Results of abundance surveys of juvenile Atlantic and gulf Menhaden, Brevoortia tyrannus and B. patronus

The estuarine populations of juvenile Atlantic and gulf menhaden (Brevoortia tyrannus and B. patronus) were sampled during two-boat, surface-trawl, abundance surveys extensively conducted in the 1970s. Juvenile Atlantic menhaden were sampled in 39 estuarine streams along the U.S. Atlantic coast from northern Florida into Massachusetts. Juvenile gulf menhaden were sampled in 29 estuarine streams along the Gulf of Mexico from southeast Texas into western Florida. A stratified, two-stage, cluster sampling design was used. Annual estimates of relative juvenile abundance for each species of menhaden were obtained from catch-effort data from the surveys. There were no significant correlations, for either species, between the relative juvenile abundance estimates and fishery-dependent estimates of year-class strength. From 1972 to 1975, the relative abundance of juvenile Atlantic menhaden in north Atlantic estuaries decreased to near zero. (PDF file contains 22 pages.

Real-time Heading Estimation using Perspective Features

There are a large number of commercially available quad-rotor helicopters available from various manufacturers. All of these systems rely on a low cost MEMS based inertial measurement system for stabilization and navigation. These low cost inertial systems are all subject to rapid error growth in their attitude and position estimates unless bounded by external measurements. This thesis created real-time algorithm to integrate measurements from visual cues with measurements from onboard sensors to estimate the attitude position and velocity of a quad-rotor helicopter in a local navigation frame, a system model for the ARDrone, and a feed-back controller for the vehicle\u27s heading. The ARDrone, by Parrot SA, is a low cost quad-rotor helicopter that comes equipped with a variety of sensors including a forward-looking high-definition camera. The vehicle is capable of using its onboard sensors to adequately constrain the errors for pitch and roll in all environments, however the yaw axis is still subject to drift. This work utilizes a RANSAC based vanishing point detection algorithm to provide a reliable heading reference and integrates the vanishing point based heading measurements with the system\u27s onboard heading measurements through an extended Kalman filter. In addition to estimating the drone\u27s heading, the Kalman filter also estimates the position and velocity of the drone as it moves through its environment. This system was able to provide a heading reference with an error of one degree for the drone and was shown to be capable of transitioning between vanishing points when the vehicle needed to change direction. The system also demonstrated that it was capable of generating an estimate of position and velocity. However because the position error was on the order of one meter, the estimate was not accurate enough for autonomous navigation