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At least 361 records · Page 20

Outline of a fast hardware implementation of Winograd's DFT algorithm

The main characteristics of the discrete Fourier transform (DFT) algorithm considered by Winograd (1976) is a significant reduction in the number of multiplications. Its primary disadvantage is a higher structural complexity. It is, therefore, difficult to translate the reduced number of multiplications into faster execution of the DFT by means of a software implementation of the algorithm. For this reason, a hardware implementation is considered in the current study, taking into account a design based on the algorithm prescription discussed by Zohar (1979). The hardware implementation of a FORTRAN subroutine is proposed, giving attention to a pipelining scheme in which 5 consecutive data batches are being operated on simultaneously, each batch undergoing one of 5 processing phases.

Zohar, S.↗

Differential Sampling For Fast Acquisition Of Frequency

Algorithm rapidly estimates frequency of sinusoidal signal corrupted by zero-mean, additive, white Gaussian noise. Incorporates differential mathematical model of signal, cyclic sampling of signal, and least-squares best-estimate criterion. Adapts to changing signal frequency. Amount of computation required to obtain estimate increases only linearly with number of successive measurements processed.

Kumar, Rajendra↗

Modified fast frequency acquisition via adaptive least squares algorithm

A method and the associated apparatus for estimating the amplitude, frequency, and phase of a signal of interest are presented. The method comprises the following steps: (1) inputting the signal of interest; (2) generating a reference signal with adjustable amplitude, frequency and phase at an output thereof; (3) mixing the signal of interest with the reference signal and a signal 90 deg out of phase with the reference signal to provide a pair of quadrature sample signals comprising respectively a difference between the signal of interest and the reference signal and a difference between the signal of interest and the signal 90 deg out of phase with the reference signal; (4) using the pair of quadrature sample signals to compute estimates of the amplitude, frequency, and phase of an error signal comprising the difference between the signal of interest and the reference signal employing a least squares estimation; (5) adjusting the amplitude, frequency, and phase of the reference signal from the numerically controlled oscillator in a manner which drives the error signal towards zero; and (6) outputting the estimates of the amplitude, frequency, and phase of the error signal in combination with the reference signal to produce a best estimate of the amplitude, frequency, and phase of the signal of interest. The preferred method includes the step of providing the error signal as a real time confidence measure as to the accuracy of the estimates wherein the closer the error signal is to zero, the higher the probability that the estimates are accurate. A matrix in the estimation algorithm provides an estimate of the variance of the estimation error.

Kumar, Rajendra↗

Particle tracing in the magnetosphere - New algorithms and results

A fast and efficient method is employed to trace charged particles through realistic magnetospheric electric and magnetic fields, greatly reducing computer simulation times. The method is applicable for particles having arbitrary charge, energy, or pitch angle but which conserve the first two adiabatic invariants. An efficient method is used to classify drift orbits, which greatly facilitates identification of the last closed drift path or other drift boundaries. The time-independent evolution of the bounce-averaged phase space density along convective drift orbits is calculated. These three tools can be used to quantitatively describe convective evolution of the particle distribution from the tail, an essential step in understanding the production of unstable distributions in the magnetosphere.

Sheldon, R. B.↗

Neural Network Back-Propagation Algorithm for Sensing Hypergols

Fast, continuous detection of a wide range of hazardous substances simultaneously is needed to achieve improved safety for personnel working with hypergolic fuels and oxidizers, as well as other hazardous substances, with a requirement for such detection systems to warn personnel immediately upon the sudden advent of hazardous conditions, with a high probability of detection and a low false alarm rate. The primary purpose of this software is to read the voltage outputs from voltage dividers containing carbon nano - tube sensors as a variable resistance leg, and to recognize quickly when a leak has occurred through recognizing that a generalized pattern change in resistivity of a carbon nanotube sensor has occurred upon exposure to dangerous substances, and, further, to identify quickly just what substance is present through detailed pattern recognition of the shape of the response provided by the carbon nanotube sensor.

Perotti, Jose↗

Computing the Fast Fourier Transform on a vector computer

Two algorithms are presented for performing a Fast Fourier Transform on a vector computer and are compared on the Control Data Corporation STAR-100. The relative merits of the two algorithms are shown to depend upon whether only a few or many independent transforms are desired. A theorem is proved which shows that a set of independent transforms can be computed by performing a partial transformation on a single vector. The results of this theorem also apply to nonvector machines and have reduced the average time per transform by a factor of two on the CDC 6600 computer.

Korn, D. G.↗

Fast Attitude Maneuvers for the Lunar Reconnaissance Orbiter

This paper describes a new operational capability for fast attitude maneuvering that is being developed for the Lunar Reconnaissance Orbiter (LRO). The LRO hosts seven scientific instruments. For some instruments, it is necessary to per-form large off-nadir slews to collect scientific data. The accessibility of off-nadir science targets has been limited by slew rates and/or occultation, thermal and power constraints along the standard slew path. The new fast maneuver (FastMan) algorithm employs a slew path that autonomously avoids constraint violations while simultaneously minimizing the slew time. The FastMan algo-rithm will open regions of observation that were not previously feasible and improve the overall science return for LRO's extended mission. The design of an example fast maneuver for LRO's Lunar Orbiter Laser Altimeter that reduc-es the slew time by nearly 40% is presented. Pre-flight, ground-test, end-to-end tests are also presented to demonstrate the readiness of FastMan. This pioneer-ing work is extensible and has potential to improve the science data collection return of other NASA spacecraft, especially those observatories in extended mission phases where new applications are proposed to expand their utility.

Karpenko, Mark↗

Impact of Tactical and Strategic Weather Avoidance on Separation Assurance

The ability to keep flights away from weather hazards while maintaining aircraft-to-aircraft separation is critically important. The Advanced Airspace Concept is an automation concept that implements a ground-based strategic conflict resolution algorithm for management of aircraft separation. The impact of dynamic and uncertain weather avoidance on this concept is investigated. A strategic weather rerouting system is integrated with the Advanced Airspace Concept, which also provides a tactical weather avoidance algorithm, in a fast time simulation of the Air Transportation System. Strategic weather rerouting is used to plan routes around weather in the 20 minute to two-hour time horizon. To address forecast uncertainty, flight routes are revised at 15 minute intervals. Tactical weather avoidance is used for short term trajectory adjustments (30 minute planning horizon) that are updated every minute to address any weather conflicts (instances where aircraft are predicted to pass through weather cells) that are left unresolved by strategic weather rerouting. The fast time simulation is used to assess the impact of tactical weather avoidance on the performance of automated conflict resolution as well as the impact of strategic weather rerouting on both conflict resolution and tactical weather avoidance. The results demonstrate that both tactical weather avoidance and strategic weather rerouting increase the algorithm complexity required to find aircraft conflict resolutions. Results also demonstrate that tactical weather avoidance is prone to higher airborne delay than strategic weather rerouting. Adding strategic weather rerouting to tactical weather avoidance reduces total airborne delays for the reported scenario by 18% and reduces the number of remaining weather violations by 13%. Finally, two features are identified that have proven important for strategic weather rerouting to realize these benefits; namely, the ability to revise reroutes and the use of maneuvers that start far ahead of encountering a weather cell when rerouting around weather.

Refai, Mohamad S.↗

Efficient algorithms for use in probabilistic finite element analysis

This paper investigates the use of Fast Probability Integration (FPI) algorithms in a Finite Element environment. A method allowing the representation of correlated fields in terms of a vector of uncorrelated transformed variables, based on the spectral decomposition of the variance-covariance matrix is developed. The response of the deterministic model corresponding to selected perturbations of these uncorrelated variables is then obtained via a Newton-type iterative scheme. The results of the perturbed problems are used to construct a local representation of the model's behavior in the neighborhood of the deterministic state, which the FPI algorithm will use to estimate the reliability of the system. Although the proposed strategy has thus far only been applied to linear elastostatics, the extension of the method to a broader class of problems appears to be feasible.

Dias, J. B.↗

Developments in Performance and Portability of BlockGen

For more than a decade Monte Carlo event generators with the current matrix element algorithms have been used for generating hard scattering events on CPU platforms, with excellent flexibility and good efficiency. While the HL-LHC is approaching and precision requirements are becoming more demanding, many studies have been made to solve the bottleneck in the current Monte Carlo event generator tool chains. The novel BlockGen family of fast matrix element algorithms shown in this report, is one of the new developments that are more suitable for GPU acceleration. We report the development experience of porting BlockGen using Kokkos. Moreover, we discuss the performance of the Kokkos version in comparison with the dedicated GPU version in CUDA.

Bothmann, E. [Gottingen U.]↗

Microprocessor implementation of an FFT for ionospheric VLF observations

A fast Fourier transform algorithm is implemented on a CMOS microprocessor for application to very low-frequency electric fields (less than 10 kHz) sensed on high-altitude scientific balloons. Two FFT's are calculated simultaneously by associating them with conjugate symmetric and conjugate antisymmetric results. One goal of the system was to detect spectral signatures associated with fast time variations present in natural signals such as whistlers and chorus. Although a full evaluation of the system was not possible for operational reasons, a measure of the system's success has been defined and evaluated.

Elvidge, J.↗

Method for hyperspectral imagery exploitation and pixel spectral unmixing

An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.

Lin, Ching-Fang↗

Atmospheric, Cloud, and Surface Parameters Retrieved from Satellite Ultra-spectral Infrared Sounder Measurements

An advanced retrieval algorithm with a fast radiative transfer model, including cloud effects, is used for atmospheric profile and cloud parameter retrieval. This physical inversion scheme has been developed, dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders, to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1-d) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression-retrieval. The solution is iterated in order to account for non-linearity in the 1-d variational solution. This retrieval algorithm is applied to the MetOp satellite Infrared Atmospheric Sounding Interferometer (IASI) launched on October 19, 2006. IASI possesses an ultra-spectral resolution of 0.25 cm(exp -1) and a spectral coverage from 645 to 2760 cm(exp -1). Preliminary retrievals of atmospheric soundings, surface properties, and cloud optical/microphysical properties with the IASI measurements are obtained and presented.

Zhou, Daniel K.↗

Anchorage Arrival Scheduling Under Off-Nominal Weather Conditions

Weather can cause flight diversions, passenger delays, additional fuel consumption and schedule disruptions at any high volume airport. The impacts are particularly acute at the Ted Stevens Anchorage International Airport in Anchorage, Alaska due to its importance as a major international portal. To minimize the impacts due to weather, a multi-stage scheduling process is employed that is iteratively executed, as updated aircraft demand and/or airport capacity data become available. The strategic scheduling algorithm assigns speed adjustments for flights that originate outside of Anchorage Center to achieve the proper demand and capacity balance. Similarly, an internal departure-scheduling algorithm assigns ground holds for pre-departure flights that originate from within Anchorage Center. Tactical flight controls in the form of airborne holding are employed to reactively account for system uncertainties. Real-world scenarios that were derived from the January 16, 2012 Anchorage visibility observations and the January 12, 2012 Anchorage arrival schedule were used to test the initial implementation of the scheduling algorithm in fast-time simulation experiments. Although over 90% of the flights in the scenarios arrived at Anchorage without requiring any delay, pre-departure scheduling was the dominant form of control for Anchorage arrivals. Additionally, tactical scheduling was used extensively in conjunction with the pre-departure scheduling to reactively compensate for uncertainties in the arrival demand. For long-haul flights, the strategic scheduling algorithm performed best when the scheduling horizon was greater than 1,000 nmi. With these long scheduling horizons, it was possible to absorb between ten and 12 minutes of delay through speed control alone. Unfortunately, the use of tactical scheduling, which resulted in airborne holding, was found to increase as the strategic scheduling horizon increased because of the additional uncertainty in the arrival times of the aircraft. Findings from these initial experiments indicate that it is possible to schedule arrivals into Anchorage with minimal delays under low-visibility conditions with less disruption to high-cost, international flights.

Grabbe, Shon↗

SIS-AOP Cueing/Segmenting Algorithm (FOA_SIS-AOP) Using the Sandia FOA 4.0 Framework

For machine vision, one of the most important operations is fast and effective object cueing or segmentation. Sandia National Labs has a long history of development and implementation of very fast and effective cueing/segmentation algorithms. This report covers the history, motivation and implementation of evolving frameworks (Sandia FOA Frameworks) upon which this long legacy of successful algorithms are built. The report describes the innovative microprocessor implementation, enabling extremely fast morphological processing, combined with a novel adaptive quantization front - end and a feature - based backend that resulted in Sandia developing fast and effective cueing in a wide variety of applications, from defect detection to SAR ATR. The report covers evolution from Sandia FOA 1.0 Framework (1995) to current Sandia FOA 4.0 Framework (2021). Requirements for the cueing algorithm for SIS - AOP (FOA_SIS - AOP) that drove the Sandia FOA 4.0 Framework development are discussed, along with information on how to use the Sandia FOA Frameworks.

97 MATHEMATICS AND COMPUTING↗

TPSAS-NF1676L-28149-DND

An Optimal Estimation (OE) inversion method has been developed to retrieve vertical profiles of aerosol size distributions, volume/surface concentrations, complex refractive indices, and single scattering albedos using the High Spectral Resolution Lidar (HSRL) and polarimeter measurements. By combining the active and passive remote sensing data, we can take advantages of the high vertical resolution of the lidar measurements and the high information content of the combined observations. There are three components to the new retrieval system: 1) simultaneous aerosol vertical profile retrieval using HSRL data, 2) standalone aerosol retrieval using Research Scanning Polarimeter (RSP), and 3) combined HSRL and RSP optimal estimation retrieval. For the LIDAR retrieval, we solve for number concentrations, mode fractions, aerosol size distributions, and real and imaginary part of refractive indices simultaneously. Based on this information, other parameters such as effective particle radius, surface and volume concentration, single scattering albedo can be derived. The combined HSRL-polarimeter algorithm will provide improved single scattering albedo and refractive index retrievals. The newly developed retrieval algorithm is very fast, and it provides an optimal estimation solution with associated error estimates. By solving for the aerosol vertical profiles using HSRL extinctions and backscattering coefficients from different layers, the new OE method can effectively reduce the impact of measurement noise on the retrieved aerosol microphysical properties. It also enables us to effectively combing HSRL and RSP data in the single OE retrieval system since the passive polarimeter instruments measure path-integrated intensity and polarizations. We have performed sensitivity studies using simulated data and applied the OE algorithms to NASA airborne field data measured by the HSRL-2 and the RSP instruments.

Xu Liu↗

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

Rajkumar, T.↗

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the QVAE has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the MODIS dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Data mining, similarity search, quantum machine le↗