Engineering the composition, morphology, and optical properties of InAsSb nanostructures via graded growth technique

Graded growth technique is utilized to realize the control over the composition, morphology, and optical properties of self-assembled InAsSb/InGaAs/InP nanostructures. By increasing the initial mole fraction of the Sb precursor during the graded growth of InAsSb, more Sb atoms can be incorporated into the InAsSb nanostructures despite the same Sb mole fraction averaged over the graded growth. This leads to a shape change from dots to dashes/wires for the InAsSb nanostructures. As a result of the composition and morphology change, photoluminescence from the InAsSb nanostructures shows different polarization and temperature characteristics. This work demonstrates a technologically important technique—graded growth, to control the growth and the resultant physical properties of self-assembled semiconductor nanostructures.Financial support from Australian Research Council is gratefully acknowledged

Note: An object detection method for active camera

To solve the problems caused by a changing background during object detection in active camera, this paper proposes a new method based on SURF (speeded up robust features) and data clustering. The SURF feature points of each image are extracted, and each cluster center is calculated by processing the data clustering of k adjacent frames. Templates for each class are obtained by calculating the histograms within the regions around the center points of the clustering classes. The window of the moving object can be located by finding the region that satisfies the histogram matching result between adjacent frames. Experimental results demonstrate that the proposed method can improve the effectiveness of object detection.Yong Chen, Ronghua Zhang, Lei Shang, and Eric H

Global Energy Matching Method for Atomistic-to-Continuum Modeling of Self-Assembling Biopolymer Aggregates

This paper studies mathematical models of biopolymer supramolecular aggregates that are formed by the self-assembly of single monomers. We develop a new multiscale numerical approach to model the structural properties of such aggregates. This theoretical approach establishes micro-macro relations between the geometrical and mechanical properties of the monomers and supramolecular aggregates. Most atomistic-to-continuum methods are constrained by a crystalline order or a periodic setting and therefore cannot be directly applied to modeling of soft matter. By contrast, the energy matching method developed in this paper does not require crystalline order and, therefore, can be applied to general microstructures with strongly variable spatial correlations. In this paper we use this method to compute the shape and the bending stiffness of their supramolecular aggregates from known chiral and amphiphilic properties of the short chain peptide monomers. Numerical implementation of our approach demonstrates consistency with results obtained by molecular dynamics simulations