Importance of storm events in controlling ecosystem structure and function in a Florida gulf coast estuary

From 8/95 to 2/01, we investigated the ecological effects of intra- and inter-annual variability in freshwater flow through Taylor Creek in southeastern Everglades National Park. Continuous monitoring and intensive sampling studies overlapped with an array of pulsed weather events that impacted physical, chemical, and biological attributes of this region. We quantified the effects of three events representing a range of characteristics (duration, amount of precipitation, storm intensity, wind direction) on the hydraulic connectivity, nutrient and sediment dynamics, and vegetation structure of the SE Everglades estuarine ecotone. These events included a strong winter storm in November 1996, Tropical Storm Harvey in September 1999, and Hurricane Irene in October 1999. Continuous hydrologic and daily water sample data were used to examine the effects of these events on the physical forcing and quality of water in Taylor Creek. A high resolution, flow-through sampling and mapping approach was used to characterize water quality in the adjacent bay. To understand the effects of these events on vegetation communities, we measured mangrove litter production and estimated seagrass cover in the bay at monthly intervals. We also quantified sediment deposition associated with Hurricane Irene's flood surge along the Buttonwood Ridge. These three events resulted in dramatic changes in surface water movement and chemistry in Taylor Creek and adjacent regions of Florida Bay as well as increased mangrove litterfall and flood surge scouring of seagrass beds. Up to 5 cm of bay-derived mud was deposited along the ridge adjacent to the creek in this single pulsed event. These short-term events can account for a substantial proportion of the annual flux of freshwater and materials between the mangrove zone and Florida Bay. Our findings shed light on the capacity of these storm events, especially when in succession, to have far reaching and long lasting effects on coastal ecosystems such as the estuarine ecotone of the SE Everglades.

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^

Enhancement of field emission from multistage structure of carbon nanotube arrays

The field emission measurements for the multistage structured nanotubes (i.e., thin-multiwall and single wall carbon nanotubes grown on multiwall carbon nanotubes) were carried out and a low turn-on field of ~0.45 V/ μm, high emission current of 450 μA at a field of IV/μm and a large field enhancement factor of ~26200 were obtained. The thin multiwall carbon nanotubes (thin-MWNTs) and single wall carbon nanotubes (SWNTs) were grown on the regular arrays of vertically aligned multi wall carbon nanotubes (MWNTs) on porous silicon substrate by Chemical Vapor Deposition (CVD) method. The thin-MWNTs and SWNTs grown on MWNTs in this way have a multistage structure which gives higher enhancement of the electric field and hence the electron field emission.