The sparsity which is implicit in MR images is exploited to significantly undersample k -space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial finite-differences or their wavelet coefficients. According to the recently developed mathematical th...

We propose a new mechanism for undersampling chaotic numbers obtained by the ring coupling of one-dimensional maps. In the case of 2 coupled maps this mechanism allows the building of a PRNG which passes all NIST Test. This new geometric undersampling is very effective for generating 2 parallel streams of pseudo-random numbers, as we show, computing carefully their properties, up to sequences o...

Methods Variable-density Poisson Disk Distribution (VD-PDD) undersampling was applied for 4D flow imaging. By applying VD-PDD independently at each time frame (Figure 1a), we achieved random undersampling in both the ky-kz plane and temporal domains. In addition, we applied an improved initial solution for SPIRIT to significantly improve reconstruction accuracy and robustness. We explored the d...

Early predicting heart attack out of stroke patients in a view data analysis is an approach to reduce high mortality rate. Stroke-patient Intensive Care Unit are imbalanced due that with the minority patients. How predict stroke-patient becomes challenge. For processing data, this paper designs algorithm by leveraging random undersampling, clustering and oversampling techniques, which called un...

INTRODUCTION In many MR applications, it is desirable to achieve high spatial and temporal resolution at the same time, such as contrast-enhanced MR angiography (CE-MRA). However, fully-sampled Cartesian acquisition for large matrices is extremely time consuming and precludes the simultaneous achievement of both goals. Several Cartesian undersampling methods, such as CAPR [1], TRIPPS [2] and va...

Abstract Uplift modeling refers to individual level causal inference. Existing research on the topic ignores one prevalent and important aspect: high class imbalance. For instance in online environments uplift is used optimally target ads discounts, but very few users ever end up clicking an ad or buying. One common approach deal with imbalance classification by undersampling dataset. In this w...

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we combine random selection under-sampling into SMOTE over-sampling technique, working with several classification algorithms from machine learning field to integrate binding site predictions. In this paper, we improve the classification result with the aid of Tomek ...

A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed the problem of high false negative positive rates in gearboxes. Normal samples raw data were subjected to undersampling first, various features labels provided with importance analysis by selection select higher label correla...

Ghost imaging reconstructs images using a single-element photodetector; it performs by illuminating an object with binary modulation patterns. This technique has various advantages, including wide wavelength, noise robustness, and high measurement sensitivity. However, one challenge is the low image quality in undersampling. The examination of patterns intended to solve this issue. In ghost ima...

Introduction: Flexible radial imaging allows multiple image sets, each having a different spatiotemporal balance, to be retrospectively reconstructed from the same dataset [1,2]. One of the applications that may benefit from this flexibility is dynamic contrast-enhanced breast imaging, in which the optimal spatiotemporal balance for image diagnosis is unknown. Images from radial undersampling h...