This paper develops and explores applications of a linear shaping transformation that minimizes the mean squared error (MSE) between the original and shaped data, i.e., that results in an output vector with the desired covariance that is as close as possible to the input, in an MSE sense. Three applications of minimum MSE shaping are considered, specifically matched filter detection, multiuser ...

The performance of the domain based local pair-natural orbital coupled-cluster (DLPNO-CCSD(T)) method has been tested to reproduce the experimental gas phase ligand dissociation enthalpy in a series of Cu(+), Ag(+), and Au(+) complexes. For 33 Cu(+)-noncovalent ligand dissociation enthalpies, all-electron calculations with the same method result in MUE below 2.2 kcal/mol, although a MSE of 1.4 ...

Antitumor activities have been described in selol, a hydrophobic mixture of molecules containing selenium in their structure, and also in maghemite magnetic nanoparticles (MNPs). Both selol and MNPs were co-encapsulated within poly(lactic-co-glycolic acid) (PLGA) nanocapsules for therapeutic purposes. The PLGA-nanocapsules loaded with MNPs and selol were labeled MSE-NC and characterized by tran...

PURPOSE Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein's unbiased risk estimate that minimizes the multichannel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods tha...

In this paper, new classes of lower bounds on the outage error probability and on the mean-square-error (MSE) in Bayesian parameter estimation are proposed. The minima of the h-outage error probability and the MSE are obtained by the generalized maximum a-posteriori probability and the minimum MSE (MMSE) estimators, respectively. However, computation of these estimators and their corresponding ...

Purpose: Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein’s unbiased risk estimate that minimizes the multichannel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods th...

Multiscale entropy (MSE) analysis provides information about complexity on various time scales. The aim of this study was to test whether MSE is able to detect autonomic dysregulation in young patients with diabetes mellitus (DM). We analyzed heart rate (HR) oscillations, systolic (SBP) and diastolic blood pressure (DBP) signals in 14 patients with DM type 1 and 14 age- and sex-matched healthy ...