Nested sampling for materials: the case of hard spheres

The recently introduced nested sampling algorithm allows the direct and efficient calculation of the partition function of atomistic systems. We demonstrate its applicability to condensed phase systems with periodic boundary conditions by studying the three dimensional hard sphere model. Having obtained the partition function, we show how easy it is to calculate the compressibility and the free energy as functions of the packing fraction and local order, verifying that the transition to crystallinity has a very small barrier, and that the entropic contribution of jammed states to the free energy is negligible for packing fractions above the phase transition. We quantify the previously proposed schematic phase diagram and estimate the extent of the region of jammed states. We find that within our samples, the maximally random jammed configuration is surprisingly disordered.

Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features

The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

Effects of multi-scattering on the performance of a single-beam acoustic manipulation device.

The effects of multiple scattering on acoustic manipulation of spherical particles using helicoidal Bessel-beams are discussed. A closed-form analytical solution is developed to calculate the acoustic radiation force resulting from a Bessel-beam on an acoustically reflective sphere, in the presence of an adjacent spherical particle, immersed in an unbounded fluid medium. The solution is based on the standard Fourier decomposition method and the effect of multi-scattering is taken into account using the addition theorem for spherical coordinates. Of particular interest here is the investigation of the effects of multiple scattering on the emergence of negative axial forces. To investigate the effects, the radiation force applied on the target particle resulting from a helicoidal Bessel-beam of different azimuthal indexes (m = 1 to 4), at different conical angles, is computed. Results are presented for soft and rigid spheres of various sizes, separated by a finite distance. Results have shown that the emergence of negative force regions is very sensitive to the level of cross-scattering between the particles. It has also been shown that in multiple scattering media, the negative axial force may occur at much smaller conical angles than previously reported for single particles, and that acoustic manipulation of soft spheres in such media may also become possible.

Scale-invariant vote-based 3D recognition and registration from point clouds

This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2013 Springer-Verlag Berlin Heidelberg.

Consensus under general convexity

A method is proposed to characterize contraction of a set through orthogonal projections. For discrete-time multi-agent systems, quantitative estimates of convergence (to a consensus) rate are provided by means of contracting convex sets. Required convexity for the sets that should include the values that the transition maps of agents take is considered in a more general sense than that of Euclidean geometry. © 2007 IEEE.

Stabilization of collective motion in three dimensions: A consensus approach

This paper proposes a methodology to stabilize relative equilibria in a model of identical, steered particles moving in three-dimensional Euclidean space. Exploiting the Lie group structure of the resulting dynamical system, the stabilization problem is reduced to a consensus problem. We first derive the stabilizing control laws in the presence of all-to-all communication. Providing each agent with a consensus estimator, we then extend the results to a general setting that allows for unidirectional and time-varying communication topologies. © 2007 IEEE.

Synchronization and balancing on the N-torus

In this paper, we study the behavior of a network of N agents, each evolving on the circle. We propose a novel algorithm that achieves synchronization or balancing in phase models under mild connectedness assumptions on the (possibly time-varying and unidirectional) communication graphs. The global convergence analysis on the N-torus is a distinctive feature of the present work with respect to previous results that have focused on convergence in the Euclidean space. © 2006 Elsevier B.V. All rights reserved.

The dynamic response of edge clamped plates loaded by spherically expanding sand shells

The dynamic deformation of both edge clamped stainless steel sandwich panels with a pyramidal truss core and equal mass monolithic plates loaded by spherically expanding shells of dry and water saturated sand has been investigated, both experimentally and via a particle based simulation methodology. The spherically expanding sand shell is generated by detonating a sphere of explosive surrounded by a shell of either dry or water saturated synthetic sand. The measurements show that the sandwich panel and plate deflections decrease with increasing stand-off between the center of the charge and the front of the test structures. Moreover, for the same charge and sand mass, the deflections of the plates are significantly higher in the water saturated sand case compared to that of dry sand. For a given stand-off, the mid-span deflection of the sandwich panel rear faces was substantially less than that of the corresponding monolithic plate for both the dry and water saturated sand cases. The experiments were simulated via a coupled discrete-particle/ finite element scheme wherein the high velocity impacting sand is modeled by interacting particles while the plate is modeled within a Lagrangian finite element setting. The simulations are in good agreement with the measurements for the dry sand impact of both the monolithic and sandwich structures. However, the simulations underestimate the effect of stand-off in the case of the water saturated sand explosion, i.e. the deflections decrease more sharply with increasing stand-off in the experiments compared to the simulations. The simulations reveal that the momentum transmitted into the sandwich and monolithic plate structures by the sand shell is approximately the same, consistent with a small fluid-structure interaction effect. The smaller deflection of the sandwich panels is therefore primarily due to the higher bending strength of sandwich structures. © 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

Sound radiation from point-driven shell structures

A numerical study is presented showing the structural response and sound radiation from a range of thin shell structures excited by a point force: a baffled flat plate, a sphere, a family of spheroids and a family of closed circular cylinders. All the structures have the same material properties, thickness and total surface area so the asymptotic modal density is the same. Dramatic differences are shown in the total radiated sound power for the different shells. It was already known that the flat plate and the sphere behave very differently. These results show that the cylinders and, particularly, the spheroids show patterns that are not intermediate between the two but instead display new features: in certain frequency ranges the radiated sound power can be at least an order of magnitude greater than either the plate or the sphere. © 2013 Elsevier Ltd.

Oxygen carrier dispersion in inert packed beds to improve performance in chemical looping combustion

Various packed beds of copper-based oxygen carriers (CuO on Al2O3) were tested over 100 cycles of low temperature (673K) Chemical Looping Combustion (CLC) with H2 as the fuel gas. The oxygen carriers were uniformly mixed with alumina (Al2O3) in order to investigate the level of separation necessary to prevent agglomeration. It was found that a mass ratio of 1:6 oxygen carrier to alumina gave the best performance in terms of stable, repeating hydrogen breakthrough curves over 100 cycles. In order to quantify the average separation achieved in the mixed packed beds, two sphere-packing models were developed. The hexagonal close-packing model assumed a uniform spherical packing structure, and based the separation calculations on a hypergeometric probability distribution. The more computationally intensive full-scale model used discrete element modelling to simulate random packing arrangements governed by gravity and contact dynamics. Both models predicted that average 'nearest neighbour' particle separation drops to near zero for oxygen carrier mass fractions of x≥0.25. For the packed bed systems studied, agglomeration was observed when the mass fraction of oxygen carrier was above this threshold. © 2013 Elsevier B.V.

Construction of cytoplasmic molecular markers distinguishing Danio rerio from Gobiocypris rarus at high identity domains based on MP-PCR strategy and Sybr Green I detection

To distinguish the cytoplasm of Danio rerio from that of Gobiocypris rarus, we cloned G. rarus COXI and constructed cytoplasmic molecular markers at the high identity domains of COXI by mutated primer PCR (MP-PCR for short). Then Sybr Green I was used to detect the single amplicon. As a result, we succeeded in getting the cytoplasmic molecular markers, G.M COXI and Z.M COXI, by MP-PCR strategy. They were used to detect the sperm-derived mtDNA in the sexual hybrid embryos (D. rerio female x G. rarus male) before the sphere stage. In the present study, all results demonstrate that MP-PCR approach and Sybr Green I detection are feasible to construct the molecular markers to identify genes that shared high identity.

Microcalorimetry as a possible tool for phylogenetic studies of Tetrahymena

Using isothermal microcalorimetry, the growth power-time curves of three strains of Tetrahymena were determined at 28 degrees C. Their Euclidean distances and cluster analysis diagram were obtained by using two thermokinetic parameters (r and Q(log)), which showed that T. thermophila BF1 and T. thermophila BF5 had a closer relationship. Compared with the single molecular biomarker (ITS1) method, microcalorimetry wasmaybe a simpler, more sensitive andmore economic technique in the phylogenetic studies of Tetrahymena species.

A derivation of the source-channel error exponent using nonidentical product distributions

This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight. © 2014 IEEE.

Mathematical symbols and computing methods in high dimensional biomimetic informatics and their applications

High dimensional biomimetic informatics (HDBI) is a novel theory of informatics developed in recent years. Its primary object of research is points in high dimensional Euclidean space, and its exploratory and resolving procedures are based on simple geometric computations. However, the mathematical descriptions and computing of geometric objects are inconvenient because of the characters of geometry. With the increase of the dimension and the multiformity of geometric objects, these descriptions are more complicated and prolix especially in high dimensional space. In this paper, we give some definitions and mathematical symbols, and discuss some symbolic computing methods in high dimensional space systematically from the viewpoint of HDBI. With these methods, some multi-variables problems in high dimensional space can be solved easily. Three detailed algorithms are presented as examples to show the efficiency of our symbolic computing methods: the algorithm for judging the center of a circle given three points on this circle, the algorithm for judging whether two points are on the same side of a hyperplane, and the algorithm for judging whether a point is in a simplex constructed by points in high dimensional space. Two experiments in blurred image restoration and uneven lighting image correction are presented for all these algorithms to show their good behaviors.

Geometrical learning, descriptive geometry, and biomimetic pattern recognition

Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size. (c) 2005 Elsevier B.V. All rights reserved.