The Murchison Widefield Array: Design Overview

The Murchison Widefield Array (MWA) is a dipole-based aperture array synthesis telescope designed to operate in the 80-300 MHz frequency range. It is capable of a wide range of science investigations, but is initially focused on three key science projects. These are detection and characterization of 3-dimensional brightness temperature fluctuations in the 21cm line of neutral hydrogen during the Epoch of Reionization (EoR) at redshifts from 6 to 10, solar imaging and remote sensing of the inner heliosphere via propagation effects on signals from distant background sources,and high-sensitivity exploration of the variable radio sky. The array design features 8192 dual-polarization broad-band active dipoles, arranged into 512 tiles comprising 16 dipoles each. The tiles are quasi-randomly distributed over an aperture 1.5km in diameter, with a small number of outliers extending to 3km. All tile-tile baselines are correlated in custom FPGA-based hardware, yielding a Nyquist-sampled instantaneous monochromatic uv coverage and unprecedented point spread function (PSF) quality. The correlated data are calibrated in real time using novel position-dependent self-calibration algorithms. The array is located in the Murchison region of outback Western Australia. This region is characterized by extremely low population density and a superbly radio-quiet environment,allowing full exploitation of the instrumental capabilities.Comment: 9 pages, 5 figures, 1 table. Accepted for publication in Proceedings of the IEE

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods

Spectral unmixing of Multispectral Lidar signals

In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design