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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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NASA NTRS2005

Spoken Language Processing in the Clarissa Procedure Browser

Clarissa, an experimental voice enabled procedure browser that has recently been deployed on the International Space Station, is as far as we know the first spoken dialog system in space. We describe the objectives of the Clarissa project and the system's architecture. In particular, we focus on three key problems: grammar-based speech recognition using the Regulus toolkit; methods for open mic speech recognition; and robust side-effect free dialogue management for handling undos, corrections and confirmations. We first describe the grammar-based recogniser we have build using Regulus, and report experiments where we compare it against a class N-gram recogniser trained off the same 3297 utterance dataset. We obtained a 15% relative improvement in WER and a 37% improvement in semantic error rate. The grammar-based recogniser moreover outperforms the class N-gram version for utterances of all lengths from 1 to 9 words inclusive. The central problem in building an open-mic speech recognition system is being able to distinguish between commands directed at the system, and other material (cross-talk), which should be rejected. Most spoken dialogue systems make the accept/reject decision by applying a threshold to the recognition confidence score. NASA shows how a simple and general method, based on standard approaches to document classification using Support Vector Machines, can give substantially better performance, and report experiments showing a relative reduction in the task-level error rate by about 25% compared to the baseline confidence threshold method. Finally, we describe a general side-effect free dialogue management architecture that we have implemented in Clarissa, which extends the "update semantics'' framework by including task as well as dialogue information in the information state. We show that this enables elegant treatments of several dialogue management problems, including corrections, confirmations, querying of the environment, and regression testing.

Rayner, M.↗

NASA NTRS1971

Automatic recognition of vector and parallel operations in a higher level language

A compiler for recognizing statements of a FORTRAN program which are suited for fast execution on a parallel or pipeline machine such as Illiac-4, Star or ASC is described. The technique employs interval analysis to provide flow information to the vector/parallel recognizer. Where profitable the compiler changes scalar variables to subscripted variables. The output of the compiler is an extension to FORTRAN which shows parallel and vector operations explicitly.

Schneck, P. B.↗

NASA NTRS1982

Advances in information extraction techniques

Sundry recent developments are presented which show some potential for affecting the automatic extraction of information from remotely sensed data. Pattern representations more abstract than Euclidean vector spaces offer some hope of unifying structural and decision theoretical approaches. The estimation of expected classification error rates is becoming more sophisticated and rigorous, but useful finite-sample results for nonparametric distributions appear unobtainable. Focus on computational complexity allows comparison of algorithms, while software engineering techniques reduce the effort necessary to develop and maintain complex image processing systems. Advances in computer systems architecture, commercial database technology, and man-machine communications should be closely monitored by the remote sensing community. A NASA-sponsored recommendation for research directions in mathematical pattern recognition are offered.

Nagy, G.↗

NASA NTRS1986

An intelligent object recognizer and classification system for astronomical use

An account is given of an image-processing system based on AI concepts, which allows input images produced by the CCT/Transit Instrument to be compared with a standard-object hierarchylike network of prototypes presented within the computer as 'frames'. Each frame contains information concerning either a standard object or the links among such objects. This method, by comparison to conventional, statistically-based pattern recognition systems, classifies data as an astronomer would and thereby lends credibility to its conclusions; it also furnishes a natural avenue for the machine's serendipitous discovery of new classes of objects.

Bernat, Andrew P.↗

NASA NTRS1994

The use of interactive computer vision and robot hand controllers for enhancing manufacturing safety

Current available robotic systems provide limited support for CAD-based model-driven visualization, sensing algorithm development and integration, and automated graphical planning systems. This paper describes ongoing work which provides the functionality necessary to apply advanced robotics to automated manufacturing and assembly operations. An interface has been built which incorporates 6-DOF tactile manipulation, displays for three dimensional graphical models, and automated tracking functions which depend on automated machine vision. A set of tools for single and multiple focal plane sensor image processing and understanding has been demonstrated which utilizes object recognition models. The resulting tool will enable sensing and planning from computationally simple graphical objects. A synergistic interplay between human and operator vision is created from programmable feedback received from the controller. This approach can be used as the basis for implementing enhanced safety in automated robotics manufacturing, assembly, repair and inspection tasks in both ground and space applications. Thus, an interactive capability has been developed to match the modeled environment to the real task environment for safe and predictable task execution.

Marzwell, Neville I.↗

NASA NTRS2022

Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition

Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.

Chow, Edward↗

NASA NTRS1993

A pattern recognition system for locating small volvanoes in Magellan SAR images of Venus

The Magellan data set constitutes an example of the large volumes of data that today's instruments can collect, providing more detail of Venus than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations put together. However, data analysis technology has not kept pace with data collection and storage technology. Due to the sheer size of the data, complete and comprehensive scientific analysis of such large volumes of image data is no longer feasible without the use of computational aids. Our progress towards developing a pattern recognition system for aiding in the detection and cataloging of small-scale natural features in large collections of images is reported. Combining classical image processing, machine learning, and a graphical user interface, the detection of the 'small-shield' volcanoes (less than 15km in diameter) that constitute the most abundant visible geologic feature in the more that 30,000 synthetic aperture radar (SAR) images of the surface of Venus are initially targeted. Our eventual goal is to provide a general, trainable tool for locating small-scale features where scientists specify what to look for simply by providing examples and attributes of interest to measure. This contrasts with the traditional approach of developing problem specific programs for detecting Specific patterns. The approach and initial results in the specific context of locating small volcanoes is reported. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanoes visible in the Magellan data. Identifying and studying these volcanoes is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is forbiddingly time-consuming. Hence, the development of techniques to partially automate this task were undertaken. The primary constraints for this particular problem are that the method must be reasonably robust and fast. Unlike most geological features, the small volcanoes of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include (1) a circular planimetric outline, (2) known diameter frequency distribution from preliminary studies, (3) a limited number of basic morphological shapes, and (4) the common occurrence of a single, circular summit pit at the center of the edifice.

Burl, M. C.↗

NASA NTRS2002

Software for Partly Automated Recognition of Targets

The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user's tendencies while the user is selecting targets and to increase the user's productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.

Opitz, David↗

NASA NTRS2003

Software for Partly Automated Recognition of Targets

The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user s tendencies while the user is selecting targets and to increase the user s productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.

Opitz, David↗

NASA NTRS1976

System for analysis of LANDSAT agricultural data: Automatic computer-assisted proportion estimation of local areas

The author has identified the following significant results. A conceptual man machine system framework was created for a large scale agricultural remote sensing system. The system is based on and can grow out of the local recognition mode of LACIE, through a gradual transition wherein computer support functions supplement and replace AI functions. Local proportion estimation functions are broken into two broad classes: (1) organization of the data within the sample segment; and (2) identification of the fields or groups of fields in the sample segment.

Nalepka, R. F.↗

NASA NTRS2001

Cyrano "Nose" The Smell of Success

Cyrano Sciences, Inc. has commercialized a simple, accurate, non- invasive tool that enables "machines to smell". The Cyranose 320 is used for quality control purposes in the food and chemical industries. Using a sensor array and onboard pattern recognition algorithms, the lightweight, portable device works by exposing an array of polymer composite sensors to the chemical components in a vapor. When the sensors come in contact with the vapor, the polymer expands like a sponge, changing the resistance of the composites. The change in resistance is measured, and from that measurement, the presence of a pre-trained substance is determined with a quick and accurate diagnosis. This real-time, portable device enables food companies to spot test raw materials for batch-to-batch consistency, spoilage, or contamination. The Cyranose 320 is also used by chemical and petrochemical companies for quick assessment of the chemical status associated with various industrial processes. Profiling a chemical environment in a hazardous materials situation allows emergency crews to accurately select fire retardants, containment strategies, and protective gear. Future applications for the Cyranose 320 are fast growing and other uses of this technology are on the horizon.

Source record↗

NASA NTRS1989

Image understanding and the man-machine interface II; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

Image understanding concepts and models, image understanding systems and applications, advanced digital processors and software tools, and advanced man-machine interfaces are among the topics discussed. Particular papers are presented on such topics as neural networks for computer vision, object-based segmentation and color recognition in multispectral images, the application of image algebra to image measurement and feature extraction, and the integration of modeling and graphics to create an infrared signal processing test bed.

Barrett, Eamon B.↗

NASA NTRS2010

Feature Extraction and Selection Strategies for Automated Target Recognition

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

computer vision↗

NASA NTRS1986

Incipient fault detection study for advanced spacecraft systems

A feasibility study to investigate the application of vibration monitoring to the rotating machinery of planned NASA advanced spacecraft components is described. Factors investigated include: (1) special problems associated with small, high RPM machines; (2) application across multiple component types; (3) microgravity; (4) multiple fault types; (5) eight different analysis techniques including signature analysis, high frequency demodulation, cepstrum, clustering, amplitude analysis, and pattern recognition are compared; and (6) small sample statistical analysis is used to compare performance by computation of probability of detection and false alarm for an ensemble of repeated baseline and faulted tests. Both detection and classification performance are quantified. Vibration monitoring is shown to be an effective means of detecting the most important problem types for small, high RPM fans and pumps typical of those planned for the advanced spacecraft. A preliminary monitoring system design and implementation plan is presented.

Milner, G. Martin↗

NASA NTRS1990

Automatic Speech Recognition in Air Traffic Control: a Human Factors Perspective

The introduction of Automatic Speech Recognition (ASR) technology into the Air Traffic Control (ATC) system has the potential to improve overall safety and efficiency. However, because ASR technology is inherently a part of the man-machine interface between the user and the system, the human factors issues involved must be addressed. Here, some of the human factors problems are identified and related methods of investigation are presented. Research at M.I.T.'s Flight Transportation Laboratory is being conducted from a human factors perspective, focusing on intelligent parser design, presentation of feedback, error correction strategy design, and optimal choice of input modalities.

Karlsson, Joakim↗

NASA NTRS2012

Optimization of Support Vector Machine (SVM) for Object Classification

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

support vector machice (SVM)↗

NASA NTRS1990

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

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

Szu, Harold H.↗

NASA NTRS1980

What's Cookin' With Helicopter Microwave Landing Systems?

This article describes a joint effort of the FAA, NASA and the Helicopter Industry to establish a data base for (Microwave Landing Systems) MLS approaches, and to aid the FAA in developing the MLS Terminal Instrument Procedures (TERPS) criteria for rotary-wing IFR operations. The latter is of particular importance for it appears to signal sincere official interest in filling in the blanks in Chapter 11 of TERPS -- that portion set aside for helicopter IFR operations and recognition of the unique capabilities of the helicopter in the instrument environment. One of the program objectives is to find out what a sample of pilots can do with the MLS in a minimum machine.

Taylor, Richard L.↗