Optical and chemical properties of polyterpenol thin films deposited via plasma-enhanced chemical vapor deposition

The development of novel organic polymer thin films is essential for the advancement of many emerging fields including organic electronics and biomedical coatings. In this study, the effect of synthesis conditions, namely radio frequency (rf) deposition power, on the material properties of polyterpenol thin films derived from nonsynthetic environmentally friendly monomer was investigated. At lower deposition powers, the polyterpenol films preserved more of the original monomer constituents, such as hydroxy functional groups; however, they were also softer and more hydrophilic compared to polymers fabricated at higher power. Enhanced monomer fragmentation and consequent reduction in the presence of the polar groups in the structure of the high-power samples reduced their optical band gap value from 2.95 eV for 10 W to 2.64 eV for 100 W. Regardless of deposition power, all samples were found to be optically transparent with smooth, defect-free, and homogenous surfaces.

Effect of iodine doping on surface and optical properties of polyterpenol thin films

This study presents the effect of iodine doping on optical and surface properties of polyterpenol thin films deposited from non-synthetic precursor by means of plasma polymerisation. Spectroscopic ellipsometry studies showed iodine doping reduced the optical band gap from 2.82 eV to 1.50 eV for pristine and doped samples respectively. Higher levels of doping notably reduced the transparency of films, an issue if material is considered for applications that require high transparency. Contact angle studies demonstrated higher hydrophilicity for films deposited at increased doping levels, results confirmed by XPS Spectroscopy and FTIR. Doping had no significant effect on the surface profile or roughness of the film.

Tissue thickness calculation in ocular optical coherence tomography

Thickness measurements derived from optical coherence tomography (OCT) images of the eye are a fundamental clinical and research metric, since they provide valuable information regarding the eye’s anatomical and physiological characteristics, and can assist in the diagnosis and monitoring of numerous ocular conditions. Despite the importance of these measurements, limited attention has been given to the methods used to estimate thickness in OCT images of the eye. Most current studies employing OCT use an axial thickness metric, but there is evidence that axial thickness measures may be biased by tilt and curvature of the image. In this paper, standard axial thickness calculations are compared with a variety of alternative metrics for estimating tissue thickness. These methods were tested on a data set of wide-field chorio-retinal OCT scans (field of view (FOV) 60° x 25°) to examine their performance across a wide region of interest and to demonstrate the potential effect of curvature of the posterior segment of the eye on the thickness estimates. Similarly, the effect of image tilt was systematically examined with the same range of proposed metrics. The results demonstrate that image tilt and curvature of the posterior segment can affect axial tissue thickness calculations, while alternative metrics, which are not biased by these effects, should be considered. This study demonstrates the need to consider alternative methods to calculate tissue thickness in order to avoid measurement error due to image tilt and curvature.

Thickness metrics in posterior segment optical coherence tomography

To develop and compare a set of metrics for calculating tissue thickness in wide-field OCT data.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.