No abstract

Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope.

Illumination of fly photoreceptors in the presence of the fluorescent dye Lucifer yellow initiates incorporation of the dye, which stains each cell down to its synaptic terminal. Unilluminated cells do not become stained. Experiments on animals in vivo show that selected cells can be stained without loss of viability. "Induced endocytosis" provides a plausible mechanism underlying this phenomenon.

Background/Aims: Glaucoma shunts vent aqueous humor to orbital tissue across a fibrous capsule. The capsule consists of collagen and fibroblasts, and its formation was assumed to be a cicatricial process. Capsule fibrosis remains the leading cause of device failure. We tested the wound healing hypothesis using morphometry and polarization microscopy and found the capsule consists not of scar tissue but rather highly organized extracellular matrix. Methods: Polarization microscopy reveals collagen orientation within the extracellular matrix. Rotation of the specimen in the plane of polarized light reveals collagen bundle orientation with respect to capsule surface geometry. Capsule thickness was measured and correlated with device dimension. Results: Capsule collagen is highly organized and not scar tissue. Relative orientation is predictable from simple engineering principles and shows tissue deposits reinforcing collagen parallel to its principle stress axes along the capsule surface. Capsule thickness and collagen orientation correlate with device dimension and surface geometry. Conclusion: The corollary is true. Geometry can be used to manage the amount, distribution and orientation of collagen and thus control hydraulic conductivity and surface area of the filtration capsule.

The Wyoming Information, Signal Processing, and Robotics Laboratory is developing a wide variety of bio-inspired vision sensors. We are interested in exploring the vision system of various insects and adapting some of their features toward the development of specialized vision sensors. We do not attempt to supplant traditional digital imaging techniques but rather develop sensor systems tailor made for the application at hand. We envision that many applications may require a hybrid approach using conventional digital imaging techniques enhanced with bio-inspired analogue sensors. In this specific project, we investigated the apposition compound eye and its characteristics commonly found in diurnal insects and certain species of arthropods. We developed and characterized an array of apposition compound eye-type sensors and tested them on an autonomous robotic vehicle. The robot exhibits the ability to follow a pre-defined target and avoid specified obstacles using a simple control algorithm.