Silk fibroin as a water-soluble bio-resist and its thermal properties

Thermal diffusivity of silk fibroin films, α = (1.6 ± 0.24) × 10-7 m2 s-1, was measured by a direct contact method. It was shown to be reduced down to ∼1 × 10-7 m2 s-1 in the crystallized phase, consistent with the multi-domain composition of β-sheet assemblies. Crystalline silk with β-sheets was made by dipping into alcohol and was used as a positive electron beam lithography (EBL) resist. It is shown by direct IR imaging of the 1619 cm-1 amide-I CO spectral signature and 3290 cm-1 amide-A N-H stretching band that an e-beam is responsible for unzipping β-sheets, which subsequently results in exposed areas returning to a water soluble state. This makes it possible to develop a water-based biocompatible silk resist and use it in lithography applications. The general principles of protein crystallization, traceable to spectral changes in IR amide bands of silk, can be used as a guide for the creation of new protein EBL resists and to quantify the electron dose required for solubility. Foam formation and laser treatments of silk can provide new approaches in surface functionalization and fabrication of 3D bio-scaffolds.

A spectral slit approach to doubletalk detection

A new class of doubletalk detector based on exploiting a spectral slit is proposed. This is achieved by spectrally deleting a frequency band in the far-end signal such that when the near-end signal is present, only the near-end spectral information is present. The proposed method relies solely on the detection of speech activity period in the slit area, and significantly, it requires no estimation of the echo path. Evaluation in typical acoustic echo setups shows that the proposed method outperforms other conventional doubletalk detectors in terms of probability of miss detection even under poor echo-to-noise ratio (ENR), low echo-to-far-end ratio (EFR) conditions, and echo path change.

Corner Inlet and Nooramunga Habitat Mapping Project – Part 2 : High resolution mapping and change detection analysis using 2011 Rapid Eye Satellite Image compared to 2009 ALOS Satellite Image

Building on a habitat mapping project completed in 2011, Deakin University was commissioned by Parks Victoria (PV) to apply the same methodology and ground-truth data to a second, more recent and higher resolution satellite image to create habitat maps for areas within the Corner Inlet and Nooramunga Marine and Coastal Park and Ramsar area. A ground-truth data set using in situ video and still photographs was used to develop and assess predictive models of benthic marine habitat distributions incorporating data from both RapidEye satellite imagery (corrected for atmospheric and water column effects by CSIRO) and LiDAR (Light Detection and Ranging) bathymetry. This report describes the results of the mapping effort as well as the methodology used to produce these habitat maps.

Overall accuracies of habitat classifications were good, with error rates similar to or better than the earlier classification (>73 % and kappa values > 0.58 for both study localities). The RapidEye classification failed to accurately detect Pyura and reef habitat classes at the Corner Inlet locality, possibly due to differences in spectral frequencies. For comparison, these categories were combined into a ‘non-seagrass’ category, similar to the one used at the Nooramunga locality in the original classification. Habitats predicted with highest accuracies differed from the earlier classification and were Posidonia in Corner Inlet (89%), and bare sediment (no-visible seagrass class) in Nooramunga (90%). In the Corner Inlet locality reef and Pyura habitat categories were not distinguishable in the repeated classification and so were combined with bare sediments. The majority of remaining classification errors were due to the misclassification of Zosteraceae as bare sediment and vice versa. Dominant habitats were the same as those from the 2011 classification with some differences in extent. For the Corner Inlet study locality the no-visible seagrass category remained the most extensive (9059 ha), followed by Posidonia (5,513 ha) and Zosteraceae (5,504 ha). In Nooramunga no-visible seagrass (6,294 ha), Zosteraceae (3,122 ha) and wet saltmarsh (1,562 ha) habitat classes were most dominant.

Change detection analyses between the 2009 and 2011 imagery were undertaken as part of this project, following the analyses presented in Monk et al. (2011) and incorporating error estimates from both classifications. These analyses indicated some shifts in classification between Posidonia and Zosteraceae as well as a general reduction in the area of Zosteraceae. Issues with classification of mixed beds were apparent, particularly in the main Posidonia bed at Nooramunga where a mosaic of Zosteraceae and Posidonia was seen that was not evident in the ALOS classification. Results of a reanalysis of the 1998-2009 change detection illustrating effects of binning of mixed beds is also provided as an appendix.

This work has been successful in providing baseline maps at an improved level of detail using a repeatable method meaning that any future changes in intertidal and shallow water marine habitats may be assessed in a consistent way with quantitative error assessments. In wider use, these maps should also allow improved conservation planning, advance fisheries and catchment management, and progress infrastructure planning to limit impacts on the Inlet environment.