Refinements in phase fraction determination of textured alloys from transmission diffraction data

The use of high-energy synchrotron X-ray diffraction sources has become increasingly common for high-quality phase fraction measurements and microstructural evolution experiments. While the high flux, large volume illuminated and large number of diffraction vectors should reduce common sources of uncertainty and bias, the distribution of the diffraction vectors may still cause bias in the phase fraction measurement. This hypothesis of bias was investigated with example experimental data and synthetic data. The authors found that there may be bias depending on the sample texture, the distribution of diffraction vectors and the hkl planes used in the phase fraction measurement, even for nearly complete coverage of a pole figure. The authors developed a series of geometry-based correction values that reduced the measurement bias due to sampling scheme and texture in the phase fraction measurement by an order of magnitude. The efficacy of these corrections was demonstrated with application to both experimental and synthetic data.

Novel Synthesis Method for Image of Materials Texture

Most patch-based texture synthesis algorithms using Markov Random Field for composite materials only considers color similarity between the corresponding pixels. The traditional algorithms are lack of adaptability, so the size of patches needs to be defined artificially in advance as the result of blurring of image texture features for composite materials. In order to improve above problems, a new patch-based sampling algorithm for synthesizing textures from an input sample image texture of composite materials is presented in this paper. By using patches of the sample texture as building blocks for image texture synthesis of composite materials, this algorithm makes high-quality texture synthesis for a wide variety of textures ranging regular to stochastic. The method is effective by our experimental results.

A New Kind of Texture Synthesis Method Based on Best Match

In order to improve above problems, this paper presents a new patch-based sampling algorithm for synthesizing textures from an input sample texture. We determine the size of block analyzing the effect of the best matched texture to synthesis result, and the result is satisfying. Moreover, the patch-based sampling algorithm remains effective when pixel-based non-parametric sampling algorithms fail to produce good results.

Bacterial and Archaeal Diversity in Sediments of West Lake Bonney, McMurdo Dry Valleys, Antarctica

ABSTRACTBacterial and archaeal diversity was examined in a sediment core from Lake Bonney, Antarctica. Members of theArchaeashowed both low abundance and diversity, whereas bacterial diversity was moderately high and some phyla were fairly abundant, even in geologically old samples. Microbial diversity correlated with sample texture and differed in silty and coarse samples.

REAL-TIME SEAMLESS TEXTURE SYNTHESIS BASED ON PATCH QUANTIZATION CLUSTERING

In this paper, we present a novel method for the optimal texture sampling and texture synthesis using vector quantization clustering. The synthesizing process selects an initial texture block from the input sample texture and then iteratively searches all the texture patches from the sampled texture space for a patch whose boundary best matches the current boundary of the synthesized texture. To control the number of the searching patches while preserving the seamless visual quality, we present two effective patch sampling techniques, namely uniformly distributed patch sampling and patch quantization clustering. The sampled texture patches will then be further optimized using principle component analysis. In this scheme, the complexity of the patch search in each step can be significantly reduced and thus the seamless texture synthesis can be performed in real-time. Experimental results show that the proposed schemes are of excellent efficiency for the visually seamless texture synthesis of a wide variety of textures ranging from regular to stochastic can be achieved.

Texture Measurement of Sintered Alumina Using the Marchdollase Function

The Rietveld method entails the calculation of a powder diffraction pattern from crystallographic, microstructural and equipment characteristics. These characteristics are related to the form of the pattern through a series of model functions. The difference between an observed and calculated pattern is then minimized by sequentially refining the physical parameters contained within the mode) functions to obtain an accurate and precise description of the specimen. A powder diffraction pattern from a specimen exhibiting cry stallo graphic texture, or preferred orientation, will display intensity values which differ systematically from those calculated for a specimen of random orientation. This systematic discrepancy can be addressed by incorporating into the Rietveld refinement a model function for sample texture. A successful model for texture will accurately assess the phase abundance and degree of texture from both oriented and randomized specimens. In this study we use the March-Dollase model function to characterize texture development in sintered alumina with respect to processing variables and sintering time.