Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “signal processing algorithms”

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.

At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Advanced Recording and Preprocessing of Physiological Signals

The measurement of the volume flow-rate of blood in an artery or vein requires both an estimate of the flow velocity and its spatial distribution and the corresponding cross-sectional area. Transcutaneous measurements of these parameters can be performed using ultrasonic techniques that are analogous to the measurement of moving objects by use of a radar. Modern digital data recording and preprocessing methods were applied to the measurement of blood-flow velocity by means of the CW Doppler ultrasonic technique. Only the average flow velocity was measured and no distribution or size information was obtained. Evaluations of current flowmeter design and performance, ultrasonic transducer fabrication methods, and other related items are given. The main thrust was the development of effective data-handling and processing methods by application of modern digital techniques. The evaluation resulted in useful improvements in both the flowmeter instrumentation and the ultrasonic transducers. Effective digital processing algorithms that provided enhanced blood-flow measurement accuracy and sensitivity were developed. Block diagrams illustrative of the equipment setup are included.

Bentley, P. B.↗
NASA NTRS1996

LCTV Holographic Imaging

Astronauts are required to interface with complex systems that require sophisticated displays to communicate effectively. Lightweight, head-mounted real-time displays that present holographic images for comfortable viewing may be the ideal solution. We describe an implementation of a liquid crystal television (LCTV) as a spatial light modulator (SLM) for the display of holograms. The implementation required the solution of a complex set of problems. These include field calculations, determination of the LCTV-SLM complex transmittance characteristics and a precise knowledge of the signal mapping between the LCTV and frame grabbing board that controls it. Realizing the hologram is further complicated by the coupling that occurs between the phase and amplitude in the LCTV transmittance. A single drive signal (a gray level signal from a framegrabber) determines both amplitude and phase. Since they are not independently controllable (as is true in the ideal SLM) one must deal with the problem of optimizing (in some sense) the hologram based on this constraint. Solutions for the above problems have been found. An algorithm has been for field calculations that uses an efficient outer product formulation. Juday's MEDOF 7 (Minimum Euclidean Distance Optimal Filter) algorithm used for originally for filter calculations has been successfully adapted to handle metrics appropriate for holography. This has solved the problem of optimizing the hologram to the constraints imposed by coupling. Two laboratory methods have been developed for determining an accurate mapping of framegrabber pixels to LCTV pixels. A friendly software system has been developed that integrates the hologram calculation and realization process using a simple set of instructions. The computer code and all the laboratory measurement techniques determining SLM parameters have been proven with the production of a high quality test image.

Knopp, Jerome↗
DOE OSTI2017

Machine learning to analyze images of shocked materials for precise and accurate measurements

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

47 OTHER INSTRUMENTATION↗
NASA NTRS1994

Task scheduling in dataflow computer architectures

Dataflow computers provide a platform for the solution of a large class of computational problems, which includes digital signal processing and image processing. Many typical applications are represented by a set of tasks which can be repetitively executed in parallel as specified by an associated dataflow graph. Research in this area aims to model these architectures, develop scheduling procedures, and predict the transient and steady state performance. Researchers at NASA have created a model and developed associated software tools which are capable of analyzing a dataflow graph and predicting its runtime performance under various resource and timing constraints. These models and tools were extended and used in this work. Experiments using these tools revealed certain properties of such graphs that require further study. Specifically, the transient behavior at the beginning of the execution of a graph can have a significant effect on the steady state performance. Transformation and retiming of the application algorithm and its initial conditions can produce a different transient behavior and consequently different steady state performance. The effect of such transformations on the resource requirements or under resource constraints requires extensive study. Task scheduling to obtain maximum performance (based on user-defined criteria), or to satisfy a set of resource constraints, can also be significantly affected by a transformation of the application algorithm. Since task scheduling is performed by heuristic algorithms, further research is needed to determine if new scheduling heuristics can be developed that can exploit such transformations. This work has provided the initial development for further long-term research efforts. A simulation tool was completed to provide insight into the transient and steady state execution of a dataflow graph. A set of scheduling algorithms was completed which can operate in conjunction with the modeling and performance tools previously developed. Initial studies on the performance of these algorithms were done to examine the effects of application algorithm transformations as measured by such quantities as number of processors, time between outputs, time between input and output, communication time, and memory size.

Katsinis, Constantine↗
NASA NTRS2019

Retrieving Aerosol Characteristics From the PACE Mission, Part 2: Multi-Angle and Polarimetry

The Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission presents new opportunities and new challenges in applying observations of two complementary multi-angle polarimeters for the space-based retrieval of global aerosol properties.Aerosol remote sensing from multi-angle radiometric-only observations enables aerosol characterization to a greater degree than single-view radiometers, as demonstrated by nearly two decades of heritage instruments. Adding polarimetry to the multi-angle observations allows for the retrieval of aerosol optical depth, Angstrom exponent,parameters of size distribution, measures of aerosol absorption, complex refractive index and degree of non-sphericity of the particles, as demonstrated by two independent retrieval algorithms applied to the heritage POLarization and Directionality of the Earth's Reflectance (POLDER) instrument. The reason why this detailed particle characterization is possible is because a multi-angle polarimeter measurement contains twice the number of Degrees of Freedom of Signal (DFS) compared to an observation from a single-view radiometer. The challenges of making use of this information content involve separating surface signal from atmospheric signal, especially when the surface is optically complex and especially in the ultraviolet portion of the spectrum where we show the necessity of polarization in making that separation. The path forward is likely to involve joint retrievalsthat will simultaneously retrieve aerosol and surface properties, although advances will berequired in radiative transfer modeling and in representing optically complex constituents in those models. Another challenge is in having the processing capability that can keep pace with the output of these instruments in an operational environment. Yet, preliminaryalgorithms applied to airborne multi-angle polarimeter observations offer encouraging results that demonstrate the advantages of these instruments to retrieve aerosol layer height, particle single scattering albedo, size distribution and spectral optical depth, and also show the necessity of polarization measurements, not just multi-angle radiometricmeasurements, to achieve these results.

Remer, Lorraine A.↗
DOE OSTI2021

Side-channel Leakage Assessment Metrics: A Case Study of GIFT Block Ciphers

Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. While there are some solutions available for resource-rich computer systems, direct application of these solutions to resource-constrained environments are often unfeasible. The fundamental problem for such resource-constrained systems is the fact that current cryptographic algorithms utilize significant energy consumption and storage overhead. Both the cryptographic algorithm and its physical implementation affect the resilience of a cryptosystem against side-channel attacks. A side-channel attack represents a process that exploits leakages in order to extract sensitive information such as the key. This paper focuses on Correlation Power Analysis (CPA) which is side-channel attack based on the power consumption leakage. In 2016 the U.S. Commerce Department’s National Institute of Standards and Technology (NIST) initiated the call for proposals of new cryptographic algorithms to strengthen the cryptographic defense of networked devices against cyberattacks and to protect the data created by those innumerable device. This work evaluates S-boxes used by NIST candidates PICCOLO, GIFT, and PRESENT, as well as several S-box variants that demonstrated sufficient weaknesses against classical cryptanalysis, for a quantitative comparison in terms of resiliency to CPA attack. Three well-known theoretical metrics are evaluated: transparency order (TO and RTO), nonlinearity, and signal-to-noise (SNR) ratio, aiming to characterize the resistance of these S-boxes against adversaries exploiting physical leakages. Experimental results from attacks on an 8- bit XMEGA were obtained via the ChipWhisperer platform and of all the S-boxes evaluated, GIFT64 with a PICCOLO S-box was found to be the most susceptible to CPA. Results showed that variations in TO and RTO were not sufficient to ensure practical CPA resistance and that among S-boxes with equal non-linearity there were no significant differences in the TO and SNR variants.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Side-channel Leakage Assessment Metrics: A Case Study of GIFT Block Ciphers

Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. While there are some solutions available for resource-rich computer systems, direct application of these solutions to resource-constrained environments are often unfeasible. The fundamental problem for such resource-constrained systems is the fact that current cryptographic algorithms utilize significant energy consumption and storage overhead. Both the cryptographic algorithm and its physical implementation affect the resilience of a cryptosystem against side-channel attacks. A side-channel attack represents a process that exploits leakages in order to extract sensitive information such as the key. This paper focuses on Correlation Power Analysis (CPA) which is side-channel attack based on the power consumption leakage. In 2016 the U.S. Commerce Department’s National Institute of Standards and Technology (NIST) initiated the call for proposals of new cryptographic algorithms to strengthen the cryptographic defense of networked devices against cyberattacks and to protect the data created by those innumerable device. This work evaluates S-boxes used by NIST candidates PICCOLO, GIFT, and PRESENT, as well as several S-box variants that demonstrated sufficient weaknesses against classical cryptanalysis, for a quantitative comparison in terms of resiliency to CPA attack. Three well-known theoretical metrics are evaluated: transparency order (TO and RTO), nonlinearity, and signal-to-noise (SNR) ratio, aiming to characterize the resistance of these S-boxes against adversaries exploiting physical leakages. Experimental results from attacks on an 8- bit XMEGA were obtained via the ChipWhisperer platform and of all the S-boxes evaluated, GIFT64 with a PICCOLO S-box was found to be the most susceptible to CPA. Results showed that variations in TO and RTO were not sufficient to ensure practical CPA resistance and that among S-boxes with equal non-linearity there were no significant differences in the TO and SNR variants.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Studies of a proton phase beam monitor for range verification in proton therapy

A primary subject of the present research in particle therapy is to ensure the precise irradiation of the target volume. The prompt gamma timing (PGT) method provides one possibility for in vivo range verification during the irradiation of patients. Prompt gamma rays with high energies are emitted promptly due to nuclear reactions of protons with tissue. The arrival time of these gammas to the detector reflects the stopping process of the primary protons in tissue and are directly correlated to the range. Due to the time resolution of the detector and the proton bunch time spread, as well as drifts of the bunch phase with respect to the accelerator frequency, timing spectra are smeared out and compromise the accuracy of range information intended for future clinical applications. Nevertheless, counteracting this limitation and recovering range information from the PGT measured spectra, corrections using a phase beam monitor can be performed. A first prototype of phase beam monitor was tested at GSI Darmstadt, where measurements of the energy profile of the ion bunches were performed. At the ELBE accelerator Helmholtz-Zentrum Dresden-Rossendorf (HZDR), set up to provide bremsstrahlung photons in very short pulses, a constant fraction algorithm for the incoming digital signals was evaluated, which is used for optimizing the time resolution. Studies of scattering experiments with different thin targets and detector positions are accomplished at Oncoray Dresden, where a clinical proton beam is available. These experiments allow a basic characterization of the proton bunch structure and the detection yield. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (> 85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. Furthermore, this suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The gravitational lensing imprints of DES Y3 superstructures on the CMB: a matched filtering approach

Low-density cosmic voids gravitationally lens the cosmic microwave background (CMB), leaving a negative imprint on the CMB convergence |$\kappa$|⁠. This effect provides insight into the distribution of matter within voids, and can also be used to study the growth of structure. We measure this lensing imprint by cross-correlating the Planck CMB lensing convergence map with voids identified in the Dark Energy Survey Year 3 (DES Y3) data set, covering approximately 4200 deg|$^2$| of the sky. We use two distinct void-finding algorithms: a 2D void-finder that operates on the projected galaxy density field in thin redshift shells, and a new code, Voxel, which operates on the full 3D map of galaxy positions. We employ an optimal matched filtering method for cross-correlation, using the Marenostrum Institut de Ciències de l’Espai N-body simulation both to establish the template for the matched filter and to calibrate detection significances. Using the DES Y3 photometric luminous red galaxy sample, we measure |$A_\kappa$|⁠, the amplitude of the observed lensing signal relative to the simulation template, obtaining |$A_\kappa = 1.03 \pm 0.22$| (⁠|$4.6\sigma$| significance) for Voxel and |$A_\kappa = 1.02 \pm 0.17$| (⁠|$5.9\sigma$| significance) for 2D voids, both consistent with Lambda cold dark matter expectations. We additionally invert the 2D void-finding process to identify superclusters in the projected density field, for which we measure |$A_\kappa = 0.87 \pm 0.15$| (⁠|$5.9\sigma$| significance). The leading source of noise in our measurements is Planck noise, implying that data from the Atacama Cosmology Telescope, South Pole Telescope and CMB-S4 will increase sensitivity and allow for more precise measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

The Evolution of Icelandic Volcano Emissions, as Observed From Space in the Era of NASA's Earth Observing System (EOS)

Volcanoes are natural phenomena that have global environmental impacts. Satellite remote sensing can help classify volcanic eruptions and track the dispersion of emissions. We assess multiple volcanic eruptions in Iceland (Eyjafjallajökull 2010, Grímsvötn 2011 and Holuhraun 2014-2015), using space-borne observations to infer information about the geological dynamics of each volcano and the properties and evolution of plume particles. We derive qualitative constraints on plume particle size, shape and light-absorption characteristics from Multi-angle Imaging SpectroRadiometer (MISR) space-borne imagery. With the MISR Research Algorithm (RA), we distinguish sulfate/water-dominated volcanic plumes from Holuhraun and ash-dominated plumes from Eyjafjallajökull and Grímsvötn, and even identify subtler changes in ash particle size and light-absorption within plumes. Additionally, plume heights are retrieved geometrically from MISR. These are combined with surface thermal anomalies from the MODerate resolution Imaging Spectroradiometer (MODIS) and SO2 concentrations derived from the Ozone Monitoring Instrument (OMI) to synthesize eruption remote-sensing chronologies. Signals related to differences in particle properties are identified and linked to evolving magma composition at Eyjafjallajökull. The results illustrate the potential to distinguish qualitative differences in eruptive magma composition based on particle light absorption and plume profile from remote-sensing. For the sulfate-rich Holuhraun plumes, the influence of aerosol hygroscopic growth during transport is inferred from such data. Three processes appear to dominate plume evolution in Iceland: downwind aerosol formation; particle hydration; and particle deposition. This work demonstrates enhanced MISR capabilities, and more generally, remote-sensing analysis that can be applied globally, especially where suborbital volcano observations are limited or entirely absent.

remote sensting↗
DOE OSTI2015

Non-intrusive, high-resolution, real-time, two-dimensional imaging of multiphase materials using acoustic array sensors

Two parallel multi-element ultrasonic acoustic arrays combined with sets of focal laws for acoustic signal generation and a classical tomographic inversion algorithm are used to generate real-time two-dimensional micro seismic acoustic images of multiphase materials. Proof of concept and calibration measurements were performed for single phase and two phase liquids, uniform polyvinyl chloride (PVC) plates, and aluminum cylinders imbedded in PVC plates. Measurement artefacts, arising from the limited range of viewing angles, and the compromise between data acquisition rate and image quality are discussed. The angle range of scanning and the image resolution were varied, and the effects on the quality of the reproduction of the speed of sound profiles of model solids and liquids with known geometries and compositions were analysed in detail. The best image quality results were obtained for a scanning angle range of [−35°, 35°] at a step size of 2.5° post processed to generate images on a 40 μm square grid. The data acquisition time for high quality images with a 30 mm × 40 mm view field is 10 min. Representation of two-phase solids with large differences in speed of sound between phases and where one phase is dispersed in the form of macroscopic objects (greater than 1 mm in diameter) proved to be the most difficult to image accurately. Liquid-liquid and liquid-vapor phase boundaries, in micro porous solids by contrast, were more readily defined. Displacement of air by water and water by heptane in natural porous limestone provides illustrative kinetic examples. Measurement results with these realistic cases demonstrate the feasibility of the technique to monitor in real time and on the micrometer length scale local composition and flow of organic liquids in inorganic porous media, one of many envisioned engineering applications. Improvement of data acquisition rate is an area for future collaborative study.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Studies of a Proton Bunch Phase Monitor for Range Verification in Proton Therapy

A primary subject of the present research in particle therapy is to ensure the precise irradiation of the target volume. The prompt gamma timing (PGT) method provides one possibility for in vivo range verification during the irradiation of patients. Prompt gamma rays with high energies are emitted promptly due to nuclear reactions of protons with tissue. The arrival time of these gammas to the detector reflects the stopping process of the primary protons in tissue and is directly correlated to the range. Due to the time resolution of the detector and the proton bunch time spread, as well as drifts of the bunch phase with respect to the accelerator frequency, timing spectra are smeared out and compromise the accuracy of range information intended for future clinical applications. Nevertheless, counteracting this limitation and recovering range information from the PGT measured spectra, corrections using a bunch phase monitor can be performed. A first prototype of bunch phase monitor was tested at GSI Darmstadt, where measurements of the energy correlation profile of the ion bunches were performed. At the ELBE accelerator at Helmholtz-Zentrum Dresden-Rossendorf (HZDR), set up to provide bremsstrahlung photons in very short pulses, a constant fraction algorithm for the incoming digital signals was evaluated, which is used for optimizing the time resolution. Studies of scattering experiments with different thin targets and detector positions are accomplished at Onco Ray Dresden, where a clinical proton beam is available. These experiments allow a basic characterization of the proton bunch structure and the detection yield. (authors)

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification

Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.

Hamza, Mohamed H.↗
NASA NTRS2017

Evaluation of Sentinel-1A Data For Above Ground Biomass Estimation in Different Forests in India

Use of remote sensing data for mapping and monitoring of forest biomass across large spatial scales can aid in addressing uncertainties in carbon cycle. Earlier, several researchers reported on the use of Synthetic Aperture Radar (SAR) data for characterizing forest structural parameters and the above ground biomass estimation. However, these studies cannot be generalized and the algorithms cannot be applied to all types of forests without additional information on the forest physiognomy, stand structure and biomass characteristics. The radar backscatter signal also saturates as forest parameters such as biomass and the tree height increase. It is also not clear how different polarizations (VV versus VH) impact the backscatter retrievals in different forested regions. Thus, it is important to evaluate the potential of SAR data in different landscapes for characterizing forest structural parameters. In this study, the SAR data from Sentinel-1A has been used to characterize forest structural parameters including the above ground biomass from tropical forests of India. Ground based data on tree density, basal area and above ground biomass data from thirty-eight different forested sites has been collected to relate to SAR data. After the pre-processing of Sentinel 1-A data for radiometric calibration, geo-correction, terrain correction and speckle filtering, the variability in the backscatter signal in relation tree density, basal area and above biomass density has been investigated. Results from the curve fitting approach suggested exponential model between the Sentinel-1A backscatter versus tree density and above ground biomass whereas the relationship was almost linear with the basal area in the VV polarization mode. Of the different parameters, tree density could explain most of the variations in backscatter. Both VV and VH backscatter signals could explain only thirty and thirty three percent of variation in above biomass in different forest sites of India. Results also suggested saturation of the Sentinel-1A backscatter signal around hundred tonnes per hectare for VV polarization and one hundred and forty five tonnes per hectare for VH polarization. The presentation will highlight the above results in addition to potentials and limitations of Sentinel-1A data for retrieving forest structural parameters. Also, background information on different forest types of India, biomass variations and forest type mapping efforts in the region will be presented.

Data↗
NASA NTRS1983

Real-time MST radar signal processing using a microcomputer running under FORTH

Data on power, correlation time, and velocity were obtained at the Urbana radar using microcomputer and a single floppy disk drive. This system includes the following features: (1) measurement of the real and imaginary components of the received signal at 20 altitudes spaced by 1.5 km; (2) coherent integration of these components over a 1/8-s time period; (3) continuous real time display of the height profiles of the two coherently integrated components; (4) real time calculation of the 1 minute averages of the power and autocovariance function up to 6 lags; (5) output of these data to floppy disk once every 2 minutes; (6) display of the 1 minute power profiles while the data are stored to the disk; (7) visual prompting for the operator to change disks when required at the end of each hour of data; and (8) continuous audible indication of the status of the interrupt service routine. Accomplishments were enabled by two developments: the use of a new correlation algorithm and the use of the FORTH language to manage the various low level and high level procedures involved.

Bowhill, S. A.↗
DOE OSTI2020

Finding wombling boundaries in LHC data with Voronoi and Delaunay tessellations

We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in the phase space of a final state signature, with the wombling boundary created by some new physics. We discuss the use of Voronoi and Delaunay tessellations of the point data for estimating the local gradients and investigate methods for sharpening the boundaries by reducing the statistical noise. The outcome from traditional wombling algorithms is a set of boundary cell candidates with relatively large gradients, whose spatial properties must then be scrutinized in order to construct the boundary and evaluate its significance. Here we propose an alternative approach where we simultaneously form and evaluate the significance of all possible boundaries in terms of the total gradient flux. We illustrate our method with several toy examples of both straight and curved boundaries with varying amounts of signal present in the data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗