Preparation and photoluminescence properties of aluminate phosphors produced by combustion synthesis.

In this work, Eu, Nd co-doped MAl2O4:Eu, Nd (M = Ca, Sr, Ba) phosphors were synthesized at low temperatures (550 °C) by the combustion method. The crystallinity of the phosphors was monitored by X-ray diffraction (XRD) and the morphology was examined by scanning electron microscope (SEM). Synthesis of phosphors, the effect of lanthanide concentrations on light emission intensity and duration investigated by using photoluminescence (PL) measurements. Narrow orange-red emissions from 500 to 750 nm in the PL spectra are assigned to 5D0 → 7Fj (j = 0,1,2,3,…) transitions of Eu3+ ion. In contrast, the broad luminescence band of the samples in the range of 400-500 nm are attributed to the 5d-4 f transitions of Eu3+ ion in the same host materials. Investigated the effects of radiation on the severity of the trap depths of these structures. The decay curves of these phosphors show how long the phosphors are attenuated. Thermoluminescence (TL) glow curves have been recorded from room temperature to 300 °C at a constant heating rate of 1 °C/s after preheat process at 130 °C for 10 s using lexsyg smart TL/OSL reader. Nd3+ trap levels can be thought of as the lanthanide element that causes long composition in the phosphorescence structure at room temperature.

Multiresolution multiresource progressive image transmission.

This paper presents a new progressive image transmission (PIT) design algorithm in which the resolution and resources (rate or distortion and storage size) at each transmission stage are allowed to be prespecified. This algorithm uses the wavelet transform and tree-structured vector quantizer (TSVQ) techniques. The wavelet transform is used to obtain a pyramid structure representation of an image. The vector quantizer technique is used to design a TSVQ for each subimage so that all the subimages that constitute the image at the current stage can be successively refined according to the resources available at that stage. The resources assigned to each subimage for the successive refinement is determined to optimize the performance at the current stage under the resource constraints. Normally, the resource constraints at each stage are determined by the specification of the transmission time or distortion for image data and the storage complexity of the TSVQ. The resolution at each stage is determined/specified according to the application or as part of the design process to optimize the visual effect.

Detection of splenosis by radionuclide scanning.

Splenosis is the autotransplantation of splenic tissue following trauma. The presence of residual nodules following splenectomy has been investigated by a sensitive scanning method employing reinjection of 99Tcm-labelled, heat-damaged autologous erythrocytes. Splenosis was detected in 11 of 19 patients who had had splenectomy for traumatic rupture of spleen. Four of them had multiple nodules, the others a single nodule. In one case, the splenic nodule did not take up the sulphur colloid, although it could be visualised on selective splenic scan. We found no splenic nodules in 23 patients who had splenectomy for non-traumatic reasons. It is concluded that the key factor in splenosis is trauma.

Modeling and segmentation of noisy and textured images using gibbs random fields.

This paper presents a new approach to the use of Gibbs distributions (GD) for modeling and segmentation of noisy and textured images. Specifically, the paper presents random field models for noisy and textured image data based upon a hierarchy of GD. It then presents dynamic programming based segmentation algorithms for noisy and textured images, considering a statistical maximum a posteriori (MAP) criterion. Due to computational concerns, however, sub-optimal versions of the algorithms are devised through simplifying approximations in the model. Since model parameters are needed for the segmentation algorithms, a new parameter estimation technique is developed for estimating the parameters in a GD. Finally, a number of examples are presented which show the usefulness of the Gibbsian model and the effectiveness of the segmentation algorithms and the parameter estimation procedures.

Estimating Components of Univariate Gaussian Mixtures Using Prony's Method.

A new technique for estimating the component parameters of a mixture of univariate Gaussian distributions using the method of moments is presented. The method of moments basically involves equating the sample moments to the corresponding mixture moments expressed in terms of component parameters and solving these equations for the unknown parameters. These moment equations, however, are nonlinear in the unknown parameters, and heretofore, an analytic solution of these equations has been obtained only for two-component mixtures [2]. Numerical solutions also tend to be unreliable for more than two components, due to the large number of nonlinear equations and parameters to be solved for. In this correspondence, under the condition that the component distributions have equal variances or equal means, the nonlinear moment equations are transformed into a set of linear equations using Prony's method. The solution of these equations for the unknown parameters is analytically feasible and numerically reliable for mixtures with several components. Numerous examples using the proposed technique for two-, three-, and four-component mixtures are presented.

Bayes smoothing algorithms for segmentation of binary images modeled by markov random fields.

A new image segmentation algorithm is presented, based on recursive Bayes smoothing of images modeled by Markov random fields and corrupted by independent additive noise. The Bayes smoothing algorithm yields the a posteriori distribution of the scene value at each pixel, given the total noisy image, in a recursive way. The a posteriori distribution together with a criterion of optimality then determine a Bayes estimate of the scene. The algorithm presented is an extension of a 1-D Bayes smoothing algorithm to 2-D and it gives the optimum Bayes estimate for the scene value at each pixel. Computational concerns in 2-D, however, necessitate certain simplifying assumptions on the model and approximations on the implementation of the algorithm. In particular, the scene (noiseless image) is modeled as a Markov mesh random field, a special class of Markov random fields, and the Bayes smoothing algorithm is applied on overlapping strips (horizontal/vertical) of the image consisting of several rows (columns). It is assumed that the signal (scene values) vector sequence along the strip is a vector Markov chain. Since signal correlation in one of the dimensions is not fully used along the edges of the strip, estimates are generated only along the middle sections of the strips. The overlapping strips are chosen such that the union of the middle sections of the strips gives the whole image. The Bayes smoothing algorithm presented here is valid for scene random fields consisting of multilevel (discrete) or continuous random variables.