On the self-learning scheme of Nagy and Shelton.
Pattern recognition model as self-learning algorithm noting Nagy and Shelton scheme
LEARNING SYSTEM↗
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Multi-Sensory Opto-Electronic Feature Extraction Neural Associative Retriever
Optical pattern recognition and neural associative memory are important research topics for optical computing.
optical↗
A Trainable Tool for Finding Small volcanoes in SAR Imagery of Venus
Volcanoes appearing in SAR images of the surface of Venus are reliably detected and located automatically by a system designed to handle general visual pattern recognition problems.
Venus volcanism SAR images of Venus SAR imagery pa↗
Development of monitoring techniques by acoustical means for mechanical checkouts Final report, 15 May - 30 Sep. 1965
Automated pattern recognition devices using sonic signature data for detecting S3D and F-1 engine valve malfunctions
VALVE↗
Process identification using a mode learning machine.
Pattern recognition techniques applied to process identification, examining capability of mode learning machines for on-line identification of both linear and nonlinear processes
OPTIMAL CONTROL↗
Using spatial statistical methods to determine an effective minimum detectable activity - 15681
Instrument minimum detectable activity (MDA) is generally established by collecting multiple measurements over time of a single representative background location to estimate a probability distribution of instrument readings. A reading that is considered to be greater than would be expected from the background probability distribution (i.e., a low-probability event) is deemed to be sufficiently unlikely that it must be attributable to added radioactivity and is therefore used as the instrument MDA. An effective MDA can be determined from spatially distributed data that are much lower than the instrument MDA. This has been accomplished using a large number of spatially distributed data points (from a radiation survey) where patterns of elevated survey readings can be distinguished from background at levels. Given the variation of the instrument response and the relatively low regulatory action levels, it is vital to establish MDA thresholds that are well below corrective action levels. Processing of radiological survey data with the use of histogram binning and geo-statistics in enabling pattern recognition can be used to establish effective field MDAs that are much lower than instrument MDAs. (authors)
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Isotope Identification
The objective of this training modules is to examine the process of using gamma spectroscopy for radionuclide identification; apply pattern recognition to gamma spectra; identify methods of verifying energy calibration; and discuss potential causes of isotope misidentification.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The Application of Artificial Intelligence and Deep Learning to Visually Identify Micrometeoroid and Orbital Debris Impacts
Recent advancements in Artificial Intelligence (AI) have made Machine Learning (ML) techniques readily available for practical applications while using a fraction of time that was previously required. In particular, the use of Deep Learning (DL) algorithms has advanced the field of image and pattern recognition. With the use of Deep Learning algorithms, Micrometeoroid and Orbital Debris(MMOD) penetrations can be identified with high accuracy and give possibilities to new understandings of hypervelocity impacts.
Deep Learning↗
The ATLAS Fast TracKer system
The ATLAS Fast TracKer (FTK) was designed to provide full tracking for the ATLAS high-level trigger by using pattern recognition based on Associative Memory (AM) chips and fitting in high-speed field programmable gate arrays. The tracks found by the FTK are based on inputs from all modules of the pixel and silicon microstrip trackers. The as-built FTK system and components are described, as is the online software used to control them while running in the ATLAS data acquisition system. Also described is the simulation of the FTK hardware and the optimization of the AM pattern banks. An optimization for long-lived particles with large impact parameter values is included. A test of the FTK system with the data playback facility that allowed the FTK to be commissioned during the shutdown between Run 2 and Run 3 of the LHC is reported. The resulting tracks from part of the FTK system covering a limited $\eta$-$\phi$ region of the detector are compared with the output from the FTK simulation. It is shown that FTK performance is in good agreement with the simulation.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
First results from the spectral DCT trigger implemented in the Cyclone V Front-End Board used for a detection of very inclined showers in the Pierre Auger surface detector Engineering Array
The paper presents the first results from the trigger based on the Discrete Cosine Transform (DCT) operating in the new Front-End Boards with Cyclone V FPGA deployed in 8 test surface detectors in the Pierre Auger Engineering Array. The patterns of the ADC traces generated by very inclined showers were obtained from the Auger database and from the CORSIKA simulation package supported next by Offline reconstruction Auger platform which gives a predicted digitized signal profiles. Simulations for many variants of the initial angle of shower, initialization depth in the atmosphere, type of particle and its initial energy gave a boundary of the DCT coefficients used next for the on-line pattern recognition in the FPGA. Preliminary results have proven a right approach. We registered several showers triggered by the DCT for 120 MSps and 160 MSps. (authors)
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Potential Collaborative Research topics with Korea’s Agency for Defense Development
This presentation provides a high level summary of current research activities at the Los Alamos National Laboratory (LANL)-University of California Jacobs School of Engineering (UCSD) Engineering Institute that will be presented at Korea's Agency for Defense Development (ADD). These research activities are at the basic engineering science level with different level of maturity ranging from initial concepts to field proof-of-concept demonstrations. We believe that all of these activities are appropriate for collaborative research activities with ADD subject to approval by each institution. All the activities summarized herein have the common theme that they are multi-disciplinary in nature and typically involved the integration of high-fidelity predictive modeling, advanced sensing technologies and new development in information technology. These activities include: Wireless Sensor Systems, Swarming Robot sensor systems, Advanced signal processing (compressed sensing) and pattern recognition, Model Verification and Validation, Optimal/robust sensor system design, Haptic systems for large-scale data processing, Cyber-physical security for robots, Multi-source energy harvesting, Reliability-based approaches to damage prognosis, SHMTools software development, and Cyber-physical systems advanced study institute.
42 ENGINEERING↗
Org.Lcsim: Event Reconstruction in Java
Maximizing the physics performance of detectors being designed for the International Linear Collider, while remaining sensitive to cost constraints, requires a powerful, efficient, and flexible simulation, reconstruction and analysis environment to study the capabilities of a large number of different detector designs. The preparation of Letters Of Intent for the International Linear Collider involved the detailed study of dozens of detector options, layouts and readout technologies; the final physics benchmarking studies required the reconstruction and analysis of hundreds of millions of events. We describe the Java-based software toolkit (org.lcsim) which was used for full event reconstruction and analysis. The components are fully modular and are available for tasks from digitization of tracking detector signals through to cluster finding, pattern recognition, track-fitting, calorimeter clustering, individual particle reconstruction, jet-finding, and analysis. The detector is defined by the same xml input files used for the detector response simulation, ensuring the simulation and reconstruction geometries are always commensurate by construction. We discuss the architecture as well as the performance.
43 PARTICLE ACCELERATORS↗
Monitoring and Control of Chemical Composition of InGaN Layers During MOCVD
This Department of Energy SBIR Phase II grant targets the inefficiency of the epitaxial processes used to deposit wide band-gap semiconductor thin films that are an essential part of the next generation high-brightness LEDs for solid state lighting. Non-uniformity of the layers over a single wafer and non-repeatability over the wafers in a single batch, separate batches and separate reactors are some of the primary source of reduced manufacturing yield. The team performed extensive research, development and validation of an in-situ spectroscopic optical monitoring and epitaxial process control system. The system monitors the LED thin films as they grow on the individual wafers inside the epitaxial reactor, records the signal in a broad spectral range and processes the information to retrieve essential film quality parameters and their variation over a single wafer, all the wafers in a batch and multiple batches. A novel software algorithm is implemented, which performs real-time spectral pattern recognition and displays any abnormalities or differences in the deposition, which is used by the engineer to optimize the epitaxial process. The project resulted in the optimization of the LED film growth and improvement of its uniformity and repeatability, reducing the cost of the final LED devices.
36 MATERIALS SCIENCE↗
Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)
Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633–643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images. The proposed CAD system contains three main stages including segmentation of volume of interest (VOI), feature extraction and classification. A 3D Fuzzy segmentation technique has been implemented to extract the VOI. The shape and texture of angiogenesis in CTLM images are significant characteristics to differentiate malignancy or benign lesions. The 3D compactness features and 3D Grey Level Co-occurrence matrix (GLCM) have been extracted from VOIs. Multilayer perceptron neural network (MLPNN) pattern recognition has developed for classification of the normal and abnormal lesion in CTLM images. The performance of the proposed CAD system has been measured with different metrics including accuracy, sensitivity, and specificity and area under receiver operative characteristics (AROC), which are 95.2, 92.4, 98.1, and 0.98%, respectively.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
SU-F-J-72: A Clinical Usable Integrated Contouring Quality Evaluation Software for Radiotherapy
Purpose: To introduce the Auto Contour Evaluation (ACE) software, which is the clinical usable, user friendly, efficient and all-in-one toolbox for automatically identify common contouring errors in radiotherapy treatment planning using supervised machine learning techniques. Methods: ACE is developed with C# using Microsoft .Net framework and Windows Presentation Foundation (WPF) for elegant GUI design and smooth GUI transition animations through the integration of graphics engines and high dots per inch (DPI) settings on modern high resolution monitors. The industrial standard software design pattern, Model-View-ViewModel (MVVM) pattern, is chosen to be the major architecture of ACE for neat coding structure, deep modularization, easy maintainability and seamless communication with other clinical software. ACE consists of 1) a patient data importing module integrated with clinical patient database server, 2) a 2D DICOM image and RT structure simultaneously displaying module, 3) a 3D RT structure visualization module using Visualization Toolkit or VTK library and 4) a contour evaluation module using supervised pattern recognition algorithms to detect contouring errors and display detection results. ACE relies on supervised learning algorithms to handle all image processing and data processing jobs. Implementations of related algorithms are powered by Accord.Net scientific computing library for better efficiency and effectiveness. Results: ACE can take patient’s CT images and RT structures from commercial treatment planning software via direct user input or from patients’ database. All functionalities including 2D and 3D image visualization and RT contours error detection have been demonstrated with real clinical patient cases. Conclusion: ACE implements supervised learning algorithms and combines image processing and graphical visualization modules for RT contours verification. ACE has great potential for automated radiotherapy contouring quality verification. Structured with MVVM pattern, it is highly maintainable and extensible, and support smooth connections with other clinical software tools.
60 APPLIED LIFE SCIENCES↗
Prospects for X(3872) Detection at P-barANDA
Monte-Carlo simulations for a resonance scan of the charmonium-like state X(3872) at P-barANDA are performed. Final state radiation hadronic background reactions are taken into account. The signal reconstruction uses a realistic pattern recognition (track finder and track fitter) and electron/pion discrimination.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Line broadening interference for high-resolution nuclear magnetic resonance spectra under inhomogeneous magnetic fields
Nuclear magnetic resonance spectroscopy serves as an important tool for analyzing chemicals and biological metabolites. However, its performance is subject to the magnetic-field homogeneity. Under inhomogeneous fields, peaks are broadened to overlap each other, introducing difficulties for assignments. Here, we propose a method termed as line broadening interference (LBI) to provide high-resolution information under inhomogeneous magnetic fields by employing certain gradients in the indirect dimension to interfere the magnetic-field inhomogeneity. The conventional spectral-line broadening is thus interfered to be non-diagonal, avoiding the overlapping among adjacent resonances. Furthermore, an inhomogeneity correction algorithm is developed based on pattern recognition to recover the high-resolution information from LBI spectra. Theoretical deductions are performed to offer systematic and detailed analyses on the proposed method. Moreover, experiments are conducted to prove the feasibility of the proposed method for yielding high-resolution spectra in inhomogeneous magnetic fields.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
CHAMPION: Intelligent Hierarchical Reasoning Agents for Enhanced Decision Support
We describe the design and development of an advanced reasoning framework employing semantic technologies, organized within a hierarchy of computational reasoning agents that interpret domain specific information. Designed based on an inspirational metaphor of the pattern recognition functions performed by the human neocortex, the CHAMPION reasoning framework represents a new computational modeling approach that derives invariant knowledge representations through memory-prediction belief propagation processes that are driven by formal ontological language specification and semantic technologies. The CHAMPION framework shows promise for enhancing complex decision making in diverse problem domains including cyber security, nonproliferation and energy consumption analysis.
99 GENERAL AND MISCELLANEOUS↗