Dual-energy computed tomography and the Alvarez and Macovski [Phys. Med. Biol. 21, 733 (1976)] transmitted intensity (AMTI) model were used in this study to estimate the maps of density (q) and atomic number (Z) of mineralogical samples. In this method, the attenuation coefficients are represented [Alvarez and Macovski, Phys. Med. Biol. 21, 733 (1976)] in the form of the two most important interactions of X-rays with atoms that is, photoelectric absorption (PE) and Compton scattering (CS). This enables material discrimination as PE and CS are, respectively, dependent on the atomic number (Z) and density (q) of materials [Alvarez and Macovski, Phys. Med. Biol. 21, 733 (1976)]. Dual-energy imaging is able to identify sample materials even if the materials have similar attenuation coefficients at single-energy spectrum. We use the full model rather than applying one of several applied simplified forms [
In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).
In statistics, the index of dispersion (or variance-to-mean ratio) is unity (σ/〈x〉 = 1) for a Poisson-distributed process with variance σ for a variable x that manifests as unit increments. Where x is a measure of some phenomenon, the index takes on a value proportional to the quanta that constitute the phenomenon. That outcome might thus be anticipated to apply for an enormously wide variety of applied measurements of quantum phenomena. However, in a photon-energy proportional radiation detector, a set of M witnessed Poisson-distributed measurements {W, W,… W} scaled so that the ideal expectation value of the quantum is unity, is generally observed to give σ/〈W〉 < 1 because of detector losses as broadly indicated by Fano [Phys. Rev. (1947), 72, 26]. In other cases where there is spectral dispersion, σ/〈W〉 > 1. Here these situations are examined analytically, in Monte Carlo simulations, and experimentally. The efforts reveal a powerful metric of quanta broadly associated with such measurements, where the extension has been made to polychromatic and lossy situations. In doing so, the index of dispersion's variously established yet curiously overlooked role as a metric of underlying quanta is indicated. The work's X-ray aspects have very diverse utility and have begun to find applications in radiography and tomography, where the ability to extract spectral information from conventional intensity detectors enables a superior level of material and source characterization.
In this paper, we develop a dual-energy ordered subsets convex method for transmission tomography based on material matching with a material dictionary. This reconstruction includes a constrained update forcing material characteristics of reconstructed atomic number (Z) and density (ρ) volumes to follow a distribution according to the material database provided. We also propose a probabilistic classification technique in order to manage this material distribution. The overall process produces a chemically segmented volume data and outperforms sequential labelling computed after tomographic reconstruction.
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