scholarly journals Nuclear effect in higher-dimensional factorial moment analysis of the $^{16}$ O-, $^{32}$ S- and $^{197}$ Au-Em interaction data at 200, 60 and 11 A GeV/c

1997 ◽  
Vol 76 (4) ◽  
pp. 659-663 ◽  
Author(s):  
M.I. Adamovich et al.
Keyword(s):  
Factorial Moment ◽  
Moment Analysis ◽  
Interaction Data ◽  
Nuclear Effect ◽  
Higher Dimensional

Scaled-factorial-moment analysis of 200A-GeV sulfur+gold interactions

1990 ◽  
Vol 65 (4) ◽  
pp. 412-415 ◽  
Author(s):  
M. I. Adamovich ◽  
M. M. Aggarwal ◽  
Y. A. Alexandrov ◽  
Z. V. Ameeva ◽  
N. P. Andreeva ◽  
...  
Keyword(s):  
Factorial Moment ◽  
Moment Analysis

scholarly journals ON THE FACTORIAL MOMENT ANALYSIS OF HIGH ENERGY EXPERIMENTAL DATA WITH NONINTEGER PARTITION NUMBER

1999 ◽  
Vol 14 (23) ◽  
pp. 3687-3697 ◽  
Author(s):  
GANG CHEN ◽  
LIANSHOU LIU ◽  
YANMIN GAO
Keyword(s):  
Experimental Data ◽  
Phase Space ◽  
Factorial Moment ◽  
High Energy ◽  
Moment Analysis ◽  
Systematic Method ◽  
Particle Correlations ◽  
Partition Number ◽  
Self Consistency

It is pointed out that in doing the factorial moment analysis with noninteger partition M of phase–space, the influence of the phase–space variation of two (or more) particle correlations has to be considered carefully. In this paper this problem is studied and a systematic method is developed to minimize this influence. The efficiency and self-consistency of this method are shown using the data of 250 GeV /c π+p and K+p collisions from the NA22 experiment as example.

“I Can See What You’re Saying”: Clinical Utility of Spectral Moment Analysis

2011 ◽  
Vol 21 (2) ◽  
pp. 44-54
Author(s):  
Kerry Callahan Mandulak
Keyword(s):  
Speech Production ◽  
Speech Signal ◽  
Clinical Utility ◽  
Acoustic Analysis ◽  
Moment Analysis ◽  
Analysis Tool ◽  
Spectral Moment ◽  
Clinical Measure ◽  
Perceptual Analysis ◽  
Disordered Speech

Spectral moment analysis (SMA) is an acoustic analysis tool that shows promise for enhancing our understanding of normal and disordered speech production. It can augment auditory-perceptual analysis used to investigate differences across speakers and groups and can provide unique information regarding specific aspects of the speech signal. The purpose of this paper is to illustrate the utility of SMA as a clinical measure for both clinical speech production assessment and research applications documenting speech outcome measurements. Although acoustic analysis has become more readily available and accessible, clinicians need training with, and exposure to, acoustic analysis methods in order to integrate them into traditional methods used to assess speech production.

Time-Dependent Bending Moment Analysis for Pile-Raft Foundation of Super Tall Buildings

2015 ◽  
Vol 105 (39) ◽  
pp. 1-8
Author(s):  
Shehong Liu ◽  
Juyun Yuan ◽  
Xin Zhao
Keyword(s):  
Tall Buildings ◽  
Bending Moment ◽  
Moment Analysis ◽  
Time Dependent ◽  
Raft Foundation

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

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