Taking environmental engineering out of the laboratory

Environmental engineering is a core component of most construction and surveying undergraduate courses. It is generally accepted that students on these courses should have an understanding of thermal comfort, heat transfer, condensation, lighting, noise transmission and acoustics. Experiments are essential in developing students’ awareness and understanding of the underlying physical concepts which drive environmental engineering solutions. Traditionally these experiments have been conducted by students working in small groups in laboratories. However, increasing student numbers and, in particular, the growth in part time study, have placed significant additional demands on limited laboratory resources. The availability of reasonably priced, simple, hand-held equipment has made it possible for students to conduct experiments outside the confines of the laboratory. Furthermore, various professional software packages (some of which are freely available online) enable the resultant data to be further developed and analysed in conjunction with the conventional textbook approach. This paper examines these alternative approaches to the traditional laboratory experiment. An assessment is provided of the types of experiment which are both possible and appropriate, and the efficacy of these approaches is considered.

On the structure and performance of a novel blind source separation based carrier phase synchronization error compensator

In this paper we carry out a detailed performance analysis of a novel blind-source-seperation (BSS) based DSP algorithm that tackles the carrier phase synchronization error problem. The results indicate that the mismatch can be effectively compensated during the normal operation as well as in the rapidly changing environments. Since the compensation is carried out before any modulation specific processing, the proposed method works with all standard modulation formats and lends itself to efficient real-time custom integrated hardware or software implementations.

Multivariate Moments Expansion Density: Application of the Dynamic Equicorrelation Model

In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of portfolio returns. This distribution, which we refer to as the multivariate moments expansion (MME), admits any non-Gaussian (multivariate) distribution as its basis because it is specified directly in terms of the basis density’s moments. To obtain the expansion of the Gaussian density, the MME is a reformulation of the multivariate Gram-Charlier (MGC), but the MME is much simpler and tractable than the MGC when positive transformations are used to produce well-defined densities. As an empirical application, we extend the dynamic conditional equicorrelation (DECO) model to an SNP framework using the MME. The resulting model is parameterized in a feasible manner to admit two-stage consistent estimation and it represents the DECO as well as the salient non-Gaussian features of portfolio return distributions. The in- and out-of-sample performance of a MME-DECO model of a portfolio of 10 assets demonstrate that it can be a useful tool for risk management purposes.