Cloud pattern interpretation <final report<
Recognition of cloud pattern from films taken by tiros satellite
PATTERN RECOGNITION↗
SEARCH · Engineering Papers
Results for “pattern recognition”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Investigation of Error Patterns in Geographical Databases
The objective of the research conducted in this project is to develop a methodology to investigate the accuracy of Airport Safety Modeling Data (ASMD) using statistical, visualization, and Artificial Neural Network (ANN) techniques. Such a methodology can contribute to answering the following research questions: Over a representative sampling of ASMD databases, can statistical error analysis techniques be accurately learned and replicated by ANN modeling techniques? This representative ASMD sample should include numerous airports and a variety of terrain characterizations. Is it possible to identify and automate the recognition of patterns of error related to geographical features? Do such patterns of error relate to specific geographical features, such as elevation or terrain slope? Is it possible to combine the errors in small regions into an error prediction for a larger region? What are the data density reduction implications of this work? ASMD may be used as the source of terrain data for a synthetic visual system to be used in the cockpit of aircraft when visual reference to ground features is not possible during conditions of marginal weather or reduced visibility. In this research, United States Geologic Survey (USGS) digital elevation model (DEM) data has been selected as the benchmark. Artificial Neural Networks (ANNS) have been used and tested as alternate methods in place of the statistical methods in similar problems. They often perform better in pattern recognition, prediction and classification and categorization problems. Many studies show that when the data is complex and noisy, the accuracy of ANN models is generally higher than those of comparable traditional methods.
Dryer, David↗
Face recognition system and method using face pattern words and face pattern bytes
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.
97 MATHEMATICS AND COMPUTING↗
Self-Amplified Optical Pattern Recognizer
Self-amplified optical pattern recognizers developed for use in recognition of patterns, in optical computing, and in optoelectronic neural networks. In recognizer, photorefractive crystal serves as medium in which one holographically records diffraction-grating filter representing pattern with which recognition sought. Apparatus "self-amplified" because signal amplified within filter to many orders of magnitude greater than in prior optical pattern recognizers. Basic principle of operation applicable to many types of correlation filters, including (but not limited to) Vander Lugt matched filters, joint-transform filters, and phase-only filters.
Liu, Hua-Kuang↗
Spatiotemporal pattern detection, generation, and computation with circuits
Abstract Implementations of neurons, delays, and synapse circuits are presented with simulations. These neural elements are used to create two small spiking neural networks, the Rate-Window and Order-Biased clusters, which are capable of detecting simple two-spike spatiotemporal patterns. A simple pattern detecting network (SPDN) is created by combining the Rate-Window and Order-Biased clusters, where clusters are small spiking neural networks, and its simple pattern detection ability is demonstrated in simulation. The SPDN is used to implement a complex pattern detecting network (CPDN) and its complex pattern detection ability is demonstrated in simulation. Methods for generating arbitrary spatiotemporal patterns are presented. The CPDN and spatiotemporal pattern generation methods are then used to implement a novel spatiotemporal computing paradigm based on detecting and responding to spatiotemporal symbols. A simulation of a spatiotemporal half adder is presented to demonstrate the computing paradigm.
97 - MATHEMATICS AND COMPUTING↗
Fast Feature-Recognizing Optoelectronic System
Proposed optoelectronic system recognizes features or classifies images by processing outputs of photosensors rapidly, in parallel, through circuits developed in research on neural networks. Array of photoconductive elements serve as photomodulated connections in electronic neural network, which provides high speed data compression to generate feature vector. System able to "learn" new patterns for subsequent recognition. Potential applications in robotic vision systems and pattern recognition.
Thakoor, S.↗
Performance of heavy-flavour jet identification in Lorentz-boosted topologies in proton-proton collisions at √(s) = 13 TeV
Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to bb̅ or cc̅, have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at √(s) = 13 TeV during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.
Pattern recognition↗
Energy calibration of germanium detectors for the Majorana Demonstrator
The Majorana Demonstrator was a search for neutrinoless double-beta decay (0νββ) in the 76 Ge isotope. It was staged at the 4850-foot level of the Sanford Underground Research Facility (SURF) in Lead, SD. The experiment consisted of 58 germanium detectors housed in a low background shield and was calibrated once per week by deploying a 228 Th line source for 1 to 2 hours. The energy scale calibration determination for the detector array was automated using custom analysis tools. We describe the offline procedure for calibration of the Demonstrator germanium detectors, including the simultaneous fitting of multiple spectral peaks, estimation of energy scale uncertainties, and the automation of the calibration procedure.
47 OTHER INSTRUMENTATION↗
Neural networks: Alternatives to conventional techniques for automatic docking
Automatic docking of orbiting spacecraft is a crucial operation involving the identification of vehicle orientation as well as complex approach dynamics. The chaser spacecraft must be able to recognize the target spacecraft within a scene and achieve accurate closing maneuvers. In a video-based system, a target scene must be captured and transformed into a pattern of pixels. Successful recognition lies in the interpretation of this pattern. Due to their powerful pattern recognition capabilities, artificial neural networks offer a potential role in interpretation and automatic docking processes. Neural networks can reduce the computational time required by existing image processing and control software. In addition, neural networks are capable of recognizing and adapting to changes in their dynamic environment, enabling enhanced performance, redundancy, and fault tolerance. Most neural networks are robust to failure, capable of continued operation with a slight degradation in performance after minor failures. This paper discusses the particular automatic docking tasks neural networks can perform as viable alternatives to conventional techniques.
Vinz, Bradley L.↗
Insect taste receptors relevant to host identification by recognition of secondary metabolite patterns of non-host plants
Highlights: • Three putative bitter gustatory receptor of silkworm respond to several secondary metabolites in non-host plants. • Secondary metabolites responsive to the gustatory receptor are representative feeding-deterrents in non-host plants. • One feeding-deterrent gustatory receptor respond to multiple feeding deterrents. • Feeding deterrent receptors are expressed in silkworm mouthparts. • Representative secondary metabolite of a non-host plant, coumarin exactly had a suppressive effect on food intake of silkworm. The taste sensing system is crucial for food recognition in insects and other animals. It is commonly believed that insect gustatory receptors (Grs) expressed in gustatory organs are indispensable for host plant selection. Many behavioral studies have shown that mono- or oligo-phagous lepidopteran insects use the balance between feeding attractants and feeding deterrents in host plants and that these are sensed by taste organs for host plant recognition. However, the molecular mechanism underlying taste recognition, especially of feeding deterrents, remains to be elucidated. To better understand this mechanism, we studied orphan Grs, including Bombyx mori Gr (BmGr) 16, BmGr18, and BmGr53, from the mono-phagous insect, Bombyx mori. Using Calcium imaging in mammalian cells, we first confirmed in lepidoptera insects that three of the putative bitter Grs widely responded to structurally different feeding deterrents. Although the phylogenetic distance of these Grs was considerable, they responded to partially overlapping deterrents of plant secondary metabolites. These findings suggest that not only these three Grs but also most of the Grs that have been assigned to putative bitter Grs are feeding-deterrent receptors that play a role in host plant recognition.
60 APPLIED LIFE SCIENCES↗
Automated recognition of oceanic cloud patterns. II - Detection of air temperature and humidity anomalies above the ocean surface from satellite imagery
Explore the source record for details and available documents.
Garand, Louis↗
Recognizing Patterns In Log-Polar Coordinates
Log-Hough transform is basis of improved method for recognition of patterns - particularly, straight lines - in noisy images. Takes advantage of rotational and scale invariance of mapping from Cartesian to log-polar coordinates, and offers economy of representation and computation. Unification of iconic and Hough domains simplifies computations in recognition and eliminates erroneous quantization of slopes attributable to finite spacing of Cartesian coordinate grid of classical Hough transform. Equally efficient recognizing curves. Log-Hough transform more amenable to massively parallel computing architectures than traditional Cartesian Hough transform. "In-place" nature makes it possible to apply local pixel-neighborhood processing.
Weiman, Carl F. R.↗
Studying the Sky/Planets Can Drown You in Images: Machine Learning Solutions at JPL/Caltech
JPL is working to develop a domain-independent system capable of small-scale object recognition in large image databases for science analysis. Two applications discussed are the cataloging of three billion sky objects in the Sky Image Cataloging and Analysis Tool (SKICAT) and the detection of possibly one million small volcanoes visible in the Magellan synthetic aperture radar images of Venus (JPL Adaptive Recognition Tool, JARTool).
geomorphology↗
Creative-Dynamics Approach To Neural Intelligence
Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.
Zak, Michail A.↗
Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data
Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10 min, eight concepts: synergy , pleiotropy , complexity , dynamics , ambiguity , circularity , personalized outcomes , and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features—such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.
60 APPLIED LIFE SCIENCES↗
Experiments on computer recognition of con- nected handwritten words
Computer recognition of handwritten words
HANDWRITING↗
Orthogonal Patterns In A Binary Neural Network
Report presents some recent developments in theory of binary neural networks. Subject matter relevant to associate (content-addressable) memories and to recognition of patterns - both of considerable importance in advancement of robotics and artificial intelligence. When probed by any pattern, network converges to one of stored patterns.
Baram, Yoram↗
Speech variability effects on recognition accuracy associated with concurrent task performance by pilots
In the present study of the responses of pairs of pilots to aircraft warning classification tasks using an isolated word, speaker-dependent speech recognition system, the induced stress was manipulated by means of different scoring procedures for the classification task and by the inclusion of a competitive manual control task. Both speech patterns and recognition accuracy were analyzed, and recognition errors were recorded by type for an isolated word speaker-dependent system and by an offline technique for a connected word speaker-dependent system. While errors increased with task loading for the isolated word system, there was no such effect for task loading in the case of the connected word system.
Simpson, C. A.↗