SEARCH · Search NASA
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.
The implementation of DDLTRN and DDLSIM programs on SEL-2 computer system is reported. These programs were tested with DDL descriptions of various complexity. An algorithm to synthesize the combinational logic using the cells available in the standard IC cell library was formulated. The algorithm is implemented as a FORTRAN program and a description of the program is given.
The present effectiveness and future promise of computational fluid dynamics (CFD) are discussed. The CFD ideal of computer-designed flight vehicles and the progress that has been made toward its achievement, expressed in terms of primary and secondary pacing items, are also addressed. The primary pacing items listed by Chapman - grid generation, turbulence modeling, solution methodology development, and mainframe computer methods and architecture - are modified and reviewed. Important secondary pacing items in CFD development are also examined: algorithm development, complex geometry definition, and predata and postata processing. In addition, user requirements placed on CFD computer codes are defined and some problems associated with the dissemination of CFD technology are identified.
Five coding techniques in the spatial and transform domains have been evaluated for SAR image compression: linear three-point predictor (LTPP), block truncation coding (BTC), microadaptive picture sequencing (MAPS), adaptive discrete cosine transform (ADCT), and adaptive Hadamard transform (AHT). These techniques have been tested with Seasat data. Both LTPP and BTC spatial domain coding techniques provide very good performance at rates of 1-2 bits/pixel. The two transform techniques, ADCT and AHT, demonstrate the capability to compress the SAR imagery to less than 0.5 bits/pixel without visible artifacts. Tradeoffs such as the rate distortion performance, the computational complexity, the algorithm flexibility, and the controllability of compression ratios are also discussed.
Convolutional codes have played and will play a key role in the downlink telemetry systems on many NASA deep-space probes, including Voyager, Magellan, and Galileo. One of the chief difficulties associated with the use of convolutional codes, however, is the notorious difficulty of analyzing them. Given a convolutional code as specified, say, by its generator polynomials, it is no easy matter to say how well that code will perform on a given noisy channel. The usual first step in such an analysis is to computer the code's free distance; this can be done with an algorithm whose complexity is exponential in the code's constraint length. The second step is often to calculate the transfer function in one, two, or three variables, or at least a few terms in its power series expansion. This step is quite hard, and for many codes of relatively short constraint lengths, it can be intractable. However, a large class of convolutional codes were discovered for which the free distance can be computed by inspection, and for which there is a closed-form expression for the three-variable transfer function. Although for large constraint lengths, these codes have relatively low rates, they are nevertheless interesting and potentially useful. Furthermore, the ideas developed here to analyze these specialized codes may well extend to a much larger class.
A ray tracing analytical tool has been developed for the simulation of spectral radiation exchange in complex systems. Algorithms are used to account for heat source spectral energy, surface directional radiation properties, and surface spectral absorptivity properties. This tool has been used to calculate the effective solar absorptivity of the geostationary operational environmental satellites (GOES) scan mirror in the calibration position. The development and design of Sounder and Imager instruments on board GOES is reviewed and the problem of calculating the effective solar absorptivity associated with the GOES hexagonal cell configuration is presented. The analytical methodology based on the Monte Carlo ray tracing technique is described and results are presented and verified by experimental measurements for selected solar incidence angles.
The emerging digital audio and video compression technology brings both an opportunity and a new challenge to IC design. The pervasive application of compression technology to consumer electronics will require high volume, low cost IC's and fast time to market of the prototypes and production units. At the same time, the algorithms used in the compression technology result in complex VLSI IC's. The conflicting challenges of algorithm complexity, low cost, and fast time to market have an impact on device architecture and design methodology. The work presented in this paper is about the design of a dedicated, high precision, Motion Picture Expert Group (MPEG) audio decoder.
The need for an adaptive active control system was realized, since a wind tunnel is subjected to variations in air velocity, temperature, air turbulence, and some other factors such as nonlinearity. Among many adaptive algorithms, the Least Mean Squares (LMS) algorithm, which is the simplest one, has been used in an Active Noise Control (ANC) system by some researchers. However, Eriksson's results, Eriksson (1985), showed instability in the ANC system with an ER filter for random noise input. The Restricted Least Squares (RLS) algorithm, although computationally more complex than the LMS algorithm, has better convergence and stability properties. The ANC system in the present work was simulated by using an FIR filter with an RLS algorithm for different inputs and for a number of plant models. Simulation results for the ANC system with acoustic feedback showed better robustness when used with the RLS algorithm than with the LMS algorithm for all types of inputs. Overall attenuation in the frequency domain was better in the case of the RLS adaptive algorithm. Simulation results with a more realistic plant model and an RLS adaptive algorithm showed a slower convergence rate than the case with an acoustic plant as a delay plant. However, the attenuation properties were satisfactory for the simulated system with the modified plant. The effect of filter length on the rate of convergence and attenuation was studied. It was found that the rate of convergence decreases with increase in filter length, whereas the attenuation increases with increase in filter length. The final design of the ANC system was simulated and found to have a reasonable convergence rate and good attenuation properties for an input containing discrete frequencies and random noise.
Between 1958-1995 a significant number of small meteorological rocketsondes were launched by United States (US) agencies from as many as 30 sites to as few as 3-4 sites in 1995. Stratospheric temperature trends were derived for many of the sites for the period 1969-1995. Similar long-term trends also were derived using rocketsondes launched from sites of the Former Soviet Union (FSU). The advantage of these two particular sets of rocket temperature measurements is the internal consistency of the data. All measurements were made with the same instrument, i.e., Datasonde in the case of the US sites and the M100B in the case of the FSU sites. Data from each instrument type were processed using its unique reduction method. Thus, all data were processed in the same manner including the method of applying corrections (necessary because of thermal heating of the thermistor from the high fall velocities experienced and from radiation effects). Straight-line least squares fit to the data made to monthly-mean measurements gave a downward change of 2-3 K per decade. A more complex fitting algorithm would have resulted in finer results but the straight-line fit was adequate for the analysis presented. Trend data are presented for 50 km, 40 km, and 25 km altitude levels. Trends for the US and FSU sites are quite similar. The 25km (50-hPa) level data are compared with radiosonde temperatures. Temperature trends over the 25-year period is different at each of the sites and not always in the same direction.
This paper discuss the problem of increased programming time for intrinsic evolvable hardware (EHW) as the complexity of the circuit grows. We develop equations for the size of the population, n, and the number of generations required for the population to converge, ngen, based on L, the length of the programming string. We show that the processing time of the computer becomes negligible for intrinsic EHW since the selection/crossover/mutation steps are only done once per generation, suggesting there is room for use of more complex evolutionary algorithms m intrinsic EHW. F i y , we review the state of the practice and discuss the notion of a system design approach for intrinsic EHW.
Explore the source record for details and available documents.
A low-complexity lossless algorithm for compression of multispectral data has been developed that takes into account pushbroom-type multispectral imagers properties in order to make the file compression more effective.
A three-channel data acquisition system was developed for the NASA Multi-Frequency Radar (MFR) system. The system is based on a commercial-off-the-shelf (COTS) industrial PC (personal computer) and two dual-channel 14-bit digital receiver cards. The decimated complex envelope representations of the three radar signals are passed to the host PC via the PCI bus, and then processed in parallel by multiple cores of the PC CPU (central processing unit). The innovation is this parallelization of the radar data processing using multiple cores of a standard COTS multi-core CPU. The data processing portion of the data acquisition software was built using autonomous program modules or threads, which can run simultaneously on different cores. A master program module calculates the optimal number of processing threads, launches them, and continually supplies each with data. The benefit of this new parallel software architecture is that COTS PCs can be used to implement increasingly complex processing algorithms on an increasing number of radar range gates and data rates. As new PCs become available with higher numbers of CPU cores, the software will automatically utilize the additional computational capacity.
Over the past 30 years most climate models have grown from relatively simple representations of a few atmospheric processes to a complex multi-disciplinary system. Computer infrastructure over that period has gone from punch card mainframes to modem parallel clusters. Model implementations have become complex, brittle, and increasingly difficult to extend and maintain. Existing verification processes for model implementations rely almost exclusively upon some combination of detailed analysis of output from full climate simulations and system-level regression tests. In additional to being quite costly in terms of developer time and computing resources, these testing methodologies are limited in terms of the types of defects that can be detected, isolated and diagnosed. Mitigating these weaknesses of coarse-grained testing with finer-grained "unit" tests has been perceived as cumbersome and counter-productive. In the commercial software sector, recent advances in tools and methodology have led to a renaissance for systematic fine-grained testing. We discuss the availability of analogous tools for scientific software and examine benefits that similar testing methodologies could bring to climate modeling software. We describe the unique challenges faced when testing complex numerical algorithms and suggest techniques to minimize and/or eliminate the difficulties.
Topics covered include: Real-Time, High-Frequency QRS Electrocardiograph; Software for Improved Extraction of Data From Tape Storage; Radio System for Locating Emergency Workers; Software for Displaying High-Frequency Test Data; Capacitor-Chain Successive-Approximation ADC; Simpler Alternative to an Optimum FQPSK-B Viterbi Receiver; Multilayer Patch Antenna Surrounded by a Metallic Wall; Software To Secure Distributed Propulsion Simulations; Explicit Pore Pressure Material Model in Carbon-Cloth Phenolic; Meshed-Pumpkin Super-Pressure Balloon Design; Corrosion Inhibitors as Penetrant Dyes for Radiography; Transparent Metal-Salt-Filled Polymeric Radiation Shields; Lightweight Energy Absorbers for Blast Containers; Brush-Wheel Samplers for Planetary Exploration; Dry Process for Making Polyimide/ Carbon-and-Boron-Fiber Tape; Relatively Inexpensive Rapid Prototyping of Small Parts; Magnetic Field Would Reduce Electron Backstreaming in Ion Thrusters; Alternative Electrochemical Systems for Ozonation of Water; Interferometer for Measuring Displacement to Within 20 pm; UV-Enhanced IR Raman System for Identifying Biohazards; Prognostics Methodology for Complex Systems; Algorithms for Haptic Rendering of 3D Objects; Modeling and Control of Aerothermoelastic Effects; Processing Digital Imagery to Enhance Perceptions of Realism; Analysis of Designs of Space Laboratories; Shields for Enhanced Protection Against High-Speed Debris; Study of Dislocation-Ordered In(x)Ga(1-x)As/GaAs Quantum Dots; and Tilt-Sensitivity Analysis for Space Telescopes.
The Quantum Adiabatic Algorithm (QAA) has been proposed as a mechanism for efficiently solving optimization problems on a quantum computer. Since adiabatic computation is analog in nature and does not require the design and use of quantum gates, it can be thought of as a simpler and perhaps more profound method for performing quantum computations that might also be easier to implement experimentally. While these features have generated substantial research in QAA, to date there is still a lack of solid evidence that the algorithm can outperform classical optimization algorithms.
A heterogeneous set of system components monitored by a varied suite of sensors and a particle-filtering (PF) framework, with the power and the flexibility to adapt to the different diagnostic and prognostic needs, has been developed. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem in order to explicitly represent and manage uncertainties in state estimation and remaining life estimation. Current state-of-the-art prognostic health management (PHM) systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to a loss in functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become for a number of reasons somewhat ungainly for successful deployment, and efficient distributed architectures can be more beneficial. The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operations based on user-defined thresholds and rules. The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver beside an embedded processing element). The CE runs in either a diagnostic or prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is formed that then carries out prognostics and fault mitigation by efficient distribution of the tasks. It should be noted that the CEs are expected not to suspend their previous tasks in the prognostic mode. When the prognostics task is over, and after appropriate actions have been taken, all CEs return to their original default configuration. Wireless technology-based implementation would ensure more flexibility in terms of sensor placement. It would also allow more sensors to be deployed because the overhead related to weights of wired systems is not present. Distributed architectures are furthermore generally robust with regard to recovery from node failures.
Advanced capabilities planned for the next generation of aircraft, including those that will operate within the Next Generation Air Transportation System (NextGen), will necessarily include complex new algorithms and non-traditional software elements. These aircraft will likely incorporate adaptive control algorithms that will provide enhanced safety, autonomy, and robustness during adverse conditions. Unmanned aircraft will operate alongside manned aircraft in the National Airspace (NAS), with intelligent software performing the high-level decision-making functions normally performed by human pilots. Even human-piloted aircraft will necessarily include more autonomy. However, there are serious barriers to the deployment of new capabilities, especially for those based upon software including adaptive control (AC) and artificial intelligence (AI) algorithms. Current civil aviation certification processes are based on the idea that the correct behavior of a system must be completely specified and verified prior to operation. This report by Rockwell Collins and SIFT documents our comprehensive study of the state of the art in intelligent and adaptive algorithms for the civil aviation domain, categorizing the approaches used and identifying gaps and challenges associated with certification of each approach.
We are developing onboard processor (OBP) technology to streamline data acquisition on-demand and explore the potential of the L-band SAR instrument onboard the proposed DESDynI mission and UAVSAR for rapid response applications. The technology would enable the observation and use of surface change data over rapidly evolving natural hazards, both as an aid to scientific understanding and to provide timely data to agencies responsible for the management and mitigation of natural disasters. We are adapting complex science algorithms for surface water extent to detect flooding, snow/water/ice classification to assist in transportation/ shipping forecasts, and repeat-pass change detection to detect disturbances. We are near completion of the development of a custom FPGA board to meet the specific memory and processing needs of L-band SAR processor algorithms and high speed interfaces to reformat and route raw radar data to/from the FPGA processor board. We have also developed a high fidelity Matlab model of the SAR processor that is modularized and parameterized for ease to prototype various SAR processor algorithms targeted for the FPGA. We will be testing the OBP and rapid response algorithms with UAVSAR data to determine the fidelity of the products.