The classical Radon transform can be thought of as a way to obtain the density of an n-dimensional object from its (n− 1)-dimensional sections in di erent directions. A generalization of this transform to in nite-dimensional spaces has the potential to allow one to obtain a function de ned on an in nite-dimensional space from its conditional expectations. We work within a standard framework in ...

Deblurring in the presence of noise is a hard problem, especially in Ultrasound and CT images. In this paper, we propose a hybrid approach of wavelet-based image deconvolution that incorporates Fourier-domain system inversion followed by wavelet-domain noise suppression. In contrast to conventional wavelet-based deconvolution methods, the algorithm employs a regularized inverse filter to operat...

The shambles mentioned at the beginning of Part I referred to the confusion in the use of standard deviations and standard errors of means,' with the latter apparently more popular, perhaps because they are so much smaller than their parent standard deviations and thus suggest higher precision. The author defined the standard deviation as representing the variation among the individual measurem...

BACKGROUND Conventional mixed models for the analysis of diet diary data have introduced several simplifying assumptions, such as that of a single standard deviation for within-person day-to-day variation which is common to all individuals. OBJECTIVE We developed a model in which the within-person standard deviation was allowed to differ from person to person. DESIGN The model was demonstra...

The time-arrival location of seismic P-wave is an important problem in the seismic research field. The modulus maxima of wavelet transform is a useful method for picking up the singularities of function. For applying the modulus maxima method to investigate the arrival time, it is necessary to eliminate the influence of random factors. Based on standard deviation, we present a method to reduce ...

We propose a statistical framework for high-level feature (HLF) extraction, which employs scale-invariant feature transform Gaussian mixture models (SIFT GMMs), acoustic features, and maximal figure-of-merit (MFoM). The MeanInfAP of our best run was 0.1679. Our team placed 11th after all of the runs and 4th among all participating teams. Notably, the InfAPs of “Singing” and “People-dancing” wer...