Carbon and nutrient storage in subtropical seagrass meadows: examples from Florida Bay and Shark Bay

Seagrass meadows in Florida Bay and Shark Bay, contain substantial stores of both organic carbon and nutrients. Soils from both systems are predominantly calcium carbonate, with an average of 82.1% CaCO3 in Florida Bay compared to 71.3% in Shark Bay. Soils from Shark Bay had, on average, 21% higher organic carbon content and 35% higher phosphorus content than Florida Bay. Further, soils from Shark Bay had lower mean dry bulk density (0.78 ± 0.01 g mL-1) than those from Florida Bay (0.84 ± 0.02 mg mL-1). The most hypersaline regions of both bays had higher organic carbon content in surficial soils. Profiles of organic carbon and phosphorus from Florida Bay indicate that this system has experienced an increase in P delivery and primary productivity over the last century; in contrast, decreasing organic carbon and phosphorus with depth in the soil profiles in Shark Bay point to a decrease in phosphorus delivery and primary productivity over the last 1000 y. The total ecosystem stocks of stored organic C in Florida Bay averages 163.5 MgCorg ha-1, lower than the average of 243.0 MgCorg ha-1 for Shark Bay; but these values place Shark and Florida Bays among the global hotspots for organic C storage in coastal ecosystems.

Field programmable gate array based target detection and gesture recognition

The move from Standard Definition (SD) to High Definition (HD) represents a six times increases in data, which needs to be processed. With expanding resolutions and evolving compression, there is a need for high performance with flexible architectures to allow for quick upgrade ability. The technology advances in image display resolutions, advanced compression techniques, and video intelligence. Software implementation of these systems can attain accuracy with tradeoffs among processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints. There is a need for new architectures to be in pace with the fast innovations in video and imaging. It contains dedicated hardware implementation of the pixel and frame rate processes on Field Programmable Gate Array (FPGA) to achieve the real-time performance. ^ The following outlines the contributions of the dissertation. (1) We develop a target detection system by applying a novel running average mean threshold (RAMT) approach to globalize the threshold required for background subtraction. This approach adapts the threshold automatically to different environments (indoor and outdoor) and different targets (humans and vehicles). For low power consumption and better performance, we design the complete system on FPGA. (2) We introduce a safe distance factor and develop an algorithm for occlusion occurrence detection during target tracking. A novel mean-threshold is calculated by motion-position analysis. (3) A new strategy for gesture recognition is developed using Combinational Neural Networks (CNN) based on a tree structure. Analysis of the method is done on American Sign Language (ASL) gestures. We introduce novel point of interests approach to reduce the feature vector size and gradient threshold approach for accurate classification. (4) We design a gesture recognition system using a hardware/ software co-simulation neural network for high speed and low memory storage requirements provided by the FPGA. We develop an innovative maximum distant algorithm which uses only 0.39% of the image as the feature vector to train and test the system design. Database set gestures involved in different applications may vary. Therefore, it is highly essential to keep the feature vector as low as possible while maintaining the same accuracy and performance^