Limb Sounding of the Lunar Limb with a Fourier Transform Spectrometer
No abstract available
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.
No abstract available
The mid-superconducting critical temperature (T(sub c) approximately 39 K) of the simple binary, intermetallic MgB, [1] makes it a very good candidate for the development of the next generation of electrooptical devices (e.g. [2]). In particular, recent advances in thin film deposition teclmiques to attain higb quality polycrystalline thin film MgB, deposited on SiN-Si substrates, with T(sub c) approximately 38K [3] coupled with the low voltage noise performance of the film [4] makes it higbly desirable for the development of moderately cooled bolometer arrays for integration into future space-bourne far infra-red (FIR) spectrometers and thermal mappers for studying the outer planets, their icy moons and other moons of interest in the 17-250 micrometer spectral wavelength range. Presently, commercially available pyroelectric detectors operating at 300 K have specific detectivity, D(*), around 7 x 10(exp 8) to 2 x 10(exp 9) centimeters square root of Hz/W. However, a MgB2 thin film based bolometer using a low-stress (less than 140 MPa) SiN membrane isolated from the substrate by a small thermal conductive link, operating at 38 K, promises to have two orders of magnitude higher specific detectivity [5][6].
Transient vibration tests offer an alternative to the conventional swept-sine vibration test, with controllable conservatism. A method of deriving a single vibration test transient from several spacecraft flight transients is described. These transients were from different physical locations on the spacecraft.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes. This scheme yields optimal performance because the amounts of normalization applied to the spatial-frequency contents of the input and filter scenes are just enough to enhance their high-spatial-frequency contents while reducing their spurious low-spatial-frequency content. As a result, in SPOF PIV processing, particle-displacement correlation peaks can readily be detected above spurious background peaks, without need for masking or background subtraction.
Receiving system for measurement of millimeter and submillimeter wavelength regions with radiometer using Fourier transforms
Several FORTRAN program modules for performing one-dimensional and two-dimensional discrete Fourier transforms, Mellin, and Fourier-Bessel transforms are described along with programs that realize the algebra of high speed Fourier transforms on a computer. The programs can perform numerical harmonic analysis of functions, synthesize complex optical filters on a computer, and model holographic image processing methods.
This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral operators, including Green's functions for wave equations and integral transforms such as Radon transforms and Fourier transforms. The proposed algorithm leverages a tensor extension of the so-called complementary low-rank property of existing matrix butterfly algorithms. The algorithm partitions the discretized integral operator tensor into subtensors of multiple levels and factorizes each subtensor at the middle level as a Tucker-type interpolative decomposition, whose factor matrices are formed in a multilevel fashion. For a d-dimensional (d > 1) integral operator discretized into a 2d-mode tensor with n2d entries, the overall CPU time and memory requirement scale as O(nd), in stark contrast to the O(nd log n) complexity of existing matrix algorithms such as matrix butterfly algorithms and fast Fourier transforms (FFTs), where n is the number of points per direction. When comparing with other tensor algorithms such as quantized tensor train (QTT), the proposed algorithm also shows superior CPU and memory performance for tensor contraction. Remarkably, the tensor butterfly algorithm can efficiently model high-frequency Green's function interactions between two unit cubes, each spanning 512 wavelengths per direction, which represents problems of scale over 512× larger than that existing butterfly algorithms can handle, with the same amount of computation resources. On the other hand, for a problem representing 64 wavelengths per direction, which is the largest size existing algebraic matrix algorithms can handle, our tensor butterfly algorithm exhibits 200x speedups and 30× memory reduction compared with existing ones. Moreover, the tensor butterfly algorithm also permits O(nd)-complexity FFTs and Radon transforms up to d = 6 dimensions.
High resolution frequency analysis methods, with application to the gravitational probe redshift experiment, are discussed. For this experiment a resolution of .00001 Hz is required to measure a slowly varying, low frequency signal of approximately 1 Hz. Major building blocks include fast Fourier transform, discrete Fourier transform, Lagrange interpolation, golden section search, and adaptive matched filter technique. Accuracy, resolution, and computer effort of these methods are investigated, including test runs on an IBM 360/65 computer.
The theory, design, and results of optical fiber pulse dispersion measurements are considered. Both the hardware and software required to perform this type of measurement are described. Hardware includes a thermoelectrically cooled injection laser diode source, an 800 GHz gain bandwidth produce avalanche photodiode and an input mode scrambler. Software for a HP 9825 computer includes fast Fourier transform, inverse Fourier transform, and optimal compensation deconvolution. Test set construction details are also included. Test results include data collected on a 1 Km fiber, a 4 Km fiber, a fused spliced, eight 600 meter length fibers concatenated to form 4.8 Km, and up to nine optical connectors.
The present invention is embodied in a charge coupled device (CCD)/charge injection device (CID) architecture capable of performing a Fourier transform by simultaneous matrix vector multiplication (MVM) operations in respective plural CCD/CID arrays in parallel in O(1) steps. For example, in one embodiment, a first CCD/CID array stores charge packets representing a first matrix operator based upon permutations of a Hartley transform and computes the Fourier transform of an incoming vector. A second CCD/CID array stores charge packets representing a second matrix operator based upon different permutations of a Hartley transform and computes the Fourier transform of an incoming vector. The incoming vector is applied to the inputs of the two CCD/CID arrays simultaneously, and the real and imaginary parts of the Fourier transform are produced simultaneously in the time required to perform a single MVM operation in a CCD/CID array.
By working in the Fourier plane, approximate removal of stripe artifacts in IRAS images can be effected. The image of interest is smoothed and subtracted from the original, giving the high-spatial-frequency part. This 'filtered' image is then clipped to remove point sources and then Fourier transformed. Subtracting the Fourier components contributing to the stripes in this image from the Fourier transform of the original and transforming back to the image plane yields substantial removal of the stripes.
These architectures are based on methods of vector processing and the discrete-Fourier-transform/inverse-discrete- Fourier-transform (DFT-IDFT) overlap-and-save method, combined with time-block separation of digital filters into frequency-domain subfilters implemented by use of sub-convolutions. The parallel-processing method implemented in these architectures enables the use of relatively small DFT-IDFT pairs, while filter tap lengths are theoretically unlimited. The size of a DFT-IDFT pair is determined by the desired reduction in processing rate, rather than on the order of the filter that one seeks to implement. The emphasis in this report is on those aspects of the underlying theory and design rules that promote computational efficiency, parallel processing at reduced data rates, and simplification of the designs of very-large-scale integrated (VLSI) circuits needed to implement high-order filters and correlators.
Traditionally, Fourier Transforms have been utilized for performing signal analysis and representation. But although it is straightforward to reconstruct a signal from its Fourier transform, no local description of the signal is included in its Fourier representation. To alleviate this problem, Windowed Fourier transforms and then wavelet transforms have been introduced, and it has been proven that wavelets give a better localization than traditional Fourier transforms, as well as a better division of the time- or space-frequency plane than Windowed Fourier transforms. Because of these properties and after the development of several fast algorithms for computing the wavelet representation of any signal, in particular the Multi-Resolution Analysis (MRA) developed by Mallat, wavelet transforms have increasingly been applied to signal analysis problems, especially real-life problems, in which speed is critical. In this paper we present and compare efficient wavelet decomposition algorithms on different parallel architectures. We report and analyze experimental measurements, using NASA remotely sensed images. Results show that our algorithms achieve significant performance gains on current high performance parallel systems, and meet scientific applications and multimedia requirements. The extensive performance measurements collected over a number of high-performance computer systems have revealed important architectural characteristics of these systems, in relation to the processing demands of the wavelet decomposition of digital images.
Fourier transform-infrared (FT-IR) spectroscopy has been used to rapidly and nondestructively analyze bacteria, bacteria-polymer mixtures, digester samples and microbial biofilms. Diffuse reflectance FT-IR (DRIFT) analysis of freeze-dried, powdered samples offered a means of obtaining structural information. The bacteria examined were divided into two groups. The first group was characterized by a dominant amide I band and the second group of organisms displayed an additional strong carbonyl stretch at approximately 1740 cm-1. The differences illustrated by the subtraction spectra obtained for microbes of the two groups suggest that FT-IR spectroscopy can be utilized to recognize differences in microbial community structure. Calculation of specific band ratios has enabled the composition of bacteria and extracellular or intracellular storage product polymer mixtures to be determined for bacteria-gum arabic (amide I/carbohydrate C-O approximately 1150 cm-1) and bacteria-poly-beta-hydroxybutyrate (amide I/carbonyl approximately 1740 cm-1). The key band ratios correlate with the compositions of the material and provide useful information for the application of FT-IR spectroscopy to environmental biofilm samples and for distinguishing bacteria grown under differing nutrient conditions. DRIFT spectra have been obtained for biofilms produced by Vibrio natriegens on stainless steel disks. Between 48 and 144 h, an increase in bands at approximately 1440 and 1090 cm-1 was seen in FT-IR spectra of the V. natriegens biofilm. DRIFT spectra of mixed culture effluents of anaerobic digesters show differences induced by shifts in input feedstocks. The use of flow-through attenuated total reflectance has permitted in situ real-time changes in biofilm formation to be monitored and provides a powerful tool for understanding the interactions within adherent microbial consortia.
The quantitative regularities of interference pattern formation by motor unit action potentials is investigated. The parameters of a single motor unit and how they relate to the Fourier transform analysis of an EMG are considered. The Fourier transform of the simulated electromyogram is compared with the Fourier transform of the actual EMG recorded from various human muscles using surface electrodes.