CROSS-SECTIONAL ANALYSIS OF PRE-TWISTED THICK BEAMS USING VARIATIONAL ASYMPTOTIC METHOD

An asymptotically-exact methodology is presented for obtaining the cross-sectional stiffness matrix of a pre-twisted moderately-thick beam having rectangular cross sections and made of transversely isotropic materials. The anisotropic beam is modeled from 3-D elasticity, without any further assumptions. The beam is allowed to have large displacements and rotations, but small strain is assumed. The strain energy of the beam is computed making use of the constitutive law and the kinematical relations derived with the inclusion of geometrical nonlinearities and initial twist. Large displacements and rotations are allowed, but small strain is assumed. The Variational Asymptotic Method is used to minimize the energy functional, thereby reducing the cross section to a point on the reference line with appropriate properties, yielding a 1-D constitutive law. In this method as applied herein, the 2-D cross-sectional analysis is performed asymptotically by taking advantage of a material small parameter and two geometric small parameters. 3-D strain components are derived using kinematics and arranged as orders of the small parameters. Warping functions are obtained by the minimization of strain energy subject to certain set of constraints that renders the 1-D strain measures well-defined. Closed-form expressions are derived for the 3-D non-linear warping and stress fields. The model is capable of predicting interlaminar and transverse shear stresses accurately up to first order.

Image reconstruction from incomplete projections data: A multiresolution modeling approach

Computerized tomography is an imaging technique which produces cross sectional map of an object from its line integrals. Image reconstruction algorithms require collection of line integrals covering the whole measurement range. However, in many practical situations part of projection data is inaccurately measured or not measured at all. In such incomplete projection data situations, conventional image reconstruction algorithms like the convolution back projection algorithm (CBP) and the Fourier reconstruction algorithm, assuming the projection data to be complete, produce degraded images. In this paper, a multiresolution multiscale modeling using the wavelet transform coefficients of projections is proposed for projection completion. The missing coefficients are then predicted based on these models at each scale followed by inverse wavelet transform to obtain the estimated projection data.

Growth and electron effective mass measurements of strained Si and Si0.94Ge0.06 on relaxed Si0.62Ge0.38 buffers grown by rapid thermal chemical vapor deposition

Abstract: We report the growth and the electron cyclotron resonance measurements of n-type Si/Si0.62Ge0.38 and Si0.94Ge0.06/Si0.62Ge0.38 modulation-doped heterostructures grown by rapid thermal chemical vapor deposition. The strained Si and Si0.94Ge0.06 channels were grown on relaxed Si0.62Ge0.38 buffer layers, which consist of 0.6 mu m uniform Si0.62Ge0.38 layers and 0.5 mu m compositionally graded relaxed SiGe layers from 0 to 38% Ge. The buffer layers were annealed at 800 degrees C for 1 h to obtain complete relaxation. A 75 Angstrom Si(SiGe) channel with a 100 Angstrom spacer and a 300 Angstrom 2 X 10(19) cm(-3) n-type supply layer was grown on the top of the buffer layers. The cross-sectional transmission electron microscope reveals that the dense dislocation network is confined to the buffer layer, and relatively few dislocations terminate on the surface. The plan-view image indicates the threading dislocation density is about 4 X 10(6) cm(-2). The far-infrared measurements of electron cyclotron resonance were performed at 4 K with the magnetic field of 4-8 T. The effective masses determined from the slope of the center frequency of the absorption peak versus applied magnetic field plot are 0.203m(0) and 0.193m(0) for the two dimensional electron gases in the Si and Si0.94Ge0.06 channels, respectively. The Si effective mass is very close to that of a two dimensional electron gas in an Si MOSFET (0.198m(0)). The electron effective mass of Si0.94Ge0.06 is reported for the first time and is about 5% lower than that of pure Si.

A MatLAB Based Virtual Phantom for 2D Electrical Impedance Tomography (MatVP2DEIT): Studying the Medical Electrical Impedance Tomography Reconstruction in Computer

Electrical Impedance Tomography (EIT) is a computerized medical imaging technique which reconstructs the electrical impedance images of a domain under test from the boundary voltage-current data measured by an EIT electronic instrumentation using an image reconstruction algorithm. Being a computed tomography technique, EIT injects a constant current to the patient's body through the surface electrodes surrounding the domain to be imaged (Omega) and tries to calculate the spatial distribution of electrical conductivity or resistivity of the closed conducting domain using the potentials developed at the domain boundary (partial derivative Omega). Practical phantoms are essentially required to study, test and calibrate a medical EIT system for certifying the system before applying it on patients for diagnostic imaging. Therefore, the EIT phantoms are essentially required to generate boundary data for studying and assessing the instrumentation and inverse solvers a in EIT. For proper assessment of an inverse solver of a 2D EIT system, a perfect 2D practical phantom is required. As the practical phantoms are the assemblies of the objects with 3D geometries, the developing of a practical 2D-phantom is a great challenge and therefore, the boundary data generated from the practical phantoms with 3D geometry are found inappropriate for assessing a 2D inverse solver. Furthermore, the boundary data errors contributed by the instrumentation are also difficult to separate from the errors developed by the 3D phantoms. Hence, the errorless boundary data are found essential to assess the inverse solver in 2D EIT. In this direction, a MatLAB-based Virtual Phantom for 2D EIT (MatVP2DEIT) is developed to generate accurate boundary data for assessing the 2D-EIT inverse solvers and the image reconstruction accuracy. MatVP2DEIT is a MatLAB-based computer program which simulates a phantom in computer and generates the boundary potential data as the outputs by using the combinations of different phantom parameters as the inputs to the program. Phantom diameter, inhomogeneity geometry (shape, size and position), number of inhomogeneities, applied current magnitude, background resistivity, inhomogeneity resistivity all are set as the phantom variables which are provided as the input parameters to the MatVP2DEIT for simulating different phantom configurations. A constant current injection is simulated at the phantom boundary with different current injection protocols and boundary potential data are calculated. Boundary data sets are generated with different phantom configurations obtained with the different combinations of the phantom variables and the resistivity images are reconstructed using EIDORS. Boundary data of the virtual phantoms, containing inhomogeneities with complex geometries, are also generated for different current injection patterns using MatVP2DEIT and the resistivity imaging is studied. The effect of regularization method on the image reconstruction is also studied with the data generated by MatVP2DEIT. Resistivity images are evaluated by studying the resistivity parameters and contrast parameters estimated from the elemental resistivity profiles of the reconstructed phantom domain. Results show that the MatVP2DEIT generates accurate boundary data for different types of single or multiple objects which are efficient and accurate enough to reconstruct the resistivity images in EIDORS. The spatial resolution studies show that, the resistivity imaging conducted with the boundary data generated by MatVP2DEIT with 2048 elements, can reconstruct two circular inhomogeneities placed with a minimum distance (boundary to boundary) of 2 mm. It is also observed that, in MatVP2DEIT with 2048 elements, the boundary data generated for a phantom with a circular inhomogeneity of a diameter less than 7% of that of the phantom domain can produce resistivity images in EIDORS with a 1968 element mesh. Results also show that the MatVP2DEIT accurately generates the boundary data for neighbouring, opposite reference and trigonometric current patterns which are very suitable for resistivity reconstruction studies. MatVP2DEIT generated data are also found suitable for studying the effect of the different regularization methods on reconstruction process. Comparing the reconstructed image with an original geometry made in MatVP2DEIT, it would be easier to study the resistivity imaging procedures as well as the inverse solver performance. Using the proposed MatVP2DEIT software with modified domains, the cross sectional anatomy of a number of body parts can be simulated in PC and the impedance image reconstruction of human anatomy can be studied.