Dark Energy in Global Brane Universe

We discuss the exact solutions of brane universes and the results indicate the Friedmann equations on the branes are modified with a new density term. Then, we assume the new term as the density of dark energy. Using Wetterich's parametrization equation of state (EOS) of dark energy, we obtain the new term varies with the red-shift z. Finally, the evolutions of the mass density parameter $\Omega_2$, dark energy density parameter $\Omega_x$ and deceleration parameter q_2 are studied.Comment: 8 pages,4 figure

Statefinder Parameters for Five-Dimensional Cosmology

We study the statefinder parameter in the five-dimensional big bounce model, and apply it to differentiate the attractor solutions of quintessence and phantom field. It is found that the evolving trajectories of these two attractor solutions in the statefinder parameters plane are quite different, and that are different from the statefinder trajectories of other dark energy models.Comment: 8 pages, 12 figures. accepted by MPL

Statefinder Parameters for Interacting Phantom Energy with Dark Matter

We apply in this paper the statefinder parameters to the interacting phantom energy with dark matter. There are two kinds of scaling solutions in this model. It is found that the evolving trajectories of these two scaling solutions in the statefinder parameter plane are quite different, and that are also different from the statefinder diagnostic of other dark energy models.Comment: 9 pages, 12 figures, some references are added, some words are modifie

Real-time Adaptive and Localized Spatiotemporal Clutter Filtering for Ultrasound Small Vessel Imaging

Effective clutter filtering is crucial in suppressing tissue clutter and extracting blood flow signal in Doppler ultrasound. Recent advances in eigen-based clutter filtering techniques have enabled ultrasound imaging of microvasculature without the need for contrast agents. However, simultaneously achieving fully adaptive, highly sensitive and real-time implementation of such eigen-based filtering techniques in clinical scanning scenarios for broad translation remains challenging. To address this, here we propose a fast spatiotemporal clutter filtering technique based on eigenvalue decomposition (EVD) and a novel localized data processing framework for robust and high-definition ultrasound imaging of blood flow. Unlike the existing local clutter filter that hard splits the ultrasound data into small blocks, our approach applies a series of 2D spatial Gaussian windows to the original data to generate local data subsets. This approach improves performance of flow detection while effectively avoiding undesired grid artifacts with dramatically reduced number of subsets required in local EVD filtering to shorten computation time. By leveraging the computational power of Graphics Processing Units (GPUs), we demonstrate the real-time implementation capability of the proposed approach. We also introduce and systematically evaluate several adaptive and automatic eigenvalue thresholding methods tailored for EVD-based filtering to facilitate optimization of blood flow imaging for either global or localized processing. The feasibility of the proposed clutter filtering technique is validated by experimental results from phantom and different in vivo studies, revealing robust clinical application potential. A tradeoff between improved performance and computational cost associated with the packet size and subset number in local processing is also investigated