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Xiao Xu

Publications and source records attributed to Xiao Xu.

112 records · Page 7Linked to original sources

Tracking and imaging of dynamic objects in scattering media by time-reversed adapted-perturbation (TRAP) optical focusing

The ability to steer light propagation inside scattering media has long been sought-after due to its potential widespread applications. To form optical foci inside scattering media, the only feasible strategy is to guide photons by using either implanted or virtual guide stars. However, all of these guide stars must be introduced extrinsically, either invasively or by physical contact, limiting the scope of their application. Here, we focus light inside scattering media by employing intrinsic dynamics as guide stars. By time-reversing the perturbed component of the scattered light adaptively, we concentrate light to the origin of the perturbation, where the permittivity varied spontaneously. We demonstrate dynamic light focusing onto moving targets and imaging of a time-variant object obscured by highly scattering media, without invasiveness and physical contact. Anticipated applications include all-weather optical communication with airplanes or satellites, tracking vehicles in thick fogs, and imaging and photoablation of angiogenic vessels in tumors.

physics.optics↗

Geometric structure of percolation clusters

We investigate the geometric properties of percolation clusters, by studying square-lattice bond percolation on the torus. We show that the density of bridges and nonbridges both tend to 1/4 for large system sizes. Using Monte Carlo simulations, we study the probability that a given edge is not a bridge but has both its loop arcs in the same loop, and find that it is governed by the two-arm exponent. We then classify bridges into two types: branches and junctions. A bridge is a {\em branch} iff at least one of the two clusters produced by its deletion is a tree. Starting from a percolation configuration and deleting the branches results in a {\em leaf-free} configuration, while deleting all bridges produces a bridge-free configuration. Although branches account for $\approx 43%$ of all occupied bonds, we find that the fractal dimensions of the cluster size and hull length of leaf-free configurations are consistent with those for standard percolation configurations. By contrast, we find that the fractal dimensions of the cluster size and hull length of bridge-free configurations are respectively given by the backbone and external perimeter dimensions. We estimate the backbone fractal dimension to be $1.643\,36(10)$.

cond-mat.stat-mech↗

Simultaneous analysis of three-dimensional percolation models

We simulate the bond and site percolation models on several three-dimensional lattices, including the diamond, body-centered cubic, and face-centered cubic lattices. As on the simple-cubic lattice [Phys. Rev. E, \textbf{87} 052107 (2013)], it is observed that in comparison with dimensionless ratios based on cluster-size distribution, certain wrapping probabilities exhibit weaker finite-size corrections and are more sensitive to the deviation from percolation threshold $p_c$, and thus provide a powerful means for determining $p_c$. We analyze the numerical data of the wrapping probabilities simultaneously such that universal parameters are shared by the aforementioned models, and thus significantly improved estimates of $p_c$ are obtained.

cond-mat.stat-mech↗

Multifractality can be a universal signature of phase transitions

Macroscopic systems often display phase transitions where certain physical quantities are singular or self-similar at different (spatial) scales. Such properties of systems are currently characterized by some order parameters and a few critical exponents. Nevertheless, recent studies show that the multifractality, where a large number of exponents are needed to quantify systems, appears in many complex systems displaying self-similarity. Here we propose a general approach and show that the appearance of the multifractality of an order parameter related quantity is the signature of a physical system transiting from one phase to another. The distribution of this quantity obtained within suitable (time) scales satisfies a $q$-Gaussian distribution plus a possible Cauchy distributed background. At the critical point the $q$-Gaussian shifts between Gaussian type with narrow tails and L$\acute{\text{e}}$vy type with fat tails. Our results suggest that the Tsallis $q$-statistics, besides the conventional Boltzmann-Gibbs statistics, may play an important role during phase transitions.

cond-mat.stat-mech↗