background: various variance reduction techniques such as forced detection (fd) have been implemented in monte carlo (mc) simulation of nuclear medicine in an effort to decrease the simulation time while keeping accuracy. however most of these techniques still result in very long mc simulation times for being implemented into routine use. materials and methods: convolution-based forced detectio...

Background: Various variance reduction techniques such as forced detection (FD) have been implemented in Monte Carlo (MC) simulation of nuclear medicine in an effort to decrease the simulation time while keeping accuracy. However most of these techniques still result in very long MC simulation times for being implemented into routine use. Materials and Methods: Convolution-based force...

This paper continues the investigation in digital signal processing of spectral analysis on certain non-commutative nite groups|wreath product groups. We describe here the generalization of discrete cyclic convolution to convolution over these groups and show how it reduces to multiplication in the spectral domain. Finite group based convolution is deened in both the spatial and spectral domain...

A rotation-based Monte Carlo (MC) simulation method (RMC) has been developed, designed for rapid calculation of downscatter through non-uniform media in SPECT. A possible application is downscatter correction in dual isotope SPECT. With RMC, only a fraction of all projections of a SPECT study have to be MC simulated in a standard manner. The other projections can be estimated rapidly using the ...

This paper proposes a novel convolution–non-convolution parallel deep network (CNCP)-based method for electricity theft detection. First, the load time series of normal residents and thieves were analyzed it was found that, compared with thieves, residents’ present more obvious periodicity in different scales, e.g., weeks years; second, times converted into 2D images according to periodicity, t...

We present a novel method that finds edges between certain image features, e.g. gray-levels, and disregards edges between other features. The method uses a channel representation of the features and performs normalized convolution using the channel values as certainties. This means that areas with certain features can be disregarded by the