scholarly journals Fast Classification of MPI Applications Using Lamport's Logical Clocks

2016 ◽  
Author(s):  
Zhou Tong ◽  
Scott Pakin ◽  
Michael Lang ◽  
Xin Yuan
Keyword(s):  
Mpi Applications ◽  
Fast Classification

scholarly journals Fast classification of MPI applications using Lamport’s logical clocks

2018 ◽  
Vol 120 ◽  
pp. 77-88 ◽  
Author(s):  
Zhou Tong ◽  
Scott Pakin ◽  
Michael Lang ◽  
Xin Yuan
Keyword(s):  
Mpi Applications ◽  
Fast Classification

Fast classification of tobacco based on laser-induced breakdown spectroscopy

2019 ◽  
Vol 12 (5) ◽  
pp. 1139-1146 ◽  
Author(s):  
李昂泽 LI Ang-ze ◽  
王宪双 WANG Xian-shuang ◽  
徐向君 XU Xiang-jun ◽  
何雅格 HE Ya-ge ◽  
郭 帅 GUO Shuai ◽  
...  
Keyword(s):  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Fast Classification

scholarly journals Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning

Sensors ◽  
2013 ◽  
Vol 13 (2) ◽  
pp. 1578-1592 ◽  
Author(s):  
Martin Längkvist ◽  
Silvia Coradeschi ◽  
Amy Loutfi ◽  
John Rayappan
Keyword(s):  
Thin Films ◽  
Feature Learning ◽  
Zno Thin Films ◽  
Unsupervised Feature Learning ◽  
Meat Spoilage ◽  
Fast Classification

Selection of Spectral Preprocessing Methods for Soil Texture Classification

2011 ◽  
Vol 181-182 ◽  
pp. 416-421 ◽  
Author(s):  
Hai Qing Yang ◽  
Bo Yan Kuang ◽  
Abdul M. Mouazen
Keyword(s):  
Texture Classification ◽  
Moving Average ◽  
Soil Samples ◽  
Normal Variation ◽  
Processing Methods ◽  
Score Plot ◽  
Standard Normal ◽  
Fast Classification ◽  
Selection Of

Fast classification of soil with different texture is essential for site-specific application of different inputs into farmland. Total 178 soil samples with five textures were collected from Silsoe Farm, Cranfield University, England. Using a Vis/NIR spectrophotometer (LabSpec2500, ASD), spectra of soil samples were scanned for the study. Amongst various pre-processing methods, smoothing with moving average(MA), standard normal variation(SNV) and 1st derivatives were mainly investigated. PCA was applied to evaluate the discriminative capacity of the pre-processing methods on soil spectra. The score plot of PCs shows that 1st derivative with variable smoothing points can help classify soil samples much more effectively than others. With the increase of smoothing points, the cumulative variance of first few PCs in PCA tends to increase while the discriminative capability based on these PCs becomes worse. A trade-off between cumulative variance and discriminative capability should be concerned. In the study, an appropriate range of smoothing points in the 1st derivative is 7-21.

Automatic fast classification of product-images with class-specific descriptor

2010 ◽  
Vol 27 (6) ◽  
pp. 808-814 ◽  
Author(s):  
Shijie Jia ◽  
Xiangwei Kong ◽  
Guang Jin
Keyword(s):  
Fast Classification
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