966 research outputs found

    Automated Pollen Image Classification

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    This Master of Science thesis reviews previous research, proposes a method anddemonstrates proof-of-concept software for the automated matching of pollen grainimages to satisfy degree requirements at the University of Tennessee. An ideal imagesegmentation algorithm and shape representation data structure is selected, alongwith a multi-phase shape matching system. The system is shown to be invariantto synthetic image translation, rotation, and to a lesser extent global contrast andintensity changes. The proof-of-concept software is used to demonstrate how pollengrains can be matched to images of other pollen grains, stored in a database, thatshare similar features with up to a 75% accuracy rate

    Systemic Risk and Hedge Funds

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    Systemic risk is commonly used to describe the possibility of a series of correlated defaults among financial institutions---typically banks---that occur over a short period of time, often caused by a single major event. However, since the collapse of Long Term Capital Management in 1998, it has become clear that hedge funds are also involved in systemic risk exposures. The hedge-fund industry has a symbiotic relationship with the banking sector, and many banks now operate proprietary trading units that are organized much like hedge funds. As a result, the risk exposures of the hedge-fund industry may have a material impact on the banking sector, resulting in new sources of systemic risks. In this paper, we attempt to quantify the potential impact of hedge funds on systemic risk by developing a number of new risk measures for hedge funds and applying them to individual and aggregate hedge-fund returns data. These measures include: illiquidity risk exposure, nonlinear factor models for hedge-fund and banking-sector indexes, logistic regression analysis of hedge-fund liquidation probabilities, and aggregate measures of volatility and distress based on regime-switching models. Our preliminary findings suggest that the hedge-fund industry may be heading into a challenging period of lower expected returns, and that systemic risk is currently on the rise.

    Digital, Automated Reactive Target System

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    In this era, technology is woven into almost every facet of our leisure activities. Although technology has innovated hobbies ranging from chess to soccer, the art of shooting has been neglected. Unnecessary insufficiency such as bullet ricochets off of mechanical steel targets, ineffective progress tracking, and general inaccessibility to outdoor training facilities are all improvable areas of this sport. The Dynamic Automated Reactive Target (D.A.R.T) System aims to fill some of these gaps and help modernize recreational marksmanship. Modeling the system after a dueling tree will optimize the use of the system and allow for different training models to challenge the marksman. The system will utilize accelerometers to collect accurate data regarding target tracking from the user during training, and custom motor controllers allow for optimized movement response times of each target. To ensure safety and increase efficiency, Bluetooth communication will grant the marksman control of the system from anywhere within the range with the use of any portable device. With the algorithms developed, multiple different training simulations can be practiced. In all, the D.A.R.T. System will revolutionize the safety and convenience of shooting sports
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