Infrared target recognition based on improved joint local ternary pattern

Science.gov (United States)

Sun, Junding; Wu, Xiaosheng

2016-05-01

This paper presents a simple, efficient, yet robust approach, named joint orthogonal combination of local ternary pattern, for automatic forward-looking infrared target recognition. It gives more advantages to describe the macroscopic textures and microscopic textures by fusing variety of scales than the traditional LBP-based methods. In addition, it can effectively reduce the feature dimensionality. Further, the rotation invariant and uniform scheme, the robust LTP, and soft concave-convex partition are introduced to enhance its discriminative power. Experimental results demonstrate that the proposed method can achieve competitive results compared with the state-of-the-art methods.

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.

Multimodality imaging and state-of-art GPU technology in discriminating benign from malignant breast lesions on real time decision support system

International Nuclear Information System (INIS)

Kostopoulos, S; Glotsos, D; Kalatzis, I; Asvestas, P; Cavouras, D; Sidiropoulos, K; Dimitropoulos, N

2014-01-01

The aim of this study was to design a pattern recognition system for assisting the diagnosis of breast lesions, using image information from Ultrasound (US) and Digital Mammography (DM) imaging modalities. State-of-art computer technology was employed based on commercial Graphics Processing Unit (GPU) cards and parallel programming. An experienced radiologist outlined breast lesions on both US and DM images from 59 patients employing a custom designed computer software application. Textural features were extracted from each lesion and were used to design the pattern recognition system. Several classifiers were tested for highest performance in discriminating benign from malignant lesions. Classifiers were also combined into ensemble schemes for further improvement of the system's classification accuracy. Following the pattern recognition system optimization, the final system was designed employing the Probabilistic Neural Network classifier (PNN) on the GPU card (GeForce 580GTX) using CUDA programming framework and C++ programming language. The use of such state-of-art technology renders the system capable of redesigning itself on site once additional verified US and DM data are collected. Mixture of US and DM features optimized performance with over 90% accuracy in correctly classifying the lesions

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.

Pattern recognition applied to uranium prospecting

Energy Technology Data Exchange (ETDEWEB)

Briggs, P L; Press, F [Massachusetts Inst. of Tech., Cambridge (USA). Dept. of Earth and Planetary Sciences

1977-07-14

It is stated that pattern recognition techniques provide one way of combining quantitative and descriptive geological data for mineral prospecting. A quantified decision process using computer-selected patterns of geological data has the potential for selecting areas with undiscovered deposits of uranium or other minerals. When a natural resource is mined more rapidly than it is discovered, its continued production becomes increasingly difficult, and it has been noted that, although a considerable uranium reserve may remain in the U.S.A., the discovery rate for uranium is decreasing exponentially with cumulative exploration footage drilled. Pattern recognition methods of organising geological information for prospecting may provide new predictive power, as well as insight into the occurrence of uranium ore deposits. Often the task of prospecting consists of three stages of information processing: (1) collection of data on known ore deposits; (2) noting any regularities common to the known examples of an ore; (3) selection of new exploration targets based on the results of the second stage. A logical pattern recognition algorithm is here described that implements this geological procedure to demonstrate the possibility of building a quantified uranium prospecting guide from diverse geologic data.

The principles of the pattern recognition of skeletal structures

International Nuclear Information System (INIS)

Motto, J.A.

2006-01-01

Request of the skeletal system form a lage proportion of plain film radiographic examinations. A sound knowledge of normal radiographic appearances is vital if abnormal patterns are to be recognized.The ABCS, SPACED and SASNOES methods of applying pattern recognition to plain radiographers of bones and joints will be presented in an attempt to make pattern recognition and offer an opinion constitutes role extension of radiographers

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

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

International Nuclear Information System (INIS)

Deputch, G.; Hoff, J.; Lipton, R.; Liu, T.; Olsen, J.; Ramberg, E.; Wu, Jin-Yuan; Yarema, R.; Shochet, M.; Tang, F.; Demarteau, M.

2011-01-01

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

Rotation-invariant neural pattern recognition system with application to coin recognition.

Science.gov (United States)

Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

1992-01-01

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

Teaching pain recognition through art: the Ramsay-Caravaggio sedation scale.

Science.gov (United States)

Poropat, Federico; Cozzi, Giorgio; Magnolato, Andrea; Monasta, Lorenzo; Borrometi, Fabio; Krauss, Baruch; Ventura, Alessandro; Barbi, Egidio

2018-01-31

Clinical observation is a key component of medical ability, enabling immediate evaluation of the patient's emotional state and contributing to a clinical clue that leads to final decision making. In medical schools, the art of learning to look can be taught using medical humanities and especially visual arts. By presenting a Ramsay sedation score (RSS) integrated with Caravaggio's paintings during a procedural sedation conference for pediatric residents, we want to test the effectiveness of this approach to improve the quality of learning. In this preliminary study, we presented videos showing sedated pediatric patients in the setting of a procedural sedation lesson to two randomized groups of residents, one attending a lesson on RSS explained through the masterpieces of Caravaggio, the other without artistic support. A week later we tested their learning with ten multi-choice questions focused on theoretical questions about sedation monitoring and ten more questions focused on recognizing the appropriate RSS viewing the videos. The primary outcome was the comparison of the total number of RSS layers properly recognized in both groups. We also evaluated the appreciation of the residents of the use of works of art integrated with the lesson. Eleven students were randomized to each group. Two residents in the standard lesson did not attend the test. The percentage of correct answers on the theoretical part was similar, 82% in the art group and 89% in the other (p > 0.05). No difference was found in the video recognition part of the RSS recognition test. Residents exposed to paintings shown great appreciation for the integration of the lesson with the Caravaggio's masterpieces. Adding artwork to a standard medical conference does not improve the performance of student tests, although this approach has been greatly appreciated by residents.

Staff Report to the Senior Department Official on Recognition Compliance Issues. Recommendation Page: National Accrediting Commission Of Cosmetology Arts and Sciences

Science.gov (United States)

US Department of Education, 2010

2010-01-01

The National Accrediting Commission of Cosmetology Arts and Sciences (NACCAS) is a national accreditor whose scope of recognition is for the accreditation throughout the United States of postsecondary schools and departments of cosmetology arts and sciences and massage therapy. The agency accredits approximately 1,300 institutions offering…

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.

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

Science.gov (United States)

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

2004-11-01

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

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.

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...

Comparative study of state-of-the-art myoelectric controllers for multigrasp prosthetic hands.

Science.gov (United States)

Segil, Jacob L; Controzzi, Marco; Weir, Richard F ff; Cipriani, Christian

2014-01-01

A myoelectric controller should provide an intuitive and effective human-machine interface that deciphers user intent in real-time and is robust enough to operate in daily life. Many myoelectric control architectures have been developed, including pattern recognition systems, finite state machines, and more recently, postural control schemes. Here, we present a comparative study of two types of finite state machines and a postural control scheme using both virtual and physical assessment procedures with seven nondisabled subjects. The Southampton Hand Assessment Procedure (SHAP) was used in order to compare the effectiveness of the controllers during activities of daily living using a multigrasp artificial hand. Also, a virtual hand posture matching task was used to compare the controllers when reproducing six target postures. The performance when using the postural control scheme was significantly better (p state machines during the physical assessment when comparing within-subject averages using the SHAP percent difference metric. The virtual assessment results described significantly greater completion rates (97% and 99%) for the finite state machines, but the movement time tended to be faster (2.7 s) for the postural control scheme. Our results substantiate that postural control schemes rival other state-of-the-art myoelectric controllers.

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

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.)

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

Science.gov (United States)

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

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

Design and testing of the first 2D Prototype Vertically Integrated Pattern Recognition Associative Memory

Energy Technology Data Exchange (ETDEWEB)

Liu, T.; Deptuch, G.; Hoff, J.; Jindariani, S.; Joshi, S.; Olsen, J.; Tran, N.; Trimpl, M.

2015-02-01

An associative memory-based track finding approach has been proposed for a Level 1 tracking trigger to cope with increasing luminosities at the LHC. The associative memory uses a massively parallel architecture to tackle the intrinsically complex combinatorics of track finding algorithms, thus avoiding the typical power law dependence of execution time on occupancy and solving the pattern recognition in times roughly proportional to the number of hits. This is of crucial importance given the large occupancies typical of hadronic collisions. The design of an associative memory system capable of dealing with the complexity of HL-LHC collisions and with the short latency required by Level 1 triggering poses significant, as yet unsolved, technical challenges. For this reason, an aggressive R&D program has been launched at Fermilab to advance state of-the-art associative memory technology, the so called VIPRAM (Vertically Integrated Pattern Recognition Associative Memory) project. The VIPRAM leverages emerging 3D vertical integration technology to build faster and denser Associative Memory devices. The first step is to implement in conventional VLSI the associative memory building blocks that can be used in 3D stacking, in other words, the building blocks are laid out as if it is a 3D design. In this paper, we report on the first successful implementation of a 2D VIPRAM demonstrator chip (protoVIPRAM00). The results show that these building blocks are ready for 3D stacking.

Fine-grained recognition of plants from images.

Science.gov (United States)

Šulc, Milan; Matas, Jiří

2017-01-01

Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

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 Open......CV library is used for signal processing. We conclude that the approach presented here is a viable solution to pattern recognition problems. Since it requires no network connection, it shows promise in fields such as wildlife research....

Pattern-recognition software detecting the onset of failures in complex systems

International Nuclear Information System (INIS)

Mott, J.; King, R.

1987-01-01

A very general mathematical framework for embodying learned data from a complex system and combining it with a current observation to estimate the true current state of the system has been implemented using nearly universal pattern-recognition algorithms and applied to surveillance of the EBR-II power plant. In this application the methodology can provide signal validation and replacement of faulty signals on a near-real-time basis for hundreds of plant parameters. The mathematical framework, the pattern-recognition algorithms, examples of the learning and estimating process, and plant operating decisions made using this methodology are discussed. The entire methodology has been reduced to a set of FORTRAN subroutines which are small, fast, robust and executable on a personal computer with a serial link to the system's data acquisition computer, or on the data acquisition computer itself

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

Description and recognition of patterns in stochastic signals. [Electroencephalograms

Energy Technology Data Exchange (ETDEWEB)

Flik, T [Technische Univ. Berlin (F.R. Germany). Informatik-Forschungsgruppe Rechnerorganisation und Schaltwerke

1975-10-01

A method is shown for the description and recognition of patterns in stochastic signals such as electroencephalograms. For pattern extraction the signal is segmented at times of minimum amplitudes. The describing features consist of geometric values of the so defined patterns. The classification algorithm is based on the regression analysis, which is well known in the field of character recognition. For an economic classification a method is proposed which reduces the number of features. The quality of this pattern recognition method is demonstrated by the detection of spike wave complexes in electroencephalograms. The pattern description and recognition are provided for processing on a digital computer. (DE)

Star pattern recognition algorithm aided by inertial information

Science.gov (United States)

Liu, Bao; Wang, Ke-dong; Zhang, Chao

2011-08-01

Star pattern recognition is one of the key problems of the celestial navigation. The traditional star pattern recognition approaches, such as the triangle algorithm and the star angular distance algorithm, are a kind of all-sky matching method whose recognition speed is slow and recognition success rate is not high. Therefore, the real time and reliability of CNS (Celestial Navigation System) is reduced to some extent, especially for the maneuvering spacecraft. However, if the direction of the camera optical axis can be estimated by other navigation systems such as INS (Inertial Navigation System), the star pattern recognition can be fulfilled in the vicinity of the estimated direction of the optical axis. The benefits of the INS-aided star pattern recognition algorithm include at least the improved matching speed and the improved success rate. In this paper, the direction of the camera optical axis, the local matching sky, and the projection of stars on the image plane are estimated by the aiding of INS firstly. Then, the local star catalog for the star pattern recognition is established in real time dynamically. The star images extracted in the camera plane are matched in the local sky. Compared to the traditional all-sky star pattern recognition algorithms, the memory of storing the star catalog is reduced significantly. Finally, the INS-aided star pattern recognition algorithm is validated by simulations. The results of simulations show that the algorithm's computation time is reduced sharply and its matching success rate is improved greatly.

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.

State of the States, 2012: Arts Education State Policy Summary

Science.gov (United States)

Arts Education Partnership (NJ1), 2012

2012-01-01

The "State of the States 2012" summarizes state policies for arts education identified in statute or code for all 50 states and the District of Columbia. Information is based primarily on results from the AEP Arts Education State Policy Survey conducted in 2010-11, and updated in April 2012.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

Science.gov (United States)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

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.

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.

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

Science.gov (United States)

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

2015-01-01

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

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.

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

Science.gov (United States)

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.

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.

Pattern Recognition of the Multiple Sclerosis Syndrome

Science.gov (United States)

Stewart, Renee; Healey, Kathleen M.

2017-01-01

During recent decades, the autoimmune disease neuromyelitis optica spectrum disorder (NMOSD), once broadly classified under the umbrella of multiple sclerosis (MS), has been extended to include autoimmune inflammatory conditions of the central nervous system (CNS), which are now diagnosable with serum serological tests. These antibody-mediated inflammatory diseases of the CNS share a clinical presentation to MS. A number of practical learning points emerge in this review, which is geared toward the pattern recognition of optic neuritis, transverse myelitis, brainstem/cerebellar and hemispheric tumefactive demyelinating lesion (TDL)-associated MS, aquaporin-4-antibody and myelin oligodendrocyte glycoprotein (MOG)-antibody NMOSD, overlap syndrome, and some yet-to-be-defined/classified demyelinating disease, all unspecifically labeled under MS syndrome. The goal of this review is to increase clinicians’ awareness of the clinical nuances of the autoimmune conditions for MS and NMSOD, and to highlight highly suggestive patterns of clinical, paraclinical or imaging presentations in order to improve differentiation. With overlay in clinical manifestations between MS and NMOSD, magnetic resonance imaging (MRI) of the brain, orbits and spinal cord, serology, and most importantly, high index of suspicion based on pattern recognition, will help lead to the final diagnosis. PMID:29064441

The art and learning patterns of knowing in nursing

Directory of Open Access Journals (Sweden)

Cristina Lavareda Baixinho

2014-12-01

Full Text Available Objective To identify the perception of the students about the use of art as a pedagogical strategy in learning the patterns of knowing in nursing; to identify the dimensions of each pattern valued in the analysis of pieces of art. Method Descriptive mixed study. Data collection used a questionnaire applied to 31 nursing students. Results In the analysis of the students’ discourse, it was explicit that empirical knowledge includes scientific knowledge, tradition and nature of care. The aesthetic knowledge implies expressiveness, subjectivity and sensitivity. Self-knowledge, experience, reflective attitude and relationships with others are the subcategories of personal knowledge and the moral and ethics support ethical knowledge. Conclusion It is possible to learn patterns of knowledge through art, especially the aesthetic, ethical and personal. It is necessary to investigate further pedagogical strategies that contribute to the learning patterns of nursing knowledge.

Improved pattern recognition systems by hybrid methods

International Nuclear Information System (INIS)

Duerr, B.; Haettich, W.; Tropf, H.; Winkler, G.; Fraunhofer-Gesellschaft zur Foerderung der Angewandten Forschung e.V., Karlsruhe

1978-12-01

This report describes a combination of statistical and syntactical pattern recongition methods. The hierarchically structured recognition system consists of a conventional statistical classifier, a structural classifier analysing the topological composition of the patterns, a stage reducing the number of hypotheses made by the first two stages, and a mixed stage based on a search for maximum similarity between syntactically generated prototypes and patterns. The stages work on different principles to avoid mistakes made in one stage in the other stages. This concept is applied to the recognition of numerals written without constraints. If no samples are rejected, a recognition rate of 99,5% is obtained. (orig.) [de

Degraded character recognition based on gradient pattern

Science.gov (United States)

Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

2010-02-01

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

Haar-like Rectangular Features for Biometric Recognition

DEFF Research Database (Denmark)

Nasrollahi, Kamal; Moeslund, Thomas B.; Rashidi, Maryam

2013-01-01

Developing a reliable, fast, and robust biometric recognition system is still a challenging task. This is because the inputs to these systems can be noisy, occluded, poorly illuminated, rotated, and of very low-resolutions. This paper proposes a probabilistic classifier using Haar-like features......, which mostly have been used for detection, for biometric recognition. The proposed system has been tested for three different biometrics: ear, iris, and hand vein patterns and it is shown that it is robust against most of the mentioned degradations and it outperforms state-of-the-art systems...

Pattern recognition in high energy physics

International Nuclear Information System (INIS)

Tenner, A.G.

1980-01-01

In high energy physics experiments tracks of elementary particles are recorded by different types of equipment. Coordinates of points of these tracks have to be measured for the geometrical reconstruction and the further analysis of the observed events. Pattern recognition methods may facilitate the detection of tracks or whole events and the separation of relevant from non-relevant information. They may also serve for the automation of measurement. Generally, all work is done by digital computation. In a bubble chamber tracks appear as strings of vapour bubbles that can be recorded photographically. Two methods of pattern recognition are discussed. The flying spot digitizer encodes the pattern on the photograph into point coordinates in the memory of a computer. The computer carries out the pattern recognition procedure entirely on the basis of the stored information. Cathode ray instruments scan the photograph by means of a computer steered optical device. Data acquisition from the film is performed in a feedback loop of the computation. In electronic experimental equipment tracks are defined by the spacial distribution of hits of counters (wire counters, scintillation counters, spark chambers). Pattern recognition is generally performed in various stages both by on-line and off-line equipment. Problems in the data handling arise both from the great abundance of data and from the time limits imposed on the on-line computation by high measuring rates. The on-line computation is carried out by hardwired logic, small computers, and to an increasing extent by microprocessors. (Auth.)

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.

Molecularly imprinted polymers for the recognition of proteins: the state of the art.

Science.gov (United States)

Bossi, A; Bonini, F; Turner, A P F; Piletsky, S A

2007-01-15

Molecular imprinting has proved to be an effective technique for the creation of recognition sites on a polymer scaffold. Protein imprinting has been a focus for many chemists working in the area of molecular recognition, since the creation of synthetic polymers that can specifically recognise proteins is a very challenging but potentially extremely rewarding objective. It is expected that molecularly imprinted polymers (MIPs) with specificity for proteins will find application in medicine, diagnostics, proteomics, environmental analysis, sensors and drug delivery. In this review, the authors provide an overview of the progress achieved in the decade between 1994 and 2005, with respect to the challenging area of MIPs for protein recognition. The discussion furnishes a comparative analysis of different approaches developed, underlining their relative advantages and disadvantages and highlighting trends and possible future directions.

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

Energy Technology Data Exchange (ETDEWEB)

Acciarri, R.; Bagby, L.; Baller, B.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Greenlee, H.; James, C.; Jostlein, H.; Ketchum, W.; Kirby, M.; Kobilarcik, T.; Lockwitz, S.; Lundberg, B.; Marchionni, A.; Moore, C.D.; Palamara, O.; Pavlovic, Z.; Raaf, J.L.; Schukraft, A.; Snider, E.L.; Spentzouris, P.; Strauss, T.; Toups, M.; Wolbers, S.; Yang, T.; Zeller, G.P. [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Adams, C. [Harvard University, Cambridge, MA (United States); Yale University, New Haven, CT (United States); An, R.; Littlejohn, B.R.; Martinez Caicedo, D.A. [Illinois Institute of Technology (IIT), Chicago, IL (United States); Anthony, J.; Escudero Sanchez, L.; De Vries, J.J.; Marshall, J.; Smith, A.; Thomson, M. [University of Cambridge, Cambridge (United Kingdom); Asaadi, J. [University of Texas, Arlington, TX (United States); Auger, M.; Ereditato, A.; Goeldi, D.; Kreslo, I.; Lorca, D.; Luethi, M.; Rudolf von Rohr, C.; Sinclair, J.; Weber, M. [Universitaet Bern, Bern (Switzerland); Balasubramanian, S.; Fleming, B.T.; Gramellini, E.; Hackenburg, A.; Luo, X.; Russell, B.; Tufanli, S. [Yale University, New Haven, CT (United States); Barnes, C.; Mousseau, J.; Spitz, J. [University of Michigan, Ann Arbor, MI (United States); Barr, G.; Bass, M.; Del Tutto, M.; Laube, A.; Soleti, S.R.; De Pontseele, W.V. [University of Oxford, Oxford (United Kingdom); Bay, F. [TUBITAK Space Technologies Research Institute, Ankara (Turkey); Bishai, M.; Chen, H.; Joshi, J.; Kirby, B.; Li, Y.; Mooney, M.; Qian, X.; Viren, B.; Zhang, C. [Brookhaven National Laboratory (BNL), Upton, NY (United States); Blake, A.; Devitt, D.; Lister, A.; Nowak, J. [Lancaster University, Lancaster (United Kingdom); Bolton, T.; Horton-Smith, G.; Meddage, V.; Rafique, A. [Kansas State University (KSU), Manhattan, KS (United States); Camilleri, L.; Caratelli, D.; Crespo-Anadon, J.I.; Fadeeva, A.A.; Genty, V.; Kaleko, D.; Seligman, W.; Shaevitz, M.H. [Columbia University, New York, NY (United States); Church, E. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Cianci, D.; Karagiorgi, G. [Columbia University, New York, NY (United States); The University of Manchester (United Kingdom); Cohen, E.; Piasetzky, E. [Tel Aviv University, Tel Aviv (Israel); Collin, G.H.; Conrad, J.M.; Hen, O.; Hourlier, A.; Moon, J.; Wongjirad, T.; Yates, L. [Massachusetts Institute of Technology (MIT), Cambridge, MA (United States); Convery, M.; Eberly, B.; Rochester, L.; Tsai, Y.T.; Usher, T. [SLAC National Accelerator Laboratory, Menlo Park, CA (United States); Dytman, S.; Graf, N.; Jiang, L.; Naples, D.; Paolone, V.; Wickremasinghe, D.A. [University of Pittsburgh, Pittsburgh, PA (United States); Esquivel, J.; Hamilton, P.; Pulliam, G.; Soderberg, M. [Syracuse University, Syracuse, NY (United States); Foreman, W.; Ho, J.; Schmitz, D.W.; Zennamo, J. [University of Chicago, IL (United States); Furmanski, A.P.; Garcia-Gamez, D.; Hewes, J.; Hill, C.; Murrells, R.; Porzio, D.; Soeldner-Rembold, S.; Szelc, A.M. [The University of Manchester (United Kingdom); Garvey, G.T.; Huang, E.C.; Louis, W.C.; Mills, G.B.; De Water, R.G.V. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Gollapinni, S. [Kansas State University (KSU), Manhattan, KS (United States); University of Tennessee, Knoxville, TN (United States); and others

2018-01-15

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies. (orig.)

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

Science.gov (United States)

Acciarri, R.; Adams, C.; An, R.; Anthony, J.; Asaadi, J.; Auger, M.; Bagby, L.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L.; Caratelli, D.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Cohen, E.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadón, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Fadeeva, A. A.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garcia-Gamez, D.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; Hourlier, A.; Huang, E.-C.; James, C.; Jan de Vries, J.; Jen, C.-M.; Jiang, L.; Johnson, R. A.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Martinez Caicedo, D. A.; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Piasetzky, E.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; Rudolf von Rohr, C.; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van De Pontseele, W.; Van de Water, R. G.; Viren, B.; Weber, M.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Yates, L.; Zeller, G. P.; Zennamo, J.; Zhang, C.

2018-01-01

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.

Pattern recognition methods in air pollution control

Energy Technology Data Exchange (ETDEWEB)

Tauber, S

1978-01-01

The use of pattern recognition methods for predicting air pollution developments is discussed. Computer analysis of historical pollution data allows comparison in graphical form. An example of crisis prediction for carbon monoxide concentrations, using the pattern recognition method of analysis, is presented. Results of the analysis agreed well with actual CO conditions. (6 graphs, 4 references, 1 table)

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.

A pattern recognition account of decision making.

Science.gov (United States)

Massaro, D W

1994-09-01

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

State-of-the-art

DEFF Research Database (Denmark)

Pecher, Arthur; Kofoed, Jens Peter

This report presents a short overview on the state-of-the-art of wave tank testing of wave energy converters (WEC). Here for, it focuses mainly on the Phase 1 and 2 development phases of wave energy converters, as these are done in the wave tank (WEC), while the other development phases are perfo......This report presents a short overview on the state-of-the-art of wave tank testing of wave energy converters (WEC). Here for, it focuses mainly on the Phase 1 and 2 development phases of wave energy converters, as these are done in the wave tank (WEC), while the other development phases...

Optical Pattern Recognition for Missile Guidance.

Science.gov (United States)

1982-11-15

directed to novel pattern recognition algo- rithms (that allow pattern recognition and object classification in the face of various geometrical and...I wats EF5 = 50) p.j/t’ni 2 (for btith image pat tern recognitio itas a preproicessing oiperatiton. Ini devices). TIhe rt’ad light intensity (0.33t mW...electrodes on its large faces . This Priz light modulator and the motivation for its devel- SLM is known as the Prom (Pockels real-time optical opment. In Sec

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.

Technical Reviews on Pattern Recognition in Process Analytical Technology

International Nuclear Information System (INIS)

Kim, Jong Yun; Choi, Yong Suk; Ji, Sun Kyung; Park, Yong Joon; Song, Kyu Seok; Jung, Sung Hee

2008-12-01

Pattern recognition is one of the first and the most widely adopted chemometric tools among many active research area in chemometrics such as design of experiment(DoE), pattern recognition, multivariate calibration, signal processing. Pattern recognition has been used to identify the origin of a wine and the time of year that the vine was grown by using chromatography, cause of fire by using GC/MS chromatography, detection of explosives and land mines, cargo and luggage inspection in seaports and airports by using a prompt gamma-ray activation analysis, and source apportionment of environmental pollutant by using a stable isotope ratio mass spectrometry. Recently, pattern recognition has been taken into account as a major chemometric tool in the so-called 'process analytical technology (PAT)', which is a newly-developed concept in the area of process analytics proposed by US Food and Drug Administration (US FDA). For instance, identification of raw material by using a pattern recognition analysis plays an important role for the effective quality control of the production process. Recently, pattern recognition technique has been used to identify the spatial distribution and uniformity of the active ingredients present in the product such as tablet by transforming the chemical data into the visual information

Artvision: State Arts Plan, 2009-2011

Science.gov (United States)

South Dakota Arts Council, 2010

2010-01-01

The South Dakota Legislature, being aware of the impact of culture on a stable economy, desires to stimulate, encourage, and give recognition and assistance to the arts which, in order to grow and flourish, depend upon freedom, imagination and individual initiative. While the development of the arts has long been considered a matter of local…

Pattern recognition and modelling of earthquake registrations with interactive computer support

International Nuclear Information System (INIS)

Manova, Katarina S.

2004-01-01

The object of the thesis is Pattern Recognition. Pattern recognition i.e. classification, is applied in many fields: speech recognition, hand printed character recognition, medical analysis, satellite and aerial-photo interpretations, biology, computer vision, information retrieval and so on. In this thesis is studied its applicability in seismology. Signal classification is an area of great importance in a wide variety of applications. This thesis deals with the problem of (automatic) classification of earthquake signals, which are non-stationary signals. Non-stationary signal classification is an area of active research in the signal and image processing community. The goal of the thesis is recognition of earthquake signals according to their epicentral zone. Source classification i.e. recognition is based on transformation of seismograms (earthquake registrations) to images, via time-frequency transformations, and applying image processing and pattern recognition techniques for feature extraction, classification and recognition. The tested data include local earthquakes from seismic regions in Macedonia. By using actual seismic data it is shown that proposed methods provide satisfactory results for classification and recognition.(Author)

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.

Hybrid methodological approach to context-dependent speech recognition

Directory of Open Access Journals (Sweden)

Dragiša Mišković

2017-01-01

Full Text Available Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human–machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

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...

Investigation of pattern recognition techniques for the indentification of splitting surfaces in Monte Carlo particle transport calculations

International Nuclear Information System (INIS)

Macdonald, J.L.

1975-08-01

Statistical and deterministic pattern recognition systems are designed to classify the state space of a Monte Carlo transport problem into importance regions. The surfaces separating the regions can be used for particle splitting and Russian roulette in state space in order to reduce the variance of the Monte Carlo tally. Computer experiments are performed to evaluate the performance of the technique using one and two dimensional Monte Carlo problems. Additional experiments are performed to determine the sensitivity of the technique to various pattern recognition and Monte Carlo problem dependent parameters. A system for applying the technique to a general purpose Monte Carlo code is described. An estimate of the computer time required by the technique is made in order to determine its effectiveness as a variance reduction device. It is recommended that the technique be further investigated in a general purpose Monte Carlo code. (auth)

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

Science.gov (United States)

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

2017-04-15

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

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...

Application of the new pattern recognition system in the new e-nose to detecting Chinese spirits

International Nuclear Information System (INIS)

Gu Yu; Li Qiang

2014-01-01

We present a new pattern recognition system based on moving average and linear discriminant analysis (LDA), which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

Environmental Sound Recognition Using Time-Frequency Intersection Patterns

Directory of Open Access Journals (Sweden)

Xuan Guo

2012-01-01

Full Text Available Environmental sound recognition is an important function of robots and intelligent computer systems. In this research, we use a multistage perceptron neural network system for environmental sound recognition. The input data is a combination of time-variance pattern of instantaneous powers and frequency-variance pattern with instantaneous spectrum at the power peak, referred to as a time-frequency intersection pattern. Spectra of many environmental sounds change more slowly than those of speech or voice, so the intersectional time-frequency pattern will preserve the major features of environmental sounds but with drastically reduced data requirements. Two experiments were conducted using an original database and an open database created by the RWCP project. The recognition rate for 20 kinds of environmental sounds was 92%. The recognition rate of the new method was about 12% higher than methods using only an instantaneous spectrum. The results are also comparable with HMM-based methods, although those methods need to treat the time variance of an input vector series with more complicated computations.

Cultivating Demand for the Arts: Arts Learning, Arts Engagement, and State Arts Policy. Summary

Science.gov (United States)

Zakaras, Laura; Lowell, Julia F.

2008-01-01

The findings summarized in this report are intended to shed light on what it means to cultivate demand for the arts, why it is necessary and important to cultivate this demand, and what state arts agencies (SAAs) and other arts and education policymakers can do to help. The research considered only the benchmark arts central to public policy:…

Instruction of pattern recognition by MATLAB practice 1

International Nuclear Information System (INIS)

1999-06-01

This book describes the pattern recognition by MATLAB practice. It includes possibility and limit of AI, introduction of pattern recognition a vector and matrix, basic status and a probability theory, a random variable and probability distribution, statistical decision theory, data-mining, gaussian mixture model, a nerve cell modeling such as Hebb's learning rule, LMS learning rule, genetic algorithm, dynamic programming and DTW, HMN on Markov model and HMM's three problems and solution, introduction of SVM with KKT condition and margin optimum, kernel trick and MATLAB practice.

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.

Algorithms for adaptive nonlinear pattern recognition

Science.gov (United States)

Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary

2011-09-01

In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral

Alterations in Resting-State Activity Relate to Performance in a Verbal Recognition Task

Science.gov (United States)

López Zunini, Rocío A.; Thivierge, Jean-Philippe; Kousaie, Shanna; Sheppard, Christine; Taler, Vanessa

2013-01-01

In the brain, resting-state activity refers to non-random patterns of intrinsic activity occurring when participants are not actively engaged in a task. We monitored resting-state activity using electroencephalogram (EEG) both before and after a verbal recognition task. We show a strong positive correlation between accuracy in verbal recognition and pre-task resting-state alpha power at posterior sites. We further characterized this effect by examining resting-state post-task activity. We found marked alterations in resting-state alpha power when comparing pre- and post-task periods, with more pronounced alterations in participants that attained higher task accuracy. These findings support a dynamical view of cognitive processes where patterns of ongoing brain activity can facilitate –or interfere– with optimal task performance. PMID:23785436

Support for Arts Education. State Arts Agency Fact Sheet

Science.gov (United States)

National Assembly of State Arts Agencies, 2011

2011-01-01

Supporting lifelong learning in the arts is a top priority for state arts agencies. By supporting arts education in the schools, state arts agencies foster young imaginations, address core academic standards, and promote the critical thinking and creativity skills essential to a 21st century work force. State arts agencies also support…

Automatic Facial Expression Recognition and Operator Functional State

Science.gov (United States)

Blanson, Nina

2012-01-01

The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions

Automatic Facial Expression Recognition and Operator Functional State

Science.gov (United States)

Blanson, Nina

2011-01-01

The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions.

Modeling the Process of Color Image Recognition Using ART2 Neural Network

Directory of Open Access Journals (Sweden)

Todor Petkov

2015-09-01

Full Text Available This paper thoroughly describes the use of unsupervised adaptive resonance theory ART2 neural network for the purposes of image color recognition of x-ray images and images taken by nuclear magnetic resonance. In order to train the network, the pixel values of RGB colors are regarded as learning vectors with three values, one for red, one for green and one for blue were used. At the end the trained network was tested by the values of pictures and determines the design, or how to visualize the converted picture. As a result we had the same pictures with colors according to the network. Here we use the generalized net to prepare a model that describes the process of the color image recognition.

State Arts Agency Fact Sheet: Support for Arts Education

Science.gov (United States)

Online Submission, 2015

2015-01-01

This national overview of state arts agency grants and services for arts education includes summary statistics and geographic distribution. The fact sheet uses data from Final Descriptive Reports of state arts agency grant-making activities submitted annually to the National Assembly of State Arts Agencies (NASAA) and the National Endowment for…

Applications of pattern recognition theory in diagnostics of nuclear power plants

International Nuclear Information System (INIS)

Cech, J.

1982-01-01

The questions are discussed of the application of the theory of pattern recognition in the diagnostics of nuclear power plants. For the future use of recognition systems in the diagnostics of nuclear power plants it is obvious that like with other complex systems, optimal models will have to be used which will organize the optimal recognition algorithm. The conclusion is presented that for the needs of nuclear power plants special systems will be more suitable for pattern recognition than digital computers which are flexible and adaptible but have a lower decision rate, an insufficient working memory, complicated programs, etc. (Z.M.)

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.

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)

Pattern recognition approach to nondestructive evaluation of materials

International Nuclear Information System (INIS)

Chen, C.H.

1987-01-01

In this paper, a pattern recognition approach to the ultrasonic nondestructive evaluation of materials is examined. Emphasis is placed on identifying effective features from time and frequency domains, correlation functions and impulse responses to classify aluminum plate specimens into three major defect geometry categories: flat, angular cut and circular hole defects. A multi-stage classification procedure is developed which can further determine the angles and sizes for defect characterization and classification. The research clearly demonstrates that the pattern recognition approach can significantly improve the nondestructive material evaluation capability of the ultrasonic methods without resorting to the solution of highly complex mathematical inverse problems

State of the States 2016: Arts Education State Policy Summary

Science.gov (United States)

Aragon, Stephanie

2016-01-01

The "State of the States 2016" summarizes state policies for arts education identified in statute or administrative code for all 50 states and the District of Columbia. Information is based on a comprehensive search of state education statute and codes on each state's relevant websites. Complete results from this review are available in…

Automated pattern recognition system for noise analysis

International Nuclear Information System (INIS)

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

1980-01-01

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

Optical character recognition systems for different languages with soft computing

CERN Document Server

Chaudhuri, Arindam; Badelia, Pratixa; K Ghosh, Soumya

2017-01-01

The book offers a comprehensive survey of soft-computing models for optical character recognition systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic algorithms, are tested using real texts written in different languages, such as English, French, German, Latin, Hindi and Gujrati, which have been extracted by publicly available datasets. The simulation studies, which are reported in details here, show that soft-computing based modeling of OCR systems performs consistently better than traditional models. Mainly intended as state-of-the-art survey for postgraduates and researchers in pattern recognition, optical character recognition and soft computing, this book will be useful for professionals in computer vision and image processing alike, dealing with different issues related to optical character recognition.

Running Improves Pattern Separation during Novel Object Recognition.

Science.gov (United States)

Bolz, Leoni; Heigele, Stefanie; Bischofberger, Josef

2015-10-09

Running increases adult neurogenesis and improves pattern separation in various memory tasks including context fear conditioning or touch-screen based spatial learning. However, it is unknown whether pattern separation is improved in spontaneous behavior, not emotionally biased by positive or negative reinforcement. Here we investigated the effect of voluntary running on pattern separation during novel object recognition in mice using relatively similar or substantially different objects.We show that running increases hippocampal neurogenesis but does not affect object recognition memory with 1.5 h delay after sample phase. By contrast, at 24 h delay, running significantly improves recognition memory for similar objects, whereas highly different objects can be distinguished by both, running and sedentary mice. These data show that physical exercise improves pattern separation, independent of negative or positive reinforcement. In sedentary mice there is a pronounced temporal gradient for remembering object details. In running mice, however, increased neurogenesis improves hippocampal coding and temporally preserves distinction of novel objects from familiar ones.

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.

State of the art

International Nuclear Information System (INIS)

Lederman, L.M.

1983-01-01

There is a large body of experience in high luminosity data taking in fixed target research. We try to consider a wide variety of high rate experiments which were limited by the detector (not by available beam) to a preset number of collisions per second. We then attempt to translate these state-of-the-art experiments to effective collider experiments. To this end, we extend the chosen detector to a comparison 4π collider detector operating near 1 TeV. There are several issues: (1) effective solid angle must be translated to approx. = 4π, (2) environments may be quite different, e.g., beam dump near fixed target or beam halo muons vs. collider backgrounds, (3) the multiplicity varies over the experiments selected and (4) we have to treat open vs closed geometries. The large variety of experiments selected is designed to average over the causes for detector limitation. Finally we chose detectors which have produced physics in order to gauge the state of the art

Image processing and recognition for biological images.

Science.gov (United States)

Uchida, Seiichi

2013-05-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. © 2013 The Author Development, Growth & Differentiation © 2013 Japanese Society of Developmental Biologists.

Multimodal emotional state recognition using sequence-dependent deep hierarchical features.

Science.gov (United States)

Barros, Pablo; Jirak, Doreen; Weber, Cornelius; Wermter, Stefan

2015-12-01

Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneous, which makes them hard to be recognized by robots. Each modality has its own restrictions and constraints which, together with the non-structured behavior of spontaneous expressions, create several difficulties for the approaches present in the literature, which are based on several explicit feature extraction techniques and manual modality fusion. Our model uses a hierarchical feature representation to deal with spontaneous emotions, and learns how to integrate multiple modalities for non-verbal emotion recognition, making it suitable to be used in an HRI scenario. Our experiments show that a significant improvement of recognition accuracy is achieved when we use hierarchical features and multimodal information, and our model improves the accuracy of state-of-the-art approaches from 82.5% reported in the literature to 91.3% for a benchmark dataset on spontaneous emotion expressions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Unconstrained Iris Acquisition and Recognition Using COTS PTZ Camera

Directory of Open Access Journals (Sweden)

Venugopalan Shreyas

2010-01-01

Full Text Available Abstract Uniqueness of iris patterns among individuals has resulted in the ubiquity of iris recognition systems in virtual and physical spaces, at high security facilities around the globe. Traditional methods of acquiring iris patterns in commercial systems scan the iris when an individual is at a predetermined location in front of the scanner. Most state-of-the-art techniques for unconstrained iris acquisition in literature use expensive custom equipment and are composed of a multicamera setup, which is bulky, expensive, and requires calibration. This paper investigates a method of unconstrained iris acquisition and recognition using a single commercial off-the-shelf (COTS pan-tilt-zoom (PTZ camera, that is compact and that reduces the cost of the final system, compared to other proposed hierarchical multicomponent systems. We employ state-of-the-art techniques for face detection and a robust eye detection scheme using active shape models for accurate landmark localization. Additionally, our system alleviates the need for any calibration stage prior to its use. We present results using a database of iris images captured using our system, while operating in an unconstrained acquisition mode at 1.5 m standoff, yielding an iris diameter in the 150–200 pixels range.

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

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.

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...

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

Science.gov (United States)

Swartz, R. Andrew

2013-01-01

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

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.

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

Structural pattern recognition methods based on string comparison for fusion databases

International Nuclear Information System (INIS)

Dormido-Canto, S.; Farias, G.; Dormido, R.; Vega, J.; Sanchez, J.; Duro, N.; Vargas, H.; Ratta, G.; Pereira, A.; Portas, A.

2008-01-01

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns are known as primitives. Selection of primitives is an essential issue in structural pattern recognition methods, because they determine what types of structural components can be constructed. However, it should be noted that there is not a general solution to extract structural features (primitives) from data. So, four different ways to compute the primitives of plasma waveforms are compared: (1) constant length primitives, (2) adaptive length primitives, (3) concavity method and (4) concavity method for noisy signals. Each method defines a code alphabet and, in this way, the pattern recognition problem is carried out via string comparisons. Results of the four methods with the TJ-II stellarator databases will be discussed

Structural pattern recognition methods based on string comparison for fusion databases

Energy Technology Data Exchange (ETDEWEB)

Dormido-Canto, S. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain)], E-mail: sebas@dia.uned.es; Farias, G.; Dormido, R. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, 28040, Madrid (Spain); Sanchez, J.; Duro, N.; Vargas, H. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain); Ratta, G.; Pereira, A.; Portas, A. [Asociacion EURATOM/CIEMAT para Fusion, 28040, Madrid (Spain)

2008-04-15

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns are known as primitives. Selection of primitives is an essential issue in structural pattern recognition methods, because they determine what types of structural components can be constructed. However, it should be noted that there is not a general solution to extract structural features (primitives) from data. So, four different ways to compute the primitives of plasma waveforms are compared: (1) constant length primitives, (2) adaptive length primitives, (3) concavity method and (4) concavity method for noisy signals. Each method defines a code alphabet and, in this way, the pattern recognition problem is carried out via string comparisons. Results of the four methods with the TJ-II stellarator databases will be discussed.

Reactor noise analysis by statistical pattern recognition methods

International Nuclear Information System (INIS)

Howington, L.C.; Gonzalez, R.C.

1976-01-01

A multivariate statistical pattern recognition system for reactor noise analysis is presented. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, updating, and data compacting capabilities. System design emphasizes control of the false-alarm rate. Its abilities to learn normal patterns, to recognize deviations from these patterns, and to reduce the dimensionality of data with minimum error were evaluated by experiments at the Oak Ridge National Laboratory (ORNL) High-Flux Isotope Reactor. Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the pattern recognition system

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...

Pattern recognition methods for acoustic emission analysis

International Nuclear Information System (INIS)

Doctor, P.G.; Harrington, T.P.; Hutton, P.H.

1979-07-01

Models have been developed that relate the rate of acoustic emissions to structural integrity. The implementation of these techniques in the field has been hindered by the noisy environment in which the data must be taken. Acoustic emissions from noncritical sources are recorded in addition to those produced by critical sources, such as flaws. A technique is discussed for prescreening acoustic events and filtering out those that are produced by noncritical sources. The methodology that was investigated is pattern recognition. Three different pattern recognition techniques were applied to a data set that consisted of acoustic emissions caused by crack growth and acoustic signals caused by extraneous noise sources. Examination of the acoustic emission data presented has uncovered several features of the data that can provide a reasonable filter. Two of the most valuable features are the frequency of maximum response and the autocorrelation coefficient at Lag 13. When these two features and several others were combined with a least squares decision algorithm, 90% of the acoustic emissions in the data set were correctly classified. It appears possible to design filters that eliminate extraneous noise sources from flaw-growth acoustic emissions using pattern recognition techniques

The time course of individual face recognition: A pattern analysis of ERP signals.

Science.gov (United States)

Nemrodov, Dan; Niemeier, Matthias; Mok, Jenkin Ngo Yin; Nestor, Adrian

2016-05-15

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

A study on the extraction of feature variables for the pattern recognition for welding flaws

International Nuclear Information System (INIS)

Kim, J. Y.; Kim, C. H.; Kim, B. H.

1996-01-01

In this study, the researches classifying the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing, feature extraction, feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function classifier, the empirical Bayesian classifier. Also, the pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack, lack of penetration, lack of fusion, porosity, and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately teamed the neural network classifier is better than stastical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

Data complexity in pattern recognition

CERN Document Server

Kam Ho Tin

2006-01-01

Machines capable of automatic pattern recognition have many fascinating uses. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. This book looks at data complexity and its role in shaping the theories and techniques in different disciplines

Surveillance of a nuclear reactor core by use of a pattern recognition method

International Nuclear Information System (INIS)

Invernizzi, Michel.

1982-07-01

A pattern recognition system is described for the surveillance of a PWR reactor. This report contains four chapters. The first one succinctly deals with statistical pattern recognition principles. In the second chapter we show how a surveillance problem may be treated by pattern recognition and we present methods for surveillances (detection of abnormalities), controls (kind of running recognition) and diagnotics (kind of abnormality recognition). The third chapter shows a surveillance method of a nuclear plant. The signals used are the neutron noise observations made by the ionization chambers inserted in the reactor. Abnormality is defined in opposition with the training set witch is supposed to be an exhaustive summary of normality. In the fourth chapter we propose a scheme for an adaptative recognition and a method based on classes modelisations by hyper-spheres. This method has been tested on simulated training sets in two-dimensional feature spaces. It gives solutions to problems of non-linear separability [fr

Processing emotional body expressions: state-of-the-art.

Science.gov (United States)

Enea, Violeta; Iancu, Sorina

2016-10-01

Processing emotional body expressions has become recently an important topic in affective and social neuroscience along with the investigation of facial expressions. The objective of the study is to review the literature on emotional body expressions in order to discuss the current state of knowledge on this topic and identify directions for future research. The following electronic databases were searched: PsychINFO, Ebsco, ERIC, ProQuest, Sagepub, and SCOPUS using terms such as "body," "bodily expression," "body perception," "emotions," "posture," "body recognition" and combinations of them. The synthesis revealed several research questions that were addressed in neuroimaging, electrophysiological and behavioral studies. Among them, one important question targeted the neural mechanisms of emotional processing of body expressions to specific subsections regarding the time course for the integration of emotional signals from face and body, as well as the role of context in the perception of emotional signals. Processing bodily expression of emotion is similar to processing facial expressions, and the holistic processing is extended to the whole person. The current state-of-the-art in processing emotional body expressions may lead to a better understanding of the underlying neural mechanisms of social behavior. At the end of the review, suggestions for future research directions are presented.

Biometric verification based on grip-pattern recognition

NARCIS (Netherlands)

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

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 is used to measure the grip pattern. An interface has been

Biometric verification based on grip-pattern recognition

NARCIS (Netherlands)

Veldhuis, Raymond N.J.; Bazen, A.M.; Kauffman, J.A.; Hartel, Pieter H.

This paper describes the design, implementation and evaluation of a user-verification system for a smart gun, which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 £ 44 piezoresistive elements is used to measure the grip pattern. An interface has been

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

Science.gov (United States)

Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

2015-11-01

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.

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...

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.

The Art of the Renaissance Capitalist State

Directory of Open Access Journals (Sweden)

Rebeka Vidrih

2007-12-01

This article also highlights the fact that the concept of art developed in Florence – a city-state that played an important role in the early-capitalist world of the Renaissance, but never assumed a leading role like Venice and Genoa. »Art« was thus created through Florence’s efforts to demonstrate its equality and importance and, although it failed to achieve this in the field of the (capitalist economy, it at least succeeded in the field of the (absolutist state that was subordinate to this economy.

Auditory orientation in crickets: Pattern recognition controls reactive steering

Science.gov (United States)

Poulet, James F. A.; Hedwig, Berthold

2005-10-01

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

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

Directory of Open Access Journals (Sweden)

Bineng Zhong

2008-06-01

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

Creating symmetry the artful mathematics of wallpaper patterns

CERN Document Server

Farris, Frank A

2015-01-01

This lavishly illustrated book provides a hands-on, step-by-step introduction to the intriguing mathematics of symmetry. Instead of breaking up patterns into blocks-a sort of potato-stamp method-Frank Farris offers a completely new waveform approach that enables you to create an endless variety of rosettes, friezes, and wallpaper patterns: dazzling art images where the beauty of nature meets the precision of mathematics. Featuring more than 100 stunning color illustrations and requiring only a modest background in math, Creating Symmetry begins by addressing the enigma of a simple curve, who

Fuzzy tree automata and syntactic pattern recognition.

Science.gov (United States)

Lee, E T

1982-04-01

An approach of representing patterns by trees and processing these trees by fuzzy tree automata is described. Fuzzy tree automata are defined and investigated. The results include that the class of fuzzy root-to-frontier recognizable ¿-trees is closed under intersection, union, and complementation. Thus, the class of fuzzy root-to-frontier recognizable ¿-trees forms a Boolean algebra. Fuzzy tree automata are applied to processing fuzzy tree representation of patterns based on syntactic pattern recognition. The grade of acceptance is defined and investigated. Quantitative measures of ``approximate isosceles triangle,'' ``approximate elongated isosceles triangle,'' ``approximate rectangle,'' and ``approximate cross'' are defined and used in the illustrative examples of this approach. By using these quantitative measures, a house, a house with high roof, and a church are also presented as illustrative examples. In addition, three fuzzy tree automata are constructed which have the capability of processing the fuzzy tree representations of ``fuzzy houses,'' ``houses with high roofs,'' and ``fuzzy churches,'' respectively. The results may have useful applications in pattern recognition, image processing, artificial intelligence, pattern database design and processing, image science, and pictorial information systems.

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem.

Directory of Open Access Journals (Sweden)

Cai Wingfield

2017-09-01

Full Text Available There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG, generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.

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

DEFF Research Database (Denmark)

Doni, Andrea; Garlanda, Cecilia; Bottazzi, Barbara

2012-01-01

The innate immune system comprises a cellular and a humoral arm. The long pentraxin PTX3 is a fluid phase pattern recognition molecule, which acts as an essential component of the humoral arm of innate immunity. PTX3 has antibody-like properties including interactions with complement components....... PTX3 interacts with C1q, ficolin-1 and ficolin-2 as well as mannose-binding lectin, recognition molecules in the classical and lectin complement pathways. The formation of these heterocomplexes results in cooperative pathogen recognition and complement activation. Interactions with C4b binding protein...

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.

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...

Pattern recognition neural-net by spatial mapping of biology visual field

Science.gov (United States)

Lin, Xin; Mori, Masahiko

2000-05-01

The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.

Quantum pattern recognition with multi-neuron interactions

Science.gov (United States)

Fard, E. Rezaei; Aghayar, K.; Amniat-Talab, M.

2018-03-01

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights (η ) should equal the numbers of unknown bits in the input pattern ( d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter α.

Optimal pattern synthesis for speech recognition based on principal component analysis

Science.gov (United States)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

An inverse problem approach to pattern recognition in industry

Directory of Open Access Journals (Sweden)

Ali Sever

2015-01-01

Full Text Available Many works have shown strong connections between learning and regularization techniques for ill-posed inverse problems. A careful analysis shows that a rigorous connection between learning and regularization for inverse problem is not straightforward. In this study, pattern recognition will be viewed as an ill-posed inverse problem and applications of methods from the theory of inverse problems to pattern recognition are studied. A new learning algorithm derived from a well-known regularization model is generated and applied to the task of reconstruction of an inhomogeneous object as pattern recognition. Particularly, it is demonstrated that pattern recognition can be reformulated in terms of inverse problems defined by a Riesz-type kernel. This reformulation can be employed to design a learning algorithm based on a numerical solution of a system of linear equations. Finally, numerical experiments have been carried out with synthetic experimental data considering a reasonable level of noise. Good recoveries have been achieved with this methodology, and the results of these simulations are compatible with the existing methods. The comparison results show that the Regularization-based learning algorithm (RBA obtains a promising performance on the majority of the test problems. In prospects, this method can be used for the creation of automated systems for diagnostics, testing, and control in various fields of scientific and applied research, as well as in industry.

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...

Sonographic Diagnosis of Tubal Cancer with IOTA Simple Rules Plus Pattern Recognition

Science.gov (United States)

Tongsong, Theera; Wanapirak, Chanane; Tantipalakorn, Charuwan; Tinnangwattana, Dangcheewan

2017-11-26

Objective: To evaluate diagnostic performance of IOTA simple rules plus pattern recognition in predicting tubal cancer. Methods: Secondary analysis was performed on prospective database of our IOTA project. The patients recruited in the project were those who were scheduled for pelvic surgery due to adnexal masses. The patients underwent ultrasound examinations within 24 hours before surgery. On ultrasound examination, the masses were evaluated using the well-established IOTA simple rules plus pattern recognition (sausage-shaped appearance, incomplete septum, visible ipsilateral ovaries) to predict tubal cancer. The gold standard diagnosis was based on histological findings or operative findings. Results: A total of 482 patients, including 15 cases of tubal cancer, were evaluated by ultrasound preoperatively. The IOTA simple rules plus pattern recognition gave a sensitivity of 86.7% (13 in 15) and specificity of 97.4%. Sausage-shaped appearance was identified in nearly all cases (14 in 15). Incomplete septa and normal ovaries could be identified in 33.3% and 40%, respectively. Conclusion: IOTA simple rules plus pattern recognition is relatively effective in predicting tubal cancer. Thus, we propose the simple scheme in diagnosis of tubal cancer as follows. First of all, the adnexal masses are evaluated with IOTA simple rules. If the B-rules could be applied, tubal cancer is reliably excluded. If the M-rules could be applied or the result is inconclusive, careful delineation of the mass with pattern recognition should be performed. Creative Commons Attribution License

Pattern Recognition-Based Analysis of COPD in CT

DEFF Research Database (Denmark)

Sørensen, Lauge Emil Borch Laurs

recognition part is used to turn the texture measures, measured in a CT image of the lungs, into a quantitative measure of disease. This is done by applying a classifier that is trained on a training set of data examples with known lung tissue patterns. Different classification systems are considered, and we...... will in particular use the pattern recognition concepts of supervised learning, multiple instance learning, and dissimilarity representation-based classification. The proposed texture-based measures are applied to CT data from two different sources, one comprising low dose CT slices from subjects with manually...... annotated regions of emphysema and healthy tissue, and one comprising volumetric low dose CT images from subjects that are either healthy or suffer from COPD. Several experiments demonstrate that it is clearly beneficial to take the lung tissue texture into account when classifying or quantifying emphysema...

Spatial pattern recognition of seismic events in South West Colombia

Science.gov (United States)

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

2013-09-01

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

Sub-pattern based multi-manifold discriminant analysis for face recognition

Science.gov (United States)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

Science.gov (United States)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

Pattern recognition receptors and the inflammasome in kidney disease

NARCIS (Netherlands)

Leemans, Jaklien C.; Kors, Lotte; Anders, Hans-Joachim; Florquin, Sandrine

2014-01-01

Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain receptors (NLRs) are families of pattern recognition receptors that, together with inflammasomes, sense and respond to highly conserved pathogen motifs and endogenous molecules released upon cell damage or stress. Evidence

Deep Learning For Sequential Pattern Recognition

OpenAIRE

Safari, Pooyan

2013-01-01

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM) In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with ...

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

Science.gov (United States)

Juday, Richard D. (Editor)

1988-01-01

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

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

Science.gov (United States)

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

1993-03-01

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

State-of-the-art inventory

NARCIS (Netherlands)

Verhagen, H.J.; Van Gerven, K.A.J.; Akkerman, G.J.

2005-01-01

The present report provides a state-of-the-art inventory of relevant information and technical concepts for the ComCoast project, being the first phase of the research stages of Work Package 3 (WP3). This project was assigned to Royal Haskoning by CUR. The information scan was set-up in a systematic

Multimodal Dialogue Management - State of the art

NARCIS (Netherlands)

Bui Huu Trung, B.H.T.

This report is about the state of the art in dialogue management. We first introduce an overview of a multimodal dialogue system and its components. Second, four main approaches to dialogue management are described (finite-state and frame-based, information-state based and probabilistic, plan-based,

Greenhouse mechanization: State of the art and future perspective

NARCIS (Netherlands)

Henten, van E.

2006-01-01

This paper reviews the state of the art and future perspective of greenhouse mechanization. Driving forces for mechanization are identified. Dutch greenhouse crop production is used as an example. Analysis of a generic crop production process combined with a review of the state of the art in

Fuel cells: state of the art

International Nuclear Information System (INIS)

Campanari, S.; Casalegno, A.

2007-01-01

This paper deals with the main features at present state-of-the-art fuel cell and hybrid cycle technologies, discussing their actual performance, possible applications, market entry perspectives and potential development [it

Towards discrete wavelet transform-based human activity recognition

Science.gov (United States)

Khare, Manish; Jeon, Moongu

2017-06-01

Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

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

Heteronomy in the arts field: state funding and British arts organizations.

Science.gov (United States)

Alexander, Victoria D

2018-03-01

For Bourdieu, the field of cultural production is comprised of an autonomous and a heteronomous sector. A heteronomous sector is one that is interpenetrated by the commercial field. I discuss an arena that, until recently, was part of the relatively autonomous sector in the field of cultural production - the supported arts sector in the United Kingdom - and argue that it became more heteronomous, due to the penetration by the state. Heteronomy due to the commercial field is present but secondary to, and driven by, the actions of the state. Political parties' attempts to diffuse and legitimate a particular economic ideology have led to state demands that arts institutions adopt neoliberal business practices in exchange for funding. Government giving to the arts, previously at arm's length, proved to be a Faustian bargain that demanded significant repayment in the form of lost autonomy. Coercive pressures from the state, enacted over time, show how the domination of one field over another can occur, even when the domination is resisted. © London School of Economics and Political Science 2017.

Simultaneous pattern recognition and track fitting by the Kalman filtering method

International Nuclear Information System (INIS)

Billoir, P.

1990-01-01

A progressive pattern recognition algorithm based on the Kalman filtering method has been tested. The algorithm starts from a small track segment or from a fitted track of a neighbouring detector, then extends the candidate tracks by adding measured points one by one. The fitted parameters and weight matrix of the candidate track are updated when adding a point, and give an increasing precision on prediction of the next point. Thus, pattern recognition and track fitting can be accomplished simultaneously. The method has been implemented and tested for track reconstruction for the vertex detector of the ZEUS experiment at DESY. Detailed procedures of the method and its performance are presented. Its flexibility is described as well. (orig.)

Radar automatic target recognition (ATR) and non-cooperative target recognition (NCTR)

CERN Document Server

Blacknell, David

2013-01-01

The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition. Topics covered include the problem as applied to th

Fusion of LBP and SWLD using spatio-spectral information for hyperspectral face recognition

Science.gov (United States)

Xie, Zhihua; Jiang, Peng; Zhang, Shuai; Xiong, Jinquan

2018-01-01

Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.

State of the art undersøgelse

DEFF Research Database (Denmark)

Nielsen, Nils

1998-01-01

Dette skrift omhandler nogle af de erfaringer der til dato er opnået i forbindelse med standby projektets udførelse. Hovedtemaet er en “State of the art undersøgelse” der omhandler standby-spændingsforsyninger, samt komponenter der kan anvendes til konstruktion af dem......Dette skrift omhandler nogle af de erfaringer der til dato er opnået i forbindelse med standby projektets udførelse. Hovedtemaet er en “State of the art undersøgelse” der omhandler standby-spændingsforsyninger, samt komponenter der kan anvendes til konstruktion af dem...

State of the art in marketing hospital foodservice departments.

Science.gov (United States)

Pickens, C W; Shanklin, C W

1985-11-01

The purposes of this study were to identify the state of the art relative to the utilization of marketing techniques within hospital foodservice departments throughout the United States and to determine whether any relationships existed between the degree of utilization of marketing techniques and selected demographic characteristics of the foodservice administrators and/or operations. A validated questionnaire was mailed to 600 randomly selected hospital foodservice administrators requesting information related to marketing in their facilities. Forty-five percent of the questionnaires were returned and analyzed for frequency of response and significant relationship between variables. Chi-square was used for nominal data and Spearman rho for ranked data. Approximately 73% of the foodservice administrators stated that marketing was extremely important in the success of a hospital foodservice department. Respondents (79%) further indicated that marketing had become more important in their departments in the past 2 years. Departmental records, professional journals, foodservice suppliers, observation, and surveys were the sources most often used to obtain marketing data, a responsibility generally assumed by the foodservice director (86.2%). Merchandising, public relations, and word-of-mouth reputation were regarded as the most important aspects of marketing. Increased sales, participation, good will, departmental recognition, and employee satisfaction were used most frequently to evaluate the success of implemented marketing techniques. Marketing audits as a means of evaluating the success of marketing were used to a limited extent by the respondents.

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.

Application of digital pattern-less molding technology to produce art casting

Directory of Open Access Journals (Sweden)

Chen Li1

2014-11-01

Full Text Available Compared with the conventional casting process, digital pattern-less casting technology has many advantages such as good machining accuracy, a short processing cycle, and low production cost. It is a new rapid manufacturing technology for castings, integrated with CAD/CAM, casting, CNC machining and many other advanced technologies. With this digital casting technology, no pattern is needed for making molds; it is precise, flexible, and green. Usually, art castings have complex structures and are made in small batches or even made in a single-piece, especially for large-sized art castings. So it has the shortcomings of high cost, low efficiency and long time for making a pattern to produce art castings with the conventional casting processes. However, the digital pattern-less casting technology can be applied to fabricate art castings, since it can greatly shorten the manufacturing cycle and lower the production cost, thus having a very good prospect. In this study, based on the digital pattern-less casting technology, a plaque casting with artistic Chinese characters (a Chinese poem was designed and manufactured, and the production process was demonstrated in detail.

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.

Where is your state of the art?

Science.gov (United States)

Ríos Gaona, Manuel

2015-04-01

Beyond the purposes of publishing, questioning and/or hypothesizing, every research has the noble aim to quench the constant human need of pushing farther away the boundaries of knowledge, may such frontier exist. The state of the art is the generic expression coined to limit the whereabouts of any particular knowledge. We use it just like a compass, it tells us how far we are, what has been done and where we should go. Take for instance quantum field theory, the state of the art in particle physics will tell you that everything that exists in this universe is made of bosons, quarks and leptons. What!?. Hold on, I was taught (and luckily maybe you weren't) that the fundamental particles were electrons, neutrons and protons, right?. Indeed, that is the state of the art but from almost one century ago. So, if I may ask... where is your state of the art? This is not about quantum physics; it is not even about hydrology; it is about everything. Today, everybody doing or not a PhD fiercely believes that their research is important, extremely unique, life changing (and help us God if we are wrong); but how can you be so sure that what you do, isn't already done?. We live in a modern world, cool environmental scientists now have tagged this era as the Anthropocene; globalization is everywhere and of course knowledge has not escaped to it. Not only knowledge is now global but is totally diversified, any crazy idea you can think of (or actually you can't) it is very likely that somebody already has his/hers hands on it. Nevertheless, this is a good thing, isn't it?. Well, that is the whole point. Every time I should write my research's state of the art, I always get overwhelmed because it actually is pretty difficult to establish, and later when I think I get it, it seems that I am solving issues that somebody already bothered to solve 30 years ago. Therefore, does it really have some purpose to be swimming in a huge pool of knowledge, not knowing where exactly I am? or

Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications

OpenAIRE

Lahiru Iddamalgoda; Partha Sarathi Das; Partha Sarathi Das; Achala Aponso; Vijayaraghava Seshadri Sundararajan; Prashanth Suravajhala; Prashanth Suravajhala; Prashanth Suravajhala; Jayaraman K Valadi

2016-01-01

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately determining the responsible genetic factors for prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern r...

Action recognition using mined hierarchical compound features.

Science.gov (United States)

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical

State of the Art in the Cramer Classification Scheme and ...

Science.gov (United States)

Slide presentation at the SOT FDA Colloquium on State of the Art in the Cramer Classification Scheme and Threshold of Toxicological Concern in College Park, MD. Slide presentation at the SOT FDA Colloquium on State of the Art in the Cramer Classification Scheme and Threshold of Toxicological Concern in College Park, MD.

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...

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

DEFF Research Database (Denmark)

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

2012-01-01

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

Using Pattern Classification and Recognition Techniques for Diagnostic and Prediction

Directory of Open Access Journals (Sweden)

MORARIU, N.

2007-04-01

Full Text Available The paper presents some aspects regarding the joint use of classification and recognition techniques for the activity evolution diagnostication and prediction by means of a set of indexes. Starting from the indexes set there is defined a measure on the patterns set, measure representing a scalar value that characterizes the activity analyzed at each time moment. A pattern is defined by the values of the indexes set at a given time. Over the classes set obtained by means of the classification and recognition techniques is defined a relation that allows the representation of the evolution from negative evolution towards positive evolution. For the diagnostication and prediction the following tools are used: pattern recognition and multilayer perceptron. The data set used in experiments describes the pollution due to CO2 emission from the consumption of fuels in Europe. The paper also presents the REFORME software written by the authors and the results of the experiment obtained with this software.

Guide to state-of-the-art electron devices

CERN Document Server

2013-01-01

Concise, high quality and comparative overview of state-of-the-art electron device development, manufacturing technologies and applications Guide to State-of-the-Art Electron Devices marks the 60th anniversary of the IEEE Electron Devices Committee and the 35th anniversary of the IEEE Electron Devices Society, as such it defines the state-of-the-art of electron devices, as well as future directions across the entire field. Spans full range of electron device types such as photovoltaic devices, semiconductor manufacturing and VLSI technology and circuits, covered by IEEE Electron and Devices Society Contributed by internationally respected members of the electron devices community A timely desk reference with fully-integrated colour and a unique lay-out with sidebars to highlight the key terms Discusses the historical developments and speculates on future trends to give a more rounded picture of the topics covered A valuable resource R&D managers; engineers in the semiconductor industry; applied scientists...

Theoretical Aspects of the Patterns Recognition Statistical Theory Used for Developing the Diagnosis Algorithms for Complicated Technical Systems

Science.gov (United States)

Obozov, A. A.; Serpik, I. N.; Mihalchenko, G. S.; Fedyaeva, G. A.

2017-01-01

In the article, the problem of application of the pattern recognition (a relatively young area of engineering cybernetics) for analysis of complicated technical systems is examined. It is shown that the application of a statistical approach for hard distinguishable situations could be the most effective. The different recognition algorithms are based on Bayes approach, which estimates posteriori probabilities of a certain event and an assumed error. Application of the statistical approach to pattern recognition is possible for solving the problem of technical diagnosis complicated systems and particularly big powered marine diesel engines.

Recognition of neural brain activity patterns correlated with complex motor activity

Science.gov (United States)

Kurkin, Semen; Musatov, Vyacheslav Yu.; Runnova, Anastasia E.; Grubov, Vadim V.; Efremova, Tatyana Yu.; Zhuravlev, Maxim O.

2018-04-01

In this paper, based on the apparatus of artificial neural networks, a technique for recognizing and classifying patterns corresponding to imaginary movements on electroencephalograms (EEGs) obtained from a group of untrained subjects was developed. The works on the selection of the optimal type, topology, training algorithms and neural network parameters were carried out from the point of view of the most accurate and fast recognition and classification of patterns on multi-channel EEGs associated with the imagination of movements. The influence of the number and choice of the analyzed channels of a multichannel EEG on the quality of recognition of imaginary movements was also studied, and optimal configurations of electrode arrangements were obtained. The effect of pre-processing of EEG signals is analyzed from the point of view of improving the accuracy of recognition of imaginary movements.

The state-of-the-art of ART sealants.

Science.gov (United States)

Frencken, Jo E

2014-03-01

Sealing caries-prone pits and fissure systems is an effective caries-preventive measure. There are basically two types of sealant materials: glass-ionomer and resin-based materials. Low- and medium-viscosity glass-ionomers were initially used and showed a low level of retention. With the advent of the ART approach in the mid-nineties, high-viscosity glass-ionomers were introduced as sealant material and the retention rate of ART sealants increased substantially. As the effectiveness of a sealant is measured by its capacity to prevent (dentine) carious lesion development, sealant retention is considered a surrogate endpoint. The ART sealant protocol is described. Systematic reviews and meta-analysis covering low- medium- and high-viscosity glass-ionomer (ART) sealants have concluded that there is no evidence that either glass-ionomer or resin-based sealants prevent dentine carious lesions better. The annual dentine carious lesion development in teeth with high-viscosity glass-ionomer ART sealants over the first three years is 1%. These ART sealants have a high capacity of preventing carious lesion development. Because no electricity and running water is required, ART sealants can be placed both inside and outside the dental surgery. High-viscosity glass-ionomer ART sealants can be used alongside resin-based sealants.41:119-124

State of the art in video system performance

Science.gov (United States)

Lewis, Michael J.

1990-01-01

The closed circuit television (CCTV) system that is onboard the Space Shuttle has the following capabilities: camera, video signal switching and routing unit (VSU); and Space Shuttle video tape recorder. However, this system is inadequate for use with many experiments that require video imaging. In order to assess the state-of-the-art in video technology and data storage systems, a survey was conducted of the High Resolution, High Frame Rate Video Technology (HHVT) products. The performance of the state-of-the-art solid state cameras and image sensors, video recording systems, data transmission devices, and data storage systems versus users' requirements are shown graphically.

The state-of-the-art of ART restorations.

Science.gov (United States)

Frencken, Jo E

2014-04-01

ART is less anxiety- and pain-provoking than traditional restorative treatments; administration of local anaesthesia is rarely required. Systematic reviews have provided evidence of the high level of effectiveness of high-viscosity glass-ionomer ART restoration in restoring single-surface cavities, both in primary and permanent posterior teeth, but its survival rates in restoring multiple-surface cavities in primary posterior teeth needs to be improved. Insufficient information is available regarding the survival rates of multiple-surface ART restorations in permanent teeth. Evidence from these reviews indicates no difference in the survival rates of single-surface high-viscosity glass-ionomer ART restorations and amalgam restorations in primary and permanent posterior teeth. Where indicated, high-viscosity glass-ionomer ART restorations can be used alongside traditional restorations. ART provides a much more acceptable introduction to dental restorative care than the traditional 'injection, drill and fill'.

Pattern-recognition system application to EBR-II plant-life extension

International Nuclear Information System (INIS)

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

1988-01-01

A computer-based pattern-recognition system, the System State Analyzer (SSA), is being used as part of the EBR-II plant-life extension program for detection of degradation and other abnormalities in plant systems. The SSA is used for surveillance of the EBR-II primary system instrumentation, primary sodium pumps, and plant heat balances. Early results of this surveillance indicate that the SSA can detect instrumentation degradation and system performance degradation over varying time intervals, and can provide derived signal values to replace signals from failed critical sensors. These results are being used in planning for extended-life operation of EBR-II

Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

Science.gov (United States)

Carpenter, Gail A.

1997-11-01

A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.

MCAW-DB: A glycan profile database capturing the ambiguity of glycan recognition patterns.

Science.gov (United States)

Hosoda, Masae; Takahashi, Yushi; Shiota, Masaaki; Shinmachi, Daisuke; Inomoto, Renji; Higashimoto, Shinichi; Aoki-Kinoshita, Kiyoko F

2018-05-11

Glycan-binding protein (GBP) interaction experiments, such as glycan microarrays, are often used to understand glycan recognition patterns. However, oftentimes the interpretation of glycan array experimental data makes it difficult to identify discrete GBP binding patterns due to their ambiguity. It is known that lectins, for example, are non-specific in their binding affinities; the same lectin can bind to different monosaccharides or even different glycan structures. In bioinformatics, several tools to mine the data generated from these sorts of experiments have been developed. These tools take a library of predefined motifs, which are commonly-found glycan patterns such as sialyl-Lewis X, and attempt to identify the motif(s) that are specific to the GBP being analyzed. In our previous work, as opposed to using predefined motifs, we developed the Multiple Carbohydrate Alignment with Weights (MCAW) tool to visualize the state of the glycans being recognized by the GBP under analysis. We previously reported on the effectiveness of our tool and algorithm by analyzing several glycan array datasets from the Consortium of Functional Glycomics (CFG). In this work, we report on our analysis of 1081 data sets which we collected from the CFG, the results of which we have made publicly and freely available as a database called MCAW-DB. We introduce this database, its usage and describe several analysis results. We show how MCAW-DB can be used to analyze glycan-binding patterns of GBPs amidst their ambiguity. For example, the visualization of glycan-binding patterns in MCAW-DB show how they correlate with the concentrations of the samples used in the array experiments. Using MCAW-DB, the patterns of glycans found to bind to various GBP-glycan binding proteins are visualized, indicating the binding "environment" of the glycans. Thus, the ambiguity of glycan recognition is numerically represented, along with the patterns of monosaccharides surrounding the binding region. The

Threshold models of recognition and the recognition heuristic

Directory of Open Access Journals (Sweden)

Edgar Erdfelder

2011-02-01

Full Text Available According to the recognition heuristic (RH theory, decisions follow the recognition principle: Given a high validity of the recognition cue, people should prefer recognized choice options compared to unrecognized ones. Assuming that the memory strength of choice options is strongly correlated with both the choice criterion and recognition judgments, the RH is a reasonable strategy that approximates optimal decisions with a minimum of cognitive effort (Davis-Stober, Dana, and Budescu, 2010. However, theories of recognition memory are not generally compatible with this assumption. For example, some threshold models of recognition presume that recognition judgments can arise from two types of cognitive states: (1 certainty states in which judgments are almost perfectly correlated with memory strength and (2 uncertainty states in which recognition judgments reflect guessing rather than differences in memory strength. We report an experiment designed to test the prediction that the RH applies to certainty states only. Our results show that memory states rather than recognition judgments affect use of recognition information in binary decisions.

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

International Nuclear Information System (INIS)

Zeng Hui; Li Qiang; Gu Yu

2016-01-01

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance (QCM) principle, and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an 8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value (A), root-mean-square value (RMS), shape factor value (S f ), crest factor value (C f ), impulse factor value (I f ), clearance factor value (CL f ), kurtosis factor value (K v ) are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis (PCA) method. Finally the back propagation (BP) neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively. (paper)

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

One Step Closer to the Marketplace for State-of-the-Art Wind Turbine

Science.gov (United States)

Drivetrain | News | NREL One Step Closer to the Marketplace for State-of-the-Art Wind Turbine Drivetrain One Step Closer to the Marketplace for State-of-the-Art Wind Turbine Drivetrain April 1, 2016 modeling, and testing in state-of-the-art facilities designed to put the drivetrain through its paces

RECOG-ORNL, Pattern Recognition Data Analysis

International Nuclear Information System (INIS)

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

2000-01-01

Description of program or function: RECOG-ORNL, a general-purpose pattern recognition code, is a modification of the RECOG program, written at Lawrence Livermore National Laboratory. RECOG-ORNL contains techniques for preprocessing, analyzing, and displaying data, and for unsupervised and supervised learning. Data preprocessing routines transform the data into useful representations by auto-calling, selecting important variables, and/or adding products or transformations of the variables of the data set. Data analysis routines use correlations to evaluate the data and interrelationships among the data. Display routines plot the multidimensional patterns in two dimensions or plot histograms, patterns, or one variable versus another. Unsupervised learning techniques search for classes contained inherently in the data. Supervised learning techniques use known information about some of the data to generate predicted properties for an unknown set

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.

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

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.

Orientation and phase mapping in the transmission electron microscope using precession-assisted diffraction spot recognition: state-of-the-art results.

Science.gov (United States)

Viladot, D; Véron, M; Gemmi, M; Peiró, F; Portillo, J; Estradé, S; Mendoza, J; Llorca-Isern, N; Nicolopoulos, S

2013-10-01

A recently developed technique based on the transmission electron microscope, which makes use of electron beam precession together with spot diffraction pattern recognition now offers the possibility to acquire reliable orientation/phase maps with a spatial resolution down to 2 nm on a field emission gun transmission electron microscope. The technique may be described as precession-assisted crystal orientation mapping in the transmission electron microscope, precession-assisted crystal orientation mapping technique-transmission electron microscope, also known by its product name, ASTAR, and consists in scanning the precessed electron beam in nanoprobe mode over the specimen area, thus producing a collection of precession electron diffraction spot patterns, to be thereafter indexed automatically through template matching. We present a review on several application examples relative to the characterization of microstructure/microtexture of nanocrystalline metals, ceramics, nanoparticles, minerals and organics. The strengths and limitations of the technique are also discussed using several application examples. ©2013 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.

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

Directory of Open Access Journals (Sweden)

Tomasz Ciszewski

2006-01-01

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

The State of Comic Art Bibliography in North America

Directory of Open Access Journals (Sweden)

Michael Rhode

2010-08-01

Full Text Available Within the past two decades, several American bibliographies of comic art have been published to aid in research on comics and cartooning. Professor John Lent’s ten-volume Comic Art Bibliographies is one of the largest such projects and he began it with a self-published volume in 1986. Librarian Randy Scott published The Comic Art Collection Catalog: An Author, Artist, Title and Subject Catalog Of The Comic Art Collection, Special Collections Division, Michigan State University Libraries, whi...

The memory state heuristic: A formal model based on repeated recognition judgments.

Science.gov (United States)

Castela, Marta; Erdfelder, Edgar

2017-02-01

The recognition heuristic (RH) theory predicts that, in comparative judgment tasks, if one object is recognized and the other is not, the recognized one is chosen. The memory-state heuristic (MSH) extends the RH by assuming that choices are not affected by recognition judgments per se, but by the memory states underlying these judgments (i.e., recognition certainty, uncertainty, or rejection certainty). Specifically, the larger the discrepancy between memory states, the larger the probability of choosing the object in the higher state. The typical RH paradigm does not allow estimation of the underlying memory states because it is unknown whether the objects were previously experienced or not. Therefore, we extended the paradigm by repeating the recognition task twice. In line with high threshold models of recognition, we assumed that inconsistent recognition judgments result from uncertainty whereas consistent judgments most likely result from memory certainty. In Experiment 1, we fitted 2 nested multinomial models to the data: an MSH model that formalizes the relation between memory states and binary choices explicitly and an approximate model that ignores the (unlikely) possibility of consistent guesses. Both models provided converging results. As predicted, reliance on recognition increased with the discrepancy in the underlying memory states. In Experiment 2, we replicated these results and found support for choice consistency predictions of the MSH. Additionally, recognition and choice latencies were in agreement with the MSH in both experiments. Finally, we validated critical parameters of our MSH model through a cross-validation method and a third experiment. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

State of the art in microfabrication

NARCIS (Netherlands)

Schmitz, Jurriaan

2014-01-01

In this review paper the state of the art in microfabrication is presented. The focus is on trends in integrated circuit fabrication by mainstream industrial players. The article starts with Moore’s Law, describing its inception as well as the evolution of Moore’s metric of the number of components

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.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

Science.gov (United States)

Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

2018-01-01

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

Science.gov (United States)

Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

2018-03-12

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

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.

Introduction of pattern recognition by MATLAB practice 2

International Nuclear Information System (INIS)

1999-06-01

The contents of this book starts introduction and examples of pattern recognition. This book describes a vector and matrix, basic statistics and a probability distribution, statistical decision theory and probability density function, liner shunt, vector quantizing and clustering GMM, PCA and KL conversion, LDA, ID 3, a nerve cell modeling, HMM, SVM and Ada boost. It has direction of MATLAB in the appendix.

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

Science.gov (United States)

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

2004-02-01

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

Human activity recognition from wireless sensor network data: benchmark and software

NARCIS (Netherlands)

van Kasteren, T.L.M.; Englebienne, G.; Kröse, B.J.A.; Chen, L.; Nugent, C.; Biswas, J.; Hoey, J.

2011-01-01

Although activity recognition is an active area of research no common benchmark for evaluating the performance of activity recognition methods exists. In this chapter we present the state of the art probabilistic models used in activity recognition and show their performance on several real world

Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Directory of Open Access Journals (Sweden)

Wen Si

2018-03-01

Full Text Available The ultra high voltage direct current (UHVDC transmission system has advantages in delivering electrical energy over long distance at high capacity. UHVDC converter transformer is a key apparatus and its insulation state greatly affects the safe operation of the transmission system. Partial discharge (PD characteristics of oil-pressboard insulation under combined AC-DC voltage are the foundation for analyzing the insulation state of UHVDC converter transformers. The defect pattern recognition based on PD characteristics is an important part of the state monitoring of converter transformers. In this paper, PD characteristics are investigated with the established experimental platform of three defect models (needle-plate, surface discharge and air gap under 1:1 combined AC-DC voltage. The different PD behaviors of three defect models are discussed and explained through simulation of electric field strength distribution and discharge mechanism. For the recognition of defect types when multiple types of sources coexist, the Random Forests algorithm is used for recognition. In order to reduce the computational layer and the loss of information caused by the extraction of traditional features, the preprocessed single PD pulses and phase information are chosen to be the features for learning and test. Zero-padding method is discussed for normalizing the features. Based on the experimental data, Random Forests and Least Squares Support Vector Machine are compared in the performance of computing time, recognition accuracy and adaptability. It is proved that Random Forests is more suitable for big data analysis.

State-of-the-art of home networking

NARCIS (Netherlands)

Koonen, A.M.J.; Popov, M.

2012-01-01

To introduce the Symposium on "Indoor Optical Networks: a Promising Way to a Converged Service Delivery", this presentation will give a brief overview of state-of-the-art home networking architectures, technologies and applications covering both technical and techno-economic aspects.

State Toleration, Religious Recognition and Equality

DEFF Research Database (Denmark)

Lægaard, Sune

2013-01-01

In debates about multiculturalism, it is widely claimed that ‘toleration is not enough’ and that we need to go ‘beyond toleration’ to some form of politics of recognition in order to satisfactorily address contemporary forms of cultural diversity (e.g. the presence in Europe of Muslim minorities...... a conceptual question of whether the relation between states and minorities can be categoriseized in terms of recognition or toleration, but about a normative question of whether and how toleration and recognition secures equality. When toleration is inadequate, this is often because it institutionaliseizes...... and upholds specific inequalities. But politics of recognition may equally well institute inequalities, and in such cases unequal recognition may not be preferable to toleration....

New simulators from old - achieving state-of-the-art simulation without state-of-the-art costs

International Nuclear Information System (INIS)

Heilmeier, H.J.; Rosser, R.M.; Fyffe, K.L.; Gaerttner, G.F.; Chulick, E.T.

1990-01-01

Achieving a state-of-the-art simulator for operator training requires neither the expensive remodeling of old simulators nor the acquisition of very expensive new machines. In this paper the authors present two distinct cases where older training simulators have been upgraded to meet training requirements with a minimum of inexpensive hardware additions, including plug-in/-out panels, and software changes

Application of pattern recognition techniques to the detection of the Phenix reactor control rods vibrations

International Nuclear Information System (INIS)

Zwingelstein, G.; Deat, M.; Le Guillou, G.

1979-01-01

The incipient detection of control rods vibrations is very important for the safety of the operating plants. This detection can be achieved by an analysis of the peaks of the power spectrum density of the neutron noise. Pattern Recognition techniques were applied to detect the rod vibrations which occured at the fast breeder Phenix (250MWe). In the first part we give a description of the basic pattern which is used to characterize the behavior of the plant. The pattern is considered as column vector in n dimensional Euclidian space where the components are the samples of the power spectral density of the neutron noise. In the second part, a recursive learning procedure of the normal patterns which provides the mean and the variance of the estimates is described. In the third part the classification problem has been framed in terms of a partitioning procedure in n dimensional space which encloses regions corresponding to normal operations. This pattern recognition scheme was applied to the detection of rod vibrations with neutron data collected at the Phenix site before and after occurence of the vibrations. The analysis was carried out with a 42-dimensional measurement space. The learned pattern was estimated with 150 measurement vectors which correspond to the period without vibrations. The efficiency of the surveillance scheme is then demonstrated by processing separately 119 measurement vectors recorded during the rod vibration period

14 CFR 1203.403 - State-of-the-art and intelligence.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false State-of-the-art and intelligence. 1203.403... PROGRAM Guides for Original Classification § 1203.403 State-of-the-art and intelligence. A logical... available from intelligence sources is known or is available to others. It is also important to consider...

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...

A self-organized learning strategy for object recognition by an embedded line of attraction

Science.gov (United States)

Seow, Ming-Jung; Alex, Ann T.; Asari, Vijayan K.

2012-04-01

on this observation we developed a self- organizing line attractor, which is capable of generating new lines in the feature space to learn unrecognized patterns. Experiments performed on UMIST pose database and CMU face expression variant database for face recognition have shown that the proposed nonlinear line attractor is able to successfully identify the individuals and it provided better recognition rate when compared to the state of the art face recognition techniques. Experiments on FRGC version 2 database has also provided excellent recognition rate in images captured in complex lighting environments. Experiments performed on the Japanese female face expression database and Essex Grimace database using the self organizing line attractor have also shown successful expression invariant face recognition. These results show that the proposed model is able to create nonlinear manifolds in a multidimensional feature space to distinguish complex patterns.

Track recognition with an associative pattern memory

International Nuclear Information System (INIS)

Bok, H.W. den; Visschers, J.L.; Borgers, A.J.; Lourens, W.

1991-01-01

Using Programmable Gate Arrays (PGAs), a prototype for a fast Associative Pattern Memory module has been realized. The associative memory performs the recognition of tracks within the hadron detector data acquisition system at NIKHEF-K. The memory matches the detector state with a set of 24 predefined tracks to identify the particle tracks that occur during an event. This information enables the trigger hardware to classify and select or discriminate the event. Mounted on a standard size (6U) VME board, several PGAs together form an associative memory. The internal logic architecture of the Gate Array is used in such a way as to minimize signal propagation delay. The memory cells, containing a binary representation of the particle tracks, are dynamically loadable through a VME bus interface, providing a high level of flexibility. The hadron detector and its readout system are briefly described and our track representation method is presented. Results from measurements under experimental conditions are discussed. (orig.)

How the United States Funds the Arts. Third Edition

Science.gov (United States)

National Endowment for the Arts, 2012

2012-01-01

The infrastructure for arts and cultural support in the United States is complex and adaptive. Citizens who enjoy the arts can choose from a wide array of drama, visual and media arts, dance, music, and literature available in formal and informal settings--theaters, museums, and concert halls, but also libraries, schools, places of worship,…

Platelet kinetics: the state of the art

International Nuclear Information System (INIS)

Heyns, A. duP

1984-01-01

In this paper an overview of the state of the art of platelet kinetics 1982 is presented. The subjects considered include a discussion of the advantages and disadvantages of some of the many radionuclide platelet labels, viz 51 Cr, 111 In, focussing briefly on models for analysis of platelets survival. (Auth.)

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.

Pattern recognition as a method of data analysis

Energy Technology Data Exchange (ETDEWEB)

Caputo, M.

1978-11-15

The method of pattern recognition has been used in biological and social sciences and has been recently introduced for the solution of geological and geophysical problems such as oil and ore prospecting and seismological prediction. The method is briefly illustrated by an application to earthquake prediction in Italy in which topographic and geologic maps are used in conjunction with earthquake catalogs. 3 figures, 1 table.

DNA pattern recognition using canonical correlation algorithm.

Science.gov (United States)

Sarkar, B K; Chakraborty, Chiranjib

2015-10-01

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

State-of-the-art in Heterogeneous Computing

Directory of Open Access Journals (Sweden)

Andre R. Brodtkorb

2010-01-01

Full Text Available Node level heterogeneous architectures have become attractive during the last decade for several reasons: compared to traditional symmetric CPUs, they offer high peak performance and are energy and/or cost efficient. With the increase of fine-grained parallelism in high-performance computing, as well as the introduction of parallelism in workstations, there is an acute need for a good overview and understanding of these architectures. We give an overview of the state-of-the-art in heterogeneous computing, focusing on three commonly found architectures: the Cell Broadband Engine Architecture, graphics processing units (GPUs, and field programmable gate arrays (FPGAs. We present a review of hardware, available software tools, and an overview of state-of-the-art techniques and algorithms. Furthermore, we present a qualitative and quantitative comparison of the architectures, and give our view on the future of heterogeneous computing.

Macrophage pattern recognition receptors in immunity, homeostasis and self tolerance.

Science.gov (United States)

Mukhopadhyay, Subhankar; Plüddemann, Annette; Gordon, Siamon

2009-01-01

Macrophages, a major component of innate immune defence, express a large repertoire of different classes of pattern recognition receptors and other surface antigens which determine the immunologic and homeostatic potential of these versatile cells. In the light of present knowledge ofmacrophage surface antigens, we discuss self versus nonself recognition, microbicidal effector functions and self tolerance in the innate immune system.

Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

Science.gov (United States)

Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki

2017-09-01

Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

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

Directory of Open Access Journals (Sweden)

Sigrid E M Heinsbroek

2008-11-01

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

The Arts and State Governments: At Arm's Length or Arm in Arm?

Science.gov (United States)

Lowell, Julia F.; Ondaatje, Elizabeth Heneghan

2006-01-01

Even though a majority of Americans claim to support public funding of the arts, state government spending on the arts is minimal--and may be losing ground relative to other types of state expenditures. Moreover, most state arts agencies, or SAAs, have not succeeded in convincing state government leaders that the arts should be integral to their…

Development of a Pattern Recognition Methodology for Determining Operationally Optimal Heat Balance Instrumentation Calibration Schedules

Energy Technology Data Exchange (ETDEWEB)

Kurt Beran; John Christenson; Dragos Nica; Kenny Gross

2002-12-15

The goal of the project is to enable plant operators to detect with high sensitivity and reliability the onset of decalibration drifts in all of the instrumentation used as input to the reactor heat balance calculations. To achieve this objective, the collaborators developed and implemented at DBNPS an extension of the Multivariate State Estimation Technique (MSET) pattern recognition methodology pioneered by ANAL. The extension was implemented during the second phase of the project and fully achieved the project goal.

From Digital Imaging to Computer Image Analysis of Fine Art

Science.gov (United States)

Stork, David G.

An expanding range of techniques from computer vision, pattern recognition, image analysis, and computer graphics are being applied to problems in the history of art. The success of these efforts is enabled by the growing corpus of high-resolution multi-spectral digital images of art (primarily paintings and drawings), sophisticated computer vision methods, and most importantly the engagement of some art scholars who bring questions that may be addressed through computer methods. This paper outlines some general problem areas and opportunities in this new inter-disciplinary research program.

77 FR 5281 - State-of-the-Art Reactor Consequence Analyses Reports

Science.gov (United States)

2012-02-02

... NUCLEAR REGULATORY COMMISSION [Docket ID: NRC-2012-0022] State-of-the-Art Reactor Consequence... release of Draft NUREG-1935, ``State-of-the-Art Reactor Consequence Analyses (SOARCA) Report,'' for public... offsite radiological health consequences for potential severe reactor accidents for the Peach Bottom...

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.

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.

The DELPHI Silicon Tracker in the global pattern recognition

CERN Document Server

Elsing, M

2000-01-01

ALEPH and DELPHI were the first experiments operating a silicon vertex detector at LEP. During the past 10 years of data taking the DELPHI Silicon Tracker was upgraded three times to follow the different tracking requirements for LEP 1 and LEP 2 as well as to improve the tracking performance. Several steps in the development of the pattern recognition software were done in order to understand and fully exploit the silicon tracker information. This article gives an overview of the final algorithms and concepts of the track reconstruction using the Silicon Tracker in DELPHI.

The DELPHI Silicon Tracker in the global pattern recognition

International Nuclear Information System (INIS)

Elsing, M.

2000-01-01

ALEPH and DELPHI were the first experiments operating a silicon vertex detector at LEP. During the past 10 years of data taking the DELPHI Silicon Tracker was upgraded three times to follow the different tracking requirements for LEP 1 and LEP 2 as well as to improve the tracking performance. Several steps in the development of the pattern recognition software were done in order to understand and fully exploit the silicon tracker information. This article gives an overview of the final algorithms and concepts of the track reconstruction using the Silicon Tracker in DELPHI

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.

State Arts Policy: Trends and Future Prospects

Science.gov (United States)

Lowell, Julia F.

2008-01-01

State arts agencies (SAAs)--key players within the U.S. system of public support for the arts--face growing economic, political, and demographic challenges to the roles and missions they adopted when founded in the mid-1960s. This report, the fourth and final in a multiyear study, looks at state arts agencies' efforts to rethink their roles and…

Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid

Directory of Open Access Journals (Sweden)

Yujia Jiang

2018-01-01

Full Text Available Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.