Spatial correlation model based observation vector clustering and MVDR beamforming for meeting recognition

2016 ◽  
Author(s):  
Shoko Araki ◽  
Masahiro Okada ◽  
Takuya Higuchi ◽  
Atsunori Ogawa ◽  
Tomohiro Nakatani
Keyword(s):  
Spatial Correlation ◽  
Correlation Model ◽  
Model Based ◽  
Vector Clustering ◽  
Observation Vector ◽  
Mvdr Beamforming

Spatial Correlation Interpretation of Effects of As+ Implantation on the Raman Spectra of GaAs

1983 ◽  
Vol 27 ◽  
Author(s):  
D.E. Aspnes ◽  
K.K. Tiong ◽  
P.M. Amirtharaj ◽  
F.H. Pollak
Keyword(s):  
Spatial Correlation ◽  
Raman Spectra ◽  
Selection Rule ◽  
Phonon Mode ◽  
Red Shift ◽  
Correlation Model ◽  
Model Based ◽  
Ion Implanted

ABSTRACTThe red shift and asymmetric broadening of the LO phonon mode of ion-implanted GaAs are both described quantitatively by a spatial correlation model based on a damage-induced relaxation of the momentum selection rule previously used by Richter, Wang, and Ley to describe similar effects in microcrystalline Si. The success of the model for a qualitatively different disorder microstructure suggests it may be possible to evaluate average sizes of crystallographically perfect regions in semiconductors from the phonon lineshapes of their Raman spectra.

A New Spatial Correlation Model Based on the Distributed RC-∏ Model

2014 ◽  
Vol 989-994 ◽  
pp. 2204-2207
Author(s):  
Xiao Xiao Liu ◽  
Jing Bo Shao ◽  
Ling Ling Zhao
Keyword(s):  
Gaussian Process ◽  
Spatial Correlation ◽  
Process Variations ◽  
Deep Submicron ◽  
Correlation Model ◽  
Crosstalk Noise ◽  
Model Based ◽  
Reduction Techniques ◽  
Non Gaussian ◽  
Tree Branch

To solve the crosstalk noise question in deep-submicron technologies, a new spatial correlation model based on the distributed RC-π model is proposed in this paper. Quiet aggressor net and tree branch reduction techniques are introduced to the distributed RC-π model, and a new spatial correlation model of both Gaussian and non-Gaussian process variations among segments is created. Experimental results show that our method maintains the efficiency of past approaches, and significantly improves on their accuracy.

Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications

2018 ◽  
Vol 48 (6) ◽  
pp. 642-649 ◽  
Author(s):  
Ronald E. McRoberts ◽  
Erik Næsset ◽  
Terje Gobakken ◽  
Gherardo Chirici ◽  
Sonia Condés ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Spatial Correlation ◽  
Laser Scanning ◽  
Satellite Image ◽  
Error Estimator ◽  
Residual Variance ◽  
Mean Square ◽  
Model Parameter ◽  
Model Based ◽  
Residual Covariance

Model-based inference is an alternative to probability-based inference for small areas or remote areas for which probability sampling is difficult. Model-based mean square error estimators incorporate three components: prediction covariance, residual variance, and residual covariance. The latter two components are often considered negligible, particularly for large areas, but no thresholds that justify ignoring them have been reported. The objectives of the study were threefold: (i) to compare analytical and bootstrap estimators of model parameter covariances as the primary factors affecting prediction covariance; (ii) to estimate the contribution of residual variance to overall variance; and (iii) to estimate thresholds for residual spatial correlation that justify ignoring this component. Five datasets were used, three from Europe, one from Africa, and one from North America. The dependent variable was either forest volume or biomass and the independent variables were either Landsat satellite image bands or airborne laser scanning metrics. Three conclusions were noteworthy: (i) analytical estimators of the model parameter covariances tended to be biased; (ii) the effects of residual variance were mostly negligible; and (iii) the effects of spatial correlation on residual covariance vary by multiple factors but decrease with increasing study area size. For study areas greater than 75 km2 in size, residual covariance could generally be ignored.

Machine-learned electron correlation model based on correlation energy density at complete basis set limit

2019 ◽  
Vol 151 (2) ◽  
pp. 024104 ◽  
Author(s):  
Takuro Nudejima ◽  
Yasuhiro Ikabata ◽  
Junji Seino ◽  
Takeshi Yoshikawa ◽  
Hiromi Nakai
Keyword(s):  
Energy Density ◽  
Electron Correlation ◽  
Correlation Energy ◽  
Basis Set ◽  
Correlation Model ◽  
Model Based ◽  
Complete Basis ◽  
Complete Basis Set ◽  
Complete Basis Set Limit

scholarly journals PROPOSAL OF THE SPATIAL DEPENDENCE EVALUATION FROM THE POWER SEMIVARIOGRAM MODEL

2017 ◽  
Vol 23 (3) ◽  
pp. 461-475 ◽  
Author(s):  
Ismael Canabarro Barbosa ◽  
Edemar Appel Neto ◽  
Enio Júnior Seidel ◽  
Marcelo Silva de Oliveira
Keyword(s):  
Spatial Correlation ◽  
Spatial Dependence ◽  
Second Order ◽  
Power Model ◽  
Semivariogram Model ◽  
Model Based ◽  
Symmetrical Distribution ◽  
Model Adjustment ◽  
Equivalent Factor

Abstract: In Geostatistics, the use of measurement to describe the spatial dependence of the attribute is of great importance, but only some models (which have second-order stationarity) are considered with such measurement. Thus, this paper aims to propose measurements to assess the degree of spatial dependence in power model adjustment phenomena. From a premise that considers the equivalent sill as the estimated semivariance value that matches the point where the adjusted power model curves intersect, it is possible to build two indexes to evaluate such dependence. The first one, SPD * , is obtained from the relation between the equivalent contribution (α) and the equivalent sill (C * = C 0 + α), and varies from 0 to 100% (based on the calculation of spatial dependence areas). The second one, SDI * , beyond the previous relation, considers the equivalent factor of model (FM * ), which depends on the exponent β that describes the force of spatial dependence in the power model (based on spatial correlation areas). The SDI * ,for β close to 2, assumes its larger scale, varying from 0 to 66.67%. Both indexes have symmetrical distribution, and allow the classification of spatial dependence in weak, moderate and strong.

Quality of ZnSe/GaAs epilayers studied by spatial correlation model of Raman scattering

1993 ◽  
Vol 62 (22) ◽  
pp. 2845-2847 ◽  
Author(s):  
J. Wang ◽  
W. H. Yao ◽  
J. B. Wang ◽  
H. Q. Lu ◽  
H. H. Sun ◽  
...  
Keyword(s):  
Raman Scattering ◽  
Spatial Correlation ◽  
Correlation Model

scholarly journals A Generalized Spatial Correlation Model for 3D MIMO Channels Based on the Fourier Coefficients of Power Spectrums

2015 ◽  
Vol 63 (14) ◽  
pp. 3671-3686 ◽  
Author(s):  
Qurrat-Ul-Ain Nadeem ◽  
Abla Kammoun ◽  
Merouane Debbah ◽  
Mohamed-Slim Alouini
Keyword(s):  
Spatial Correlation ◽  
Fourier Coefficients ◽  
Correlation Model ◽  
Mimo Channels ◽  
3D Mimo
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