WorldWideScience

Sample records for network pattern recognition

  1. 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.

  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

    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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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...

  14. 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.

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

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    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.

  16. Page 1 Artificial neural networks for pattern recognition 221 The ...

    Indian Academy of Sciences (India)

    The Boltzmann learning law (see figure 17) allows us to represent a given environ- ment by the network (Ackley et al 1985; Hinton & Sejnowski 1986; Aleksander. & Morton 1990). The law uses an information theoretic measure to evaluate how well the environment is represented in the network. If a perfect representation is.

  17. 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…

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

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    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’

  19. Pattern Recognition with Stochastic Resonance in a Generic Neural Network

    Science.gov (United States)

    Tan, Z.; Ali, M. K.

    We discuss stochastic resonance in associative memory with a canonical neural network model that describes the generic behavior of a large family of dynamical systems near bifurcation. Our result shows that stochastic resonance helps memory association. The relationship between stochastic resonance, associative memory, storage load, history of memory and initial states are studied. In intelligent systems like neural networks, it is likely that stochastic resonance combined with synaptic information enhances memory recalls.

  20. Network Security via Biometric Recognition of Patterns of Gene Expression

    Science.gov (United States)

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time expression and assay of gene expression products.

  1. Network Security via Biometric Recognition of Patterns of Gene Expression

    Science.gov (United States)

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT (Information Technology) organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time assays of gene expression products.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

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    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.

  12. 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,

  13. Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

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    TIMCHENKO, L.

    2012-11-01

    Full Text Available Propositions necessary for development of parallel-hierarchical (PH network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.

  14. Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

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    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.

  15. 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.

  16. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Xiaolong Zhai

    2017-07-01

    Full Text Available 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. 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...

  18. Using artificial bat sonar neural networks for complex pattern recognition: recognizing faces and the speed of a moving target.

    Science.gov (United States)

    Dror, I E; Florer, F L; Rios, D; Zagaeski, M

    1996-04-01

    Two sets of studies examined the viability of using bat-like sonar input for artificial neural networks in complex pattern recognition tasks. In the first set of studies, a sonar neural network was required to perform two face recognition tasks. In the first task, the network was trained to recognize different faces regardless of facial expressions. Following training, the network was tested on its ability to generalize and correctly recognize faces using echoes of novel facial expressions that were not included in the training set. The neural network was able to recognize novel echoes of faces almost perfectly (above 96% accuracy) when it was required to recognize up to five faces. In the second face recognition task, a sonar neural network was trained to recognize the sex of 16 faces (eight males and eight females). After training, the network was able to correctly recognize novel echoes of those faces as 'male' or as 'female' faces with accuracy levels of 88%. However, the network was not able to recognize novel faces as 'male' or 'female' faces. In the second set of studies, a sonar neural network was required to learn to recognize the speed of a target that was moving towards the viewer. During training, the target was presented in a variety of orientations, and the network's performance was evaluated when the target was presented in novel orientations that were not included in the training set. The different orientations dramatically affected the amplitude and the frequency composition of the echoes. The neural network was able to learn and recognize the speed of a moving target, and to generalize to new orientations of the target. However, the network was not able to generalize to new speeds that were not included in the training set. The potential and limitations of using bat-like sonar as input for artifical neural networks are discussed.

  19. Pattern recognition via synchronization in phase-locked loop neural networks.

    Science.gov (United States)

    Hoppensteadt, F C; Izhikevich, E M

    2000-01-01

    We propose a novel architecture of an oscillatory neural network that consists of phase-locked loop (PLL) circuits. It stores and retrieves complex oscillatory patterns as synchronized states with appropriate phase relations between neurons.

  20. 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.

  1. 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

  2. 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...

  3. Mining Emerging Sequential Patterns for Activity Recognition in Body Sensor Networks

    DEFF Research Database (Denmark)

    Gu, Tao; Wang, Liang; Chen, Hanhua

    2010-01-01

    Body Sensor Networks oer many applications in healthcare, well-being and entertainment. One of the emerging applications is recognizing activities of daily living. In this paper, we introduce a novel knowledge pattern named Emerging Sequential Pattern (ESP)|a sequential pattern that discovers...... signicant class dierences|to recognize both simple (i.e., sequential) and complex (i.e., interleaved and concurrent) activities. Based on ESPs, we build our complex activity models directly upon the sequential model to recognize both activity types. We conduct comprehensive empirical studies to evaluate...

  4. Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

    Science.gov (United States)

    Srimani, P. K.; Parimala, Y. G.

    2011-12-01

    A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

  5. 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.

  6. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

    Science.gov (United States)

    Han, Bing; Taha, Tarek M

    2010-04-01

    There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

  7. Pattern Recognition and Classification of Fatal Traffic Accidents in Israel A Neural Network Approach

    DEFF Research Database (Denmark)

    Prato, Carlo Giacomo; Gitelman, Victoria; Bekhor, Shlomo

    2011-01-01

    This article provides a broad picture of fatal traffic accidents in Israel to answer an increasing need of addressing compelling problems, designing preventive measures, and targeting specific population groups with the objective of reducing the number of traffic fatalities. The analysis focuses...... on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns......: (1) single-vehicle accidents of young drivers, (2) multiple-vehicle accidents between young drivers, (3) accidents involving motorcyclists or cyclists, (4) accidents where elderly pedestrians crossed in urban areas, and (5) accidents where children and teenagers cross major roads in small urban areas...

  8. Collaborative Localization in Wireless Sensor Networks via Pattern Recognition in Radio Irregularity Using Omnidirectional Antennas

    Directory of Open Access Journals (Sweden)

    Tzu-Yun Lai

    2010-01-01

    Full Text Available In recent years, various received signal strength (RSS-based localization estimation approaches for wireless sensor networks (WSNs have been proposed. RSS-based localization is regarded as a low-cost solution for many location-aware applications in WSNs. In previous studies, the radiation patterns of all sensor nodes are assumed to be spherical, which is an oversimplification of the radio propagation model in practical applications. In this study, we present an RSS-based cooperative localization method that estimates unknown coordinates of sensor nodes in a network. Arrangement of two external low-cost omnidirectional dipole antennas is developed by using the distance-power gradient model. A modified robust regression is also proposed to determine the relative azimuth and distance between a sensor node and a fixed reference node. In addition, a cooperative localization scheme that incorporates estimations from multiple fixed reference nodes is presented to improve the accuracy of the localization. The proposed method is tested via computer-based analysis and field test. Experimental results demonstrate that the proposed low-cost method is a useful solution for localizing sensor nodes in unknown or changing environments.

  9. Pattern recognition in lithology classification: modeling using neural networks, self-organizing maps and genetic algorithms

    Science.gov (United States)

    Sahoo, Sasmita; Jha, Madan K.

    2017-03-01

    Effective characterization of lithology is vital for the conceptualization of complex aquifer systems, which is a prerequisite for the development of reliable groundwater-flow and contaminant-transport models. However, such information is often limited for most groundwater basins. This study explores the usefulness and potential of a hybrid soft-computing framework; a traditional artificial neural network with gradient descent-momentum training (ANN-GDM) and a traditional genetic algorithm (GA) based ANN (ANN-GA) approach were developed and compared with a novel hybrid self-organizing map (SOM) based ANN (SOM-ANN-GA) method for the prediction of lithology at a basin scale. This framework is demonstrated through a case study involving a complex multi-layered aquifer system in India, where well-log sites were clustered on the basis of sand-layer frequencies; within each cluster, subsurface layers were reclassified into four depth classes based on the maximum drilling depth. ANN models for each depth class were developed using each of the three approaches. Of the three, the hybrid SOM-ANN-GA models were able to recognize incomplete geologic pattern more reasonably, followed by ANN-GA and ANN-GDM models. It is concluded that the hybrid soft-computing framework can serve as a promising tool for characterizing lithology in groundwater basins with missing lithologic patterns.

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

    Science.gov (United States)

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

    2015-10-30

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

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

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2015-10-01

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

  12. 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.

  13. Development of Neural Network Architectures for Self- Organizing Pattern Recognition and Robotics

    Science.gov (United States)

    1993-03-01

    ARFI 2 recognition svysterni A notlher new andaog adapt ivYe r"ncrsl lf iodt Mi Pz / v A HI) incorporates, conipu)t1ations fro fuzzy smet heory into...gjenerator is t he Vector lnte-grati’ni to FrolpoIrit. ( lITE 1niode0 for >,VThrrl(’iroio1Ž arno ’ speed cont rol of tri ilt ijoi nt mrovements. VliTE

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network

    Directory of Open Access Journals (Sweden)

    Babar Khan

    2017-10-01

    Full Text Available Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance and stability (selectivity of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.

  20. 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...

  1. Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

    Directory of Open Access Journals (Sweden)

    Jonathan C Tapson

    2013-08-01

    Full Text Available The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify – arbitrarily - neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.

  2. Pattern recognition and analysis of short duration disturbance based on neural network

    Science.gov (United States)

    Wang, Huaying

    2008-10-01

    For quantitative detection of distortions of voltage waveform, a novel approach based on wavelet transform (WT) to detect and locate the power quality (PQ) disturbances is proposed. Due to expansion of power electronics devices, the wide diffusion of nonlinear and time-variant loads has caused massive serious PQ problems in power system. The signal containing noise is de-noised by WT, and then become input node to the wavelet neural network. The standard genetic algorithm is utilized to complete the network structure, and then the fundamental component of the signal is estimated to extract the mixed information. Therefore the disturbance signal is acquired by subtracting the fundamental component. In processing of disturbances signal, the principle of singularity detection using WT modulus maxima is presented with dyadic WT approach for the detection and localization of the PQ. The simulation results demonstrate that the proposed method is effective.

  3. 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.

  4. Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

    Science.gov (United States)

    Szu, Harold H.

    1990-01-01

    In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

  5. Minimum Constructive Back Propagation Neural Network Based on Fuzzy Logic for Pattern Recognition of Electronic Nose System

    Directory of Open Access Journals (Sweden)

    Radi Radi

    2011-08-01

    Full Text Available Constructive Back Propagation Neural Network (CBPNN is a kind of back propagation neural network trained with constructive algorithm. Training of CBPNN is mainly conducted by developing the network’s architecture which commonly done by adding a number of new neuron units on learning process. Training of the network usually implements fixed method to develop its structure gradually by adding new units constantly. Although this method is simple and able to create an adaptive network for data pattern complexity, but it is wasteful and inefficient for computing. New unit addition affects directly to the computational load of training, speed of convergence, and structure of the final neural network. While increases training load significantly, excessive addition of units also tends to generate a large size of final network. Moreover, addition pattern with small unit number tends to drop off the adaptability of the network and extends time of training. Therefore, there is important to design an adaptive structure development pattern for CBPNN in order to minimize computing load of training. This study proposes Fuzzy Logic (FL algorithm to manage and develop structure of CBPNN. FL method was implemented on two models of CBPNN, i.e. designed with one and two hidden layers, used to recognize aroma patterns on an electronic nose system. The results showed that this method is effective to be applied due to its capability to minimize time of training, to reduce load of computational learning, and generate small size of network.

  6. GIAO C-H COSY Simulations Merged with Artificial Neural Networks Pattern Recognition Analysis. Pushing the Structural Validation a Step Forward.

    Science.gov (United States)

    Zanardi, María M; Sarotti, Ariel M

    2015-10-02

    The structural validation problem using quantum chemistry approaches (confirm or reject a candidate structure) has been tackled with artificial neural network (ANN) mediated multidimensional pattern recognition from experimental and calculated 2D C-H COSY. In order to identify subtle errors (such as regio- or stereochemical), more than 400 ANNs have been built and trained, and the most efficient in terms of classification ability were successfully validated in challenging real examples of natural product misassignments.

  7. Selective networks and recognition automata.

    Science.gov (United States)

    Reeke, G N; Edelman, G M

    1984-01-01

    The results we have presented demonstrate that a network based on a selective principle can function in the absence of forced learning or an a priori program to give recognition, classification, generalization, and association. While Darwin II is not a model of any actual nervous system, it does set out to solve one of the same problems that evolution had to solve--the need to form categories in a bottom-up manner from information in the environment, without incorporating the assumptions of any particular observer. The key features of the model that make this possible are (1) Darwin II incorporates selective networks whose initial specificities enable them to respond without instruction to unfamiliar stimuli; (2) degeneracy provides multiple possibilities of response to any one stimulus, at the same time providing functional redundancy against component failure; (3) the output of Darwin II is a pattern of response, making use of the simultaneous responses of multiple degenerate groups to avoid the need for very high specificity and the combinatorial disaster that would imply; (4) reentry within individual networks vitiates the limitations described by Minsky and Papert for a class of perceptual automata lacking such connections; and (5) reentry between intercommunicating networks with different functions gives rise to new functions, such as association, that either one alone could not display. The two kinds of network are roughly analogous to the two kinds of category formation that people use: Darwin, corresponding to the exemplar description of categories, and Wallace, corresponding to the probabilistic matching description of categories. These principles lead to a new class of pattern-recognizing machine of which Darwin II is just an example. There are a number of obvious extensions to this work that we are pursuing. These include giving Darwin II the capability to deal with stimuli that are in motion, an ability that probably precedes the ability of biological

  8. Neural Network and Letter Recognition.

    Science.gov (United States)

    Lee, Hue Yeon

    Neural net architectures and learning algorithms that recognize hand written 36 alphanumeric characters are studied. The thin line input patterns written in 32 x 32 binary array are used. The system is comprised of two major components, viz. a preprocessing unit and a Recognition unit. The preprocessing unit in turn consists of three layers of neurons; the U-layer, the V-layer, and the C -layer. The functions of the U-layer is to extract local features by template matching. The correlation between the detected local features are considered. Through correlating neurons in a plane with their neighboring neurons, the V-layer would thicken the on-cells or lines that are groups of on-cells of the previous layer. These two correlations would yield some deformation tolerance and some of the rotational tolerance of the system. The C-layer then compresses data through the 'Gabor' transform. Pattern dependent choice of center and wavelengths of 'Gabor' filters is the cause of shift and scale tolerance of the system. Three different learning schemes had been investigated in the recognition unit, namely; the error back propagation learning with hidden units, a simple perceptron learning, and a competitive learning. Their performances were analyzed and compared. Since sometimes the network fails to distinguish between two letters that are inherently similar, additional ambiguity resolving neural nets are introduced on top of the above main neural net. The two dimensional Fourier transform is used as the preprocessing and the perceptron is used as the recognition unit of the ambiguity resolver. One hundred different person's handwriting sets are collected. Some of these are used as the training sets and the remainders are used as the test sets. The correct recognition rate of the system increases with the number of training sets and eventually saturates at a certain value. Similar recognition rates are obtained for the above three different learning algorithms. The minimum error

  9. 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).

  10. 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

  11. Pulse image recognition using fuzzy neural network.

    Science.gov (United States)

    Xu, L S; Meng, Max Q -H; Wang, K Q

    2007-01-01

    The automatic recognition of pulse images is the key in the research of computerized pulse diagnosis. In order to automatically differentiate the pulse patterns by using small samples, a fuzzy neural network to classify pulse images based on the knowledge of experts in traditional Chinese pulse diagnosis was designed. The designed classifier can make hard decision and soft decision for identifying 18 patterns of pulse images at the accuracy of 91%, which is better than the results that achieved by back-propagation neural network.

  12. 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.

  13. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

  14. Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets.

    Science.gov (United States)

    Zhang, Ai-hua; Sun, Hui; Han, Ying; Yan, Guang-li; Yuan, Ye; Song, Gao-chen; Yuan, Xiao-xia; Xie, Ning; Wang, Xi-jun

    2013-08-06

    Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology.

  15. Facial expression recognition based on improved deep belief networks

    Science.gov (United States)

    Wu, Yao; Qiu, Weigen

    2017-08-01

    In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

  16. Human Face Recognition Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Răzvan-Daniel Albu

    2009-10-01

    Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.

  17. 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.

  18. 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.

  19. Hidden neural networks: application to speech recognition

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1998-01-01

    We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks

    Science.gov (United States)

    Nikelshpur, Dmitry O.

    2014-01-01

    Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…

  8. Electrolarynx Voice Recognition Utilizing Pulse Coupled Neural Network

    Directory of Open Access Journals (Sweden)

    Fatchul Arifin

    2010-08-01

    Full Text Available The laryngectomies patient has no ability to speak normally because their vocal chords have been removed. The easiest option for the patient to speak again is by using electrolarynx speech. This tool is placed on the lower chin. Vibration of the neck while speaking is used to produce sound. Meanwhile, the technology of "voice recognition" has been growing very rapidly. It is expected that the technology of "voice recognition" can also be used by laryngectomies patients who use electrolarynx.This paper describes a system for electrolarynx speech recognition. Two main parts of the system are feature extraction and pattern recognition. The Pulse Coupled Neural Network – PCNN is used to extract the feature and characteristic of electrolarynx speech. Varying of β (one of PCNN parameter also was conducted. Multi layer perceptron is used to recognize the sound patterns. There are two kinds of recognition conducted in this paper: speech recognition and speaker recognition. The speech recognition recognizes specific speech from every people. Meanwhile, speaker recognition recognizes specific speech from specific person. The system ran well. The "electrolarynx speech recognition" has been tested by recognizing of “A” and "not A" voice. The results showed that the system had 94.4% validation. Meanwhile, the electrolarynx speaker recognition has been tested by recognizing of “saya” voice from some different speakers. The results showed that the system had 92.2% validation. Meanwhile, the best β parameter of PCNN for electrolarynx recognition is 3.

  9. 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.

  10. Multilayer bootstrap network for unsupervised speaker recognition

    OpenAIRE

    Zhang, Xiao-Lei

    2015-01-01

    We apply multilayer bootstrap network (MBN), a recent proposed unsupervised learning method, to unsupervised speaker recognition. The proposed method first extracts supervectors from an unsupervised universal background model, then reduces the dimension of the high-dimensional supervectors by multilayer bootstrap network, and finally conducts unsupervised speaker recognition by clustering the low-dimensional data. The comparison results with 2 unsupervised and 1 supervised speaker recognition...

  11. Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

    Science.gov (United States)

    Zhang, Jianhua; Li, Sunan; Wang, Rubin

    2017-01-01

    In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

  12. Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Jianhua Zhang

    2017-05-01

    Full Text Available In this paper, we deal with the Mental Workload (MWL classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers and parameter optimization algorithms for the Convolutional Neural Networks (CNN. The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

  13. Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data.

    Science.gov (United States)

    dos Santos Siqueira, Anderson; Biazoli Junior, Claudinei Eduardo; Comfort, William Edgar; Rohde, Luis Augusto; Sato, João Ricardo

    2014-01-01

    The framework of graph theory provides useful tools for investigating the neural substrates of neuropsychiatric disorders. Graph description measures may be useful as predictor variables in classification procedures. Here, we consider several centrality measures as predictor features in a classification algorithm to identify nodes of resting-state networks containing predictive information that can discriminate between typical developing children and patients with attention-deficit/hyperactivity disorder (ADHD). The prediction was based on a support vector machines classifier. The analyses were performed in a multisite and publicly available resting-state fMRI dataset of healthy children and ADHD patients: the ADHD-200 database. Network centrality measures contained little predictive information for the discrimination between ADHD patients and healthy subjects. However, the classification between inattentive and combined ADHD subtypes was more promising, achieving accuracies higher than 65% (balance between sensitivity and specificity) in some sites. Finally, brain regions were ranked according to the amount of discriminant information and the most relevant were mapped. As hypothesized, we found that brain regions in motor, frontoparietal, and default mode networks contained the most predictive information. We concluded that the functional connectivity estimations are strongly dependent on the sample characteristics. Thus different acquisition protocols and clinical heterogeneity decrease the predictive values of the graph descriptors.

  14. [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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Stimulated Deep Neural Network for Speech Recognition

    Science.gov (United States)

    2016-09-08

    approaches yield state-of-the-art performance in a range of tasks, including speech recognition . However, the parameters of the network are hard to analyze...advantage of the smoothness con- straints that stimulated training offers. The approaches are eval- uated on two large vocabulary speech recognition

  20. Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

    Science.gov (United States)

    Kim, Ju-Won; Park, Seunghee

    2018-01-02

    In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

  1. 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.

  2. 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.

  3. 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

  4. 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 ...

  5. 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.

  6. 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…

  7. 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…

  8. 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.

  9. 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.

  10. Constructive autoassociative neural network for facial recognition.

    Directory of Open Access Journals (Sweden)

    Bruno J T Fernandes

    Full Text Available Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network. CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer. Neurons in the CANet output layer present lateral inhibitory connections that improve the recognition rate. Experiments in face recognition and facial expression recognition show that the CANet outperforms other methods presented in the literature.

  11. 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.

  12. 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

  13. 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

  14. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  15. Hand Recognition Using Thermal Image and Extension Neural Network

    Directory of Open Access Journals (Sweden)

    Meng-Hui Wang

    2012-01-01

    Full Text Available Hand recognition is one of the popular biometry methods for access control systems. In this paper, a new scheme for personal recognition using thermal images of the hand and an extension neural network (ENN is presented. The features of the recognition system are extracted from gray level hand images, which are taken by an infrared camera. The main advantage of the thermal image is that it can reduce errors and noise in the features extracted stage, which is most important to increase the accuracy of recognition systems. Moreover, a new recognition method based on the ENN is proposed to perform the core functions of the hand recognition system. The proposed ENN-based recognition method also permits rapid adaptive processing for a new pattern, as it only tunes the boundaries of classified features or adds a new neural node. It is feasible to implement the proposed method on a Microcomputer for a portable personal recognition device. From the tested examples, the proposed method has a significantly high degree of recognition accuracy and shows good tolerance to errors added.

  16. 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 ...

  17. 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).

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology.

    Science.gov (United States)

    Cavalli, Fabio; Lusnig, Luca; Trentin, Edmondo

    2017-05-01

    Sex determination on skeletal remains is one of the most important diagnosis in forensic cases and in demographic studies on ancient populations. Our purpose is to realize an automatic operator-independent method to determine the sex from the bone shape and to test an intelligent, automatic pattern recognition system in an anthropological domain. Our multiple-classifier system is based exclusively on the morphological variants of a curve that represents the sagittal profile of the calvarium, modeled via artificial neural networks, and yields an accuracy higher than 80 %. The application of this system to other bone profiles is expected to further improve the sensibility of the methodology.

  4. Urdu character recognition using fourier descriptors for optical networks

    Science.gov (United States)

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

    2005-08-01

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

  5. Convolutional Neural Network for Image Recognition

    CERN Document Server

    Seifnashri, Sahand

    2015-01-01

    The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. Convolutional neural networks and face recognition task

    Science.gov (United States)

    Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

    2017-09-01

    Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

  11. Convolution neural networks for ship type recognition

    Science.gov (United States)

    Rainey, Katie; Reeder, John D.; Corelli, Alexander G.

    2016-05-01

    Algorithms to automatically recognize ship type from satellite imagery are desired for numerous maritime applications. This task is difficult, and example imagery accurately labeled with ship type is hard to obtain. Convolutional neural networks (CNNs) have shown promise in image recognition settings, but many of these applications rely on the availability of thousands of example images for training. This work attempts to under- stand for which types of ship recognition tasks CNNs might be well suited. We report the results of baseline experiments applying a CNN to several ship type classification tasks, and discuss many of the considerations that must be made in approaching this problem.

  12. MBVCNN: Joint convolutional neural networks method for image recognition

    Science.gov (United States)

    Tong, Tong; Mu, Xiaodong; Zhang, Li; Yi, Zhaoxiang; Hu, Pei

    2017-05-01

    Aiming at the problem of objects in image recognition rectangle, but objects which are input into convolutional neural networks square, the object recognition model was put forward which was based on BING method to realize object estimate, used vectorization of convolutional neural networks to realize input square image in convolutional networks, therefore, built joint convolution neural networks, which achieve multiple size image input. Verified by experiments, the accuracy of multi-object image recognition was improved by 6.70% compared with single vectorization of convolutional neural networks. Therefore, image recognition method of joint convolutional neural networks can enhance the accuracy in image recognition, especially for target in rectangular shape.

  13. Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

    Science.gov (United States)

    Popko, E. A.; Weinstein, I. A.

    2016-08-01

    Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

  14. 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.

  15. 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.

  16. 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

  17. Review network for scene text recognition

    Science.gov (United States)

    Li, Shuohao; Han, Anqi; Chen, Xu; Yin, Xiaoqing; Zhang, Jun

    2017-09-01

    Recognizing text in images captured in the wild is a fundamental preprocessing task for many computer vision and machine learning applications and has gained significant attention in recent years. This paper proposes an end-to-end trainable deep review neural network for scene text recognition, which is a combination of feature extraction, feature reviewing, feature attention, and sequence recognition. Our model can generate the predicted text without any segmentation or grouping algorithm. Because the attention model in the feature attention stage lacks global modeling ability, a review network is applied to extract the global context of sequence data in the feature reviewing stage. We perform rigorous experiments across a number of standard benchmarks, including IIIT5K, SVT, ICDAR03, and ICDAR13 datasets. Experimental results show that our model is comparable to or outperforms state-of-the-art techniques.

  18. Hierarchical Neural Network Structures for Phoneme Recognition

    CERN Document Server

    Vasquez, Daniel; Minker, Wolfgang

    2013-01-01

    In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Object recognition with hierarchical discriminant saliency networks

    Directory of Open Access Journals (Sweden)

    Sunhyoung eHan

    2014-09-01

    Full Text Available The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognitionmodel, the hierarchical discriminant saliency network (HDSN, whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. The HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a neuralnetwork implementation, all layers are convolutional and implement acombination of filtering, rectification, and pooling. The rectificationis performed with a parametric extension of the now popular rectified linearunits (ReLUs, whose parameters can be tuned for the detection of targetobject classes. This enables a number of functional enhancementsover neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation ofsaliency responses by the discriminant power of the underlying features,and the ability to detect both feature presence and absence.In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity totarget object classes and invariance. The resulting performance demonstrates benefits for all the functional enhancements of the HDSN.

  5. Animal Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Tibor Trnovszky

    2017-01-01

    Full Text Available In this paper, the performances of well-known image recognition methods such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Local Binary Patterns Histograms (LBPH and Support Vector Machine (SVM are tested and compared with proposed convolutional neural network (CNN for the recognition rate of the input animal images. In our experiments, the overall recognition accuracy of PCA, LDA, LBPH and SVM is demonstrated. Next, the time execution for animal recognition process is evaluated. The all experimental results on created animal database were conducted. This created animal database consist of 500 different subjects (5 classes/ 100 images for each class. The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set. For proposed CNN we have obtained a recognition accuracy of 98%. The proposed method based on CNN outperforms the state of the art methods.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Applying Artificial Neural Networks for Face Recognition

    Directory of Open Access Journals (Sweden)

    Thai Hoang Le

    2011-01-01

    Full Text Available This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

  12. 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.

  13. 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.

  14. AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JIDE JULIUS POPOOLA

    2014-04-01

    Full Text Available In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox.

  15. 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.

  16. 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...

  17. 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.

  18. 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.

  19. 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.

  20. Sign language recognition using competitive learning in the HAVNET neural network

    Science.gov (United States)

    Sujan, Vivek A.; Meggiolaro, Marco A.

    2000-04-01

    An optical modeless Sign Language Recognition (SLR) system is presented. The system uses the HAusdorf-Voronoi NETwork (HAVNET), an artificial neural network designed for 2D binary pattern recognition. It uses adaptation of the Hausdorff distance to determine the similarity between an input pattern and a learned representation. A detailed review of the architecture, the learning equations, and the recognition equations for the HAVNET network are presented. Competitive learning has been implemented in training the network using a nearest-neighbor technique. The SLR system is applied to the optical recognition of 24 static symbols from the American Sign Language convention. The SLR system represents the target images in a 80 X 80 pixel format. The implemented HAVNET network classifies the inputs into categories representing each of the symbols, using an output layer of 24 nodes. The network is trained with 5 different formats for each symbol and is tested with all 24 symbols in 15 new formats. Results from the SLR system without competitive training show shape identification problems, when distinguishing symbols with similar shapes. Implementation of competitive learning in the HAVNET neural network improved recognition accuracy on this task to 89%. The hand gestures are identified through a window search algorithm. Feature recognition is obtained from edge enhancement by applying a Laplacian filter and thresholding, which provides robustness to pose, color and background variations.

  1. Handwritten Sindhi Character Recognition Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Shafique Ahmed Awan

    2018-01-01

    Full Text Available OCR (OpticalCharacter Recognition is a technology in which text image is used to understand and write text by machines. The work on languages containing isolated characters such as German, English, French and others is at its peak. The OCR and ICR (Intelligent Character Recognition research in Sindhi script is currently at in starting stages and not sufficient work have been cited in this area even though Sindhi language is rich in culture and history. This paper presents one of the initial steps in recognizing Sindhi handwritten characters. The isolated characters of Sindhi script written by thesubjects have been recognized. The various subjects were asked to write Sindhi characters in unconstrained form and then the written samples were collected and scanned through a flatbed scanner. The scanned documents were preprocessedwith the help of binary conversion, removing noise by pepper noise and the lines were segmented with the help of horizontal profile technique. The segmented lines were used to extract characters from scanned pages.This character segmentation was done by vertical projection. The extracted characters have been used to extract features so that the characters can be classified easily. Zoning was used for the feature extraction technique. For the classification, neural network has been used. The recognized characters converted into editable text with an average accuracy of 85%.

  2. 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.

  3. 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.

  4. Distorted Character Recognition Via An Associative Neural Network

    Science.gov (United States)

    Messner, Richard A.; Szu, Harold H.

    1987-03-01

    The purpose of this paper is two-fold. First, it is intended to provide some preliminary results of a character recognition scheme which has foundations in on-going neural network architecture modeling, and secondly, to apply some of the neural network results in a real application area where thirty years of effort has had little effect on providing the machine an ability to recognize distorted objects within the same object class. It is the author's belief that the time is ripe to start applying in ernest the results of over twenty years of effort in neural modeling to some of the more difficult problems which seem so hard to solve by conventional means. The character recognition scheme proposed utilizes a preprocessing stage which performs a 2-dimensional Walsh transform of an input cartesian image field, then sequency filters this spectrum into three feature bands. Various features are then extracted and organized into three sets of feature vectors. These vector patterns that are stored and recalled associatively. Two possible associative neural memory models are proposed for further investigation. The first being an outer-product linear matrix associative memory with a threshold function controlling the strength of the output pattern (similar to Kohonen's crosscorrelation approach [1]). The second approach is based upon a modified version of Grossberg's neural architecture [2] which provides better self-organizing properties due to its adaptive nature. Preliminary results of the sequency filtering and feature extraction preprocessing stage and discussion about the use of the proposed neural architectures is included.

  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. A STUDY OF VARIABLES CHARACTERIZING DRAINAGE PATTERNS IN RIVER NETWORKS

    Directory of Open Access Journals (Sweden)

    L. Zhang

    2012-07-01

    Full Text Available In GIS and in terrain analysis, drainage systems are important components. Due to local topography and subsurface geology, a drainage system achieves a particular drainage pattern based on the form and texture of its network of stream channels and tributaries. Drainage pattern recognition helps to provide a qualitative description of the terrain for analysis and classification and is useful for terrain modelling and visualization and applications in environment. Much research has been done on the description of drainage patterns in geography and hydrology. However automatic drainage pattern recognition in river networks is not well developed. This paper introduces a method based on geometric quantitative indicators to recognize drainage patterns in a river network automatically. Experiment results are presented and discussed.

  14. 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...

  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. 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.

  17. 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.

  18. 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.

  19. IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

    Directory of Open Access Journals (Sweden)

    A. S. Potapov

    2014-09-01

    Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

  20. 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.

  1. 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.

  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. 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.

  4. 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.

  5. 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).

  6. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.

  7. Increased Efficiency of Face Recognition System using Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Rajani Muraleedharan

    2006-02-01

    Full Text Available This research was inspired by the need of a flexible and cost effective biometric security system. The flexibility of the wireless sensor network makes it a natural choice for data transmission. Swarm intelligence (SI is used to optimize routing in distributed time varying network. In this paper, SI maintains the required bit error rate (BER for varied channel conditions while consuming minimal energy. A specific biometric, the face recognition system, is discussed as an example. Simulation shows that the wireless sensor network is efficient in energy consumption while keeping the transmission accuracy, and the wireless face recognition system is competitive to the traditional wired face recognition system in classification accuracy.

  8. 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...

  9. 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

  10. 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

  11. 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...

  12. 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.

  13. 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.

  14. 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.

  15. Recognition of faults patterns in electric generators using artificial neurons networks; Reconocimiento de patrones de fallas en generadores electricos empleando redes neuronales artificiales

    Energy Technology Data Exchange (ETDEWEB)

    Rocha Sanchez, Martha Alicia

    1999-03-01

    This work present the development of a reprocessing method to reduce the information of the original data and to maintain the essential information of the data that enter the reduction process. The obtaining of these data is performed with the aid of the ICM ++ (ICMsystems), from which vectors or n-uplos of elements are obtained. This investigation allowed to analyze an evaluation of the paradigms of artificial neural networks, with the intention of detecting which of these paradigms would evolve better with the problem of fault diagnosis in electric generators. From this a prototype system was developed called diagnosis of faults in electrical generators, which automatically will recognize faults in electrical generators by means of the interpretation of the recording of partial discharges. [Spanish] El presente trabajo presenta el desarrollo de un metodo de reprocesamiento para reducir informacion de los datos originales y mantener la informacion esencial de los datos que entran al proceso de reduccion. La obtencion de estos datos se realiza con la ayuda del ICM ++ (ICMsystems), de los cuales se obtienen vectores o n-uplos de elementos. Esta investigacion permitio analizar una evaluacion de los paradigmas de redes neuronales artificiales, con el objeto de detectar cual de estos paradigmas se desempenaria mejor con el problema de diagnostico de fallas en generadores electricos. A partir de esto se desarrollo un sistema prototipo llamado diagnostico de fallas en generadores electricos, el cual automaticamente reconocera fallas en los generadores electricos mediante la interpretacion de registro de descargas parciales.

  16. 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.

  17. 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.

  18. 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.

  19. Pattern recognition in the ALFALFA.70 and Sloan Digital Sky Surveys: a catalogue of ˜500 000 H I gas fraction estimates based on artificial neural networks

    Science.gov (United States)

    Teimoorinia, Hossen; Ellison, Sara L.; Patton, David R.

    2017-02-01

    The application of artificial neural networks (ANNs) for the estimation of H I gas mass fraction (M_{H I}/{{M}_{*}}) is investigated, based on a sample of 13 674 galaxies in the Sloan Digital Sky Survey (SDSS) with H I detections or upper limits from the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA). We show that, for an example set of fixed input parameters (g - r colour and I-band surface brightness), a multidimensional quadratic model yields M_{H I}/{{M}_{*}} scaling relations with a smaller scatter (0.22 dex) than traditional linear fits (0.32 dex), demonstrating that non-linear methods can lead to an improved performance over traditional approaches. A more extensive ANN analysis is performed using 15 galaxy parameters that capture variation in stellar mass, internal structure, environment and star formation. Of the 15 parameters investigated, we find that g - r colour, followed by stellar mass surface density, bulge fraction and specific star formation rate have the best connection with M_{H I}/{{M}_{*}}. By combining two control parameters, that indicate how well a given galaxy in SDSS is represented by the ALFALFA training set (PR) and the scatter in the training procedure (σfit), we develop a strategy for quantifying which SDSS galaxies our ANN can be adequately applied to, and the associated errors in the M_{H I}/{{M}_{*}} estimation. In contrast to previous works, our M_{H I}/{{M}_{*}} estimation has no systematic trend with galactic parameters such as M⋆, g - r and star formation rate. We present a catalogue of M_{H I}/{{M}_{*}} estimates for more than half a million galaxies in the SDSS, of which ˜150 000 galaxies have a secure selection parameter with average scatter in the M_{H I}/{{M}_{*}} estimation of 0.22 dex.

  20. Recognition of sign language gestures using neural networks

    Directory of Open Access Journals (Sweden)

    Simon Vamplew

    2007-04-01

    Full Text Available This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan hand gestures.

  1. Face Recognition using Artificial Neural Network | Endeshaw | Zede ...

    African Journals Online (AJOL)

    Face recognition (FR) is one of the biometric methods to identify the individuals by the features of face. Two Face Recognition Systems (FRS) based on Artificial Neural Network (ANN) have been proposed in this paper based on feature extraction techniques. In the first system, Principal Component Analysis (PCA) has been ...

  2. 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…

  3. 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

  4. Hierarchical polynomial network approach to automated target recognition

    Science.gov (United States)

    Kim, Richard Y.; Drake, Keith C.; Kim, Tony Y.

    1994-02-01

    A hierarchical recognition methodology using abductive networks at several levels of object recognition is presented. Abductive networks--an innovative numeric modeling technology using networks of polynomial nodes--results from nearly three decades of application research and development in areas including statistical modeling, uncertainty management, genetic algorithms, and traditional neural networks. The systems uses pixel-registered multisensor target imagery provided by the Tri-Service Laser Radar sensor. Several levels of recognition are performed using detection, classification, and identification, each providing more detailed object information. Advanced feature extraction algorithms are applied at each recognition level for target characterization. Abductive polynomial networks process feature information and situational data at each recognition level, providing input for the next level of processing. An expert system coordinates the activities of individual recognition modules and enables employment of heuristic knowledge to overcome the limitations provided by a purely numeric processing approach. The approach can potentially overcome limitations of current systems such as catastrophic degradation during unanticipated operating conditions while meeting strict processing requirements. These benefits result from implementation of robust feature extraction algorithms that do not take explicit advantage of peculiar characteristics of the sensor imagery, and the compact, real-time processing capability provided by abductive polynomial networks.

  5. 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.

  6. 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.

  7. 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...

  8. 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.

  9. 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.

  10. 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.

  11. 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)

  12. Hand gesture recognition based on convolutional neural networks

    Science.gov (United States)

    Hu, Yu-lu; Wang, Lian-ming

    2017-11-01

    Hand gesture has been considered a natural, intuitive and less intrusive way for Human-Computer Interaction (HCI). Although many algorithms for hand gesture recognition have been proposed in literature, robust algorithms have been pursued. A recognize algorithm based on the convolutional neural networks is proposed to recognize ten kinds of hand gestures, which include rotation and turnover samples acquired from different persons. When 6000 hand gesture images were used as training samples, and 1100 as testing samples, a 98% recognition rate was achieved with the convolutional neural networks, which is higher than that with some other frequently-used recognition algorithms.

  13. 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.

  14. 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.

  15. Improvement of the detection response time of gas sensors using the association of artificial neural networks with pattern recognition technique; Amelioration de la reponse temporelle de capteurs de gaz par reconnaissance de forme a l'aide de reseaux de neurones

    Energy Technology Data Exchange (ETDEWEB)

    Bordieu, Ch.; Rebiere, D. [Bordeaux-1 Univ., Lab. IXL, UMR CNRS 5818, 33 (France); Pistre, J.; Planata, R. [Centre d' Etudes du Bouchet, 91 - Vert-le-Petit (France)

    1999-07-01

    The association of artificial neural networks (multilayer perceptrons) with a real time pattern recognition technique (shifting windows) allowed the development of systems for the detection and the quantification of gases. Shifting window technique is presented and offers an interesting way to improve the detection response time. The partial detector characterization with regard to its parameters was realized. Applications dealing with the detection of gas compounds using surface acoustic sensors permit to show the shifting window technique feasibility. (author)

  16. 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.

  17. Three-dimensional fingerprint recognition by using convolution neural network

    Science.gov (United States)

    Tian, Qianyu; Gao, Nan; Zhang, Zonghua

    2018-01-01

    With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

  18. 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.

  19. 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.

  20. 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.

  1. 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...

  2. 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.

  3. 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...

  4. 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...

  5. 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

  6. 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

  7. 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.…

  8. 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...

  9. 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

  10. 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

  11. Higher-order neural network software for distortion invariant object recognition

    Science.gov (United States)

    Reid, Max B.; Spirkovska, Lilly

    1991-01-01

    The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. [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.

  19. Braille Character Recognition Using Artificial Neural Network

    OpenAIRE

    Subur, Joko; Sardjono, Tri Arief; Mardiyanto, Ronny

    2015-01-01

    Braille letter is characters designed for the blind, consist of six embossed points, arranged in a standard braille character. Braille letters is touched and read using fingers, therefore the sensitivity of the fingers is important. Those characters need to be memorized, so it is very difficult to be learned. The aim of this research is to create a braille characters recognition system and translate it to alpha-numeric text. Webcam camera is used to capture braille image from braille characte...

  20. 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.

  1. Reject mechanisms for massively parallel neural network character recognition systems

    Science.gov (United States)

    Garris, Michael D.; Wilson, Charles L.

    1992-12-01

    Two reject mechanisms are compared using a massively parallel character recognition system implemented at NIST. The recognition system was designed to study the feasibility of automatically recognizing hand-printed text in a loosely constrained environment. The first method is a simple scalar threshold on the output activation of the winning neurode from the character classifier network. The second method uses an additional neural network trained on all outputs from the character classifier network to accept or reject assigned classifications. The neural network rejection method was expected to perform with greater accuracy than the scalar threshold method, but this was not supported by the test results presented. The scalar threshold method, even though arbitrary, is shown to be a viable reject mechanism for use with neural network character classifiers. Upon studying the performance of the neural network rejection method, analyses show that the two neural networks, the character classifier network and the rejection network, perform very similarly. This can be explained by the strong non-linear function of the character classifier network which effectively removes most of the correlation between character accuracy and all activations other than the winning activation. This suggests that any effective rejection network must receive information from the system which has not been filtered through the non-linear classifier.

  2. 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.

  3. 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.

  4. 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.

  5. Isolated Speech Recognition Using Artificial Neural Networks

    National Research Council Canada - National Science Library

    Polur, Prasad

    2001-01-01

    .... A small size vocabulary containing the words YES and NO is chosen. Spectral features using cepstral analysis are extracted per frame and imported to a feedforward neural network which uses a backpropagation with momentum training algorithm...

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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,

  14. A model of traffic signs recognition with convolutional neural network

    Science.gov (United States)

    Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

    2016-10-01

    In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

  15. 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.

  16. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    Energy Technology Data Exchange (ETDEWEB)

    Vargas, Lorena P [Lorena Vargas Quintero, Optic and Computer Science Group - Universidad Popular del Cesar (Colombia); Barba, Leiner; Torres, C O; Mattos, L, E-mail: vargas.lorena@yahoo.com [Optic and Computer Science Group - Popular of Cesar University, Km 12, Valledupar (Colombia)

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  17. Progressive changes in a recognition memory network in Parkinson's disease.

    Science.gov (United States)

    Segura, Bàrbara; Ibarretxe-Bilbao, Naroa; Sala-Llonch, Roser; Baggio, Hugo Cesar; Martí, María Jose; Valldeoriola, Francesc; Vendrell, Pere; Bargalló, Nuria; Tolosa, Eduard; Junqué, Carme

    2013-04-01

    In a previous functional MRI (fMRI) study, we found that patients with Parkinson's disease (PD) presented with dysfunctions in the recruitment of recognition memory networks. We aimed to investigate the changes in these networks over time. We studied 17 PD patients and 13 age and sex matched healthy subjects. In both groups fMRI (recognition memory paradigm) and neuropsychological assessments were obtained at baseline and at follow-up. To analyse changes over time in functional networks, model free (independent component analysis) analyses of the fMRI data were carried out. Then, a cross correlation approach was used to assess the changes in the strength of functional connectivity. At follow-up, patients showed reduced recruitment of one network, including decreased activation in the orbitofrontal cortices, middle frontal gyri, frontal poles, anterior paracingulate cortex, superior parietal lobes and left middle temporal gyrus, as well as decreased deactivation in the anterior paracingulate gyrus and precuneus. Cross correlation analyses over time showed a decrease in the strength of functional connectivity between the middle frontal gyrus and the superior parietal lobe in PD patients. Model free fMRI and cross correlation connectivity analyses were able to detect progressive changes in functional networks involved in recognition memory in PD patients at early disease stages and without overt clinical deterioration. Functional connectivity analyses could be useful to monitor changes in brain networks underlying neuropsychological deficits in PD.

  18. 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.

  19. Dynamic Image Networks for Action Recognition

    NARCIS (Netherlands)

    Bilen, H.; Fernando, B.; Gavves, E.; Vedaldi, A.; Gould, S.

    2016-01-01

    We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used. The dynamic image is based on the rank pooling concept and is obtained through the parameters of a ranking machine that encodes

  20. The effect of network degradation on speech recognition

    CSIR Research Space (South Africa)

    Joubert, G

    2005-11-01

    Full Text Available The authors describe a system, based on open-source tools, that was developed in order to study the effect of network degenerations in Voice-over-Internet-Protocol applications on speech-recognition accuracy. Sophisticated play-out algorithms...

  1. 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

  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. Activity Recognition Using Complex Network Analysis.

    Science.gov (United States)

    Jalloul, Nahed; Poree, Fabienne; Viardot, Geoffrey; L'Hostis, Phillipe; Carrault, Guy

    2017-10-12

    In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a Random Forest (RF) classifier, and when considering a monitoring system composed of only two modules positioned at the Neck and Thigh of the subject's body.

  4. Pattern reverberation in networks of excitable systems with connection delays

    Science.gov (United States)

    Lücken, Leonhard; Rosin, David P.; Worlitzer, Vasco M.; Yanchuk, Serhiy

    2017-01-01

    We consider the recurrent pulse-coupled networks of excitable elements with delayed connections, which are inspired by the biological neural networks. If the delays are tuned appropriately, the network can either stay in the steady resting state, or alternatively, exhibit a desired spiking pattern. It is shown that such a network can be used as a pattern-recognition system. More specifically, the application of the correct pattern as an external input to the network leads to a self-sustained reverberation of the encoded pattern. In terms of the coupling structure, the tolerance and the refractory time of the individual systems, we determine the conditions for the uniqueness of the sustained activity, i.e., for the functionality of the network as an unambiguous pattern detector. We point out the relation of the considered systems with cyclic polychronous groups and show how the assumed delay configurations may arise in a self-organized manner when a spike-time dependent plasticity of the connection delays is assumed. As excitable elements, we employ the simplistic coincidence detector models as well as the Hodgkin-Huxley neuron models. Moreover, the system is implemented experimentally on a Field-Programmable Gate Array.

  5. 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.

  6. 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.

  7. 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.

  8. Video-based face recognition via convolutional neural networks

    Science.gov (United States)

    Bao, Tianlong; Ding, Chunhui; Karmoshi, Saleem; Zhu, Ming

    2017-06-01

    Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.

  9. 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.

  10. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  11. 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

  12. Neural Network Architectures for General Image Recognition.

    Science.gov (United States)

    1992-07-21

    Adomian and Adomian [43], the first two terms of an operator decomposition solution, X0 and X 1 , are given by: X0 = Bf(I) (A.24) XI = A(Xo) + Xo (A.25) 91... Adomian and Adomian [43] for a description.] A.5 Examples Consider designing an 0/0 NN that is sensitive to horizontal binary patterns, to a resolution of...Nonlinear Functional Analysis, New York, NY: Springer-Verlag (1985), pp. 186- 216. 43. G. Adomian and G.E. Adomian ,"A global method for solution of complex

  13. Sign Language Recognition using Neural Networks

    Directory of Open Access Journals (Sweden)

    Sabaheta Djogic

    2014-11-01

    Full Text Available – Sign language plays a great role as communication media for people with hearing difficulties.In developed countries, systems are made for overcoming a problem in communication with deaf people. This encouraged us to develop a system for the Bosnian sign language since there is a need for such system. The work is done with the use of digital image processing methods providing a system that teaches a multilayer neural network using a back propagation algorithm. Images are processed by feature extraction methods, and by masking method the data set has been created. Training is done using cross validation method for better performance thus; an accuracy of 84% is achieved.

  14. Recognition of Gestures using Artifical Neural Network

    Directory of Open Access Journals (Sweden)

    Marcel MORE

    2013-12-01

    Full Text Available Sensors for motion measurements are now becoming more widespread. Thanks to their parameters and affordability they are already used not only in the professional sector, but also in devices intended for daily use or entertainment. One of their applications is in control of devices by gestures. Systems that can determine type of gesture from measured motion have many uses. Some are for example in medical practice, but they are still more often used in devices such as cell phones, where they serve as a non-standard form of input. Today there are already several approaches for solving this problem, but building sufficiently reliable system is still a challenging task. In our project we are developing solution based on artificial neural network. In difference to other solutions, this one doesn’t require building model for each measuring system and thus it can be used in combination with various sensors just with minimal changes in his structure.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Distributed Recognition of Reference Nodes for Wireless Sensor Network Localization

    Directory of Open Access Journals (Sweden)

    M. Simek

    2012-04-01

    Full Text Available All known localization techniques for wireless sensor and ad-hoc networks require certain set of reference nodes being used for position estimation. The anchor-free techniques in contrast to anchor-based do not require reference nodes called anchors to be placed in the network area before localization operation itself, but they can establish own reference coordinate system to be used for the relative position estimation. We observed that contemporary anchor-free localization algorithms achieve a low localization error, but dissipate significant energy reserves during the recognition of reference nodes used for the position estimation. Therefore, we have proposed the optimized anchor-free localization algorithm referred to as BRL (Boundary Recognition aided Localization, which achieves a low localization error and mainly reduces the communication cost of the reference nodes recognition phase. The proposed BRL algorithm was investigated throughout the extensive simulations on the database of networks with the different number of nodes and densities and was compared in terms of communication cost and localization error with the known related algorithms such as AFL and CRP. Through the extensive simulations we have observed network conditions where novel BRL algorithm excels in comparison with the state of art.

  2. 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.

  3. On the relevance of using Bayesian belief networks in wireless sensor networks situation recognition.

    Science.gov (United States)

    Bagula, Antoine B; Osunmakinde, Isaac; Zennaro, Marco

    2010-01-01

    Achieving situation recognition in ubiquitous sensor networks (USNs) is an important issue that has been poorly addressed by both the research and practitioner communities. This paper describes some steps taken to address this issue by effecting USN middleware intelligence using an emerging situation awareness (ESA) technology. We propose a situation recognition framework where temporal probabilistic reasoning is used to derive and emerge situation awareness in ubiquitous sensor networks. Using data collected from an outdoor environment monitoring in the city of Cape Town, we illustrate the use of the ESA technology in terms of sensor system operating conditions and environmental situation recognition.

  4. On the Relevance of Using Bayesian Belief Networks in Wireless Sensor Networks Situation Recognition

    Directory of Open Access Journals (Sweden)

    Marco Zennaro

    2010-12-01

    Full Text Available Achieving situation recognition in ubiquitous sensor networks (USNs is an important issue that has been poorly addressed by both the research and practitioner communities. This paper describes some steps taken to address this issue by effecting USN middleware intelligence using an emerging situation awareness (ESA technology. We propose a situation recognition framework where temporal probabilistic reasoning is used to derive and emerge situation awareness in ubiquitous sensor networks. Using data collected from an outdoor environment monitoring in the city of Cape Town, we illustrate the use of the ESA technology in terms of sensor system operating conditions and environmental situation recognition.

  5. Broken symmetries in a location-invariant word recognition network.

    Science.gov (United States)

    Hannagan, Thomas; Dandurand, Frédéric; Grainger, Jonathan

    2011-01-01

    We studied the feedforward network proposed by Dandurand et al. (2010), which maps location-specific letter inputs to location-invariant word outputs, probing the hidden layer to determine the nature of the code. Hidden patterns for words were densely distributed, and K-means clustering on single letter patterns produced evidence that the network had formed semi-location-invariant letter representations during training. The possible confound with superseding bigram representations was ruled out, and linear regressions showed that any word pattern was well approximated by a linear combination of its constituent letter patterns. Emulating this code using overlapping holographic representations (Plate, 1995) uncovered a surprisingly acute and useful correspondence with the network, stemming from a broken symmetry in the connection weight matrix and related to the group-invariance theorem (Minsky & Papert, 1969). These results also explain how the network can reproduce relative and transposition priming effects found in humans.

  6. 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.

  7. Finding pattern in biochemical reaction networks - a sub-graph mining approach

    OpenAIRE

    Henkel, Ron; Lambusch, Fabienne; Waltemath, Dagmar

    2015-01-01

    Biological questions today are often answered with the help of simulation models. Many of these models encode biological processes as biochemical reaction networks. The increasing amount of published models and the growing size of encoded reaction networks demand methods to analyse models. Specifically, researchers need to identify reoccurring and biologically relevant patterns. However, pattern recognition in large networks is a hard problem, and only partial solutions for very specific biol...

  8. 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.

  9. 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.

  10. Noisy Ocular Recognition Based on Three Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Min Beom Lee

    2017-12-01

    Full Text Available In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera, specular reflection (SR and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs. Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II training dataset (selected from the university of Beira iris (UBIRIS.v2 database, mobile iris challenge evaluation (MICHE database, and institute of automation of Chinese academy of sciences (CASIA-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  11. 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.

  12. 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.

  13. 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.

  14. Neural Network for Nanoscience Scanning Electron Microscope Image Recognition.

    Science.gov (United States)

    Modarres, Mohammad Hadi; Aversa, Rossella; Cozzini, Stefano; Ciancio, Regina; Leto, Angelo; Brandino, Giuseppe Piero

    2017-10-16

    In this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.

  15. 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.

  16. 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...

  17. 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

  18. A small-world network model of facial emotion recognition.

    Science.gov (United States)

    Takehara, Takuma; Ochiai, Fumio; Suzuki, Naoto

    2016-01-01

    Various models have been proposed to increase understanding of the cognitive basis of facial emotions. Despite those efforts, interactions between facial emotions have received minimal attention. If collective behaviours relating to each facial emotion in the comprehensive cognitive system could be assumed, specific facial emotion relationship patterns might emerge. In this study, we demonstrate that the frameworks of complex networks can effectively capture those patterns. We generate 81 facial emotion images (6 prototypes and 75 morphs) and then ask participants to rate degrees of similarity in 3240 facial emotion pairs in a paired comparison task. A facial emotion network constructed on the basis of similarity clearly forms a small-world network, which features an extremely short average network distance and close connectivity. Further, even if two facial emotions have opposing valences, they are connected within only two steps. In addition, we show that intermediary morphs are crucial for maintaining full network integration, whereas prototypes are not at all important. These results suggest the existence of collective behaviours in the cognitive systems of facial emotions and also describe why people can efficiently recognize facial emotions in terms of information transmission and propagation. For comparison, we construct three simulated networks--one based on the categorical model, one based on the dimensional model, and one random network. The results reveal that small-world connectivity in facial emotion networks is apparently different from those networks, suggesting that a small-world network is the most suitable model for capturing the cognitive basis of facial emotions.

  19. 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

  20. 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

  1. 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

  2. One solution to recognition of artistic pictures for guide robots by using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Lukić Luka

    2009-01-01

    Full Text Available In this paper is presented one solution to efficient, robust and cheap recognition of artistic pictures on the walls of museums and exhibit halls that reveals satisfactory measure of universality in order to be applied in the areas of trade, process industry, quality control, etc. This solution can be used in a wide range of applications where there is a demand of classifying objects on basis of their visual properties in a large number of existing classes. Here is proposed a method of selective grouping of pattern vectors as training sets for classifiers (artificial neural networks in this case, providing a smaller number of hidden layers in networks, achieving more precise performances and significantly expanding a number of classes to be classified. Selection approach is used in the very classification as well - neural networks are fed with input pattern vectors chosen from subsets determined by additional coefficients. .

  3. 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.

  4. 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.

  5. Neural networks for data compression and invariant image recognition

    Science.gov (United States)

    Gardner, Sheldon

    1989-01-01

    An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

  6. 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...

  7. 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.

  8. 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).

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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…

  14. Recognizing changing seasonal patterns using neural networks

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); G. Draisma (Gerrit)

    1997-01-01

    textabstractIn this paper we propose a graphical method based on an artificial neural network model to investigate how and when seasonal patterns in macroeconomic time series change over time. Neural networks are useful since the hidden layer units may become activated only in certain seasons or

  15. 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.

  16. 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.

  17. Pattern Formation on Networks: from Localised Activity to Turing Patterns

    Science.gov (United States)

    McCullen, Nick; Wagenknecht, Thomas

    2016-06-01

    Networks of interactions between competing species are used to model many complex systems, such as in genetics, evolutionary biology or sociology and knowledge of the patterns of activity they can exhibit is important for understanding their behaviour. The emergence of patterns on complex networks with reaction-diffusion dynamics is studied here, where node dynamics interact via diffusion via the network edges. Through the application of a generalisation of dynamical systems analysis this work reveals a fundamental connection between small-scale modes of activity on networks and localised pattern formation seen throughout science, such as solitons, breathers and localised buckling. The connection between solutions with a single and small numbers of activated nodes and the fully developed system-scale patterns are investigated computationally using numerical continuation methods. These techniques are also used to help reveal a much larger portion of of the full number of solutions that exist in the system at different parameter values. The importance of network structure is also highlighted, with a key role being played by nodes with a certain so-called optimal degree, on which the interaction between the reaction kinetics and the network structure organise the behaviour of the system.

  18. 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.

  19. 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.

  20. Automatic speech recognition using a predictive echo state network classifier.

    Science.gov (United States)

    Skowronski, Mark D; Harris, John G

    2007-04-01

    We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

  1. 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.

  2. Face Recognition Using MLP and RBF Neural Network with Gabor and Discrete Wavelet Transform Characterization: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Fatma Zohra Chelali

    2015-01-01

    Full Text Available Face recognition has received a great attention from a lot of researchers in computer vision, pattern recognition, and human machine computer interfaces in recent years. Designing a face recognition system is a complex task due to the wide variety of illumination, pose, and facial expression. A lot of approaches have been developed to find the optimal space in which face feature descriptors are well distinguished and separated. Face representation using Gabor features and discrete wavelet has attracted considerable attention in computer vision and image processing. We describe in this paper a face recognition system using artificial neural networks like multilayer perceptron (MLP and radial basis function (RBF where Gabor and discrete wavelet based feature extraction methods are proposed for the extraction of features from facial images using two facial databases: the ORL and computer vision. Good recognition rate was obtained using Gabor and DWT parameterization with MLP classifier applied for computer vision dataset.

  3. Virtualized Network Function Orchestration System and Experimental Network Based QR Recognition for a 5G Mobile Access Network

    Directory of Open Access Journals (Sweden)

    Misun Ahn

    2017-12-01

    Full Text Available This paper proposes a virtualized network function orchestration system based on Network Function Virtualization (NFV, one of the main technologies in 5G mobile networks. This system should provide connectivity between network devices and be able to create flexible network function and distribution. This system focuses more on access networks. By experimenting with various scenarios of user service established and activated in a network, we examine whether rapid adoption of new service is possible and whether network resources can be managed efficiently. The proposed method is based on Bluetooth transfer technology and mesh networking to provide automatic connections between network machines and on a Docker flat form, which is a container virtualization technology for setting and managing key functions. Additionally, the system includes a clustering and recovery measure regarding network function based on the Docker platform. We will briefly introduce the QR code perceived service as a user service to examine the proposal and based on this given service, we evaluate the function of the proposal and present analysis. Through the proposed approach, container relocation has been implemented according to a network device’s CPU usage and we confirm successful service through function evaluation on a real test bed. We estimate QR code recognition speed as the amount of network equipment is gradually increased, improving user service and confirm that the speed of recognition is increased as the assigned number of network devices is increased by the user service.

  4. Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Ali A. Alani

    2017-11-01

    Full Text Available Handwritten digit recognition is an open problem in computer vision and pattern recognition, and solving this problem has elicited increasing interest. The main challenge of this problem is the design of an efficient method that can recognize the handwritten digits that are submitted by the user via digital devices. Numerous studies have been proposed in the past and in recent years to improve handwritten digit recognition in various languages. Research on handwritten digit recognition in Arabic is limited. At present, deep learning algorithms are extremely popular in computer vision and are used to solve and address important problems, such as image classification, natural language processing, and speech recognition, to provide computers with sensory capabilities that reach the ability of humans. In this study, we propose a new approach for Arabic handwritten digit recognition by use of restricted Boltzmann machine (RBM and convolutional neural network (CNN deep learning algorithms. In particular, we propose an Arabic handwritten digit recognition approach that works in two phases. First, we use the RBM, which is a deep learning technique that can extract highly useful features from raw data, and which has been utilized in several classification problems as a feature extraction technique in the feature extraction phase. Then, the extracted features are fed to an efficient CNN architecture with a deep supervised learning architecture for the training and testing process. In the experiment, we used the CMATERDB 3.3.1 Arabic handwritten digit dataset for training and testing the proposed method. Experimental results show that the proposed method significantly improves the accuracy rate, with accuracy reaching 98.59%. Finally, comparison of our results with those of other studies on the CMATERDB 3.3.1 Arabic handwritten digit dataset shows that our approach achieves the highest accuracy rate.

  5. 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.

  6. 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.

  7. AN APPLICATION OF SPEAKER RECOGNITION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Murat CANER

    2006-02-01

    Full Text Available In this study an artificial neural network (ANN is implemented, which has been used frequently as an implementation model in recent years, to recognize speaker identification. Generally, recognition is consist of three stages that, processing of signal, obtaining attributes and comparing them. Speech samples are transformed into digital data according to voice card of PC. In the analysis of voice stage, recurrent periods and white noise of voice data are trimmed by hamming window method and voice attribute part of the digital data is obtained. For obtaining attribute of voice data LPC (linear predictive coding and DFT (discrete fourier transform methods are used. Of those 28 coefficents, that is used for speaker recognition, 16 were obtained by the analysis of DFT and 12 were obtained by the analysis of LPC. The parameters that represent speaker voice, is used for training and test of ANN. Multilayer perceptron model is used as an architecture of ANN and backpropagation algorithm is used for training method. Voices of "a" is taken from 7 different person and their attributes are found. ANN is trained with these features to find the speaker who is the owner of the sample voice. And then using the test data that is not used for training part, recognition achievement of ANN is tested. As a result, good results were obtained with low failure rate.

  8. Automatic Pavement Crack Recognition Based on BP Neural Network

    Directory of Open Access Journals (Sweden)

    Li Li

    2014-02-01

    Full Text Available A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application.

  9. 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...

  10. Object Recognition in Aerial Images Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Matija Radovic

    2017-06-01

    Full Text Available There are numerous applications of unmanned aerial vehicles (UAVs in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5% and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once” platform, objects can be tracked, detected (“seen”, and classified (“comprehended” from video feeds supplied by UAVs in real-time.

  11. Finding patterns in biochemical reaction networks

    OpenAIRE

    Henkel, Ron; Lambusch, Fabienne; Wolkenhauer, Olaf; Sandkuhl, Kurt; Rosenke, Christian; Waltemath, Dagmar

    2016-01-01

    Computational models in biology encode molecular and cell biological processes. Many of them can be represented as biochemical reaction networks. Studying such networks, one is often interested in systems that share similar reactions and mechanisms. Typical goals are to understand the parts of a model, to identify reoccurring patterns, and to find biologically relevant motifs. The large number of models are available for such a search, but also the large size of models require automated metho...

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Searching social networks for subgraph patterns

    Science.gov (United States)

    Ogaard, Kirk; Kase, Sue; Roy, Heather; Nagi, Rakesh; Sambhoos, Kedar; Sudit, Moises

    2013-06-01

    Software tools for Social Network Analysis (SNA) are being developed which support various types of analysis of social networks extracted from social media websites (e.g., Twitter). Once extracted and stored in a database such social networks are amenable to analysis by SNA software. This data analysis often involves searching for occurrences of various subgraph patterns (i.e., graphical representations of entities and relationships). The authors have developed the Graph Matching Toolkit (GMT) which provides an intuitive Graphical User Interface (GUI) for a heuristic graph matching algorithm called the Truncated Search Tree (TruST) algorithm. GMT is a visual interface for graph matching algorithms processing large social networks. GMT enables an analyst to draw a subgraph pattern by using a mouse to select categories and labels for nodes and links from drop-down menus. GMT then executes the TruST algorithm to find the top five occurrences of the subgraph pattern within the social network stored in the database. GMT was tested using a simulated counter-insurgency dataset consisting of cellular phone communications within a populated area of operations in Iraq. The results indicated GMT (when executing the TruST graph matching algorithm) is a time-efficient approach to searching large social networks. GMT's visual interface to a graph matching algorithm enables intelligence analysts to quickly analyze and summarize the large amounts of data necessary to produce actionable intelligence.

  18. Executable Code Recognition in Network Flows Using Instruction Transition Probabilities

    Science.gov (United States)

    Kim, Ikkyun; Kang, Koohong; Choi, Yangseo; Kim, Daewon; Oh, Jintae; Jang, Jongsoo; Han, Kijun

    The ability to recognize quickly inside network flows to be executable is prerequisite for malware detection. For this purpose, we introduce an instruction transition probability matrix (ITPX) which is comprised of the IA-32 instruction sets and reveals the characteristics of executable code's instruction transition patterns. And then, we propose a simple algorithm to detect executable code inside network flows using a reference ITPX which is learned from the known Windows Portable Executable files. We have tested the algorithm with more than thousands of executable and non-executable codes. The results show that it is very promising enough to use in real world.

  19. Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

    Directory of Open Access Journals (Sweden)

    Abdullahi Abubakar Mas’ud

    2016-07-01

    Full Text Available In order to investigate how artificial neural networks (ANNs have been applied for partial discharge (PD pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1 determining the optimum weights in training the ANN; (2 using PD data captured over long stressing period in training the ANN; (3 ANN recognizing different PD degradation levels; (4 using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5 understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6 developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

  20. Network approach to patterns in stratocumulus clouds

    Science.gov (United States)

    Glassmeier, Franziska; Feingold, Graham

    2017-10-01

    Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth’s climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis’s Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav–Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.

  1. Network approach to patterns in stratocumulus clouds.

    Science.gov (United States)

    Glassmeier, Franziska; Feingold, Graham

    2017-10-03

    Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth's climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis's Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav-Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.

  2. 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.  

  3. Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks

    Directory of Open Access Journals (Sweden)

    Joaquín Luque

    2016-05-01

    Full Text Available Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance.

  4. Convolutional neural networks with balanced batches for facial expressions recognition

    Science.gov (United States)

    Battini Sönmez, Elena; Cangelosi, Angelo

    2017-03-01

    This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.

  5. POLISH EMOTIONAL SPEECH RECOGNITION USING ARTIFICAL NEURAL NETWORK

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    Paweł Powroźnik

    2014-11-01

    Full Text Available The article presents the issue of emotion recognition based on polish emotional speech analysis. The Polish database of emotional speech, prepared and shared by the Medical Electronics Division of the Lodz University of Technology, has been used for research. The following parameters extracted from sampled and normalised speech signal has been used for the analysis: energy of signal, speaker’s sex, average value of speech signal and both the minimum and maximum sample value for a given signal. As an emotional state a classifier fof our layers of artificial neural network has been used. The achieved results reach 50% of accuracy. Conducted researches focused on six emotional states: a neutral state, sadness, joy, anger, fear and boredom.

  6. A Chinese Named Entity Recognition System with Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Hui-Kang

    2017-01-01

    Full Text Available Named entity recognition (NER is a typical sequential labeling problem that plays an important role in natural language processing (NLP systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006.

  7. Peeking Network States with Clustered Patterns

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jinoh [Texas A & M Univ., Commerce, TX (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-10-20

    Network traffic monitoring has long been a core element for effec- tive network management and security. However, it is still a chal- lenging task with a high degree of complexity for comprehensive analysis when considering multiple variables and ever-increasing traffic volumes to monitor. For example, one of the widely con- sidered approaches is to scrutinize probabilistic distributions, but it poses a scalability concern and multivariate analysis is not gen- erally supported due to the exponential increase of the complexity. In this work, we propose a novel method for network traffic moni- toring based on clustering, one of the powerful deep-learning tech- niques. We show that the new approach enables us to recognize clustered results as patterns representing the network states, which can then be utilized to evaluate “similarity” of network states over time. In addition, we define a new quantitative measure for the similarity between two compared network states observed in dif- ferent time windows, as a supportive means for intuitive analysis. Finally, we demonstrate the clustering-based network monitoring with public traffic traces, and show that the proposed approach us- ing the clustering method has a great opportunity for feasible, cost- effective network monitoring.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Optimal flux patterns in cellular metabolic networks

    Energy Technology Data Exchange (ETDEWEB)

    Almaas, E

    2007-01-20

    The availability of whole-cell level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy-tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations have relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reaction are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central-carbon metabolic pathways for the sample of random environments.

  14. 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

  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. 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.

  17. 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.

  18. 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.

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

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    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.

  20. 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

  1. Pattern-recognition techniques applied to performance monitoring of the DSS 13 34-meter antenna control assembly

    Science.gov (United States)

    Mellstrom, J. A.; Smyth, P.

    1991-01-01

    The results of applying pattern recognition techniques to diagnose fault conditions in the pointing system of one of the Deep Space network's large antennas, the DSS 13 34-meter structure, are discussed. A previous article described an experiment whereby a neural network technique was used to identify fault classes by using data obtained from a simulation model of the Deep Space Network (DSN) 70-meter antenna system. Described here is the extension of these classification techniques to the analysis of real data from the field. The general architecture and philosophy of an autonomous monitoring paradigm is described and classification results are discussed and analyzed in this context. Key features of this approach include a probabilistic time-varying context model, the effective integration of signal processing and system identification techniques with pattern recognition algorithms, and the ability to calibrate the system given limited amounts of training data. Reported here are recognition accuracies in the 97 to 98 percent range for the particular fault classes included in the experiments.

  2. Discovering Preferential Patterns in Sectoral Trade Networks.

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    Isabella Cingolani

    Full Text Available We analyze the patterns of import/export bilateral relations, with the aim of assessing the relevance and shape of "preferentiality" in countries' trade decisions. Preferentiality here is defined as the tendency to concentrate trade on one or few partners. With this purpose, we adopt a systemic approach through the use of the tools of complex network analysis. In particular, we apply a pattern detection approach based on community and pseudocommunity analysis, in order to highlight the groups of countries within which most of members' trade occur. The method is applied to two intra-industry trade networks consisting of 221 countries, relative to the low-tech "Textiles and Textile Articles" and the high-tech "Electronics" sectors for the year 2006, to look at the structure of world trade before the start of the international financial crisis. It turns out that the two networks display some similarities and some differences in preferential trade patterns: they both include few significant communities that define narrow sets of countries trading with each other as preferential destinations markets or supply sources, and they are characterized by the presence of similar hierarchical structures, led by the largest economies. But there are also distinctive features due to the characteristics of the industries examined, in which the organization of production and the destination markets are different. Overall, the extent of preferentiality and partner selection at the sector level confirm the relevance of international trade costs still today, inducing countries to seek the highest efficiency in their trade patterns.

  3. Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

    Science.gov (United States)

    Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

    2015-09-01

    In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. 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.

  5. Comparison of crisp and fuzzy character networks in handwritten word recognition

    Science.gov (United States)

    Gader, Paul; Mohamed, Magdi; Chiang, Jung-Hsien

    1992-01-01

    Experiments involving handwritten word recognition on words taken from images of handwritten address blocks from the United States Postal Service mailstream are described. The word recognition algorithm relies on the use of neural networks at the character level. The neural networks are trained using crisp and fuzzy desired outputs. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level.

  6. 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.

  7. The regulation of social recognition, social communication and aggression: vasopressin in the social behavior neural network.

    Science.gov (United States)

    Albers, H Elliott

    2012-03-01

    Neuropeptides in the arginine vasotocin/arginine vasopressin (AVT/AVP) family play a major role in the regulation of social behavior by their actions in the brain. In mammals, AVP is found within a circuit of recriprocally connected limbic structures that form the social behavior neural network. This review examines the role played by AVP within this network in controlling social processes that are critical for the formation and maintenance of social relationships: social recognition, social communication and aggression. Studies in a number of mammalian species indicate that AVP and AVP V1a receptors are ideally suited to regulate the expression of social processes because of their plasticity in response to factors that influence social behavior. The pattern of AVP innervation and V1a receptors across the social behavior neural network may determine the potential range and intensity of social responses that individuals display in different social situations. Although fundamental information on how social behavior is wired in the brain is still lacking, it is clear that different social behaviors can be influenced by the actions of AVP in the same region of the network and that AVP can act within multiple regions of this network to regulate the expression of individual social behaviors. The existing data suggest that AVP can influence social behavior by modulating the interpretation of sensory information, by influencing decision making and by triggering complex motor outputs. This article is part of a Special Issue entitled Oxytocin, Vasopressin, and Social Behavior. Copyright © 2011 Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Jahed Mehran

    2007-12-01

    are time and time-frequency domain. In this work we demonstrate the capability of an EMG pattern recognition system using ANFIS as classifier with a real-time learning method. Our results reveal that the utilized real-time ANFIS approach along with the user evaluation provides a 96.7% average accuracy. This rate is superior to the previously reported result utilizing artificial neural networks (ANN real-time method 1. Conclusion This study shows that ANFIS real-time learning method coupled with mixed time and time-frequency features as EMG features can provide acceptable results for designing sEMG pattern recognition system suitable for hand prosthesis control.

  9. 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

  10. Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition

    Directory of Open Access Journals (Sweden)

    YuKang Jia

    2017-01-01

    Full Text Available Long Short-Term Memory (LSTM is a kind of Recurrent Neural Networks (RNN relating to time series, which has achieved good performance in speech recogniton and image recognition. Long Short-Term Memory Projection (LSTMP is a variant of LSTM to further optimize speed and performance of LSTM by adding a projection layer. As LSTM and LSTMP have performed well in pattern recognition, in this paper, we combine them with Connectionist Temporal Classification (CTC to study piano’s continuous note recognition for robotics. Based on the Beijing Forestry University music library, we conduct experiments to show recognition rates and numbers of iterations of LSTM with a single layer, LSTMP with a single layer, and Deep LSTM (DLSTM, LSTM with multilayers. As a result, the single layer LSTMP proves performing much better than the single layer LSTM in both time and the recognition rate; that is, LSTMP has fewer parameters and therefore reduces the training time, and, moreover, benefiting from the projection layer, LSTMP has better performance, too. The best recognition rate of LSTMP is 99.8%. As for DLSTM, the recognition rate can reach 100% because of the effectiveness of the deep structure, but compared with the single layer LSTMP, DLSTM needs more training time.

  11. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    interesting property of many biological networks that was recently brought to attention of the scientific community [3, 4, 5] is an extremely broad distribution of node connectivities defined as the number of immediate neighbors of a given node in the network. While the majority of nodes have just a few edges connecting them to other nodes in the network, there exist some nodes, that we will refer to as ''hubs'', with an unusually large number of neighbors. The connectivity of the most connected hub in such a network is typically several orders of magnitude larger than the average connectivity in the network. Often the distribution of connectivities of individual nodes can be approximated by a scale-free power law form [3] in which case the network is referred to as scale-free. Among biological networks distributions of node connectivities in metabolic [4], protein interaction [5], and brain functional [6] networks can be reasonably approximated by a power law extending for several orders of magnitude. The set of connectivities of individual nodes is an example of a low-level (single-node) topological property of a network. While it answers the question about how many neighbors a given node has, it gives no information about the identity of those neighbors. It is clear that most functional properties of networks are defined at a higher topological level in the exact pattern of connections of nodes to each other. However, such multi-node connectivity patterns are rather difficult to quantify and compare between networks. In this work we concentrate on multi-node topological properties of protein networks. These networks (as any other biological networks) lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as mutations within individual genes, and gene duplications. As a result their connections are characterized by a large degree of randomness. One may wonder which

  12. 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...

  13. 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...

  14. 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.

  15. 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...

  16. 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.

  17. 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.

  18. 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.

  19. Transferring knowledge of activity recognition across sensor networks

    NARCIS (Netherlands)

    van Kasteren, T.L.M.; Englebienne, G.; Kröse, B.J.A.

    2010-01-01

    A problem in performing activity recognition on a large scale (i.e. in many homes) is that a labelled data set needs to be recorded for each house activity recognition is performed in. This is because most models for activity recognition require labelled data to learn their parameters. In this paper

  20. Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

    Science.gov (United States)

    Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.

    2016-01-01

    According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.

  1. A modified artificial immune system based pattern recognition approach--an application to clinical diagnostics.

    Science.gov (United States)

    Zhao, Weixiang; Davis, Cristina E

    2011-05-01

    This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function--partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of "replacement threshold=0.3", the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for study 2 are 0.49% (AIS-RBFPLS) and 6.61% (AIS

  2. A modified artificial immune system based pattern recognition approach -- an application to clinic diagnostics

    Science.gov (United States)

    Zhao, Weixiang; Davis, Cristina E.

    2011-01-01

    Objective This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Methods and materials Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function – partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). Results The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of “replacement threshold = 0.3”, the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for

  3. An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System

    Directory of Open Access Journals (Sweden)

    Yepeng Ni

    2016-09-01

    Full Text Available With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability.

  4. An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System.

    Science.gov (United States)

    Ni, Yepeng; Liu, Jianbo; Liu, Shan; Bai, Yaxin

    2016-09-08

    With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability.

  5. 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 ...

  6. 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

  7. Architecture of the parallel hierarchical network for fast image recognition

    Science.gov (United States)

    Timchenko, Leonid; Wójcik, Waldemar; Kokriatskaia, Natalia; Kutaev, Yuriy; Ivasyuk, Igor; Kotyra, Andrzej; Smailova, Saule

    2016-09-01

    Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

  8. Connectivity, excitability and activity patterns in neuronal networks

    NARCIS (Netherlands)

    le Feber, Jakob; Stoyanova, Irina; Chiappalone, Michela

    2014-01-01

    Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods

  9. A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Si-Yao Fu

    2012-01-01

    Full Text Available In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs. By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people’s facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.

  10. Perfect Match: Biometrics and Body Patterning in a Networked World

    Directory of Open Access Journals (Sweden)

    Gillian Fuller

    2003-01-01

    Full Text Available Bodies are increasingly becoming collectively integrated into informational processes which are open to biotechnical forms of regulation. Biometrics, the use of body measurements such as retina scanning, face recognition and fingerprinting is now being uncontroversially introduced throughout the world under the aegis of security and efficient traffic management. Fields that once molded the individual through bodily confinement and observation are dispersing and converging into the regimes of logistics and control. This paper looks at the operations of biometrics to consider the biopolitical ramifications of body measurement as power is made operational by controlling movement via the haptic techniques of information architecture rather than the more familiar modes of discipline and panoptical vision machines. Biometrics is the perfect control for the networked individual as we divide across infinite planes and dimensions, reconfiguring endlessly to become pattern matches in expanding databases of everyday life.

  11. 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....

  12. 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

  13. 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.

  14. Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput

    OpenAIRE

    Inoue, Masaya; Inoue, Sozo; Nishida, Takeshi

    2016-01-01

    In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The "high throughput" refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition ...

  15. 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

  16. 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.

  17. 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.

  18. Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

    NARCIS (Netherlands)

    Kruithof, M.C.; Bouma, H.; Fischer, N.M.; Schutte, K.

    2016-01-01

    Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an

  19. Feature Acquisition and Analysis for Facial Expression Recognition Using Convolutional Neural Networks

    National Research Council Canada - National Science Library

    Taiki Nishime; Satoshi Endo; Naruaki Toma; Koji Yamada; Yuhei Akamine

    2017-01-01

    .... Therefore, it is difficult to evaluate the reliability of the result from recognition accuracy alone, and the analysis for explaining the result and feature learned by Convolutional Neural Networks (CNN...

  20. Systematic discovery of new recognition peptides mediating protein interaction networks.

    Directory of Open Access Journals (Sweden)

    2005-12-01

    Full Text Available Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or "linear motif" (e.g., SH3 binding to PxxP. Many domains are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues, and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical molecular details of how interaction networks are constructed, and can explain how one protein is able to bind to very different partners. Here we show that binding motifs can be detected using data from genome-scale interaction studies, and thus avoid the normally slow discovery process. Our approach based on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others. Direct binding experiments reveal that two predicted motifs are indeed protein-binding modules: a DxxDxxxD protein phosphatase 1 binding motif with a KD of 22 microM and a VxxxRxYS motif that binds Translin with a KD of 43 microM. We estimate that there are dozens or even hundreds of linear motifs yet to be discovered that will give molecular insight into protein networks and greatly illuminate cellular processes.

  1. 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.

  2. 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...

  3. 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…

  4. A symmetry perceiving adaptive neural network and facial image recognition.

    Science.gov (United States)

    Sinha, P

    1998-11-30

    The paper deals with the forensic problem of comparing nearly from view and facial images for personal identification. The human recognition process for such problems, is primarily based on both holistic as well as feature-wise symmetry perception aided by subjective analysis for detecting ill-defined features. It has been attempted to approach the modelling of such a process by designing a robust symmetry perceiving adaptive neural network. The pair of images to be compared should be presented to the proposed neural network (NN) as source (input) and target images. The NN learns about the symmetry between the pair of images by analysing examples of associated feature pairs belonging to the source and the target images. In order to prepare a paired example of associated features for training purpose, when we select one particular feature on the source image as a unique pixel, we must associate it with the corresponding feature on the target image also. But, in practice, it is not always possible to fix the latter feature also as a unique pixel due to pictorial ambiguity. The robust or fault tolerant NN takes care of such a situation and allows fixing the associated target feature as a rectangular array of pixels, rather than fixing it as a unique pixel, which is pretty difficult to be done with certainty. From such a pair of sets of associated features, the NN searches out proper locations of the target features from the sets of ambiguous target features by a fuzzy analysis during its learning. If any of target features, searched out by the NN, lies outside the prespecified zone, the training of the NN is unsuccessful. This amounts to non-existence of symmetry between the pair of images and confirms non-identity. In case of a successful training, the NN gets adapted with appropriate symmetry relation between the pair of images and when the source image is input to the trained NN, it responds by outputting a processed source image which is superimposable over the

  5. Dialogue act recognition under uncertainty using Bayesian networks

    NARCIS (Netherlands)

    Keizer, S.; op den Akker, Hendrikus J.A.

    2007-01-01

    In this paper we discuss the task of dialogue act recognition as a part of interpreting user utterances in context. To deal with the uncertainty that is inherent in natural language processing in general and dialogue act recognition in particular we use machine learning techniques to train

  6. 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.

  7. 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.

  8. 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

  9. Multi-input Multi-output Beta Wavelet Network: Modeling of Acoustic Units for Speech Recognition

    OpenAIRE

    Chokri Ben Amar; Mourad Zaied; Ridha Ejbali

    2012-01-01

    In this paper, we propose a novel architecture of wavelet network called Multi-input Multi-output Wavelet Network MIMOWN as a generalization of the old architecture of wavelet network. This newel prototype was applied to speech recognition application especially to model acoustic unit of speech. The originality of our work is the proposal of MIMOWN to model acoustic unit of speech. This approach was proposed to overcome limitation of old wavelet network model. The use of the multi-input multi...

  10. A Particle Swarm Optimization Algorithm for Neural Networks in Recognition of Maize Leaf Diseases

    Directory of Open Access Journals (Sweden)

    Zhiyong ZHANG

    2014-03-01

    Full Text Available The neural networks have significance on recognition of crops disease diagnosis? but it has disadvantage of slow convergent speed and shortcoming of local optimum. In order to identify the maize leaf diseases by using machine vision more accurately, we propose an improved particle swarm optimization algorithm for neural networks. With the algorithm, the neural network property is improved. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in the image recognition. At last, an example of the emulation shows that neural network model based on recognizes significantly better than without optimization. Model accuracy has been improved to a certain extent to meet the actual needs of maize leaf diseases recognition.

  11. 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.

  12. 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.

  13. Investigation of an HMM/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals.

    Science.gov (United States)

    Polur, Prasad D; Miller, Gerald E

    2006-10-01

    Computer speech recognition of individuals with dysarthria, such as cerebral palsy patients requires a robust technique that can handle conditions of very high variability and limited training data. In this study, application of a 10 state ergodic hidden Markov model (HMM)/artificial neural network (ANN) hybrid structure for a dysarthric speech (isolated word) recognition system, intended to act as an assistive tool, was investigated. A small size vocabulary spoken by three cerebral palsy subjects was chosen. The effect of such a structure on the recognition rate of the system was investigated by comparing it with an ergodic hidden Markov model as a control tool. This was done in order to determine if this modified technique contributed to enhanced recognition of dysarthric speech. The speech was sampled at 11 kHz. Mel frequency cepstral coefficients were extracted from them using 15 ms frames and served as training input to the hybrid model setup. The subsequent results demonstrated that the hybrid model structure was quite robust in its ability to handle the large variability and non-conformity of dysarthric speech. The level of variability in input dysarthric speech patterns sometimes limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor impaired individuals holds sufficient promise.

  14. 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...

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. 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

  2. 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 ...

  3. 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

  4. EMG Pattern Classification by Split and Merge Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Hyeon-min Shim

    2016-12-01

    Full Text Available In this paper; we introduce an enhanced electromyography (EMG pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN. Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have been applied in several previously published studies. A DBN is a fast greedy learning algorithm that can identify a fairly good set of weights rapidly—even in deep networks with a large number of parameters and many hidden layers. To reduce overfitting and to enhance performance, the adopted optimization method was based on genetic algorithms (GA. As a result, the performance of the SM-DBN was 12.06% higher than conventional DBN. Additionally, SM-DBN results in a short convergence time, thereby reducing the training epoch. It is thus efficient in reducing the risk of overfitting. It is verified that the optimization was improved using GA.

  5. 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.

  6. Entropy-based generation of supervised neural networks for classification of structured patterns.

    Science.gov (United States)

    Tsai, Hsien-Leing; Lee, Shie-Jue

    2004-03-01

    Sperduti and Starita proposed a new type of neural network which consists of generalized recursive neurons for classification of structures. In this paper, we propose an entropy-based approach for constructing such neural networks for classification of acyclic structured patterns. Given a classification problem, the architecture, i.e., the number of hidden layers and the number of neurons in each hidden layer, and all the values of the link weights associated with the corresponding neural network are automatically determined. Experimental results have shown that the networks constructed by our method can have a better performance, with respect to network size, learning speed, or recognition accuracy, than the networks obtained by other methods.

  7. Energy prediction using spatiotemporal pattern networks

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun; Henze, Gregor P.; Sarkar, Soumik

    2017-11-01

    This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

  8. 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.

  9. 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).

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. Neuro-parity pattern recognition system and method

    Science.gov (United States)

    Gross, Kenneth C.; Singer, Ralph M.; Van Alstine, Rollin G.; Wegerich, Stephan W.; Yue, Yong

    2000-01-01

    A method and system for monitoring a process and determining its condition. Initial data is sensed, a first set of virtual data is produced by applying a system state analyzation to the initial data, a second set of virtual data is produced by applying a neural network analyzation to the initial data and a parity space analyzation is applied to the first and second set of virtual data and also to the initial data to provide a parity space decision about the condition of the process. A logic test can further be applied to produce a further system decision about the state of the process.

  16. A Hierarchical Approach to Real-time Activity Recognition in Body Sensor Networks

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Tao, Xianping

    2012-01-01

    Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we rst use a fast and lightweight al...

  17. Real-time Human Activity Recognition using a Body Sensor Network

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Chen, Hanhua

    2010-01-01

    Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a realtime, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network...

  18. 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.

  19. 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

  20. Dynamical Networks for Smog Pattern Analysis

    CERN Document Server

    Zong, Linqi; Zhu, Jia

    2015-01-01

    Smog, as a form of air pollution, poses as a serious problem to the environment, health, and economy of the world[1-4] . Previous studies on smog mostly focused on the components and the effects of smog [5-10]. However, as the smog happens with increased frequency and duration, the smog pattern which is critical for smog forecast and control, is rarely investigated, mainly due to the complexity of the components, the causes, and the spreading processes of smog. Here we report the first analysis on smog pattern applying the model of dynamical networks with spontaneous recovery. We show that many phenomena such as the sudden outbreak and dissipation of smog and the long duration smog can be revealed with the mathematical mechanism under a random walk simulation. We present real-world air quality index data in accord with the predictions of the model. Also we found that compared to external causes such as pollution spreading from nearby, internal causes such as industrial pollution and vehicle emission generated...

  1. Social networking patterns/hazards among teenagers.

    Science.gov (United States)

    Machold, C; Judge, G; Mavrinac, A; Elliott, J; Murphy, A M; Roche, E

    2012-05-01

    Social Networking Sites (SNSs) have grown substantially, posing new hazards to teenagers. This study aimed to determine general patterns of Internet usage among Irish teenagers aged 11-16 years, and to identify potential hazards, including; bullying, inappropriate contact, overuse, addiction and invasion of users' privacy. A cross-sectional study design was employed to survey students at three Irish secondary schools, with a sample of 474 completing a questionnaire. 202 (44%) (n = 460) accessed the Internet using a shared home computer. Two hours or less were spent online daily by 285(62%), of whom 450 (98%) were unsupervised. 306 (72%) (n = 425) reported frequent usage of SNSs, 403 (95%) of whom were Facebook users. 42 (10%) males and 51 (12%) females experienced bullying online, while 114 (27%) reported inappropriate contact from others. Concerning overuse and the risk of addiction, 140 (33%) felt they accessed SNSs too often. These patterns among Irish teenagers suggest that SNS usage poses significant dangers, which are going largely unaddressed.

  2. Social networking patterns/hazards among teenagers.

    LENUS (Irish Health Repository)

    Machold, C

    2012-05-01

    Social Networking Sites (SNSs) have grown substantially, posing new hazards to teenagers. This study aimed to determine general patterns of Internet usage among Irish teenagers aged 11-16 years, and to identify potential hazards, including; bullying, inappropriate contact, overuse, addiction and invasion of users\\' privacy. A cross-sectional study design was employed to survey students at three Irish secondary schools, with a sample of 474 completing a questionnaire. 202 (44%) (n = 460) accessed the Internet using a shared home computer. Two hours or less were spent online daily by 285(62%), of whom 450 (98%) were unsupervised. 306 (72%) (n = 425) reported frequent usage of SNSs, 403 (95%) of whom were Facebook users. 42 (10%) males and 51 (12%) females experienced bullying online, while 114 (27%) reported inappropriate contact from others. Concerning overuse and the risk of addiction, 140 (33%) felt they accessed SNSs too often. These patterns among Irish teenagers suggest that SNS usage poses significant dangers, which are going largely unaddressed.

  3. Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network.

    Science.gov (United States)

    Fu, H C; Xu, Y Y; Chang, H Y

    1999-12-01

    Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

  4. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

    Science.gov (United States)

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-01-01

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods. PMID:27399696

  5. 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

  6. A comparison study between MLP and convolutional neural network models for character recognition

    Science.gov (United States)

    Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.

    2017-05-01

    Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.

  7. Pattern recognition of clouds and ice in polar regions

    Science.gov (United States)

    Welch, R. M.; Sengupta, S. K.; Sundar, C. A.; Kuo, K. S.; Carsey, F. D.

    1990-01-01

    The study is based on AVHRR imagery and results from Landsat high-spatial-resolution scenes. Among the textual features investigated are the gray level difference vector (GLDV), and sum and difference histogram (SADH) approaches as well as gray level run length, spatial-coherence, and spectral-histogram measures. The traditional stepwise discriminant analysis and neural-network analysis are used for the identification of 20 Arctic surface and cloud classes. A principal-component analysis and hybrid architecture employing a modularized competitive learning layer are utilized. It is pointed out that the cloud-classification accuracy comparable to that of back-propagation could be achieved with a training time two orders of magnitude faster.

  8. 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.

  9. 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.

  10. 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.

  11. Cascading neural networks for upper-body gesture recognition

    CSIR Research Space (South Africa)

    Mangera, R

    2014-01-01

    Full Text Available Gesture recognition has many applications ranging from health care to entertainment. However for it to be a feasible method of human-computer interaction it is essential that only intentional movements are interpreted and that the system can work...

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

    Science.gov (United States)

    Boone, Bradley G.; Shukla, Oodaye B.

    1993-01-01

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

  13. An integrated pattern recognition approach for intrusion detection.

    Science.gov (United States)

    Pandit, Amod; Stanley, R Joe; McMillin, Bruce

    2002-01-01

    Intrusion detection systems (IDS) attempt to address the vulnerability of computer-based systems for abuse by insiders and to penetration by outsiders. An IDS is required to examine an enormous amount of data generated by computer networks to assist in the abuse detection process. Thus, there is a need to develop automated tools that address these requirements to assist system operators in the detection of violations of existing security policies. In this research, an automated IDS is proposed for insider threats in a distributed system. The proposed IDS functions as an anomaly detector for insider system operations based on the analysis of the system's log files. The approach integrates dynamic programming and adaptive resonance theory (ART1) clustering. The integrated approach aligns sequences of log events with prototypical sequences of events for performing tasks and classifies the aligned sequences for intrusion detection. The system examined for this research is a Boots System for controlling the movement of boots from one place to another under specific security restrictions related to the boot orders. We present the proposed model, the results achieved and the analysis of an implemented prototype.

  14. 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...

  15. 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.

  16. 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.

  17. 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...

  18. 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

  19. 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.

  20. 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

  1. 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…

  2. 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…

  3. 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…

  4. 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…

  5. 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

  6. 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

  7. Quantitative recognition of flammable and toxic gases with artificial neural network using metal oxide gas sensors in embedded platform

    Directory of Open Access Journals (Sweden)

    B. Mondal

    2015-06-01

    Full Text Available Artificial Neural Network (ANN based pattern recognition technique is used for ensuring the reliable evaluation of responses from an array of Zinc Oxide (ZnO based sensors comprising of pure ZnO nano-rods and composites of ZnO–SnO2. All the sensors were fabricated in the lab. The paper first reports the development of an artificial neural network based model for successfully recognizing different concentration of hydrogen, methane and carbon mono-oxide. Feed forward back propagation neural network was used for the classification of the gases at critical concentrations. The optimized ANN algorithm is then embedded in the microcontroller based circuit and finally verified under lab conditions.

  8. Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

    Science.gov (United States)

    He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

    2017-03-01

    Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

  9. 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.

  10. Optimizing text-independent speaker recognition using an LSTM neural network

    OpenAIRE

    Larsson, Joel

    2014-01-01

    In this paper a novel speaker recognition system is introduced. Automated speaker recognition has become increasingly popular to aid in crime investigations and authorization processes with the advances in computer science. Here, a recurrent neural network approach is used to learn to identify ten speakers within a set of 21 audio books. Audio signals are processed via spectral analysis into Mel Frequency Cepstral Coefficients that serve as speaker specific features, which are input to the ne...

  11. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  12. Characterization of Galician (N.W. Spain) quality brand potatoes: a comparison study of several pattern recognition techniques.

    Science.gov (United States)

    Padín, P M; Peña, R M; García, S; Iglesias, R; Barro, S; Herrero, C

    2001-01-01

    Authenticity is an important food quality criterion and rapid methods to guarantee it are widely demanded by food producers, processors, consumers and regulatory bodies. The objective of this work was to develop a classification system in order to confirm the authenticity of Galician potatoes with a Certified Brand of Origin and Quality (CBOQ) 'Denominación Específica: Patata de Galicia' and to differentiate them from other potatoes that did not have this CBOQ. Ten selected metals were determined by atomic spectroscopy in 102 potato samples which were divided into two categories: CBOQ and non-CBOQ potatoes. Multivariate chemometric techniques, such as cluster analysis and principal component analysis, were applied to perform a preliminary study of the data structure. Four supervised pattern recognition procedures [including linear discriminant analysis (LDA), K-nearest neighbours (KNN), soft independent modelling of class analogy (SIMCA) and multilayer feed-forward neural networks (MLF-ANN)] were used to classify samples into the two categories considered on the basis of the chemical data. Results for LDA, KNN and MLF-ANN are acceptable for the non-CBOQ class, whereas SIMCA showed better recognition and prediction abilities for the CBOQ class. A more sophisticated neural network approach performed by the combination of the self-organizing with adaptive neighbourhood network (SOAN) and MLF network was employed to optimize the classification. Using this combined method, excellent performance in terms of classification and prediction abilities was obtained for the two categories with a success rate ranging from 98 to 100%. The metal profiles provided sufficient information to enable classification rules to be developed for identifying potatoes according to their origin brand based on SOAN-MLF neural networks.

  13. 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

  14. 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.

  15. Mexican sign language recognition using normalized moments and artificial neural networks

    Science.gov (United States)

    Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

    2014-09-01

    This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.

  16. Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

    Directory of Open Access Journals (Sweden)

    Nabil M. Hewahi

    2011-12-01

    Full Text Available We claim that knowing the ethnic group of human would increase the accuracy of the emotion recognition. This is due to the difference between the face appearances and expressions of
    various ethnic groups. To test our claim, we developed an approach based on Artificial Neural Networks (ANN using backpropgation algorithm to recognize the human emotion through facial expressions. Our approach has been tested by using MSDEF dataset, and we found that, there is a positive effect on the accuracy of the recognition of emotion if we consider the ethnic group as input factor while building the recognition model. We achieved 8% improvement rate.

  17. 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

  18. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  19. Optimal Recognition Method of Human Activities Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Oniga Stefan

    2015-12-01

    Full Text Available The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

  20. Dynamic Bayesian Networks for Audio-Visual Speech Recognition

    Directory of Open Access Journals (Sweden)

    Liang Luhong

    2002-01-01

    Full Text Available The use of visual features in audio-visual speech recognition (AVSR is justified by both the speech generation mechanism, which is essentially bimodal in audio and visual representation, and by the need for features that are invariant to acoustic noise perturbation. As a result, current AVSR systems demonstrate significant accuracy improvements in environments affected by acoustic noise. In this paper, we describe the use of two statistical models for audio-visual integration, the coupled HMM (CHMM and the factorial HMM (FHMM, and compare the performance of these models with the existing models used in speaker dependent audio-visual isolated word recognition. The statistical properties of both the CHMM and FHMM allow to model the state asynchrony of the audio and visual observation sequences while preserving their natural correlation over time. In our experiments, the CHMM performs best overall, outperforming all the existing models and the FHMM.

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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...

  9. 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.

  10. Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network.

    Science.gov (United States)

    Sun, Xin; Qian, Huinan

    2016-01-01

    Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin.

  11. Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network.

    Directory of Open Access Journals (Sweden)

    Xin Sun

    Full Text Available Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin.

  12. Discovering functional interaction patterns in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Can Tolga

    2008-06-01

    Full Text Available Abstract Background In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution of PPI networks and identification of conserved subnetworks across different species, discovery of modules of interaction, use of PPI networks for functional annotation of uncharacterized proteins, and improvement of the accuracy of currently available networks. Results In this article, we map known functional annotations of proteins onto a PPI network in order to identify frequently occurring interaction patterns in the functional space. We propose a new frequent pattern identification technique, PPISpan, adapted specifically for PPI networks from a well-known frequent subgraph identification method, gSpan. Existing module discovery techniques either look for specific clique-like highly interacting protein clusters or linear paths of interaction. However, our goal is different; instead of single clusters or pathways, we look for recurring functional interaction patterns in arbitrary topologies. We have applied PPISpan on PPI networks of Saccharomyces cerevisiae and identified a number of frequently occurring functional interaction patterns. Conclusion With the help of PPISpan, recurring functional interaction patterns in an organism's PPI network can be identified. Such an analysis offers a new perspective on the modular organization of PPI networks. The complete list of identified functional interaction patterns is available at http://bioserver.ceng.metu.edu.tr/PPISpan/.

  13. An object recognition using structured light and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Byeong Gab; Kim, Dong Gi; Kang, E Sok [Chungnam National Univ., Taejon (Korea, Republic of); Yoon, Ji Sup [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1997-12-31

    This paper presents a 3D image processing which uses neural networks to combine a 2D vision camera and a laser slit beam. A laser slit beam from laser source is slitted by a set of cylindrical lenses and the line image of the networks allow to get the 3D image parameters such as the size, the position and the orientation from the line image without knowing the camera intrinsic parameters. (author). 7 refs., 3 tabs., 5 figs.

  14. Toward an optimal convolutional neural network for traffic sign recognition

    Science.gov (United States)

    Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec

    2015-12-01

    Convolutional Neural Networks (CNN) beat the human performance on German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters 27% and 22% compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (ReLU) as the activation function that only needs a few operations to produce the result. Specifically, compared with the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication operation which makes it computationally much more efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best reported classification accuracy while it reduces the overall number of parameters 85% compared with the winner network in the competition.

  15. Optimization of multilayer neural network parameters for speaker recognition

    Science.gov (United States)

    Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

    2016-05-01

    This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

  16. Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

    Directory of Open Access Journals (Sweden)

    Min Peng

    2017-10-01

    Full Text Available Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.

  17. Self-assembled supramolecular networks at interfaces: Molecular immobilization and recognition using nanoporous templates

    Science.gov (United States)

    Li, Min; Zeng, Qingdao; Wang, Chen

    2011-10-01

    In this review a series of organic-based open porous networks are discussed, in which hydrogen bonds play an important role in network formation. Using these open networks as molecular templates: 1) a wealth of functional guest species can be immobilized; 2) fullerene molecules can be separated and recognized; 3) photoisomerization reactions can be observed by STM; 4) 1D molecular arrays can be constructed; and 5) heterogeneous bilayer structures can be formed. It is envisioned that these supramolecular networks might be developed into a new family of useful soft frameworks for studies toward shape-selective catalysis, molecular recognition and host-guest supramolecular chemistry.

  18. Type I interferon and pattern recognition receptor signaling following particulate matter inhalation

    Directory of Open Access Journals (Sweden)

    Erdely Aaron

    2012-07-01

    Full Text Available Abstract Background Welding, a process that generates an aerosol containing gases and metal-rich particulates, induces adverse physiological effects including inflammation, immunosuppression and cardiovascular dysfunction. This study utilized microarray technology and subsequent pathway analysis as an exploratory search for markers/mechanisms of in vivo systemic effects following inhalation. Mice were exposed by inhalation to gas metal arc – stainless steel (GMA-SS welding fume at 40 mg/m3 for 3 hr/d for 10 d and sacrificed 4 hr, 14 d and 28 d post-exposure. Whole blood cells, aorta and lung were harvested for global gene expression analysis with subsequent Ingenuity Pathway Analysis and confirmatory qRT-PCR. Serum was collected for protein profiling. Results The novel finding was a dominant type I interferon signaling network with the transcription factor Irf7 as a central component maintained through 28 d. Remarkably, these effects showed consistency across all tissues indicating a systemic type I interferon response that was complemented by changes in serum proteins (decreased MMP-9, CRP and increased VCAM1, oncostatin M, IP-10. In addition, pulmonary expression of interferon α and β and Irf7 specific pattern recognition receptors (PRR and signaling molecules (Ddx58, Ifih1, Dhx58, ISGF3 were induced, an effect that showed specificity when compared to other inflammatory exposures. Also, a canonical pathway indicated a coordinated response of multiple PRR and associated signaling molecules (Tlr7, Tlr2, Clec7a, Nlrp3, Myd88 to inhalation of GMA-SS. Conclusion This methodological approach has the potential to identify consistent, prominent and/or novel pathways and provides insight into mechanisms that contribute to pulmonary and systemic effects following toxicant exposure.

  19. Pattern Recognition and Pathway Analysis with Genetic Algorithms in Mass Spectrometry Based Metabolomics

    Directory of Open Access Journals (Sweden)

    Wei Zou

    2009-04-01

    Full Text Available A robust and complete workflow for metabolic profiling and data mining was described in detail. Three independent and complementary analytical techniques for metabolic profiling were applied: hydrophilic interaction chromatography (HILIC–LC–ESI–MS, reversed-phase liquid chromatography (RP–LC–ESI–MS, and gas chromatography (GC–TOF–MS all coupled to mass spectrometry (MS. Unsupervised methods, such as principle component analysis (PCA and clustering, and supervised methods, such as classification and PCA-DA (discriminatory analysis were used for data mining. Genetic Algorithms (GA, a multivariate approach, was probed for selection of the smallest subsets of potentially discriminative predictors. From thousands of peaks found in total, small subsets selected by GA were considered as highly potential predictors allowing discrimination among groups. It was found that small groups of potential top predictors selected with PCA-DA and GA are different and unique. Annotated GC–TOF–MS data generated identified feature metabolites. Metabolites putatively detected with LC–ESI–MS profiling require further elemental composition assignment with accurate mass measurement by Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS and structure elucidation by nuclear magnetic resonance spectroscopy (NMR. GA was also used to generate correlated networks for pathway analysis. Several case studies, comprising groups of plant samples bearing different genotypes and groups of samples of human origin, namely patients and healthy volunteers’ urine samples, demonstrated that such a workflow combining comprehensive metabolic profiling and advanced data mining techniques provides a powerful approach for pattern recognition and biomarker discovery

  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.