Results 11 to 20 of about 148,752 (225)
Remote Sensing, 2017 The Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) method to downscale Landsat-8 Operational Land Imager (OLI) 30-m data to Sentinel-2 multi-spectral instrument (MSI) 20-m resolution is presented. The method first downscales the Landsat-8 30-m OLI
Zhongbin Li +5 more
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A Dispersion Compensation Method Based on Resampling of Modulated Signal for FMCW Lidar
Sensors, 2021 In order to eliminate the nonlinearity in the laser modulation process, the dual-interferometers system is often adopted in the frequency modulation continuous wave (FMCW) laser ranging.
Shuo Jiang, Bo Liu, Shengjie Wang
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Reference sample size for multiple regression in corn [PDF]
Pesquisa Agropecuária Brasileira, 2019 : The objective of this work was to determine the number of plants required to model corn grain yield (Y) as a function of ear length (X1) and ear diameter (X2), using the multiple regression model Y = β0 + β1X1 + β2X2.
Alberto Cargnelutti Filho, Marcos Toebe
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Frontiers in Applied Mathematics and Statistics, 2023 When initial sample information falls short of enabling industrial engineers to confidently make decisions about lot quality assessment, repetitive sampling emerges as a solution.
Jose J. Muñoz +2 more
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Revealing Traces of Image Resampling and Resampling Antiforensics
Advances in Multimedia, 2017 Image resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis.
Anjie Peng, Yadong Wu, Xiangui Kang
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Arrhythmia detection using resampling and deep learning methods on unbalanced data
Компьютерная оптика, 2022 Due to cardiovascular diseases millions of people die around the world. One way to detect abnormality in the heart condition is with the help of electrocardiogram signal (ECG) analysis.
E.Y. Shchetinin, A.G. Glushkova
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Resampling: an improvement of Importance Sampling in varying population size models [PDF]
, 2016 Sequential importance sampling algorithms have been defined to estimate likelihoods in models of ancestral population processes. However, these algorithms are based on features of the models with constant population size, and become inefficient when the ...
Leblois, Raphaël +3 more
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Sample size for estimation of the Pearson correlation coefficient in cherry tomato tests
Ciência Rural, 2017 : The aim of this study was to determine the required sample size for estimation of the Pearson coefficient of correlation between cherry tomato variables. Two uniformity tests were set up in a protected environment in the spring/summer of 2014.
Bruno Giacomini Sari +5 more
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A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
Applied Sciences, 2020 Imbalanced classification is one of the most important problems of machine learning and data mining, existing in many real datasets. In the past, many basic classifiers such as SVM, KNN, and so on have been used for imbalanced datasets in which the ...
Huajuan Duan +3 more
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A sequential Monte Carlo approach to computing tail probabilities in stochastic models [PDF]
, 2011 Sequential Monte Carlo methods which involve sequential importance sampling and resampling are shown to provide a versatile approach to computing probabilities of rare events.
Chan, Hock Peng, Lai, Tze Leung
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