – The paper sets out to integrate what is known about the concept of tacit knowledge and proposes a pattern recognition and synthesis (PRS) framework as an explanation of how tacit knowledge is created. Design\\/methodology\\/approach – In this conceptual piece it is argued that knowledge is monistic and that the dichotic distinction between tacit and explicit is an artifact

William H. A. Johnson

2007-01-01

191

An Adaptively Evolving Intrusion Detection System using Pattern Recognition Techniques  

Microsoft Academic Search

With networking technology evolving so rapidly, computer security has been receiving a lot of attention in the recent years. Conventional intrusion detection methods in the field of computer security are anomaly detection and misuse detection - the former suffers from high false alarm rates while the latter lacks generalization capabilities and cannot detect new attack types. Pattern recognition techniques can

Devi Parikh

192

Pattern recognition for sensor array signals using Fuzzy ARTMAP  

Microsoft Academic Search

A Fuzzy ARTMAP classifier for pattern recognition in chemical sensor array was developed based on Fuzzy Set Theory and Adaptive Resonance Theory. In contrast to most current classifiers with difficulty in detecting new analytes, the Fuzzy ARTMAP system can identify untrained analytes with comparatively high probability. And to detect presence of new analyte, the Fuzzy ARTMAP classifier does not need

Zhe Xu; Xiajing Shi; Lingyan Wang; Jin Luo; Chuan-Jian Zhong; Susan Lu

2009-01-01

193

Phoneme sequence pattern recognition using fuzzy neural network  

Microsoft Academic Search

In this paper, a 2-D phoneme sequence pattern recognition using the fuzzy neural network is presented. The self-organizing map and the learning vector quantization are used to organize the phoneme feature vectors of short and long phonemes segmented from speech samples to obtain the phoneme maps. The 2-D phoneme response sequences of the speech samples are formed on the phoneme

H. K. Kwan; X. Dong

2003-01-01

194

Neural network designed volume holographic wavelet correlator for pattern recognition  

Microsoft Academic Search

Neural network (NN) techniques have been introduced to design wavelet filters and wavelet transform systems for pattern recognition. Based on the theory of wavelet matched filtering and the associative characteristic of volume holographic storage in a photo refractive crystal, a novel volume holographic wavelet correlator is constructed. A neural network is proposed to optimize parameters of the wavelet filters to

Wenyi Feng; Yingbai Yan; Guofan Jin; Minxian Wu; Qingsheng He

1999-01-01

195

Pattern recognition applied to earthquake epicenters in California  

Microsoft Academic Search

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

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

1976-01-01

196

Pattern Recognition as a Guide in Diagnosing Headache  

PubMed Central

This paper presents the family physician with a comprehensive yet simple classification that is easily followed. By a system known as ‘pattern recognition’, the physician may determine cause in the majority of headaches in his own office. With use of ancillary investigatory procedures where necessary, he will refer only when the diagnosis warrants it.

Nurse, E. G.

1974-01-01

197

Characterization of ultrasonic transducers using pattern recognition techniques  

SciTech Connect

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

Ekis, J.W.

1990-09-01

198

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

PubMed

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

Love, Ryan J; Jones, Kim S

2013-04-24

199

Online pattern recognition in intensive care medicine.  

PubMed Central

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

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

2001-01-01

200

Computational operators for dynamical complex pattern recognition  

NASA Astrophysics Data System (ADS)

Spatially extended systems yield complex patterns arising from the coupled dynamics of its different regions. In this paper we introduce a matrix computational operator (the so-called RSV operator), F sub(A), for the characterization of asymmetric amplitude fragmentation in extended systems. For a given matrix of amplitudes this operation results in an asymmetric-triangulation field composed by L points and I straigth lines. The parameter (I - L)/L is a new quantitative measure of the local complexity defined in terms of the asymmetry in the gradient field of the amplitudes. This asymmetric fragmentation parameter is a measure of the degree of structural complexity and characterizes the localized regions of a spatially extended system and symmetry breaking along the evolution of the system. For the case of a random field, in the real domain, which has total asymmetry, this asymmetric fragmentation parameter is expected to have the highest value and this is used to normalize the values for the other cases. Here, we present a detailed description of the operator F sub(A) and some of the fundamental conjectures that arises from its application in spatio-temporal asymmetric patterns. We also compare the performance of correlation length, entropies and RSV operators applied mainly in non-equilibrium plasma extended systems. The complex regimes we study are stochasticity, symmetry breaking, chaoticity and localized turbulence. The main result is the high performance of the complex entropy and RSV operator to quantify non-linear amplitude fragmentation and localized turbulence in spatio-temporal dynamics. *%K Correlation methods, Mathematical operators, Mathematical models, Random processes

Rosa, R.; Neto, C.; Ramos, F.; Sharma, A.; Valdivia, J.

1999-09-01

201

A new concept of vertically integrated pattern recognition associative memory  

SciTech Connect

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

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

2011-11-01

202

Quantum Mechanics, Pattern Recognition, and the Mammalian Brain  

NASA Astrophysics Data System (ADS)

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

Chapline, George

2008-10-01

203

Action Recognition for Surveillance Applications Using Optic Flow and SVM  

Microsoft Academic Search

Low quality images taken by surveillance cameras pose a great challenge to human action recognition algorithms. This is because\\u000a they are usually noisy, of low resolution and of low frame rate. In this paper we propose an action recognition algorithm\\u000a to overcome the above challenges. We use optic flow to construct motion descriptors and apply a SVM to classify them.

Somayeh Danafar; Niloofar Gheissari

2007-01-01

204

A Voting-Based Sequential Pattern Recognition Method  

PubMed Central

We propose a novel method for recognizing sequential patterns such as motion trajectory of biological objects (i.e., cells, organelle, protein molecules, etc.), human behavior motion, and meteorological data. In the proposed method, a local classifier is prepared for every point (or timing or frame) and then the whole pattern is recognized by majority voting of the recognition results of the local classifiers. The voting strategy has a strong benefit that even if an input pattern has a very large deviation from a prototype locally at several points, they do not severely influence the recognition result; they are treated just as several incorrect votes and thus will be neglected successfully through the majority voting. For regularizing the recognition result, we introduce partial-dependency to local classifiers. An important point is that this dependency is introduced to not only local classifiers at neighboring point pairs but also to those at distant point pairs. Although, the dependency makes the problem non-Markovian (i.e., higher-order Markovian), it can still be solved efficiently by using a graph cut algorithm with polynomial-order computations. The experimental results revealed that the proposed method can achieve better recognition accuracy while utilizing the above characteristics of the proposed method.

Ogawara, Koichi; Fukutomi, Masahiro; Uchida, Seiichi; Feng, Yaokai

2013-01-01

205

Activity recognition using correlated pattern mining for people with dementia.  

PubMed

Due to the rapidly aging population around the world, senile dementia is growing into a prominent problem in many societies. To monitor the elderly dementia patients so as to assist them in carrying out their basic Activities of Daily Living (ADLs) independently, sensors are deployed in their homes. The sensors generate a stream of context information, i.e., snippets of the patient's current happenings, and pattern mining techniques can be applied to recognize the patient's activities based on these micro contexts. Most mining techniques aim to discover frequent patterns that correspond to certain activities. However, frequent patterns can be poor representations of activities. In this paper, instead of using frequent patterns, we propose using correlated patterns to represent activities. Using simulation data collected in a smart home testbed, our experimental results show that using correlated patterns rather than frequent ones improves the recognition performance by 35.5% on average. PMID:22256096

Sim, Kelvin; Phua, Clifton; Yap, Ghim-Eng; Biswas, Jit; Mokhtari, Mounir

2011-01-01

206

Modulational-induced optical pattern formation in a passive optical-feedback system  

NASA Astrophysics Data System (ADS)

The evolution of extremely rich dynamic patterns across the transverse cross section of a laser beam is demonstrated for a passive optical-feedback ring-cavity system. The instability responsible for pattern formation is primarily modulational in nature, leading to a competitive interaction between strongly saturated solitary-wave ringlike and filamentary spatial structures. The complexity of pattern formation depends sensitively on saturated filament density, which can be conveniently controlled in an optical-feedback arrangement. It is suggested that the enormous degeneracy of potentially unstable two-dimensional modes in K-space means that pattern evolution may be a sensitive function of seeding, either from numerical noise or from an externally imposed seed pattern. The high degeneracy offers the possibility of designing neural networks of pattern recognition systems in the future.

Moloney, J. V.; Adachihara, H.; Indik, R.; Lizarraga, C.; Northcutt, R.

1990-06-01

207

Structural pattern recognition methods based on string comparison for fusion databases  

Microsoft Academic Search

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

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

2008-01-01

208

Pattern recognition software and techniques for biological image analysis.  

PubMed

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

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

2010-11-24

209

Pattern Recognition Software and Techniques for Biological Image Analysis  

PubMed Central

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

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

2010-01-01

210

Cerebellar LTD and Pattern Recognition by Purkinje Cells  

PubMed Central

Summary Many theories of cerebellar function assume that long-term depression (LTD) of parallel fiber (PF) synapses enables Purkinje cells to learn to recognize PF activity patterns. We have studied the LTD-based recognition of PF patterns in a biophysically realistic Purkinje-cell model. With simple-spike firing as observed in vivo, the presentation of a pattern resulted in a burst of spikes followed by a pause. Surprisingly, the best criterion to distinguish learned patterns was the duration of this pause. Moreover, our simulations predicted that learned patterns elicited shorter pauses, thus increasing Purkinje-cell output. We tested this prediction in Purkinje-cell recordings both in vitro and in vivo. In vitro, we found a shortening of pauses when decreasing the number of active PFs or after inducing LTD. In vivo, we observed longer pauses in LTD-deficient mice. Our results suggest a novel form of neural coding in the cerebellar cortex.

Steuber, Volker; Mittmann, Wolfgang; Hoebeek, Freek E.; Silver, R. Angus; De Zeeuw, Chris I.; Hausser, Michael; De Schutter, Erik

2007-01-01

211

Learning pattern recognition and decision making in the insect brain  

NASA Astrophysics Data System (ADS)

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.

Huerta, R.

2013-01-01

212

Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays  

PubMed Central

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

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

2012-01-01

213

Optical fingerprint recognition based on local minutiae structure coding.  

PubMed

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

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

2013-07-15

214

A new paradigm for pattern recognition of drugs.  

PubMed

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

Potemkin, Vladimir A; Grishina, Maria A

2008-03-21

215

Musical instrument identification: A pattern-recognition approach  

Microsoft Academic Search

A statistical pattern-recognition technique was applied to the classification of musical instrument tones within a taxonomic hierarchy. Perceptually salient acoustic features— related to the physical properties of source excitation and resonance structure—were measured from the output of an auditory model (the log-lag correlogram) for 1023 isolated tones over the full pitch ranges of 15 orchestral instruments. The data set included

Keith D. Martin; Youngmoo E. Kim

1998-01-01

216

Soft Computing, f-Granulation and Pattern Recognition  

Microsoft Academic Search

\\u000a Different components of soft computing (e.g., fuzzy logic, artificial neural networks, rough sets and genetic algorithms)\\u000a and machine intelligence, and their relevance to pattern recognition and data mining are explained. Characteristic features\\u000a of these tools are described conceptually. Various ways of integrating these tools for application specific merits are described.\\u000a Tasks like case (prototype) generation, rule generation, knowledge encoding, classification

Sankar K. Pal

2010-01-01

217

Pattern recognition used to investigate multivariate data in analytical chemistry  

SciTech Connect

Pattern recognition and allied multivariate methods provide an approach to the interpretation of the multivariate data often encountered in analytical chemistry. Widely used methods include mapping and display, discriminant development, clustering, and modeling. Each has been applied to a variety of chemical problems, and examples are given. The results of two recent studies are shown, a classification of subjects as normal or cystic fibrosis heterozygotes and simulation of chemical shifts of carbon-13 nuclear magnetic resonance spectra by linear model equations.

Jurs, P.C.

1986-06-06

218

Neural networks and pattern recognition in human-computer interaction  

Microsoft Academic Search

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

Janet Finlay; Russell Beale

1993-01-01

219

On Visual Semantic Algebra (VSA) and the cognitive process of pattern recognition  

Microsoft Academic Search

A new form of denotational mathematics known as visual semantic algebra (VSA) is presented for abstract visual object and architecture manipulation. The cognitive theories for pattern recognition, such as cognitive principles of visual perception and basic mechanisms of object and pattern recognition, are explored. A number of case studies on VSA in pattern recognition are presented to demonstrate VAS' expressive

Yingxu Wang

2008-01-01

220

A comparison of proportional control methods for pattern recognition control.  

PubMed

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

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

2011-01-01

221

Spatial pattern recognition of seismic events in South West Colombia  

NASA Astrophysics Data System (ADS)

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

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

2013-09-01

222

Classification of simultaneous movements using surface EMG pattern recognition.  

PubMed

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

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

2012-12-10

223

Recognition of dynamic patterns in dc-dc switching converters  

NASA Astrophysics Data System (ADS)

Techniques are presented for analyzing the dynamic patterns of power flow in high-frequency dc-dc switching converters. Emphasis is placed on exploring methods for developing reduced-order models of power electronic circuits for simulation and state-space averaging. Tools from selective modal analysis are used to develop the dynamic pattern recognition algorithm that facilitates the classification of circuit modules. The algorithm is intended for the analysis of converters derived from the canonical switching call, i.e., the buck, boost, and flyback converters.

Leeb, Steven B.; Kirtley, James L.; Verghese, George C.

224

Defect Classification based on Pattern Recognition of Distributed Points  

NASA Astrophysics Data System (ADS)

A spatial pattern recognition algorithm is proposed for the effective sampling method to characterize defect occurrence of an entire wafer by reviewing a small number of defect samples. The algorithm classifies defects into clustered defects or regional defects or random defects based on analysis of defect densities and distances between neighboring points. Voronoi diagrams are applied to calculate the densities and the distances. Regional defects are classified into four major classes: rings, blobs, lines and arcs. Ring and blob patterns are detected by template matching techniques, while line and arc patterns are detected by utilizing their geometric properties. The Hough transform is used to detect line patterns and the detected pattern is verified by analyzing features. The proposed algorithm was evaluated using 916 sample wafers obtained in a real semiconductor process. 93.3% of the sample wafers with ring or blob patterns (250/268) were processed correctly while 3.9% of the sample wafers on which ring or blob patterns are detected (13/332) were false. 95.1% of the sample wafers with line patterns (117/123) were processed correctly while 18.7% of the sample wafers on which line patterns are detected (70/375) were false.

Shibuya, Hisae; Takagi, Yuji; Nakagawa, Yasuo

225

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

ERIC Educational Resources Information Center

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

Marsden, Jim

1993-01-01

226

Optical character recognition of handwritten Arabic using hidden Markov models  

SciTech Connect

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

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

2011-01-01

227

A control chart pattern recognition system using a statistical correlation coefficient method  

Microsoft Academic Search

This paper presents a control chart pattern recognition system using a statistical correlation coefficient method. Pattern recognition techniques have been widely applied to identify unnatural patterns in control charts. Most of them are capable of recognizing a single unnatural pattern for different abnormal types. However, before an unnatural pattern occurs, a change point from normal to abnormal may appear at

Jenn-Hwai Yang; Miin-Shen Yang

2005-01-01

228

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

NASA Astrophysics Data System (ADS)

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

Millán, María S.

2012-10-01

229

A pattern recognition approach to voiced-unvoiced-silence classification with applications to speech recognition  

Microsoft Academic Search

In speech analysis, the voiced-unvoiced decision is usually performed in conjunction with pitch analysis. The linking of voiced-unvoiced (V-UV) decision to pitch analysis not only results in unnecessary complexity, but makes it difficult to classify short speech segments which are less than a few pitch periods in duration. In this paper, we describe a pattern recognition approach for deciding whether

BISHNU S. ATAL; LAWRENCE R. RABINER

1976-01-01

230

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

NASA Astrophysics Data System (ADS)

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

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

2008-03-01

231

Optimizing automated gas turbine fault detection using statistical pattern recognition  

NASA Astrophysics Data System (ADS)

A method enabling the automated diagnosis of Gas Turbine Compressor blade faults, based on the principles of statistical pattern recognition is initially presented. The decision making is based on the derivation of spectral patterns from dynamic measurements data and then the calculation of discriminants with respect to reference spectral patterns of the faults while it takes into account their statistical properties. A method of optimizing the selection of discriminants using dynamic measurements data is also presented. A few scalar discriminants are derived, in such a way that the maximum available discrimination potential is exploited. In this way the success rate of automated decision making is further improved, while the need for intuitive discriminant selection is eliminated. The effectiveness of the proposed methods is demonstrated by application to data coming from an Industrial Gas Turbine while extension to other aspects of Fault Diagnosis is discussed.

Loukis, E.; Mathioudakis, K.; Papailiou, K.

1992-06-01

232

Adaptive pattern recognition based control system and method  

US Patent & Trademark Office Database

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation that may be efficiently processed to determine correspondence. Applications of the interface and system include a video cassette recorder (VCR), medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Hoffberg; Steven M. (West Harrison, NY); Hoffberg-Borghesani; Linda I. (Acton, MA)

2002-06-04

233

Genetic granular cognitive fuzzy neural networks and human brains for pattern recognition  

Microsoft Academic Search

Biological neural networks in the human brain can recognize different patterns with noise by the unknown biologically cognitive pattern recognition method. Since the human brain consists of biological neural networks that are the major components performing pattern recognition, it is very interesting and very important to investigate how the biological neural networks and the artificial neural networks recognize different patterns.

Jun Li; Natasha Barrett; Yan-qing Zhang; David A. Washburn

2005-01-01

234

Wavelet invariant pattern recognition system based on the volume holographic correlator  

Microsoft Academic Search

In this paper, based on the volume holographic storage in a photorefractive crystal, a new rotation-, shift-invariant pattern recognition system, with the wavelet transform, has been set up. Parameters of rotated input pattern are first estimated and normalized. Rotation-, shift-invariant pattern recognition is then achieved by correlating the normalized input pattern with the undistorted reference, using the system. Simulation result

Qingzeng Xue; Wenzhao Tan; Yingbai Yan; Qingshong He

2002-01-01

235

Neural substrates for visual pattern recognition learning in Igo.  

PubMed

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

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

2008-06-28

236

Pattern recognition via multispectral, hyperspectral, and polarization-based imaging  

NASA Astrophysics Data System (ADS)

Pattern recognition deals with the detection and identification of a specific target in an unknown input scene. Target features such as shape, color, surface dynamics, and material characteristics are common target attributes used for identification and detection purposes. Pattern recognition using multispectral (MS), hyperspectral (HS), and polarization-based spectral (PS) imaging can be effectively exploited to highlight one or more of these attributes for more efficient target identification and detection. In general, pattern recognition involves two steps: gathering target information from sensor data and identifying and detecting the desired target from sensor data in the presence of noise, clutter, and other artifacts. Multispectral and hyperspectral imaging (MSI/HSI) provide both spectral and spatial information about the target. As the reflection or emission spectral signatures depend on the elemental composition of objects residing within the scene, the polarization state of radiation is sensitive to the surface features such as relative smoothness or roughness, surface material, shapes and edges, etc. Therefore, polarization information imparted by surface reflections of the target yields unique and discriminatory signatures which could be used to augment spectral target detection techniques, through the fusion of sensor data. Sensor data fusion is currently being used to effectively recognize and detect one or more of the target attributes. However, variations between sensors and temporal changes within sensors can introduce noise in the measurements, contributing to additional target variability that hinders the detection process. This paper provides a quick overview of target identification and detection using MSI/HSI, highlighting the advantages and disadvantages of each. It then discusses the effectiveness of using polarization-based imaging in highlighting some of the target attributes at single and multiple spectral bands using polarization spectral imaging (PSI), known as spectropolarimetry imaging.

El-Saba, Aed; Alam, Mohammad S.; Sakla, Wesam A.

2010-04-01

237

A new paradigm for pattern recognition of drugs  

Microsoft Academic Search

A new paradigm is suggested for pattern recognition of drugs. The approach is based on the combined application of the 4D\\/3D\\u000a quantitative structure–activity relationship (QSAR) algorithms BiS and ConGO. The first algorithm, BiS\\/MC (multiconformational),\\u000a is used for the search for the conformers interacting with a receptor. The second algorithm, ConGO, has been suggested for\\u000a the detailed study of the selected

Vladimir A. Potemkin; Maria A. Grishina

2008-01-01

238

Control chart pattern recognition using a back propagation neural network  

NASA Astrophysics Data System (ADS)

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.

Spoerre, Julie K.; Perry, Marcus B.

2000-10-01

239

Memristor-MOS analog correlator for pattern recognition system.  

PubMed

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

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

2013-05-01

240

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

SciTech Connect

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

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

1994-09-01

241

Fuzzy neural networks pattern recognition method and its application in ultrasonic detection for bonding defect of thin composite materials  

Microsoft Academic Search

Aiming at the problems in pattern recognition of bonding defect of thin composite materials, a new fuzzy neural network (FNN) pattern recognition method was proposed via taking full advantage of processing fuzzy information of the fuzzy pattern recognition and self-learning of the neural network (NN) pattern recognition. The structure characteristics and realization approach of the algorithm were discussed detailed. The

Xu Yan-hong; Zhang Ze; Liu Kun; Zhang Guan-ying

2009-01-01

242

Resolving the limb position effect in myoelectric pattern recognition.  

PubMed

Reported studies on pattern recognition of electromyograms (EMG) for the control of prosthetic devices traditionally focus on classification accuracy of signals recorded in a laboratory. The difference between the constrained nature in which such data are often collected and the unpredictable nature of prosthetic use is an example of the semantic gap between research findings and a viable clinical implementation. In this paper, we demonstrate that the variations in limb position associated with normal use can have a substantial impact on the robustness of EMG pattern recognition, as illustrated by an increase in average classification error from 3.8% to 18%. We propose to solve this problem by: 1) collecting EMG data and training the classifier in multiple limb positions and by 2) measuring the limb position with accelerometers. Applying these two methods to data from ten normally limbed subjects, we reduce the average classification error from 18% to 5.7% and 5.0%, respectively. Our study shows how sensor fusion (using EMG and accelerometers) may be an efficient method to mitigate the effect of limb position and improve classification accuracy. PMID:21846608

Fougner, Anders; Scheme, Erik; Chan, Adrian D C; Englehart, Kevin; Stavdahl, Oyvind

2011-08-15

243

Wavelet-based moment invariants for pattern recognition  

NASA Astrophysics Data System (ADS)

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

Chen, Guangyi; Xie, Wenfang

2011-07-01

244

Gene prediction by pattern recognition and homology search  

SciTech Connect

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

Xu, Y.; Uberbacher, E.C.

1996-05-01

245

Pattern recognition in a high-rate GEM-TPC  

NASA Astrophysics Data System (ADS)

A pattern recognition software for a continuously operating high-rate Time Projection Chamber with Gas Electron Multiplier amplification (GEM-TPC) has been designed and tested. Space points are delivered by a track-independent clustering algorithm. A true 3-dimensional track follower combines them to helical tracks, without constraints on the vertex position. Fast helix fits, based on a conformal mapping on the Riemann sphere, are the basis for deciding whether points belong to one track. To assess the performance of the algorithm in a high-rate environment, pp interactions at a rate of 2 × 107 s-1, the maximum rate foreseen for PANDA, have been simulated. The pattern recognition is capable of finding different kinds of track topologies with high efficiency and provides excellent seed values for track fitting or online event selection. The feasibility of event deconvolution has been demonstrated: Different techniques to retain the tracks from an event with known time from other tracks in the TPC are presented in this paper.

Rauch, J.

2012-12-01

246

Recognition of Acoustic Emission Patterns from Mixed Mode Wood Fracture.  

NASA Astrophysics Data System (ADS)

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

Lee, Shih-Hao

247

Drosophila Scavenger Receptor CI Is a Pattern Recognition Receptor for Bacteria  

Microsoft Academic Search

One hallmark of innate immunity apparently conserved from primitive life forms through to humans is the ability of the host to recognize pathogen-associated molecular patterns (PAMPs). Since macrophage pattern recognition receptors are not well defined in Drosophila, we set out to identify such receptors. Our findings reveal that Drosophila macrophages express multiple pattern recognition receptors and that the Drosophila scavenger

Mika Rämet; Alan Pearson; Pascal Manfruelli; Xiohung Li; Henry Koziel; Verena Göbel; Ed Chung; Monty Krieger; R. Alan B. Ezekowitz

2001-01-01

248

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

Microsoft Academic Search

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the

Barry K. Lavine; Nikhil Mirjankar; Robert K. Vander Meer

2010-01-01

249

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

Microsoft Academic Search

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the

Barry K. Lavine; Nikhil Mirjankar; Robert K. Vander Meer

2011-01-01

250

A New Unsupervised Neural Network for Pattern Recognition with Spiking Neurons  

Microsoft Academic Search

In this paper we propose a three-layered neural network for binary pattern recognition and memorization. Unlike the classic approach to pattern recognition, our net works organizing itself in an unsupervised way, to distinguish beetween different patterns or to recognize similar ones. If we present a binary input to the first layer, after some time steps we could read the output

Lorenzo Riano; Riccardo Rizzo; Antonio Chella

2006-01-01

251

Coaxial optical structure for iris recognition from a distance  

NASA Astrophysics Data System (ADS)

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

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

2011-05-01

252

Multi-kernel SVM based star pattern recognition for Celestial Navigation  

Microsoft Academic Search

This paper presents a combination of intelligent learning algorithm, the Support Vector Machine, and the recognition of star pattern in Celestial Navigation. Considering the star pattern recognition's character, noticing the advantages of SVM in learning competence, the paper proposes a solution to star pattern recognition with multi-kernel SVM. A multi-kernel algorithm bases on Genetic Programming is designed. Topics of multi-kernel

Taiyang Liu; Shicheng Wang; Zhiguo Liu; Haibo Min; Renbing Li

2010-01-01

253

Pattern recognition as rule-guided inductive inference.  

PubMed

The determination of pattern recognition rules is viewed as a problem of inductive inference, guided by generalization rules, which control the generalization process, and problem knowledge rules, which represent the underlying semantics relevant to the recognition problem under consideration. The paper formulates the theoretical framework and a method for inferring general and optimal (according to certain criteria) descriptions of object classes from examples of classification or partial descriptions. The language for expressing the class descriptions and the guidance rules is an extension of the first-order predicate calculus, called variable-valued logic calculus VL21. VL21 involves typed variables and contains several new operators especially suited for conducting inductive inference, such as selector, internal disjunction, internal conjunction, exception, and generalization. Important aspects of the theory include: 1) a formulation of several kinds of generalization rules; 2) an ability to uniformly and adequately handle descriptors (i.e., variables, functions, and predicates) of different type (nominal, linear, and structured) and of different arity (i.e., different number of arguments); 3) an ability to generate new descriptors, which are derived from the initial descriptors through a rule-based system (i.e., an ability to conduct the so called constructive induction); 4) an ability to use the semantics underlying the problem under consideration. An experimental computer implementation of the method is briefly described and illustrated by an example. PMID:21868911

Michalski, R S

1980-04-01

254

Comparative evaluation of pattern recognition techniques for detection of microcalcifications  

NASA Astrophysics Data System (ADS)

Computer detection of microcalcifications in mammographic images will likely require a multi-stage algorithm that includes segmentation of possible microcalcifications, pattern recognition techniques to classify the segmented objects, a method to determine if a cluster of calcifications exists, and possibly a method to determine the probability of malignancy. This paper will focus on the classification of segmented objects as being either (1) microcalcifications or (2) non-microcalcifications. Six classifiers (2 Bayesian, 2 dynamic neural networks, a standard backpropagation network, and a K-nearest neighbor) are compared. Methods of segmentation and feature selection are described, although they are not the primary concern of this paper. A database of digitized film mammograms is used for training and testing. Detection accuracy is compared across the six methods.

Woods, K. S.; Solka, Jeffrey L.; Priebe, Carey E.; Doss, Chris C.; Bowyer, Kevin W.; Clarke, Laurence P.

1993-07-01

255

The role of bacteria and pattern recognition receptors in GVHD.  

PubMed

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

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

2010-10-31

256

Using Decision Trees for Comparing Pattern Recognition Feature Sets  

SciTech Connect

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

Proctor, D D

2005-08-18

257

Pattern recognition of magnetic resonance images with application to atherosclerosis  

SciTech Connect

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.

Carman, C.S.

1989-01-01

258

Asymptotic analysis of pattern-theoretic object recognition  

NASA Astrophysics Data System (ADS)

Automated target recognition (ATR) is a problem of great importance in a wide variety of applications: from military target recognition to recognizing flow-patterns in fluid- dynamics to anatomical shape-studies. The basic goal is to utilize observations (images, signals) from remote sensors (such as videos, radars, MRI or PET) to identify the objects being observed. In a statistical framework, probability distributions on parameters representing the object unknowns are derived an analyzed to compute inferences (please refer to [1] for a detailed introduction). An important challenge in ATR is to determine efficient mathematical models for the tremendous variability of object appearance which lend themselves to reasonable inferences. This variation may be due to differences in object shapes, sensor-mechanisms or scene- backgrounds. To build models for object variabilities, we employ deformable templates. In brief, the object occurrences are described through their typical representatives (called templates) and transformations/deformations which particularize the templates to the observed objects. Within this pattern-theoretic framework, ATR becomes a problem of selecting appropriate templates and estimating deformations. For an object (alpha) (epsilon) A, let I(alpha ) denote a template (for example triangulated CAD-surface) and let s (epsilon) S be a particular transformation, then denote the transformed template by sI(alpha ). Figure 1 shows instances of the template for a T62 tank at several different orientations. For the purpose of object classification, the unknown transformation s is considered a nuisance parameter, leading to a classical formulation of Bayesian hypothesis- testing in presence of unknown, random nuisance parameters. S may not be a vector-space, but it often has a group structure. For rigid objects, the variation in translation and rotation can be modeled through the action of special Euclidean group SE(n). For flexible objects, such as anatomical shapes, higher-dimensional groups such as a diffeomorphisms are utilized.

Cooper, Matthew L.; Srivastava, Anuj

2000-08-01

259

Structural patterns or discrete events? A link between pattern recognition and discrete-event systems  

Microsoft Academic Search

There is an interesting analogy between recognition of noisy, distorted, or incomplete structural patterns using the weighted distance based on symbol-to-symbol operations and modelling of actual discrete-event systems, where different types of uncertainty can occur. A method of analysis and modelling of a complex system's behaviour represented by a sufficiently long time series of formal symbols is considered. The ideas

J. Pik

1992-01-01

260

Wavelet-based neural pattern analyzer for behaviorally significant burst pattern recognition.  

PubMed

Closed-loop neural prosthesis systems rely on accurately recording neural data from multiple neurons and detecting behaviorally meaningful patterns before representing them in a highly compressed form for wireless transmission over a limited-bandwidth link. We present a novel wavelet-based approach for detecting spikes, grouping them as bursts and building a dynamic vocabulary of meaningful burst patterns. Simulation results on pre-recorded in vivo multi-channel extracellular neural data from the buccal ganglion of Aplysia demonstrate the feasibility of behavior recognition as well as data compression (>500X) by the proposed approach. PMID:19162588

Narasimhan, Seetharam; Cullins, Miranda; Chiel, Hillel J; Bhunia, Swarup

2008-01-01

261

Recognition of Compton scattering patterns in advanced Compton telescopes  

NASA Astrophysics Data System (ADS)

The next generation of Compton telescopes (such as MEGA or NCT) will detect impinging gamma rays by measuring one or more Compton interactions, possibly electron tracks, and a final photo absorption. However, the recovery of the original parameters of the photon, especially its energy and direction, is a challenging task, since the measured data only consists of a set of energy and position measurements and their ordering, i.e. the path of the photon, is unknown. Thus the main tasks of the pattern recognition algorithm are to identify the interaction sequence of the photon (i.e. which hit is the start point) and distinguish the pattern from background signatures, especially incompletely absorbed events. The most promising approach up to now is based on Bayesian statistics: The Compton interactions are parameterized in a multi-dimensional data space, which contains the interaction information of the Compton sequence as well as geometry information of the detector. For each data space cell the probability that the corresponding interaction sequence is one of a correctly ordered, completely absorbed source photon can be determined by Bayesian statistics and detailed simulations. This probability can then be used to distinguish source photons from incompletely absorbed photons. Simulations show that the Bayesian approach can improve the 68% event containment of the ARM distribution by up to 40%, and results in a much better separation between "good" and "bad" events. In addition, sensitivity improvements up to a factor 1.7 can be achieved.

Zoglauer, Andreas; Boggs, Steven E.; Andritschke, Robert; Kanbach, Gottfried

2007-10-01

262

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

Microsoft Academic Search

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

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

2011-01-01

263

Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  

Microsoft Academic Search

This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern recognition processes and lear ning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern recognition

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

1993-01-01

264

Contribution of Flagellin Pattern Recognition to Intestinal Inflammation during Salmonella enterica Serotype Typhimurium Infection  

Microsoft Academic Search

Salmonella enterica serotype Typhimurium causes acute inflammatory diarrhea in humans. Flagella contrib- ute to intestinal inflammation, but the mechanism remains unclear since most mutations abrogating pattern recognition of flagellin also prevent motility and reduce bacterial invasion. To determine the contribution of flagellin pattern recognition to the generation of innate immune responses, we compared in two animal models a nonmotile, but

Sebastian E. Winter; P. Thiennimitr; S.-P. Nuccio; T. Haneda; M. G. Winter; R. P. Wilson; J. M. Russell; T. Henry; Q. T. Tran; S. D. Lawhon; G. Gomez; C. L. Bevins; H. Russmann; D. M. Monack; L. G. Adams; A. J. Baumler

2009-01-01

265

Pseudo-phase portrait applied to pattern recognition in flavonoid-protein interactions  

Microsoft Academic Search

Pattern recognition represents an attractive scientific field in the study of biological interactions. The interactions between flavonoids and proteins are extremely important in the explanation of the biological activity of these compounds. In this paper, the geometrical morphology of pseudo-phase portrait of the primary sequence of several proteins is evaluated, in order to have an approach to the pattern recognition

Alberto Rolo-naranjo; Rocio Rebollido-rios; Kenia Melchor-rodriguez; Edelsys Codorniu-hernández

2009-01-01

266

Spectral classification using pattern-recognition techniques. II. Application to curium energy levels  

Microsoft Academic Search

Curium energy levels have been classified according to configuration using pattern-recognition techniques. Four features: energy level, Lande g, J, and isotope shift: have been used to describe each level. Forty levels have been assigned with high probability based on consistent results obtained by various pattern recognition techniques. This represents an increase of 9% for even levels and 19% for odd

K. L. Peterson; D. L. Anderson; M. L. Parsons

1978-01-01

267

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

Microsoft Academic Search

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

Mitra Basu; Horst Bunke; Alberto Del Bimbo

2005-01-01

268

Combining fuzzy pattern recognition and fuzzy control in an AI driven neural network  

Microsoft Academic Search

We combined fuzzy pattern recognition and fuzzy control in the testing program of a fuzzy ANN. In pattern recognition for each input vector the network provides a spectrum of “object to a class belongingness” with respect to all classes. A fuzzy controller using some of the components of the same input vector generates a numerical output by simulating an equation.

M. E. Ulug

1996-01-01

269

Design of neuro-fuzzy based modular architecture for pattern recognition  

Microsoft Academic Search

Many pattern recognition methodologies and design techniques have been developed over the years and new approaches continue to emerge. For the solution of complex problems in pattern recognition and more generally machine intelligence, involving heterogeneous data sources of both numeric and symbolic information, the fundamental design philosophy is to employ hybrid methodologies rather than attempting to produce the solution using

S. Mercy Shalinie

2002-01-01

270

Protein Fold Pattern Recognition Using Bayesian Ensemble of RBF Neural Networks  

Microsoft Academic Search

Protein fold pattern recognition has been one of the most challenging problems in biology during the last 40 years. Recently due to the vast improvement in machine learning and pattern recognition methods many computer scientists have applied these methods to solve this problem. However, protein folding problem is much more complicated than ordinary machine learning problems because of its natural

Homa Baradaran Hashemi; Azadeh Shakery; Mahdi Pakdaman Naeini

2009-01-01

271

Pattern recognition using the fuzzy Sugeno integral for response integration in modular neural networks  

Microsoft Academic Search

We describe in this paper a new approach for pattern recognition using modular neural networks with a fuzzy logic method for response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the particular case of human fingerprints. Also, the method for achieving response integration is based on the fuzzy Sugeno integral. Response integration is

Patricia Melin; Angeles Quezada; Oscar Castillo

2004-01-01

272

Nanoparticle-structured sensing array materials and pattern recognition for VOC detection  

Microsoft Academic Search

Nanostructured sensing arrays combined with pattern-recognition analysis provide new opportunities for enhancing the design of sensor materials in terms of sensitivity and selectivity. In this work, we report findings of an investigation of nanostructured sensing arrays for the detection of volatile organic compounds (VOCs) and nitro-aromatic compounds (NACs) and the data analysis based on pattern recognition using principle component analysis

Li Han; Xiajing Shi; Wendy Wu; F. Louis Kirk; Jin Luo; Lingyan Wang; Derrick Mott; Lisa Cousineau; Stephanie I-Im Lim; Susan Lu; Chuan-Jian Zhong

2005-01-01

273

Distortion-invariant kernel filters for general pattern recognition  

NASA Astrophysics Data System (ADS)

We note several key general pattern recognition (GPR) issues that have been ignored in all prior distortion-invariant kernel filter (kernel DIF) work. These include: the unrealistic assumption of centered test data, the lack of a fast FFTbased on-line implementation, the significantly larger storage and on-line computation requirements, incorrect formulation of the kernel filter in the FT domain, incorrect formulation of prior image-domain kernel SDF and Mace filters, and the unrealistic use of test set data for parameter selection. We present several improvements to prior kernel DIF work. Our primary objective is to examine the viability of kernel DIFs for GPR and automatic target recognition (ATR) applications (where the location of the object in the test input is not known). Thus, in this paper, we apply our improved kernel DIFs to CAD ATR data. We address range and full 360° aspect view variations; we also address rejection of unseen confuser objects and clutter. We use training and validation set data (not test set data) to select the kernel parameter. We show that kernel filters (higher-order features) can improve classification and confuser rejection performance. We consider only kernel SDF filters, since their on-line computation requirements are reasonable; we present test results for both polynomial and Gaussian kernels. The main purposes of this paper are to: note issues of importance ignored in all prior kernel DIF work, detail how to properly perform energy minimization in kernel DIFs, show that kernel SDF filters can correct errors for ATR data, and compare the performance of kernel SDF filters and standard Minace DIFs. We also introduce our new Minace-preprocessed kernel SDF filter.

Patnaik, Rohit; Casasent, David

2008-03-01

274

Scalable pattern recognition for large-scale scientific data mining  

SciTech Connect

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.

Kamath, C.; Musick, R.

1998-03-23

275

Algorithms for pattern recognition in images of cell cultures  

NASA Astrophysics Data System (ADS)

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.

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

2001-06-01

276

Chemical Sensor Pattern Recognition System Using a Self-Training Neural Network Classifier With Automated Outlier Detection.  

National Technical Information Service (NTIS)

A device and method for a pattern recognition system using a self-training neural network classifier with automated outlier detection for use in chemical sensor array systems. The pattern recognition system uses a Probabilistic Neural Network (PNN) traini...

R. E. Shaffer

1998-01-01

277

Pattern recognition as a nursing intervention with Japanese women with ovarian cancer.  

PubMed

This article explores the process of pattern recognition, contained within Newman's theory of health as expanding consciousness, as a nursing intervention with adults with ovarian cancer. The process of the nurse-client interactive pattern revealed four nonlinear phases: the client-nurse mutual concern, pattern recognition, vision and action potential, and transformation. Most participants found meaning in their lives and experienced personal growth in expanding consciousness. PMID:9595174

Endo, E

1998-06-01

278

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

NASA Astrophysics Data System (ADS)

The manuscript has investigated the application of near-infrared (NIR) spectroscopy for differentiation gastric cancer. The 90 spectra from cancerous and normal tissues were collected from a total of 30 surgical specimens using Fourier transform near-infrared spectroscopy (FT-NIR) equipped with a fiber-optic probe. Major spectral differences were observed in the CH-stretching second overtone (9000-7000 cm-1), CH-stretching first overtone (6000-5200 cm-1), and CH-stretching combination (4500-4000 cm-1) regions. By use of unsupervised pattern recognition, such as principal component analysis (PCA) and cluster analysis (CA), all spectra were classified into cancerous and normal tissue groups with accuracy up to 81.1%. The sensitivity and specificity was 100% and 68.2%, respectively. These present results indicate that CH-stretching first, combination band and second overtone regions can serve as diagnostic markers for gastric cancer.

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

2013-01-01

279

Pattern recognition control outperforms conventional myoelectric control in upper limb patients with targeted muscle reinnervation.  

PubMed

Pattern recognition myoelectric control shows great promise as an alternative to conventional amplitude based control to control multiple degree of freedom prosthetic limbs. Many studies have reported pattern recognition classification error performances of less than 10% during offline tests; however, it remains unclear how this translates to real-time control performance. In this contribution, we compare the real-time control performances between pattern recognition and direct myoelectric control (a popular form of conventional amplitude control) for participants who had received targeted muscle reinnervation. The real-time performance was evaluated during three tasks; 1) a box and blocks task, 2) a clothespin relocation task, and 3) a block stacking task. Our results found that pattern recognition significantly outperformed direct control for all three performance tasks. Furthermore, it was found that pattern recognition was configured much quicker. The classification error of the pattern recognition systems used by the patients was found to be 16% ±(1.6%) suggesting that systems with this error rate may still provide excellent control. Finally, patients qualitatively preferred using pattern recognition control and reported the resulting control to be smoother and more consistent. PMID:24110008

Hargrove, Levi J; Lock, Blair A; Simon, Ann M

2013-07-01

280

Optical nanoantennas with tunable radiation patterns.  

PubMed

We address new optical nanoantenna systems with tunable highly directional radiation patterns. The antenna comprises a regular linear array of metal nanoparticles in the proximity of an interface with high dielectric contrast. We show that the radiation pattern of the system can be controlled by changing parameters of the excitation, such as the polarization and/or incidence angles. In the case of excitation under the total reflection condition, the system operates as a nanoscopic source of radiation, converting the macroscopic incident plane wavefront into a narrow beam of light with adjustable characteristics. We derive also simple analytical formulas which give an excellent description of the radiation pattern and provide a useful tool for analysis and antenna design. PMID:23339692

Munárriz, J; Malyshev, A V; Malyshev, V A; Knoester, J

2013-02-01

281

Rotation-invariant, time-integrating optical correlation recognition system  

NASA Astrophysics Data System (ADS)

The speed at which one or more targets can be recognized in a time-sequencing rotation-invariant binary phase-only filter (BPOF) optical recognition system can be improved significantly by integrating sequential correlation responses and using the integrated peak responses as inputs to the same statistical correlation plane filter (CPF) used for individual sequential correlation responses. Since commercially available BPOFs can be written at very high frame rates (350 frames per second), more than 10 correlation responses can be integrated during the frame time of an output camera operating at video rates. Therefore, the use of of integrated rather than individual sequential correlation responses reduces the processing time by a factor of 10 or more if the same standard video rate camera is used at the correlation plane. This paper presents results obtained using a prototype time-integrating BPOF correlator to achieve near real-time rotation-invariant recognition of both single and multiple targets in a noisy and cluttered input scene.

Walsh, Thomas R.; Carhart, Gary W.; Draayer, Bret F.; Billings, Paul A.; Giles, Michael K.

1990-12-01

282

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

NASA Astrophysics Data System (ADS)

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

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

283

Multiresolution pattern recognition of small volcanos in Magellan data  

NASA Astrophysics Data System (ADS)

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 105 to 106 of these volcanos visible in the Magellan data. Identifying and studying these volcanos is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is very time-consuming. Hence, we have undertaken the development of techniques to partially automate this task. The goal is not the unrealistic one of total automation, but rather the development of a useful tool to aid the project scientists. The primary constraints for this particular problem are as follows: (1) the method must be reasonably robust; and (2) the method must be reasonably fast. Unlike most geological features, the small volcanos of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include the following: (1) a circular planimetric outline; (2) known diameter frequency distribution from preliminary studies; (3) a limited number of basic morphological shapes; and (4) the common occurrence of a single, circular summit pit at the center of the edifice.

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

1992-12-01

284

Efficient unfolding pattern recognition in single molecule force spectroscopy data  

PubMed Central

Background Single-molecule force spectroscopy (SMFS) is a technique that measures the force necessary to unfold a protein. SMFS experiments generate Force-Distance (F-D) curves. A statistical analysis of a set of F-D curves reveals different unfolding pathways. Information on protein structure, conformation, functional states, and inter- and intra-molecular interactions can be derived. Results In the present work, we propose a pattern recognition algorithm and apply our algorithm to datasets from SMFS experiments on the membrane protein bacterioRhodopsin (bR). We discuss the unfolding pathways found in bR, which are characterised by main peaks and side peaks. A main peak is the result of the pairwise unfolding of the transmembrane helices. In contrast, a side peak is an unfolding event in the alpha-helix or other secondary structural element. The algorithm is capable of detecting side peaks along with main peaks. Therefore, we can detect the individual unfolding pathway as the sequence of events labeled with their occurrences and co-occurrences special to bR's unfolding pathway. We find that side peaks do not co-occur with one another in curves as frequently as main peaks do, which may imply a synergistic effect occurring between helices. While main peaks co-occur as pairs in at least 50% of curves, the side peaks co-occur with one another in less than 10% of curves. Moreover, the algorithm runtime scales well as the dataset size increases. Conclusions Our algorithm satisfies the requirements of an automated methodology that combines high accuracy with efficiency in analyzing SMFS datasets. The algorithm tackles the force spectroscopy analysis bottleneck leading to more consistent and reproducible results.

2011-01-01

285

The role of binocular disparity in rapid scene and pattern recognition  

PubMed Central

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.

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

2013-01-01

286

Real-time computer control using pattern recognition of the electromyogram  

Microsoft Academic Search

A real-time system is discussed which recognizes users gestures using neural networks for pattern recognition of the electromyogram (EMG). A novel application for dynamic computer control is presented. In addition to gesture recognition, an estimate of muscular exertion is provided to facilitate control of continuously varying parameters. The use of this system for a real-time graphical user interface (GUI) is

William Putnam; R. Benjamin Knapp

1993-01-01

287

Pattern Recognition Approach for Music Style Identification Using Shallow Statistical Descriptors  

Microsoft Academic Search

In the field of computer music, pattern recognition algorithms are very relevant for music information retrieval applications. One challenging task in this area is the automatic recognition of musical style, having a number of applications like indexing and selecting musical databases. From melodies symbolically represented as digital scores (standard musical instrument digital interface files), a number of melodic, harmonic, and

Pedro Ponce De Len; Jos Iesta

2007-01-01

288

Pattern Recognition in South Indian Classical Music Using a Hybrid of HMM and DTW  

Microsoft Academic Search

Automatic recognition of musical patterns plays a crucial part in Musicological and Ethno-musicological research and can become an indispensable tool for search and comparison of music extracts within a large multimedia database. This paper suggests an efficient method for recognizing isolated musical patterns in a monophonic environment. Each pattern to be recognized is converted into a sequence of frequency jumps

M. S. Sinith; K. Rajeev

2007-01-01

289

Prototype learning for structured pattern representation applied to on-line recognition of handwritten Japanese characters  

Microsoft Academic Search

This paper describes prototype learning for structured pattern representation with common sub- patterns shared among multiple character prototypes for on-line recognition of handwritten Japanese charac- ters. Prototype learning algorithms have not yet been shown to be useful for structured or hierarchical pattern representation. In this paper, we incorporate cost-free parallel translation to negate the location distributions of subpatterns when they

Akihito Kitadai; Masaki Nakagawa

2007-01-01

290

Computerized Pattern Recognition: A New Technique for the Analysis of Chemical Communication  

Microsoft Academic Search

Computerized pattern recognition techniques can be applied to the study of complex chemical communication systems. Analysis of high resolution gas chromatographic concentration patterns of the major volatile components of the scent marks of a South American primate, Saguinus fuscicollis, demonstrates that the concentration patterns can be used to predict the gender and subspecies of unknown donors.

Amos B. Smith; Anne M. Belcher; Gisela Epple; Peter C. Jurs; Barry Lavine

1985-01-01

291

GRAPH BASED APPROACHES FOR RECOGNITION OF PATTERNS AND IMPLICIT INFORMATION IN ROAD NETWORKS  

Microsoft Academic Search

The paper will introduce into the subject of recognition of typical patterns in road networks. We will first describe the design and lay-out of roads. Applications to detect patterns and to use them for finding more knowledge in vector data are shown. We will familiarise the reader with different patterns in road networks and approaches for the automatic detection of

F. Heinzle; K.-H. Anders; M. Sester

292

A Pattern-Recognition Model Applied to the Perception of Pitch.  

National Technical Information Service (NTIS)

A mathematical model for pattern recognition is applied to the perception of complex tones. A possibly infinite and continuous space (over which sensory stimuli range) is mapped into a finite space of discrete points (the classifications of such stimuli)....

D. Rothenberg

1969-01-01

293

Pattern Recognition of EEG-EOG as a Technique for All-Night Sleep Stage Scoring.  

National Technical Information Service (NTIS)

The study reports an approach to using a pattern recognition system, simulated on a digital computer, for automatic, all-night sleep stage scoring. A detailed comparison of percent agreement for the computer and three experienced human judges is presented...

W. B. Martin S. S. Viglione R. D. Joseph L. C. Johnson P. Naitoh

1971-01-01

294

New-distinction measure for pattern recognition in fuzzy features space  

NASA Astrophysics Data System (ADS)

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

Zlotnikov, Konstantin A.; Fyodorov, Boris F.

1999-08-01

295

Pattern recognition of multivariate information based on non-statistical techniques  

Microsoft Academic Search

A novel method for non-statistical PR\\/dimensionality reduction technology of multivariate information is proposed, which based on graphical techniques, quantitative non-statistical technique, grey system theory, fuzzy system theory, Information entropy theory, neronet technique et al. In nowadays pattern recognition field, statistical pattern recognition techniques are exercising dominion over. But there are many real-world problems the sample is not obey the known

Gao Haibo; Hong Wenxue; Cui Jianxin; Zhao Yong; Meng Hui

2008-01-01

296

Pattern Recognition for Industrial Security Using the Fuzzy Sugeno Integral and Modular Neural Networks  

Microsoft Academic Search

We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition.\\u000a Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason,\\u000a the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of\\u000a magnitude more difficult

Patricia Melin; Alejandra Mancilla; Miguel Lopez; Daniel Solano; Miguel Soto; Oscar Castillo

297

Pattern Recognition Using Modular Neural Networks and Fuzzy Integral as Method for Response Integration  

Microsoft Academic Search

\\u000a We describe in this paper a new approach for pattern recognition using modular neural networks with a fuzzy logic method for\\u000a response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the\\u000a particular case of human fingerprints. Also, the method for achieving response integration is based on the fuzzy Sugeno integral.\\u000a Response integration is

Patricia Melin; Gabriela Martinez; Claudia Gonzalez; Diana Bravo; Felma Gonzalez

298

Pattern Recognition Using Modular Neural Networks and Fuzzy Integral as Method for Response Integration  

Microsoft Academic Search

We describe in this paper a new approach for pattern recognition using modular neural networks with a fuzzy logic method for\\u000a response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the\\u000a particular case of human fingerprints. Also, the method for achieving response integration is based on the fuzzy Sugeno integral.\\u000a Response integration is

Patricia Melin; Melchor Carranza; Pedro Antonio Salazar-tejeda; Rene Nuñez

2007-01-01

299

ExPRESS - Extraction Pattern Recognition Engine and Specification Suite  

Microsoft Academic Search

The emergence of information extraction (IE) oriented pattern engines has been observed during the last decade. Most of them exploit heavily finite-state devices. This paper introduces EXPRESS - a new extraction pattern engine, whose rules are regular expressions over flat feature structures. The underlying pattern language can be seen as a blend of two previously introduced IE oriented pattern formalisms,

Jakub Piskorski

300

A fuzzy neural network and its application to pattern recognition  

Microsoft Academic Search

Defines four types of fuzzy neurons and proposes the structure of a four-layer feedforward fuzzy neural network (FNN) and its associated learning algorithm. The proposed four-layer FNN performs well when used to recognize shifted and distorted training patterns. When an input pattern is provided, the network first fuzzifies this pattern and then computes the similarities of this pattern to all

Hon Keung Kwan; Yaling Cai

1994-01-01

301

The application of pattern recognition in wood processing industry  

NASA Astrophysics Data System (ADS)

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

Wang, Yeqin; Wang, Hui

2010-08-01

302

Building Emerging Pattern (EP) Random forest for recognition  

Microsoft Academic Search

The Random forest classifier comes to be the working horse for visual recognition community. It predicts the class label of an input data by aggregating the votes of multiple tree classifiers. However, the classification performances of these tree classifiers are different. The random forest classifier ignores the difference by simply assigning them equal weights in voting for the final classification

Liang Wang; Yizhou Wang; Debin Zhao

2010-01-01

303

SEMG feature extraction methods for pattern recognition of upper limbs  

Microsoft Academic Search

In this paper, a new feature of surface electromyo- graphy (sEMG) by using discrete wavelet transform (DWT) is proposed for motion recognition of upper limbs, and this method can be eventually used for rehabilitation robot control. Seven traditional features of sEMG are also extracted for comparative study, they are integral of absolute value (IAV), difference absolute mean value (DAMV), zero

Feng Zhang; Pengfeng Li; Zeng-Guang Hou; Yixiong Chen; Fei Xu; Jin Hu; Qingling Li; Min Tan

2011-01-01

304

Improving surgical pattern recognition through repetitive viewing of video clips  

Microsoft Academic Search

Previous field studies show that surgery residents and medical students have difficulty recognizing appropriate anatomic cues during laparoscopic surgery, causing delays in procedures and errors. Such observations led to the development of an anatomy recognition training intervention, specifically the use of an ordered set of video clips that show the main steps of a laparoscopic pro- cedure. Each procedural step

Stephanie Guerlain; Kristen Brook Green; Marcel C. Lafollette; T. C. Mersch; B. A. Mitchell; G. R. Poole; J. F. Calland; Lv Jianhong; E. G. Chekan

2004-01-01

305

Online Handwriting Recognition Using Multiple Pattern Class Models  

Microsoft Academic Search

The field of personal computing has begun to make a transition from the desktop to handheld devices, thereby requiring input paradigms that are more suited for single hand entry than a keyboard and recent developments in online handwriting recognition allow for such input modalities. Data entry using a pen forms a natural, convenient interface. The large number of writing styles

Scott D. Connell

2000-01-01

306

Temporal cortex activation during speech recognition: an optical topography study.  

PubMed

Cortical activity during speech recognition was examined using optical topography (OT), a recently developed non-invasive technique. To assess relative changes in hemoglobin oxygenation, local changes in near-infrared light absorption were measured simultaneously from 44 points in both hemispheres. A dichotic listening paradigm was used in this experiment, in which target stimuli and non-target stimuli were presented to different ears. Subjects were asked to track targets and to press a button when targets shifted from one ear to the other. We compared three tasks: (i) a control task, in which a tone was used as the target; (ii) a repeat task, in which the target was one repeated sentence; (iii) a story task, in which the targets were continuous sentences of a story. The activity for the story task, compared with the repeat task, was localized in the left superior temporal cortex. Relative to the control task, we observed in this region a larger increase in oxyhemoglobin concentration and a decrease in deoxyhemoglobin concentration in the story task than those in the repeat task. These results suggest that the activity in the left temporal association area reflects the load of auditory, memory, and language information processing. PMID:10585521

Sato, H; Takeuchi, T; Sakai, K L

1999-12-17

307

Human tactile pattern recognition: active versus passive touch, velocity effects, and patterns of confusion.  

PubMed

1. Subjects without any previous experience in a tactile psychophysics task participated in a study of tactile letter recognition employing active and passive touch. In the active task, subjects reached through a curtain and examined embossed letters with horizontal, unidirectional finger strokes. In the passive task, subjects sat with their arms and hands immobilized while a rotating drum stimulator pressed the embossed letters onto the right index finger. The stimulus conditions in the passive task were identical to those used in neurophysiological experiments with monkeys. 2. A survey of 40 naive subjects who were not screened in any way showed a wide range of performance levels. There was no difference between the subjects in the active and passive tasks, either in overall mean percent correct scores, which were 49.0 and 50.7%, respectively or in the percent correct scores for individual letters whose product-moment correlation coefficient was 0.94. The active and passive groups, which contained 25 and 15 members, respectively, had no members in common. 3. Videotapes of the finger movements of eight subjects in the active task showed a characteristic V-shaped velocity profile (velocity vs. lateral position) starting at approximately 100 mm/s at the left-hand edge of the plate containing the embossed letter, decelerating to a minimum when the center of the finger was directly over the letter, and then accelerating away from the letter. The average minimum scanning velocity was 17 mm/s. 4. Scanning velocity had no significant effect on performance in the passive task between 20 and 40 mm/s. An increase to 80 mm/s produced a 16% decline in percent correct identifications. 5. Learning effects were evident across sessions even though subjects were given no feedback or training. The increase in mean percent correct judgments averaged 4% per session, which lasted for approximately 1 h. 6. Data from 64 subjects were pooled for detailed comparison of identification patterns in active and passive touch. The results were analyzed and found to be consistent with the hypothesis that the identification and confusion probabilities are identical in the two modes. We conclude that there is no difference between active and passive touch in form recognition when the stimulus pattern is smaller than a finger pad. 7. Data from all experiments were pooled to produce a single confusion matrix with 324 presentations per letter. The majority of erroneous responses are grouped in a small number of confusion pairs and the majority of those confusion pairs are strongly asymmetric. The probable neural mechanisms of some confusion patterns are discussed. PMID:2051193

Vega-Bermudez, F; Johnson, K O; Hsiao, S S

1991-03-01

308

A simple and efficient optical character recognition system for basic symbols in printed Kannada text  

Microsoft Academic Search

Optical Character Recognition (OCR) systems have been effectively developed for the recognition of printed characters of non-Indian\\u000a languages. Efforts are on the way for the development of efficient OCR systems for Indian languages, especially for Kannada,\\u000a a popular South Indian language. We present in this paper an OCR system developed for the recognition of basic characters\\u000a (vowels and consonants) in

R. Sanjeev Kunte; R. D. Sudhaker Samuel

2007-01-01

309

Movement pattern recognition in basketball free-throw shooting  

Microsoft Academic Search

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

Andrea Schmidt

310

Application of pattern recognition to shock-Trauma studies  

Microsoft Academic Search

This paper reports the results of a study to determine pattern vectors (Profiles) composed of physiological and biochemical measurements which reflect the severity of injury to traumatized individuals. Profiles, selected by clinicians at the University of Maryland Center for the Study of Trauma, were obtained from the Center data bank and subjected to pattern analyses using OLPARS, an on-line pattern

R. Cowley; S. Turney; W. Copes; J. Sperrazza; C. Masaitis; W. Sacco

1972-01-01

311

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

ERIC Educational Resources Information Center

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…

Perez, Ernest

1990-01-01

312

Pattern recognition in a compartmental model of a CA1 pyramidal neuron  

Microsoft Academic Search

Computer simulation of a CA1 hippocampal pyramidal neuron is used to estimate the effects of synaptic and spatio-temporal noise on such a cell's ability to accurately calculate the weighted sum of its inputs, presented in the form of transient patterns of activity. Comparison is made between the pattern recognition capability of the cell in the presence of this noise and

Bruce P Graham

2001-01-01

313

Artificial Intelligent Based Human Motion Pattern Recognition and Prediction for the Surface Electromyographic Signals  

Microsoft Academic Search

In this research, the artificial intelligent method based human motion pattern recognition for surface electromyographic (EMG) signal is proposed. As the EMG signal is a measurement of anatomical and physiological characteristic of the given muscle, the macroscopical movement patterns of the human body can be classified and recognized. By using the technology of wavelet packet transformation, the high-frequency noises can

Xu Guo; Hu Yu; Gao Zhen; Liu Yuliang; Zhang Yong; Zhang Ying

2009-01-01

314

Electronic tongues for environmental monitoring based on sensor arrays and pattern recognition: a review  

Microsoft Academic Search

The use of sensor arrays and pattern recognition applied to the obtained signal patterns for environmental monitoring are discussed in some detail. Different types of electronic tongues are described and evaluated for monitoring purposes. More specifically the performance of multielectrode arrays used for voltammetric analysis of aqueous samples is described. It is, e.g. shown how such an ‘electronic tongue’ can

Christina Krantz-Rülcker; Maria Stenberg; Fredrik Winquist; Ingemar Lundström

2001-01-01

315

A coherence-based approach for the pattern recognition of time series  

Microsoft Academic Search

A pattern recognition approach based on the frequency domain measure of squared coherence is a useful approach to identify linearly related groupings of time series over different periods of time. It is considered in an application to identify similar patterns of the yearly rates of change in the Gross Domestic Product (GDP) of twenty two highly developed countries in an

Elizabeth Ann Maharaj; Pierpaolo D'Urso

2010-01-01

316

Combining pattern recognition techniques with Akaike's information criteria for identifying ARMA models  

Microsoft Academic Search

ARMA models are identified by combining pattern recognition techniques with Akaike's (1974, 1979) information criteria. First, pattern vectors of ARMA models are obtained by the extended sample autocorrelation functions method proposed by Tsay and Tiao (1984). Second, decision functions of various training samples are specified by the perceptron algorithm used in learning machines. Third, Akaike's AIC and BIC criteria are

Liang Wang; Gaetan A. Libert

1994-01-01

317

Comparative evaluation of pattern recognition algorithms: statistical, neural, fuzzy, and neuro-fuzzy techniques  

Microsoft Academic Search

Pattern recognition by fuzzy, neural, and neuro-fuzzy approaches, has gained popularity partly because of intelligent decision processes involved in some of the above techniques, thus providing better classification and partly because of simplicity in computation required by these methods as opposed to traditional statistical approaches for complex data structures. However, the accuracy of pattern classification by various methods is often

Sunanda Mitra; Ramiro Castellanos

1998-01-01

318

Pattern Recognition in Neural Networks with Competing Dynamics: Coexistence of Fixed-Point and Cyclic Attractors  

PubMed Central

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

Herrera-Aguilar, Jose L.; Larralde, Hernan; Aldana, Maximino

2012-01-01

319

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

Microsoft Academic Search

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

Gerald S. Pollack; Ronald R. Hoy

1979-01-01

320

Pattern recognition of multiple EMG signals applied to the description of human gait  

Microsoft Academic Search

The application of pattern recognition to the classification of normal and four pathological gaits is described. The classification is based on construction of a pattern feature vector whose elements are obtained by processing EMG signals obtained from 6 muscles responsible for movement of the foot at the ankle. The paper describes the basic actions of these muscles and the resulting

GEORGE A. BEKEY; Chi-Wu Chang; JACQUELINE PERRY; M. M. Hoffer

1977-01-01

321

A Pattern Recognition Neural Network Using Many Sets of Weights and Biases  

Microsoft Academic Search

In supervised training, we often try to find out a set of weights and biases for a pattern recognition neural network in order to classify all patterns in a training data set. However, it would be difficult if the neural network was not big enough for learning a large training data set. In this paper, we propose a training method

Le Dung; Makoto Mizukawa

2007-01-01

322

Control chart pattern recognition using K-MICA clustering and neural networks.  

PubMed

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved. PMID:22035774

Ebrahimzadeh, Ataollah; Addeh, Jalil; Rahmani, Zahra

2011-10-28

323

Human Activity Recognition and Pathological Gait Pattern Identification  

Microsoft Academic Search

Human activity analysis has attracted great interest from computer vision researchers due to its promising applications in many areas such as automated visual surveillance, computer-human interactions, and motion-based identification and diagnosis. This dissertation presents work in two areas: general human activity recognition from video, and human activity analysis for the purpose of identifying pathological gait from both 3D captured data

Feng Niu

2007-01-01

324

Stage-Specific Sampling by Pattern Recognition Receptors during Candida albicans Phagocytosis  

PubMed Central

Candida albicans is a medically important pathogen, and recognition by innate immune cells is critical for its clearance. Although a number of pattern recognition receptors have been shown to be involved in recognition and phagocytosis of this fungus, the relative role of these receptors has not been formally examined. In this paper, we have investigated the contribution of the mannose receptor, Dectin-1, and complement receptor 3; and we have demonstrated that Dectin-1 is the main non-opsonic receptor involved in fungal uptake. However, both Dectin-1 and complement receptor 3 were found to accumulate at the site of uptake, while mannose receptor accumulated on C. albicans phagosomes at later stages. These results suggest a potential role for MR in phagosome sampling; and, accordingly, MR deficiency led to a reduction in TNF-? and MCP-1 production in response to C. albicans uptake. Our data suggest that pattern recognition receptors sample the fungal phagosome in a sequential fashion.

Heinsbroek, Sigrid E. M.; Taylor, Philip R.; Martinez, Fernando O.; Martinez-Pomares, Luisa; Brown, Gordon D.; Gordon, Siamon

2008-01-01

325

Contribution of flagellin pattern recognition to intestinal inflammation during Salmonella enterica serotype typhimurium infection.  

PubMed

Salmonella enterica serotype Typhimurium causes acute inflammatory diarrhea in humans. Flagella contribute to intestinal inflammation, but the mechanism remains unclear since most mutations abrogating pattern recognition of flagellin also prevent motility and reduce bacterial invasion. To determine the contribution of flagellin pattern recognition to the generation of innate immune responses, we compared in two animal models a nonmotile, but flagellin-expressing and -secreting serotype Typhimurium strain (flgK mutant) to a nonmotile, non-flagellin-expressing strain (flgK fliC fljB mutant). In vitro, caspase-1 can be activated by cytosolic delivery of flagellin, resulting in release of the interferon gamma inducing factor interleukin-18 (IL-18). Experiments with streptomycin-pretreated caspase-1-deficient mice suggested that induction of gamma interferon expression in the murine cecum early (12 h) after serotype Typhimurium infection was caspase-1 dependent but independent of flagellin pattern recognition. In addition, mRNA levels of the CXC chemokines macrophage inflammatory protein 2 and keratinocyte-derived chemokine were markedly increased early after serotype Typhimurium infection of streptomycin-pretreated wild-type mice regardless of flagellin expression. In contrast, in bovine ligated ileal loops, flagellin pattern recognition contributed to increased mRNA levels of macrophage inflammatory protein 3alpha and more fluid accumulation at 2 h after infection. Collectively, our data suggest that pattern recognition of flagellin contributes to early innate host responses in the bovine ileal mucosa but not in the murine cecal mucosa. PMID:19237529

Winter, Sebastian E; Thiennimitr, Parameth; Nuccio, Sean-Paul; Haneda, Takeshi; Winter, Maria G; Wilson, R Paul; Russell, Joseph M; Henry, Thomas; Tran, Quynh T; Lawhon, Sara D; Gomez, Gabriel; Bevins, Charles L; Rüssmann, Holger; Monack, Denise M; Adams, L Garry; Bäumler, Andreas J

2009-02-23

326

The Recognition Of Myoelectric Patterns For Prosthetic Limb Control  

Microsoft Academic Search

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

Bernard Hudgins; Philip Parker; Robert N. Scott

1991-01-01

327

Synergetic brain model for human-like motion patterns recognition  

Microsoft Academic Search

The ability of human brain in making decisions based on the available information stored in the memory and the information provided through cognitive process are the motivations of simulating human intelligence. This paper presents a brain model able to recognize biological behavioral patterns of human locomotion using synergetic approach. Two human-like motion patterns are studied here: slow and fast walking

I. Za'balawi; Loo Chu Kiong; Wong Eng Kiong

2010-01-01

328

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

NASA Astrophysics Data System (ADS)

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

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

1993-03-01

329

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

Microsoft Academic Search

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

Joerg Schwanke; Roland Megnet; Peter F. Jensch

1993-01-01

330

Distinct patterns of viewpoint-dependent BOLD activity during common-object recognition and mental rotation.  

PubMed

A fundamental but unanswered question about the human visual system concerns the way in which misoriented objects are recognized. One hypothesis maintains that representations of incoming stimuli are transformed via parietally based spatial normalization mechanisms (eg mental rotation) to match view-specific representations in long-term memory. Using fMRI, we tested this hypothesis by directly comparing patterns of brain activity evoked during classic mental rotation and misoriented object recognition involving everyday objects. BOLD activity increased systematically with stimulus rotation within the ventral visual stream during object recognition and within the dorsal visual stream during mental rotation. More specifically, viewpoint-dependent activity was significantly greater in the right superior parietal lobule during mental rotation than during object recognition. In contrast, viewpoint-dependent activity was significantly greater in the right fusiform gyrus during object recognition than during mental rotation. In addition to these differences in viewpoint-dependent activity, object recognition and mental rotation produced distinct patterns of brain activity, independent of stimulus rotation: object recognition resulted in greater overall activity within ventral stream visual areas and mental rotation resulted in greater overall activity within dorsal stream visual areas. The present results are inconsistent with the hypothesis that misoriented object recognition is mediated by structures within the parietal lobe that are known to be involved in mental rotation. PMID:17214381

Wilson, Kevin D; Farah, Martha J

2006-01-01

331

Bottom-up approach to the ECG pattern-recognition problem  

Microsoft Academic Search

A bottom-up approach to the recognition problem in ECG waveforms is presented in the paper. This approach is based on the\\u000a assumption that ECG waveforms are composite entities that can be decomposed into other simpler entities, these into other\\u000a simpler ones etc., until peak patterns and segment paterrns are obtained. The peak patterns and the segment patterns are considered\\u000a primitive

P. Trahanias; E. Skordalakis

1989-01-01

332

Pattern recognition: the miracle of foresight, with the benefit of hindsight.  

PubMed

One of the central skills integral to effective Bodywork and Movement Therapies is pattern recognition. The ability to be able identify key patterns of data and piece them together using skill, knowledge, intuition and reasoning is what can separate great results from mediocre results. One such pattern that has been observed by movement therapists over the last century, infant developmental progressions, are highlighted and contextualized below in the light of evolution. PMID:23036884

Wallden, Matt

2012-09-05

333

Analog design of a new neural network for optical character recognition  

Microsoft Academic Search

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

Ian P. Morns; Satnam Singh Dlay

1999-01-01

334

A topological and temporal correlator network for spatiotemporal pattern learning, recognition, and recall.  

PubMed

In this paper, we describe the design of an artificial neural network for spatiotemporal pattern recognition and recall. This network has a five-layered architecture and operates in two modes: pattern learning and recognition mode, and pattern recall mode. In pattern learning and recognition mode, the network extracts a set of topologically and temporally correlated features from each spatiotemporal input pattern based on a variation of Kohonen's self-organizing maps. These features are then used to classify the input into categories based on the fuzzy ART network. In the pattern recall mode, the network can reconstruct any of the learned categories when the appropriate category node is excited or probed. The network performance was evaluated via computer simulations of time-varying, two-dimensional and three-dimensional data. The results show that the network is capable of both recognition and recall of spatiotemporal data in an on-line and self-organized fashion. The network can also classify repeated events in the spatiotemporal input and is robust to noise in the input such as distortions in the spatial and temporal content. PMID:18252532

Srinivasa, N; Ahuja, N

1999-01-01

335

Paper Cut-Out Patterns Recognition Based on Geometrical Features  

Microsoft Academic Search

In this paper, we investigate the geometrical shape of cut-out patterns and the classification techniques, then introduce the six geometry features definition, including shape-factor, complexity, extendability, eccentricity, solidity and modal-ratio and propose the application of BP neural networks to train, classify and identify the patterns. The proposed scheme has the advantages of classifying and identifying the excessive geometrical artistic deformations.

Xianquan Zhang; Fangyuan Qin; Guoxiang Li

2009-01-01

336

Real-time holographic pattern recognition with bacteriorhodopsin films  

Microsoft Academic Search

The biological photochrome bacteriorhodopsin (BR) has attractive photophysical properties which allow its use as the photoactive component for dynamic recording media for optical applications. Purple membrane (PM) patches, which contain BR in a two-dimensional crystalline lattice, are isolated from Halobacterium halobium. Polymeric films with embedded PM are well suited reversible media for holographic recording. In addition, artificial derivatives of BR

Norbert Hampp; Ralph Thoma; Christoph R. Braeuchle; Dieter Oesterhelt

1993-01-01

337

Depth-of-interaction recognition using optical filters for nuclear medicine imaging  

Microsoft Academic Search

This paper describes a depth-of-interaction recognition method that can be applied to radiation detectors for positron emission tomography. The proposed method uses optical filters to differentiate wavelength distribution of scintillation photons depending on crystal depth positions. Then the crystal of interaction can be identified with a multianode photodetector whose sensitive surface is partly covered with optical filters. A validation experiment

Tomoyuki Hasegawa; Mitsuo Ishikawa; Koichi Maruyama; Naoko Inadama; Eiji Yoshida; Hideo Murayama

2005-01-01

338

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

SciTech Connect

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.

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

1994-05-01

339

Reticle programmed defect size measurement using low-voltage SEM and pattern recognition techniques  

NASA Astrophysics Data System (ADS)

The use of programmed defect test reticles to characterize automatic defect inspection equipment has long been an established practice in the maskmaking industry. Measurement of the defect sizes on these programmed defect test masks is not necessary if one only desires to qualitatively investigate differences in system performance. However, more meaningful comparisons in inspection system performance require a calibrated programmed defect test mask. Historically, commercially available programmed defect test reticles have not had traceable or well-documented defect sizing methods nor was information regarding the precision of these measurements provided. This paper describes the methods used and results obtained from the work performed to address these issues. Using a low voltage scanning electron microscope as an image acquisition system, defect sizing is accomplished using automated pattern recognition software. The software reports defect size metrics such as maximum inscribed circle diameter and area. Measurement precision better than 30 nm has been demonstrated for the maximum inscribed circle method. The correlation of SEM based measurements to historical optical metrology measurements ia also discussed.

Zurbrick, Larry S.; Khanna, Steve; Lee, Jay; Greed, James J.; Laird, Ellen R.; Blanquies, Rene M.

2000-02-01

340

Dynamic array generation and pattern formation for optical tweezers  

Microsoft Academic Search

The generalised phase contrast approach is used for the generation of optical arrays of arbitrary beam shape, suitable for applications in optical tweezers for the manipulation of biological specimens. This approach offers numerous advantages over current techniques involving the use of computer-generated holograms or diffractive optical elements. We demonstrate a low-loss system for generating intensity patterns suitable for the trapping

Paul C Mogensen; Jesper Glückstad

2000-01-01

341

Scale-invariant pattern recognition system based on volume holographic wavelet correlator  

NASA Astrophysics Data System (ADS)

In this paper, based on the volume holographic storage in a photorefractive crystal, a new scale-invariant pattern recognition system, with the wavelet transform, has been set up. The wavelet filter can increase the discrimination capability of the correlator. However the wavelet-filtered image is edge-enhanced, the phase-only logarithmic radial harmonic (LRH) filter is not suitable for such image when regarding the scale invariance. The LRH filter is modified to achieve scale invariant pattern recognition. Simulation result validates the theory.

Xue, Qingzeng; Yan, Yingbai; He, Qingshong

2001-09-01

342

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

NASA Astrophysics Data System (ADS)

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

Seijas, Leticia; Segura, Enrique

343

Classifying two-dimensional orbits using pattern recognition  

NASA Astrophysics Data System (ADS)

We present a fast algorithm to identify both regular and irregular orbits that map out a sustained shape in configuration space. The method, which we dub 'pattern autocorrelation' (PACO), detects a repeating pattern in time-series constructed from binary sign changes in phase-space coordinates reduced to two dimensions. This is achieved by computing the autocorrelation function of the time-series, and by retrieving a pattern and a pattern-to-signal ratio. We apply the method to two-dimensional orbits in the logarithmic potential in an application to spiral galaxies with an asymptotically flat rotation curve; the general case of three-dimensional orbits is sketched. We find that irregular orbits can yet sustain the smooth morphological features of a galaxy for a substantial fraction of a Hubble time: this fraction is quantified through the pattern-to-signal ratio. In the case where a central supermassive black hole is added to the potential, we find that up to ?16% of initial conditions space yields irregular motion that may sustain long-lived regular features. The method further detects and distinguishes orbits that are not based on Lissajous theory of resonant motion.

Faber, N. T.; Flitti, F.; Boily, C. M.; Collet, C.; Patsis, P. A.; Portegies Zwart, S.

2013-12-01

344

EEG-based communication: a pattern recognition approach  

Microsoft Academic Search

Presents an overview of the authors' research into brain-computer interfacing (BCI). This comprises an offline study of the effect of motor imagery on EEG and an online study that uses pattern classifiers incorporating parameter uncertainty and temporal information to discriminate between different cognitive tasks in real-time

William D. Penny; Stephen J. Roberts; Eleanor A. Curran; Maria J. Stokes

2000-01-01

345

Applying Novel QuickLearn Algorithm for Pattern Recognition  

Microsoft Academic Search

In this paper we describe novel QuickLearn (QL) algorithm for pattern classification which has two steps. The mapping level, first step, includes a multi input-single output mapping function (MF) with fixed weights and input data were presented to it during the training phase. It is only a kind of data shifting and scaling before presenting to next level. After shifting

Alireza Akhbardeh; Alpo Värri

2006-01-01

346

Recent results on structural pattern recognition for Fusion massive databases  

Microsoft Academic Search

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

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

2007-01-01

347

Generation of Polynomial Discriminant Functions for Pattern Recognition  

Microsoft Academic Search

A practical method of determining weights for crossproduct and power terms in the variable inputs to an adaptive threshold element used for statistical pattern classification is derived. The objective is to make it possible to realize general nonlinear decision surfaces, in contrast with the linear (hyperplanar) decision surfaces that can be realized by a threshold element using only first-order terms

Donald F. Specht

1967-01-01

348

Earth Observations: Pattern Recognition of the Earth System  

NSDL National Science Digital Library

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

Nyman, Matthew

349

Wind pattern recognition in neural fuzzy wind turbine control system  

Microsoft Academic Search

This paper introduces a new approach utilizing a fuzzy classifier and a modular temporal neural network to predict wind speed and direction for advanced wind turbine control systems. The fuzzy classifier estimates wind patterns and then assigns weights accordingly to each module of the temporal neural network. The finite-duration impulse response multiple-layer structure of the temporal network makes it possible

Guangdian G. Wu; Zhijie Dou

1994-01-01

350

A Novel Fuzzy Neural Network for Pattern Recognition  

Microsoft Academic Search

Neural network system is a self-learning adaptive system, and it is easy to associate, synthesize and generalize with its properties of fault-tolerance and robustness. Therefore, it is available to process the pattern information, which is hard to describe with language. In consideration of the shortage of fuzzy theory and the advantage of vague set, that is fuzzy membership function has

Yibiao Zhao; Song Wang; Shun Zhang; Jian Pu; Rui Fang

2006-01-01

351

Probabilistic SVM outputs for pattern recognition using analytical geometry  

Microsoft Academic Search

We present an alternative way of interpreting and modifying the outputs of the support vector machine (SVM) classifiers. Stemming from the geometrical interpretation of the SVM outputs as a distance of individual patterns from the hyperplane, allows us to calculate its posterior probability, i.e. to construct a probability-based measure of belonging to one of the classes, depending on the vector's

Ana Madevska-bogdanova; Dragan Nikolik; Leopold Curfs

2004-01-01

352

PROMENADE - An On-Line Pattern Recognition System.  

National Technical Information Service (NTIS)

The report describes research on man-computer interaction using, as principal interface, a high-performance CRT screen for computer output and a typewriter-like keyboard for input to the computer. The PROMENADE system is used to perform pattern recognitio...

D. J. Hall G. H. Ball

1967-01-01

353

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

NASA Astrophysics Data System (ADS)

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

Alawadi, Fahad

2009-09-01

354

Nanoscale DNA tetrahedra improve biomolecular recognition on patterned surfaces.  

PubMed

The bottom-up approach of DNA nano-biotechnology can create biomaterials with defined properties relevant for a wide range of applications. This report describes nanoscale DNA tetrahedra that are beneficial to the field of biosensing and the targeted immobilization of biochemical receptors on substrate surfaces. The DNA nanostructures act as immobilization agents that are able to present individual molecules at a defined nanoscale distance to the solvent thereby improving biomolecular recognition of analytes. The tetrahedral display devices are self-assembled from four oligonucleotides. Three of the four tetrahedron vertices are equipped with disulfide groups to enable oriented binding to gold surfaces. The fourth vertex at the top of the bound tetrahedron presents the biomolecular receptor to the solvent. In assays testing the molecular accessibility via DNA hybridization and protein capturing, tetrahedron-tethered receptors outperformed conventional immobilization approaches with regard to specificity and amount of captured polypeptide by a factor of up to seven. The bottom-up strategy of creating DNA tetrahedrons is also compatible with the top-down route of nanopatterning of inorganic substrates, as demonstrated by the specific coating of micro- to nanoscale gold squares amid surrounding blank or poly(ethylene glycol)-passivated glass surfaces. DNA tetrahedra can create biofunctionalized surfaces of rationally designed properties that are of relevance in analytical chemistry, cell biology, and single-molecule biophysics. PMID:22083943

Schlapak, Robert; Danzberger, Jürgen; Armitage, David; Morgan, David; Ebner, Andreas; Hinterdorfer, Peter; Pollheimer, Philipp; Gruber, Hermann J; Schäffler, Friedrich; Howorka, Stefan

2011-11-15

355

Revolving interference patterns for the rotation of optically trapped particles  

Microsoft Academic Search

Optically trapped objects are rotated controllably in the interference pattern between a Laguerre–Gaussian (LG) beam and a Gaussian beam. In this work the interference pattern is analysed and its properties as it propagates are modelled, showing the important role played by the Guoy-phase of the two interfering beams. An analysis of producing controlled rotation of the interference pattern using a

M. P. MacDonald; K. Volke-Sepulveda; L. Paterson; J. Arlt; W. Sibbett; K. Dholakia

2002-01-01

356

Recognition of higher order patterns in proteins: immunologic kernels.  

PubMed

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

Bremel, Robert D; Homan, E Jane

2013-07-29

357

Local binary pattern based texture analysis for visual fire recognition  

Microsoft Academic Search

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

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

2010-01-01

358

A sequential dynamic heteroassociative memory for multistep pattern recognition and one-to-many association.  

PubMed

Bidirectional associative memories (BAMs) have been widely used for auto and heteroassociative learning. However, few research efforts have addressed the issue of multistep vector pattern recognition. We propose a model that can perform multi step pattern recognition without the need for a special learning algorithm, and with the capacity to learn more than two pattern series in the training set. The model can also learn pattern series of different lengths and, contrarily to previous models, the stimuli can be composed of gray-level images. The paper also shows that by adding an extra autoassociative layer, the model can accomplish one-to-many association, a task that was exclusive to feedforward networks with context units and error backpropagation learning. PMID:16526476

Chartier, Sylvain; Boukadoum, Mounir

2006-01-01

359

The mannose receptor is a pattern recognition receptor involved in host defense  

Microsoft Academic Search

The mannose receptor recognizes the patterns of carbohydrates that decorate the surfaces and cell walls of infectious agents. This macrophage and dendritic cell pattern-recognition receptor mediates endocytosis and phagocytosis. The mannose receptor is the prototype of a new family of multilectin receptor proteins (membrane-spanning receptors containing eight-ten lectin-like domains, which appear to play a key role in host defense) and

Philip D Stahl; R Alan B Ezekowitz

1998-01-01

360

Foundations for a syntatic pattern recognition system for genomic DNA sequences  

SciTech Connect

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

Searles, D.B.

1993-03-01

361

A voice pattern matching LSI based on a new speaker-independent continuous voice recognition scheme  

Microsoft Academic Search

A novel CMOS special-purpose LSI, the MSM6851, is described which realizes a voice-pattern-matching scheme for speaker-independent continuous voice recognition. A voice signal was specified by a specific pattern drawn on a plane with two axes: time axis and frequency axis. A simultaneous matching procedure on two axes was realized by a vector-processing LSI to absorb the difference of voice uttered

A. Shimbo; Y. Takizawa; H. Ando

1988-01-01

362

Fuzzy-Neural Networks (FNNs) Algorithm for Partial Discharge Pattern Recognition  

Microsoft Academic Search

In order to develop reliable on-site partial discharge (PD) pattern recognition algorithm, the fuzzy inference-based polynomial network pattern classifier (PNC), one of the fuzzy-neural networks, was investigated. This algorithm was designed and tested using PD data measured from laboratory defect models. Considering the on-site situation in which it is difficult to obtain voltage phases in PRPDA (phase resolved partial discharge

Jeong-Tae Kim; Won Choi; Sung-Kwun Oh; Keon-Jun Park; Gil-Sung Kim; Stanislaw Grzybowski

2008-01-01

363

Genetic Granular Cognitive Fuzzy Neural Networks and Human Brains for Pattern Recognition  

Microsoft Academic Search

With ever-improving information technologies and high performance computational power, recent techniques in granular computing,\\u000a soft computing and cognitive science have allowed an increasing understanding of normal and abnormal brain functions, especially\\u000a in the research of human’s pattern recognition by means of computational intelligence. It is well understood that normal brains\\u000a have high intelligence to recognize different geometrical patterns, but a

Cui Lin; Jun Li; Natasha Barrett; Yan-qing Zhang; David A. Washburn

2006-01-01

364

Health as expanding consciousness: pattern recognition and incarcerated mothers, a transforming experience.  

PubMed

Margaret Newman's theory, Health as Expanding Consciousness (HEC), was used to examine the life pattern of two incarcerated mothers awaiting release from prison. Pattern recognition provided a way to approach understanding the experience of incarceration in a novel fashion. The process increased trust between the mothers and the nurse, and was a useful strategy in articulating the contribution of nursing science and humanistic care. PMID:17679268

Hayes, Margaret Oot; Jones, Dorothy

2007-01-01

365

Process out-of-control cause diagnosis of coal preparation plant based on fuzzy pattern recognition  

Microsoft Academic Search

A new method of fuzzy pattern recognition is proposed considering the importance of eigenvector in recognizing fuzzy patterns. Aiming at the coal preparation process of the coal preparation plant, the eigenvector of process out-of-control cause is attained by using of the integrated environment and other application systems of CIMS. The method to diagnose process out-of-control cause is given based on

Sun Wei; Gong Dunwei; Wang Xuesong

2000-01-01

366

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

Microsoft Academic Search

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

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

2009-01-01

367

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 ...

368

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

ERIC Educational Resources Information Center

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

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

2010-01-01

369

Circular harmonic phase filters for efficient rotation-invariant pattern recognition  

Microsoft Academic Search

A generalized approach for pattern recognition using spatial filters with reduced tolerance requirements was described in some recent publications. This approach leads to various possible implementations such as the composite matched filter, the circular harmonic matched filter, or the composite circular harmonic matched filter. The present work describes new examples leading to very high selectivity filters retaining rotation invariance and

Joseph Rosen; Joseph Shamir

1988-01-01

370

An Intelligent Biofeedback System Based on Pattern Recognition and Electroacupuncture Imitating Traditional Chinese Medical Acupuncture  

Microsoft Academic Search

This paper presented a novel intelligent biofeedback system based on the pattern recognition of physiological signals and electroacupuncture that imitates the traditional Chinese medical acupuncture. The system is composed of circuit of signal acquisition, computer, intelligent electroacupuncture and relevant softwares. The main purpose of research is to make decisions and judgments by artificial intelligence technique instead of physicians, that is,

Jun Jing; Weiping Zhang; Yuchun Wang; Shiwen Yuan

2006-01-01

371

Wavelet-based Denoised and Feature Extraction of NMR Spectroscopy Based on Pattern Recognition  

Microsoft Academic Search

According to the shortages of application of MRS and MRI to the clinical cancer diagnosis, an effective method to analyze and process the raw data of nuclear magnetic resonance is brought forward based on wavelet transform and pattern recognition technologies. Aiming at the characteristics of FID signals and MRS, de-nosing of FID and MRS data was performed using wavelet threshold

Dong Guangbo; Sun Zengqi; Ma Jian; Xie Guihai

2005-01-01

372

Disease pattern recognition testing for rheumatoid arthritis using infrared spectra of human serum  

Microsoft Academic Search

Background: In view of the importance of the diagnosis of rheumatoid arthritis, a novel diagnostic method based on spectroscopic pattern recognition in combination with laboratory parameters such as the rheumatoid factor is described in the paper. Results of a diagnostic study of rheumatoid arthritis employing this method are presented. Method: The method uses classification of infrared (IR) spectra of serum

A. Staib; B. Dolenko; D. J. Fink; J. Früh; A. E. Nikulin; M. Otto; M. S. Pessin-Minsley; O. Quarder; R. Somorjai; U. Thienel; G. Werner; W. Petrich

2001-01-01

373

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

Microsoft Academic Search

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

L. T. Wille

2004-01-01

374

Experimental design protocol for the pattern recognition analysis of bandpass filtered Fourier transform infrared interferograms  

Microsoft Academic Search

Signal processing techniques are described for open path Fourier transform infrared (FTIR) measurements that overcome fundamental limitations in conventional data analysis strategies. Bandpass digital filters are applied directly to FTIR interferograms to isolate spectral frequencies of interest, followed by pattern recognition analysis of segments of the filtered interferogram to provide an automated means for detecting a target analyte. In this

Ronald E. Shaffer; Gary W. Small; Roger J. Combs; Robert B. Knapp; Robert T. Kroutil

1995-01-01

375

Antagonizing the innate pattern recognition receptor CD204 to improve dendritic cell-targeted cancer immunotherapy  

PubMed Central

Extensive studies have established a role of scavenger receptor CD204 in pattern recognition and ligand uptake. Strikingly, we recently revealed a previously unrecognized feature of CD204 action in attenuating T-cell activation and antitumor immunity. Blocking its activity in dendritic cells represents a promising approach to the improvement of cancer immunotherapy.

Yu, Xiaofei; Wang, Xiang-Yang

2012-01-01

376

Growing subspace pattern recognition methods and their neural-network models  

Microsoft Academic Search

In statistical pattern recognition, the decision of which features to use is usually left to human judgment. If possible, automatic methods are desirable. Like multilayer perceptrons, learning subspace methods (LSMs) have the potential to integrate feature extraction and classification. In this paper, we propose two new algorithms, along with their neural-network implementations, to overcome certain limitations of the earlier LSMs.

M. Prakash; M. Narasimha Murty

1997-01-01

377

Brain-wave bio potentials based mobile robot control: wavelet-neural network pattern recognition approach  

Microsoft Academic Search

We show how a recently developed wavelet methodology could be useful for EOG state classification. The work focuses on using EOG and EMG signals to drive a robot and our approach is characterized by our emphasis on wavelet pattern recognition methods rather than on the experimenter's feedback training. In our experimental system, we used a control device activated by human

C. K. Ho; Minoru SASAKI

2001-01-01

378

A bi-directional associative memory used in a pattern recognition system  

Microsoft Academic Search

Associative memories are used in a pattern-recognition system as core classifiers. For this application, the training procedure for the BAM (bidirectional associative memory) proposed by B. Kosko (1988) has been substantially modified. By introducing an orthogonal set of vectors as the intermediate codes, the IGIA (indirect generalized inverse algorithm) can be incorporated into the recalling procedure. Thus, better system performance

P. Li; R. S. Nutter

1990-01-01

379

IEEE computer society conference on computer vision and pattern recognition (CVPR 1989)  

SciTech Connect

The authors reports on the current theory and experimentation in computer vision and pattern recognition. Topics covered include: Robust edge detection; Shape understanding from Lambertian photometric flow fields; Adding scale to the primal sketch; Outdoor vehicle navigation using passive 3D vision; Processing of line drawings in a hierarchical environment; and Parallel memory systems for image processing.

Not Available

1989-01-01

380

Application of computer pattern recognition to metal ion chemical ionization mass spectrometry data  

Microsoft Academic Search

The first application of computer pattern recognition to the analysis of low pressure transition metal ion chemical ionization (MICI) data is described. The data have been collected using a conventional ion cyclotron resonance (ICR) mass spectrometer and a Fourier transform mass spectrometer (FTMS) equipped with laser ionization sources. Chemical ionization data for organic compounds of several classes with various transition

1986-01-01

381

Short time traffic speed prediction using pattern recognition and feature selection methods  

Microsoft Academic Search

In this study we predict traffic speed on Istanbul roads using RTMS (remote traffic microwave sensor) speed measurements obtained from the Istanbul Municipality web site. We use two different pattern recognition methods, k-nearest neighbor (kNN) and support vector regression machine (SVM). In order to predict the speed at a short time (5 minutes to 60 minutes) ahead, we use speed

Ülkem Yildirim; Zehra Çataltepe

2008-01-01

382

Symbolic, Neural and Neuro-fuzzy Approaches to Pattern Recognition in Cardiotocograms  

Microsoft Academic Search

In this paper, several approaches to computer supported recognition of accelerative and decelerative patterns in the Foetal Heart Rate signal are presented, in order to automate the diagnosis of foetal well being. The classifiers discussed in here evolve from a rule-based approach to a neuro-fuzzy system, through classical neural network architectures. The main problem regarding the symbolic approach was a

Oscar Fontenla-romero; Bertha Guijarro-berdiñas; Amparo Alonso-betanzos

2002-01-01

383

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

ERIC Educational Resources Information Center

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

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

2012-01-01

384

Classification of Brazilian vinegars according to their 1H NMR spectra by pattern recognition analysis  

Microsoft Academic Search

This work describes using 1H NMR data and pattern recognition analysis to classify vinegars. Vinegar authenticity is linked to raw ingredient source and manufacturing conditions. Application of PCA and HCA methods resulted in the natural clustering of the samples according to the raw material used. Wine vinegars were characterized by a high concentration of ethyl acetate, glycerol, methanol and tartaric

Elisangela F. Boffo; Leila A. Tavares; Márcia M. C. Ferreira; Antonio G. Ferreira

2009-01-01

385

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

SciTech Connect

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

Ma, H.

1994-12-31

386

Quantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses  

Microsoft Academic Search

We evaluated real-time myoelectric pattern recognition control of a virtual arm by transradial amputees. Five unilateral patients performed 10 wrist and hand movements using their amputated and intact arms. In order to demonstrate the value of information from intrinsic hand muscles, this data was included in EMG recordings from the intact arm. With both arms, motions were selected in approximately

Guanglin Li; Aimee E. Schultz; Todd A. Kuiken

2010-01-01

387

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

Microsoft Academic Search

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

Sholom M. Weiss; Ioannis Kapouleas

1989-01-01

388

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...

389

Characterization of a pattern recognition molecule vitellogenin from carp ( Cyprinus carpio)  

Microsoft Academic Search

Pattern recognition proteins function in innate immune responses by binding to molecules on the surface of invading pathogens and initiating host defense reactions. To explore the role of vitellogenin (Vg) in fish innate immunity, we purified Vg from Carp by gel filtration combined with diethylaminoethyl (DEAE) chromatography. The purified Vg was confirmed by MALDI-TOF mass spectrometry. Antibacterial activity analysis showed

Qing-Hui Liu; Shi-Cui Zhang; Zhao-Jie Li; Chun-Ren Gao

2009-01-01

390

Identification of metal ion solutions using acoustic plate mode devices and pattern recognition  

Microsoft Academic Search

The temperature dependence of acoustic plate mode (APM) devices used as probes for dilute electrolytes is described. Specifically, the probe responses that consist of the frequency change and device loss were studied for dilute aqueous solutions of alkali metal ions. It is shown that by integrating the temperature dependence of the APM probe responses with pattern recognition techniques, valuable information

Reiner Dahint; Zack A. Shana; Fabien Josse; Susan A. Riedel; Michael Grunze

1993-01-01

391

Damage detection by using FBGs and strain field pattern recognition techniques  

NASA Astrophysics Data System (ADS)

A novel methodology for damage detection and location in structures is proposed. The methodology is based on strain measurements and consists in the development of strain field pattern recognition techniques. The aforementioned are based on PCA (principal component analysis) and damage indices (T2 and Q). We propose the use of fiber Bragg gratings (FBGs) as strain sensors.

Sierra-Pérez, Julián; Güemes, Alfredo; Mujica, Luis E.

2013-02-01

392

A Change in Orientation: Recognition of Rotated Patterns by Bumble Bees  

Microsoft Academic Search

In three experiments, bumble bees were trained to discriminate between a reinforcing pattern (S+) and a nonreinforcing one (S-) which differed only in the configuration of four artificial petals. They were subsequently tested for recognition of the S+ rotated by 90° (S + 90). Experiment 1 used petals of four colors, and the other experiments used four symbols. The symbols

C. M. S. Plowright; F. Landry; D. Church; J. Heyding; N. Dupuis-Roy; J. P. Thivierge; V. Simonds

2001-01-01

393

Classification of regions of solar activity based on methods of pattern recognition theory  

Microsoft Academic Search

Methods for predicting manifestations of solar activity based on pattern recognition theory were developed. The logical procedure for solving problems of forecasting solar activity is similar to forecasting by the synoptic method, but it is objective and can be fully automated. The actual execution of the scheme consists of the following: the general forecasting problem is formulated as forecasting the

S. I. Avdyushin; B. O. Berlyand; P. B. Dernshteyn; V. A. Burov

1983-01-01

394

Comparative analysis of statistical, fuzzy, and artificial neural pattern recognition techniques  

Microsoft Academic Search

A common mathematical foundation for the comparative analysis of statistical, fuzzy, and artificial neural pattern recognition or decision making systems is presented. This development uses abstract algebraic techniques to characterize the functions generating decision surfaces and the learning\\/training processes involved in each technique.

Jay B. Jordan; Howon Choe

1992-01-01

395

A new flatness pattern recognition model based on CA-CMAC network  

Microsoft Academic Search

In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learn assignment, slow convergence, and local minimal in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, it has been proved that the model is time-consuming and

Yan Li; Hong-Li Yuan; Yong-Zheng Li

2009-01-01

396

Pattern recognition using neural-fuzzy networks based on improved particle swam optimization  

Microsoft Academic Search

This paper introduces a recurrent neural-fuzzy network (RNFN) based on improved particle swarm opti- mization (IPSO) for pattern recognition applications. The proposed IPSO method consists of the modified evolutionary direction operator (MEDO) and the traditional PSO. A novel MEDO combining the evolution- ary direction operator (EDO) and the migration operation is also proposed. Hence, the proposed IPSO method can improve

Cheng-jian Lin; Jun-guo Wang; Chi-yung Lee

2009-01-01

397

A method of pattern recognition based upon synthetic technology of fuzzy logic and neural network  

Microsoft Academic Search

A method which uses the synthesis of fuzzy logic and neural networks is provided to solve 2D pattern recognition with incomplete information. When the object set is confirmed, we use a neural network detector to discover the edges and corners. After we have obtained that information, we get the conclusion from the fuzzy reasoning. Computer simulation shows that, although there

Chen Yong; Sheng Minghao; He Yongbao

1993-01-01

398

A New Flatness Pattern Recognition Model Based on Cerebellar Model Articulation Controllers Network  

Microsoft Academic Search

In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and local minimum in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, the model is time-consuming and complex. Thus, a new approach

Hai-tao HE; Yan LI

2008-01-01

399

Pattern recognition of power quality events using Fuzzy neural network based rule generation  

Microsoft Academic Search

This paper presents pattern recognition of time series data and subsequent temporal data mining of power signal disturbance events that occur frequently in power distribution networks using multiresolution S-transform and Fuzzy Multilayer Perceptron network (Fuzzy MLP). The muliresolution S-transform yields relevant features, which are used in a Fuzzy expert system to separate the transient time series data and steady state

Lalit Kumar Behera; Maya Nayak

2012-01-01

400

Application research on intelligent pattern recognition methods in hail identification of weather radar  

Microsoft Academic Search

Firstly, advantages of the learning ability of intelligent pattern recognition models, which have been used in hail identification of weather radar based on echo parameters, is discussed. Then, structures and working principles of hail identification models which based on fuzzy neural network and support vector machines (SVM) are described respectively. Finally, effect validation of hail identification has been finished by

She Yong; Yu Lei; Wei Yi

2010-01-01

401

A genetic algorithm for pattern recognition analysis of pyrolysis gas chromatographic data  

Microsoft Academic Search

The development of a genetic algorithm (GA) for pattern recognition analysis of pyrolysis gas chromatographic data is reported. The GA selects features that optimize the separation of the classes in a plot of the two largest principal components (PCs) of the data. Because the largest PCs capture the bulk of the variance in the data, the peaks chosen by the

Barry K. Lavine; Anthony Moores; Lisa K. Helfend

1999-01-01

402

Fuel Spill Identification by Gas Chromatography - Genetic Algorithms\\/Pattern Recognition Techniques  

Microsoft Academic Search

Gas chromatography and pattern recognition methods were used to develop a potential method for typing jet fuels so a spill sample in the environment can be traced to its source. The test data consisted of 256 gas chromatograms of neat jet fuels. 31 fuels that have undergone weathering in a subsurface environment were correctly identified by type using discriminants developed

Barry K. Lavine; Anthony J. Moores; Howard T. Mayfield; Abdullah Faruque

1998-01-01

403

A New Training Algorithm for Pattern Recognition Technique Based on Straight Line Segments  

Microsoft Academic Search

Abstract Recently, a new Pattern Recognition technique based on straight line segments (SLSs) was presented. The key issue in this new technique is to find a function based on dis- tances between points and two sets of SLSs that minimizes a certain error or risk criterion. An algorithm for solving this optimization problem is called training algorithm. Al- though this

João Henrique Burckas Ribeiro; Ronaldo Fumio Hashimoto

2008-01-01

404

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

Microsoft Academic Search

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

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

2011-01-01

405

Diagnosis by pattern recognition for PMSM used in more electric aircraft  

Microsoft Academic Search

Presently, condition monitoring and fault diagnostics in electric drives are essential to optimize maintenance operations and increase reliability levels. This paper presents a diagnosis method for electrical and mechanical faults detection. This method combines a detection method based on expertise with a pattern recognition approach so as to detect different faults appearing on the system but also to classify their

O. Ondel; E. Boutleux; G. Clerc

2011-01-01

406

Pattern recognition using a family of design algorithms based upon the generalized probabilistic descent method  

Microsoft Academic Search

This paper provides a comprehensive introduction to a novel approach to pattern recognition which is based on the generalized probabilistic descent method (GPD) and its related design algorithms. The paper contains a survey of recent recognizer design techniques, the formulation of GPD, the concept of minimum classification error learning that is closely related to the GPD formalization, a relational analysis

SHIGERU KATAGIRI; Biing-Hwang Juang; Chin-Hui Lee

1998-01-01

407

Pattern Recognition-A Technique for Induction Machines Rotor Broken Bar Detection  

Microsoft Academic Search

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

M. Haji; H. Toliyat

2001-01-01

408

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

Microsoft Academic Search

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

M. Haji; H. A. Toliyat

2002-01-01

409

Pattern recognition-a technique for induction machines rotor fault detection “broken bar fault”  

Microsoft Academic Search

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

Masoud Haji; Hamid A. Toliyat

2001-01-01

410

Pattern recognition-a technique for induction machines rotor fault detection “eccentricity and broken bar fault”  

Microsoft Academic Search

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

Masoud Haji; Hamid A. Toliyat

2001-01-01

411

Accuracy of ultrasound subjective ‘pattern recognition’ for the diagnosis of borderline ovarian tumors  

Microsoft Academic Search

Objectives To assess the value of pattern recognition for the preoperative ultrasound diagnosis of borderline ovarian tumors (BOTs). Methods This was a prospective study of women who were referred to our regional cancer center with the diagnosis of an adnexal mass on a Level II (routine) gynecological ultrasound scan. Women with lesions of uncertain nature were referred for a Level

J. Yazbek; K. S. Raju; J. Ben-Nagi; T. Holland; K. Hillaby; D. Jurkovic

2007-01-01

412

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...

413

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

Microsoft Academic Search

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

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

2009-01-01

414

A New Method for the Algorithm of Close Degree in Fuzzy Pattern Recognition  

Microsoft Academic Search

Some algorithms of close degree in fuzzy pattern recognition were researched and their limitations in application were found. So a new algorithm of close degree was proposed based on fluctuation thought in this paper. The whole relation between various elements and fuzzy set was considered in the new algorithm. It could avoid that the little change of membership grade in

Zhigang Li; Quanming Zhao; Jungang Zhou; Lingling Li

2009-01-01

415

Structural basis of recognition of pathogen-associated molecular patterns and inhibition of proinflammatory cytokines by camel peptidoglycan recognition protein.  

PubMed

Peptidoglycan recognition proteins (PGRPs) are involved in the recognition of pathogen-associated molecular patterns. The well known pathogen-associated molecular patterns include LPS from Gram-negative bacteria and lipoteichoic acid (LTA) from Gram-positive bacteria. In this work, the crystal structures of two complexes of the short form of camel PGRP (CPGRP-S) with LPS and LTA determined at 1.7- and 2.1-Å resolutions, respectively, are reported. Both compounds were held firmly inside the complex formed with four CPGRP-S molecules designated A, B, C, and D. The binding cleft is located at the interface of molecules C and D, which is extendable to the interface of molecules A and C. The interface of molecules A and B is tightly packed, whereas that of molecules B and D forms a wide channel. The hydrophilic moieties of these compounds occupy a common region, whereas hydrophobic chains interact with distinct regions in the binding site. The binding studies showed that CPGRP-S binds to LPS and LTA with affinities of 1.6 × 10(-9) and 2.4 × 10(-8) M, respectively. The flow cytometric studies showed that both LPS- and LTA-induced expression of the proinflammatory cytokines TNF-? and IL-6 was inhibited by CPGRP-S. The results of animal studies using mouse models indicated that both LPS- and LTA-induced mortality rates decreased drastically when CPGRP-S was administered. The recognition of both LPS and LTA, their high binding affinities for CPGRP-S, the significant decrease in the production of LPS- and LTA-induced TNF-? and IL-6, and the drastic reduction in the mortality rates in mice by CPGRP-S indicate its useful properties as an antibiotic agent. PMID:21454594

Sharma, Pradeep; Dube, Divya; Singh, Amar; Mishra, Biswajit; Singh, Nagendra; Sinha, Mau; Dey, Sharmistha; Kaur, Punit; Mitra, Dipendra K; Sharma, Sujata; Singh, Tej P

2011-03-21

416

A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury.  

PubMed

This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of nine subjects with incomplete cervical SCI while they tried to perform six different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (> 97%) were achieved in classification of seven different classes (six intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury. PMID:23033334

Liu, Jie; Zhou, Ping

2012-09-27

417

Understanding Complexity: Pattern Recognitions, Emergent Phenomena and Causal Coupling  

NASA Astrophysics Data System (ADS)

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

Raia, F.

2010-12-01

418

Critical song features for auditory pattern recognition in crickets.  

PubMed

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

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

2013-02-20

419

Critical Song Features for Auditory Pattern Recognition in Crickets  

PubMed Central

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.

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

2013-01-01

420

Thermal imaging for face recognition in optical security systems  

NASA Astrophysics Data System (ADS)

Face recognition belongs to the most fundamental human perceptual capabilities. Today, the technology is encapsulated in commercial software that runs on any low cost personal computer and is capable to detect the human faces anywhere in a scene and rapidly identifying them by matching the face against its database of known faces.

Kobel, Joanna; Holowacz, Iwona; Podbielska, Halina

2001-08-01

421

Application of segmentation based on optical flow for gait recognition  

Microsoft Academic Search

The quality of human silhouettes has a direct effect on gait recognition performance. This paper proposes a robust gait representation scheme to suppress the influence of silhouette incompleteness. By means of dividing human body area in a video sequence into several sub-areas, representing each sub-area through an ellipse whose parameters can be calculated from the corresponding motion information extracted from

Sun Xiaoying; Zhang Qiuhong; Xu Yanqun

2010-01-01

422

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

PubMed

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates. PMID:21035667

Lavine, Barry K; Mirjankar, Nikhil; Vander Meer, Robert K

2010-09-16

423

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

PubMed

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates. PMID:21215868

Lavine, Barry K; Mirjankar, Nikhil; Vander Meer, Robert K

2010-12-04

424

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures  

NASA Astrophysics Data System (ADS)

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

Yao, Ruigen; Pakzad, Shamim N.

2012-08-01

425

Selective homopolymer adsorption on structured surfaces as a model for pattern recognition  

NASA Astrophysics Data System (ADS)

Homopolymer adsorption onto chemically structured periodic surfaces and its potential for pattern recognition is investigated using Monte Carlo simulations. To analyze the surface-induced selective adsorption on a fundamental geometric level polymer chains are represented by freely jointed chains with a fixed bond length whose monomers are attracted by the sites of regular lattice patterns. The structural properties of the adsorbed low-temperature state are comprehensively discussed for different lattices by looking at the radius of gyration and the inter bond angle distributions. These observables show a non-trivial dependence on the commensurability of characteristic lengths given by the lattice constant and by the bond length. Reasons for this behavior are given by exploiting geometric and entropic arguments. The findings are examined in the context of pattern recognition by polymer adsorption. Furthermore, the adsorption transition is discussed briefly. For certain incommensurable situations the adsorption occurs in two steps due to entropic restrictions.

Gemünden, Patrick; Behringer, Hans

2013-01-01

426

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

PubMed

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

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

2012-01-01

427

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

PubMed

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

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

1990-04-10

428

Retinotopically specific reorganization of visual cortex for tactile pattern recognition.  

PubMed

Although previous studies have shown that Braille reading and other tactile discrimination tasks activate the visual cortex of blind and sighted people, it is not known whether this kind of crossmodal reorganization is influenced by retinotopic organization. We have addressed this question by studying "S," a visually impaired adult with the rare ability to read print visually and Braille by touch. S had normal visual development until 6 years of age, and thereafter severe acuity reduction due to corneal opacification, but no evidence of visual-field loss. Functional magnetic resonance imaging revealed that, in S's early visual areas, tactile information processing activated what would be the foveal representation for normally sighted individuals, and visual information processing activated what would be the peripheral representation. Control experiments showed that this activation pattern was not due to visual imagery. S's high-level visual areas, which correspond to shape- and object-selective areas in normally sighted individuals, were activated by both visual and tactile stimuli. The retinotopically specific reorganization in early visual areas suggests an efficient redistribution of neural resources in the visual cortex. PMID:19361999

Cheung, Sing-Hang; Fang, Fang; He, Sheng; Legge, Gordon E

2009-04-14

429

A coherence-based approach for the pattern recognition of time series  

NASA Astrophysics Data System (ADS)

A pattern recognition approach based on the frequency domain measure of squared coherence is a useful approach to identify linearly related groupings of time series over different periods of time. It is considered in an application to identify similar patterns of the yearly rates of change in the Gross Domestic Product (GDP) of twenty two highly developed countries in an econophysics context. The approach is also tested in simulation studies using linearly related time series, and it is shown to have a very good success rate of correct pattern matching.

Maharaj, Elizabeth Ann; D'Urso, Pierpaolo

2010-09-01

430

Optical character recognition: an illustrated guide to the frontier  

NASA Astrophysics Data System (ADS)

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

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

1999-12-01

431

Gain, detuning, and radiation patterns of nanoparticle optical antennas  

Microsoft Academic Search

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

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

2008-01-01

432

Patterns of diabetic macular edema with optical coherence tomography  

Microsoft Academic Search

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

Tomohiro Otani; Shoji Kishi; Yasuhiro Maruyama

1999-01-01

433

Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks  

NASA Astrophysics Data System (ADS)

This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune recognition and learning mechanisms. The structure damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to generate recognition feature vectors that are able to match the training data. In addition, the immune learning algorithm can learn and remember various data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) Structural Health Monitoring Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms.

Chen, Bo; Zang, Chuanzhi

2009-03-01

434

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

PubMed

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

Mian, Ajmal

2011-04-11

435

A Study of Car Park Control System Using Optical Character Recognition  

Microsoft Academic Search

This paper presents a study and design of car park control system using optical character recognition (OCR) devices. The system uses client server environment. The administrator will monitor the system and the database from the server side. Furthermore the parking information will be displayed static based on the database shared by the server. Server application and database will be stored

Anton Satria Prabuwono; Ariff Idris

2008-01-01

436

Optical method of sub-cooled water recognition in dew point hygrometer  

Microsoft Academic Search

A new optical method of sub-cooled water recognition in dew point hygrometer is presented. The physical phenomenon of sub-cooled water and its influence on the hygrometer accuracy is described. A hypothesis relating scattering of light by water and ice layers is proposed. A new type of dew point hygrometer based on the silicone mirror with the embedded capacitive detector and

Jerzy Weremczuk

2000-01-01

437

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

Microsoft Academic Search

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

Kai Ding; Lianwen Jin; Hanyu Yan

2008-01-01

438

Photorefractive moiré like pattern as optical numerical code generator  

NASA Astrophysics Data System (ADS)

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

de Oliveira, G. N.; de Oliveira, M. E.; dos Santos, P. A. M.

2012-09-01

439

EBR-II (Experimental Breeder Reactor-II) system surveillance using pattern recognition software  

SciTech Connect

The problem of most accurately determining the Experimental Breeder Reactor-II (EBR-II) reactor outlet temperature from currently available plant signals is investigated. Historically, the reactor outlet pipe was originally instrumented with 8 temperature sensors but, during 22 years of operation, all these instruments have failed except for one remaining thermocouple, and its output had recently become suspect. Using pattern recognition methods to compare values of 129 plant signals for similarities over a 7 month period spanning reconfiguration of the core and recalibration of many plant signals, it was determined that the remaining reactor outlet pipe thermocouple is still useful as an indicator of true mixed mean reactor outlet temperature. Application of this methodology to investigate one specific signal has automatically validated the vast majority of the 129 signals used for pattern recognition and also highlighted a few inconsistent signals for further investigation.

Mott, J.E.; Radtke, W.H.; King, R.W.

1986-02-01

440

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

NASA Astrophysics Data System (ADS)

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

Hölzel, Robert W.; Krischer, Katharina

2013-11-01

441

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

NASA Astrophysics Data System (ADS)

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.

Li, Ying; Jiao, Licheng

2001-09-01

442

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

NASA Astrophysics Data System (ADS)

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

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

2011-04-01

443

An overview of pattern recognition in the central arms of the PHENIX detector  

SciTech Connect

It is predicted that a Au+Au event in the PHENIX Detector at RHIC will produce up to 800 charged particles in the PHENIX central arms. Pattern recognition algorithms are being developed to handle this hostile tracking environment. To facilitate the development of these algorithms, a suite of evaluators and event displays have been developed to calculate efficiencies and identify weaknesses in the algorithms. An overview of these algorithms and procedures will be discussed.

Mitchell, J.T.

1997-02-17

444

A Hybrid Methodology for Pattern Recognition in Signaling Cervical Cancer Pathways  

Microsoft Academic Search

\\u000a Cervical Cancer (CC) is the result of the infection of high risk Human Papilloma Viruses. mRNA microarray expression data\\u000a provides biologists with evidences of cellular compensatory gene expression mechanisms in the CC progression. Pattern recognition\\u000a of signalling pathways through expression data can reveal interesting insights for the understanding of CC. Consequently,\\u000a gene expression data should be submitted to different pre-processing

David Escarcega; Fernando Ramos; Ana Espinosa; Jaime Berumen

445

Recognition of Binding Patterns Common to a Set of Protein Structures  

Microsoft Academic Search

We present a novel computational method, MultiBind, for recognition of binding patterns common to a set of protein structures. It is the first method which performs a multiple alignment between pro- tein binding sites in the absence of overall sequence, fold or binding partner similarity. MultiBind recognizes common spatial arrangements of physico-chemical properties in the binding sites. These should be

Maxim Shatsky; Alexandra Shulman-peleg; Ruth Nussinov; Haim J. Wolfson

2005-01-01

446

Pattern Recognition of Surface EMG Biological Signals by Means of Hilbert Spectrum and Fuzzy Clustering  

Microsoft Academic Search

\\u000a A novel method for hand movement pattern recognition from electromyography (EMG) biological signals is proposed. These signals\\u000a are recorded by a three-channel data acquisition system using surface electrodes placed over the forearm, and then processed\\u000a to recognize five hand movements: opening, closing, supination, flexion, and extension. Such method combines the Hilbert–Huang\\u000a analysis with a fuzzy clustering classifier. A set of

Ruben-Dario Pinzon-Morales; Katherine-Andrea Baquero-Duarte; Alvaro-Angel Orozco-Gutierrez; Victor-Hugo Grisales-Palacio

447

A Comparative Study of Pattern Recognition Algorithms for Classification of Ultrasonic Signals  

Microsoft Academic Search

An extensive discrimination study was conducted on ultrasonic signals very similar to each other obtained from artificial\\u000a inserts in a carbon fibre reinforced epoxy plate. The performance of fifteen classification schemes consisting of non-parametric\\u000a pattern recognition and Artificial Neural System (ANS) algorithms is assessed in this paper. The purpose of this study is\\u000a to define an upper bound for the

A. A. Anastassopoulos; V. N. Nikolaidis; T. P. Philippidis

1999-01-01

448

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

Microsoft Academic Search

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

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

2007-01-01

449

Pattern recognition in high energy physics with artificial neural networks - JETNET 2.0  

Microsoft Academic Search

A F77 package of adaptive artificial neural network algorithms, JETNET 2.0, is presented. Its primary target is the high energy physics community, but it is general enough to be used in any pattern-recognition application area. The basic ingredients are the multilayer perceptron back-propagation algorithm and the topological self-organizing map. The package consists of a set of subroutines, which can either

Leif Lönnblad; Carsten Peterson; Thorsteinn Rögnvalsson

1992-01-01

450

Econometric Modelling based on Pattern recognition via the Fuzzy c-Means Clustering Algorithm  

Microsoft Academic Search

In this paper we consider the use of fuzzy modelling in the context of econometric analysis of both time-series and cross-section data. We discuss and demonstrate a semi-parametric methodology for model identification and estimation that is based on the Fuzzy c-Means algorithm that is widely used in the context of pattern recognition, and the Takagi-Sugeno approach to modelling fuzzy systems.

David E. A. Giles; Robert Draeseke

2001-01-01

451

An approach for pattern recognition of hand activities based on EEG and Fuzzy neural network  

Microsoft Academic Search

Electroencephalography (EEG) is another interesting bio-electrical signal to differ from EMG (Electromyography). In order\\u000a to pursue its application in the control of the multi-fingered robot hand or the prosthetic hand, the pattern recognition\\u000a technology of the human hand activities based on EEG should be investigated as a very important and elementary research objective\\u000a at first. After discussing our research strategy

Xiao Dong Zhan; Taehun Kang; Hyouk Ryeol Choi

2005-01-01

452

Partial Discharge Pattern Recognition Using Fuzzy-Neural Networks (FNNs) Algorithm  

Microsoft Academic Search

In order to develop reliable on-site partial discharge (PD) pattern recognition algorithm, the fuzzy set-based fuzzy neural network (FNN) was investigated and designed. Using PD data measured from laboratory defect models, this algorithm was designed and tested. Considering the on-site situation where it is not easy to obtain voltage phases in PRPDA (phase resolved partial discharge analysis), the measured PD

Jeong-Tae Kim; Won Choi; Sung-Kwun Oh; Keon-Jun Park; Stanislaw Grzybowski

2008-01-01

453

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots  

Microsoft Academic Search

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning\\u000a to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories\\u000a if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors

Daejung Shin; Seung You Na; Jin Young Kim; Seong-joon Baek

2008-01-01

454

AUTHENTICATION OF FUEL SPILL STANDARDS USING GAS CHROMATOGRAPHY\\/PATTERN RECOGNITION TECHNIQUES  

Microsoft Academic Search

Gas chromatography and pattern recognition techniques have been used to develop a potential method to authenticate fuel spill identification standards. The test data consisted of 65 gas chromatograms of JP-4, Jet-A, JP-7, JPTS, JP-5, AVGAS, and Diesel fuels, which were obtained from Mukilteo and Wright Patterson Energy Management Laboratories. A genetic algorithm (GA) was used to identify features in the

B. K. Lavine; Anja Vesanen; D. M. Brzozowski; H. T. Mayfield

2001-01-01

455

Scale-invariant pattern recognition system based on volume holographic wavelet correlator  

Microsoft Academic Search

In this paper, based on the volume holographic storage in a photorefractive crystal, a new scale-invariant pattern recognition system, with the wavelet transform, has been set up. The wavelet filter can increase the discrimination capability of the correlator. However the wavelet-filtered image is edge-enhanced, the phase-only logarithmic radial harmonic (LRH) filter is not suitable for such image when regarding the

Qingzeng Xue; Yingbai Yan; Qingshong He

2001-01-01

456

Paper Cut-Out Patterns Recognition Based on Wavelet Decomposition and NMI  

Microsoft Academic Search

This paper proposes a new Paper Cut-Out Patterns Recognition method combining the Wavelet Transform with NMI feature. The NMI feature of binary image has the ability of anti-gray distortion and TRS invariance, and its extraction method is sample. By Wavelet Transform, we can compute the NMI feature using low-frequency components instead of the original image. Experiments show that it can

Zhang Xianquan; Li Guoxiang; Qin Fangyuan

2009-01-01

457

Pattern recognition methods: a novel analysis for the pupillographic sleepiness test  

Microsoft Academic Search

The aim of this paper is to improve the information gained by the most commonly applied fit-for-duty sleepiness test (Pupillographic Sleepiness test, PST) by using pattern recognition approaches. The pupil diameter based sleepiness detection is enriched by several new features and machine learning methods. Using all newly computed pupil diameter features we achieved on the two-class detection problem (moderate sleepiness

Jarek Krajewski; Thomas Schnupp; Sebastian Schnieder; David Sommer; Christian Heinze; Martin Golz

2010-01-01

458

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

Microsoft Academic Search

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

WenJun Zhang

2007-01-01

459

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

PubMed

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

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

2013-08-22

460

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

Microsoft Academic Search

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

Rong Liu; Guorong Lv; Bin He; Kexin Xu

2011-01-01

461

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

SciTech Connect

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.

Foley, M.G.

1991-02-01

462

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

SciTech Connect

Many next-generation physics experiments will be characterized by the collection of large quantities of data, taken in rapid succession, from which scientists will have to unravel the underlying physical processes. In most cases, large backgrounds will overwhelm the physics signal. Since the quantity of data that can be stored for later analysis is limited, real-time event selection is imperative to retain the interesting events while rejecting the background. Scaling of current technologies is unlikely to satisfy the scientific needs of future projects, so investments in transformational new technologies need to be made. For example, future particle physics experiments looking for rare processes will have to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare processes. In this proposal, we intend to develop hardware-based technology that significantly advances the state-of-the-art for fast pattern recognition within and outside HEP using the 3D vertical integration technology that has emerged recently in industry. The ultimate physics reach of the LHC experiments will crucially depend on the tracking trigger's ability to help discriminate between interesting rare events and the background. Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing pattern recognition for a track trigger, requiring about three orders of magnitude more associative memory patterns than what was used in the original CDF SVT. Significant improvement in the architecture of associative memory structures is needed to run fast pattern recognition algorithms of this scale. We are proposing the development of 3D integrated circuit technology as a way to implement new associative memory structures for fast pattern recognition applications. Adding a 'third' dimension to the signal processing chain, as compared to the two-dimensional nature of printed circuit boards, Field Programmable Gate Arrays (FPGAs), etc., opens up the possibility for new architectures that could dramatically enhance pattern recognition capability. We are currently performing preliminary design work to demonstrate the feasibility of this approach. In this proposal, we seek to develop the design and perform the ASIC engineering necessary to realize a prototype device. While our focus here is on the Energy Frontier (e.g. the LHC), the approach may have applications in experiments in the Intensity Frontier and the Cosmic Frontier as well as other scientific and medical projects. In fact, the technique that we are proposing is very generic and could have wide applications far beyond track trigger, both within and outside HEP.

Deputch, G.; Hoff, J.; Lipton, R.; Liu, T.; Olsen, J.; Ramberg, E.; Wu, Jin-Yuan; Yarema, R.; /Fermilab; Shochet, M.; Tang, F.; /Chicago U.; Demarteau, M.; /Argonne /INFN, Padova

2011-04-13

463

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

PubMed Central

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

Lee, Sean; Nitin, Mantri

2012-01-01

464

A strategy for minimizing the effect of misclassifications during real time pattern recognition myoelectric control.  

PubMed

Pattern recognition myoelectric control in combination with targeted muscle reinnervation (TMR) may provide better real-time control of upper limb prostheses. Current pattern recognition algorithms can classify movements with an off-line accuracy of approximately 95%. When amputees use these systems to control prostheses, motion misclassifications may hinder their performance. This study investigated the use of a decision based velocity profile that limited movement speed when there was a change in classifier decision. The goal of this velocity ramp was to improve prosthesis positioning by minimizing the effect of unintended movements. Two patients who had undergone TMR surgery controlled either a virtual or physical prosthesis. They completed a Target Achievement Control Test where they commanded a virtual prosthesis into a target posture. Participants showed improved performance metrics of 34% increase in completion rate and 13% faster overall time with the velocity ramp compared to without the velocity ramp. One participant controlled a physical prosthesis and in three minutes was able to create a tower of 1" cubes seven blocks tall with the velocity ramp compared to a tower of only two blocks tall in the control condition. These results suggest that using a pattern recognition system with a decision based velocity profile may improve user performance. PMID:19964513

Simon, Ann M; Hargrove, Levi J; Lock, Blair A; Kuiken, Todd A

2009-01-01

465

A digital procedure for ground water recharge and discharge pattern recognition and rate estimation.  

PubMed

A digital procedure to estimate recharge/discharge rates that requires relatively short preparation time and uses readily available data was applied to a setting in central Wisconsin. The method requires only measurements of the water table, fluxes such as stream baseflows, bottom of the system, and hydraulic conductivity to delineate approximate recharge/discharge zones and to estimate rates. The method uses interpolation of the water table surface, recharge/discharge mapping, pattern recognition, and a parameter estimation model. The surface interpolator used is based on the theory of radial basis functions with thin-plate splines. The recharge/discharge mapping is based on a mass-balance calculation performed using MODFLOW. The results of the recharge/discharge mapping are critically dependent on the accuracy of the water table interpolation and the accuracy and number of water table measurements. The recharge pattern recognition is performed with the help of a graphical user interface (GUI) program based on several algorithms used in image processing. Pattern recognition is needed to identify the recharge/discharge zonations and zone the results of the mapping method. The parameter estimation program UCODE calculates the parameter values that provide a best fit between simulated heads and flows and calibration head-and-flow targets. A model of the Buena Vista Ground Water Basin in the Central Sand Plains of Wisconsin is used to demonstrate the procedure. PMID:12772823

Lin, Yu-Feng; Anderson, Mary P

466

A Software Algorithm Prototype for Optical Recognition of Embossed Braille  

Microsoft Academic Search

Braille is a tactile format of written communication for sight-impaired people worldwide. This paper proposes a software solution prototype to optically recognise single sided embossed Braille documents using a simple image processing algorithm and probabilistic neural network. The output is a Braille text file formatted to preserve the layout of the original document which can be sent to an electronic

Lisa Wong; Waleed H. Abdulla; Stephan Hussmann

2004-01-01

467

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

PubMed

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

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

2013-08-19

468

tFPR: A fuzzy and structural pattern recognition system of multi-variate time-dependent pattern classes based on sigmoidal functions  

Microsoft Academic Search

tFPR is a hybrid fuzzy and structural pattern recognition system that uses fuzzy sets to represent multi-variate pattern classes that can be either static or dynamic depending on time or some other parameter space. The membership functions of the fuzzy sets that represent pattern classes are modeled by sigmoidal functions. The choice of sigmoidal functions was motivated by their ability

John A. Drakopoulos; Barbara Hayes-Roth

1998-01-01

469

Doppler encoded excitation pattern tomographic optical microscopy.  

PubMed

Most far-field optical imaging systems rely on lenses and spatially resolved detection to probe distinct locations on the object. We describe and demonstrate a high-speed wide-field approach to imaging that instead measures the complex spatial Fourier transform of the object by detecting its spatially integrated response to dynamic acousto-optically synthesized structured illumination. Tomographic filtered backprojection is applied to reconstruct the object in two or three dimensions. This technique decouples depth of field and working distance from resolution, in contrast to conventional imaging, and can be used to image biological and synthetic structures in fluoresced or scattered light employing coherent or broadband illumination. We discuss the electronically programmable transfer function of the optical system and its implications for imaging dynamic processes. We also explore wide-field fluorescence imaging in scattering media by coherence gating. Finally, we present two-dimensional high-resolution tomographic image reconstructions in both scattered and fluoresced light demonstrating a thousandfold improvement in the depth of field compared to conventional lens-based microscopy. PMID:21124527

Feldkhun, Daniel; Wagner, Kelvin H

2010-12-01

470

Structural Pattern Recognition Techniques for Data Retrieval in Massive Fusion Databases  

NASA Astrophysics Data System (ADS)

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

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

2008-03-01

471

Structural Pattern Recognition Techniques for Data Retrieval in Massive Fusion Databases  

SciTech Connect

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

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

2008-03-12

472

Immulectin-2, a pattern recognition receptor that stimulates hemocyte encapsulation and melanization in the tobacco hornworm, Manduca sexta  

Microsoft Academic Search

In insects, encapsulation followed by melanization is a major defense mechanism against metazoan parasites. However, insects must recognize and differentiate nonself before they mount an immune response. Recognition of pathogens in insects is accomplished by a set of pattern recognition receptors (PRRs). Binding of PRRs to pathogens is linked to a variety of immune responses including phagocytosis, nodule formation, encapsulation,

Xiao-Qiang Yu; Michael R Kanost

2004-01-01

473

Using artificial bat sonar neural networks for complex pattern recognition: Recognizing faces and the speed of a moving target  

Microsoft Academic Search

Two sets of studies examined the viability of using bat-like sonar input for artificial neural networks in complex pattern recognition tasks. In the first set of studies, a sonar neural network was required to perform two face recognition tasks. In the first task, the network was trained to recognize different faces regardless of facial expressions. Following training, the network was

Itiel E. Dror; Faith L. Florer; Damien Rios; Mark Zagaeski

1996-01-01

474

Automated classification of single airborne particles from two-dimension, angle-resolved optical scattering (TAOS) patterns  

NASA Astrophysics Data System (ADS)

Two-dimension, angle-resolved optical scattering (TAOS) is an experimental technique by which patterns of LASER light intensity scattered by single (micrometer or sub-micrometer sized) airborne particles are collected. In the past 10 years TAOS instrumentation has evolved from laboratory prototypes to field-deployable equipment; patterns are collected by the thousands during indoor or outdoor sampling in short times. Although comparison between experimental and computed scattering patterns has been carried out extensively, there is no satisfactory way to relate a given pattern to the particle it comes from. This paper reports about the ongoing development and implementation of a method which is aimed at classifying patterns, rather than identifying original particles. A machine learning algorithm includes the extraction of morphological features and their multivariate statistical analysis. A classifier is trained and validated in a supervised mode, by relying on patterns from known materials. Then the tuned classifier is applied to the recognition of patterns of unknown origin.

Crosta, Giovanni F.; Pan, Yong-Le; Chang, Richard K.

2011-05-01

475

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

PubMed Central

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

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

2013-01-01

476

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

NASA Astrophysics Data System (ADS)

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

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

2009-05-01

477

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

PubMed

Processing noisy signals using the ideal binary mask improves automatic speech