Estimation of missing data using windowed linear prediction in laterally truncated projection & in cone beam CT

In this paper, we address the reconstruction problem from laterally truncated helical cone-beam projections. The reconstruction problem from lateral truncation, though similar to that of interior radon problem, is slightly different from it as well as the local (lambda) tomography and pseudo-local tomography in the sense that we aim to reconstruct the entire object being scanned from a region-of-interest (ROI) scan data. The method proposed in this paper is a projection data completion approach followed by the use of any standard accurate FBP type reconstruction algorithm. In particular, we explore a windowed linear prediction (WLP) approach for data completion and compare the quality of reconstruction with the linear prediction (LP) technique proposed earlier.

Behavior of Sandwich Beams With Functionally Graded Rubber Core in Three Point Bending

The three-point bending behavior of sandwich beams made up of jute epoxy skins and piecewise linear functionally graded (FG) rubber core reinforced with fly ash filler is investigated. This work studies the influence of the parameters such as weight fraction of fly ash, core to thickness ratio, and orientation of jute on specific bending modulus and strength. The load displacement response of the sandwich is traced to evaluate the specific modulus and strength. FG core samples are prepared by using conventional casting technique and sandwich by hand layup. Presence of gradation is quantified experimentally. Results of bending test indicate that specific modulus and strength are primarily governed by filler content and core to sandwich thickness ratio. FG sandwiches with different gradation configurations (uniform, linear, and piecewise linear) are modeled using finite element analysis (ANSYS 5.4) to evaluate specific strength which is subsequently compared with the experimental results and the best gradation configuration is presented. POLYM. COMPOS., 32:1541-1551, 2011. (C) 2011 Society of Plastics Engineers

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.

Real-time Computer Simulation of Three Dimensional Elastostatics using the Finite Point Method

Real-time simulation of deformable solids is essential for some applications such as biological organ simulations for surgical simulators. In this work, deformable solids are approximated to be linear elastic, and an easy and straight forward numerical technique, the Finite Point Method (FPM), is used to model three dimensional linear elastostatics. Graphics Processing Unit (GPU) is used to accelerate computations. Results show that the Finite Point Method, together with GPU, can compute three dimensional linear elastostatic responses of solids at rates suitable for real-time graphics, for solids represented by reasonable number of points.

Phase Characterization and Study of Molecular Order of a Three-Ring Mesogen by (13)C NMR in Smectic C and Nematic Phases

Molecules exhibiting a thermotropic liquid-crystalline property have acquired significant importance due to their sensitivity to external stimuli such as temperature, mechanical forces, and electric and magnetic fields. As a result, several novel mesogens have been synthesized by the introduction of various functional groups in the vicinity of the aromatic core as well as in the side chains and their properties have been studied. In the present study, we report three-ring mesogens with hydroxyl groups at one terminal. These mesogens were synthesized by a multistep route, and structural characterization was accomplished by spectral techniques. The mesophase properties were studied by hot-stage optical polarizing microscopy, differential scanning calorimetry, and small-angle X-ray scattering. An enantiotropic nematic phase was noticed for lower homologues, while an additional smectic C phase was found for higher homologues. Solid-state high-resolution natural abundance (13)C NMR studies of a typical mesogen in the solid phase and in the mesophases have been carried out. The (13)C NMR spectrum of the mesogen in the smectic C and nematic phases indicated spontaneous alignment of the molecule in the magnetic field. By utilizing the two-dimensional separated local field (SLF) NMR experiment known as SAMPI4, (13)C-(1)H dipolar couplings have been obtained, which were utilized to determine the orientational order parameters of the mesogen.

3D Image Reconstruction From Truncated Helical Cone Beam Projection Data - A Linear Prediction Approach

With the introduction of 2D flat-panel X-ray detectors, 3D image reconstruction using helical cone-beam tomography is fast replacing the conventional 2D reconstruction techniques. In 3D image reconstruction, the source orbit or scanning geometry should satisfy the data sufficiency or completeness condition for exact reconstruction. The helical scan geometry satisfies this condition and hence can give exact reconstruction. The theoretically exact helical cone-beam reconstruction algorithm proposed by Katsevich is a breakthrough and has attracted interest in the 3D reconstruction using helical cone-beam Computed Tomography.In many practical situations, the available projection data is incomplete. One such case is where the detector plane does not completely cover the full extent of the object being imaged in lateral direction resulting in truncated projections. This result in artifacts that mask small features near to the periphery of the ROI when reconstructed using the convolution back projection (CBP) method assuming that the projection data is complete. A number of techniques exist which deal with completion of missing data followed by the CBP reconstruction. In 2D, linear prediction (LP)extrapolation has been shown to be efficient for data completion, involving minimal assumptions on the nature of the data, producing smooth extensions of the missing projection data.In this paper, we propose to extend the LP approach for extrapolating helical cone beam truncated data. The projection on the multi row flat panel detectors has missing columns towards either ends in the lateral direction in truncated data situation. The available data from each detector row is modeled using a linear predictor. The available data is extrapolated and this completed projection data is backprojected using the Katsevich algorithm. Simulation results show the efficacy of the proposed method.