Three-dimensional Interactive Data Language pole figure visualization

2006 ◽  
Vol 21 (2) ◽  
pp. 102-104 ◽  
Author(s):  
Colleen S. Frazer ◽  
Mark A. Rodriguez ◽  
Ralph G. Tissot
Keyword(s):  
Pole Figure ◽  
Three Dimensional ◽  
Large Data ◽  
Peak Height ◽  
Large Data Sets ◽  
Data Sets ◽  
Interactive Data Language ◽  
Area Detector ◽  
Position Sensitive ◽  
Interactive Data

The Interactive Data Language has been used to produce a software program capable of advanced three-dimensional visualizations of pole figure and θ-2θ data. The data can also be used to calculate quantitative properties such as strain level and to minimize the peak-height texture effects in individual θ-2θ scans. The collection of the large data sets necessary for the analyses is facilitated by use of a position sensitive detector or area detector.

A modified Pinkerton-type helium gas-flow system for high-accuracy data collection at the X3 SUNY synchrotron beamline at NSLS

2001 ◽  
Vol 34 (1) ◽  
pp. 76-79 ◽  
Author(s):  
Lynn Ribaud ◽  
Guang Wu ◽  
Yuegang Zhang ◽  
Philip Coppens
Keyword(s):  
Data Collection ◽  
Gas Flow ◽  
Flow System ◽  
Three Dimensional ◽  
Large Data ◽  
Large Data Sets ◽  
Typical Result ◽  
Data Sets ◽  
Helium Gas ◽  
Synchrotron Light

As the combination of high-intensity synchrotron sources and area detectors allows collection of large data sets in a much shorter time span than previously possible, the use of open helium gas-flow systems is much facilitated. A flow system installed at the SUNY X3 synchrotron beamline at the National Synchrotron Light Source has been used for collection of a number of large data sets at a temperature of ∼16 K. Instability problems encountered when using a helium cryostat for three-dimensional data collection are eliminated. Details of the equipment, its temperature calibration and a typical result are described.

Inversion for magnetic anomalies of arbitrary three‐dimensional bodies

Geophysics ◽  
1990 ◽  
Vol 55 (10) ◽  
pp. 1321-1326 ◽  
Author(s):  
X. Wang ◽  
R. O. Hansen
Keyword(s):  
Three Dimensional ◽  
Large Data ◽  
Magnetic Anomalies ◽  
Large Data Sets ◽  
Data Sets ◽  
Inversion Algorithm ◽  
Geologic Model ◽  
Profile Inversion ◽  
Inversion Techniques ◽  
Medicine Lake

Two‐dimensional (profile) inversion techniques for magnetic anomalies are widely used in exploration geophysics: but, until now, the three‐dimensional (3-D) methods available have been restricted in their geologic applicability, dependent upon good initial values or limited by the capabilities of existing computers. We have developed a fully 3-D inversion algorithm intended for routine application to large data sets. The algorithm based on a Fourier transform expression for the magnetic field of homogeneous polyhedral bodies (Hansen and Wang, 1998), is a 3-D generalization of CompuDepth (O’Brien, 1972). Like CompuDepth, the new inversion algorithm employs thespatial equivalent of frequency‐domain autoregression to determine a series of coefficients from which the depths and locations of polyhedral vertices are calculated by solving complex polynomials. These vertices are used to build a 3-D geologic model. Application to the Medicine Lake Volcano aeromagnetic anomaly resulted in a geologically reasonable model of the source.

Wavelet Compression of Three-Dimensional Time-Lapse Biological Image Data

2005 ◽  
Vol 11 (1) ◽  
pp. 9-17 ◽  
Author(s):  
H. Narfi Stefansson ◽  
Kevin W. Eliceiri ◽  
Charles F. Thomas ◽  
Amos Ron ◽  
Ron DeVore ◽  
...  
Keyword(s):  
High Speed ◽  
Three Dimensional ◽  
Image Data ◽  
Large Data ◽  
Time Lapse ◽  
Large Data Sets ◽  
Multidimensional Data ◽  
Data Sets ◽  
Optical Sectioning ◽  
Dynamic Events

The use of multifocal-plane, time-lapse recordings of living specimens has allowed investigators to visualize dynamic events both within ensembles of cells and individual cells. Recordings of such four-dimensional (4D) data from digital optical sectioning microscopy produce very large data sets. We describe a wavelet-based data compression algorithm that capitalizes on the inherent redunancies within multidimensional data to achieve higher compression levels than can be obtained from single images. The algorithm will permit remote users to roam through large 4D data sets using communication channels of modest bandwidth at high speed. This will allow animation to be used as a powerful aid to visualizing dynamic changes in three-dimensional structures.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

Using Large Data Sets and Statistical Tools to Define “Failure” and Demonstrate the Safety of DPR

2018 ◽  
Vol 2018 (6) ◽  
pp. 38-39
Author(s):  
Austa Parker ◽  
Yan Qu ◽  
David Hokanson ◽  
Jeff Soller ◽  
Eric Dickenson ◽  
...  
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Statistical Tools
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