University of New South Wales

About: University of New South Wales is a education organization based out in Sydney, New South Wales, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 51197 authors who have published 153634 publications receiving 4880608 citations. The organization is also known as: UNSW & UNSW Australia.

Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces

Abstract: In this paper, intelligent reflecting surfaces (IRSs) are employed to enhance the physical layer security in a challenging radio environment. In particular, a multi-antenna access point (AP) has to serve multiple single-antenna legitimate users, which do not have line-of-sight communication links, in the presence of multiple multi-antenna potential eavesdroppers whose channel state information (CSI) is not perfectly known. Artificial noise (AN) is transmitted from the AP to deliberately impair the eavesdropping channels for security provisioning. We investigate the joint design of the beamformers and AN covariance matrix at the AP and the phase shifters at the IRSs for maximization of the system sum-rate while limiting the maximum information leakage to the potential eavesdroppers. To this end, we formulate a robust non-convex optimization problem taking into account the impact of the imperfect CSI of the eavesdropping channels. To address the non-convexity of the optimization problem, an efficient algorithm is developed by capitalizing on alternating optimization, a penalty-based approach, successive convex approximation, and semidefinite relaxation. Simulation results show that IRSs can significantly improve the system secrecy performance compared to conventional architectures without IRS. Furthermore, our results unveil that, for physical layer security, uniformly distributing the reflecting elements among multiple IRSs is preferable over deploying them at a single IRS.

Flame spray pyrolysis: An enabling technology for nanoparticles design and fabrication

Abstract: Combustion of appropriate precursor sprays in a flame spray pyrolysis (FSP) process is a highly promising and versatile technique for the rapid and scalable synthesis of nanostuctural materials with engineered functionalities. The technique was initially derived from the fundamentals of the well-established vapour-fed flame aerosols reactors that was widely practised for the manufacturing of simple commodity powders such as pigmentary titania, fumed silica, alumina, and even optical fibers. In the last 10 years however, FSP knowledge and technology was developed substantially and a wide range of new and complex products have been synthesised, attracting major industries in a diverse field of applications. Key innovations in FSP reactor engineering and precursor chemistry have enabled flexible designs of nanostructured loosely-agglomerated powders and particulate films of pure or mixed oxides and even pure metals and alloys. Unique material morphologies such as core–shell structures and nanorods are possible using this essentially one step and continuous FSP process. Finally, research challenges are discussed and an outlook on the next generation of engineered combustion-made materials is given.

The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

Abstract: The coefficient of determination R2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R2 for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R2 that we called R2GLMM for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients ICC using Poisson and binomial GLMMs. In this article, we expand our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen9s inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen9s inequality has important implications for biologically meaningful interpretation of GLMMs, while the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments.