Accelerating dark energy models in bianchi Type-V space-time

Some new exact solutions of Einstein's field equations in a spatially homogeneous and anisotropic Bianchi type-V space-time with minimally interaction of perfect fluid and dark energy components have been obtained. To prevail the deterministic solution we choose the scale factor $a(t) = \sqrt{t^{n}e^{t}}$, which yields a time dependent deceleration parameter (DP), representing a model which generates a transition of the universe from the early decelerating phase to the recent accelerating phase. We find that for $n \geq 1$, the quintessence model is reproducible with present and expected future evolution of the universe. The other models (for $n < 1$), we observe the phantom scenario. The quintessence as well as phantom models approach to isotropy at late time. For different values of $n$, we can generate a class of physically viable DE models. The cosmic jerk parameter in our descended model is also found to be in good concordance with the recent data of astrophysical observations under appropriate condition. The physical and geometric properties of spatially homogeneous and anisotropic cosmological models are discussed.Comment: 12 pages, 6 figure

Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion

Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180$^\circ$ view of the object. This is impractical in a limited angle scenario, when the viewing angle is less than 180$^\circ$, which can occur due to different factors including restrictions on scanning time, limited flexibility of scanner rotation, etc. The sinograms obtained as a result, cause existing techniques to produce highly artifact-laden reconstructions. In this paper, we propose to address this problem through implicit sinogram completion, on a challenging real world dataset containing scans of common checked-in luggage. We propose a system, consisting of 1D and 2D convolutional neural networks, that operates on a limited angle sinogram to directly produce the best estimate of a reconstruction. Next, we use the x-ray transform on this reconstruction to obtain a "completed" sinogram, as if it came from a full 180$^\circ$ measurement. We feed this to standard analytical and iterative reconstruction techniques to obtain the final reconstruction. We show with extensive experimentation that this combined strategy outperforms many competitive baselines. We also propose a measure of confidence for the reconstruction that enables a practitioner to gauge the reliability of a prediction made by our network. We show that this measure is a strong indicator of quality as measured by the PSNR, while not requiring ground truth at test time. Finally, using a segmentation experiment, we show that our reconstruction preserves the 3D structure of objects effectively.Comment: Spotlight presentation at CVPR 201