WorldWideScience

Sample records for net pattern recognition

  1. Beyond Pattern Recognition With Neural Nets

    Science.gov (United States)

    Arsenault, Henri H.; Macukow, Bohdan

    1989-02-01

    Neural networks are finding many areas of application. Although they are particularly well-suited for applications related to associative recall such as content-addressable memories, neural nets can perform many other applications ranging from logic operations to the solution of optimization problems. The training of a recently introduced model to perform boolean logical operations such as XOR is described. Such simple systems can be combined to perform any complex boolean operation. Any complex task consisting of parallel and serial operations including fuzzy logic that can be described in terms of input-output relations can be accomplished by combining modules such as the ones described here. The fact that some modules can carry out their functions even when their inputs contain erroneous data, and the fact that each module can carry out its functions in parallel with itself and other modules promises some interesting applications.

  2. Pattern recognition

    CERN Document Server

    Theodoridis, Sergios

    2003-01-01

    Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

  3. Computer-aided star pattern recognition with astrometry.net: in-flight support of telescope operations on SOFIA

    Science.gov (United States)

    Schindler, Karsten; Lang, Dustin; Moore, Liz; Hümmer, Martin; Wolf, Jürgen; Krabbe, Alfred

    2016-08-01

    SOFIA is an airborne observatory, operating a gyroscopically stabilized telescope with an effective aperture of 2.5 m on-board a modified Boeing 747SP. Its primary objective is to conduct observations at mid- to far-infrared wavelengths. When SOFIA opens its door to the night sky, the initial telescope pointing is estimated from the aircraft's position and heading as well as the telescope's attitude relative to the aircraft. This initial pointing estimate needs to be corrected using stars that are manually identified in tracking camera images; telescope pointing also needs to be verified and refined at the beginning of each flight leg. We report about the implementation of the astrometry.net package on the telescope operator workstations on-board SOFIA. This package provides a very robust, reliable and fast algorithm for blind astrometric image calibration. Using images from SOFIA's Wide Field Imager, we are able to display an almost instant, continuous feedback of calculated right ascension, declination and field rotation in the GUI for the telescope operator. The computer-aided recognition of star patterns will support telescope pointing calibrations in the future, further increasing the efficiency of the observatory. We also discuss other current and future use cases of the astrometry.net package in the SOFIA project and at the German SOFIA Institute (DSI).

  4. Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

    Science.gov (United States)

    Baskaran, Subbiah; Ramachandran, Narayanan; Noever, David

    1998-01-01

    The use of probabilistic (PNN) and multilayer feed forward (MLFNN) neural networks are investigated for calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of networks are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this article.

  5. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  6. Statistical Pattern Recognition

    CERN Document Server

    Webb, Andrew R

    2011-01-01

    Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

  7. Pattern Recognition Control Design

    Science.gov (United States)

    Gambone, Elisabeth A.

    2018-01-01

    Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

  8. Pattern recognition in spectra

    Science.gov (United States)

    Gebran, M.; Paletou, F.

    2017-06-01

    We present a new automated procedure that simultaneously derives the effective temperature Teff, surface gravity log g, metallicity [Fe/H], and equatorial projected rotational velocity ve sin i for stars. The procedure is inspired by the well-known PCA-based inversion of spectropolarimetric full-Stokes solar data, which was used both for Zeeman and Hanle effects. The efficiency and accuracy of this procedure have been proven for FGK, A, and late type dwarf stars of K and M spectral types. Learning databases are generated from the Elodie stellar spectra library using observed spectra for which fundamental parameters were already evaluated or with synthetic data. The synthetic spectra are calculated using ATLAS9 model atmospheres. This technique helped us to detect many peculiar stars such as Am, Ap, HgMn, SiEuCr and binaries. This fast and efficient technique could be used every time a pattern recognition is needed. One important application is the understanding of the physical properties of planetary surfaces by comparing aboard instrument data to synthetic ones.

  9. Algebraic pattern recognition

    Science.gov (United States)

    Przybyłek, Michał R.

    2014-01-01

    This paper offers an algebraic explanation for the phenomenon of a new and prosperous branch of evolutionary metaheuristics - "skeletal algorithms". We show how this explanation gives rise to algorithms for recognition of algebraic theories and present sample applications.

  10. Pattern recognition and string matching

    CERN Document Server

    Cheng, Xiuzhen

    2002-01-01

    The research and development of pattern recognition have proven to be of importance in science, technology, and human activity. Many useful concepts and tools from different disciplines have been employed in pattern recognition. Among them is string matching, which receives much theoretical and practical attention. String matching is also an important topic in combinatorial optimization. This book is devoted to recent advances in pattern recognition and string matching. It consists of twenty eight chapters written by different authors, addressing a broad range of topics such as those from classifica­ tion, matching, mining, feature selection, and applications. Each chapter is self-contained, and presents either novel methodological approaches or applications of existing theories and techniques. The aim, intent, and motivation for publishing this book is to pro­ vide a reference tool for the increasing number of readers who depend upon pattern recognition or string matching in some way. This includes student...

  11. Pattern recognition receptors: an update.

    Science.gov (United States)

    Goutagny, Nadege; Fitzgerald, Katherine A

    2006-07-01

    The vertebrate immune system consists of two inter-related components, the innate and adaptive responses, which are required for the resolution of infection. The innate immune response is critical for the immediate protection from infection and for marshalling the B- and T-cell responses of the adaptive response. A key component of the innate immune response is germline-encoded pattern recognition receptors that detect pathogens. Several families of these pattern recognition receptors have now been described. Microbial recognition by these receptors triggers appropriate immune responses, including the direct uptake and killing of pathogens and/or initiation of intracellular signaling pathways that culminate in the activation of immune responsive transcriptional programs. Pattern recognition receptors include soluble receptors in serum (collectins), transmembrane receptors on cell surfaces or vacuolar membranes (C-type lectins and Toll-like receptors) or cytoplasmic sensors (NACHT-LRR proteins and RNA helicases). Roles for these pattern recognition receptor families are emerging in the susceptibility to bacterial and viral infections and in acute and chronic conditions, such as sepsis, autoimmune disease and atherosclerosis. These findings suggest that the selective targeting of pattern recognition receptors and the pathways they trigger may be useful clinically. Progress towards therapeutics designed to target Toll-like receptor signaling is already well underway. This review will describe our current understanding of innate immune sensors and the mechanisms regulating their activity.

  12. RICH pattern recognition for LHCb

    CERN Document Server

    Forty, Roger W

    1999-01-01

    An algorithm for performing pattern recognition in RICH detectors is described. It uses a maximum-likelihood technique, comparing the predicted and observed distributions of detected photoelectrons. The algorithm is able to handle regions of high track density, for which LHCb provides a good example. (5 refs).

  13. Data structures, computer graphics, and pattern recognition

    CERN Document Server

    Klinger, A; Kunii, T L

    1977-01-01

    Data Structures, Computer Graphics, and Pattern Recognition focuses on the computer graphics and pattern recognition applications of data structures methodology.This book presents design related principles and research aspects of the computer graphics, system design, data management, and pattern recognition tasks. The topics include the data structure design, concise structuring of geometric data for computer aided design, and data structures for pattern recognition algorithms. The survey of data structures for computer graphics systems, application of relational data structures in computer gr

  14. HANPP Collection: Global Patterns in Net Primary Productivity (NPP)

    Data.gov (United States)

    National Aeronautics and Space Administration — The Global Patterns in Net Primary Productivity (NPP) portion of the Human Appropriation of Net Primary Productivity (HANPP) Collection maps the net amount of solar...

  15. Pattern recognition in speech and language processing

    CERN Document Server

    Chou, Wu

    2003-01-01

    Minimum Classification Error (MSE) Approach in Pattern Recognition, Wu ChouMinimum Bayes-Risk Methods in Automatic Speech Recognition, Vaibhava Goel and William ByrneA Decision Theoretic Formulation for Adaptive and Robust Automatic Speech Recognition, Qiang HuoSpeech Pattern Recognition Using Neural Networks, Shigeru KatagiriLarge Vocabulary Speech Recognition Based on Statistical Methods, Jean-Luc GauvainToward Spontaneous Speech Recognition and Understanding, Sadaoki FuruiSpeaker Authentication, Qi Li and Biing-Hwang JuangHMMs for Language Processing Problems, Ri

  16. MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

    Directory of Open Access Journals (Sweden)

    Artur Popko

    2013-06-01

    Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

  17. Optical Pattern Recognition With Self-Amplification

    Science.gov (United States)

    Liu, Hua-Kuang

    1994-01-01

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

  18. Acoustic Pattern Recognition on Android Devices

    DEFF Research Database (Denmark)

    Møller, Maiken Bjerg; Gaarsdal, Jesper; Steen, Kim Arild

    2013-01-01

    an Android application developed for acoustic pattern recognition of bird species. The acoustic data is recorded using a built-in microphone, and pattern recognition is performed on the device, requiring no network connection. The algorithm is implemented in C++ as a native Android module and the OpenCV...

  19. Introduction to pattern recognition a MATLAB approach

    CERN Document Server

    Theodoridis, Sergios; Koutroumbas, Konstantinos; Cavouras, Dionisis

    2010-01-01

    An accompanying manual to Theodoridis/Koutroumbas, Pattern Recognition, that includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. *Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition 4e.*Solved examples in Matlab, including real-life data sets in imaging and audio recognition*Available separately or at a special package price with the mai

  20. HANPP Collection: Global Patterns in Net Primary Productivity (NPP)

    Data.gov (United States)

    National Aeronautics and Space Administration — The Global Patterns in Net Primary Productivity (NPP) portion of the HANPP Collection maps the net amount of solar energy converted to plant organic matter through...

  1. Pattern recognition and simulation in ecology

    Directory of Open Access Journals (Sweden)

    Xiaozhuo Han

    2015-12-01

    Full Text Available In ecology, the patterns usually refer to all kinds of nonrandom spatial and temporal structures of ecosystems driving by multiple ecological processes. Pattern recognition is an important step to reveal the complicated relationship between ecological patterns and processes. To review and present some advances about ecological modeling, patterns recognition, and computer simulation, an international workshop on Mathematical and Numerical Ecology with the theme "Pattern recognition and simulation in ecology" was held in in October 2014 in Guangzhou, China, and the International Society of Computational Ecology was the co-sponsor. Eight peer-reviewed papers those were originally presented at this workshop covering three themes: patterns in phylogeny, patterns in communities and ecosystems, and spatial pattern analysis are included in this special issue.

  2. Pattern recognition and classification an introduction

    CERN Document Server

    Dougherty, Geoff

    2012-01-01

    The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer visi

  3. Pattern Activation/Recognition Theory of Mind

    Directory of Open Access Journals (Sweden)

    Bertrand edu Castel

    2015-07-01

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

  4. Pattern activation/recognition theory of mind.

    Science.gov (United States)

    du Castel, Bertrand

    2015-01-01

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

  5. Pattern activation/recognition theory of mind

    Science.gov (United States)

    du Castel, Bertrand

    2015-01-01

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

  6. Distortion invariant pattern recognition with phase filters

    Science.gov (United States)

    Rosen, Joseph; Shamir, Joseph

    1987-01-01

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

  7. Visual cluster analysis and pattern recognition methods

    Science.gov (United States)

    Osbourn, Gordon Cecil; Martinez, Rubel Francisco

    2001-01-01

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

  8. Pattern recognition using linguistic fuzzy logic predictors

    Science.gov (United States)

    Habiballa, Hashim

    2016-06-01

    The problem of pattern recognition has been solved with numerous methods in the Artificial Intelligence field. We present an unconventional method based on Lingustic Fuzzy Logic Forecaster which is primarily used for the task of time series analysis and prediction through logical deduction wtih linguistic variables. This method should be used not only to the time series prediction itself, but also for recognition of patterns in a signal with seasonal component.

  9. An Implementation of Nested Pattern Matching in Interaction Nets

    Directory of Open Access Journals (Sweden)

    Abubakar Hassan

    2010-03-01

    Full Text Available Reduction rules in interaction nets are constrained to pattern match exactly one argument at a time. Consequently, a programmer has to introduce auxiliary rules to perform more sophisticated matches. In this paper, we describe the design and implementation of a system for interaction nets which allows nested pattern matching on interaction rules. We achieve a system that provides convenient ways to express interaction net programs without defining auxiliary rules.

  10. Fast Radioactive Nuclide Recognition Method Study Based on Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Yonggang Huo

    2014-01-01

    Full Text Available Based on pattern recognition method, applied the nuclear radiation digital measurement and analysis system platform, through synthetically making use of the radioactive nuclide’s ray information, selected radiation characteristic information of the radioactive nuclide, established the characteristic arrays database of radioactive nuclides, the recognition method is designed and applied to the identification of radionuclide radiation while using middle or low-resolution detector in this paper. Verified by experiments, when the count value of the traditional low-resolution spectrometer system is not reach single full energy peak’s statistical lower limit value, the three kinds of mixed radioactive nuclides’ true discrimination rate reached more than 90 % in the digital measurement and analysis system using fast radionuclide recognition method. The results show that this method is obviously superior to the traditional methods, and effectively improve the rapid identification ability to radioactive nuclide.

  11. 2nd International Conference on Pattern Recognition

    CERN Document Server

    Marsico, Maria

    2015-01-01

    This book contains the extended and revised versions of a set of selected papers from the 2nd International Conference on Pattern Recognition (ICPRAM 2013), held in Barcelona, Spain, from 15 to 18 February, 2013. ICPRAM was organized by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and was held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI). The hallmark of this conference was to encourage theory and practice to meet in a single venue. The focus of the book is on contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods.

  12. Associative Pattern Recognition In Analog VLSI Circuits

    Science.gov (United States)

    Tawel, Raoul

    1995-01-01

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

  13. Grip-pattern recognition for smart guns

    NARCIS (Netherlands)

    Kauffman, J.A.; Bazen, A.M.; Gerez, Sabih H.; Veldhuis, Raymond N.J.

    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

  14. DNA pattern recognition using canonical correlation algorithm

    Indian Academy of Sciences (India)

    Home; Journals; Journal of Biosciences; Volume 40; Issue 4. DNA pattern recognition using canonical correlation algorithm ... As a case study, the potential of CCA was demonstrated on the sequence found from HIV-1 preferred integration sites. The subsequences on the left and right flanking from the integration site were ...

  15. Multiple degree of freedom optical pattern recognition

    Science.gov (United States)

    Casasent, D.

    1987-01-01

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

  16. Conformal Predictions in Multimedia Pattern Recognition

    Science.gov (United States)

    Nallure Balasubramanian, Vineeth

    2010-01-01

    The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…

  17. Pattern Recognition by Retina-Like Devices.

    Science.gov (United States)

    Weiman, Carl F. R.; Rothstein, Jerome

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

  18. Pattern Recognition in Time Series

    Science.gov (United States)

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

    2012-03-01

    , 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.

  19. Explicit pattern recognition models for speech perception

    Science.gov (United States)

    Nearey, Terrance M.

    2003-10-01

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

  20. Applications of chaotic neurodynamics in pattern recognition

    Science.gov (United States)

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

    1991-08-01

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

  1. Pattern recognition monitoring of PEM fuel cell

    Science.gov (United States)

    Meltser, Mark Alexander

    1999-01-01

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

  2. Granular neural networks, pattern recognition and bioinformatics

    CERN Document Server

    Pal, Sankar K; Ganivada, Avatharam

    2017-01-01

    This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinf...

  3. Pattern Recognition by Hierarchical Temporal Memory

    OpenAIRE

    Maltoni, Prof. Davide

    2011-01-01

    Hierarchical Temporal Memory (HTM) is still largely unknown by the pattern recognition community and only a few studies have been published in the scientific literature. This paper reviews HTM architecture and related learning algorithms by using formal notation and pseudocode description. Novel approaches are then proposed to encode coincidence-group membership (fuzzy grouping) and to derive temporal groups (maxstab temporal clustering). Systematic experiments on three line-drawing datasets ...

  4. VLSI Microsystem for Rapid Bioinformatic Pattern Recognition

    Science.gov (United States)

    Fang, Wai-Chi; Lue, Jaw-Chyng

    2009-01-01

    A system comprising very-large-scale integrated (VLSI) circuits is being developed as a means of bioinformatics-oriented analysis and recognition of patterns of fluorescence generated in a microarray in an advanced, highly miniaturized, portable genetic-expression-assay instrument. Such an instrument implements an on-chip combination of polymerase chain reactions and electrochemical transduction for amplification and detection of deoxyribonucleic acid (DNA).

  5. Pattern recognition of neurotransmitters using multimode sensing.

    Science.gov (United States)

    Stefan-van Staden, Raluca-Ioana; Moldoveanu, Iuliana; van Staden, Jacobus Frederick

    2014-05-30

    Pattern recognition is essential in chemical analysis of biological fluids. Reliable and sensitive methods for neurotransmitters analysis are needed. Therefore, we developed for pattern recognition of neurotransmitters: dopamine, epinephrine, norepinephrine a method based on multimode sensing. Multimode sensing was performed using microsensors based on diamond paste modified with 5,10,15,20-tetraphenyl-21H,23H-porphyrine, hemin and protoporphyrin IX in stochastic and differential pulse voltammetry modes. Optimized working conditions: phosphate buffer solution of pH 3.01 and KCl 0.1mol/L (as electrolyte support), were determined using cyclic voltammetry and used in all measurements. The lowest limits of quantification were: 10(-10)mol/L for dopamine and epinephrine, and 10(-11)mol/L for norepinephrine. The multimode microsensors were selective over ascorbic and uric acids and the method facilitated reliable assay of neurotransmitters in urine samples, and therefore, the pattern recognition showed high reliability (RSDneurotransmitters on biological fluids at a lower determination level than chromatographic methods. The sampling of the biological fluids referees only to the buffering (1:1, v/v) with a phosphate buffer pH 3.01, while for chromatographic methods the sampling is laborious. Accordingly with the statistic evaluation of the results at 99.00% confidence level, both modes can be used for pattern recognition and quantification of neurotransmitters with high reliability. The best multimode microsensor was the one based on diamond paste modified with protoporphyrin IX. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Weighted Local Active Pixel Pattern (WLAPP for Face Recognition in Parallel Computation Environment

    Directory of Open Access Journals (Sweden)

    Gundavarapu Mallikarjuna Rao

    2013-10-01

    Full Text Available Abstract  - The availability of multi-core technology resulted totally new computational era. Researchers are keen to explore available potential in state of art-machines for breaking the bearer imposed by serial computation. Face Recognition is one of the challenging applications on so ever computational environment. The main difficulty of traditional Face Recognition algorithms is lack of the scalability. In this paper Weighted Local Active Pixel Pattern (WLAPP, a new scalable Face Recognition Algorithm suitable for parallel environment is proposed.  Local Active Pixel Pattern (LAPP is found to be simple and computational inexpensive compare to Local Binary Patterns (LBP. WLAPP is developed based on concept of LAPP. The experimentation is performed on FG-Net Aging Database with deliberately introduced 20% distortion and the results are encouraging. Keywords — Active pixels, Face Recognition, Local Binary Pattern (LBP, Local Active Pixel Pattern (LAPP, Pattern computing, parallel workers, template, weight computation.  

  7. Automatic fishing net detection and recognition based on optical gated viewing for underwater obstacle avoidance

    Science.gov (United States)

    Liu, Xiaoquan; Wang, Xinwei; Ren, Pengdao; Cao, Yinan; Zhou, Yan; Liu, Yuliang

    2017-08-01

    An automatic fishing net detection and recognition method for underwater obstacle avoidance is proposed. In the method, optical gated viewing technology is utilized to obtain high-resolution fishing net images and extend detection distance by suppressing water backscattering and background noise. The fishing net recognition is based on the proposed histograms of slope lines (HSLs) descriptors plus a support vector machine classifier. The extraction of HSL descriptors includes five steps of contrast-limited adaptive histogram equalization, the Gaussian low-pass filtering, the Canny detection, the Hough transform, and weighted vote. In the proof experiments, the detection distance of the fishing net reaches 5.7 attenuation length and the recognition accuracy reaches 93.79%.

  8. Sequential File Programming Patterns and Performance with .NET

    OpenAIRE

    Kukol, Peter; Gray, Jim

    2005-01-01

    Programming patterns for sequential file access in the .NET Framework are described and the performance is measured. The default behavior provides excellent performance on a single disk - 50 MBps both reading and writing. Using large request sizes and doing file pre-allocation when possible have quantifiable benefits. When one considers disk arrays, .NET unbuffered IO delivers 800 MBps on a 16-disk array, but buffered IO delivers about 12% of that performance. Consequently, high-performance f...

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

    Science.gov (United States)

    Chao, Tien-Hsin

    1991-01-01

    The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

  10. Developing Signal-Pattern-Recognition Programs

    Science.gov (United States)

    Shelton, Robert O.; Hammen, David

    2006-01-01

    Pattern Interpretation and Recognition Application Toolkit Environment (PIRATE) is a block-oriented software system that aids the development of application programs that analyze signals in real time in order to recognize signal patterns that are indicative of conditions or events of interest. PIRATE was originally intended for use in writing application programs to recognize patterns in space-shuttle telemetry signals received at Johnson Space Center's Mission Control Center: application programs were sought to (1) monitor electric currents on shuttle ac power busses to recognize activations of specific power-consuming devices, (2) monitor various pressures and infer the states of affected systems by applying a Kalman filter to the pressure signals, (3) determine fuel-leak rates from sensor data, (4) detect faults in gyroscopes through analysis of system measurements in the frequency domain, and (5) determine drift rates in inertial measurement units by regressing measurements against time. PIRATE can also be used to develop signal-pattern-recognition software for different purposes -- for example, to monitor and control manufacturing processes.

  11. Artificial intelligence tools for pattern recognition

    Science.gov (United States)

    Acevedo, Elena; Acevedo, Antonio; Felipe, Federico; Avilés, Pedro

    2017-06-01

    In this work, we present a system for pattern recognition that combines the power of genetic algorithms for solving problems and the efficiency of the morphological associative memories. We use a set of 48 tire prints divided into 8 brands of tires. The images have dimensions of 200 x 200 pixels. We applied Hough transform to obtain lines as main features. The number of lines obtained is 449. The genetic algorithm reduces the number of features to ten suitable lines that give thus the 100% of recognition. Morphological associative memories were used as evaluation function. The selection algorithms were Tournament and Roulette wheel. For reproduction, we applied one-point, two-point and uniform crossover.

  12. Feature selection for data and pattern recognition

    CERN Document Server

    Jain, Lakhmi

    2015-01-01

    This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.

  13. Phase Transitions in Quantum Pattern Recognition

    CERN Document Server

    Trugenberger, Carlo Andrea

    2002-01-01

    With the help of quantum mechanics one can formulate a model of associative memory with optimal storage capacity. I generalize this model by introducing a parameter playing the role of an effective temperature. The corresponding thermodynamics provides criteria to tune the efficiency of quantum pattern recognition. I show that the associative memory undergoes a phase transition from a disordered high-temperature phase with no correlation between input and output to an ordered, low-temperature phase with minimal input-output Hamming distance.

  14. Similarity-based pattern analysis and recognition

    CERN Document Server

    Pelillo, Marcello

    2013-01-01

    This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models. Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification alg

  15. Success potential of automated star pattern recognition

    Science.gov (United States)

    Van Bezooijen, R. W. H.

    1986-01-01

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

  16. Inverse Scattering Approach to Improving Pattern Recognition

    Energy Technology Data Exchange (ETDEWEB)

    Chapline, G; Fu, C

    2005-02-15

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

  17. Inverse scattering approach to improving pattern recognition

    Science.gov (United States)

    Chapline, George; Fu, Chi-Yung

    2005-05-01

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

  18. Pattern recognition receptors in microbial keratitis

    Science.gov (United States)

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

    2015-01-01

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

  19. Intrusion detection using pattern recognition methods

    Science.gov (United States)

    Jiang, Nan; Yu, Li

    2007-09-01

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

  20. Supervised adaptive Hamming net for classification of multiple-valued patterns.

    Science.gov (United States)

    Hung, C A; Lin, S F

    1997-04-01

    A Supervised Adaptive Hamming Net (SAHN) is introduced for incremental learning of recognition categories in response to arbitrary sequences of multiple-valued or binary-valued input patterns. The binary-valued SAHN derived from the Adaptive Hamming Net (AHN) is functionally equivalent to a simplified ARTMAP, which is specifically designed to establish many-to-one mappings. The generalization to learning multiple-valued input patterns is achieved by incorporating multiple-valued logic into the AHN. In this paper, we examine some useful properties of learning in a P-valued SAHN. In particular, an upper bound is derived on the number of epochs required by the P-valued SAHN to learn a list of input-output pairs that is repeatedly presented to the architecture. Furthermore, we connect the P-valued SAHN with the binary-valued SAHN via the thermometer code.

  1. Pattern-Recognition Processor Using Holographic Photopolymer

    Science.gov (United States)

    Chao, Tien-Hsin; Cammack, Kevin

    2006-01-01

    proposed joint-transform optical correlator (JTOC) would be capable of operating as a real-time pattern-recognition processor. The key correlation-filter reading/writing medium of this JTOC would be an updateable holographic photopolymer. The high-resolution, high-speed characteristics of this photopolymer would enable pattern-recognition processing to occur at a speed three orders of magnitude greater than that of state-of-the-art digital pattern-recognition processors. There are many potential applications in biometric personal identification (e.g., using images of fingerprints and faces) and nondestructive industrial inspection. In order to appreciate the advantages of the proposed JTOC, it is necessary to understand the principle of operation of a conventional JTOC. In a conventional JTOC (shown in the upper part of the figure), a collimated laser beam passes through two side-by-side spatial light modulators (SLMs). One SLM displays a real-time input image to be recognized. The other SLM displays a reference image from a digital memory. A Fourier-transform lens is placed at its focal distance from the SLM plane, and a charge-coupled device (CCD) image detector is placed at the back focal plane of the lens for use as a square-law recorder. Processing takes place in two stages. In the first stage, the CCD records the interference pattern between the Fourier transforms of the input and reference images, and the pattern is then digitized and saved in a buffer memory. In the second stage, the reference SLM is turned off and the interference pattern is fed back to the input SLM. The interference pattern thus becomes Fourier-transformed, yielding at the CCD an image representing the joint-transform correlation between the input and reference images. This image contains a sharp correlation peak when the input and reference images are matched. The drawbacks of a conventional JTOC are the following: The CCD has low spatial resolution and is not an ideal square

  2. Targeting pattern recognition receptors in cancer immunotherapy.

    Science.gov (United States)

    Goutagny, Nadège; Estornes, Yann; Hasan, Uzma; Lebecque, Serge; Caux, Christophe

    2012-03-01

    Pattern recognition receptors (PRRs) are known for many years for their role in the recognition of microbial products and the subsequent activation of the immune system. The 2011 Nobel Prize for medicine indeed rewarded J. Hoffmann/B. Beutler and R. Steinman for their revolutionary findings concerning the activation of the immune system, thus stressing the significance of understanding the mechanisms of activation of the innate immunity. Such immunostimulatory activities are of major interest in the context of cancer to induce long-term antitumoral responses. Ligands for the toll-like receptors (TLRs), a well-known family of PRR, have been shown to have antitumoral activities in several cancers. Those ligands are now undergoing extensive clinical investigations both as immunostimulant molecules and as adjuvant along with vaccines. However, when considering the use of these ligands in tumor therapy, one shall consider the potential effect on the tumor cells themselves as well as on the entire organism. Recent data indeed demonstrate that TLR activation in tumor cells could trigger both pro- or antitumoral effect depending on the context. This review discusses this balance between the intrinsic activation of PRR in tumor cells and the extrinsic microenvironment activation in term of overall effect of PRR ligands on tumor development. We review recent advances in the field and underline appealing prospects for clinical development of PRR agonists in the light of our current knowledge on their expression and activation.

  3. Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition.

    Science.gov (United States)

    Wang, Zhe; Wang, Limin; Wang, Yali; Zhang, Bowen; Qiao, Yu

    2017-02-09

    Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods for image recognition. In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition. To this end, we make three main contributions from the following aspects. First, we propose a patch-level and end-to-end architecture to model the appearance of local patches, called PatchNet. PatchNet is essentially a customized network trained in a weakly supervised manner, which uses the image-level supervision to guide the patch-level feature extraction. Second, we present a hybrid visual representation, called VSAD, by utilizing the robust feature representations of PatchNet to describe local patches and exploiting the semantic probabilities of PatchNet to aggregate these local patches into a global representation. Third, based on the proposed VSAD representation, we propose a new state-of-the-art scene recognition approach, which achieves an excellent performance on two standard benchmarks: MIT Indoor67 (86.2%) and SUN397 (73.0%).

  4. Pattern recognition: recent insights from Dectin-1.

    Science.gov (United States)

    Reid, Delyth M; Gow, Neil A R; Brown, Gordon D

    2009-02-01

    The beta-glucan receptor Dectin-1 is an archetypical non-toll-like pattern recognition receptor expressed predominantly by myeloid cells, which can induce its own intracellular signalling and can mediate a variety of cellular responses, such as cytokine production. Recent identification of the components of these signalling pathways, such as Syk kinase, CARD9 and Raf-1, has provided novel insights into the molecular mechanisms underlying Dectin-1 function. Furthermore, a broader appreciation of the cellular responses mediated by this receptor and the effects of interactions with other receptors, including the TLRs, have greatly furthered our understanding of innate immunity and how this drives the development of adaptive immunity, particularly Th17 responses. Recent studies have highlighted the importance of Dectin-1 in anti-fungal immunity, in both mice and humans, and have suggested a possible involvement of this receptor in the control of mycobacterial infections.

  5. Pattern recognition receptors in HIV transmission

    Directory of Open Access Journals (Sweden)

    Teunis B. Geijtenbeek

    2012-03-01

    Full Text Available Dendritic cells (DCs, Langerhans cells (LCs and macrophages are innate immune cells that reside in genital and intestinal mucosal tissues susceptible to HIV-1 infection. These innate cells play distinct roles in initiation of HIV-1 infection and induction of anti-viral immunity. DCs are potent migratory cells that capture HIV-1 and transfer virus to CD4+ T cells in the lymph nodes, whereas LCs have a protective anti-viral function, and macrophages function as viral reservoirs since they produce viruses over prolonged times. These differences are due to the different immune functions of these cells partly dependent on the expression of specific pattern recognition receptors. Expression of Toll-like receptors, C-type lectin receptors and cell-specific machinery for antigen uptake and processing strongly influence the outcome of virus interactions.

  6. NIRExpNet: Three-Stream 3D Convolutional Neural Network for Near Infrared Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Zhan Wu

    2017-11-01

    Full Text Available Facial expression recognition (FER under active near-infrared (NIR illumination has the advantages of illumination invariance. In this paper, we propose a three-stream 3D convolutional neural network, named as NIRExpNet for NIR FER. The 3D structure of NIRExpNet makes it possible to extract automatically, not just spatial features, but also, temporal features. The design of multiple streams of the NIRExpNet enables it to fuse local and global facial expression features. To avoid over-fitting, the NIRExpNet has a moderate size to suit the Oulu-CASIA NIR facial expression database that is a medium-size database. Experimental results show that the proposed NIRExpNet outperforms some previous state-of-art methods, such as Histogram of Oriented Gradient to 3D (HOG 3D, Local binary patterns from three orthogonal planes (LBP-TOP, deep temporal appearance-geometry network (DTAGN, and adapt 3D Convolutional Neural Networks (3D CNN DAP.

  7. Pattern recognition with simple oscillating circuits

    Science.gov (United States)

    Hölzel, R. W.; Krischer, K.

    2011-07-01

    Neural network devices that inherently possess parallel computing capabilities are generally difficult to construct because of the large number of neuron-neuron connections. However, there exists a theoretical approach (Hoppensteadt and Izhikevich 1999 Phys. Rev. Lett. 82 2983) that forgoes the individual connections and uses only a global coupling: systems of weakly coupled oscillators with a time-dependent global coupling are capable of performing pattern recognition in an associative manner similar to Hopfield networks. The information is stored in the phase shifts of the individual oscillators. However, to date, even the feasibility of controlling phase shifts with this kind of coupling has not yet been established experimentally. We present an experimental realization of this neural network device. It consists of eight sinusoidal electrical van der Pol oscillators that are globally coupled through a variable resistor with the electric potential as the coupling variable. We estimate an effective value of the phase coupling strength in our experiment. For that, we derive a general approach that allows one to compare different experimental realizations with each other as well as with phase equation models. We demonstrate that individual phase shifts of oscillators can be experimentally controlled by a weak global coupling. Furthermore, supplied with a distorted input image, the oscillating network can indeed recognize the correct image out of a set of predefined patterns. It can therefore be used as the processing unit of an associative memory device.

  8. Spectral feature classification and spatial pattern recognition

    Science.gov (United States)

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

    1979-01-01

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

  9. LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

    OpenAIRE

    Hoi, Steven C. H.; Wu, Xiongwei; Liu, Hantang; Wu, Yue; Wang, Huiqiong; Xue, Hui; Wu, Qiang

    2015-01-01

    Logo detection from images has many applications, particularly for brand recognition and intellectual property protection. Most existing studies for logo recognition and detection are based on small-scale datasets which are not comprehensive enough when exploring emerging deep learning techniques. In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images. To facilitate research, LOGO-Net has two datasets: ...

  10. Online and Offline Pattern Recognition in PANDA

    Science.gov (United States)

    Boca, Gianluigi

    2016-11-01

    PANDA is one of the four experiments that will run at the new facility FAIR that is being built in Darmstadt, Germany. It is a fixed target experiment: a beam of antiprotons collides on a jet proton target (the maximum center of mass energy is 5.46 GeV). The interaction rate at the startup will be 2MHz with the goal of reaching 20MHz at full luminosity. The beam of antiprotons will be essentially continuous. PANDA will have NO hardware trigger but only a software trigger, to allow for maximum flexibility in the physics program. All those characteristics are severe challenges for the reconstruction code that 1) must be fast, since it has to be validated up to 20MHz interaction rate; 2) must be able to reject fake tracks caused by the remnant hits, belonging to previous or later events in some slow detectors, for example the straw tubes in the central region. The Pattern Recognition (PR) of PANDA will have to run both online to achieve a first fast selection, and offline, at lower rate, for a more refined selection. In PANDA the PR code is continuously evolving; this contribution shows the present status. I will give an overview of three examples of PR following different strategies and/or implemented on different hardware (FPGA, GPUs, CPUs) and, when available, I will report the performances.

  11. Pattern-recognition receptors in human eosinophils.

    Science.gov (United States)

    Kvarnhammar, Anne Månsson; Cardell, Lars Olaf

    2012-05-01

    The pattern-recognition receptor (PRR) family includes Toll-like receptors (TLRs), nucleotide-binding oligomerization domain (NOD) -like receptors (NLRs), RIG-I-like receptors (RLRs), C-type lectin receptors (CLRs) and the receptor for advanced glycation end products (RAGE). They recognize various microbial signatures or host-derived danger signals and trigger an immune response. Eosinophils are multifunctional leucocytes involved in the pathogenesis of several inflammatory processes, including parasitic helminth infection, allergic diseases, tissue injury and tumour immunity. Human eosinophils express several PRRs, including TLR1-5, TLR7, TLR9, NOD1, NOD2, Dectin-1 and RAGE. Receptor stimulation induces survival, oxidative burst, activation of the adhesion system and release of cytokines (interleukin-1β, interleukin-6, tumour necrosis factor-α and granulocyte-macrophage colony-stimulating factor), chemokines (interleukin-8 and growth-related oncogene-α) 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. © 2012 The Authors. Immunology © 2012 Blackwell Publishing Ltd.

  12. Online and Offline Pattern Recognition in PANDA

    Directory of Open Access Journals (Sweden)

    Boca Gianluigi

    2016-01-01

    Full Text Available PANDA is one of the four experiments that will run at the new facility FAIR that is being built in Darmstadt, Germany. It is a fixed target experiment: a beam of antiprotons collides on a jet proton target (the maximum center of mass energy is 5.46 GeV. The interaction rate at the startup will be 2MHz with the goal of reaching 20MHz at full luminosity. The beam of antiprotons will be essentially continuous. PANDA will have NO hardware trigger but only a software trigger, to allow for maximum flexibility in the physics program. All those characteristics are severe challenges for the reconstruction code that 1 must be fast, since it has to be validated up to 20MHz interaction rate; 2 must be able to reject fake tracks caused by the remnant hits, belonging to previous or later events in some slow detectors, for example the straw tubes in the central region. The Pattern Recognition (PR of PANDA will have to run both online to achieve a first fast selection, and offline, at lower rate, for a more refined selection. In PANDA the PR code is continuously evolving; this contribution shows the present status. I will give an overview of three examples of PR following different strategies and/or implemented on different hardware (FPGA, GPUs, CPUs and, when available, I will report the performances.

  13. Image pattern recognition supporting interactive analysis and graphical visualization

    Science.gov (United States)

    Coggins, James M.

    1992-01-01

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

  14. Pattern recognition receptors and gastric cancer

    Directory of Open Access Journals (Sweden)

    Natalia eCastaño-Rodriguez

    2014-07-01

    Full Text Available Chronic inflammation has been associated with an increased risk of several human malignancies, a classic example being gastric cancer (GC. Development of GC is known to result from infection of the gastric mucosa by Helicobacter pylori, which initially induces acute inflammation and, in a subset of patients, progresses over time to chronic inflammation, gastric atrophy, intestinal metaplasia, dysplasia and finally intestinal-type GC.Germ-line encoded receptors known as pattern-recognition receptors (PRRs are critical for generating mature pro-inflammatory cytokines that are crucial for both Th1 and Th2 responses. Given that H. pylori is initially targeted by PRRs, it is conceivable that dysfunction within genes of this arm of the immune system could modulate the host response against H. pylori infection, and subsequently influence the emergence of GC.Current evidence suggests that Toll-like receptors (TLRs (TLR2, TLR3, TLR4, TLR5 and TLR9, nucleotide-binding oligomerization domain (NOD-like receptors (NLRs (NOD1, NOD2 and NLRP3, a C-type lectin receptor (DC-SIGN and retinoic acid-inducible gene (RIG-I-like receptors (RIG-I and MDA5, are involved in both the recognition of H. pylori and gastric carcinogenesis. In addition, polymorphisms in genes involved in the TLR (TLR1, TLR2, TLR4, TLR5, TLR9 and CD14 and NLR (NOD1, NOD2, NLRP3, NLRP12, NLRX1, CASP1, ASC and CARD8 signalling pathways have been shown to modulate the risk of H. pylori infection, gastric precancerous lesions and/or GC. Further, the modulation of PRRs has been suggested to suppress H. pylori-induced inflammation and enhance GC cell apoptosis, highlighting their potential relevance in GC therapeutics. In this review, we present current advances in our understanding of the role of the TLR and NLR signalling pathways in the pathogenesis of GC, address the involvement of other recently identified PRRs in GC, and discuss the potential implications of PRRs in GC immunotherapy.

  15. Searching for Pulsars Using Image Pattern Recognition

    Science.gov (United States)

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

    2014-02-01

    In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ~9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The

  16. Searching for pulsars using image pattern recognition

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-02-01

    In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ∼9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The

  17. Application Of t-Cherry Junction Trees in Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Edith Kovacs

    2010-06-01

    Full Text Available Pattern recognition aims to classify data (patterns based ei-
    ther on a priori knowledge or on statistical information extracted from the data. In this paper we will concentrate on statistical pattern recognition using a new probabilistic approach which makes possible to select the so called 'informative' features. We develop a pattern recognition algorithm which is based on the conditional independence structure underlying the statistical data. Our method was succesfully applied on a real problem of recognizing Parkinson's disease on the basis of voice disorders.

  18. Classification complexity in myoelectric pattern recognition.

    Science.gov (United States)

    Nilsson, Niclas; Håkansson, Bo; Ortiz-Catalan, Max

    2017-07-10

    Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject's intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition. CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback-Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec. NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers' sensitivity to such redundancy. This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance.

  19. Grasping Pattern Recognition and Grasping Force Estimation For Prosthetic Hands

    OpenAIRE

    Zhang Bing-Ke; Zhong Guo-Liang; Deng Hua

    2016-01-01

    Human’s movement can be decoded by surface electromyography (EMG), and the prosthetic hand can be controlled freely through EMG signal. This paper proposes a grasping pattern and force synchronized decoding method for prosthetic hands. Considering pattern recognition and force estimation simultaneously, this paper analyzes whether different muscle contraction levels affect pattern recognition and whether different grasping modes have impact on force estimation, then proposes two schemes to co...

  20. Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets.

    Science.gov (United States)

    Khan, A M; Lee, Y K; Kim, T S

    2008-01-01

    Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.

  1. NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

    Science.gov (United States)

    Mezgec, Simon; Koroušić Seljak, Barbara

    2017-06-27

    Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86 . 72 % , along with an accuracy of 94 . 47 % on a detection dataset containing 130 , 517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson's disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55 % , which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson's disease patients.

  2. NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment

    Science.gov (United States)

    Koroušić Seljak, Barbara

    2017-01-01

    Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86.72%, along with an accuracy of 94.47% on a detection dataset containing 130,517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson’s disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55%, which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson’s disease patients. PMID:28653995

  3. Pattern recognition in the satellite temperature retrieval problem

    Science.gov (United States)

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

    1985-01-01

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

  4. EMG Pattern Recognition based on Evidence Accumulation for Prosthesis Control

    Energy Technology Data Exchange (ETDEWEB)

    Lee, S.P. [Daewoo Electronics Co., Ltd., Seoul (Korea, Republic of); Park, S.H. [Yonsei University, Seoul (Korea, Republic of)

    1997-12-01

    We present a method of electromyography(EMG) pattern recognition to identify motion commands for the control of a prosthetic arm by evidence accumulation with multiple parameters. 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 the EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition. (author). 29 refs., 11 figs., 7 tabs.

  5. DNA sequence pattern recognition methods in GRAIL

    Energy Technology Data Exchange (ETDEWEB)

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

    1995-12-31

    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.

  6. Scalable pattern recognition algorithms applications in computational biology and bioinformatics

    CERN Document Server

    Maji, Pradipta

    2014-01-01

    Reviews the development of scalable pattern recognition algorithms for computational biology and bioinformatics Includes numerous examples and experimental results to support the theoretical concepts described Concludes each chapter with directions for future research and a comprehensive bibliography

  7. The Pandora software development kit for pattern recognition

    Energy Technology Data Exchange (ETDEWEB)

    Marshall, J.S.; Thomson, M.A. [University of Cambridge, Cavendish Laboratory, Cambridge (United Kingdom)

    2015-09-15

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

  8. The Pandora software development kit for pattern recognition

    Science.gov (United States)

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

    2015-09-01

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

  9. Pattern recognition in service of people with disabilities

    CSIR Research Space (South Africa)

    Coetzee, L

    2004-11-01

    Full Text Available This paper analyses a number of disabilities, their assistive devices and the associated technologies in order to highlight the role pattern recognition plays in enabling accessibility and improving human computer interaction for people living...

  10. Visual cluster analysis and pattern recognition template and methods

    Science.gov (United States)

    Osbourn, Gordon Cecil; Martinez, Rubel Francisco

    1999-01-01

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

  11. Photonic correlator pattern recognition: Application to autonomous docking

    Science.gov (United States)

    Sjolander, Gary W.

    1991-01-01

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

  12. Applications of evolutionary computation in image processing and pattern recognition

    CERN Document Server

    Cuevas, Erik; Perez-Cisneros, Marco

    2016-01-01

    This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an...

  13. Data analysis and pattern recognition in multiple databases

    CERN Document Server

    Adhikari, Animesh; Pedrycz, Witold

    2014-01-01

    Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyse them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery, and mining patterns of select items provide different...

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

    Science.gov (United States)

    Yevin, Igor; Koblyakov, Alexander

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

  15. Emotion recognition a pattern analysis approach

    CERN Document Server

    Konar, Amit

    2014-01-01

    Offers both foundations and advances on emotion recognition in a single volumeProvides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domainsInspires young researchers to prepare themselves for their own researchDemonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.

  16. Spatial patterns and cell surface clusters in perineuronal nets.

    Science.gov (United States)

    Arnst, Nikita; Kuznetsova, Svetlana; Lipachev, Nikita; Shaikhutdinov, Nurislam; Melnikova, Anastasiya; Mavlikeev, Mikhail; Uvarov, Pavel; Baltina, Tatyana V; Rauvala, Heikki; Osin, Yuriy N; Kiyasov, Andrey P; Paveliev, Mikhail

    2016-10-01

    Perineuronal nets (PNN) ensheath GABAergic and glutamatergic synapses on neuronal cell surface in the central nervous system (CNS), have neuroprotective effect in animal models of Alzheimer disease and regulate synaptic plasticity during development and regeneration. Crucial insights were obtained recently concerning molecular composition and physiological importance of PNN but the microstructure of the network remains largely unstudied. Here we used histochemistry, fluorescent microscopy and quantitative image analysis to study the PNN structure in adult mouse and rat neurons from layers IV and VI of the somatosensory cortex. Vast majority of meshes have quadrangle, pentagon or hexagon shape with mean mesh area of 1.29µm(2) in mouse and 1.44µm(2) in rat neurons. We demonstrate two distinct patterns of chondroitin sulfate distribution within a single mesh - with uniform (nonpolar) and node-enriched (polar) distribution of the Wisteria floribunda agglutinin-positive signal. Vertices of the node-enriched pattern match better with local maxima of chondroitin sulfate density as compared to the uniform pattern. PNN is organized into clusters of meshes with distinct morphologies on the neuronal cell surface. Our findings suggest the role for the PNN microstructure in the synaptic transduction and plasticity. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Type-2 fuzzy graphical models for pattern recognition

    CERN Document Server

    Zeng, Jia

    2015-01-01

    This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.

  18. Threats of Password Pattern Leakage Using Smartwatch Motion Recognition Sensors

    Directory of Open Access Journals (Sweden)

    Jihun Kim

    2017-06-01

    Full Text Available Thanks to the development of Internet of Things (IoT technologies, wearable markets have been growing rapidly. Smartwatches can be said to be the most representative product in wearable markets, and involve various hardware technologies in order to overcome the limitations of small hardware. Motion recognition sensors are a representative example of those hardware technologies. However, smartwatches and motion recognition sensors that can be worn by users may pose security threats of password pattern leakage. In the present paper, passwords are inferred through experiments to obtain password patterns inputted by users using motion recognition sensors, and verification of the results and the accuracy of the results is shown.

  19. Patterns recognition of electric brain activity using artificial neural networks

    Science.gov (United States)

    Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

    2017-04-01

    An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

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

    Energy Technology Data Exchange (ETDEWEB)

    Zheng, Yufeng

    2014-12-23

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

  1. HANPP Collection: Global Patterns in Human Appropriation of Net Primary Productivity (HANPP)

    Data.gov (United States)

    National Aeronautics and Space Administration — The Global Patterns in Human Appropriation of Net Primary Productivity (HANPP) portion of the HANPP Collection represents a digital map of human appropriation of net...

  2. Recognition of control chart patterns with incomplete samples

    Science.gov (United States)

    Miftah Abdelrahman Senoussi, Ahmed; Masood, Ibrahim; Nasrull Abdol Rahman, Mohd; Fahrul Hassan, Mohd

    2017-08-01

    In quality control, automated recognition of statistical process control (SPC) chart patterns is an effective technique for monitoring unnatural variation (UV) in manufacturing process. In most studies, focus was given on complete patterns by assuming there is no constrain in the SPC samples. Nevertheless, there is in-practice case whereby the SPC samples cannot be captured properly due to measurement sensor error or human error. Thus, this research aims to design a recognition scheme for incomplete samples pattern that will be useful for an industrial application. The design methodology involves three phases: (i) simulation of UV and SPC chart patterns, (ii) design of pattern recognition scheme, and (iii) evaluation of performance recognition. It involves modelling of the simulated SPC samples in bivariate quality control, raw data input representation, and recognizer training and testing. The proposed technique indicates a high recognition accuracy (normal pattern = 99.5%, shift patterns = 97.5%). This research will provide a new perspective in SPC charting scheme when dealing with constraint in terms of incomplete samples, which is greatly useful for an industrial practitioner in finding the solution for corrective action.

  3. Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

    Science.gov (United States)

    Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

    2017-12-01

    The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

    1993-04-01

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

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

    Science.gov (United States)

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

  6. Pattern Recognition as a Human Centered non-Euclidean Problem

    NARCIS (Netherlands)

    Duin, R.P.W.

    2010-01-01

    Regularities in the world are human defined. Patterns in the observed phenomena are there because we define and recognize them as such. Automatic pattern recognition tries to bridge the gap between human judgment and measurements made by artificial sensors. This is done in two steps: representation

  7. Grip-pattern recognition: Applied to a smart gun

    NARCIS (Netherlands)

    Shang, X.

    2008-01-01

    In our work 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

  8. Pattern Mining for Named Entity Recognition

    OpenAIRE

    Nouvel, Damien; Antoine, Jean-Yves; Friburger, Nathalie

    2014-01-01

    International audience; Many evaluation campaigns have shown that knowledge-based and data-driven approaches remain equally competitive for Named Entity Recognition. Our re-search team has developed CasEN, a symbolic system based on finite state tran-ducers, which achieved promising results during the Ester2 French-speaking eval-uation campaign. Despite these encouraging results, manually extending the cov-erage of such a hand-crafted system is a difficult task. In this paper, we present a no...

  9. Pattern Recognition of mtDNA with Associative Models

    Directory of Open Access Journals (Sweden)

    Acevedo María Elena

    2016-01-01

    Full Text Available In this paper we applied an associative memory for the pattern recognition of mtDNA that can be useful to identify bodies and human remains. In particular, we used both morphological hetroassociative memories: max and min. We process the problem of pattern recognition as a classification task. Our proposal showed a correct recall, we obtained the 100% of recalling of all the learned patterns. We simulated a corrupted sample of mtDNA by adding noise of two types: additive and subtractive. The memory showed a correct recall when we applied less or equal than 55% of both types of noise.

  10. Grasping Pattern Recognition and Grasping Force Estimation For Prosthetic Hands

    Directory of Open Access Journals (Sweden)

    Zhang Bing-Ke

    2016-01-01

    Full Text Available Human’s movement can be decoded by surface electromyography (EMG, and the prosthetic hand can be controlled freely through EMG signal. This paper proposes a grasping pattern and force synchronized decoding method for prosthetic hands. Considering pattern recognition and force estimation simultaneously, this paper analyzes whether different muscle contraction levels affect pattern recognition and whether different grasping modes have impact on force estimation, then proposes two schemes to complete EMG simultaneously decoding. Experiments compare the accuracy of the two methods. The results show that there is no much difference between two methods in force estimation, the former’s accuracy of pattern recognition is a little higher than the latter.

  11. Associative Pattern Recognition Through Macro-molecular Self-Assembly

    Science.gov (United States)

    Zhong, Weishun; Schwab, David J.; Murugan, Arvind

    2017-05-01

    We show that macro-molecular self-assembly can recognize and classify high-dimensional patterns in the concentrations of N distinct molecular species. Similar to associative neural networks, the recognition here leverages dynamical attractors to recognize and reconstruct partially corrupted patterns. Traditional parameters of pattern recognition theory, such as sparsity, fidelity, and capacity are related to physical parameters, such as nucleation barriers, interaction range, and non-equilibrium assembly forces. Notably, we find that self-assembly bears greater similarity to continuous attractor neural networks, such as place cell networks that store spatial memories, rather than discrete memory networks. This relationship suggests that features and trade-offs seen here are not tied to details of self-assembly or neural network models but are instead intrinsic to associative pattern recognition carried out through short-ranged interactions.

  12. Face Recognition Using Local Quantized Patterns and Gabor Filters

    Science.gov (United States)

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

    2015-05-01

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

  13. Self-amplified optical pattern-recognition technique

    Science.gov (United States)

    Liu, Hua-Kuang

    1992-01-01

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

  14. Subspace methods for pattern recognition in intelligent environment

    CERN Document Server

    Jain, Lakhmi

    2014-01-01

    This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

  15. Bifurcating optical pattern recognition in photorefractive crystals

    Science.gov (United States)

    Liu, Hua-Kuang

    1993-01-01

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

  16. The Relationship between Arithmetic and Reading Achievement and Visual Pattern Recognition in First Grade Children.

    Science.gov (United States)

    Bragman, Ruth; Hardy, Robert C.

    1982-01-01

    Results from testing 20 first graders in a remedial class in Maryland indicated that: same pattern recognition was significantly higher than reverse pattern recognition; identical pattern recognition did not affect performance on reading and arithmetic achievement; reverse pattern recognition significantly affected performance on reading and…

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

    Huntsberger, Terry

    2011-01-01

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

  19. Neural nets with varying topology for high-energy particle recognition: an outlook of computational dynamics

    Science.gov (United States)

    Perrone, Antonio L.; Messi, Roberto; Pasqualucci, Enrico; Basti, Gianfranco

    1993-09-01

    With respect to Rosenblatt linear perceptron, a classical limitation theorem demonstrated by M. Minsky and S. Papert is discussed. This theorem, '$PSIOne-in-a-box', ultimately concern the intrinsic limitations of parallel calculations in pattern calculations in pattern recognition problems. We demonstrate a possible solution of this limitation problem by substituting the static definition of characteristic functions and of their domains in the 'geometrical' perceptron, with their dynamic definition. This dynamics consists in the mutual redefinition of the characteristic function and of its domain depending on the matching with the input. We show an application of this 'dynamic' perceptron scheme in particle tracks recognition in high energy physics. Actually, this algorithm is being used for real time automatic triggering of ADONE e+e- storage ring (Frascati, Rome) to evaluate the neutron time-like electromagnetic form factor in the context of 'Fenice' collaboration by Italian Institute of Nuclear Physics (INFN).

  20. 2D DOST based local phase pattern for face recognition

    Science.gov (United States)

    Moniruzzaman, Md.; Alam, Mohammad S.

    2017-05-01

    A new two dimensional (2-D) Discrete Orthogonal Stcokwell Transform (DOST) based Local Phase Pattern (LPP) technique has been proposed for efficient face recognition. The proposed technique uses 2-D DOST as preliminary preprocessing and local phase pattern to form robust feature signature which can effectively accommodate various 3D facial distortions and illumination variations. The S-transform, is an extension of the ideas of the continuous wavelet transform (CWT), is also known for its local spectral phase properties in time-frequency representation (TFR). It provides a frequency dependent resolution of the time-frequency space and absolutely referenced local phase information while maintaining a direct relationship with the Fourier spectrum which is unique in TFR. After utilizing 2-D Stransform as the preprocessing and build local phase pattern from extracted phase information yield fast and efficient technique for face recognition. The proposed technique shows better correlation discrimination compared to alternate pattern recognition techniques such as wavelet or Gabor based face recognition. The performance of the proposed method has been tested using the Yale and extended Yale facial database under different environments such as illumination variation and 3D changes in facial expressions. Test results show that the proposed technique yields better performance compared to alternate time-frequency representation (TFR) based face recognition techniques.

  1. Pattern storage and recognition using ferrofluids

    Science.gov (United States)

    Ban, Shuai; Korenivski, V.

    2006-04-01

    An implementation of an associative memory based on a ferromagnetic nanocolloid is proposed. The design contains inductive input and output units for training the ferrofluid as well as sensors incorporated into the output units for performing recall. Using Monte Carlo simulations of the system we demonstrate the possibility of creating nanoparticle configurations that can serve to associate input/output pattern pairs.

  2. Diagnosis of Equipment Failures by Pattern Recognition

    DEFF Research Database (Denmark)

    Pau, L. F.

    1974-01-01

    The main problems in relation to automatic fault finding and diagnosis in equipments or production systems are discussed: 1) compression of the syndrome and observation spaces for better discrimination between failure modes; 2) simultaneous display of the failure patterns and the failure instants...

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

    Science.gov (United States)

    Liu, Hua-Kuang (Editor)

    1989-01-01

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

  4. Global Patterns in Human Consumption of Net Primary Production

    Science.gov (United States)

    Imhoff, Marc L.; Bounoua, Lahouari; Ricketts, Taylor; Loucks, Colby; Harriss, Robert; Lawrence William T.

    2004-01-01

    The human population and its consumption profoundly affect the Earth's ecosystems. A particularly compelling measure of humanity's cumulative impact is the fraction of the planet's net primary production that we appropriate for our Net primary production-the net amount of solar energy converted to plant organic matter through photosynthesis-can be measured in units of elemental carbon and represents the primary food energy source for the world's ecosystems. Human appropriation of net primary production, apart from leaving less for other species to use, alters the composition of the atmosphere, levels of biodiversity, flows within food webs and the provision of important primary production required by humans and compare it to the total amount generated on the landscape. We then derive a spatial ba!mce sheet of net primary production supply and demand for the world. We show that human appropriation of net primary production varies spatially from almost zero to many times the local primary production. These analyses reveal the uneven footprint of human consumption and related environmental impacts, indicate the degree to which human populations depend on net primary production "imports" and suggest policy options for slowing future growth of human appropriation of net primary production.

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

    Science.gov (United States)

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

    2008-05-06

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

  6. A Compact Prototype of an Optical Pattern Recognition System

    Science.gov (United States)

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

    1996-01-01

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

  7. Learned pattern recognition using synthetic-discriminant-functions

    Science.gov (United States)

    Jared, David A.; Ennis, David J.

    1986-01-01

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

  8. Application of pattern recognition techniques to crime analysis

    Energy Technology Data Exchange (ETDEWEB)

    Bender, C.F.; Cox, L.A. Jr.; Chappell, G.A.

    1976-08-15

    The initial goal was to evaluate the capabilities of current pattern recognition techniques when applied to existing computerized crime data. Performance was to be evaluated both in terms of the system's capability to predict crimes and to optimize police manpower allocation. A relation was sought to predict the crime's susceptibility to solution, based on knowledge of the crime type, location, time, etc. The preliminary results of this work are discussed. They indicate that automatic crime analysis involving pattern recognition techniques is feasible, and that efforts to determine optimum variables and techniques are warranted. 47 figures (RWR)

  9. TECA: Petascale pattern recognition for climate science

    Energy Technology Data Exchange (ETDEWEB)

    Prabhat, . [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Byna, Surendra [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Vishwanath, Venkatram [Argonne National Lab. (ANL), Argonne, IL (United States); Dart, Eli [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Wehner, Michael [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Collins, William D. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-08-26

    Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBM BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.

  10. Pattern recognition with parallel associative memory

    Science.gov (United States)

    Toth, Charles K.; Schenk, Toni

    1990-01-01

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

  11. Analog parallel processor hardware for high speed pattern recognition

    Science.gov (United States)

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

    1990-01-01

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

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

    Science.gov (United States)

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

    1984-01-01

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

  13. Two Challenges of Correct Validation in Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Thomas eNowotny

    2014-09-01

    Full Text Available Supervised pattern recognition is the process of mapping patterns to class labelsthat define their meaning. The core methods for pattern recognitionhave been developed by machine learning experts but due to their broadsuccess an increasing number of non-experts are now employing andrefining them. In this perspective I will discuss the challenge ofcorrect validation of supervised pattern recognition systems, in particular whenemployed by non-experts. To illustrate the problem I will give threeexamples of common errors that I have encountered in the lastyear. Much of this challenge can be addressed by strict procedure invalidation but there are remaining problems of correctlyinterpreting comparative work on exemplary data sets, which I willelucidate on the example of the well-used MNIST data set of handwrittendigits.

  14. Self-amplified optical pattern recognition system

    Science.gov (United States)

    Liu, Hua-Kuang (Inventor)

    1994-01-01

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

  15. Fuzzy Pattern Recognition Based on Symmetric Fuzzy Relative Entropy

    OpenAIRE

    Shi, Y F; He, L.H.; Chen, J.

    2009-01-01

    Based on fuzzy similarity degree, entropy, relative entropy and fuzzy entropy, the symmetric fuzzy relative entropy is presented, which not only has a full physical meaning, but also has succinct practicability. The symmetric fuzzy relative entropy can be used to measure the divergence between different fuzzy patterns. The example demonstrates that the symmetric fuzzy relative entropy is valid and reliable for fuzzy pattern recognition and classification, and its classification precision is v...

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

    Science.gov (United States)

    Amador, Jose J (Inventor)

    2007-01-01

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

  17. Speech Signal Analysis and Pattern Recognition in Diagnosis of Dysarthria

    OpenAIRE

    Thoppil, Minu George; Kumar, C. Santhosh; Kumar, Anand; Amose, John

    2017-01-01

    Background: Dysarthria refers to a group of disorders resulting from disturbances in muscular control over the speech mechanism due to damage of central or peripheral nervous system. There is wide subjective variability in assessment of dysarthria between different clinicians. In our study, we tried to identify a pattern among types of dysarthria by acoustic analysis and to prevent intersubject variability. Objectives: (1) Pattern recognition among types of dysarthria with software tool and t...

  18. Quantitative EEG Applying the Statistical Recognition Pattern Method

    DEFF Research Database (Denmark)

    Engedal, Knut; Snaedal, Jon; Hoegh, Peter

    2015-01-01

    BACKGROUND/AIM: The aim of this study was to examine the discriminatory power of quantitative EEG (qEEG) applying the statistical pattern recognition (SPR) method to separate Alzheimer's disease (AD) patients from elderly individuals without dementia and from other dementia patients. METHODS...

  19. Statistical pattern recognition for automatic writer identification and verification

    NARCIS (Netherlands)

    Bulacu, Marius Lucian

    2007-01-01

    The thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document

  20. Magnetic resonance imaging pattern recognition in hypomyelinating disorders

    NARCIS (Netherlands)

    Steenweg, M.E.; Vanderver, A.; Blaser, S.; Blizzi, A.; de Koning, T.J.; Mancini, G.M.S.; van Wieringen, W.N.; Barkhof, F.; Wolf, N.I.; van der Knaap, M.S.

    2010-01-01

    Hypomyelination is observed in the context of a growing number of genetic disorders that share clinical characteristics. The aim of this study was to determine the possible role of magnetic resonance imaging pattern recognition in distinguishing different hypomyelinating disorders, which would

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

    Science.gov (United States)

    Suresh, Rahul; Mosser, David M.

    2013-01-01

    Infection by pathogenic microbes initiates a set of complex interactions between the pathogen and the host mediated by pattern recognition receptors. Innate immune responses play direct roles in host defense during the early stages of infection, and they also exert a profound influence on the generation of the adaptive immune responses that ensue.…

  2. Pattern recognition approach to quantify the atomic structure of graphene

    DEFF Research Database (Denmark)

    Kling, Jens; Vestergaard, Jacob Schack; Dahl, Anders Bjorholm

    2014-01-01

    We report a pattern recognition approach to detect the atomic structure in high-resolution transmission electron microscopy images of graphene. The approach provides quantitative information such as carbon-carbon bond lengths and bond length variations on a global and local scale alike. © 2014...

  3. Learning Driving Behavior by Timed Syntactic Pattern Recognition

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2011-01-01

    We advocate the use of an explicit time representation in syntactic pattern recognition because it can result in more succinct models and easier learning problems. We apply this approach to the real-world problem of learning models for the driving behavior of truck drivers. We discretize the values

  4. [Chemical pattern recognition of traditional Chinese medicine kudingcha (II)].

    Science.gov (United States)

    Su, W; Wu, Z; He, X; Chen, J

    1998-04-01

    In this paper, the HPLC data from 78 samples of Kudingcha were treated with back propagation algorithm of artifical neural network pattern recognition, and the computer-aided classification of Ilex cornuta Lindl., Ilex latifolia Thunb. and Ligustrum lucidum Ait. was accomplished. This paper provides a scientific, advanced and feasible method for identification of traditional Chinese medicine.

  5. Full-body movement pattern recognition in climbing*

    NARCIS (Netherlands)

    Seifert, Ludovic; Dovgalecs, Vladislavs; Boulanger, Jérémie; Orth, Dominic; Hérault, Romain; Davids, Keith

    2014-01-01

    The aim of this study was to propose a method for full-body movement pattern recognition in climbing, by computing the 3D unitary vector of the four limbs and pelvis during performance. One climber with an intermediate skill level traversed two easy routes of similar rates of difficulty (5c

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

    Science.gov (United States)

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

    2012-12-31

    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: (а) to simplify the procedure of choice of recognition signs (criteria); (b) to obtain one-type δ-like recognition signals irrespective of the type of objects; (с) 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.

  7. An application of syntactic pattern recognition to seismic discrimination

    Science.gov (United States)

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

    1981-08-01

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

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

    Science.gov (United States)

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

    2013-02-01

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

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

    Science.gov (United States)

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

    2012-06-01

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

  10. Natural Cytotoxicity Receptors: Pattern Recognition and Involvement of Carbohydrates

    Directory of Open Access Journals (Sweden)

    Angel Porgador

    2005-01-01

    Full Text Available Natural cytotoxicity receptors (NCRs, expressed by natural killer (NK cells, trigger NK lysis of tumor and virus-infected cells on interaction with cell-surface ligands of these target cells. We have determined that viral hemagglutinins expressed on the surface of virus-infected cells are involved in the recognition by the NCRs, NKp44 and NKp46. Recognition of tumor cells by the NCRs NKp30 and NKp46 involves heparan sulfate epitopes expressed on the tumor cell membrane. Our studies provide new evidence for the identity of the ligands for NCRs and indicate that a broader definition should be applied to pathological patterns recognized by innate immune receptors. Since nonmicrobial endogenous carbohydrate structures contribute significantly to this recognition, there is an imperative need to develop appropriate tools for the facile sequencing of carbohydrate moieties.

  11. Pattern recognition software and techniques for biological image analysis.

    Directory of Open Access Journals (Sweden)

    Lior Shamir

    2010-11-01

    Full Text Available 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.

  12. FACE RECOGNITION BASED ON LOCAL DERIVATIVE TETRA PATTERN

    Directory of Open Access Journals (Sweden)

    A Geetha

    2017-02-01

    Full Text Available This paper proposes a new face recognition algorithm called local derivative tetra pattern (LDTrP. The new technique LDTrP is used to alleviate the face recognition rate under real-time challenges. Local derivative pattern (LDP is a directional feature extraction method to encode directional pattern features based on local derivative variations. The nth -order LDP is proposed to encode the first (n-1th order local derivative direction variations. The LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. The local tetra pattern (LTrP encodes the relationship between the reference pixel and its neighbours by using the first-order derivatives in vertical and horizontal directions. LTrP extracts values which are based on the distribution of edges which are coded using four directions. The LDTrP combines the higher order directional feature from both LDP and LTrP. Experimental results on ORL and JAFFE database show that the performance of LDTrP is consistently better than LBP, LTP and LDP for face identification under various conditions. The performance of the proposed method is measured in terms of recognition rate.

  13. Pattern Recognition in Optical Remote Sensing Data Processing

    Science.gov (United States)

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

    Computational procedures of the land surface biophysical parameters retrieval imply that modeling techniques are available of the outgoing radiation description together with monitoring techniques of remote sensing data processing using registered radiances between the related optical sensors and the land surface objects called “patterns”. Pattern recognition techniques are a valuable approach to the processing of remote sensing data for images of the land surface - atmosphere system. Many simplified codes of the direct and inverse problems of atmospheric optics are considered applicable for the imagery processing of low and middle spatial resolution. Unless the authors are not interested in the accuracy of the final information products, they utilize these standard procedures. The emerging necessity of processing data of high spectral and spatial resolution given by imaging spectrometers puts forward the newly defined pattern recognition techniques. The proposed tools of using different types of classifiers combined with the parameter retrieval procedures for the forested environment are maintained to have much wider applications as compared with the image features and object shapes extraction, which relates to photometry and geometry in pixel-level reflectance representation of the forested land cover. The pixel fraction and reflectance of “end-members” (sunlit forest canopy, sunlit background and shaded background for a particular view and solar illumination angle) are only a part in the listed techniques. It is assumed that each pixel views collections of the individual forest trees and the pixel-level reflectance can thus be computed as a linear mixture of sunlit tree tops, sunlit background (or understory) and shadows. Instead of these photometry and geometry constraints, the improved models are developed of the functional description of outgoing spectral radiation, in which such parameters of the forest canopy like the vegetation biomass density for

  14. Contiguous Uniform Deviation for Multiple Linear Regression in Pattern Recognition

    Science.gov (United States)

    Andriana, A. S.; Prihatmanto, D.; Hidaya, E. M. I.; Supriana, I.; Machbub, C.

    2017-01-01

    Understanding images by recognizing its objects is still a challenging task. Face elements detection has been developed by researchers but not yet shows enough information (low resolution in information) needed for recognizing objects. Available face recognition methods still have error in classification and need a huge amount of examples which may still be incomplete. Another approach which is still rare in understanding images uses pattern structures or syntactic grammars describing shape detail features. Image pixel values are also processed as signal patterns which are approximated by mathematical function curve fitting. This paper attempts to add contiguous uniform deviation method to curve fitting algorithm to increase applicability in image recognition system related to object movement. The combination of multiple linear regression and contiguous uniform deviation method are applied to the function of image pixel values, and show results in higher resolution (more information) of visual object detail description in object movement.

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

    Science.gov (United States)

    Huerta, R.

    2013-01-01

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

  16. Neurocomputing methods for pattern recognition in nuclear physics

    Energy Technology Data Exchange (ETDEWEB)

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

    1991-12-31

    We review recent progress on the development and applications of novel neurocomputing techniques for pattern recognition problems of relevance to RHIC experiments. The Elastic Tracking algorithm is shown to achieve sub-pad two track resolution without preprocessing. A high pass neural filter is developed for jet analysis and singular deconvolution methods are shown to recover the primordial jet distribution to a surprising high degree of accuracy.

  17. Linear Programming and Its Application to Pattern Recognition Problems

    Science.gov (United States)

    Omalley, M. J.

    1973-01-01

    Linear programming and linear programming like techniques as applied to pattern recognition problems are discussed. Three relatively recent research articles on such applications are summarized. The main results of each paper are described, indicating the theoretical tools needed to obtain them. A synopsis of the author's comments is presented with regard to the applicability or non-applicability of his methods to particular problems, including computational results wherever given.

  18. Advanced Pattern Recognition Techniques (Techniques avancees de reconnaissance de forme)

    Science.gov (United States)

    1998-09-01

    we will need a teacher, vances in pattern recognition techniques have come to tell us that our favourite group is, e.g. Darjeeling from better...34Estimating the Time-Delay and Frequency-Decay Parameter of Scattering Components using a Modified MUSIC Algorithm," IEEE Trans. on Antennas and Prop...available Modified MUSIC Algorithm," IEEE Trans. on from Fourier techniques. Antennas and Propagation, Vol. 42, No. 10, pp. 1412-1419, October, 1994. 11

  19. Achromatic optical correlator for white light pattern recognition

    Science.gov (United States)

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

    1987-01-01

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

  20. Real-valued composite filters for optical pattern recognition

    Science.gov (United States)

    Balendra, A.; Rajan, P. K.

    1993-01-01

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

  1. Track Pattern Recognition for the SHiP Spectrometer Tracker

    Science.gov (United States)

    Hushchyn, M.; Ustyuzhanin, A.; Alenkin, O.; van Herwijnen, E.

    2017-10-01

    SHiP is a new proposed fixed-target experiment at the CERN SPS accelerator. The goal of the experiment is to search for hidden particles predicted by models of Hidden Sectors. The purpose of the SHiP Spectrometer Tracker is to reconstruct tracks of charged particles from the decay of neutral New Physics objects with high efficiency. The goal is to develop a method of pattern recognition based on the SHiP Spectrometer Tracker design.

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

    Science.gov (United States)

    Singley, M. E.

    1984-01-01

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

  3. Conditional Random Fields for Pattern Recognition Applied to Structured Data

    Directory of Open Access Journals (Sweden)

    Tom Burr

    2015-07-01

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

  4. Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

    Directory of Open Access Journals (Sweden)

    Jian Wu

    2014-01-01

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

  5. PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics

    Science.gov (United States)

    Lutich, Andrey

    2017-07-01

    This research considers the problem of generating compact vector representations of physical design patterns for analytics purposes in semiconductor patterning domain. PatterNet uses a deep artificial neural network to learn mapping of physical design patterns to a compact Euclidean hyperspace. Distances among mapped patterns in this space correspond to dissimilarities among patterns defined at the time of the network training. Once the mapping network has been trained, PatterNet embeddings can be used as feature vectors with standard machine learning algorithms, and pattern search, comparison, and clustering become trivial problems. PatterNet is inspired by the concepts developed within the framework of generative adversarial networks as well as the FaceNet. Our method facilitates a deep neural network (DNN) to learn directly the compact representation by supplying it with pairs of design patterns and dissimilarity among these patterns defined by a user. In the simplest case, the dissimilarity is represented by an area of the XOR of two patterns. Important to realize that our PatterNet approach is very different to the methods developed for deep learning on image data. In contrast to "conventional" pictures, the patterns in the CAD world are the lists of polygon vertex coordinates. The method solely relies on the promise of deep learning to discover internal structure of the incoming data and learn its hierarchical representations. Artificial intelligence arising from the combination of PatterNet and clustering analysis very precisely follows intuition of patterning/optical proximity correction experts paving the way toward human-like and human-friendly engineering tools.

  6. Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet

    OpenAIRE

    Xie, Jianwen; Zhu, Song-Chun; Wu, Ying Nian

    2016-01-01

    Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability is defined by a spatial-temporal ConvNet that consists of multiple layers of spatial-tem...

  7. Do subitizing deficits in developmental dyscalculia involve pattern recognition weakness?

    Science.gov (United States)

    Ashkenazi, Sarit; Mark-Zigdon, Nitza; Henik, Avishai

    2013-01-01

    The abilities of children diagnosed with developmental dyscalculia (DD) were examined in two types of object enumeration: subitizing, and small estimation (5-9 dots). Subitizing is usually defined as a fast and accurate assessment of a number of small dots (range 1 to 4 dots), and estimation is an imprecise process to assess a large number of items (range 5 dots or more). Based on reaction time (RT) and accuracy analysis, our results indicated a deficit in the subitizing and small estimation range among DD participants in relation to controls. There are indications that subitizing is based on pattern recognition, thus presenting dots in a canonical shape in the estimation range should result in a subitizing-like pattern. In line with this theory, our control group presented a subitizing-like pattern in the small estimation range for canonically arranged dots, whereas the DD participants presented a deficit in the estimation of canonically arranged dots. The present finding indicates that pattern recognition difficulties may play a significant role in both subitizing and subitizing deficits among those with DD. © 2012 Blackwell Publishing Ltd.

  8. A star pattern recognition algorithm for autonomous attitude determination

    Science.gov (United States)

    Van Bezooijen, R. W. H.

    1990-01-01

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

  9. Speech Signal Analysis and Pattern Recognition in Diagnosis of Dysarthria

    Science.gov (United States)

    Thoppil, Minu George; Kumar, C. Santhosh; Kumar, Anand; Amose, John

    2017-01-01

    Background: Dysarthria refers to a group of disorders resulting from disturbances in muscular control over the speech mechanism due to damage of central or peripheral nervous system. There is wide subjective variability in assessment of dysarthria between different clinicians. In our study, we tried to identify a pattern among types of dysarthria by acoustic analysis and to prevent intersubject variability. Objectives: (1) Pattern recognition among types of dysarthria with software tool and to compare with normal subjects. (2) To assess the severity of dysarthria with software tool. Materials and Methods: Speech of seventy subjects were recorded, both normal subjects and the dysarthric patients who attended the outpatient department/admitted in AIMS. Speech waveforms were analyzed using Praat and MATHLAB toolkit. The pitch contour, formant variation, and speech duration of the extracted graphs were analyzed. Results: Study population included 25 normal subjects and 45 dysarthric patients. Dysarthric subjects included 24 patients with extrapyramidal dysarthria, 14 cases of spastic dysarthria, and 7 cases of ataxic dysarthria. Analysis of pitch of the study population showed a specific pattern in each type. F0 jitter was found in spastic dysarthria, pitch break with ataxic dysarthria, and pitch monotonicity with extrapyramidal dysarthria. By pattern recognition, we identified 19 cases in which one or more recognized patterns coexisted. There was a significant correlation between the severity of dysarthria and formant range. Conclusions: Specific patterns were identified for types of dysarthria so that this software tool will help clinicians to identify the types of dysarthria in a better way and could prevent intersubject variability. We also assessed the severity of dysarthria by formant range. Mixed dysarthria can be more common than clinically expected. PMID:29184336

  10. Local Directional Ternary Pattern for Facial Expression Recognition.

    Science.gov (United States)

    Ryu, Byungyong; Rivera, Adin Ramirez; Kim, Jaemyun; Chae, Oksam

    2017-07-11

    This paper presents a new face descriptor, local directional ternary pattern (LDTP), for facial expression recognition. LDTP efficiently encodes information of emotion-related features (i.e., eyes, eyebrows, upper nose, and mouth) by using the directional information and ternary pattern in order to take advantage of the robustness of edge patterns in the edge region while overcoming weaknesses of edge-based methods in smooth regions. Our proposal, unlike existing histogram-based face description methods that divide the face into several regions and sample the codes uniformly, uses a two level grid to construct the face descriptor while sampling expression-related information at different scales. We use a coarse grid for stable codes (highly related to non-expression), and a finer one for active codes (highly related to expression). This multi-level approach enables us to do a finer grain description of facial motions, while still characterizing the coarse features of the expression. Moreover, we learn the active LDTP codes from the emotionrelated facial regions. We tested our method by using persondependent and independent cross-validation schemes to evaluate the performance. We show that our approaches improve the overall accuracy of facial expression recognition on six datasets.

  11. Pattern Recognition for a Flight Dynamics Monte Carlo Simulation

    Science.gov (United States)

    Restrepo, Carolina; Hurtado, John E.

    2011-01-01

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

  12. Collocation and Pattern Recognition Effects on System Failure Remediation

    Science.gov (United States)

    Trujillo, Anna C.; Press, Hayes N.

    2007-01-01

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

  13. Pattern-Recognition Algorithm for Locking Laser Frequency

    Science.gov (United States)

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

    2006-01-01

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

  14. The role of pattern recognition receptors in lung sarcoidosis.

    Science.gov (United States)

    Mortaz, Esmaeil; Adcock, Ian M; Abedini, Atefhe; Kiani, Arda; Kazempour-Dizaji, Mehdi; Movassaghi, Masoud; Garssen, Johan

    2017-08-05

    Sarcoidosis is a granulomatous disorder of unknown etiology. Infection, genetic factors, autoimmunity and an aberrant innate immune system have been explored as potential causes of sarcoidosis. The etiology of sarcoidosis remains unknown, and it is thought that it might be caused by an infectious agent in a genetically predisposed, susceptible host. Inflammation results from recognition of evolutionarily conserved structures of pathogens (Pathogen-associated molecular patterns, PAMPs) and/or from reaction to tissue damage associated patterns (DAMPs) through recognition by a limited number of germ line-encoded pattern recognition receptors (PRRs). Due to the similar clinical and histopathological picture of sarcoidosis and tuberculosis, Mycobacterium tuberculosis antigens such early secreted antigen (ESAT-6), heat shock proteins (Mtb-HSP), catalase-peroxidase (katG) enzyme and superoxide dismutase A peptide (sodA) have been often considered as factors in the etiopathogenesis of sarcoidosis. Potential non-TB-associated PAMPs include lipopolysaccharide (LPS) from the outer membrane of Gram-negative bacteria, peptidoglycan, lipoteichoic acid, bacterial DNA, viral DNA/RNA, chitin, flagellin, leucine-rich repeats (LRR), mannans in the yeast cell wall, and microbial HSPs. Furthermore, exogenous non-organic antigens such as metals, silica, pigments with/without aluminum in tattoos, pesticides, and pollen have been evoked as potential causes of sarcoidosis. Exposure of the airways to diverse infectious and non-infectious agents may be important in the pathogenesis of sarcoidosis. The current review provides and update on the role of PPRs and DAMPs in the pathogenesis of sarcoidsis. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Pattern recognition and image processing for environmental monitoring

    Science.gov (United States)

    Siddiqui, Khalid J.; Eastwood, DeLyle

    1999-12-01

    Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.

  16. 6th International Conference on Pattern Recognition and Machine Intelligence

    CERN Document Server

    Gawrysiak, Piotr; Kryszkiewicz, Marzena; Rybiński, Henryk

    2016-01-01

    This book presents valuable contributions devoted to practical applications of Machine Intelligence and Big Data in various branches of the industry. All the contributions are extended versions of presentations delivered at the Industrial Session the 6th International Conference on Pattern Recognition and Machine Intelligence (PREMI 2015) held in Warsaw, Poland at June 30- July 3, 2015, which passed through a rigorous reviewing process. The contributions address real world problems and show innovative solutions used to solve them. This volume will serve as a bridge between researchers and practitioners, as well as between different industry branches, which can benefit from sharing ideas and results.

  17. Pattern recognition in menstrual bleeding diaries by statistical cluster analysis

    Directory of Open Access Journals (Sweden)

    Wessel Jens

    2009-07-01

    Full Text Available Abstract Background The aim of this paper is to empirically identify a treatment-independent statistical method to describe clinically relevant bleeding patterns by using bleeding diaries of clinical studies on various sex hormone containing drugs. Methods We used the four cluster analysis methods single, average and complete linkage as well as the method of Ward for the pattern recognition in menstrual bleeding diaries. The optimal number of clusters was determined using the semi-partial R2, the cubic cluster criterion, the pseudo-F- and the pseudo-t2-statistic. Finally, the interpretability of the results from a gynecological point of view was assessed. Results The method of Ward yielded distinct clusters of the bleeding diaries. The other methods successively chained the observations into one cluster. The optimal number of distinctive bleeding patterns was six. We found two desirable and four undesirable bleeding patterns. Cyclic and non cyclic bleeding patterns were well separated. Conclusion Using this cluster analysis with the method of Ward medications and devices having an impact on bleeding can be easily compared and categorized.

  18. Terminal Attractor Optical Associative Memory for Pattern Recognition

    Science.gov (United States)

    Lin, Xin; Mori, Masahiko; Ohtsubo, Junji; Watanabe, Masanobu

    2000-02-01

    Optical associative memory with terminal attractor (TA) is proposed for pattern recognition. With numerical simulations, the optimal control parameter in the TA model associative memory is determined. The optimal control parameter is also used in an optical experiment. The capacity of TA model associative memory is also investigated based on the consistency between the stored pattern and the obtained equilibrium state in statistical thermodynamics. The results of numerical simulations indicate that the memory rate of the TA associative memory is greater than 0.35. We also compare TA model with the conventional Hopfield model, and show that the TA model can eliminate spurious states in the Hopfield model and increase recalling ability and memory capacity.

  19. Inductive class representation and its central role in pattern recognition

    Energy Technology Data Exchange (ETDEWEB)

    Goldfarb, L. [Univ. of New Brunswick, Fredericton, New Brunswick (Canada)

    1996-12-31

    The definition of inductive learning (IL) based on the new concept of inductive class representation (ICR) is given. The ICR, in addition to the ability to recognize a noise-corrupted object from the class, must also provide the means to generate every element in the resulting approximation of the class, i.e., the emphasis is on the generative capability of the ICR. Thus, the IL problem absorbs the main difficulties associated with a satisfactory formulation of the pattern recognition problem. This formulation of the IL problem appeared gradually as a result of the development of a fundamentally new formal model of IL--evolving transformation system (ETS) model. The model with striking clarity suggests that IL is the basic process which produces all the necessary {open_quotes}structures{close_quotes} for the recognition process, which is built directly on top of it. Based on the training set, the IL process, constructs optimal discriminatory (symbolic) weighted {open_quotes}features{close_quotes} which induce the corresponding optimal (symbolic) distance measure. The distance measure is a generalization of the weighted Levenshtein, or edit, distance defined on strings over a finite alphabet. The ETS model has emerged as a result of an attempt to unify two basic, but inadequate, approaches to pattern recognition: the classical vector space based and the syntactic approaches. ETS also elucidates with remarkable clarity the nature of the interrelationships between the corresponding symbolic and numeric mechanisms, in which the symbolic mechanisms play a more fundamental part. The model, in fact, suggests the first formal definition of the symbolic mathematical structure and also suggests a fundamentally different, more satisfactory, way of introducing the concept of fuzziness. The importance of the ICR concept to semiotics and semantics should become apparent as soon as one fully realizes that it represents the class and specifies the semantics of the class.

  20. Pattern recognition with TiOx-based memristive devices

    Directory of Open Access Journals (Sweden)

    Finn Zahari

    2015-07-01

    Full Text Available We report on the development of TiOx-based memristive devices for bio-inspired neuromorphic systems. In particular, capacitor like structures of Al/AlOx/TiOx/Al with, respectively 20 nm and 50 nm thick TiOx-layers were fabricated and analyzed in terms of their use in neural network circuits. Therefore, an equivalent circuit model is presented which mimics the observed device properties on a qualitative level and relies on mobile oxygen ions by taking electronic transport through local conducting filaments and hopping between TiOx defect states into account. The model also comprises back diffusion of oxygen ions and allows for a realistic description of the experimental recorded device characteristics. The in Refs. [1-3] reported computing paradigms for pattern recognition have been used as guidelines for a device performance investigation at the network level. In particular, simulations of a spiking neural network are presented which allows for pattern recognition. As input patterns hand written digits taken from the MNIST Data base have been used. Within the network the memristive devices are arranged in a cross-bar array connected by 196 input neurons and ten output neurons. While, each input neuron corresponds to a specific pixel of the image of the input pattern, the output neurons were implemented as spiking neurons. In addition, the output neurons were inhibitory linked within an winner-take-it-all network and consist of a homeostasis-like behavior for their spiking thresholds. Based on the network simulation essential requirements for the development of optimal memristive device for neuromorphic circuits are discussed.

  1. Wavelet-based moment invariants for pattern recognition

    Science.gov (United States)

    Chen, Guangyi; Xie, Wenfang

    2011-07-01

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

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

    Science.gov (United States)

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

    1986-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Almeida, Leandro G. [Institut de Biologie de l’École Normale Supérieure (IBENS), Inserm 1024- CNRS 8197,46 rue d’Ulm, 75005 Paris (France); Backović, Mihailo [Center for Cosmology, Particle Physics and Phenomenology - CP3,Universite Catholique de Louvain,Louvain-la-neuve (Belgium); Cliche, Mathieu [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States); Lee, Seung J. [Department of Physics, Korea Advanced Institute of Science and Technology,335 Gwahak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); School of Physics, Korea Institute for Advanced Study,Seoul 130-722 (Korea, Republic of); Perelstein, Maxim [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States)

    2015-07-17

    Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image' of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p{sub T} in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

  4. Computational intelligence in multi-feature visual pattern recognition hand posture and face recognition using biologically inspired approaches

    CERN Document Server

    Pisharady, Pramod Kumar; Poh, Loh Ai

    2014-01-01

    This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good...

  5. Pattern-Recognition System for Approaching a Known Target

    Science.gov (United States)

    Huntsberger, Terrance; Cheng, Yang

    2008-01-01

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

  6. Albedo Pattern Recognition and Time-Series Analyses in Malaysia

    Science.gov (United States)

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

    2012-07-01

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

  7. ALBEDO PATTERN RECOGNITION AND TIME-SERIES ANALYSES IN MALAYSIA

    Directory of Open Access Journals (Sweden)

    S. A. Salleh

    2012-07-01

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

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

    Science.gov (United States)

    Baird, Bill

    1986-10-01

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

  9. Tree-based indexing for real-time ConvNet landmark-based visual place recognition

    Directory of Open Access Journals (Sweden)

    Yi Hou

    2017-01-01

    Full Text Available Recent impressive studies on using ConvNet landmarks for visual place recognition take an approach that involves three steps: (a detection of landmarks, (b description of the landmarks by ConvNet features using a convolutional neural network, and (c matching of the landmarks in the current view with those in the database views. Such an approach has been shown to achieve the state-of-the-art accuracy even under significant viewpoint and environmental changes. However, the computational burden in step (c significantly prevents this approach from being applied in practice, due to the complexity of linear search in high-dimensional space of the ConvNet features. In this article, we propose two simple and efficient search methods to tackle this issue. Both methods are built upon tree-based indexing. Given a set of ConvNet features of a query image, the first method directly searches the features’ approximate nearest neighbors in a tree structure that is constructed from ConvNet features of database images. The database images are voted on by features in the query image, according to a lookup table which maps each ConvNet feature to its corresponding database image. The database image with the highest vote is considered the solution. Our second method uses a coarse-to-fine procedure: the coarse step uses the first method to coarsely find the top-N database images, and the fine step performs a linear search in Hamming space of the hash codes of the ConvNet features to determine the best match. Experimental results demonstrate that our methods achieve real-time search performance on five data sets with different sizes and various conditions. Most notably, by achieving an average search time of 0.035 seconds/query, our second method improves the matching efficiency by the three orders of magnitude over a linear search baseline on a database with 20,688 images, with negligible loss in place recognition accuracy.

  10. Carbon Nanotube Synaptic Transistor Network for Pattern Recognition.

    Science.gov (United States)

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

    2015-11-18

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

  11. Pattern Recognition Of Blood Vessel Networks In Ocular Fundus Images

    Science.gov (United States)

    Akita, K.; Kuga, H.

    1982-11-01

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

  12. Terahertz Imaging for Biomedical Applications Pattern Recognition and Tomographic Reconstruction

    CERN Document Server

    Yin, Xiaoxia; Abbott, Derek

    2012-01-01

    Terahertz Imaging for Biomedical Applications: Pattern Recognition and Tomographic Reconstruction presents the necessary algorithms needed to assist screening, diagnosis, and treatment, and these algorithms will play a critical role in the accurate detection of abnormalities present in biomedical imaging. Terahertz biomedical imaging has become an area of interest due to its ability to simultaneously acquire both image and spectral information. Terahertz imaging systems are being commercialized with an increasing number of trials performed in a biomedical setting. Terahertz tomographic imaging and detection technology contributes to the ability to identify opaque objects with clear boundaries,and would be useful to both in vivo and ex vivo environments. This book also: Introduces terahertz radiation techniques and provides a number of topical examples of signal and image processing, as well as machine learning Presents the most recent developments in an emerging field, terahertz radiation Utilizes new methods...

  13. Pattern recognition issues on anisotropic smoothed particle hydrodynamics

    Science.gov (United States)

    Pereira Marinho, Eraldo

    2014-03-01

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

  14. Pattern recognition and reconstruction on a FPGA coprocessor board

    CERN Document Server

    Männer, R; Simmler, H

    2000-01-01

    High energy accelerator labs use huge detector systems to track particles. The ATLAS detector at CERN, Geneva (Switzerland), will provide complex three-dimensional images. A trigger system at the detector output is used to reduce the amount of data to a manageable size. Each trigger applies certain filter algorithms to select the very rare physically interesting events. The algorithm presented, processes data from a special detector called TRT, to generate a trigger decision within approximately=10 ms. System supervisors then decide together with other results whether the event will be rejected or passed to the next trigger level. Due to the restricted execution time for calculating the decision, fast pattern recognition algorithms are required. These algorithms require a high I/O bandwidth and high computing power. These reasons and the high degree of parallelism make it best suited for custom computing machines. (3 refs).

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

    Science.gov (United States)

    Ebert, Elizabeth E.

    1992-01-01

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

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

    Science.gov (United States)

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

    1994-01-01

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

  17. Pattern Recognition and Natural Language Processing: State of the Art

    Directory of Open Access Journals (Sweden)

    Mirjana Kocaleva

    2016-05-01

    Full Text Available Development of information technologies is growing steadily. With the latest software technologies development and application of the methods of artificial intelligence and machine learning intelligence embededs in computers, the expectations are that in near future computers will be able to solve problems themselves like people do. Artificial intelligence emulates human behavior on computers. Rather than executing instructions one by one, as theyare programmed, machine learning employs prior experience/data that is used in the process of system’s training. In this state of the art paper, common methods in AI, such as machine learning, pattern recognition and the natural language processing (NLP are discussed. Also are given standard architecture of NLP processing system and the level thatisneeded for understanding NLP. Lastly the statistical NLP processing and multi-word expressions are described.

  18. Vocal folds disorder detection using pattern recognition methods.

    Science.gov (United States)

    Wang, Jianglin; Jo, Cheolwoo

    2007-01-01

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

  19. Distorted Pattern Recognition and Analysis with the Help of IEf Graph Representation

    Directory of Open Access Journals (Sweden)

    Adam Sedziwy

    2002-01-01

    Full Text Available An algorithm for distorted pattern recognition is presented. lt's generalization of M Flasinski results (Pattern Recognition, 27, 1-16, 1992. A new formalism allows to make both qualitative and quantitive distortion analysis. It also enlarges parser flexibility by extending the set of patterns which may be recognized.

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

    Science.gov (United States)

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

    2006-01-01

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

  1. Pattern recognition techniques and the measurement of solar magnetic fields

    Science.gov (United States)

    Lopez Ariste, Arturo; Rees, David E.; Socas-Navarro, Hector; Lites, Bruce W.

    2001-11-01

    Measuring vector magnetic fields in the solar atmosphere using the profiles of the Stokes parameters of polarized spectral lines split by the Zeeman effect is known as Stokes Inversion. This inverse problem is usually solved by least-squares fitting of the Stokes profiles. However least-squares inversion is too slow for the new generation of solar instruments (THEMIS, SOLIS, Solar-B, ...) which will produce an ever-growing flood of spectral data. The solar community urgently requires a new approach capable of handling this information explosion, preferably in real-time. We have successfully applied pattern recognition and machine learning techniques to tackle this problem. For example, we have developed PCA-inversion, a database search technique based on Principal Component Analysis of the Stokes profiles. Search is fast because it is carried out in low dimensional PCA feature space, rather than the high dimensional space of the spectral signals. Such a data compression approach has been widely used for search and retrieval in many areas of data mining. PCA-inversion is the basis of a new inversion code called FATIMA (Fast Analysis Technique for the Inversion of Magnetic Atmospheres). Tests on data from HAO's Advanced Stokes Polarimeter show that FATIMA isover two orders of magnitude faster than least squares inversion. Initial tests on an alternative code (DIANNE - Direct Inversion based on Artificial Neural NEtworks) show great promise of achieving real-time performance. In this paper we present the latest achievements of FATIMA and DIANNE, two powerful examples of how pattern recognition techniques can revolutionize data analysis in astronomy.

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

    Energy Technology Data Exchange (ETDEWEB)

    Deptuch, Gregory; Hoff, Jim; Kwan, Simon; Lipton, Ron; Liu, Ted; Ramberg, Erik; Todri, Aida; Yarema, Ray; /Fermilab; Demarteua, Marcel,; Drake, Gary; Weerts, Harry; /Argonne /Chicago U. /Padua U. /INFN, Padua

    2010-10-01

    Future particle physics experiments looking for rare processes will have no choice but to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare process. The authors propose to develop a 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) chip for HEP applications, to advance the state-of-the-art for pattern recognition and track reconstruction for fast triggering.

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

    Science.gov (United States)

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

    1986-01-01

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

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

    Science.gov (United States)

    Guseman, L. F., Jr. (Principal Investigator)

    1984-01-01

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

  5. Spiking neural network-based control chart pattern recognition

    Directory of Open Access Journals (Sweden)

    Medhat H.A. Awadalla

    2012-03-01

    Full Text Available Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC is usually used to improve the quality of products. Control charting plays the most important role in SPC. Control charts help to monitor the behavior of the process to determine whether it is stable or not. Unnatural patterns in control charts mean that there are some unnatural causes for variations in SPC. Spiking neural networks (SNNs are the third generation of artificial neural networks that consider time as an important feature for information representation and processing. In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR. Furthermore, enhancements to the SpikeProp learning algorithm are proposed. These enhancements provide additional learning rules for the synaptic delays, time constants and for the neurons thresholds. Simulated experiments have been conducted and the achieved results show a remarkable improvement in the overall performance compared with artificial neural networks.

  6. Fixation patterns during recognition of personally familiar and unfamiliar faces

    Directory of Open Access Journals (Sweden)

    Goedele Van Belle

    2010-06-01

    Full Text Available Previous studies recording eye movements have rendered inconclusive findings with respect to processing differences between familiar and unfamiliar faces. We argue that this can be attributed to a number of factors that differ across studies: the type and extent of familiarity with stimuli presented, the varying spatial resolution of visual information and/or specifics, the definition of areas of interest subject to analyses, as well as the fact that the time course of scan patterns is often neglected. Here we sought to address these issues by recording gaze patterns in a face recognition task with personally familiar and unfamiliar faces, without predefining areas of interest and investigating potential processing differences at each individual fixation. We found that after a first fixation on the center of mass of the face—suggesting an initial holistic perception—and a subsequent left eye bias, local features were focused and explored more for familiar than unfamiliar faces. Although the number of fixations made did not differ for un-/familiar faces, the locations of fixations began to differ much earlier than when the familiarity decisions were provided. This suggests that without strict time constraints, different types of information are collected relatively early on familiar and unfamiliar faces.

  7. Facial expression recognition based on improved local ternary pattern and stacked auto-encoder

    Science.gov (United States)

    Wu, Yao; Qiu, Weigen

    2017-08-01

    In order to enhance the robustness of facial expression recognition, we propose a method of facial expression recognition based on improved Local Ternary Pattern (LTP) combined with Stacked Auto-Encoder (SAE). This method uses the improved LTP extraction feature, and then uses the improved depth belief network as the detector and classifier to extract the LTP feature. The combination of LTP and improved deep belief network is realized in facial expression recognition. The recognition rate on CK+ databases has improved significantly.

  8. Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach

    National Research Council Canada - National Science Library

    Marquand, Andre F; Filippone, Maurizio; Ashburner, John; Girolami, Mark; Mourao-Miranda, Janaina; Barker, Gareth J; Williams, Steven C R; Leigh, P Nigel; Blain, Camilla R V

    2013-01-01

    .... In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders...

  9. FPGA design of correlation-based pattern recognition

    Science.gov (United States)

    Jridi, Maher; Alfalou, Ayman

    2017-05-01

    Optical/Digital pattern recognition and tracking based on optical/digital correlation are a well-known techniques to detect, identify and localize a target object in a scene. Despite the limited number of treatments required by the correlation scheme, computational time and resources are relatively high. The most computational intensive treatment required by the correlation is the transformation from spatial to spectral domain and then from spectral to spatial domain. Furthermore, these transformations are used on optical/digital encryption schemes like the double random phase encryption (DRPE). In this paper, we present a VLSI architecture for the correlation scheme based on the fast Fourier transform (FFT). One interesting feature of the proposed scheme is its ability to stream image processing in order to perform correlation for video sequences. A trade-off between the hardware consumption and the robustness of the correlation can be made in order to understand the limitations of the correlation implementation in reconfigurable and portable platforms. Experimental results obtained from HDL simulations and FPGA prototype have demonstrated the advantages of the proposed scheme.

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

    Energy Technology Data Exchange (ETDEWEB)

    Steinlin, M.; Boltshauser, E. [Department of Neurology, University Children`s Hospital, Zurich (Switzerland); Blaser, S. [Division of Paediatric Neuroradiology, Hospital for Sick Children, Toronto (Canada)

    1998-06-01

    Inborn errors of metabolism can affect the cerebellum during development, maturation and later during life. We have established criteria for pattern recognition of cerebellar abnormalities in metabolic disorders. The abnormalities can be divided into four major groups: cerebellar hypoplasia (CH), hyperplasia, cerebellar atrophy (CA), cerebellar white matter abnormalities (WMA) or swelling, and involvement of the dentate nuclei (DN) or cerebellar cortex. CH can be an isolated typical finding, as in adenylsuccinase deficiency, but is also occasionally seen in many other disorders. Differentiation from CH and CA is often difficult, as in carbohydrate deficient glycoprotein syndrome or 2-l-hydroxyglutaric acidaemia. In cases of atrophy the relationship of cerebellar to cerebral atrophy is important. WMA may be diffuse or patchy, frequently predominantly around the DN. Severe swelling of white matter is present during metabolic crisis in maple syrup urine disease. The DN can be affected by metabolite deposition, necrosis, calcification or demyelination. Involvement of cerebellar cortex is seen in infantile neuroaxonal dystrophy. Changes in DN and cerebellar cortex are rather typical and therefore most helpful; additional features should be sought as they are useful in narrowing down the differential diagnosis. (orig.) With 6 figs., 6 tabs., 41 refs.

  11. Pattern Recognition by Dinamic Feature Analysis Based on PCA

    Directory of Open Access Journals (Sweden)

    Juliana Valencia-Aguirre

    2009-06-01

    Full Text Available Usually, in pattern recognition problems we represent the observations by mean of measures on appropriate variables of data set, these measures can be categorized as Static and Dynamic Features. Static features are not always an accurate representation of data. In these sense, many phenomena are better modeled by dynamic changes on their measures. The advantage of using an extended form (dynamic features is the inclusion of new information that allows us to get a better representation of the object. Nevertheless, sometimes it is difficult in a classification stage to deal with dynamic features, because the associated computational cost often can be higher than we deal with static features. For analyzing such representations, we use Principal Component Analysis (PCA, arranging dynamic data in such a way we can consider variations related to the intrinsic dynamic of observations. Therefore, the method made possible to evaluate the dynamic information about of the observations on a lower dimensionality feature space without decreasing the accuracy performance. Algorithms were tested on real data to classify pathological speech from normal voices, and using PCA for dynamic feature selection, as well.

  12. Discriminant pattern recognition using transformation-invariant neurons.

    Science.gov (United States)

    Sona, D; Sperduti, A; Starita, A

    2000-06-01

    To overcome the problem of invariant pattern recognition, Simard, LeCun, and Denker (1993) proposed a successful nearest-neighbor approach based on tangent distance, attaining state-of-the-art accuracy. Since this approach needs great computational and memory effort, Hastie, Simard, and Säckinger (1995) proposed an algorithm (HSS) based on singular value decomposition (SVD), for the generation of nondiscriminant tangent models. In this article we propose a different approach, based on a gradient-descent constructive algorithm, called TD-Neuron, that develops discriminant models. We present as well comparative results of our constructive algorithm versus HSS and learning vector quantization (LVQ) algorithms. Specifically, we tested the HSS algorithm using both the original version based on the two-sided tangent distance and a new version based on the one-sided tangent distance. Empirical results over the NIST-3 database show that the TD-Neuron is superior to both SVD- and LVQ-based algorithms, since it reaches a better trade-off between error and rejection.

  13. Session Introduction: Challenges of Pattern Recognition in Biomedical Data.

    Science.gov (United States)

    Verma, Shefali Setia; Verma, Anurag; Basile, Anna Okula; Bishop, Marta-Byrska; Darabos, Christian

    2018-01-01

    The analysis of large biomedical data often presents with various challenges related to not just the size of the data, but also to data quality issues such as heterogeneity, multidimensionality, noisiness, and incompleteness of the data. The data-intensive nature of computational genomics problems in biomedical informatics warrants the development and use of massive computer infrastructure and advanced software tools and platforms, including but not limited to the use of cloud computing. Our session aims to address these challenges in handling big data for designing a study, performing analysis, and interpreting outcomes of these analyses. These challenges have been prevalent in many studies including those which focus on the identification of novel genetic variant-phenotype associations using data from sources like Electronic Health Records (EHRs) or multi-omic data. One of the biggest challenges to focus on is the imperfect nature of the biomedical data where a lot of noise and sparseness is observed. In our session, we will present research articles that can help in identifying innovative ways to recognize and overcome newly arising challenges associated with pattern recognition in biomedical data.

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

    Science.gov (United States)

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

    2001-03-01

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

  15. Large-area settlement pattern recognition from Landsat-8 data

    Science.gov (United States)

    Wieland, Marc; Pittore, Massimiliano

    2016-09-01

    The study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work include built-up area identification, settlement type classification and urban structure types pattern recognition. Besides commonly used accuracy assessments of the resulting map products, thorough performance evaluations are carried out under varying conditions to tune algorithm parameters and assess their applicability for the given tasks. As part of this, several research questions are being addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on the SVM performance to identify built-up areas and urban structure types are evaluated. Also the influence of an extended feature space including digital elevation model features is tested for mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and compared to the heterogeneity of the building stock within the computational unit of the segments. The study concludes that the information content of Landsat-8 images is sufficient for the tested classification tasks and even detailed urban structures could be extracted with satisfying accuracy. Freely available ancillary settlement point location data could further improve the built-up area classification. Digital elevation features and pan-sharpening could, however, not significantly improve the classification results. The study highlights the importance of dynamically tuned classifier parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as a valid measure of the spatial distribution of classification uncertainties.

  16. Multiresolution pattern recognition of small volcanos in Magellan data

    Science.gov (United States)

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

    1992-01-01

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

  17. Efficient unfolding pattern recognition in single molecule force spectroscopy data

    Directory of Open Access Journals (Sweden)

    Labudde Dirk

    2011-06-01

    Full Text Available Abstract 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.

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

    Science.gov (United States)

    Vidal, Karine; Donnet-Hughes, Anne

    2008-01-01

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

  19. Vascular dysfunction following polymicrobial sepsis: role of pattern recognition receptors.

    Directory of Open Access Journals (Sweden)

    Stefan Felix Ehrentraut

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

  20. Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition

    Directory of Open Access Journals (Sweden)

    Bineng Zhong

    2008-06-01

    Full Text Available This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP, which is based on Daugman’s method for iris recognition and the local XOR pattern (LXP operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.

  1. Soft computing approach to pattern classification and object recognition a unified concept

    CERN Document Server

    Ray, Kumar S

    2012-01-01

    Soft Computing Approach to Pattern Classification and Object Recognition establishes an innovative, unified approach to supervised pattern classification and model-based occluded object recognition. The book also surveys various soft computing tools, fuzzy relational calculus (FRC), genetic algorithm (GA) and multilayer perceptron (MLP) to provide a strong foundation for the reader. The supervised approach to pattern classification and model-based approach to occluded object recognition are treated in one framework , one based on either a conventional interpretation or a new interpretation of

  2. The Role of Binocular Disparity in Rapid Scene and Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Matteo Valsecchi

    2013-04-01

    Full Text Available 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.

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

    Science.gov (United States)

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

    1983-05-01

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

  4. Exploring How User Routine Affects the Recognition Performance of a Lock Pattern

    NARCIS (Netherlands)

    de Wide, Lisa; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

    2015-01-01

    To protect an Android smartphone against attackers, a lock pattern can be used. Nevertheless, shoulder-surfing and smudge attacks can be used to get access despite of this protection. To combat these attacks, biometric recognition can be added to the lock pattern, such that the lock-pattern

  5. Peptide Pattern Recognition for high-throughput protein sequence analysis and clustering

    DEFF Research Database (Denmark)

    Busk, Peter Kamp

    2017-01-01

    Large collections of protein sequences with divergent sequences are tedious to analyze for understanding their phylogenetic or structure-function relation. Peptide Pattern Recognition is an algorithm that was developed to facilitate this task but the previous version does only allow a limited...... number of sequences as input. I implemented Peptide Pattern Recognition as a multithread software designed to handle large numbers of sequences and perform analysis in a reasonable time frame. Benchmarking showed that the new implementation of Peptide Pattern Recognition is twenty times faster than...... the previous implementation on a small protein collection with 673 MAP kinase sequences. In addition, the new implementation could analyze a large protein collection with 48,570 Glycosyl Transferase family 20 sequences without reaching its upper limit on a desktop computer. Peptide Pattern Recognition...

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

    Science.gov (United States)

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

  7. The Pandora multi-algorithm approach to automated pattern recognition in LAr TPC detectors

    Science.gov (United States)

    Marshall, J. S.; Blake, A. S. T.; Thomson, M. A.; Escudero, L.; de Vries, J.; Weston, J.; MicroBooNE Collaboration

    2017-09-01

    The development and operation of Liquid Argon Time Projection Chambers (LAr TPCs) for neutrino physics has created a need for new approaches to pattern recognition, in order to fully exploit the superb imaging capabilities offered by this technology. The Pandora Software Development Kit provides functionality to aid the process of designing, implementing and running pattern recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition: individual algorithms each address a specific task in a particular topology; a series of many tens of algorithms then carefully builds-up a picture of the event. The input to the Pandora pattern recognition is a list of 2D Hits. The output from the chain of over 70 algorithms is a hierarchy of reconstructed 3D Particles, each with an identified particle type, vertex and direction.

  8. Utility and recognition of lines and linear patterns on electronic displays depicting aeronautical charting information

    Science.gov (United States)

    2009-01-01

    This report describes a study conducted to explore the utility and recognition of lines and linear patterns on electronic displays depicting aeronautical charting information. The study gathered data from a large number of pilots who conduct all type...

  9. Compressed imagery detection rate through map seeking circuit, and histogram of oriented gradient pattern recognition

    Science.gov (United States)

    Newtson, Kathy A.; Creusere, Charles C.

    2017-05-01

    This research investigates the features retained after image compression for automatic pattern recognition purposes. Many raw images with vehicles in them were collected for these experiments. These raw images were significantly compressed using open-source JPEG and JPEG2000 compression algorithms. The original and compressed images are processed with a Map Seeking Circuit (MSC) pattern recognition algorithm, as well as a Histogram of Oriented Gradient (HOG) with Support Vector Machine (SVM) pattern recognition program. Detection rates are given for these images that demonstrates the feature extraction capabilities as well as false alarm rates when the compression was increased. JPEG2000 compression results show preservation of the features needed for automatic pattern recognition which was better than the JPEG standard image compression results.

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

    Science.gov (United States)

    Guseman, L. F., Jr.

    1983-01-01

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

  11. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  12. Binary optical filters for scale invariant pattern recognition

    Science.gov (United States)

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

    1992-01-01

    Binary synthetic discriminant function (BSDF) optical filters which are invariant to scale changes in the target object of more than 50 percent are demonstrated in simulation and experiment. Efficient databases of scale invariant BSDF filters can be designed which discriminate between two very similar objects at any view scaled over a factor of 2 or more. The BSDF technique has considerable advantages over other methods for achieving scale invariant object recognition, as it also allows determination of the object's scale. In addition to scale, the technique can be used to design recognition systems invariant to other geometric distortions.

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

    Directory of Open Access Journals (Sweden)

    Pavol Partila

    2015-01-01

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

  14. Gender recognition using local binary pattern and Naive Bayes ...

    African Journals Online (AJOL)

    Automatic gender classification has many important applications, for example, intelligent user interface, surveillance, identity authentication, access control and human-computer interaction amongst others. Gender recognition is a fundamental task for human beings, as many social functions critically depend on the correct ...

  15. Sparse Representation for Computer Vision and Pattern Recognition

    Science.gov (United States)

    2009-05-01

    recognition [71], image super-resolution [75], motion and data segmentation [33], [56], supervised denoising and inpainting [51] and background...denosing with this approach, and [49], [51] for numerous additional examples, comparisons, and applications in image demosaic- ing, image inpainting , and

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

    Directory of Open Access Journals (Sweden)

    Shouyi Yin

    2015-01-01

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

  17. Fast traffic sign recognition with a rotation invariant binary pattern based feature.

    Science.gov (United States)

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

    2015-01-19

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

  18. Modelling Feature Interaction Patterns in Nokia Mobile Phones using Coloured Petri Nets and Design/CPN

    DEFF Research Database (Denmark)

    Lorentsen, Louise; Tuovinen, Antti-Pekka; Xu, Jianli

    2002-01-01

    This paper describes the first results of a project on modelling of important feature interaction patterns of Nokia mobile phones using Coloured Petri Nets. A modern mobile phone supports many features: voice and data calls, text messaging, personal information management (phonebook and calendar)...... successfully identified inconsistencies in the specifications. Furthermore, the construction of the CPN model has lead to interesting ideas for possible improvements in the architecture of the mobile phone UI software system.......This paper describes the first results of a project on modelling of important feature interaction patterns of Nokia mobile phones using Coloured Petri Nets. A modern mobile phone supports many features: voice and data calls, text messaging, personal information management (phonebook and calendar......), WAP browsing, games, etc. All these features are packaged into a handset with a small screen and a special purpose keypad. The limited user interface and the seamless intertwining of logically separate features cause many problems in the software development of the user interface of mobile phones...

  19. Fingerprint Recognition using Fuzzy Logic with Triangular Pattern Template

    DEFF Research Database (Denmark)

    Hussain, Dil Muhammad Akbar

    2006-01-01

    in a fingerprint for identification.  However, we propose a fingerprint recognition technique using fuzzy logic.  This approach utilizes two types of features; ridge ending and bifurcation to construct triangles and the fuzzy logic determine the degree of membership for matching.  The unique difference in our...... implementation is that fuzzy concept is used for matching rather than the feature extraction.  The laboratory results obtained indicate that matching process efficiency can be improved using this technique....

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

    Science.gov (United States)

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

    2016-03-01

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

  1. Fetal heart rate pattern recognition by the method of auscultation.

    Science.gov (United States)

    Miller, F C; Pearse, K E; Paul, R H

    1984-09-01

    The thesis that obstetric health care personnel can discriminate characteristics of baseline fetal heart rate (FHR) and FHR patterns by auscultation needs to be tested. For this study, audiotones of the FHR signals were recorded for eight representative FHR patterns. Each recording was for three minutes and included one uterine contraction. Physicians and nurses who use continuous electronic FHR monitoring on a regular basis listened to the eight recordings and attempted to identify the baseline rate, variability, and periodic patterns, and then matched their perceptions with the eight corresponding FHR tracings (not in order). Baseline FHR and FHR without periodic patterns were most frequently identified correctly. Late decelerations with and without good baseline variability were misdiagnosed 18.4 and 33% of the time, respectively. Although the FHR characteristics and periodic patterns were correctly identified most of the time, failure to recognize significant periodic patterns by as many as one-third of the participants is unacceptable in modern obstetrics.

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

    Science.gov (United States)

    Ming, Yue; Wang, Guangchao; Fan, Chunxiao

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Wenjia Liu

    2013-01-01

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

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

    Science.gov (United States)

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

    1988-01-01

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

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

    Science.gov (United States)

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

  6. Students' Dichotomous Experiences of the Illuminating and Illusionary Nature of Pattern Recognition in Mathematics

    Science.gov (United States)

    Mhlolo, Michael Kainose

    2016-01-01

    The concept of pattern recognition lies at the heart of numerous deliberations concerned with new mathematics curricula, because it is strongly linked to improved generalised thinking. However none of these discussions has made the deceptive nature of patterns an object of exploration and understanding. Yet there is evidence showing that pattern…

  7. All optical logic for optical pattern recognition and networking applications

    Science.gov (United States)

    Khoury, Jed

    2017-05-01

    In this paper, we propose architectures for the implementation 16 Boolean optical gates from two inputs using externally pumped phase- conjugate Michelson interferometer. Depending on the gate to be implemented, some require single stage interferometer and others require two stages interferometer. The proposed optical gates can be used in several applications in optical networks including, but not limited to, all-optical packet routers switching, and all-optical error detection. The optical logic gates can also be used in recognition of noiseless rotation and scale invariant objects such as finger prints for home land security applications.

  8. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  9. Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

    Directory of Open Access Journals (Sweden)

    Chia-Hung Lin

    2010-01-01

    Full Text Available This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD from a two-dimensional (2D image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

  10. Latitudinal patterns of magnitude and interannual variability in net ecosystem exchange regulated by biological and environmental variables

    NARCIS (Netherlands)

    Yuan, W.P.; Luo, Y.Q.; Richardson, A.D.; Oren, R.; Luyssaert, S.; Janssens, I.A.; Ceulemans, R.; Zhou, X.H.; Grunwald, T.; Aubinet, M.; Berhofer, C.; Baldocchi, D.D.; Chen, J.Q.; Dunn, A.L.; Deforest, J.L.; Dragoni, D.; Goldstein, A.H.; Moors, E.J.; Munger, J.W.; Monson, R.K.; Suyker, A.E.; Star, G.; Scott, R.L.; Tenhunen, J.; Verma, S.B.; Vesala, T.; Wofsy, S.

    2009-01-01

    Over the last two and half decades, strong evidence showed that the terrestrial ecosystems are acting as a net sink for atmospheric carbon. However the spatial and temporal patterns of variation in the sink are not well known. In this study, we examined latitudinal patterns of interannual

  11. Artificial neural network for bubbles pattern recognition on the images

    Science.gov (United States)

    Poletaev, I. E.; Pervunin, K. S.; Tokarev, M. P.

    2016-10-01

    Two-phase bubble flows have been used in many technological and energy processes as processing oil, chemical and nuclear reactors. This explains large interest to experimental and numerical studies of such flows last several decades. Exploiting of optical diagnostics for analysis of the bubble flows allows researchers obtaining of instantaneous velocity fields and gaseous phase distribution with the high spatial resolution non-intrusively. Behavior of light rays exhibits an intricate manner when they cross interphase boundaries of gaseous bubbles hence the identification of the bubbles images is a complicated problem. This work presents a method of bubbles images identification based on a modern technology of deep learning called convolutional neural networks (CNN). Neural networks are able to determine overlapping, blurred, and non-spherical bubble images. They can increase accuracy of the bubble image recognition, reduce the number of outliers, lower data processing time, and significantly decrease the number of settings for the identification in comparison with standard recognition methods developed before. In addition, usage of GPUs speeds up the learning process of CNN owning to the modern adaptive subgradient optimization techniques.

  12. Finger Vein Recognition Using Local Line Binary Pattern

    Directory of Open Access Journals (Sweden)

    Bakhtiar Affendi Rosdi

    2011-11-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Sulavik, S.B.; Spencer, R.P.; Weed, D.A.; Shapiro, H.R.; Shiue, S.T.; Castriotta, R.J. (Univ. of Connecticut School of Medicine, Farmington (USA))

    1990-12-01

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

  14. Supervised and Unsupervised Classification for Pattern Recognition Purposes

    Directory of Open Access Journals (Sweden)

    Catalina COCIANU

    2006-01-01

    Full Text Available A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a mathematical model allowing to express the similarity degree between couples of particular objects, quasi-metrics expressing the similarity between an object an a cluster and between clusters, respectively. In supervised classification, we are provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern. Typically, the given training patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. The final section of the paper presents a new methodology for supervised learning based on PCA. The classes are represented in the measurement/feature space by a continuous repartitions

  15. Ceramics with Net Pattern from the 2003 Excavations on the Lbishche Fortified Settlement

    Directory of Open Access Journals (Sweden)

    Chizhevsky Andrei A.

    2012-06-01

    Full Text Available The Lbishche fortified settlement is one of the largest settlements of the Samara Bend. Its upper layers refer to the early Middle Ages and have been well studied. The lower layers referring to the Early Iron Age have not been subjected to serious examination until recently. In 2003, 778 pottery fragments were discovered in these layers, 12 of them with basket, or net, pattern, and all found in the same room. According to the author, ceramics of this type refers to the Gorodets culture of the third period, and dates from no earlier than the 3rd-2nd centuries BC, when the carriers of this culture migrated to the Samara Volga river region from the Don river basin. In this region There are 30 Gorodets culture sites; of these, 8 fortified settlement sites, mainly in the southern part of the Samara Bend.

  16. Mobile phone use patterns and preferences in safety net office-based buprenorphine patients.

    Science.gov (United States)

    Tofighi, Babak; Grossman, Ellie; Buirkle, Emily; McNeely, Jennifer; Gourevitch, Marc; Lee, Joshua D

    2015-01-01

    Integrating mobile phone technologies in addiction treatment is of increasing importance and may optimize patient engagement with their care and enhance the delivery of existing treatment strategies. Few studies have evaluated mobile phone and text message (TM) use patterns in persons enrolled in addiction treatment, and none have assessed the use in safety net, office-based buprenorphine practices. A 28-item, quantitative and qualitative semistructured survey was administered to opiate-dependent adults in an urban, publicly funded, office-based buprenorphine program. Survey domains included demographic characteristics, mobile phone and TM use patterns, and preferences pertaining to their recovery. Surveyors approached 73 of the 155 eligible subjects (47%); 71 respondents completed the survey. Nearly all participants reported mobile phone ownership (93%) and TM use (93%), and most reported "very much" or "somewhat" comfort sending TM (79%). Text message contact with 12-step group sponsors, friends, family members, and counselors was also described (32%). Nearly all preferred having their providers' mobile phone number (94%), and alerting the clinic via TM in the event of a potential relapse to receive both supportive TM and a phone call from their buprenorphine provider was also well received (62%). Mobile phone and TM use patterns and preferences among this sample of office-based buprenorphine participants highlight the potential of adopting patient-centered mobile phone-based interventions in this treatment setting.

  17. An exploratory study of treated-bed nets in Timor-Leste: patterns of intended and alternative usage

    Directory of Open Access Journals (Sweden)

    Wilder-Smith Annelies

    2011-07-01

    Full Text Available Abstract Background The Timor-Leste Ministry of Health has recently finalized the National Malaria Control Strategy for 2010-2020. A key component of this roadmap is to provide universal national coverage with long-lasting insecticide-treated nets (LLINs in support of achieving the primary goal of reducing both morbidity and mortality from malaria by 30% in the first three years, followed by a further reduction of 20% by end of the programme cycle in 2020 1. The strategic plan calls for this target to be supported by a comprehensive information, education and communication (IEC programme; however, there is limited prior research into household and personal usage patterns to assist in the creation of targeted, effective, and socio-culturally specific behaviour change materials. Methods Nine separate focus group discussions (FGDs were carried out in Dili, Manatuto, and Covalima districts, Democratic Republic of Timor-Leste, in July 2010. These focus groups primarily explored themes of perceived malaria risk, causes of malaria, net usage patterns within families, barriers to correct and consistent usage, and the daily experience of users (both male and female in households with at least one net. Comprehensive qualitative analysis utilized open source analysis software. Results The primary determinants of net usage were a widespread perception that nets could or should only be used by pregnant women and young children, and the availability of sufficient sleeping space under a limited number of nets within households. Both nuisance biting and disease prevention were commonly cited as primary motivations for usage, while seasonality was not a significant factor. Long-term net durability and ease of hanging were seen as key attributes in net design preference. Very frequent washing cycles were common, potentially degrading net effectiveness. Finally, extensive re-purposing of nets (fishing, protecting crops was both reported and observed, and may

  18. An exploratory study of treated-bed nets in Timor-Leste: patterns of intended and alternative usage.

    Science.gov (United States)

    Lover, Andrew A; Sutton, Brett A; Asy, Angelina J; Wilder-Smith, Annelies

    2011-07-21

    The Timor-Leste Ministry of Health has recently finalized the National Malaria Control Strategy for 2010-2020. A key component of this roadmap is to provide universal national coverage with long-lasting insecticide-treated nets (LLINs) in support of achieving the primary goal of reducing both morbidity and mortality from malaria by 30% in the first three years, followed by a further reduction of 20% by end of the programme cycle in 2020 1. The strategic plan calls for this target to be supported by a comprehensive information, education and communication (IEC) programme; however, there is limited prior research into household and personal usage patterns to assist in the creation of targeted, effective, and socio-culturally specific behaviour change materials. Nine separate focus group discussions (FGDs) were carried out in Dili, Manatuto, and Covalima districts, Democratic Republic of Timor-Leste, in July 2010.These focus groups primarily explored themes of perceived malaria risk, causes of malaria, net usage patterns within families, barriers to correct and consistent usage, and the daily experience of users (both male and female) in households with at least one net. Comprehensive qualitative analysis utilized open source analysis software. The primary determinants of net usage were a widespread perception that nets could or should only be used by pregnant women and young children, and the availability of sufficient sleeping space under a limited number of nets within households. Both nuisance biting and disease prevention were commonly cited as primary motivations for usage, while seasonality was not a significant factor. Long-term net durability and ease of hanging were seen as key attributes in net design preference. Very frequent washing cycles were common, potentially degrading net effectiveness. Finally, extensive re-purposing of nets (fishing, protecting crops) was both reported and observed, and may significantly decrease availability of nighttime sleeping

  19. Classifying two-dimensional orbits using pattern recognition

    Science.gov (United States)

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

    2013-12-01

    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.

  20. Machine Learning Method for Pattern Recognition in Volcano Seismic Spectra

    Science.gov (United States)

    Radic, V.; Unglert, K.; Jellinek, M.

    2016-12-01

    Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as Self-Organizing Maps (SOM), Principal Component Analysis (PCA) and clustering methods can help to quickly and automatically identify important patterns related to impending eruptions. In this study we develop and evaluate an algorithm applied on a set of synthetic volcano seismic spectra as well as observed spectra from Kılauea Volcano, Hawai`i. Our goal is to retrieve a set of known spectral patterns that are associated with dominant phases of volcanic tremor before, during, and after periods of volcanic unrest. The algorithm is based on training a SOM on the spectra and then identifying local maxima and minima on the SOM 'topography'. The topography is derived from the first two PCA modes so that the maxima represent the SOM patterns that carry most of the variance in the spectra. Patterns identified in this way reproduce the known set of spectra. Our results show that, regardless of the level of white noise in the spectra, the algorithm can accurately reproduce the characteristic spectral patterns and their occurrence in time. The ability to rapidly classify spectra of volcano seismic data without prior knowledge of the character of the seismicity at a given volcanic system holds great potential for real time or near-real time applications, and thus ultimately for eruption forecasting.

  1. Pattern Recognition-Based Analysis of COPD in CT

    DEFF Research Database (Denmark)

    Sørensen, Lauge Emil Borch Laurs

    Computed tomography (CT), a medical imaging technique, offers a detailed view of the human body that can be used for direct inspection of the lung tissue. This allows for in vivo measurement of subtle disease patterns such as the patterns associated with chronic obstructive pulmonary disease (COPD......). COPD, also commonly referred to as “smokers’ lungs”, is a lung disease characterized by limitation of the airflow to and from the lungs causing shortness of breath. The disease is expected to rank as the fifth most burdening disease worldwide by 2020 according the the World Health Organization. COPD...... individual lung voxel in the CT image and counts the number of voxels below the threshold relative to the total amount of lung voxels. This thesis presents several methods for texture-based quantification of emphysema and/or COPD in CT images of the lungs. The methods rely on image processing and pattern...

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

    Science.gov (United States)

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

    1989-01-01

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

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

    Science.gov (United States)

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

    2015-04-01

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

  4. Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

    Directory of Open Access Journals (Sweden)

    Zhaoqin Peng

    2013-01-01

    Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

  5. The Role of Verbal Instruction and Visual Guidance in Training Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Jamie S. North

    2017-09-01

    Full Text Available We used a novel approach to examine whether it is possible to improve the perceptual–cognitive skill of pattern recognition using a video-based training intervention. Moreover, we investigated whether any improvements in pattern recognition transfer to an improved ability to make anticipation judgments. Finally, we compared the relative effectiveness of verbal and visual guidance interventions compared to a group that merely viewed the same sequences without any intervention and a control group that only completed pre- and post-tests. We found a significant effect for time of testing. Participants were more sensitive in their ability to perceive patterns and distinguish between novel and familiar sequences at post- compared to pre-test. However, this improvement was not influenced by the nature of the intervention, despite some trends in the data. An analysis of anticipation accuracy showed no change from pre- to post-test following the pattern recognition training intervention, suggesting that the link between pattern perception and anticipation may not be strong. We present a series of recommendations for scientists and practitioners when employing training methods to improve pattern recognition and anticipation.

  6. Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

    Directory of Open Access Journals (Sweden)

    Md. Abdullah-al-mamun

    2015-08-01

    Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

  7. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    Science.gov (United States)

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  8. Postural pattern recognition in children with unilateral cerebral palsy

    Directory of Open Access Journals (Sweden)

    Domagalska-Szopa M

    2014-02-01

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

  9. Determination of ocular torsion by means of automatic pattern recognition

    NARCIS (Netherlands)

    Groen, Eric; Bos, Jelte E.; Nacken, Peter F M; De Graaf, Bernd

    A new, automatic method for determination of human ocular torsion (OT) was developed based on the tracking of iris patterns in digitized video images. Instead of quantifying OT by means of cross-correlation of circular iris samples, a procedure commonly applied, this new method automatically selects

  10. Determination of ocular torsion by means of automatic pattern recognition

    NARCIS (Netherlands)

    Groen, E.L.; Bos, J.E.; Nacken, P.F.M.; Graaf, B. de

    1996-01-01

    A new, automatic method for determination of human ocular torsion (OT) was devel-oped based on the tracking of iris patterns in digitized video images. Instead of quanti-fying OT by means of cross-correlation of circular iris samples, a procedure commonly applied, this new method automatically

  11. Pattern recognition in paediatric ecgs: the hidden secrets to clinical ...

    African Journals Online (AJOL)

    In general, medical practitioners (and paediatricians in particular) are trained to recognise common patterns of abnormalities in order to derive clues to disease diagnosis. This is typical of most clinical settings, especially in history taking and clinical examination, but also for investigations such as X-rays and common routine ...

  12. Increased neutrophil expression of pattern recognition receptors during COPD exacerbations

    NARCIS (Netherlands)

    Pouwels, Simon D.; Van Geffen, Wouter H.; Jonker, Marnix R.; Kerstjens, Huib A. M.; Nawijn, Martijn C.; Heijink, Irene H.

    Previously, we observed increased serum levels of damage-associated molecular patterns (DAMPs) during COPD exacerbations. Here, gene expression of DAMP receptors was measured in peripheral blood neutrophils of COPD patients during stable disease and severe acute exacerbation. The expression of

  13. Pattern Recognition for Reliability Assessment of Water Distribution Networks

    NARCIS (Netherlands)

    Trifunović, N.

    2012-01-01

    The study presented in this manuscript investigates the patterns that describe reliability of water distribution networks focusing to the node connectivity, energy balance, and economics of construction, operation and maintenance. A number of measures to evaluate the network resilience has been

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

    Science.gov (United States)

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

    2015-01-01

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

  15. Recognition of Cherenkov Ring Patterns with the HMPID-RICH Detector in ALICE at LHC

    CERN Document Server

    Di Bari, D

    1999-01-01

    A high momentum particle identification detector (HMPID) covering about 50f the ALICE central barrel region has been designed and prototyped.The detector consists of seven RICH modules with a proximity focusing geometry, covering 12m2. The very large density of hits on the detector (80÷90 part/m2 in the extreme cases) makes the recognition of the Cherenkov photon patterns a complex and crucial task. A study of the pattern recognition based on the Hough transformation in terms of particle identification efficiency and particle contamination will be presented.

  16. Recognition of Cherenkov ring patterns with the HMPID-RICH detector in ALICE at LHC

    CERN Document Server

    Cozza, D; De Cataldo, G; Di Bari, D; Di Mauro, A; Elia, D; Franco, A; Goret, B; Martinengo, P; Nappi, E; Paic, G; Piuz, François; Posa, F; Williams, T D

    1999-01-01

    A high momentum particle identification detector (HMPID) covering about 5% of the ALICE central barrel region has been designed and prototyped. The detector consists of seven RICH modules with a proximity focusing geometry, covering 12 m/sup 2/. The very large density of hits on the detector (80/90 part/m/sup 2/ in the extreme cases) makes the recognition of the Cherenkov photon patterns a complex and crucial task. A study of the pattern recognition strategy based on the Hough transformation in terms of particle identification efficiency and particle contamination will be presented. (7 refs).

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

    Science.gov (United States)

    Seijas, Leticia; Segura, Enrique

    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.

  18. RECOG-SIZE: Pattern recognition code RECOG-ORNL. [In FORTRAN IV for IBM 360

    Energy Technology Data Exchange (ETDEWEB)

    Begovich, C. L.; Larson, N. M.

    1977-05-01

    RECOG-ORNL is a general-purpose pattern recognition code that is an ORNL modification of the program RECOG, written and implemented at Lawrence Livermore Laboratory. The code contains various pattern recognition methods for data analysis, preprocessing and display of data, plus unsupervised and supervised learning. The algorithms used and the input data required are described for each available option. A sample run is included. An auxiliary program that calculates the array sizes needed for a data set is also listed. 2 figures, 12 tables.

  19. Rough-fuzzy pattern recognition applications in bioinformatics and medical imaging

    CERN Document Server

    Maji, Pradipta

    2012-01-01

    Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems dev

  20. Recognition of higher order patterns in proteins: immunologic kernels.

    Directory of Open Access Journals (Sweden)

    Robert D Bremel

    Full Text Available 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.

  1. Optical Correlator in Industrial Image Pattern Recognition System

    Directory of Open Access Journals (Sweden)

    Dávid Solus

    2016-12-01

    Full Text Available The aim of this paper is design a system for recognizing industrial image pattern using optical correlator. The proposed system is used to obtain information about the accuracy of the model and the industrial form of images, in this case – pavements. Cambridge optical correlator is used in designed system as comparator. Several experiments have been done by software called “Fourier Optics Experimenter”. Results and conclusion are discussed.

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

    Science.gov (United States)

    Sick, J. N.

    2003-12-01

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

  3. Malaria transmission pattern resilience to climatic variability is mediated by insecticide-treated nets

    Directory of Open Access Journals (Sweden)

    Taleo George

    2008-06-01

    Full Text Available Abstract Background Malaria is an important public-health problem in the archipelago of Vanuatu and climate has been hypothesized as important influence on transmission risk. Beginning in 1988, a major intervention using insecticide-treated bed nets (ITNs was implemented in the country in an attempt to reduce Plasmodium transmission. To date, no study has addressed the impact of ITN intervention in Vanuatu, how it may have modified the burden of disease, and whether there were any changes in malaria incidence that might be related to climatic drivers. Methods and findings Monthly time series (January 1983 through December 1999 of confirmed Plasmodium falciparum and Plasmodium vivax infections in the archipelago were analysed. During this 17 year period, malaria dynamics underwent a major regime shift around May 1991, following the introduction of bed nets as a control strategy in the country. By February of 1994 disease incidence from both parasites was reduced by at least 50%, when at most 20% of the population at risk was covered by ITNs. Seasonal cycles, as expected, were strongly correlated with temperature patterns, while inter-annual cycles were associated with changes in precipitation. Following the bed net intervention, the influence of environmental drivers of malaria dynamics was reduced by 30–80% for climatic forces, and 33–54% for other factors. A time lag of about five months was observed for the qualitative change ("regime shift" between the two parasites, the change occurring first for P. falciparum. The latter might be explained by interspecific interactions between the two parasites within the human hosts and their distinct biology, since P. vivax can relapse after a primary infection. Conclusion The Vanuatu ITN programme represents an excellent example of implementing an infectious disease control programme. The distribution was undertaken to cover a large, local proportion (~80% of people in villages where malaria was

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

    Science.gov (United States)

    Raia, F.

    2010-12-01

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

  5. Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

    Science.gov (United States)

    Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

    2017-06-01

    We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

  6. A Dynamic Interval-Valued Intuitionistic Fuzzy Sets Applied to Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Zhenhua Zhang

    2013-01-01

    Full Text Available We present dynamic interval-valued intuitionistic fuzzy sets (DIVIFS, which can improve the recognition accuracy when they are applied to pattern recognition. By analyzing the degree of hesitancy, we propose some DIVIFS models from intuitionistic fuzzy sets (IFS and interval-valued IFS (IVIFS. And then we present a novel ranking condition on the distance of IFS and IVIFS and introduce some distance measures of DIVIFS satisfying the ranking condition. Finally, a pattern recognition example applied to medical diagnosis decision making is given to demonstrate the application of DIVIFS and its distances. The simulation results show that the DIVIFS method is more comprehensive and flexible than the IFS method and the IVIFS method.

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

    Science.gov (United States)

    Ding, Li; Zhou, Runjing; Liu, Guiying

    2010-08-01

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

  8. Protein recognition by a pattern-generating fluorescent molecular probe

    Science.gov (United States)

    Pode, Zohar; Peri-Naor, Ronny; Georgeson, Joseph M.; Ilani, Tal; Kiss, Vladimir; Unger, Tamar; Markus, Barak; Barr, Haim M.; Motiei, Leila; Margulies, David

    2017-12-01

    Fluorescent molecular probes have become valuable tools in protein research; however, the current methods for using these probes are less suitable for analysing specific populations of proteins in their native environment. In this study, we address this gap by developing a unimolecular fluorescent probe that combines the properties of small-molecule-based probes and cross-reactive sensor arrays (the so-called chemical 'noses/tongues'). On the one hand, the probe can detect different proteins by generating unique identification (ID) patterns, akin to cross-reactive arrays. On the other hand, its unimolecular scaffold and selective binding enable this ID-generating probe to identify combinations of specific protein families within complex mixtures and to discriminate among isoforms in living cells, where macroscopic arrays cannot access. The ability to recycle the molecular device and use it to track several binding interactions simultaneously further demonstrates how this approach could expand the fluorescent toolbox currently used to detect and image proteins.

  9. Facial Recognition Patterns of Children and Adults Looking at Robotic Faces

    OpenAIRE

    Eunil Park; Ki Joon Kim; Del Pobil, Angel P.

    2012-01-01

    The present study investigates whether adults and children exhibit different eye‐fixation patterns when they look at human faces, machinelike robotic faces, and humanlike robotic faces. The results from two between‐ subject experiments showed that children and adults did have different facial recognition patterns; children tended to fixate more on the mouth of both machinelike and humanlike robotic faces than they do on human faces, while adults focused more on the eyes. The implications of n...

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

    Energy Technology Data Exchange (ETDEWEB)

    Searles, D.B.

    1993-03-01

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

  11. Cluster analysis and multiplet pattern recognition in two-dimensional NMR spectra

    Science.gov (United States)

    Neidig, Klaus-Peter; Saffrich, Rainer; Lorenz, Michael; Kalbitzer, Hans Robert

    A general and efficient strategy for the recognition of arbitrary multiplet patterns in two-dimensional nuclear magnetic resonance spectra has been developed. It comprises cluster analysis, feature extraction, and pattern matching techniques. The corresponding C routines embedded in the graphical environment of the program AURELIA were tested successfully on two-dimensional nuclear magnetic resonance spectra of the neuropeptide head activator and the HPr proteins of Staphylococcus aureus and Streptococcus faecalis.

  12. Comparative Analysis of Signaling Pathways Triggered by Different Pattern-recognition Receptor-types

    OpenAIRE

    Wan,Wei-Lin

    2017-01-01

    Plant cell surface receptors sense microbial pathogens by recognizing microbial structures called pathogen or microbe-associated molecular patterns (PAMPs/MAMPs). There are two major types of plant pattern recognition receptors: 1. Leucine-rich repeat receptor proteins (LRR-RP) and LRR receptor kinases (LRR-RK) and 2. Plant receptor proteins and receptor kinases carrying ectopic lysin motifs (LysM-RP and LysM-RK). Although many studies focused on the signal pathways triggered by these recepto...

  13. A Pattern Recognition Study of the Influence of Material Properties on the Prediction of Crater Geometries.

    Science.gov (United States)

    1981-02-01

    1973. 13. Kowalski, B.R., T. F. Schatzki , and F. H. Stross, "Classification of Archaeological Artifacts by Applying Pattern Recognition to Trace Element...1839-1847, 1977. 16. Duewer, D.L., B. R. Kowalski and T. F. Schatzki , Anal. Chem. (in press August or September 1975). 17. Kowalski, B.R., Chemtech, May

  14. Fuzzy logic analysis optimizations for pattern recognition - Implementation and experimental results

    Science.gov (United States)

    Hires, Matej; Habiballa, Hashim

    2017-07-01

    The article presents an practical results of optimization of the fuzzy logic analysis method for pattern recognition. The theoretical background of the proposed theory is shown in the former article extending the original fuzzy logic analysis method. This article shows the implementation and experimental verification of the approach.

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

    Science.gov (United States)

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

    2012-01-01

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

  16. Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition

    NARCIS (Netherlands)

    Leon Rincon, Carlos; Moreno, José Fernando; Cely, Jorge

    2017-01-01

    The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks’

  17. LacdiNAc-glycans constitute a parasite pattern for galectin-3-mediated immune recognition

    NARCIS (Netherlands)

    van den Berg, Timo K.; Honing, Henk; Franke, Niels; van Remoortere, Alexandra; Schiphorst, Wietske E. C. M.; Liu, Fu-Tong; Deelder, André M.; Cummings, Richard D.; Hokke, Cornelis H.; van Die, Irma

    2004-01-01

    Although Gal beta 1-4GlcNAc (LacNAc) moieties are the most common constituents of N-linked glycans on vertebrate proteins, GalNAc beta 1-4GlcNAc (LacdiNAc, LDN)-containing glycans are widespread in invertebrates, such as helminths. We postulated that LDN might be a molecular pattern for recognition

  18. Effects of Cooperative Group Work Activities on Pre-School Children's Pattern Recognition Skills

    Science.gov (United States)

    Tarim, Kamuran

    2015-01-01

    The aim of this research is twofold; to investigate the effects of cooperative group-based work activities on children's pattern recognition skills in pre-school and to examine the teachers' opinions about the implementation process. In line with this objective, for the study, 57 children (25 girls and 32 boys) were chosen from two private schools…

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

    Science.gov (United States)

    Silberstang, Joyce; London, Manuel

    2009-01-01

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

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

    Science.gov (United States)

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

    2010-01-01

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

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

    Science.gov (United States)

    Welk, Dorette Sugg

    2002-01-01

    Sophomore nursing students (n=162) examined scenarios depicting typical and atypical signs of heart attack. Examples were structured to include essential and nonessential symptoms, enabling pattern recognition and improved performance. The method provides a way to prepare students to anticipate and recognize life-threatening situations. (Contains…

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

    Science.gov (United States)

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

    1993-01-01

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

  3. Pattern recognition in cyclic and discrete skills performance from inertial measurement units

    NARCIS (Netherlands)

    Seifert, Ludovic; L'Hermette, Maxime; Komar, John; Orth, Dominic; Mell, Florian; Merriaux, Pierre; Grenet, Pierre; Caritu, Yanis; Hérault, Romain; Dovgalecs, Vladislavs; Davids, Keith

    2014-01-01

    The aim of this study is to compare and validate an Inertial Measurement Unit (IMU) relative to an optic system, and to propose methods for pattern recognition to capture behavioural dynamics during sport performance. IMU validation was conducted by comparing the motions of the two arms of a

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

    CERN Document Server

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

    1981-01-01

    A single board MC 68000 based computer has been assembled and benchmarked against the CDC 7600 running portions of the pattern recognition code used at the MPS. This computer has a floating coprocessor to achieve throughputs equivalent to several percent that of the 7600. A major part of this work was the construction of a FORTR

  5. Pattern recognition in volcano seismology - Reducing spectral dimensionality

    Science.gov (United States)

    Unglert, K.; Radic, V.; Jellinek, M.

    2015-12-01

    Variations in the spectral content of volcano seismicity can relate to changes in volcanic activity. Low-frequency seismic signals often precede or accompany volcanic eruptions. However, they are commonly manually identified in spectra or spectrograms, and their definition in spectral space differs from one volcanic setting to the next. Increasingly long time series of monitoring data at volcano observatories require automated tools to facilitate rapid processing and aid with pattern identification related to impending eruptions. Furthermore, knowledge transfer between volcanic settings is difficult if the methods to identify and analyze the characteristics of seismic signals differ. To address these challenges we evaluate whether a machine learning technique called Self-Organizing Maps (SOMs) can be used to characterize the dominant spectral components of volcano seismicity without the need for any a priori knowledge of different signal classes. This could reduce the dimensions of the spectral space typically analyzed by orders of magnitude, and enable rapid processing and visualization. Preliminary results suggest that the temporal evolution of volcano seismicity at Kilauea Volcano, Hawai`i, can be reduced to as few as 2 spectral components by using a combination of SOMs and cluster analysis. We will further refine our methodology with several datasets from Hawai`i and Alaska, among others, and compare it to other techniques.

  6. Pattern Extraction Algorithm for NetFlow-Based Botnet Activities Detection

    Directory of Open Access Journals (Sweden)

    Rafał Kozik

    2017-01-01

    Full Text Available As computer and network technologies evolve, the complexity of cybersecurity has dramatically increased. Advanced cyber threats have led to current approaches to cyber-attack detection becoming ineffective. Many currently used computer systems and applications have never been deeply tested from a cybersecurity point of view and are an easy target for cyber criminals. The paradigm of security by design is still more of a wish than a reality, especially in the context of constantly evolving systems. On the other hand, protection technologies have also improved. Recently, Big Data technologies have given network administrators a wide spectrum of tools to combat cyber threats. In this paper, we present an innovative system for network traffic analysis and anomalies detection to utilise these tools. The systems architecture is based on a Big Data processing framework, data mining, and innovative machine learning techniques. So far, the proposed system implements pattern extraction strategies that leverage batch processing methods. As a use case we consider the problem of botnet detection by means of data in the form of NetFlows. Results are promising and show that the proposed system can be a useful tool to improve cybersecurity.

  7. Hypothesis testing for evaluating a multimodal pattern recognition framework applied to speaker detection

    Directory of Open Access Journals (Sweden)

    Kunt Murat

    2008-03-01

    Full Text Available Abstract Background Speaker detection is an important component of many human-computer interaction applications, like for example, multimedia indexing, or ambient intelligent systems. This work addresses the problem of detecting the current speaker in audio-visual sequences. The detector performs with few and simple material since a single camera and microphone meets the needs. Method A multimodal pattern recognition framework is proposed, with solutions provided for each step of the process, namely, the feature generation and extraction steps, the classification, and the evaluation of the system performance. The decision is based on the estimation of the synchrony between the audio and the video signals. Prior to the classification, an information theoretic framework is applied to extract optimized audio features using video information. The classification step is then defined through a hypothesis testing framework in order to get confidence levels associated to the classifier outputs, allowing thereby an evaluation of the performance of the whole multimodal pattern recognition system. Results Through the hypothesis testing approach, the classifier performance can be given as a ratio of detection to false-alarm probabilities. Above all, the hypothesis tests give means for measuring the whole pattern recognition process effciency. In particular, the gain offered by the proposed feature extraction step can be evaluated. As a result, it is shown that introducing such a feature extraction step increases the ability of the classifier to produce good relative instance scores, and therefore, the performance of the pattern recognition process. Conclusion The powerful capacities of hypothesis tests as an evaluation tool are exploited to assess the performance of a multimodal pattern recognition process. In particular, the advantage of performing or not a feature extraction step prior to the classification is evaluated. Although the proposed framework is

  8. Listening for recollection: a multi-voxel pattern analysis of recognition memory retrieval strategies

    Directory of Open Access Journals (Sweden)

    Joel R Quamme

    2010-08-01

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

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

    Science.gov (United States)

    Noh, Soo Rim; Isaacowitz, Derek M.

    2014-01-01

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

  10. Brain MRI Pattern Recognition Translated to Clinical Scenarios

    Directory of Open Access Journals (Sweden)

    Andreia V. Faria

    2017-10-01

    Full Text Available We explored the performance of structure-based computational analysis in four neurodegenerative conditions [Ataxia (AT, n = 16, Huntington's Disease (HD, n = 52, Alzheimer's Disease (AD, n = 66, and Primary Progressive Aphasia (PPA, n = 50], all characterized by brain atrophy. The independent variables were the volumes of 283 anatomical areas, derived from automated segmentation of T1-high resolution brain MRIs. The segmentation based volumetric quantification reduces image dimensionality from the voxel level [on the order of O(106] to anatomical structures [O(102] for subsequent statistical analysis. We evaluated the effectiveness of this approach on extracting anatomical features, already described by human experience and a priori biological knowledge, in specific scenarios: (1 when pathologies were relatively homogeneous, with evident image alterations (e.g., AT; (2 when the time course was highly correlated with the anatomical changes (e.g., HD, an analogy for prediction; (3 when the pathology embraced heterogeneous phenotypes (e.g., AD so the classification was less efficient but, in compensation, anatomical and clinical information were less redundant; and (4 when the entity was composed of multiple subgroups that had some degree of anatomical representation (e.g., PPA, showing the potential of this method for the clustering of more homogeneous phenotypes that can be of clinical importance. Using the structure-based quantification and simple linear classifiers (partial least square, we achieve 87.5 and 73% of accuracy on differentiating AT and pre-symptomatic HD patents from controls, respectively. More importantly, the anatomical features automatically revealed by the classifiers agreed with the patterns previously described on these pathologies. The accuracy was lower (68% on differentiating AD from controls, as AD does not display a clear anatomical phenotype. On the other hand, the method identified PPA clinical phenotypes and their

  11. Performance Study of the First 2D Prototype of Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

    Energy Technology Data Exchange (ETDEWEB)

    Deptuch, Gregory [Fermilab; Hoff, James [Fermilab; Jindariani, Sergo [Fermilab; Liu, Tiehui [Fermilab; Olsen, Jamieson [Fermilab; Tran, Nhan [Fermilab; Joshi, Siddhartha [Northwestern U.; Li, Dawei [Northwestern U.; Ogrenci-Memik, Seda [Northwestern U.

    2017-09-24

    Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high luminosity LHC (HL-LHC) running conditions. Associative Memory (AM) based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available and speed performance must be improved over previous systems while maintaining a comparable number of patterns. The Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) Project aims to achieve the target pattern density and performance goal using 3DIC technology. The first step taken in the VIPRAM work was the development of a 2D prototype (protoVIPRAM00) in which the associative memory building blocks were designed to be compatible with the 3D integration. In this paper, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3D stacking. We also present performance boundary characterization of the chip under extreme conditions.

  12. Visual Scanning Patterns and Executive Function in Relation to Facial Emotion Recognition in Aging

    Science.gov (United States)

    Circelli, Karishma S.; Clark, Uraina S.; Cronin-Golomb, Alice

    2012-01-01

    Objective The ability to perceive facial emotion varies with age. Relative to younger adults (YA), older adults (OA) are less accurate at identifying fear, anger, and sadness, and more accurate at identifying disgust. Because different emotions are conveyed by different parts of the face, changes in visual scanning patterns may account for age-related variability. We investigated the relation between scanning patterns and recognition of facial emotions. Additionally, as frontal-lobe changes with age may affect scanning patterns and emotion recognition, we examined correlations between scanning parameters and performance on executive function tests. Methods We recorded eye movements from 16 OA (mean age 68.9) and 16 YA (mean age 19.2) while they categorized facial expressions and non-face control images (landscapes), and administered standard tests of executive function. Results OA were less accurate than YA at identifying fear (precognition of sad expressions and with scanning patterns for fearful, sad, and surprised expressions. Conclusion We report significant age-related differences in visual scanning that are specific to faces. The observed relation between scanning patterns and executive function supports the hypothesis that frontal-lobe changes with age may underlie some changes in emotion recognition. PMID:22616800

  13. FEM enhanced signal processing approach for pattern recognition in the SQUID based NDE system

    Science.gov (United States)

    Sarreshtedari, F.; Jahed, N. M. S.; Hosseni, N.; Pourhashemi, A.; banzet, Marko; schubert, Juergen; Fardmanesh, M.

    2010-06-01

    An efficient Non-Destructive Evaluation algorithm has been developed in order to extract the required information for pattern recognition of defects in the conductive samples. Using high-Tc gradiometer RF-SQUIDs in unshielded environments and incorporating an automated two dimensional non-magnetic scanning robot, samples with different intentional defects have been tested. We have used a developed noise cancellation approach for the improvement of the effectiveness of the used inverse-problem technique. In this approach we have used a well examined Finite Element Method (FEM) to apply a noise reduction filtering on the obtained raw magnetic image data before incorporating the signal processing analysis. By applying this noise cancellation filter and incorporating three different signal processing algorithms and comparing the results of the predicted images by the pattern of the intentionally made defects, we have investigated the ability of these methods for pattern recognition of unknown defects.

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

    Science.gov (United States)

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

    2011-01-15

    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.

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

    Science.gov (United States)

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

    1989-01-01

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

  16. Using genetic algorithm feature selection in neural classification systems for image pattern recognition

    Directory of Open Access Journals (Sweden)

    Margarita R. Gamarra A.

    2012-09-01

    Full Text Available Pattern recognition performance depends on variations during extraction, selection and classification stages. This paper presents an approach to feature selection by using genetic algorithms with regard to digital image recognition and quality control. Error rate and kappa coefficient were used for evaluating the genetic algorithm approach Neural networks were used for classification, involving the features selected by the genetic algorithms. The neural network approach was compared to a K-nearest neighbor classifier. The proposed approach performed better than the other methods.

  17. Comparative study of the CCF-like pattern recognition protein in different Lumbricid species.

    Science.gov (United States)

    Silerová, Marcela; Procházková, Petra; Josková, Radka; Josens, Guy; Beschin, Alain; De Baetselier, Patrick; Bilej, Martin

    2006-01-01

    Coelomic fluid of the Lumbricid Eisenia fetida contains a 42-kDa pattern recognition protein named coelomic cytolytic factor (CCF) that binds microbial cell wall components and triggers the activation of the prophenoloxidase cascade, an important invertebrate defense pathway. Here we report on the sequence characterization of CCF-like molecules of other Lumbricids: Aporrectodea caliginosa, Aporrectodea icterica, Aporrectodea longa, Aporrectodea rosea, Dendrobaena veneta, Lumbricus rubellus and Lumbricus terrestris, and show that CCF from E. fetida has a broader saccharide-binding specificity, being the only one recognizing N,N'-diacetylchitobiose. We suggest that the broad recognition repertoire of E. fetida CCF reflects a particular microbial environment this species lives in.

  18. Binary pattern flavored feature extractors for Facial Expression Recognition: An overview

    DEFF Research Database (Denmark)

    Kristensen, Rasmus Lyngby; Tan, Zheng-Hua; Ma, Zhanyu

    2015-01-01

    This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression Recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER...... terminology, describes open challenges, and provides recommendations to scientific evaluation of FER systems. Lastly, it studies the facial expression recognition accuracy and blur invariance of the Local Frequency Descriptor. The paper seeks to bring together disjointed studies, and the main contribution...

  19. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic.

    Science.gov (United States)

    Hou, Shi-Wang; Feng, Shunxiao; Wang, Hui

    2016-01-01

    Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

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

    Science.gov (United States)

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

    2015-11-01

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

  1. Interactions of the humoral pattern recognition molecule PTX3 with the complement system

    DEFF Research Database (Denmark)

    Doni, Andrea; Garlanda, Cecilia; Bottazzi, Barbara

    2012-01-01

    The innate immune system comprises a cellular and a humoral arm. The long pentraxin PTX3 is a fluid phase pattern recognition molecule, which acts as an essential component of the humoral arm of innate immunity. PTX3 has antibody-like properties including interactions with complement components...... and factor H, the principal regulators of the classical, lectin and alternative complement pathways, show that PTX3 also may have a major influence on the regulation of the complement system. The complex interaction of PTX3 with the complement system at different levels has broad implications for host....... PTX3 interacts with C1q, ficolin-1 and ficolin-2 as well as mannose-binding lectin, recognition molecules in the classical and lectin complement pathways. The formation of these heterocomplexes results in cooperative pathogen recognition and complement activation. Interactions with C4b binding protein...

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

    Science.gov (United States)

    Chen, Bo; Zang, Chuanzhi

    2009-03-01

    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.

  3. A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

    Directory of Open Access Journals (Sweden)

    He-Yuan Lin

    2008-03-01

    Full Text Available A novel motion-adaptive deinterlacing algorithm with edge-pattern recognition and hybrid motion detection is introduced. The great variety of video contents makes the processing of assorted motion, edges, textures, and the combination of them very difficult with a single algorithm. The edge-pattern recognition algorithm introduced in this paper exhibits the flexibility in processing both textures and edges which need to be separately accomplished by line average and edge-based line average before. Moreover, predicting the neighboring pixels for pattern analysis and interpolation further enhances the adaptability of the edge-pattern recognition unit when motion detection is incorporated. Our hybrid motion detection features accurate detection of fast and slow motion in interlaced video and also the motion with edges. Using only three fields for detection also renders higher temporal correlation for interpolation. The better performance of our deinterlacing algorithm with higher content-adaptability and less memory cost than the state-of-the-art 4-field motion detection algorithms can be seen from the subjective and objective experimental results of the CIF and PAL video sequences.

  4. A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Li Hsin-Te

    2008-01-01

    Full Text Available Abstract A novel motion-adaptive deinterlacing algorithm with edge-pattern recognition and hybrid motion detection is introduced. The great variety of video contents makes the processing of assorted motion, edges, textures, and the combination of them very difficult with a single algorithm. The edge-pattern recognition algorithm introduced in this paper exhibits the flexibility in processing both textures and edges which need to be separately accomplished by line average and edge-based line average before. Moreover, predicting the neighboring pixels for pattern analysis and interpolation further enhances the adaptability of the edge-pattern recognition unit when motion detection is incorporated. Our hybrid motion detection features accurate detection of fast and slow motion in interlaced video and also the motion with edges. Using only three fields for detection also renders higher temporal correlation for interpolation. The better performance of our deinterlacing algorithm with higher content-adaptability and less memory cost than the state-of-the-art 4-field motion detection algorithms can be seen from the subjective and objective experimental results of the CIF and PAL video sequences.

  5. Stage-specific sampling by pattern recognition receptors during Candida albicans phagocytosis.

    Directory of Open Access Journals (Sweden)

    Sigrid E M Heinsbroek

    2008-11-01

    Full Text Available 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-alpha 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.

  6. LOCAL LINE BINARY PATTERN FOR FEATURE EXTRACTION ON PALM VEIN RECOGNITION

    Directory of Open Access Journals (Sweden)

    Jayanti Yusmah Sari

    2015-08-01

    Full Text Available In recent years, palm vein recognition has been studied to overcome problems in conventional systems in biometrics technology (finger print, face, and iris. Those problems in biometrics includes convenience and performance. However, due to the clarity of the palm vein image, the veins could not be segmented properly. To overcome this problem, we propose a palm vein recognition system using Local Line Binary Pattern (LLBP method that can extract robust features from the palm vein images that has unclear veins. LLBP is an advanced method of Local Binary Pattern (LBP, a texture descriptor based on the gray level comparison of a neighborhood of pixels. There are four major steps in this paper, Region of Interest (ROI detection, image preprocessing, features extraction using LLBP method, and matching using Fuzzy k-NN classifier. The proposed method was applied on the CASIA Multi-Spectral Image Database. Experimental results showed that the proposed method using LLBP has a good performance with recognition accuracy of 97.3%. In the future, experiments will be conducted to observe which parameter that could affect processing time and recognition accuracy of LLBP is needed

  7. Item and order memory for novel visual patterns assessed by two-choice recognition.

    Science.gov (United States)

    Avons, S E; Ward, Geoff; Melling, Lindsay

    2004-07-01

    Five experiments examined item and order memory for short lists of novel visual patterns. Memory was tested either by an item recognition test, choosing between a target and a similar foil (Experiments 1, 3a, and 4), or by a relative recency decision between two patterns that occupied adjacent list positions (Experiments 2, 3b, and 5). For both item recognition and relative recency tasks, accuracy was in most cases constant across serial positions, except for a recency advantage that was usually restricted to the most recent item or recency decision. Only a small and marginally significant effect of list length was observed for item recognition. Relative recency was more sensitive to list length and fell to near-chance levels with lists of eight items. We conclude that for these materials, prerecency item recognition depends on stable, context-free descriptions of items. Relative recency judgements are sensitive to list properties, but fail to show evidence of primacy or extended recency that are observed when other techniques are used to study serial order memory. We discuss the results in relation to four current models of serial order memory that embody different assumptions in the way that serial order is represented. Copyright 2004 The Experimental Psychology Society

  8. Analyzing dynamic cellular morphology in time-lapsed images enabled by cellular deformation pattern recognition.

    Science.gov (United States)

    Li, Heng; Liu, Zhiwen; Pang, Fengqian; Fan, Zhiyi; Shi, Yonggang

    2015-01-01

    Computational analysis of cellular morphology aims to provide quantitative information of the global organizational and physiological state of cells, and has long been a major topic of biomedical research. Instead of analyzing morphology of static cells, we concentrate on live-cell deformation in a period of time. According to our observation of dynamic cell behavior, we have assumed that the pattern of cellular deformation is relevant to the cellular state. Moreover, based on our assumption an innovative approach for characterizing the deformation pattern is described and applied into cell classification. After normalizing and aligning cell image sequences, we extract the continuity of deformation at each angle through time-lapse. Then the deformation pattern is given by the histogram of the continuity of deformation. Experimental results demonstrate that the cellular deformation pattern provided by our approach can be applied to discriminate cellular activation. In addition, the deformation pattern recognition makes remarkable progress in the classification of cells.

  9. A Pattern Recognition Technique Based on Wavelet Decomposition for Identification of Patients With Congestive Heart Failure

    Directory of Open Access Journals (Sweden)

    Abdulnasir Hossen

    2009-12-01

    Full Text Available A pattern recognition technique based on approximate estimation of power spectral densities (PSD of sub-bands resulted from wavelet decomposition of R-R interval (RRI data for identification of patients with Congestive Heart Failure (CHF is investigated. Both trial and test data used in this work are drawn from MIT databases. Two standard patterns of the base-2 logarithmic values of the reciprocal of the probability measure of the approximated PSD of CHF patients and normal subjects are derived by averaging all corresponding values of all sub-bands of 12 CHF data and 12 normal subjects in the trial set. The computed pattern of each data under test is then compared band-by-band with both standard patterns of CHF and normal subjects to find the closest pattern. The new technique resulted in an identification accuracy of about 90% by applying it on the test data.

  10. Sequential Learning and Recognition of Comprehensive Behavioral Patterns Based on Flow of People

    Science.gov (United States)

    Gibo, Tatsuya; Aoki, Shigeki; Miyamoto, Takao; Iwata, Motoi; Shiozaki, Akira

    Recently, surveillance cameras have been set up everywhere, for example, in streets and public places, in order to detect irregular situations. In the existing surveillance systems, as only a handful of surveillance agents watch a large number of images acquired from surveillance cameras, there is a possibility that they may miss important scenes such as accidents or abnormal incidents. Therefore, we propose a method for sequential learning and the recognition of comprehensive behavioral patterns in crowded places. First, we comprehensively extract a flow of people from input images by using optical flow. Second, we extract behavioral patterns on the basis of change-point detection of the flow of people. Finally, in order to recognize an observed behavioral pattern, we draw a comparison between the behavioral pattern and previous behavioral patterns in the database. We verify the effectiveness of our approach by placing a surveillance camera on a campus.

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

    Science.gov (United States)

    Hinton, Yolanda L.

    1999-01-01

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

  12. Pattern recognition algorithms for data mining scalability, knowledge discovery and soft granular computing

    CERN Document Server

    Pal, Sankar K

    2004-01-01

    Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

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

    Energy Technology Data Exchange (ETDEWEB)

    Newsam, S; Kamath, C

    2004-12-10

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

  14. An Approach for Pattern Recognition of EEG Applied in Prosthetic Hand Drive

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Zhang

    2011-12-01

    Full Text Available For controlling the prosthetic hand by only electroencephalogram (EEG, it has become the hot spot in robotics research to set up a direct communication and control channel between human brain and prosthetic hand. In this paper, the EEG signal is analyzed based on multi-complicated hand activities. And then, two methods of EEG pattern recognition are investigated, a neural prosthesis hand system driven by BCI is set up, which can complete four kinds of actions (arm’s free state, arm movement, hand crawl, hand open. Through several times of off-line and on-line experiments, the result shows that the neural prosthesis hand system driven by BCI is reasonable and feasible, the C-support vector classifiers-based method is better than BP neural network on the EEG pattern recognition for multi-complicated hand activities.

  15. Assay for the pattern recognition molecule collectin liver 1 (CL-L1)

    DEFF Research Database (Denmark)

    Axelgaard, Esben; Jensenius, Jens Christian; Jensen, Lisbeth

    Collectin liver 1 (also termed collectin 10 and CL-L1) is a C-type lectin that functions as a pattern recognition molecule (PRM) in the innate immune system1. We have produced antibodies against CL-L1 and have developed a sandwich-type time-resolved immuno-fluorometric assay (TRIFMA) for the meas......Collectin liver 1 (also termed collectin 10 and CL-L1) is a C-type lectin that functions as a pattern recognition molecule (PRM) in the innate immune system1. We have produced antibodies against CL-L1 and have developed a sandwich-type time-resolved immuno-fluorometric assay (TRIFMA...

  16. Real-time pattern recognition using an optical generalized Hough transform.

    Science.gov (United States)

    Fernández, Ariel; Flores, Jorge L; Alonso, Julia R; Ferrari, José A

    2015-12-20

    We present some pattern recognition applications of a generalized optical Hough transform and the temporal multiplexing strategies for dynamic scale and orientation-variant detection. Unlike computer-based implementations of the Hough transform, in principle its optical implementation does not impose restrictions on the execution time or on the resolution of the images or frame rate of the videos to be processed, which is potentially useful for real-time applications. Validation experiments are presented.

  17. Epitope recognition patterns of thyroid peroxidase autoantibodies in healthy individuals and patients with Hashimoto's thyroiditis*

    DEFF Research Database (Denmark)

    Nielsen, Claus H; Brix, Thomas H; Gardas, Andrzej

    2008-01-01

    Thyroid peroxidase antibodies (TPOAb) are markers of autoimmune thyroid disease (AITD), including Hashimoto's thyroiditis (HT), but naturally occurring TPOAb are also detectable in healthy, euthyroid individuals. In AITD, circulating TPOAb react mainly with two immunodominant regions (IDR), IDR-A......-A and IDR-B. The present study was undertaken in order to compare the epitope recognition pattern of TPOAb in HT patients and healthy subjects....

  18. Value recognition and eating patterns of Kimchi in female middle school students and their mothers

    OpenAIRE

    Kim, Jung-Hyun; Lee, Min-June; Yoon, In-Kyung

    2007-01-01

    This study analyzed Kimchi eating culture in 178 households with female middle school children located in Incheon and Seosan areas, investigated the Kimchi eating patterns of female middle school students, and also analyzed the differences in value recognition for Kimchi between mothers and their female middle school students. Results showed that 23.0% of subject households answered eat Kimchi at every meal and the main reason for eating Kimchi in most households was good for taste. Most hous...

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

    Science.gov (United States)

    Morrell, J. S.

    1975-01-01

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

  20. Oxidation-specific epitopes are danger-associated molecular patterns recognized by pattern recognition receptors of innate immunity

    DEFF Research Database (Denmark)

    Miller, Yury I; Choi, Soo-Ho; Wiesner, Philipp

    2011-01-01

    Oxidation reactions are vital parts of metabolism and signal transduction. However, they also produce reactive oxygen species, which damage lipids, proteins and DNA, generating "oxidation-specific" epitopes. In this review, we discuss the hypothesis that such common oxidation-specific epitopes...... are a major target of innate immunity, recognized by a variety of "pattern recognition receptors" (PRRs). By analogy with microbial "pathogen-associated molecular patterns" (PAMPs), we postulate that host-derived, oxidation-specific epitopes can be considered to represent "danger (or damage......)-associated molecular patterns" (DAMPs). We also argue that oxidation-specific epitopes present on apoptotic cells and their cellular debris provided the primary evolutionary pressure for the selection of such PRRs. Furthermore, because many PAMPs on microbes share molecular identity and/or mimicry with oxidation...

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

    Science.gov (United States)

    Cesqui, Benedetta; Tropea, Peppino; Micera, Silvestro; Krebs, Hermano Igo

    2013-07-15

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

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

    Science.gov (United States)

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

    2013-07-01

    To determine if pattern recognition techniques applied to high-resolution manometry (HRM) spatiotemporal plots of the pharyngeal swallow can identify features of disordered swallowing reported on the Modified Barium Swallow Impairment Profile (MBSImP). Case series evaluating new method of data analysis. University hospital. Simultaneous HRM and videofluoroscopy was performed on 30 subjects (335 swallows) with dysphagia. Videofluoroscopic studies were scored according to the MBSImP guidelines while HRM plots were analyzed using a novel program. Pattern recognition using a multilayer perceptron artificial neural network (ANN) was performed to determine if 7 pharyngeal components of the MBSImP as well as penetration/aspiration status could be identified from the HRM plot alone. Receiver operating characteristic (ROC) analysis was also performed. MBSImP parameters were identified correctly as normal or disordered at an average rate of approximately 91% (area under the ROC curve ranged from 0.902 to 0.981). Classifications incorporating two MBSImP parameters resulted in classification accuracies over 93% (area under the ROC curve ranged from 0.963 to 0.989). Pattern recognition coupled with multiparameter quantitative analysis of HRM spatiotemporal plots can be used to identify swallowing abnormalities, which are currently assessed using videofluoroscopy. The ability to provide quantitative, functional data at the bedside while avoiding radiation exposure makes HRM an appealing tool to supplement and, at times, replace traditional videofluoroscopic studies.

  3. Surface acoustic wave sensor array system for trace organic vapor detection using pattern recognition analysis

    Science.gov (United States)

    Rose-Pehrsson, Susan L.; Grate, Jay W.; Klusty, Mark

    1993-03-01

    A sensor system using surface acoustic wave (SAW) vapor sensors has been fabricated and tested against hazardous organic vapors, simulants of these vapors, and potential background vapors. The vapor tests included two- and three-component mixtures, and covered a wide relative humidity range. The sensor system was compared of four SAW devices coated with different sorbent materials with different vapor selectivities. Preconcentrators were included to improve sensitivity. The vapor experiments were organized into a large data set analyzed using pattern recognition techniques. Pattern recognition algorithms were developed to identify two different classes of hazards. The algorithms were verified against a second data set not included in the training. Excellent sensitivity was achieved by the sensor coatings, and the pattern recognition analysis, and was also examined by the preconcentrators. The system can detect hazardous vapors of interest in the ppb range even in varying relative humidity and in the presence of background vapors. The system does not false alarm to a variety of other vapors including gasoline, jet fuel, diesel fuel and cigarette smoke.

  4. An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control

    Science.gov (United States)

    Adewuyi, Adenike A.; Hargrove, Levi J.; Kuiken, Todd A.

    2015-01-01

    Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for partial-hand applications. PMID:25955989

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

    Science.gov (United States)

    Lee, Sean; Nitin, Mantri

    2012-01-01

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

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

    Science.gov (United States)

    Kozłowski, Adam; Królak, Aleksandra

    2009-06-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-04-13

    Many next-generation physics experiments will be characterized by the collection of large quantities of data, taken in rapid succession, from which scientists will have to unravel the underlying physical processes. In most cases, large backgrounds will overwhelm the physics signal. Since the quantity of data that can be stored for later analysis is limited, real-time event selection is imperative to retain the interesting events while rejecting the background. Scaling of current technologies is unlikely to satisfy the scientific needs of future projects, so investments in transformational new technologies need to be made. For example, future particle physics experiments looking for rare processes will have to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare processes. In this proposal, we intend to develop hardware-based technology that significantly advances the state-of-the-art for fast pattern recognition within and outside HEP using the 3D vertical integration technology that has emerged recently in industry. The ultimate physics reach of the LHC experiments will crucially depend on the tracking trigger's ability to help discriminate between interesting rare events and the background. Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing pattern

  8. Door and cabinet recognition using convolutional neural nets and real-time method fusion for handle detection and grasping

    DEFF Research Database (Denmark)

    Maurin, Adrian Llopart; Ravn, Ole; Andersen, Nils Axel

    2017-01-01

    In this paper we present a new method that robustly identifies doors, cabinets and their respective handles, with special emphasis on extracting useful features from handles to be then manipulated. The novelty of this system relies on the combination of a Convolutional Neural Net (CNN), as a form...

  9. Automated Facial Expression Recognition Using Gradient-Based Ternary Texture Patterns

    Directory of Open Access Journals (Sweden)

    Faisal Ahmed

    2013-01-01

    Full Text Available Recognition of human expression from facial image is an interesting research area, which has received increasing attention in the recent years. A robust and effective facial feature descriptor is the key to designing a successful expression recognition system. Although much progress has been made, deriving a face feature descriptor that can perform consistently under changing environment is still a difficult and challenging task. In this paper, we present the gradient local ternary pattern (GLTP—a discriminative local texture feature for representing facial expression. The proposed GLTP operator encodes the local texture of an image by computing the gradient magnitudes of the local neighborhood and quantizing those values in three discrimination levels. The location and occurrence information of the resulting micropatterns is then used as the face feature descriptor. The performance of the proposed method has been evaluated for the person-independent face expression recognition task. Experiments with prototypic expression images from the Cohn-Kanade (CK face expression database validate that the GLTP feature descriptor can effectively encode the facial texture and thus achieves improved recognition performance than some well-known appearance-based facial features.

  10. ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    VIMALA C.

    2015-05-01

    Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

  11. Epileptic Pattern Recognition and Discovery of the Local Field Potential in Amygdala Kindling Process.

    Science.gov (United States)

    Wang, Yu-Lin; Chen, Yin-Lin; Su, Alvin Wen-Yu; Shaw, Fu-Zen; Liang, Sheng-Fu

    2016-03-01

    Epileptogenesis, which occurs in an epileptic brain, is an important focus for epilepsy. The spectral analysis has been popularly applied to study the electrophysiological activities. However, the resolution is dominated by the window function of the algorithm used and the sample size. In this report, a temporal waveform analysis method is proposed to investigate the relationship of electrophysiological discharges and motor outcomes with a kindling process. Wistar rats were subjected to electrical amygdala kindling to induce temporal lobe epilepsy. During the kindling process, different morphologies of afterdischarges (ADs) were found and a recognition method, using template matching techniques combined with morphological comparators, was developed to automatically detect the epileptic patterns. The recognition results were compared to manually labeled results, and 79%-91% sensitivity was found. In addition, the initial ADs (the first 10 s) of different seizure stages were specifically utilized for recognition, and an average of 85% sensitivity was achieved. Our study provides an alternative viewpoint away from frequency analysis and time-frequency analysis to investigate epileptogenesis in an epileptic brain. The recognition method can be utilized as a preliminary inspection tool to identify remarkable changes in a patient's electrophysiological activities for clinical use. Moreover, we demonstrate the feasibility of predicting behavioral seizure stages from the early epileptiform discharges.

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

    Science.gov (United States)

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

    2015-12-01

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

  13. Role of the dual interaction of fungal pathogens with pattern recognition receptors in the activation and modulation of host defence.

    NARCIS (Netherlands)

    Netea, M.G.; Meer, J.W.M. van der; Kullberg, B.J.

    2006-01-01

    Recognition of pathogen-associated molecular patterns (PAMPs) of microorganisms by pathogen recognition receptors induces signals responsible for the activation of genes important for an effective host defence, especially those of pro-inflammatory cytokines. Toll-like receptors (TLRs) and

  14. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition.

    Science.gov (United States)

    Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

    2017-06-13

    Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).

  15. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Directory of Open Access Journals (Sweden)

    Liangji Zhou

    2017-01-01

    Full Text Available As a typical deep-learning model, Convolutional Neural Networks (CNNs can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases.

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

    Science.gov (United States)

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

    2011-02-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

  18. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Science.gov (United States)

    Huo, Guanying

    2017-01-01

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

  19. Seasonal hysteresis of net ecosystem exchange in response to temperature change: Patterns and causes

    NARCIS (Netherlands)

    Niu, S.; Luo, Y.; Montagnani, L.; Janssens, I.A.; Gielen, B.; Rambal, S.; Moors, E.J.; Matteucci, G.

    2011-01-01

    Understanding how net ecosystem exchange (NEE) changes with temperature is central to the debate on climate change-carbon cycle feedbacks, but still remains unclear. Here, we used eddy covariance measurements of NEE from 20 FLUXNET sites (203 site-years of data) in mid- and high-latitude forests to

  20. Testing of hypothesis of two-dimensional random variables independence on the basis of algorithm of pattern recognition

    Science.gov (United States)

    Lapko, A. V.; Lapko, V. A.; Yuronen, E. A.

    2016-11-01

    The new technique of testing of hypothesis of random variables independence is offered. Its basis is made by nonparametric algorithm of pattern recognition. The considered technique doesn't demand sampling of area of values of random variables.

  1. Assay for the pattern recognition molecule collectin liver 1 (CL-L1)

    DEFF Research Database (Denmark)

    Axelgaard, Esben; Jensenius, Jens Christian; Thiel, Steffen

    Collectin liver 1 (also termed collectin 10 and CL-L1) is a C-type lectin that functions as a pattern recognition molecule (PRM) in the innate immune system1. We have produced antibodies against CL-L1 and have developed a sandwich-type time-resolved immuno-fluorometric assay (TRIFMA...... to co-purify with MASPs, possibly rendering it a role in complement. CL-L1 showed binding activity towards mannose-TSK beads in a Ca2+-dependent manner. This binding could be inhibited by mannose and glucose, but not by galactose, indicating that CL-L1 binds via its carbohydrate-recognition domain (CRD)....

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

    Science.gov (United States)

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

    1992-01-01

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

  3. Local absolute binary patterns as image preprocessing for grip-pattern recognition in smart gun

    NARCIS (Netherlands)

    Shang, X.; Veldhuis, Raymond N.J.; Bowyer, K.W.; Flynn, P.J.; Govindaraju, V.; Ratha, N.

    2007-01-01

    In a biometric verification system of a smart gun, the rightful user is recognized based on his handpressure pattern. The main factor which affects the verification performance of this system is the variation between the probe image and the gallery image of a subject, in particular when the probe

  4. epSICAR: An Emerging Patterns based Approach to Sequential, Interleaved and Concurrent Activity Recognition

    DEFF Research Database (Denmark)

    Gu, Tao; Wu, Zhanqing; Tao, Xianping

    2009-01-01

    upon the training dataset for complex activities, we build our activity models by mining a set of Emerging Patterns from the sequential activity trace only and apply our models in recognizing sequential, interleaved and concurrent activities. We conduct our empirical studies in a real smart home...... and concurrent) manners in real life. In this paper, we propose a novel Emerging Patterns based approach to Sequential, Interleaved and Concurrent Activity Recognition (epSICAR). We exploit Emerging Patterns as powerful discriminators to differentiate activities. Different from other learning-based models built...... environment, and the results demonstrate that with a time slice of 15 seconds, we achieve an accuracy of 90.96% for sequential activity, 87.98% for interleaved activity and 78.58% for concurrent activity....

  5. [Proposal for recognition of the comfort pattern in clients with pemphigus vulgaris using Fuzzy Logic].

    Science.gov (United States)

    Brandão, Euzeli da Silva; dos Santos, Iraci; Lanzillotti, Regina Serrão; Moreira, Augusto Júnior

    2013-08-01

    The objective was to propose the use of Fuzzy Logic for recognition of comfort patterns in people undergoing a technology of nursing care because of pemphigus vulgaris, a rare mucocutaneous disease that affects mainly adults. The proposal applied experimental methods, with subjects undergoing a qualitative-quantitative comparison (taxonomy/relevance) of the comfort patterns before and after the intervention. A record of a chromatic scale corresponding to the intensity of each attribute was required: pain, mobility and impaired self-image. The Fuzzy rules established by an inference engine set the standard for comfort in maximum, median and minimum discomfort, reflecting the effectiveness of nursing care. Although rarely used in the area of nursing, this logic enabled viable research without a priori scaling of the number of subjects depending on the estimation of population parameters. It is expected to evaluate the pattern of comfort in the client with pemphigus, before the applied technology, in a personalized way, leading to a comprehensive evaluation.

  6. Optimizing Photosynthetic and Respiratory Parameters Based on the Seasonal Variation Pattern in Regional Net Ecosystem Productivity Obtained from Atmospheric Inversion

    Science.gov (United States)

    Chen, Z.; Chen, J.; Zheng, X.; Jiang, F.; Zhang, S.; Ju, W.; Yuan, W.; Mo, G.

    2014-12-01

    In this study, we explore the feasibility of optimizing ecosystem photosynthetic and respiratory parameters from the seasonal variation pattern of the net carbon flux. An optimization scheme is proposed to estimate two key parameters (Vcmax and Q10) by exploiting the seasonal variation in the net ecosystem carbon flux retrieved by an atmospheric inversion system. This scheme is implemented to estimate Vcmax and Q10 of the Boreal Ecosystem Productivity Simulator (BEPS) to improve its NEP simulation in the Boreal North America (BNA) region. Simultaneously, in-situ NEE observations at six eddy covariance sites are used to evaluate the NEE simulations. The results show that the performance of the optimized BEPS is superior to that of the BEPS with the default parameter values. These results have the implication on using atmospheric CO2 data for optimizing ecosystem parameters through atmospheric inversion or data assimilation techniques.

  7. Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.

    Directory of Open Access Journals (Sweden)

    Andre F Marquand

    Full Text Available Progressive supranuclear palsy (PSP, multiple system atrophy (MSA and idiopathic Parkinson's disease (IPD can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs. An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i a subcortical motor network; (ii each of its component regions and (iii the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.

  8. Mechanisms of Expression and Internalisation of FIBCD1; a novel Pattern Recognition Receptor in the Gut Mucosa

    DEFF Research Database (Denmark)

    Hammond, Mark; Schlosser, Anders; Dubey, Lalit Kumar

    2012-01-01

    is a carbohydrate recognition domain also expressed by the ficolins, which are pattern recognition molecules that activate the complement system via the lectin pathway. Chitin is a highly ace¬tylated homopolymer of β-1,4-N-acetyl-glucosamine carbohydrate found abundantly in nature in organisms such as fungi...... pattern recognition receptor that binds chitin and directs acetylated structures for de¬gradation in the endosome via clathrin-mediated endocytosis. The localisation of FIBCD1 in the intestinal mucosal epithelia points towards a functional role in innate immunity and/or gut homeostasis....

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

    Science.gov (United States)

    Baird, Bill

    1986-08-01

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

  10. Fast pattern recognition with the ATLAS L1 track trigger for the HL-LHC

    CERN Document Server

    Martensson, Mikael; The ATLAS collaboration

    2016-01-01

    A fast hardware based track trigger for high luminosity upgrade of the Large Hadron Collider (HL- LHC) is being developed in ATLAS. The goal is to achieve trigger levels in high pileup collisions that are similar or even better than those achieved at low pile-up running of LHC by adding tracking information to the ATLAS hardware trigger which is currently based on information from calorimeters and muon trigger chambers only. Two methods for fast pattern recognition are investigated. The first is based on matching tracker hits to pattern banks of simulated high momentum tracks which are stored in a custom made Associative Memory (AM) ASIC. The second is based on the Hough transform where detector hits are transformed into 2D Hough space with one variable related to track pt and one to track direction. Hits found by pattern recognition will be sent to a track fitting step which calculates the track parameters . The speed and precision of the track fitting depends on the quality of the hits selected by the patte...

  11. Fast pattern recognition with the ATLAS L1Track trigger for HL-LHC

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00530554; The ATLAS collaboration

    2016-01-01

    A fast hardware based track trigger is being developed in ATLAS for the High Luminosity upgrade of the Large Hadron Collider. The goal is to achieve trigger levels in the high pile-up conditions of the High Luminosity Large Hadron Collider that are similar or better than those achieved at low pile-up conditions by adding tracking information to the ATLAS hardware trigger. A method for fast pattern recognition using the Hough transform is investigated. In this method, detector hits are mapped onto a 2D parameter space with one parameter related to the transverse momentum and one to the initial track direction. The performance of the Hough transform is studied at different pile-up values. It is also compared, using full event simulation of events with average pile-up of 200, with a method based on matching detector hits to pattern banks of simulated tracks stored in a custom made Associative Memory ASICs. The pattern recognition is followed by a track fitting step which calculates the track parameters. The spee...

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

    Directory of Open Access Journals (Sweden)

    Serge Thomas Mickala Bourobou

    2015-05-01

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

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

    Science.gov (United States)

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-05-21

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

  14. Accuracy of ultrasound subjective 'pattern recognition' for the diagnosis of borderline ovarian tumors.

    Science.gov (United States)

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

    2007-05-01

    To assess the value of pattern recognition for the preoperative ultrasound diagnosis of borderline ovarian tumors (BOTs). 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 III (expert) ultrasound scan in our tertiary center. The tumor pattern recognition method was used to differentiate between various types of ovarian tumors. Morphological features suggestive of BOTs were: unilocular cyst with a positive ovarian crescent sign and extensive papillary projections arising from the inner wall, or a cyst with a well defined multilocular nodule. The ultrasound findings were compared with the final histological diagnosis. A total of 224 women with an adnexal mass of uncertain nature were referred for an expert scan, 166 (74.1%) of whom underwent surgery. In this group of women the final histological diagnoses were: 99 (60%) benign lesions, 32 (19%) invasive ovarian cancer and 35 (21%) BOTs. Using pattern recognition combining the different morphological features, a correct preoperative diagnosis of BOT was made in 24/35 (68.6%) women: area under the receiver-operating characteristics curve 0.812 (standard error 0.049; 95% CI, 0.716-0.908), sensitivity 0.69 (95% CI, 0.52-0.81), specificity 0.94 (95% CI, 0.88-0.97), positive likelihood ratio 11.3 (95% CI, 5.53-22.8) and negative likelihood ratio 0.34 (95% CI, 0.21-0.55). Ultrasound diagnosis of BOTs is highly specific. However, typical features are absent in one-third of cases, which are typically misdiagnosed as benign lesions. Copyright (c) 2007 ISUOG.

  15. Morphological characterization of Mycobacterium tuberculosis in a MODS culture for an automatic diagnostics through pattern recognition.

    Directory of Open Access Journals (Sweden)

    Alicia Alva

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

  16. Folk Dance Pattern Recognition Over Depth Images Acquired via Kinect Sensor

    Science.gov (United States)

    Protopapadakis, E.; Grammatikopoulou, A.; Doulamis, A.; Grammalidis, N.

    2017-02-01

    The possibility of accurate recognition of folk dance patterns is investigated in this paper. System inputs are raw skeleton data, provided by a low cost sensor. In particular, data were obtained by monitoring three professional dancers, using a Kinect II sensor. A set of six traditional Greek dances (without their variations) consists the investigated data. A two-step process was adopted. At first, the most descriptive skeleton data were selected using a combination of density based and sparse modelling algorithms. Then, the representative data served as training set for a variety of classifiers.

  17. A pattern recognition method for the RICH-based HMPID detector in ALICE

    CERN Document Server

    Elia, D; Cozza, D; Di Bari, D; Di Mauro, A; Morsch, Andreas; Nappi, E; Paic, G; Piuz, François; Stucchi, S; Tomasicchio, G

    1999-01-01

    A pattern recognition method developed for the High Momentum Particle IDentification (HMPID) detector in the ALICE experiment at CERN is presented. The algorithm is based on the Hough transform with a mapping of the pad coordinate space directly to the Cherenkov angle parameter space. Cherenkov angle reconstruction has been studied as a function of different particle densities in the photodetector using real data taken in the ALICE tests at the CERN SPS: a satisfactory resolution can be achieved even in events where the occupancy reaches more than 12, which is the situation we may be confronted with in central Pb-Pb interactions at LHC. (9 refs).

  18. FOLK DANCE PATTERN RECOGNITION OVER DEPTH IMAGES ACQUIRED VIA KINECT SENSOR

    Directory of Open Access Journals (Sweden)

    E. Protopapadakis

    2017-02-01

    Full Text Available The possibility of accurate recognition of folk dance patterns is investigated in this paper. System inputs are raw skeleton data, provided by a low cost sensor. In particular, data were obtained by monitoring three professional dancers, using a Kinect II sensor. A set of six traditional Greek dances (without their variations consists the investigated data. A two-step process was adopted. At first, the most descriptive skeleton data were selected using a combination of density based and sparse modelling algorithms. Then, the representative data served as training set for a variety of classifiers.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1981-01-01

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

  20. Is it worth changing pattern recognition methods for structural health monitoring?

    Science.gov (United States)

    Bull, L. A.; Worden, K.; Cross, E. J.; Dervilis, N.

    2017-05-01

    The key element of this work is to demonstrate alternative strategies for using pattern recognition algorithms whilst investigating structural health monitoring. This paper looks to determine if it makes any difference in choosing from a range of established classification techniques: from decision trees and support vector machines, to Gaussian processes. Classification algorithms are tested on adjustable synthetic data to establish performance metrics, then all techniques are applied to real SHM data. To aid the selection of training data, an informative chain of artificial intelligence tools is used to explore an active learning interaction between meaningful clusters of data.

  1. Identification of a β-glucosidase from the Mucor circinelloides genome by peptide pattern recognition

    DEFF Research Database (Denmark)

    Yuhong, Huang; Busk, Peter Kamp; Grell, Morten Nedergaard

    2014-01-01

    Mucor circinelloides produces plant cell wall degrading enzymes that allow it to grow on complex polysaccharides. Although the genome of M. circinelloides has been sequenced, only few plant cell wall degrading enzymes are annotated in this species. We applied peptide pattern recognition, which......-nitrophenyl-β-d-glucopyranoside as substrate was 0.20mM and 2.41U/mg, respectively. The enzyme was not inhibited by glucose and retained 84% activity at glucose concentrations up to 140mM. Although zygomycetes are not considered to be important degraders of lignocellulosic biomass in nature, the present finding of an active β...

  2. Immunopathological Roles of Cytokines, Chemokines, Signaling Molecules, and Pattern-Recognition Receptors in Systemic Lupus Erythematosus

    Science.gov (United States)

    Yu, Shui-Lian; Kuan, Woon-Pang; Wong, Chun-Kwok; Li, Edmund K.; Tam, Lai-Shan

    2012-01-01

    Systemic lupus erythematosus (SLE) is an autoimmune disease with unknown etiology affecting more than one million individuals each year. It is characterized by B- and T-cell hyperactivity and by defects in the clearance of apoptotic cells and immune complexes. Understanding the complex process involved and the interaction between various cytokines, chemokines, signaling molecules, and pattern-recognition receptors (PRRs) in the immune pathways will provide valuable information on the development of novel therapeutic targets for treating SLE. In this paper, we review the immunopathological roles of novel cytokines, chemokines, signaling molecules, PRRs, and their interactions in immunoregulatory networks and suggest how their disturbances may implicate pathological conditions in SLE. PMID:22312407

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

    CERN Document Server

    Kamada, K; Ogawa, S

    1999-01-01

    A pattern recognition method was applied to the neutron-gamma discrimination of the pulses from the liquid scintillator, NE-213. The circuit for the discrimination is composed of A/D converter, fast SCA, memory control circuit, two digital delay lines and two buffer memories. All components are packed on a small circuit board and are installed into a personal computer. Experiments using a weak sup 2 sup 5 sup 2 Cf n-gamma source were undertaken to test the feasibility of the circuit. The circuit is of very easy adjustment and, at the same time, of very economical price when compared with usual discrimination circuits, such as the TAC system.

  4. The automated differential; pattern recognition systems, precision, and the spun smear.

    Science.gov (United States)

    Kingsley, T C

    1980-01-01

    Utilizing normal patient blood samples, two automated differential pattern recognition systems, the ADC-500 and the Hematrak 360, were compared in a duplicate study with the manual eye method, using spun and wedge smear preparations. The study showed a superior precision for the 500-cell differential, but occasional data points with spun smears were unusually drifted from the central cluster for both the manual and instrument methods. Analysis of the data uncovered at least two reasons for these unusual outliers: "star artifacts" and warped glass slides. The importrance of these method-related artifacts are discussed, alo ng with the future of the 500-cell differential with today's automated technology.

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

    Science.gov (United States)

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

    2014-01-01

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

  6. Pattern recognition in RICH counters using the Possibilistic C- Spherical Shell algorithm

    CERN Document Server

    Massone, A M; Masulli, F

    2000-01-01

    The pattern recognition problem in RICH counters concerns the identification of an unknown number of imperfect roughly-circular rings made of a low number of discrete points in presence of background. We present some preliminary results obtained using the Possibilistic C-Spherical Shell algorithm. In particular, we show that the algorithm is very tolerant and robust to noise (outliers rate) level. Moreover, for complex images full of rings, we introduce an iterative scheme that greatly improves performance. Also, the rings are not required to be complete, arcs are sufficient to recognize the underlying rings. (4 refs).

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

    Science.gov (United States)

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

    1985-01-01

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

  8. An Application of Discriminant Analysis to Pattern Recognition of Selected Contaminated Soil Features in Thin Sections

    DEFF Research Database (Denmark)

    Ribeiro, Alexandra B.; Nielsen, Allan Aasbjerg

    1997-01-01

    qualitative microprobe results: present elements Al, Si, Cr, Fe, As (associated with others). Selected groups of calibrated images (same light conditions and magnification) submitted to discriminant analysis, in order to find a pattern of recognition in the soil features corresponding to contamination already...... concentrations of contaminants are indicated by chemical wet analysis, these contaminants must occur directly in the solid phase. Thin sections of soil aggregates were scanned for Cu, Cr and As using an electron microprobe, and qualitative analysis was made on selected areas. Microphotographs of thin sections...

  9. Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

    Science.gov (United States)

    Nasertdinova, A. D.; Bochkarev, V. V.

    2017-11-01

    Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.

  10. Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

    Science.gov (United States)

    Decker, Arthur J.

    1999-01-01

    Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

  11. A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies.

    Science.gov (United States)

    Benatti, Simone; Milosevic, Bojan; Farella, Elisabetta; Gruppioni, Emanuele; Benini, Luca

    2017-04-15

    Poliarticulated prosthetic hands represent a powerful tool to restore functionality and improve quality of life for upper limb amputees. Such devices offer, on the same wearable node, sensing and actuation capabilities, which are not equally supported by natural interaction and control strategies. The control in state-of-the-art solutions is still performed mainly through complex encoding of gestures in bursts of contractions of the residual forearm muscles, resulting in a non-intuitive Human-Machine Interface (HMI). Recent research efforts explore the use of myoelectric gesture recognition for innovative interaction solutions, however there persists a considerable gap between research evaluation and implementation into successful complete systems. In this paper, we present the design of a wearable prosthetic hand controller, based on intuitive gesture recognition and a custom control strategy. The wearable node directly actuates a poliarticulated hand and wirelessly interacts with a personal gateway (i.e., a smartphone) for the training and personalization of the recognition algorithm. Through the whole system development, we address the challenge of integrating an efficient embedded gesture classifier with a control strategy tailored for an intuitive interaction between the user and the prosthesis. We demonstrate that this combined approach outperforms systems based on mere pattern recognition, since they target the accuracy of a classification algorithm rather than the control of a gesture. The system was fully implemented, tested on healthy and amputee subjects and compared against benchmark repositories. The proposed approach achieves an error rate of 1.6% in the end-to-end real time control of commonly used hand gestures, while complying with the power and performance budget of a low-cost microcontroller.

  12. Changes in pattern completion--a key mechanism to explain age-related recognition memory deficits?

    Science.gov (United States)

    Vieweg, Paula; Stangl, Matthias; Howard, Lorelei R; Wolbers, Thomas

    2015-03-01

    Accurate memory retrieval from partial or degraded input requires the reactivation of memory traces, a hippocampal mechanism termed pattern completion. Age-related changes in hippocampal integrity have been hypothesized to shift the balance of memory processes in favor of the retrieval of already stored information (pattern completion), to the detriment of encoding new events (pattern separation). Using a novel behavioral paradigm, we investigated the impact of cognitive aging (1) on recognition performance across different levels of stimulus completeness, and (2) on potential response biases. Participants were required to identify previously learned scenes among new ones. Additionally, all stimuli were presented in gradually masked versions to alter stimulus completeness. Both young and older adults performed increasingly poorly as the scenes became less complete, and this decline in performance was more pronounced in elderly participants indicative of a pattern completion deficit. Intriguingly, when novel scenes were shown, only the older adults showed an increased tendency to identify these as familiar scenes. In line with theoretical models, we argue that this reflects an age-related bias towards pattern completion. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. The pattern-recognition molecule mannan-binding lectin (MBL) in the pathophysiology of diabetic nephropathy

    DEFF Research Database (Denmark)

    Axelgaard, Esben; Thiel, Steffen; Hansen, Troels Krarup

    The pattern-recognition molecule mannan-binding lectin (MBL) in the pathophysiology of diabetic nephropathy Esben Axelgaard*; Steffen Thiel*; Jakob Appel Østergaard† and Troels Krarup Hansen† *Department of Biomedicine, Aarhus University, Wilhelm Meyer´s Allé 4, 8000 Aarhus C, Denmark. †Department...... of Clinical Medicine, Aarhus University and The Danish Diabetes Academy, Nørrebrogade 44, build. 3, 8000 Aarhus C, Denmark The complement system is part of the innate immune system and is an important part of the first line of defence against pathogens. Mannan-binding lectin (MBL) is one of the pattern...... to carbohydrates of both specific type and density, which thus provides sufficient binding avidity. The character of MBL binding-sites on host cells remain unknown, but it is speculated that altered protein glycation in diabetes permits MBL binding. Based on new studies using MBL/double knockout C57bl/6j mice, we...

  14. Chaos Synchronization Error Technique-Based Defect Pattern Recognition for GIS through Partial Discharge Signal Analysis

    Directory of Open Access Journals (Sweden)

    Hung-Cheng Chen

    2014-08-01

    Full Text Available The work is aimed at using the chaos synchronization error dynamics (CSED technique for defect pattern recognition in gas insulated switchgear (GIS. The radiated electromagnetic waves generated due to internal defects were measured by the self-made ultrahigh frequency (UHF micro-strip antenna, so as to determine whether partial discharge will occur. Firstly, a data pretreatment is performed on the measured raw data for the purpose of computational burden reduction. A characteristic matrix is then constructed according to dynamic error trajectories in a chaos synchronization system, subsequent to which characteristics are extracted. A comparison with the existing Hilbert-Huang Transform (HHT method reveals that the two characteristics extracted from the CSED results presented herein using the fractal theory were recognized at a higher rate pattern.

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

    Science.gov (United States)

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

    2015-12-05

    In this paper, near infrared spectroscopy (NIR) in cooperation with the pattern recognition techniques were used to determine the type of neat acetonitrile and the adulteration in acetonitrile. NIR spectra were collected between 400 nm and 2498 nm. The experimental data were first subjected to analysis of principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Then support vector machine (SVM) were applied to develop classification models and the best parameter combination was selected by grid search. Under the best parameter combination, the classification accuracy rates of three types of neat acetonitrile reached 87.5%, and 100% for the adulteration with different concentration levels. The results showed that NIR spectroscopy combined with SVM could be utilized for determining the potential adulterants including water, ethanol, isopropyl alcohol, acrylonitrile, methanol, and by-products associated with the production of acetonitrile. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    Savchenko, A V

    2013-10-01

    The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n-grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%-7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition.

    Science.gov (United States)

    Lu, Zhiyuan; Chen, Xiang; Zhang, Xu; Tong, Kay-Yu; Zhou, Ping

    2017-08-01

    Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user's intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.

  18. Pattern Recognition via the Toll-Like Receptor System in the Human Female Genital Tract

    Directory of Open Access Journals (Sweden)

    Kaei Nasu

    2010-01-01

    Full Text Available The mucosal surface of the female genital tract is a complex biosystem, which provides a barrier against the outside world and participates in both innate and acquired immune defense systems. This mucosal compartment has adapted to a dynamic, non-sterile environment challenged by a variety of antigenic/inflammatory stimuli associated with sexual intercourse and endogenous vaginal microbiota. Rapid innate immune defenses against microbial infection usually involve the recognition of invading pathogens by specific pattern-recognition receptors recently attributed to the family of Toll-like receptors (TLRs. TLRs recognize conserved pathogen-associated molecular patterns (PAMPs synthesized by microorganisms including bacteria, fungi, parasites, and viruses as well as endogenous ligands associated with cell damage. Members of the TLR family, which includes 10 human TLRs identified to date, recognize distinct PAMPs produced by various bacterial, fungal, and viral pathogens. The available literature regarding the innate immune system of the female genital tract during human reproductive processes was reviewed in order to identify studies specifically related to the expression and function of TLRs under normal as well as pathological conditions. Increased understanding of these molecules may provide insight into site-specific immunoregulatory mechanisms in the female reproductive tract.

  19. HPLC fingerprint analysis combined with chemometrics for pattern recognition of ginger.

    Science.gov (United States)

    Feng, Xu; Kong, Weijun; Wei, Jianhe; Ou-Yang, Zhen; Yang, Meihua

    2014-03-01

    Ginger, the fresh rhizome of Zingiber officinale Rosc. (Zingiberaceae), has been used worldwide; however, for a long time, there has been no standard approbated internationally for its quality control. To establish an efficacious and combinational method and pattern recognition technique for quality control of ginger. A simple, accurate and reliable method based on high-performance liquid chromatography with photodiode array (HPLC-PDA) detection was developed for establishing the chemical fingerprints of 10 batches of ginger from different markets in China. The method was validated in terms of precision, reproducibility and stability; and the relative standard deviations were all less than 1.57%. On the basis of this method, the fingerprints of 10 batches of ginger samples were obtained, which showed 16 common peaks. Coupled with similarity evaluation software, the similarities between each fingerprint of the sample and the simulative mean chromatogram were in the range of 0.998-1.000. Then, the chemometric techniques, including similarity analysis, hierarchical clustering analysis and principal component analysis were applied to classify the ginger samples. Consistent results were obtained to show that ginger samples could be successfully classified into two groups. This study revealed that HPLC-PDA method was simple, sensitive and reliable for fingerprint analysis, and moreover, for pattern recognition and quality control of ginger.

  20. Design and testing of the first 2D Prototype Vertically Integrated Pattern Recognition Associative Memory

    Energy Technology Data Exchange (ETDEWEB)

    Liu, T.; Deptuch, G.; Hoff, J.; Jindariani, S.; Joshi, S.; Olsen, J.; Tran, N.; Trimpl, M.

    2015-02-01

    An associative memory-based track finding approach has been proposed for a Level 1 tracking trigger to cope with increasing luminosities at the LHC. The associative memory uses a massively parallel architecture to tackle the intrinsically complex combinatorics of track finding algorithms, thus avoiding the typical power law dependence of execution time on occupancy and solving the pattern recognition in times roughly proportional to the number of hits. This is of crucial importance given the large occupancies typical of hadronic collisions. The design of an associative memory system capable of dealing with the complexity of HL-LHC collisions and with the short latency required by Level 1 triggering poses significant, as yet unsolved, technical challenges. For this reason, an aggressive R&D program has been launched at Fermilab to advance state of-the-art associative memory technology, the so called VIPRAM (Vertically Integrated Pattern Recognition Associative Memory) project. The VIPRAM leverages emerging 3D vertical integration technology to build faster and denser Associative Memory devices. The first step is to implement in conventional VLSI the associative memory building blocks that can be used in 3D stacking, in other words, the building blocks are laid out as if it is a 3D design. In this paper, we report on the first successful implementation of a 2D VIPRAM demonstrator chip (protoVIPRAM00). The results show that these building blocks are ready for 3D stacking.

  1. Cardinality as a Highly Descriptive Feature in Myoelectric Pattern Recognition for Decoding Motor Volition

    Directory of Open Access Journals (Sweden)

    Max eOrtiz-Catalan

    2015-10-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  3. Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

    Science.gov (United States)

    Miller, Vonda H; Jansen, Ben H

    2008-12-01

    Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

  4. Pattern recognition as a concept for multiple-choice questions in a national licensing exam.

    Science.gov (United States)

    Freiwald, Tilo; Salimi, Madjid; Khaljani, Ehsan; Harendza, Sigrid

    2014-11-14

    Multiple-choice questions (MCQ) are still widely used in high stakes medical exams. We wanted to examine whether and to what extent a national licensing exam uses the concept of pattern recognition to test applied clinical knowledge. We categorized all 4,134 German National medical licensing exam questions between October 2006 and October 2012 by discipline, year, and type. We analyzed questions from the four largest disciplines: internal medicine (n = 931), neurology (n = 305), pediatrics (n = 281), and surgery (n = 233), with respect to the following question types: knowledge questions (KQ), pattern recognition questions (PRQ), inverse PRQ (IPRQ), and pseudo PRQ (PPRQ). A total 51.1% of all questions were of a higher taxonomical order (PRQ and IPRQ) with a significant decrease in the percentage of these questions (p exam to test applied clinical knowledge. Being aware of this concept may aid in the design and balance of MCQs in an exam with respect to testing clinical reasoning as a desired skill at the threshold of postgraduate medical education.

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

    Science.gov (United States)

    Rajan, P. K.; Balendra, Anushia

    1992-01-01

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

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

    Science.gov (United States)

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

    2015-10-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

  8. NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis.

    Science.gov (United States)

    Zhang, Ai-hua; Sun, Hui; Qiu, Shi; Wang, Xi-jun

    2013-09-01

    Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice. In this review, we will present several special examples of a successful application of NMR for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and discuss possible future improvements. Copyright © 2013 John Wiley & Sons, Ltd.

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

    Science.gov (United States)

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

    2013-01-01

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

  10. Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images

    Directory of Open Access Journals (Sweden)

    Marc Wieland

    2014-03-01

    Full Text Available In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS and very high resolution (WorldView-2, Quickbird, Ikonos multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based, have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.

  11. Scanning patterns of faces do not explain impaired emotion recognition in Huntington Disease: Evidence for a high level mechanism

    Directory of Open Access Journals (Sweden)

    Marieke evan Asselen

    2012-02-01

    Full Text Available Previous studies in patients with amygdala lesions suggested that deficits in emotion recognition might be mediated by impaired scanning patterns of faces. Here we investigated whether scanning patterns also contribute to the selective impairment in recognition of disgust in Huntington disease (HD. To achieve this goal, we recorded eye movements during a two-alternative forced choice emotion recognition task. HD patients in presymptomatic (n=16 and symptomatic (n=9 disease stages were tested and their performance was compared to a control group (n=22. In our emotion recognition task, participants had to indicate whether a face reflected one of six basic emotions. In addition, and in order to define whether emotion recognition was altered when the participants were forced to look at a specific component of the face, we used a second task where only limited facial information was provided (eyes/mouth in partially masked faces. Behavioural results showed no differences in the ability to recognize emotions between presymptomatic gene carriers and controls. However, an emotion recognition deficit was found for all 6 basic emotion categories in early stage HD. Analysis of eye movement patterns showed that patient and controls used similar scanning strategies. Patterns of deficits were similar regardless of whether parts of the faces were masked or not, thereby confirming that selective attention to particular face parts is not underlying the deficits. These results suggest that the emotion recognition deficits in symptomatic HD patients cannot be explained by impaired scanning patterns of faces. Furthermore, no selective deficit for recognition of disgust was found in presymptomatic HD patients.

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

    Science.gov (United States)

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

    2010-01-01

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

  13. Complement activating soluble pattern recognition molecules with collagen-like regions, mannan-binding lectin, ficolins and associated proteins

    DEFF Research Database (Denmark)

    Thiel, Steffen

    2007-01-01

    Mannan-binding lectin (MBL), L-ficolin, M-ficolin and H-ficolin are all complement activating soluble pattern recognition molecules with recognition domains linked to collagen-like regions. All four may form complexes with four structurally related proteins, the three MBL-associated serine...... proteases (MASPs), MASP-1, MASP-2 and MASP-3, and a smaller MBL-associated protein (MAp19). The four recognition molecules recognize patterns of carbohydrate or acetyl-group containing ligands. After binding to the relevant targets all four are able to activate the complement system. We thus have a system...... where four different and/or overlapping patterns of microbial origin or patterns of altered-self may be recognized, but in all cases the signalling molecules, the MASPs, are shared. MASP-1 and MASP-3 are formed from one gene, MASP1/3, by alternative splicing generating two different mRNAs from a single...

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

    Directory of Open Access Journals (Sweden)

    Jahed Mehran

    2007-12-01

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

  15. Phoneme Error Pattern by Heritage Speakers of Spanish on an English Word Recognition Test.

    Science.gov (United States)

    Shi, Lu-Feng

    2017-04-01

    Heritage speakers acquire their native language from home use in their early childhood. As the native language is typically a minority language in the society, these individuals receive their formal education in the majority language and eventually develop greater competency with the majority than their native language. To date, there have not been specific research attempts to understand word recognition by heritage speakers. It is not clear if and to what degree we may infer from evidence based on bilingual listeners in general. This preliminary study investigated how heritage speakers of Spanish perform on an English word recognition test and analyzed their phoneme errors. A prospective, cross-sectional, observational design was employed. Twelve normal-hearing adult Spanish heritage speakers (four men, eight women, 20-38 yr old) participated in the study. Their language background was obtained through the Language Experience and Proficiency Questionnaire. Nine English monolingual listeners (three men, six women, 20-41 yr old) were also included for comparison purposes. Listeners were presented with 200 Northwestern University Auditory Test No. 6 words in quiet. They repeated each word orally and in writing. Their responses were scored by word, word-initial consonant, vowel, and word-final consonant. Performance was compared between groups with Student's t test or analysis of variance. Group-specific error patterns were primarily descriptive, but intergroup comparisons were made using 95% or 99% confidence intervals for proportional data. The two groups of listeners yielded comparable scores when their responses were examined by word, vowel, and final consonant. However, heritage speakers of Spanish misidentified significantly more word-initial consonants and had significantly more difficulty with initial /p, b, h/ than their monolingual peers. The two groups yielded similar patterns for vowel and word-final consonants, but heritage speakers made significantly

  16. Derelict fishing nets in Puget Sound and the Northwest Straits: patterns and threats to marine fauna.

    Science.gov (United States)

    Good, Thomas P; June, Jeffrey A; Etnier, Michael A; Broadhurst, Ginny

    2010-01-01

    Derelict fishing gear remains in the marine environment for years, entangling, and killing marine organisms worldwide. Since 2002, hundreds of derelict nets containing over 32,000 marine animals have been recovered from Washington's inland waters. Analysis of 870 gillnets found many were derelict for years; most were recovered from northern Puget Sound and high-relief rocky habitats and were relatively small, of recent construction, in good condition, stretched open, and in relatively shallow water. Marine organisms documented in recovered gillnets included 31,278 invertebrates (76 species), 1036 fishes (22 species), 514 birds (16 species), and 23 mammals (4 species); 56% of invertebrates, 93% of fish, and 100% of birds and mammals were dead when recovered. For all taxa, mortality was generally associated with gillnet effectiveness (total area, age and condition, and suspension in the water). Mortality from derelict fishing gear is underestimated at recovery and may be important for species of economic and conservation concern. Published by Elsevier Ltd.

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

    Science.gov (United States)

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

    2013-09-01

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

  18. Two-phase flow patterns recognition and parameters estimation through natural circulation test loop image analysis

    Energy Technology Data Exchange (ETDEWEB)

    Mesquita, R.N.; Libardi, R.M.P.; Masotti, P.H.F.; Sabundjian, G.; Andrade, D.A.; Umbehaun, P.E.; Torres, W.M.; Conti, T.N.; Macedo, L.A. [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil). Nuclear Engineering Center], e-mail: rnavarro@ipen.br

    2009-07-01

    Visualization of natural circulation test loop cycles is used to study two-phase flow patterns associated with phase transients and static instabilities of flow. Experimental studies on natural circulation flow were originally related to accidents and transient simulations relative to nuclear reactor systems with light water refrigeration. In this regime, fluid circulation is mainly caused by a driving force ('thermal head') which arises from density differences due to temperature gradient. Natural circulation phenomenon has been important to provide residual heat removal in cases of 'loss of pump power' or plant shutdown in nuclear power plant accidents. The new generation of compact nuclear reactors includes natural circulation of their refrigerant fluid as a security mechanism in their projects. Two-phase flow patterns have been studied for many decades, and the related instabilities have been object of special attention recently. Experimental facility is an all glass-made cylindrical tubes loop which contains about twelve demineralized water liters, a heat source by an electrical resistor immersion heater controlled by a Variac, and a helicoidal heat exchanger working as cold source. Data is obtained through thermo-pairs distributed over the loop and CCD cameras. Artificial intelligence based algorithms are used to improve (bubble) border detection and patterns recognition, in order to estimate and characterize, phase transitions patterns and correlate them with the periodic static instability (chugging) cycle observed in this circuit. Most of initial results show good agreement with previous numerical studies in this same facility. (author)

  19. Pattern Recognition of Signals for the Fault-Slip Type of Rock Burst in Coal Mines

    Directory of Open Access Journals (Sweden)

    X. S. Liu

    2015-01-01

    Full Text Available The fault-slip type of rock burst is a major threat to the safety of coal mining, and effectively recognizing its signals patterns is the foundation for the early warning and prevention. At first, a mechanical model of the fault-slip was established and the mechanism of the rock burst induced by the fault-slip was revealed. Then, the patterns of the electromagnetic radiation, acoustic emission (AE, and microseismic signals in the fault-slip type of rock burst were proposed, in that before the rock burst occurs, the electromagnetic radiation intensity near the sliding surface increases rapidly, the AE energy rises exponentially, and the energy released by microseismic events experiences at least one peak and is close to the next peak. At last, in situ investigations were performed at number 1412 coal face in the Huafeng Mine, China. Results showed that the signals patterns proposed are in good agreement with the process of the fault-slip type of rock burst. The pattern recognition can provide a basis for the early warning and the implementation of relief measures of the fault-slip type of rock burst.

  20. The pattern recognition molecule ficolin-1 exhibits differential binding to lymphocyte subsets, providing a novel link between innate and adaptive immunity

    DEFF Research Database (Denmark)

    Genster, Ninette; Ma, Ying Jie; Munthe-Fog, Lea

    2014-01-01

    Ficolin-1 is a soluble pattern recognition molecule synthesized by myeloid cells and capable of activating the lectin pathway of complement on the surface of pathogens. It is tethered to the membranes of monocytes and granulocytes; however, the biological significance of cell-associated ficolin-1...... is unknown. Recognition of healthy host cells by a pattern recognition molecule constitutes a potential hazard to self cells and tissues, emphasizing the importance of further elucidating the reported self-recognition. In the current study we investigated the potential recognition of lymphocytes by ficolin-1...... and point toward additional immune modulating functions of the molecule besides its role in pathogen recognition....

  1. Variability in the recognition of distinctive immunofluorescence patterns in different brands of HEp-2 cell slides

    Directory of Open Access Journals (Sweden)

    Alessandra Dellavance

    2013-06-01

    Full Text Available INTRODUCTION: Indirect immunofluorescence on HEp-2 cells is considered the gold standard for the detection of autoantibodies against cellular antigens. However, the culture conditions, cell fixation and permeabilization processes interfere directly in the preservation and spatial distribution of antigens. Therefore, one can assume that certain peculiarities in the processing of cellular substrate may affect the recognition of indirect immunofluorescence patterns associated with several autoantibodies. OBJECTIVE: To evaluate a panel of serum samples representing nuclear, nucleolar, cytoplasmic, mitotic apparatus, and chromosome plate patterns on HEp-2 cell substrates from different suppliers. MATERIALS AND METHODS: Seven blinded observers, independent from the three selected reference centers, evaluated 17 samples yielding different nuclear, nucleolar, cytoplasmic and mitotic apparatus patterns on HEp-2 cell slides from eight different brands. The slides were coded to maintain confidentiality of both brands and participating centers. RESULTS: The 17 HEp-2 cell patterns were identified on most substrates. Nonetheless, some slides showed deficit in the expression of several patterns: nuclear coarse speckled/U1-ribonucleoprotein associated with antibodies against RNP (U1RNP, centromeric protein F (CENP-F, proliferating cell nuclear antigen (PCNA, cytoplasmic fine speckled associated with anti-Jo-1 antibodies (histidyl synthetase, nuclear mitotic apparatus protein 1 (NuMA-1 and nuclear mitotic apparatus protein 2 (NuMA-2. CONCLUSION: Despite the overall good quality of the assessed HEp-2 substrates, there was considerable inconsistency in results among different commercial substrates. The variations may be due to the evaluated batches, hence generalizations cannot be made as to the respective brands. It is recommended that each new batch or new brand be tested with a panel of reference sera representing the various patterns.

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

    Science.gov (United States)

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

    1987-01-01

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

  3. Discovering novel causal patterns from biomedical natural-language texts using Bayesian nets.

    Science.gov (United States)

    Atkinson, John; Rivas, Alejandro

    2008-11-01

    Most of the biomedicine text mining approaches do not deal with specific cause--effect patterns that may explain the discoveries. In order to fill this gap, this paper proposes an effective new model for text mining from biomedicine literature that helps to discover cause--effect hypotheses related to diseases, drugs, etc. The supervised approach combines Bayesian inference methods with natural-language processing techniques in order to generate simple and interesting patterns. The results of applying the model to biomedicine text databases and its comparison with other state-of-the-art methods are also discussed.

  4. RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition

    Science.gov (United States)

    Jiang, Yuning; Kang, Jinfeng; Wang, Xinan

    2017-03-01

    Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today’s electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementing k-nearest neighbor classification on metal-oxide RRAM crossbar arrays. Metal-oxide RRAM with gradual RESET behaviors is chosen as both the storage and computing components. The proposed architecture is tested by the MNIST database. High speed (~100 ns per example) and high recognition accuracy (97.05%) are obtained. The influence of several non-ideal device properties is also discussed, and it turns out that the proposed architecture shows great tolerance to device variations. This work paves a new way to achieve RRAM-based parallel computing hardware systems with high performance.

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

    Science.gov (United States)

    Rajan, P. K.; Khan, Ajmal

    1993-01-01

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

  6. New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data

    Directory of Open Access Journals (Sweden)

    Kyeong-Sang Lee

    2017-01-01

    Full Text Available Snow cover plays an important role in climate and hydrology, at both global and regional scales. Most previous studies have used static threshold techniques to detect snow cover, which can lead to errors such as misclassification of snow and clouds, because the reflectance of snow cover exhibits variability and is affected by several factors. Therefore, we present a simple new algorithm for mapping snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS data using dynamic wavelength warping (DWW, which is based on dynamic time warping (DTW. DTW is a pattern recognition technique that is widely used in various fields such as human action recognition, anomaly detection, and clustering. Before performing DWW, we constructed 49 snow reflectance spectral libraries as reference data for various solar zenith angle and digital elevation model conditions using approximately 1.6 million sampled data. To verify the algorithm, we compared our results with the MODIS swath snow cover product (MOD10_L2. Producer’s accuracy, user’s accuracy, and overall accuracy values were 92.92%, 78.41%, and 92.24%, respectively, indicating good overall classification accuracy. The proposed algorithm is more useful for discriminating between snow cover and clouds than threshold techniques in some areas, such as those with a high viewing zenith angle.

  7. Connectivity strategies for higher-order neural networks applied to pattern recognition

    Science.gov (United States)

    Spirkovska, Lilly; Reid, Max B.

    1990-01-01

    Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.

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

    Directory of Open Access Journals (Sweden)

    Yandan Wang

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

  9. Human blood dendritic cell subsets exhibit discriminative pattern recognition receptor profiles

    Science.gov (United States)

    Lundberg, Kristina; Rydnert, Frida; Greiff, Lennart; Lindstedt, Malin

    2014-01-01

    Dendritic cells (DCs) operate as the link between innate and adaptive immunity. Their expression of pattern recognition receptors (PRRs), such as Toll-like receptors (TLRs) and C-type lectin receptors (CLRs), enables antigen recognition and mediates appropriate immune responses. Distinct subsets of human DCs have been identified; however their expression of PRRs is not fully clarified. Expressions of CLRs by DC subpopulations, in particular, remain elusive. This study aimed to identify and compare PRR expressions on human blood DC subsets, including CD1c+, CD141+ and CD16+ myeloid DCs and CD123+ plasmacytoid DCs, in order to understand their capacity to recognize different antigens as well as their responsiveness to PRR-directed targeting. Whole blood was obtained from 13 allergic and six non-allergic individuals. Mononuclear cells were purified and multi-colour flow cytometry was used to assess the expression of 10 CLRs and two TLRs on distinct DC subsets. PRR expression levels were shown to differ between DC subsets for each PRR assessed. Furthermore, principal component analysis and random forest test demonstrated that the PRR profiles were discriminative between DC subsets. Interestingly, CLEC9A was expressed at lower levels by CD141+ DCs from allergic compared with non-allergic donors. The subset-specific PRR expression profiles suggests individual responsiveness to PRR-targeting and supports functional specialization. PMID:24444310

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

    Science.gov (United States)

    2012-01-01

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

  11. Role of pattern recognition receptors of the neurovascular unit in inflamm-aging.

    Science.gov (United States)

    Wilhelm, Imola; Nyúl-Tóth, Ádám; Kozma, Mihály; Farkas, Attila E; Krizbai, István A

    2017-11-01

    Aging is associated with chronic inflammation partly mediated by increased levels of damage-associated molecular patterns, which activate pattern recognition receptors (PRRs) of the innate immune system. Furthermore, many aging-related disorders are associated with inflammation. PRRs, such as Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain-like receptors (NLRs), are expressed not only in cells of the innate immune system but also in other cells, including cells of the neurovascular unit and cerebral vasculature forming the blood-brain barrier. In this review, we summarize our present knowledge about the relationship between activation of PRRs expressed by cells of the neurovascular unit-blood-brain barrier, chronic inflammation, and aging-related pathologies of the brain. The most important damage-associated molecular pattern-sensing PRRs in the brain are TLR2, TLR4, and NLR family pyrin domain-containing protein-1 and pyrin domain-containing protein-3, which are activated during physiological and pathological aging in microglia, neurons, astrocytes, and possibly endothelial cells and pericytes. Copyright © 2017 the American Physiological Society.

  12. A novel grey-fuzzy-Markov and pattern recognition model for industrial accident forecasting

    Science.gov (United States)

    Edem, Inyeneobong Ekoi; Oke, Sunday Ayoola; Adebiyi, Kazeem Adekunle

    2017-10-01

    Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially spark more lively academic, value-added discussions that will be of practical significance to members of the safety community. In this communication, a new grey-fuzzy-Markov time series model, developed from nondifferential grey interval analytical framework has been presented for the first time. This instrument forecasts future accident occurrences under time-invariance assumption. The actual contribution made in the article is to recognise accident occurrence patterns and decompose them into grey state principal pattern components. The architectural framework of the developed grey-fuzzy-Markov pattern recognition (GFMAPR) model has four stages: fuzzification, smoothening, defuzzification and whitenisation. The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations. The novelty of the work lies in the capability of the model in making highly accurate predictions and forecasts based on the availability of small or incomplete accident data.

  13. A non-linear discrete transform for pattern recognition of discrete chaotic systems

    CERN Document Server

    Karanikas, C

    2003-01-01

    It is shown, by an invertible non-linear discrete transform that any finite sequence or any collection of strings of any length can be presented as a random walk on trees. These transforms create the mathematical background for coding any information, for exploring its local variability and diversity. With the underlying computational algorithms, with several examples and applications we propose that these transforms can be used for pattern recognition of immune type. In other words we propose a mathematical platform for detecting self and non-self strings of any alphabet, based on a negative selection algorithms, for scouting data's periodicity and self-similarity and for measuring the diversity of chaotic strings with fractal dimension methods. In particular we estimate successfully the entropy and the ratio of chaotic data with self similarity. Moreover we give some applications of a non-linear denoising filter.

  14. Medical Pattern Recognition: Applying an Improved Intuitionistic Fuzzy Cross-Entropy Approach

    Directory of Open Access Journals (Sweden)

    Kuo-Chen Hung

    2012-01-01

    Full Text Available One of the toughest challenges in medical diagnosis is the handling of uncertainty. Since medical diagnosis with respect to the symptoms uncertain, they will be assumed to have an intuitive nature. Thus, to obtain the uncertain optimism degree of the doctor, fuzzy linguistic quantifiers will be used. The aim of this article is to provide an improved nonprobabilistic entropy approach to support doctors examining the work of the preliminary diagnosing. The proposed entropy measure is based on intuitionistic fuzzy sets, extrainformation regarding hesitation degree, and an intuitive and mathematical connection between the notions of entropy in terms of fuzziness and intuitionism has been revealed. An illustrative example for medical pattern recognition demonstrates the usefulness of this study. Furthermore, in order to make computing and ranking results easier and to increase the recruiting productivity, a computer-based interface system has been developed to support doctors in making more efficient judgments.

  15. Immunopathological Roles of Cytokines, Chemokines, Signaling Molecules, and Pattern-Recognition Receptors in Systemic Lupus Erythematosus

    Directory of Open Access Journals (Sweden)

    Shui-Lian Yu

    2012-01-01

    Full Text Available Systemic lupus erythematosus (SLE is an autoimmune disease with unknown etiology affecting more than one million individuals each year. It is characterized by B- and T-cell hyperactivity and by defects in the clearance of apoptotic cells and immune complexes. Understanding the complex process involved and the interaction between various cytokines, chemokines, signaling molecules, and pattern-recognition receptors (PRRs in the immune pathways will provide valuable information on the development of novel therapeutic targets for treating SLE. In this paper, we review the immunopathological roles of novel cytokines, chemokines, signaling molecules, PRRs, and their interactions in immunoregulatory networks and suggest how their disturbances may implicate pathological conditions in SLE.

  16. TLC Fingerprinting and Pattern Recognition Methods in the Assessment of Authenticity of Poplar-Type Propolis.

    Science.gov (United States)

    Milojković Opsenica, Dušanka; Ristivojević, Petar; Trifković, Jelena; Vovk, Irena; Lušić, Dražen; Tešić, Živoslav

    2016-08-01

    Propolis is a "natural" remedy with prominent biological activity, which is used as dietary supplement. In the absence of clinical studies that would substantiate these claims, information on the biological activity of propolis is valuable. This study comprises chromatographic, image processing and chemometric approach for phenolic profiling of Serbian, Croatian and Slovenian propolis test solutions. Modern thin-layer chromatography equipment in combination with software for image processing was applied for fingerprinting and data acquisition, whereas the principal component analysis was used as pattern recognition method. Characterization of phenolic profile was performed along with the determination of the botanical and geographical origin of propolis. High-performance thin-layer chromatograms reveal that Central and Southeastern European propolis samples are rich in flavonoids. In addition, phenolic compounds proved to be suitable markers for the determination of European propolis authenticity. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  17. How pattern recognition receptor triggering influences T cell responses: a new look into the system.

    Science.gov (United States)

    Padovan, Elisabetta; Landmann, Regine M; De Libero, Gennaro

    2007-07-01

    Signaling through pattern recognition receptors (PRRs) in antigen-presenting cells (APCs) is required for the induction of T cell responses. PRR triggering in APCs induces important cellular modifications that have profound effects on antigen internalization, processing, MHC loading and antigen presentation. Accumulating experimental evidence also suggests that the fate of T cell responses depends strongly on the type of PRR triggered and the timing of PRR signaling. Here, we discuss the beneficial effects of PRR stimulation in the context of priming naive T cells, the generation and maintenance of effector/memory T cells, and the induction or break of tolerance. We propose a new classification into opsonic, phagocytic and instructive PRRs based on the functional properties of the receptors.

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

    Science.gov (United States)

    Smyth, P.; Mellstrom, J.

    1990-01-01

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

  19. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

    Directory of Open Access Journals (Sweden)

    Mario Sansone

    2013-01-01

    Full Text Available Computer systems for Electrocardiogram (ECG analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units or in prompt detection of dangerous events (e.g., ventricular fibrillation. Together with clinical applications (arrhythmia detection and heart rate variability analysis, ECG is currently being investigated in biometrics (human identification, an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

  20. Pattern recognition of concrete surface cracks and defects using integrated image processing algorithms

    Science.gov (United States)

    Balbin, Jessie R.; Hortinela, Carlos C.; Garcia, Ramon G.; Baylon, Sunnycille; Ignacio, Alexander Joshua; Rivera, Marco Antonio; Sebastian, Jaimie

    2017-06-01

    Pattern recognition of concrete surface crack defects is very important in determining stability of structure like building, roads or bridges. Surface crack is one of the subjects in inspection, diagnosis, and maintenance as well as life prediction for the safety of the structures. Traditionally determining defects and cracks on concrete surfaces are done manually by inspection. Moreover, any internal defects on the concrete would require destructive testing for detection. The researchers created an automated surface crack detection for concrete using image processing techniques including Hough transform, LoG weighted, Dilation, Grayscale, Canny Edge Detection and Haar Wavelet Transform. An automatic surface crack detection robot is designed to capture the concrete surface by sectoring method. Surface crack classification was done with the use of Haar trained cascade object detector that uses both positive samples and negative samples which proved that it is possible to effectively identify the surface crack defects.

  1. Several Similarity Measures of Interval Valued Neutrosophic Soft Sets and Their Application in Pattern Recognition Problems

    Directory of Open Access Journals (Sweden)

    Anjan Mukherjee

    2014-12-01

    Full Text Available Interval valued neutrosophic soft set introduced by Irfan Deli in 2014[8] is a generalization of neutrosophic set introduced by F. Smarandache in 1995[19], which can be used in real scientific and engineering applications. In this paper the Hamming and Euclidean distances between two interval valued neutrosophic soft sets (IVNS sets are defined and similarity measures based on distances between two interval valued neutrosophic soft sets are proposed. Similarity measure based on set theoretic approach is also proposed. Some basic properties of similarity measures between two interval valued neutrosophic soft sets is also studied. A decision making method is established for interval valued neutrosophic soft set setting using similarity measures between IVNS sets. Finally an example is given to demonstrate the possible application of similarity measures in pattern recognition problems.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ratta, G.A. [Asociacion EURATOM/CIEMAT para Fusion (Spain)], E-mail: giuseppe.ratta@ciemat.es; Vega, J.; Pereira, A.; Portas, A.; Luna, E. de la [Asociacion EURATOM/CIEMAT para Fusion (Spain); Dormido-Canto, S.; Farias, G.; Dormido, R.; Sanchez, J.; Duro, N.; Vargas, H. [Dpto. Informatica y Automatica-UNED, 28040 Madrid (Spain); Santos, M.; Pajares, G. [Dpto. Arquitectura de Computadores y Automatica-UCM, 28040 Madrid (Spain); Murari, A. [Consorzio RFX-Associazione EURATOM ENEA per la Fusione, Padua (Italy)

    2008-04-15

    Structural pattern recognition techniques allow the identification of plasma behaviours. Physical properties are encoded in the morphological structure of signals. Intelligent access methods have been applied to JET databases to retrieve data according to physical criteria. On the one hand, the structural form of signals has been used to develop general purpose data retrieval systems to search for both similar entire waveforms and similar structural shapes inside waveforms. On the other hand, domain dependent knowledge was added to the structural information of signals to create particular data retrieval methods for specific physical phenomena. The inclusion of explicit knowledge assists in data analysis. The latter has been applied in JET to look for first, cut-offs in ECE heterodyne radiometer signals and, second, L-H transitions.

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

    Science.gov (United States)

    Koblesky, Theodore

    2012-03-01

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

  4. Recognition of Epileptiform Patterns in the Human Electroencephalogram Using Multi-Layer Perceptron

    Directory of Open Access Journals (Sweden)

    V. Mokran

    1995-06-01

    Full Text Available Automatic detection of epileptiform patterns is highly desirable during continuous monitoring of patients with epilepsy. This paper describes an unconvential system for automatic off-line recognition of epileptic sharp transients in the human electroencephalogram (EEG, based on a standard neural network architecture - multi-layer perceptron (MLP, and implemented on a Silicon Graphics Indigo workstation. The system makes comprehensive use of wide spatial contextual information available on 12 channels of EEG and takes advantage of discrete dyadic wavelet transform (DDWT for efficient parameterisation of EEG data. The EEG database consists of 12 patients, 7 of which are used in the process of training of MLP. The resulting MLP is presented with the testing data set consisting of all data vectors from all 12 patients, and is shown to be capable to recognise a wide variety of epileptic signals.

  5. The factors affecting competitiveness of companies: contribution and limits of the statistical pattern recognition methods

    Directory of Open Access Journals (Sweden)

    Ladislav Blažek

    2011-01-01

    Full Text Available The paper elaborates the methodical side of empirical research of factors influencing the economic success of companies. The analysis is based on the selective sample of more than 400 stock listed (share holding companies and limited partnerships located in the Czech Republic. The main goal of the research is to verify, methodically and theoretically, the hypothesis that there is significant mutual dependency between certain types of economic success of companies and a certain typical configuration of values of selected characteristics which describe these companies. The paper concentrates on an analysis of applying the statistical pattern recognition methodology in the course of verifying this hypothesis. Our analysis confirms the potential gains connected with the method. Within the sample we identified group of potential factors of competitiveness which can characterize the interdependence between competitiveness and economic performance.

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

    Science.gov (United States)

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

    2013-01-01

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

  7. Chemical Vapor Identification by Plasma Treated Thick Film Tin Oxide Gas Sensor Array and Pattern Recognition

    Directory of Open Access Journals (Sweden)

    J. K. Srivastava

    2011-02-01

    Full Text Available Present study deals the class recognition potential of a four element plasma treated thick film tin oxide gas sensor array exposed with volatile organic compounds (VOCs. Methanol, Ethanol and Acetone are selected as target VOCs and exposed on sensor array at different concentration in range from 100-1000 ppm. Sensor array consist of four tin oxide sensors doped with 1-4 % PbO concentrations were fabricated by thick film technology and then treated with oxygen plasma for 5-10 minute durations. Sensor signal is analyzed by principal component analysis (PCA for visual classification of VOCs. Further output of PCA is used as input for classification of VOCs by four pattern classification techniques as: linear discriminant analysis (LDA, k-nearest neighbor (KNN, back propagation neural network (BPNN and support vector machine (SVM. All the four classifier results 100 % correct classification rate of VOCs by response analysis of sensor array treated with plasma for 5 minute.

  8. Nondestructive diagnosis of rotation components of a railway vehicle using infrared thermography and pattern recognitions

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Seok Jin; Kim, Min Su; Seo, Jung Won [New Transportation Research Center, Korea Railroad Research Institute, Uiwang (Korea, Republic of); Kang, Bu Beong [Dept. of of Railway Vehicle System Engineering, Woosong University, Daejeon (Korea, Republic of)

    2016-08-15

    The faults in railway vehicle components may result in either the stoppage of the service and the derailment of the vehicle. Therefore, it is important to diagnose and monitor the main components of a railway vehicle. The use of temperature is one of the basic methods for the diagnosis of abnormal conditions in the rotational components of a railway vehicle, such as bearings, reduction gears, brake discs, wheels and traction motors. In the present study, the diagnose of the rotational components using infrared thermography and a pattern recognition technique was carried out and a field test was performed. The results show that this method of diagnosis using infrared thermography can be used to identify abnormal conditions in rotational components of a railway vehicle.

  9. A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades.

    Science.gov (United States)

    Tang, Jialin; Soua, Slim; Mares, Cristinel; Gan, Tat-Hean

    2017-11-01

    The identification of particular types of damage in wind turbine blades using acoustic emission (AE) techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency-frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency-MARSE, and average frequency-peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope). The results show that these parameters are representative for the classification of the failure modes.

  10. A galectin from Eriocheir sinensis functions as pattern recognition receptor enhancing microbe agglutination and haemocytes encapsulation.

    Science.gov (United States)

    Wang, Mengqiang; Wang, Lingling; Huang, Mengmeng; Yi, Qilin; Guo, Ying; Gai, Yunchao; Wang, Hao; Zhang, Huan; Song, Linsheng

    2016-08-01

    Galectins are a family of β-galactoside binding lectins that function as pattern recognition receptors (PRRs) in innate immune system of both vertebrates and invertebrates. The cDNA of Chinese mitten crab Eriocheir sinensis galectin (designated as EsGal) was cloned via rapid amplification of cDNA ends (RACE) technique based on expressed sequence tags (ESTs) analysis. The full-length cDNA of EsGal was 999 bp. Its open reading frame encoded a polypeptide of 218 amino acids containing a GLECT/Gal-bind_lectin domain and a proline/glycine rich low complexity region. The deduced amino acid sequence and domain organization of EsGal were highly similar to those of crustacean galectins. The mRNA transcripts of EsGal were found to be constitutively expressed in a wide range of tissues and mainly in hepatopancreas, gill and haemocytes. The mRNA expression level of EsGal increased rapidly and significantly after crabs were stimulated by different microbes. The recombinant EsGal (rEsGal) could bind various pathogen-associated molecular patterns (PAMPs), including lipopolysaccharide (LPS), peptidoglycan (PGN) and glucan (GLU), and exhibited strong activity to agglutinate Escherichia coli, Vibrio anguillarum, Bacillus subtilis, Micrococcus luteus, Staphylococcus aureus and Pichia pastoris, and such agglutinating activity could be inhibited by both d-galactose and α-lactose. The in vitro encapsulation assay revealed that rEsGal could enhance the encapsulation of haemocytes towards agarose beads. These results collectively suggested that EsGal played crucial roles in the immune recognition and elimination of pathogens and contributed to the innate immune response against various microbes in crabs. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

    Energy Technology Data Exchange (ETDEWEB)

    Smolinska, Agnieszka, E-mail: A.Smolinska@science.ru.nl [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands); Blanchet, Lionel [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands); Department of Biochemistry, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Nijmegen (Netherlands); Buydens, Lutgarde M.C.; Wijmenga, Sybren S. [Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen (Netherlands)

    2012-10-31

    Highlights: Black-Right-Pointing-Pointer Procedures for acquisition of different biofluids by NMR. Black-Right-Pointing-Pointer Recent developments in metabolic profiling of different biofluids by NMR are presented. Black-Right-Pointing-Pointer The crucial steps involved in data preprocessing and multivariate chemometric analysis are reviewed. Black-Right-Pointing-Pointer Emphasis is given on recent findings on Multiple Sclerosis via NMR and pattern recognition methods. - Abstract: Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).

  12. Dual window pattern recognition classifier for improved partial-hand prosthesis control

    Directory of Open Access Journals (Sweden)

    Eric Joseph Earley

    2016-02-01

    Full Text Available Although partial-hand amputees largely retain the ability to use their wrist, it is difficult to preserve wrist motion while using a myoelectric partial-hand prosthesis without severely impacting control performance. Electromyogram (EMG pattern recognition is a well-studied control method; however, EMG from wrist motion can obscure myoelectric finger control signals. Thus, to accommodate wrist motion and to provide high classification accuracy and minimize system latency, we developed a training protocol and a classifier that switches between long and short EMG analysis window lengths.Seventeen non-amputee and two partial-hand amputee subjects participated in a study to determine the effects of including EMG from different arm and hand locations during static and/or dynamic wrist motion in the classifier training data. We evaluated several real-time classification techniques to determine which control scheme yielded the highest performance in virtual real-time tasks using a 3-way ANOVA. We found significant interaction between analysis window length and the number of grasps available. Including static and dynamic wrist motion and intrinsic hand muscle EMG with extrinsic muscle EMG significantly reduced pattern recognition classification error by 35%. Classification delay or majority voting techniques significantly improved real-time task completion rates (17%, selection (23% and completion (11% times, and selection attempts (15% for non-amputee subjects, and the dual window classifier significantly reduced the time (8% and average number of attempts required to complete grasp selections (14% made in various wrist positions. Amputee subjects demonstrated improved task timeout rates, and made fewer grasp selection attempts, with classification delay or majority voting techniques.Thus, the proposed techniques show promise for improving control of partial-hand prostheses and more effectively restoring function to individuals using these devices.

  13. Authentication of Whey Protein Powders by Portable Mid-Infrared Spectrometers Combined with Pattern Recognition Analysis.

    Science.gov (United States)

    Wang, Ting; Tan, Siow Ying; Mutilangi, William; Aykas, Didem P; Rodriguez-Saona, Luis E

    2015-10-01

    The objective of this study was to develop a simple and rapid method to differentiate whey protein types (WPC, WPI, and WPH) used for beverage manufacturing by combining the spectral signature collected from portable mid-infrared spectrometers and pattern recognition analysis. Whey protein powders from different suppliers are produced using a large number of processing and compositional variables, resulting in variation in composition, concentration, protein structure, and thus functionality. Whey protein powders including whey protein isolates, whey protein concentrates and whey protein hydrolysates were obtained from different suppliers and their spectra collected using portable mid-infrared spectrometers (single and triple reflection) by pressing the powder onto an Attenuated Total Reflectance (ATR) diamond crystal with a pressure clamp. Spectra were analyzed by soft independent modeling of class analogy (SIMCA) generating a classification model showing the ability to differentiate whey protein types by forming tight clusters with interclass distance values of >3, considered to be significantly different from each other. The major bands centered at 1640 and 1580 cm(-1) were responsible for separation and were associated with differences in amide I and amide II vibrations of proteins, respectively. Another important band in whey protein clustering was associated with carboxylate vibrations of acidic amino acids (∼1570 cm(-1)). The use of a portable mid-IR spectrometer combined with pattern recognition analysis showed potential for discriminating whey protein ingredients that can help to streamline the analytical procedure so that it is more applicable for field-based screening of ingredients. A rapid, simple and accurate method was developed to authenticate commercial whey protein products by using portable mid-infrared spectrometers combined with chemometrics, which could help ensure the functionality of whey protein ingredients in food applications. © 2015

  14. Bolstering Immunity through Pattern Recognition Receptors: A Unique Approach to Control Tuberculosis

    Directory of Open Access Journals (Sweden)

    Susanta Pahari

    2017-08-01

    Full Text Available The global control of tuberculosis (TB presents a continuous health challenge to mankind. Despite having effective drugs, TB still has a devastating impact on human health. Contributing reasons include the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb, the AIDS-pandemic, and the absence of effective vaccines against the disease. Indeed, alternative and effective methods of TB treatment and control are urgently needed. One such approach may be to more effectively engage the immune system; particularly the frontline pattern recognition receptor (PRR systems of the host, which sense pathogen-associated molecular patterns (PAMPs of Mtb. It is well known that 95% of individuals infected with Mtb in latent form remain healthy throughout their life. Therefore, we propose that clues can be found to control the remainder by successfully manipulating the innate immune mechanisms, particularly of nasal and mucosal cavities. This article highlights the importance of signaling through PRRs in restricting Mtb entry and subsequently preventing its infection. Furthermore, we discuss whether this unique therapy employing PRRs in combination with drugs can help in reducing the dose and duration of current TB regimen.

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

    Science.gov (United States)

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

    2015-01-01

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

  16. Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection.

    Science.gov (United States)

    Li, Zhifeng; Gong, Dihong; Li, Xuelong; Tao, Dacheng

    2016-05-01

    Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called local pattern selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized. At the second level, higher level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH data set (the largest face aging data set available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods.

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

    Directory of Open Access Journals (Sweden)

    Shouyi Yin

    2014-10-01

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

  18. Sequential Filtering Processes Shape Feature Detection in Crickets: A Framework for Song Pattern Recognition.

    Science.gov (United States)

    Hedwig, Berthold G

    2016-01-01

    Intraspecific acoustic communication requires filtering processes and feature detectors in the auditory pathway of the receiver for the recognition of species-specific signals. Insects like acoustically communicating crickets allow describing and analysing the mechanisms underlying auditory processing at the behavioral and neural level. Female crickets approach male calling song, their phonotactic behavior is tuned to the characteristic features of the song, such as the carrier frequency and the temporal pattern of sound pulses. Data from behavioral experiments and from neural recordings at different stages of processing in the auditory pathway lead to a concept of serially arranged filtering mechanisms. These encompass a filter for the carrier frequency at the level of the hearing organ, and the pulse duration through phasic onset responses of afferents and reciprocal inhibition of thoracic interneurons. Further, processing by a delay line and coincidence detector circuit in the brain leads to feature detecting neurons that specifically respond to the species-specific pulse rate, and match the characteristics of the phonotactic response. This same circuit may also control the response to the species-specific chirp pattern. Based on these serial filters and the feature detecting mechanism, female phonotactic behavior is shaped and tuned to the characteristic properties of male calling song.

  19. Fully Exploiting the Potential of the Periodic Table through Pattern Recognition

    Science.gov (United States)

    Schultz, Emeric

    2005-11-01

    This article describes an approach to learning chemical concepts that uses simple rules and pattern recognition to make the formulas of the oxides and hydrides of selected elements in the periodic table. The concepts that emerge from these exercises include: (i) how and why we write formulas as we do; (ii) the greatest and smallest values for oxidation numbers for important elements; (iii) the electronegativity scale; (iv) the acidic or basic characteristics of oxides depending on the formula and its location in the periodic table. This method involves a step-by-step progression in the student's ability to write formulas and balance equations without requiring a previous knowledge of chemical species or charge. Exceptions discovered in obtaining patterns are used to develop additional concepts as well as to discover regions of the table in which exceptions may be expected. This approach stresses finding commonalities rather than differences. This approach contrasts to current ways of presenting foundational materials in which, subject material is presented without regard to a rational development and in which exceptions abound. The current approach, in the author's opinion, sets the student on the path of learning chemistry bit by bit instead of in a holistic fashion.

  20. A pattern recognition system for locating small volvanoes in Magellan SAR images of Venus

    Science.gov (United States)

    Burl, M. C.; Fayyad, U. M.; Smyth, P.; Aubele, J. C.; Crumpler, L. S.

    1993-01-01

    The Magellan data set constitutes an example of the large volumes of data that today's instruments can collect, providing more detail of Venus than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations put together. However, data analysis technology has not kept pace with data collection and storage technology. Due to the sheer size of the data, complete and comprehensive scientific analysis of such large volumes of image data is no longer feasible without the use of computational aids. Our progress towards developing a pattern recognition system for aiding in the detection and cataloging of small-scale natural features in large collections of images is reported. Combining classical image processing, machine learning, and a graphical user interface, the detection of the 'small-shield' volcanoes (less than 15km in diameter) that constitute the most abundant visible geologic feature in the more that 30,000 synthetic aperture radar (SAR) images of the surface of Venus are initially targeted. Our eventual goal is to provide a general, trainable tool for locating small-scale features where scientists specify what to look for simply by providing examples and attributes of interest to measure. This contrasts with the traditional approach of developing problem specific programs for detecting Specific patterns. The approach and initial results in the specific context of locating small volcanoes is reported. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanoes visible in the Magellan data. Identifying and studying these volcanoes is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is forbiddingly time-consuming. Hence, the development of techniques to partially automate this task were undertaken. The primary

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

    Directory of Open Access Journals (Sweden)

    Lucie eTrda

    2015-04-01

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

  2. Blueprints of signaling interactions between pattern recognition receptors: implications for the design of vaccine adjuvants.

    Science.gov (United States)

    Timmermans, Kim; Plantinga, Theo S; Kox, Matthijs; Vaneker, Michiel; Scheffer, Gert Jan; Adema, Gosse J; Joosten, Leo A B; Netea, Mihai G

    2013-03-01

    Innate immunity activation largely depends on recognition of microorganism structures by Pattern Recognition Receptors (PRRs). PRR downstream signaling results in production of pro- and anti-inflammatory cytokines and other mediators. Moreover, PRR engagement in antigen-presenting cells initiates the activation of adaptive immunity. Recent reports suggest that for the activation of innate immune responses and initiation of adaptive immunity, synergistic effects between two or more PRRs are necessary. No systematic analysis of the interaction between the major PRR pathways were performed to date. In this study, a systematical analysis of the interactions between PRR signaling pathways was performed. PBMCs derived from 10 healthy volunteers were stimulated with either a single PRR ligand or a combination of two PRR ligands. Known ligands for the major PRR families were used: Toll-like receptors (TLRs), C-type lectin receptors (CLRs), NOD-like receptors (NLRs), and RigI-helicases. After 24 h of incubation, production of tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, and IL-10 was measured in supernatants by enzyme-linked immunosorbent assay (ELISA). The consistency of the PRR interactions (both inhibitory and synergistic) between the various individuals was assessed. A number of PRR-dependent signaling interactions were found to be consistent, both between individuals and with regard to multiple cytokines. The combinations of TLR2 and NOD2, TLR5 and NOD2, TLR5 and TLR3, and TLR5 and TLR9 acted as synergistic combinations. Surprisingly, inhibitory interactions between TLR4 and TLR2, TLR4 and Dectin-1, and TLR2 and TLR9 as well as TLR3 and TLR2 were observed. These consistent signaling interactions between PRR combinations may represent promising targets for immunomodulation and vaccine adjuvant development.

  3. Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition.

    Science.gov (United States)

    Borgwardt, Stefan; Koutsouleris, Nikolaos; Aston, Jacqueline; Studerus, Erich; Smieskova, Renata; Riecher-Rössler, Anita; Meisenzahl, Eva M

    2013-09-01

    The at-risk mental state for psychosis (ARMS) and the first episode of psychosis have been associated with structural brain abnormalities that could aid in the individualized early recognition of psychosis. However, it is unknown whether the development of these brain alterations predates the clinical deterioration of at-risk individuals, or alternatively, whether it parallels the transition to psychosis at the single-subject level. We evaluated the performance of an magnetic resonance imaging (MRI)-based classification system in classifying disease stages from at-risk individuals with subsequent transition to psychosis (ARMS-T) and patients with first-episode psychosis (FE). Pairwise and multigroup biomarkers were constructed using the structural MRI data of 22 healthy controls (HC), 16 ARMS-T and 23 FE subjects. The performance of these biomarkers was measured in unseen test cases using repeated nested cross-validation. The classification accuracies in the HC vs FE, HC vs ARMS-T, and ARMS-T vs FE analyses were 86.7%, 80.7%, and 80.0%, respectively. The neuroanatomical decision functions underlying these discriminative results particularly involved the frontotemporal, cingulate, cerebellar, and subcortical brain structures. Our findings suggest that structural brain alterations accumulate at the onset of psychosis and occur even before transition to psychosis allowing for the single-subject differentiation of the prodromal and first-episode stages of the disease. Pattern regression techniques facilitate an accurate prediction of these structural brain dynamics at the early stage of psychosis, potentially allowing for the early recognition of individuals at risk of developing psychosis.

  4. Value recognition and eating patterns of Kimchi in female middle school students and their mothers.

    Science.gov (United States)

    Kim, Jung-Hyun; Lee, Min-June; Yoon, In-Kyung

    2007-01-01

    This study analyzed Kimchi eating culture in 178 households with female middle school children located in Incheon and Seosan areas, investigated the Kimchi eating patterns of female middle school students, and also analyzed the differences in value recognition for Kimchi between mothers and their female middle school students. Results showed that 23.0% of subject households answered eat Kimchi at every meal and the main reason for eating Kimchi in most households was good for taste. Most households made their own Kimchi, and only 12.3% of households bought Kimchi. Subject households preferred hot and spicy taste (34.8%) and pleasing taste (20.2%), and 44.4% of middle school children answered as eating Kimchi at every meal, and the source for information on Kimchi was home in 51.6% and mass media in 33.7%, suggesting the lack of school education. Both mothers and their female middle school students placed high value on Kimchi for its nutritional aspect and on Kimchi from the market for its convenience. Mothers showed significantly higher value (pKimchi compared to their middle school students, and female middle school students showed significantly higher value (pKimchi as an international food compared to their mothers. Also, the value for hot pepper powder was high among other additional ingredients, and both mothers and middle school students had high values for Kimchi stew among other food dishes using Kimchi, and middle school students showed higher values (pKimchi such as Kimchi pizza and Kimchi spaghetti compared to the mothers group. Therefore, based on these results, the development of educational programs on Kimchi is needed not only at home but also at schools, by re-emphasizing the importance of value recognition for KImchi in our food culture.

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

    Science.gov (United States)

    Maas, Christian; Schmalzl, Jörg

    2013-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Wang Cheng-Li

    2012-08-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Searles, D.B.

    1993-03-01

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

  8. Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.

    Directory of Open Access Journals (Sweden)

    Amir Eftekhar

    Full Text Available This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70-100% and low false predictions (dependant on training procedure. The cases of highest false predictions are found in the frontal origin with 0.31-0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40-50% for a false prediction rate of less than 0.15/hour.

  9. Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.

    Science.gov (United States)

    Eftekhar, Amir; Juffali, Walid; El-Imad, Jamil; Constandinou, Timothy G; Toumazou, Christofer

    2014-01-01

    This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70-100% and low false predictions (dependant on training procedure). The cases of highest false predictions are found in the frontal origin with 0.31-0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40-50% for a false prediction rate of less than 0.15/hour.

  10. Distinct patterns of functional and effective connectivity between perirhinal cortex and other cortical regions in recognition memory and perceptual discrimination.

    Science.gov (United States)

    O'Neil, Edward B; Protzner, Andrea B; McCormick, Cornelia; McLean, D Adam; Poppenk, Jordan; Cate, Anthony D; Köhler, Stefan

    2012-01-01

    Traditionally, the medial temporal lobe (MTL) is thought to be dedicated to declarative memory. Recent evidence challenges this view, suggesting that perirhinal cortex (PrC), which interfaces the MTL with the ventral visual pathway, supports highly integrated object representations in recognition memory and perceptual discrimination. Even with comparable representational demands, perceptual and memory tasks differ in numerous task demands and the subjective experience they evoke. Here, we tested whether such differences are reflected in distinct patterns of connectivity between PrC and other cortical regions, including differential involvement of prefrontal control processes. We examined functional magnetic resonance imaging data for closely matched perceptual and recognition memory tasks for faces that engaged right PrC equivalently. Multivariate seed analyses revealed distinct patterns of interactions: Right ventrolateral prefrontal and posterior cingulate cortices exhibited stronger functional connectivity with PrC in recognition memory; fusiform regions were part of the pattern that displayed stronger functional connectivity with PrC in perceptual discrimination. Structural equation modeling revealed distinct patterns of effective connectivity that allowed us to constrain interpretation of these findings. Overall, they demonstrate that, even when MTL structures show similar involvement in recognition memory and perceptual discrimination, differential neural mechanisms are reflected in the interplay between the MTL and other cortical regions.

  11. Transcriptional modulation of pattern recognition receptors in chronic colitis in mice is accompanied with Th1 and Th17 response

    NARCIS (Netherlands)

    Zheng, Bin|info:eu-repo/dai/nl/313932166; Morgan, Mary E|info:eu-repo/dai/nl/273313215; van de Kant, Hendrik J G|info:eu-repo/dai/nl/304838799; Garssen, Johan|info:eu-repo/dai/nl/086369962; Folkerts, Gert|info:eu-repo/dai/nl/087131811; Kraneveld, Aletta D|info:eu-repo/dai/nl/126612838

    2017-01-01

    Pattern recognition receptors (PRRs) may contribute to inflammatory bowel diseases (IBD) development due to their microbial-sensing ability and the unique microenvironment in the inflamed gut. In this study, the PRR mRNA expression profile together with T cell-associated factors in the colon was

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

    Science.gov (United States)

    Heydorn, R. D.

    1984-01-01

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

  13. The innate pattern recognition molecule Ficolin-1 is secreted by monocytes/macrophages and is circulating in human plasma

    DEFF Research Database (Denmark)

    Honoré, Christian; Rørvig, Sara; Munthe-Fog, Lea

    2008-01-01

    Ficolin-1 (M-Ficolin) is a pattern recognition molecule of the complement system that is expressed by myeloid cells and type II alveolar epithelial cells. Ficolin-1 has been shown to localize in the secretory granules of these cells and attached to cell surfaces, but whether Ficolin-1 exists...

  14. Polymorphisms of innate pattern recognition receptors, response to interferon-beta and development of neutralizing antibodies in multiple sclerosis patients

    DEFF Research Database (Denmark)

    Enevold, Christian; Oturai, Annette Bang; Sørensen, Per Soelberg

    2010-01-01

    Interferon-beta therapy of patients with relapsing-remitting multiple sclerosis involves repeated 'immunizations' with exogenous protein solutions. Innate pattern recognition receptors play an important role in immune responses towards foreign substances and may thus be related to treatment outcome....

  15. The pattern recognition molecule deleted in malignant brain tumors 1 (DMBT1) and synthetic mimics inhibit liposomal nucleic acid delivery

    DEFF Research Database (Denmark)

    Lund Hansen, Pernille; Blaich, Stephanie; End, Caroline

    2011-01-01

    Liposomal nucleic acid delivery is a preferred option for therapeutic settings. The cellular pattern recognition molecule DMBT1, secreted at high levels in various diseases, and synthetic mimics efficiently inhibit liposomal nucleic acid delivery to human cells. These findings may have relevance ...

  16. Structure of the ectodomain of Drosophila peptidoglycan-recognition protein LCa suggests a molecular mechanism for pattern recognition.

    Science.gov (United States)

    Chang, Chung-I; Ihara, Kentaro; Chelliah, Yogarany; Mengin-Lecreulx, Dominique; Wakatsuki, Soichi; Deisenhofer, Johann

    2005-07-19

    The peptidoglycan-recognition protein LCa (PGRP-LCa) is a transmembrane receptor required for activation of the Drosophila immune deficiency pathway by monomeric Gram-negative peptidoglycan. We have determined the crystal structure of the ectodomain of PGRP-LCa at 2.5-A resolution and found two unique helical insertions in the LCa ectodomain that disrupt an otherwise L-shaped peptidoglycan-docking groove present in all other known PGRP structures. The deficient binding of PGRP-LCa to monomeric peptidoglycan was confirmed by biochemical pull-down assays. Recognition of monomeric peptidoglycan involves both PGRP-LCa and -LCx. We showed that association of the LCa and LCx ectodomains in vitro depends on monomeric peptidoglycan. The presence of a defective peptidoglycan-docking groove, while preserving a unique role in mediating monomeric peptidoglycan induction of immune response, suggests that PGRP-LCa recognizes the exposed structural features of a monomeric muropeptide when the latter is bound to and presented by the ectodomain of PGRP-LCx. Such features include N-acetyl glucosamine and the anhydro bond in the glycan of the muropeptide, which have been demonstrated to be critical for immune stimulatory activity.

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

    Science.gov (United States)

    Smith, William L.; Ebert, Elizabeth

    1990-01-01

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

  18. Pattern recognition receptor genes expression profiling in indigenous chickens of India and White Leghorn.

    Science.gov (United States)

    Haunshi, S; Burramsetty, Arun Kumar; Kannaki, T R; Ravindra, K S Raja; Chatterjee, R N

    2017-09-01

    Pattern recognition receptors (PRR) such as Toll-like receptors, NOD-like receptors, RIG-I helicase receptors, and C-type lectin receptors play a critical role in innate immunity as a first line of defense against invading pathogens through recognition of pathogen and/or damage-associated molecular patterns. Genetic makeup of birds is known to play a role in resistance or susceptibility to various infectious diseases. Therefore, the present study was carried out to elucidate the differential expression of PRR and some of the cytokine genes in peripheral blood mononuclear cells of indigenous chicken breeds such as Ghagus and Nicobari and an exotic chicken breed, White Leghorn (WLH). The stability of expression of reference genes in peripheral blood mononuclear cells of 3 breeds was first determined using NormFinder and BestKeeper programs. NormFinder determined B2M and G6PDH reference genes as the best combination with stability value of 0.38. Out of total 14 genes studied, expression of ten genes was found to be significantly different among 3 breeds after normalization with these reference genes. Ghagus breed showed higher level of expression of TLR1LB, TLR7, NOD1, NOD5, B-Lec, IFNβ, IL1β, and IL8 genes when compared to Nicobari breed. Further, Ghagus showed higher expression of TLR1LB, MDA5, LGP2, B-Lec, IL1β, and IL8 genes as compared to WLH breed. Higher expression of LGP2 and MDA5 genes was observed in Nicobari compared to the WLH breed while higher expression of TLR7, NOD1, NOD5, and IFNβ genes was observed in WLH as compared to Nicobari breed. No difference was observed in the expression of TLR1LA, TLR3, B-NK, and IFNα genes among 3 breeds. Study revealed significant breed effect in expression profile of PRR and some of the cytokine genes and Ghagus breed seems to have better expression profile of these genes linked to the innate immunity when compared to the WLH and Nicobar breeds. © 2017 Poultry Science Association Inc.

  19. Automatic disease screening method using image processing for dried blood microfluidic drop stain pattern recognition.

    Science.gov (United States)

    Sikarwar, Basant S; Roy, Mukesh; Ranjan, Priya; Goyal, Ayush

    2016-07-01

    This paper examines programmed automatic recognition of infection from samples of dried stains of micro-scale drops of patient blood. This technique has the upside of being low-cost and less-intrusive and not requiring puncturing the patient with a needle for drawing blood, which is especially critical for infants and the matured. It also does not require expensive pathological blood test laboratory equipment. The method is shown in this work to be successful for ailment identification in patients suffering from tuberculosis and anaemia. Illness affects the physical properties of blood, which thus influence the samples of dried micro-scale blood drop stains. For instance, if a patient has a severe drop in platelet count, which is often the case of dengue or malaria patients, the blood's physical property of viscosity drops substantially, i.e. the blood is thinner. Thus, the blood micro-scale drop stain samples can be utilised for diagnosing maladies. This paper presents programmed automatic examination of the dried micro-scale drop blood stain designs utilising an algorithm based on pattern recognition. The samples of micro-scale blood drop stains of ordinary non-infected people are clearly recognisable as well as the samples of micro-scale blood drop stains of sick people, due to key distinguishing features. As a contextual analysis, the micro-scale blood drop stains of patients infected with tuberculosis have been contrasted with the micro-scale blood drop stains of typical normal healthy people. The paper dives into the fundamental flow mechanics behind how the samples of the dried micro-scale blood drop stain is shaped. What has been found is a thick ring like feature in the dried micro-scale blood drop stains of non-ailing people and thin shape like lines in the dried micro-scale blood drop stains of patients with anaemia or tuberculosis disease. The ring like feature at the periphery is caused by an outward stream conveying suspended particles to the edge

  20. Analysis of spatial and temporal patterns of aboveground net primary productivity in the Eurasian steppe region from 1982 to 2013.

    Science.gov (United States)

    Jiao, Cuicui; Yu, Guirui; Ge, Jianping; Chen, Xi; Zhang, Chi; He, Nianpeng; Chen, Zhi; Hu, Zhongmin

    2017-07-01

    To explore the importance of the Eurasian steppe region (EASR) in global carbon cycling, we analyzed the spatiotemporal dynamics of the aboveground net primary productivity (ANPP) of the entire EASR from 1982 to 2013. The ANPP in the EASR was estimated from the Integrated ANPPNDVI model, which is an empirical model developed based on field-observed ANPP and long-term normalized difference vegetation index (NDVI) data. The optimal composite period of NDVI data was identified by considering spatial heterogeneities across the study area in the Integrated ANPPNDVI model. EASR's ANPP had apparent zonal patterns along hydrothermal gradients, and the mean annual value was 43.78 g C m(-2) yr(-1), which was lower than the global grasslands average. Compared to other important natural grasslands, EASR's ANPP was lower than the North American, South American, and African grasslands. The total aboveground net primary productivity (TANPP) was found to be 378.97 Tg C yr(-1), which accounted for 8.18%-36.03% of the TANPP for all grasslands. In addition, EASR's TANPP was higher than that of the grasslands in North America, South America, and Africa. The EASR's TANPP increased in a fluctuating manner throughout the entire period of 1982-2013. The increasing trend was greater than that for North American and South American and was lower than that for African grasslands over the same period. The years 1995 and 2007 were two turning points at which trends in EASR's TANPP significantly changed. Our analysis demonstrated that the EASR has been playing a substantial and progressively more important role in global carbon sequestration. In addition, in the development of empirical NDVI-based ANPP models, the early-middle growing season averaged NDVI, the middle-late growing season averaged NDVI and the annual maximum NDVI are recommended for use for semi-humid regions, semi-arid regions, and desert vegetation in semi-arid regions, respectively.

  1. Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures.

    Science.gov (United States)

    Chuk, Tim; Crookes, Kate; Hayward, William G; Chan, Antoni B; Hsiao, Janet H

    2017-12-01

    It remains controversial whether culture modulates eye movement behavior in face recognition. Inconsistent results have been reported regarding whether cultural differences in eye movement patterns exist, whether these differences affect recognition performance, and whether participants use similar eye movement patterns when viewing faces from different ethnicities. These inconsistencies may be due to substantial individual differences in eye movement patterns within a cultural group. Here we addressed this issue by conducting individual-level eye movement data analysis using hidden Markov models (HMMs). Each individual's eye movements were modeled with an HMM. We clustered the individual HMMs according to their similarities and discovered three common patterns in both Asian and Caucasian participants: holistic (looking mostly at the face center), left-eye-biased analytic (looking mostly at the two individual eyes in addition to the face center with a slight bias to the left eye), and right-eye-based analytic (looking mostly at the right eye in addition to the face center). The frequency of participants adopting the three patterns did not differ significantly between Asians and Caucasians, suggesting little modulation from culture. Significantly more participants (75%) showed similar eye movement patterns when viewing own- and other-race faces than different patterns. Most importantly, participants with left-eye-biased analytic patterns performed significantly better than those using either holistic or right-eye-biased analytic patterns. These results suggest that active retrieval of facial feature information through an analytic eye movement pattern may be optimal for face recognition regardless of culture. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks

    DEFF Research Database (Denmark)

    Garrido, Jesús A.; Luque, Niceto R.; Tolu, Silvia

    2016-01-01

    The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly...... understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input synapses...... and at the inhibitory interneuron-interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity...

  3. Acoustic-Based Cutting Pattern Recognition for Shearer through Fuzzy C-Means and a Hybrid Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2016-10-01

    Full Text Available As the conventional cutting pattern recognition methods for shearer are huge in size, have low recognition reliability and an inconvenient contacting measurement method, a fast and reliable coal-rock cutting pattern recognition system is always a baffling problem worldwide. However, the recognition rate has a direct relation with the outputs of coal mining and the safety quality of staff. In this paper, a novel cutting pattern identification method through the cutting acoustic signal of the shearer is proposed. The signal is clustering by fuzzy C-means (FCM and a hybrid optimization algorithm, combining the fruit fly and genetic optimization algorithm (FGOA. Firstly, an industrial microphone is installed on the shearer and the acoustic signal is collected as the source signal due to its obvious advantages of compact size, non-contact measurement and ease of remote transmission. The original sound is decomposed by multi-resolution wavelet packet transform (WPT, and the normalized energy of each node is extracted as a feature vector. Then, FGOA, by introducing a genetic proportion coefficient into the basic fruit fly optimization algorithm (FOA, is applied to overcome the disadvantages of being time-consuming and sensitivity to initial centroids of the traditional FCM. A simulation example, with the accuracy of 95%, and some comparisons prove the effectiveness and superiority of the proposed scheme. Finally, an industrial test validates the practical effect.

  4. Soluble Collectin-12 (CL-12) Is a Pattern Recognition Molecule Initiating Complement Activation via the Alternative Pathway

    DEFF Research Database (Denmark)

    Ma, Ying Jie; Hein, Estrid; Munthe-Fog, Lea

    2015-01-01

    -recognition molecule. Using recombinant CL-12 full length or CL-12 extracellular domain, we determined the occurrence of soluble CL-12 shed from in vitro cultured cells. Western blot showed that soluble recombinant CL-12 migrated with a band corresponding to ∼ 120 kDa under reducing conditions, whereas under...... of the terminal complement complex. These results demonstrate the existence of CL-12 in a soluble form and indicate a novel mechanism by which the alternative pathway of complement may be triggered directly by a soluble pattern-recognition molecule....

  5. A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition.

    Science.gov (United States)

    Zhang, Xiaorong; Huang, He

    2015-02-19

    Unreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. While the proposed SFTM has shown great promise, the previous design is impractical. A practical SFTM has to be fast enough, lightweight, automatic, and robust under different conditions with or without disturbances. This paper presented a real-time, practical SFTM towards robust EMG PR. A novel fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect disturbances and recover the PR performance immediately. These components of SFTM were then integrated with the EMG PR module and tested on five able-bodied subjects and a transradial amputee in real-time for classifying multiple hand and wrist motions under different conditions with different disturbance types and levels. The proposed fast LDA retraining algorithm significantly shortened the retraining time from nearly 1 s to less than 4 ms when tested on the embedded system prototype, which demonstrated the feasibility of a nearly "zero-delay" SFTM that is imperceptible to the users. The results of the real-time tests suggested that the SFTM was able to handle different types of disturbances investigated in this study and significantly improve the classification performance when one or multiple EMG signals were disturbed. In addition, the SFTM could also maintain the system's classification performance when there was no disturbance. This paper presented a real-time, lightweight, and automatic

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-12-31

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

  7. Nanoparticle impact on innate immune cell pattern-recognition receptors and inflammasomes activation.

    Science.gov (United States)

    Silva, Ana Luísa; Peres, Carina; Conniot, João; Matos, Ana I; Moura, Liane; Carreira, Bárbara; Sainz, Vanessa; Scomparin, Anna; Satchi-Fainaro, Ronit; Préat, Véronique; Florindo, Helena F

    2017-09-20

    Nanotechnology-based strategies can dramatically impact the treatment, prevention and diagnosis of a wide range of diseases. Despite the unprecedented success achieved with the use of nanomaterials to address unmet biomedical needs and their particular suitability for the effective application of a personalized medicine, the clinical translation of those nanoparticulate systems has still been impaired by the limited understanding on their interaction with complex biological systems. As a result, unexpected effects due to unpredicted interactions at biomaterial and biological interfaces have been underlying the biosafety concerns raised by the use of nanomaterials. This review explores the current knowledge on how nanoparticle (NP) physicochemical and surface properties determine their interactions with innate immune cells, with particular attention on the activation of pattern-recognition receptors and inflammasome. A critical perspective will additionally address the impact of biological systems on the effect of NP on immune cell activity at the molecular level. We will discuss how the understanding of the NP-innate immune cell interactions can significantly add into the clinical translation by guiding the design of nanomedicines with particular effect on targeted cells, thus improving their clinical efficacy while minimizing undesired but predictable toxicological effects. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Holographic memory for high-density data storage and high-speed pattern recognition

    Science.gov (United States)

    Gu, Claire

    2002-09-01

    As computers and the internet become faster and faster, more and more information is transmitted, received, and stored everyday. The demand for high density and fast access time data storage is pushing scientists and engineers to explore all possible approaches including magnetic, mechanical, optical, etc. Optical data storage has already demonstrated its potential in the competition against other storage technologies. CD and DVD are showing their advantages in the computer and entertainment market. What motivated the use of optical waves to store and access information is the same as the motivation for optical communication. Light or an optical wave has an enormous capacity (or bandwidth) to carry information because of its short wavelength and parallel nature. In optical storage, there are two types of mechanism, namely localized and holographic memories. What gives the holographic data storage an advantage over localized bit storage is the natural ability to read the stored information in parallel, therefore, meeting the demand for fast access. Another unique feature that makes the holographic data storage attractive is that it is capable of performing associative recall at an incomparable speed. Therefore, volume holographic memory is particularly suitable for high-density data storage and high-speed pattern recognition. In this paper, we review previous works on volume holographic memories and discuss the challenges for this technology to become a reality.

  9. Global neural pattern similarity as a common basis for categorization and recognition memory.

    Science.gov (United States)

    Davis, Tyler; Xue, Gui; Love, Bradley C; Preston, Alison R; Poldrack, Russell A

    2014-05-28

    Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. Copyright © 2014 the authors 0270-6474/14/347472-13$15.00/0.

  10. Investigation into diagnostic agreement using automated computer-assisted histopathology pattern recognition image analysis

    Science.gov (United States)

    Webster, Joshua D.; Michalowski, Aleksandra M.; Dwyer, Jennifer E.; Corps, Kara N.; Wei, Bih-Rong; Juopperi, Tarja; Hoover, Shelley B.; Simpson, R. Mark

    2012-01-01

    The extent to which histopathology pattern recognition image analysis (PRIA) agrees with microscopic assessment has not been established. Thus, a commercial PRIA platform was evaluated in two applications using whole-slide images. Substantial agreement, lacking significant constant or proportional errors, between PRIA and manual morphometric image segmentation was obtained for pulmonary metastatic cancer areas (Passing/Bablok regression). Bland-Altman analysis indicated heteroscedastic measurements and tendency toward increasing variance with increasing tumor burden, but no significant trend in mean bias. The average between-methods percent tumor content difference was -0.64. Analysis of between-methods measurement differences relative to the percent tumor magnitude revealed that method disagreement had an impact primarily in the smallest measurements (tumor burden 0.988, indicating high reproducibility for both methods, yet PRIA reproducibility was superior (C.V.: PRIA = 7.4, manual = 17.1). Evaluation of PRIA on morphologically complex teratomas led to diagnostic agreement with pathologist assessments of pluripotency on subsets of teratomas. Accommodation of the diversity of teratoma histologic features frequently resulted in detrimental trade-offs, increasing PRIA error elsewhere in images. PRIA error was nonrandom and influenced by variations in histomorphology. File-size limitations encountered while training algorithms and consequences of spectral image processing dominance contributed to diagnostic inaccuracies experienced for some teratomas. PRIA appeared better suited for tissues with limited phenotypic diversity. Technical improvements may enhance diagnostic agreement, and consistent pathologist input will benefit further development and application of PRIA. PMID:22616030

  11. Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

    Directory of Open Access Journals (Sweden)

    Amir LAKZIAN

    2010-09-01

    Full Text Available This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC, -33 kPa, and permanent wilting point (PWP, -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN, Artificial Neural Network (ANN and pedotransfer functions (PTF were used to predict the soil water at each sampling location. Mean square deviation (MSD and its components, index of agreement (d, root mean square difference (RMSD and normalized RMSD (RMSDr were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

  12. IMPROVEMENTS IN WATER SUPPLY SYSTEMS BASED ON OPTIMIZATION AND RECOGNITION OF CONSUMPTION PATTERNS

    Directory of Open Access Journals (Sweden)

    A. M. F. DINIZ

    2015-05-01

    Full Text Available Water supply systems consume large amounts of energy because of the pumping processes involved. The operational strategy of using frequency converters enables the system to work with better adjusted discharge rate to meet demand. In this case, an optimization strategy can establish an optimal procedure in order to schedule the rotational speed of pumps over a period and guarantee a volume of water in the supply tank. This work presents and solves an optimization problem that provides the optimal schedule for the rotational speed of pumps in a real water supply system considering minimizing the use of electricity and the cost thereof and maintenance. The optimization problem is based on two Artificial Neural Networks (ANN models that provide the total power consumption in the pumping system and level of water in the tank. Pattern recognition techniques in univariate time series based on the real data are used to forecast the demand curve according to the season ofthe year. The results show the potential savings generated by the proposed method and show the feasibility of scheduling the rotational speed of the pumps to ensure the minimum energy cost without affecting hourly demand and the security of the supply system.

  13. An Implementation of Exploratory Start for Power Quality Disturbance Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Turgay Yalcin

    2016-10-01

    Full Text Available Identification of system disturbances and detection of them guarantees smart grids power quality system reliability and long lasting life of the power system. The key goal of this study is to generate non - time consuming features for CPU, for recognizing different types of non-stationary and non-linear smart grid faults based on signal processing techniques. This paper proposes a new solution for real time power system monitoring against power quality faults focusing on voltage sag and noise. EEMD is used for noise reduction with first intrinsic mode function (imf1. Hilbert Huang Transform (HHT is used for generating instantaneous amplitude (IA and instantaneous frequency (IF feature of real time voltage sag power signal. The proposed power system monitoring system is able to detect power system voltage sag disturbances and capable of recognize and remove EMI (Electromagnetic Interference-Noise.  In this study based on experimental studies, Hilbert Huang based pattern recognition technique was used to investigate power signal to diagnose voltage sag in power grid. SVM and Decision Tree (C4.5 were operated and their achievements were matched for calculation error and CPU time. According to the analysis, decision tree algorithm without dimensionality reduction produces the best solution.

  14. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    Science.gov (United States)

    Sanchez, Elie

    1991-10-01

    With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

  15. Pattern recognition based forearm motion classification for patients with chronic hemiparesis.

    Science.gov (United States)

    Geng, Yanjuan; Zhang, Liangqing; Tang, Dan; Zhang, Xiufeng; Li, Guanglin

    2013-01-01

    To make full use of electromyography (EMG) that contains rich information of muscular activities in active rehabilitation for motor hemiparetic patients, a couple of recent studies have explored the feasibility of applying pattern recognition technique to the classification of multiple motion classes for stroke survivors and reported some promising results. However, it still remains unclear if kinematics signals could also bring good motion classification performance, particularly for patients after traumatic brain damage. In this study, the kinematics signals was used for motion classification analysis in three stroke survivors and two patients after traumatic brain injury, and compared with EMG. The results showed that an average classification error of 7.9 ± 6.8% for the affected arm over all subjects could be achieved with a linear classifier when they performed multiple fine movements, 7.9% lower than that when using EMG. With either kind of signals, the motor control ability of the affected arm degraded significantly in comparison to the intact side. The preliminary results suggested the great promise of kinematics information as well as the biological signals in detecting user's conscious effort for robot-aided active rehabilitation.

  16. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network.

    Science.gov (United States)

    Zhai, Xiaolong; Jelfs, Beth; Chan, Rosa H M; Tin, Chung

    2017-01-01

    Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

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

    Science.gov (United States)

    Marzwell, Neville

    1995-01-01

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

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

    Science.gov (United States)

    Choudry, A.

    1987-01-01

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

  19. E3 Ubiquitin Ligases Pellinos as Regulators of Pattern Recognition Receptor Signaling and Immune responses

    Science.gov (United States)

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

    2015-01-01

    SUMMARY Pellinos are a family of E3 ubiquitin ligases discovered for their role in catalyzing K63-linked polyubiquitination of Pelle, an IL-1 receptor-associated kinase homologue in the Drosophila Toll pathway. Subsequent studies have revealed the central and non-redundant roles of mammalian Pellino-1, Pellino-2 and Pelino-3 in signaling pathways emanating from IL-1 receptors, Toll-like receptors, NOD-like receptors, T- and B-cell receptors. While Pellinos ability to interact with many signaling intermediates suggested their scaffolding roles, recent findings in mice expressing ligase-inactive Pellinos demonstrated the importance of Pellino ubiquitin ligase activity. Cell-specific functions of Pellinos have emerged, e.g., Pellino-1 being a negative regulator in T-lymphocytes and a positive regulator in myeloid cells, and details of molecular regulation of receptor signaling by various members of the Pellino family have been revealed. In this review, we have summarized current information about Pellino-mediated regulation of signaling by pattern recognition receptors, T-cell and B-cell receptors and TNF receptors, and discuss Pellino’s role in sepsis and infectious diseases, as well as in autoimmune, inflammatory and allergic disorders. We also provide our perspective on the potential of targeting Pellinos with peptide- or small molecule-based drug compounds as a new therapeutic approach for septic shock and autoimmune pathologies. PMID:26085210

  20. A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades

    Directory of Open Access Journals (Sweden)

    Jialin Tang

    2017-11-01

    Full Text Available The identification of particular types of damage in wind turbine blades using acoustic emission (AE techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency−frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency−MARSE, and average frequency−peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope. The results show that these parameters are representative for the classification of the failure modes.