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About 5,199 papers for “Undersampling”
2024
Citation: 'undersampling' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.08297 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.
Cited by 0
Chevalier, Stéphane · Mendeley Data · 2025
The totally pre-processed data from the camera isn't included here, as it is weighing 2GB. It means that the first few sections from the Matlab code won't work, but everything is available starting from the section 3 "Linear Régressions". Links to relevant repos https://github.com/liuyang12/3DCS/tree/master/packages/TVAL3_beta2.4 https://pylops.readthedocs.io/ https://github.com/humatic/pylbfgs The setup.py file from the repo didn't work form me So the version I used is included here, but some modifications will be needed on a different machine. Not sure if it is Windows-compatible. Good luck!
Cited by 0Open access
Gary McGinley (6919646), Bård A. Bendiksen (6919649), Lili Zhang (120447) et al. · Figshare · 2019
<p>An example of representative undersampling masks for different undersampling factors, together with the magnitude data from the CS reconstructions.</p>
Cited by 0Open access
Chevalier, Stéphane · Mendeley Data · 2025
The totally pre-processed data from the camera isn't included here, as it is weighing 2GB. It means that the first few sections from the Matlab code won't work, but everything is available starting from the section 3 "Linear Régressions". Links to relevant repos https://github.com/liuyang12/3DCS/tree/master/packages/TVAL3_beta2.4 https://pylops.readthedocs.io/ https://github.com/humatic/pylbfgs The setup.py file from the repo didn't work form me So the version I used is included here, but some modifications will be needed on a different machine. Not sure if it is Windows-compatible. Good luck!
Cited by 0Open access
Bo Sun, Qian Zhou, Zhijun Wang et al. · Neurocomputing · 2023
Cited by 21
Gonzalo Nápoles, Isel Grau · Lecture notes in computer science · 2023
Cited by 0Open access
Pace, Phillip E., Leino, Richard E., Styer, David · Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School) · 2000
An antenna receives an analog waveform and an analog signal indicative of the amplitude and frequency of the analog waveform. The analog signal is processed in a plurality of parallel digital processing channels each arranged to digitize the analog signal at a corresponding sampling frequency fsi to produce a plurality of digital signals. A discrete Fourier transform is applied to each of the digital signals output to produce a corresponding plurality of unique Fourier spectra of length mi=(fs;)(TL;) where TLi is the integration time for the discrete Fourier transform for each digital processing channel. The lengths of the Fourier spectra (m;) are selected to be pairwise relatively prime. The discrete Fourier transform encodes the signals in same form as the symmetrical number system (SNS). A SNS-to-decimal algorithm is then applied to the detected bin values (a;) to determine the numerical value of the frequency f of the analog waveform. The receiver resolves all undersampling ambiguities exactly, thereby relaxing the speed requirements on the digital section of the receiver.
Cited by 0
David J. Field, Robert F. Hess · Vision Research · 1996
In a recent paper of ours [Hess & Field (1993). Vision Research, 33, 2663-2670], we claim that there was a predictable relationship between position errors and contrast errors for an undersampled system. In this paper we re-state our main points. We feel that the response to that paper by Levi and Klein in the accompanying article does not require us to produce changes in our original position. We believe that the data support the notion that the principal causes of the positional errors in the normal periphery and the in the amblyopic visual system are due to uncalibrated distortions in the local signs of visual neurons. We believe that undersampling plays a major role in producing positional errors only in the far periphery at, or very near, the acuity limit. We maintain that our initial studies provide strong evidence that undersampling is insufficient as an explanation for the positional errors in the periphery of normals (Hess & Field, 1993) or the central field of amblyopes [Hess & Field (1994). Vision Research, 34, 3397-3406.
Cited by 11Open access
Epigani · Zenodo (CERN European Organization for Nuclear Research) · 2023
release for publication
Cited by 0Open access
Epigani · Zenodo (CERN European Organization for Nuclear Research) · 2023
release for publication
Cited by 0Open access
Ekramul Haque Tusher (21324362), Mohd Arfian Ismail (18522493), Abdullah Akib (21609026) et al. · Figshare · 2025
<div> Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a pressing need for early diagnostic methods for HCV. This study investigates the prognostic accuracy of several ensemble machine learning (ML) models for diagnosing HCV infection. The study utilizes a dataset comprising demographic information of 615 individuals suspected of having HCV infection. Additionally, the research employs oversampling and undersampling techniques to address class imbalances in the dataset and conducts feature reduction using the F-test in one-way analysis of variance. Ensemble ML methods, including Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT), are used to predict HCV infection. The performance of these ensemble methods is evaluated using metrics such as accuracy, recall, precision, F1 score, G-mean, balanced accuracy, cross-validation (CV), area under the curve (AUC), standard deviation, and error rate. Compared with previous studies, the Bagging k-NN model demonstrated superior performance under oversampling conditions, achieving 98.37% accuracy, 98.23% CV score, 97.67% precision, 97.93% recall, 98.18% selectivity, 97.79% F1 score, 98.06% balanced accuracy, 98.05% G-mean, a 1.63% error rate, 0.98 AUC, and a standard deviation of 0.192. This study highlights the potential of ensemble ML approaches in improving the diagnosis of HCV. The findings provide a foundation for developing accurate predictive methods for HCV diagnosis. </div>
Cited by 0Open access
Haoming Wang (3185421), Xiangdong Liu (79858) · Figshare · 2021
<p>Without undersampling, the highest F2-score is 0.3975. Continuously increasing the undersampling rate does not blindly reduce the evaluation score. It is non-decreasing but increases when the undersampling rate is approximately 54% and 80%.</p>
Cited by 0Open access
Hatef Monajemi, David L. Donoho · Information and Inference A Journal of the IMA · 2018
Abstract We study anisotropic undersampling schemes like those used in multi-dimensional magnetic resonance (MR) spectroscopy and imaging, which sample exhaustively in certain time dimensions and randomly in others. Our analysis shows that anisotropic undersampling schemes are equivalent to certain block-diagonal measurement systems. We develop novel exact formulas for the sparsity/undersampling tradeoffs in such measurement systems, assuming uniform sparsity fractions in each column. Our formulas predict finite-$N$ phase transition behavior differing substantially from the well-known asymptotic phase transitions for classical Gaussian undersampling. Extensive empirical work shows that our formulas accurately describe observed finite-$N$ behavior, while the usual formulas based on universality are substantially inaccurate at the moderate $N$ involved in realistic applications. We also vary the anisotropy, keeping the total number of samples fixed, and for each variation we determine the precise sparsity/undersampling tradeoff (phase transition). We show that, other things being equal, the ability to recover a sparse object decreases with an increasing number of exhaustively sampled dimensions.
Cited by 8
Haoming Wang (3185421), Xiangdong Liu (79858) · Figshare · 2021
<p>LDA has a higher F2-measure when the undersampling is 30%, with a value of 0.3994, and has the lowest value at 54%; it is non-decreasing but increases after 54%.</p>
Cited by 0Open access
Hatef Monajemi, David L. Donoho · arXiv (Cornell University) · 2017
We study anisotropic undersampling schemes like those used in multi-dimensional NMR spectroscopy and MR imaging, which sample exhaustively in certain time dimensions and randomly in others. Our analysis shows that anisotropic undersampling schemes are equivalent to certain block-diagonal measurement systems. We develop novel exact formulas for the sparsity/undersampling tradeoffs in such measurement systems. Our formulas predict finite-N phase transition behavior differing substantially from the well known asymptotic phase transitions for classical Gaussian undersampling. Extensive empirical work shows that our formulas accurately describe observed finite-N behavior, while the usual formulas based on universality are substantially inaccurate. We also vary the anisotropy, keeping the total number of samples fixed, and for each variation we determine the precise sparsity/undersampling tradeoff (phase transition). We show that, other things being equal, the ability to recover a sparse object decreases with an increasing number of exhaustively-sampled dimensions.
Cited by 1Open access
Haoming Wang (3185421), Xiangdong Liu (79858) · Figshare · 2021
<p>The ENN score was the highest (0.423), the RENN score was slightly lower (0.079), and the other three undersampling scores were similar.</p>
Cited by 0Open access
Haoming Wang (3185421), Xiangdong Liu (79858) · Figshare · 2021
<p>All five sampling methods had similar scores; however, NCR had the lowest standard deviation.</p>
Cited by 0Open access
Hatef Monajemi, David L. Donoho · arXiv (Cornell University) · 2017
We study anisotropic undersampling schemes like those used in\nmulti-dimensional NMR spectroscopy and MR imaging, which sample exhaustively in\ncertain time dimensions and randomly in others.\n Our analysis shows that anisotropic undersampling schemes are equivalent to\ncertain block-diagonal measurement systems. We develop novel exact formulas for\nthe sparsity/undersampling tradeoffs in such measurement systems. Our formulas\npredict finite-N phase transition behavior differing substantially from the\nwell known asymptotic phase transitions for classical Gaussian undersampling.\nExtensive empirical work shows that our formulas accurately describe observed\nfinite-N behavior, while the usual formulas based on universality are\nsubstantially inaccurate.\n We also vary the anisotropy, keeping the total number of samples fixed, and\nfor each variation we determine the precise sparsity/undersampling tradeoff\n(phase transition). We show that, other things being equal, the ability to\nrecover a sparse object decreases with an increasing number of\nexhaustively-sampled dimensions.\n
Cited by 0Open access
Azam Dekamin, M.I.M. Wahab, Aziz Guergachi et al. · Clinica Chimica Acta · 2021
Cited by 0
Tamal K. Dey, Joachim Giesen · 2001
Current surface reconstruction algorithms perform satisfactorily on we ll-sampled, smooth surfaces without boundaries. However, these algorithms face difficulty with undersampling. Cases of undersampling are prevalent in real data since often they sample a part of the boundary of an object, or are derived from a surface with high curvature or nonsmoothness. In this paper we present an algorithm to detect the boundaries where dense sampling stops and undersampling begins. This information can be used to reconstruct surfaces with boundaries, and also to localize small and sharp features where usually undersampling happens. We report the effectiveness of the algorithm with a number of experimental results. Theoretically, we justify the algorithm with some mild assumptions that are valid for most practical data.
Cited by 109