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At least 919 records · Page 51

Squeezing Every Last 'Bit' of Information from Enceladus Mass Spectrometry

Potential opportunities to return to Enceladus in Discovery and Flagship class missions inspire development of next-generation instruments and creative approaches to sample collection, sample analysis, and data analysis and transmission strategies. Mass spectrometers (MS) are ideally suited to future Enceladus missions due to their analytical power in identifying a range of molecular and ionic compositions – including complex organics – and potentially astrobiologically-important features such as isotope ratios, chirality, and enantiomeric excess. However, long communication delays from Enceladus and limited bandwidth limits the data transmission from these higher-data-volume instruments, likely delaying mission-related response to new data. We explore the utility of data science and machine learning (ML) on isotope ratio (IR)MS data collected from laboratory analogs of Enceladus to: 1) process data quickly for rapid ground-based analyses, 2) understand if compositional and biosignature information could be extracted from IRMS data, and 3) evaluate whether onboard ML techniques could improve sample analysis, cadence, and transmission prioritization. Laboratory analogs analyzed isotopes of volatile CO2 that interacted with seawaters of varying composition, and include both abiotic and biotic (microbially-influenced) experiments. Enceladus’s alkaline oceans promote speciation of carbon into multiple forms (e.g., H2CO3 / CO2, HCO3-, and CO32-), each of which could be isotopically fractionated by abiotic or biotic reactions. Large (>2‰) changes in carbon isotopes (δ13C) are observed from some biotic experiments inoculated with complex microbial ecosystems relative to the abiotic seawaters. ML training and classification suggests that microbial samples can be distinguished from abiotic samples, yet that a broad range of microbial experiments are necessary to train ML models to cover a range of complexities including disequilibria, and isotopic and compositional fractionation.

geochemistry↗

Making a Metal-Lined Composite-Overwrapped Pressure Vessel

process has been devised for the fabrication of a pressure vessel that comprises a composite-material (matrix/fiber) shell with a metal liner on its inner surface. The use of the composite material makes it possible for the tank to be strong enough to withstand the anticipated operating pressure and yet weigh less than does an equivalent all-metal tank. The metal liner is used as a barrier against permeation: In the absence of such a barrier, the pressurized gas in the tank could leak by diffusing through the composite-material shell. The figure depicts workpieces at four key stages in the process, which consists of the following steps: 1. A mandrel that defines the size and shape of the pressure vessel is made by either molding or machining a piece of tooling wax. 2. Silver paint is applied to the surface of the mandrel to make it electrically conductive. 3. The ends of the mandrel are fitted with metal bosses. 4. The mandrel is put into a plating bath, wherein the metal liner is electrodeposited. Depending on the applications, the liner metal could be copper, nickel, gold, or an alloy. Typical liner thicknesses range from 1 to 10 mils (0.025 to 0.25 mm). 5. The wax is melted from within, leaving the thin metal liner. 6. A hollow shaft that includes holes and fittings through which the liner can be pressurized is sealed to both ends of the liner. The liner is pressurized to stiffen (and hence stabilize) it for the next step. 7. The pressurized liner is placed in a filament-winding machine, which is then operated to cover the liner with multiple layers of an uncured graphite-fiber/epoxy-matrix or other suitable composite material. 8. The composite-overwrapped liner is cured in an oven. 9. The pressure is relieved and the shaft is removed. The tank is then ready for use. The process as described above accommodates variations: a) The mandrel could be made of a wax that melts at a higher temperature and not removed until the tank is cured in the oven. b) The tank need not be cylindrical or axisymmetric, as long as the filament-winding machine can accommodate the chosen shape. c) Shallow grooves could be formed on the surface of the mandrel to give the liner a bellows-like character for reinforcement and/or to accommodate expansion and contraction.

DeLay, Tom↗

Time-sharing switch for vacuum brazing

Switching unit changes power and cooling-water connections between two vacuum-brazing machines. It allows both units to be powered by single radio-frequency (RF) generator. One machine can be used for brazing while bell jar of other is being evacuated (20 minute process) in preparation for brazing or is being cooled after brazing (10 minute process).

Stein, J. A.↗

Documentation for the machine-readable version of a deep objective-prism survey for large Magellanic cloud members

This catalog contains 1273 proven or probable Large Magellanic Cloud (LMC) members, as found on deep objective-prism plates taken with the Curtis Schmidt telescope at Cerro Tololo Inter-American Observatory in Chile. The stars are generally brighter than about photographic magnitude 14. Approximate spectral types were determined by examination of the 580 A/mm objective-prism spectra; approximate 1975 positions were obtained by measuring relative to the 1975 coordinate grids on the Uppsala-Mount Stromlo Atlas of the LMC (Gascoigne and Westerlund 1961), and approximate photographic magnitudes were determined by averaging image density measures from the plates and image-diameter measures on the 'B' charts. The machine-readable version of the LMC survey catalog is described to enable users to read and process the tape file without problems or guesswork.

Warren, W. H., Jr.↗

An overview of computer-based natural language processing

Computer based Natural Language Processing (NLP) is the key to enabling humans and their computer based creations to interact with machines in natural language (like English, Japanese, German, etc., in contrast to formal computer languages). The doors that such an achievement can open have made this a major research area in Artificial Intelligence and Computational Linguistics. Commercial natural language interfaces to computers have recently entered the market and future looks bright for other applications as well. This report reviews the basic approaches to such systems, the techniques utilized, applications, the state of the art of the technology, issues and research requirements, the major participants and finally, future trends and expectations. It is anticipated that this report will prove useful to engineering and research managers, potential users, and others who will be affected by this field as it unfolds.

Gevarter, W. B.↗

Activities of the Remote Sensing Information Sciences Research Group

Topics on the analysis and processing of remotely sensed data in the areas of vegetation analysis and modelling, georeferenced information systems, machine assisted information extraction from image data, and artificial intelligence are investigated. Discussions on support field data and specific applications of the proposed technologies are also included.

John E Estes↗

PISCES: An environment for parallel scientific computation

The parallel implementation of scientific computing environment (PISCES) is a project to provide high-level programming environments for parallel MIMD computers. Pisces 1, the first of these environments, is a FORTRAN 77 based environment which runs under the UNIX operating system. The Pisces 1 user programs in Pisces FORTRAN, an extension of FORTRAN 77 for parallel processing. The major emphasis in the Pisces 1 design is in providing a carefully specified virtual machine that defines the run-time environment within which Pisces FORTRAN programs are executed. Each implementation then provides the same virtual machine, regardless of differences in the underlying architecture. The design is intended to be portable to a variety of architectures. Currently Pisces 1 is implemented on a network of Apollo workstations and on a DEC VAX uniprocessor via simulation of the task level parallelism. An implementation for the Flexible Computing Corp. FLEX/32 is under construction. An introduction to the Pisces 1 virtual computer and the FORTRAN 77 extensions is presented. An example of an algorithm for the iterative solution of a system of equations is given. The most notable features of the design are the provision for several granularities of parallelism in programs and the provision of a window mechanism for distributed access to large arrays of data.

Pratt, T. W.↗

Close to real life

NASA's Numerical Aerodynamic Simulation (NAS) facility for CFD modeling of highly complex aerodynamic flows employs as its basic hardware two Cray-2s, an ETA-10 Model Q, an Amdahl 5880 mainframe computer that furnishes both support processing and access to 300 Gbytes of disk storage, several minicomputers and superminicomputers, and a Thinking Machines 16,000-device 'connection machine' processor. NAS, which was the first supercomputer facility to standardize operating-system and communication software on all processors, has done important Space Shuttle aerodynamics simulations and will be critical to the configurational refinement of the National Aerospace Plane and its intergrated powerplant, which will involve complex, high temperature reactive gasdynamic computations.

Peterson, Victor L.↗

Automated implementation of rule-based expert systems with neural networks for time-critical applications

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Ramamoorthy, P. A.↗

Experimental Semiautonomous Vehicle

Semiautonomous rover vehicle serves as testbed for evaluation of navigation and obstacle-avoidance techniques. Designed to traverse variety of terrains. Concepts developed applicable to robots for service in dangerous environments as well as to robots for exploration of remote planets. Called Robby, vehicle 4 m long and 2 m wide, with six 1-m-diameter wheels. Mass of 1,200 kg and surmounts obstacles as large as 1 1/2 m. Optimized for development of machine-vision-based strategies and equipped with complement of vision and direction sensors and image-processing computers. Front and rear cabs steer and roll with respect to centerline of vehicle. Vehicle also pivots about central axle, so wheels comply with almost any terrain.

Wilcox, Brian H.↗

A distributed Petri Net controller for a dual arm testbed

This thesis describes the design and functionality of a Distributed Petri Net Controller (DPNC). The controller runs under X Windows to provide a graphical interface. The DPNC allows users to distribute a Petri Net across several host computers linked together via a TCP/IP interface. A sub-net executes on each host, interacting with the other sub-nets by passing a token vector from host to host. One host has a command window which monitors and controls the distributed controller. The input to the DPNC is a net definition file generated by Great SPN. Thus, a net may be designed, analyzed and verified using this package before implementation. The net is distributed to the hosts by tagging transitions that are host-critical with the appropriate host number. The controller will then distribute the remaining places and transitions to the hosts by generating the local nets, the local marking vectors and the global marking vector. Each transition can have one or more preconditions which must be fulfilled before the transition can fire, as well as one or more post-processes to be executed after the transition fires. These implement the actual input/output to the environment (machines, signals, etc.). The DPNC may also be used to simulate a Great SPN net since stochastic and deterministic firing rates are implemented in the controller for timed transitions.

Bjanes, Atle↗

Progress in integrated-circuit horn antennas for receiver applications. Part 1: Antenna design

The purpose of this work is to present a systematic method for the design of multimode quasi-integrated horn antennas. The design methodology is based on the Gaussian beam approach and the structures are optimized for achieving maximum fundamental Gaussian coupling efficiency. For this purpose, a hybrid technique is employed in which the integrated part of the antennas is treated using full-wave analysis, whereas the machined part is treated using an approximate method. This results in a simple and efficient design process. The developed design procedure has been applied for the design of a 20, a 23, and a 25 dB quasi-integrated horn antennas, all with a Gaussian coupling efficiency exceeding 97 percent. The designed antennas have been tested and characterized using both full-wave analysis and 90 GHz/370 GHz measurements.

Eleftheriades, George V.↗

Document analysis with neural net circuits

Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.

Graf, Hans Peter↗

Balanced-Rotating-Spray Tank-And-Pipe-Cleaning System

Spray head translates and rotates to clean entire inner surface of tank or pipe. Cleansing effected by three laterally balanced gas/liquid jets from spray head that rotates about longitudinal axis. Uses much less liquid. Cleaning process in system relies on mechanical action of jets instead of contaminant dissolution. Eliminates very difficult machining needed to make multiple converging/diverging nozzles within one spray head. Makes nozzle much smaller. Basic two-phase-flow, supersonic-nozzle design applied to other spray systems for interior or exterior cleaning.

Thaxton, Eric A.↗

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Dominguez, Jesus A.↗

Learning to Control Advanced Life Support Systems

Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for advanced life support.

Subramanian, Devika↗

NASA Tech Briefs, March 2009

Topics covered include: Improved Instrument for Detecting Water and Ice in Soil; Real-Time Detection of Dust Devils from Pressure Readings; Determining Surface Roughness in Urban Areas Using Lidar Data; DSN Data Visualization Suite; Hamming and Accumulator Codes Concatenated with MPSK or QAM; Wide-Angle-Scanning Reflectarray Antennas Actuated by MEMS; Biasable Subharmonic Membrane Mixer for 520 to 600 GHz; Hardware Implementation of Serially Concatenated PPM Decoder; Symbolic Processing Combined with Model-Based Reasoning; Presentation Extensions of the SOAP; Spreadsheets for Analyzing and Optimizing Space Missions; Processing Ocean Images to Detect Large Drift Nets; Alternative Packaging for Back-Illuminated Imagers; Diamond Machining of an Off-Axis Biconic Aspherical Mirror; Laser Ablation Increases PEM/Catalyst Interfacial Area; Damage Detection and Self-Repair in Inflatable/Deployable Structures; Polyimide/Glass Composite High-Temperature Insulation; Nanocomposite Strain Gauges Having Small TCRs; Quick-Connect Windowed Non-Stick Penetrator Tips for Rapid Sampling; Modeling Unsteady Cavitation and Dynamic Loads in Turbopumps; Continuous-Flow System Produces Medical-Grade Water; Discrimination of Spore-Forming Bacilli Using spoIVA; nBn Infrared Detector Containing Graded Absorption Layer; Atomic References for Measuring Small Accelerations; Ultra-Broad-Band Optical Parametric Amplifier or Oscillator; Particle-Image Velocimeter Having Large Depth of Field; Enhancing SERS by Means of Supramolecular Charge Transfer; Improving 3D Wavelet-Based Compression of Hyperspectral Images; Improved Signal Chains for Readout of CMOS Imagers; SOI CMOS Imager with Suppression of Cross-Talk; Error-Rate Bounds for Coded PPM on a Poisson Channel; Biomorphic Multi-Agent Architecture for Persistent Computing; and Using Covariance Analysis to Assess Pointing Performance.

Source record↗

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

Huang, Yiteng↗