A new spectral apparent horizon finder for 3D numerical relativity

We present a new spectral-method-based algorithm for finding apparent horizons in three-dimensional space-like hypersurfaces without symmetries. While there are already a wide variety of algorithms for finding apparent horizons, our new algorithm does not suffer from the same weakness as previous spectral apparent horizon finders: namely the monopolar coefficient ($\ell=0$ in terms of the spherical harmonics decomposition) needed to be determined by a root-finding procedure. Hence, this leads to a much faster and more robust spectral apparent horizon finder. The finder is tested with the Kerr-Schild and Brill-Lindquist data. Our finder is accurate and is as efficient as the currently fastest methods developed recently by Schnetter (2003 Class. Quantum Grav. {\bf 20}, 4719) and Thornburg (2004 Class. Quantum Grav. {\bf 21}, 743). At typical resolutions it takes only 0.5 second to find the apparent horizon of a Kerr-Schild black hole with $a=0.9M$ to the accuracy $\sim 10^{-5}$ for the fractional error in the horizon's location on a 2 GHz processor.Comment: Minor changes and references added. Published in Class. Quantum Gra

Optical imaging techniques in microfluidics and their applications

Microfluidic devices have undergone rapid development in recent years and provide a lab-on-a-chip solution for many biomedical and chemical applications. Optical imaging techniques are essential in microfluidics for observing and extracting information from biological or chemical samples. Traditionally, imaging in microfluidics is achieved by bench-top conventional microscopes or other bulky imaging systems. More recently, many novel compact microscopic techniques have been developed to provide a low-cost and portable solution. In this review, we provide an overview of optical imaging techniques used in microfluidics followed with their applications. We first discuss bulky imaging systems including microscopes and interferometer-based techniques, then we focus on compact imaging systems that can be better integrated with microfluidic devices, including digital in-line holography and scanning-based imaging techniques. The applications in biomedicine or chemistry are also discussed along with the specific imaging techniques

On the beta-drift of an initially circular vortex patch

The nonlinear inviscid evolution of a vortex patch in a single-layer quasi-geostrophic fluid and within a background planetary vorticity gradient is examined numerically at unprecedented spatial resolution. The evolution is governed by two dimensionless parameters: the initial size (radius) of the vortex compared to the Rossby deformation radius, and the initial strength of the vortex compared to the variation of the planetary vorticity across the vortex. It is found that the zonal speed of a vortex increases with its strength. However, the meridional speed reaches a maximum at intermediate vortex strengths. Both large and weak vortices are readily deformed, often into elliptical and tripolar shapes. This deformation is shown to be related to an instability of the instantaneous vorticity distribution in the absence of the planetary vorticity gradient β. The extremely high numerical resolution employed reveals a striking feature of the flow evolution, namely the generation of very sharp vorticity gradients surrounding the vortex and extending downstream of it in time. These gradients form as the vortex forces background planetary vorticity contours out of its way as it propagates. The contours close to the vortex swirl rapidly around the vortex and homogenize, but at some critical distance the swirl is not strong enough and, instead, a sharp vorticity gradient forms. The region inside this sharp gradient is called the ‘trapped zone’, though it shrinks slowly in time and leaks. This leaking occurs in a narrow wake called the ‘trailing front’, another zone of sharp vorticity gradients, extending behind the vortex

Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework

Light field (LF) cameras provide perspective information of scenes by taking directional measurements of the focusing light rays. The raw outputs are usually dark with additive camera noise, which impedes subsequent processing and applications. We propose a novel LF denoising framework based on anisotropic parallax analysis (APA). Two convolutional neural networks are jointly designed for the task: first, the structural parallax synthesis network predicts the parallax details for the entire LF based on a set of anisotropic parallax features. These novel features can efficiently capture the high frequency perspective components of a LF from noisy observations. Second, the view-dependent detail compensation network restores non-Lambertian variation to each LF view by involving view-specific spatial energies. Extensive experiments show that the proposed APA LF denoiser provides a much better denoising performance than state-of-the-art methods in terms of visual quality and in preservation of parallax details