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Now showing 1 - 10 of 21841
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    Throughput optimization in MIMO networks
    (Georgia Institute of Technology, 2011-08-22) Srinivasan, Ramya ; Blough, Douglas M. ; Ammar, Mostafa ; Ferri, Bonnie ; Ingram, Mary Ann ; Sivakumar, Raghupathy ; Electrical and Computer Engineering
    Enabling multi-hop wireless mesh networks with multi-input multi-output (MIMO) functionality boosts network throughput by transmitting over multiple orthogonal spatial channels (spatial multiplexing) and by performing interference cancellation, to allow links within interference range to be concurrently active. Furthermore, if the channel is in a deep fade, then multiple antenna elements at the transmitter and/or receiver can be used to transmit a single stream, thereby improving signal quality (diversity gain). However, there is a fundamental trade-off between boosting individual link performance and reducing interference, which must be modeled in the process of optimizing network throughput. This is called the diversity-multiplexing-interference suppression trade-off. Optimizing network throughput therefore, requires optimizing the trade-off between the amounts of diversity employed on each link, the number of streams multiplexed on each link and the number of interfering links allowed to be simultaneously active in the network. We present a set of efficient heuristics for one-shot link scheduling and stream allocation that approximately solve the problem of optimizing network throughput in a single time slot. We identify the fundamental problem of verifying the feasibility of a given stream allocation. The problems of general link scheduling and stream allocation are very closely related to the problem of verifying feasibility. We present a set of efficient heuristic feasibility tests which can be easily incorporated into practical scheduling schemes. We show for some special MIMO network scenarios that feasibility is of polynomial complexity. However, we conjecture that in general, this problem, which is a variation of Boolean Satisablility, is NP-Complete.
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    Development and characterization of novel reduction-oxidation active materials for two-step solar thermochemical cycles
    (Georgia Institute of Technology, 2019-05-21) Bush, Hagan E. ; Loutzenhiser, Peter G. ; Jeter, Sheldon ; Kumar, Satish ; Orlando, Thomas ; Ranjan, Devesh ; Mechanical Engineering
    Solar thermochemistry enables concentrating solar technologies to store or produce energy and materials in new, more versatile ways. In this work, binary and perovskite metal oxide candidates for high-temperature reduction-oxidation (redox) thermochemical cycles were synthesized and characterized to determine their potential for solar applications. First, the experimental infrastructure required to study rapidly reacting, high temperature metal oxides was developed. A high flux solar simulator (HFSS) capable of rapid heating was coupled to an upward flow reactor (UFR) to thermally reduce oxide samples, and O2 product gas flows were measured to calculate thermal reduction rates. The radiative input from the HFSS was characterized and coupled to computational models of the UFR to predict gas dynamics and redox sample heating. Dispersion modeling was used to correct temporal O2 measurements downstream of reducing samples. Thermal reduction experiments with the well-studied binary oxide pair Co3O4/CoO were performed to validate the computational models. Next, the UFR and a thermogravimetric analyzer (TGA) were used to evaluate candidate materials. Fe2O3/Fe3O4 were kinetically characterized via TGA and evaluated in thermodynamic cycle models. The results suggested the oxides were promising candidates for solar thermochemical electricity production. Al-doped SrFeO3-δ was synthesized and reaction models were developed with TGA to predict equilibrium nonstoichiometry and redox thermodynamics. The results were incorporated into a thermodynamic cycle model, and redox cycling experiments were performed in the UFR. The analyses determined that the oxides were well-suited to air separation for NH3 production.
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    Survival and regrowth of fecal enterococci in desiccated and rewetted sediments
    (Georgia Institute of Technology, 2005-04) Hartel, Peter G. ; Rodgers, Karen ; Fisher, Jared A. ; McDonald, Jennifer L. ; Gentit, Lisa C. ; Otero, Ernesto ; Rivera-Torres, Yaritza ; Bryant, Tamara L. ; Jones, Stephen H. ; University of Georgia. Dept. of Crop and Soil Sciences ; University of Georgia. Marine Extension Service ; University of Puerto Rico (Mayagüez Campus). Dept. of Marine Sciences ; University of New Hampshire. Jackson Estuarine Laboratory ; Hatcher, Kathryn J.
    Fecal enterococci are bacteria widely used as indicators of fecal contamination in marine and estuarine waters. One assumption is that these bacteria do not persist or regrow in the environment. Our continuing problems with high numbers of fecal enterococci in sediment suggested that these bacteria may persist and regrow. Therefore, we conducted experiments with fecal enterococci to determine their ability to survive desiccation and to regrow in marine and estuarine sediments from Georgia, New Hampshire, and Puerto Rico after 0, 2, 30, and 60 days. Although numbers of fecal enterococci generally decreased with increased length of drying, many fecal enterococci survived desiccation and regrew in rewetted sediment, violating the assumption that fecal bacteria not persist or regrow in the environment. Because there is not a better alternative to fecal enterococci as fecal indicator bacteria, these results suggest that care should be taken not to disturb the sediment when sampling water for fecal contamination, or if the sediment is already disturbed (e.g., on windy days or during runoff conditions), then the influence of sediment should be considered.
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    Using sample-based continuation techniques to efficiently compute subspace reachable sets and Pareto surfaces
    (Georgia Institute of Technology, 2019-11-11) Brew, Julian ; Lightsey, E. Glenn ; Holzinger, Marcus J. ; Schuet, Stefan ; Tsiotras, Panagiotis ; Rogers, Jonathan ; Aerospace Engineering
    For a given continuous-time dynamical system with control input constraints and prescribed state boundary conditions, one can compute the reachable set at a specified time horizon. Forward reachable sets contain all states that can be reached using a feasible control policy at the specified time horizon. Alternatively, backwards reachable sets contain all initial states that can reach the prescribed state boundary condition using a feasible control policy at the specified time horizon. The computation of reachable sets has been applied to many problems such as vehicle collision avoidance, operational safety planning, system capability demonstration, and even economic modeling and weather forecasting. However, computing reachable volumes for general nonlinear systems is very difficult to do both accurately and efficiently. The first contribution of this thesis investigates computational techniques for alleviating the curse of dimensionality by computing reachable sets on subspaces of the full state dimension and computing point solutions for the reachable set boundary. To compute these point solutions, optimal control problems are reduced to initial value problems using continuation methods and then solved. The sample-based continuation techniques are computationally efficient in that they are easily parallelizable. However, the distribution of samples on the reachable set boundary is not directly controlled. The second contribution presents necessary conditions for distributed computation convergence, as well as necessary conditions for curvature- or uniform coverage-based sampling methods. Solutions to multi-objective optimization problems are generally defined using a set of feasible solutions such that for any one objective to improve it is necessary for other objectives to degrade. This suggests there is a connection between the two fields with the potential of cross-fertilization of computational techniques and theory. The third contribution explores analytical connections between reachability theory and multi-objective optimization with investigation into properties, constraints, and special cases.
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    Methodology for global optimization of computationally expensive design problems
    (Georgia Institute of Technology, 2013-06-25) Koullias, Stefanos ; Mavris, Dimitri N. ; Griendling, Kelly ; Mahadevan, Sankaran ; Schrage, Daniel P. ; German, Brian J. ; Aerospace Engineering
    The design of unconventional aircraft requires early use of high-fidelity physics-based tools to search the unfamiliar design space for optimum designs. Current methods for incorporating high-fidelity tools into early design phases for the purpose of reducing uncertainty are inadequate due to the severely restricted budgets that are common in early design as well as the unfamiliar design space of advanced aircraft. This motivates the need for a robust and efficient global optimization algorithm. This research presents a novel surrogate model-based global optimization algorithm to efficiently search challenging design spaces for optimum designs. The algorithm searches the design space by constructing a fully Bayesian Gaussian process model through a set of observations and then using the model to make new observations in promising areas where the global minimum is likely to occur. The algorithm is incorporated into a methodology that reduces failed cases, infeasible designs, and provides large reductions in the objective function values of design problems. Results on four sets of algebraic test problems are presented and the methodology is applied to an airfoil section design problem and a conceptual aircraft design problem. The method is shown to solve more nonlinearly constrained algebraic test problems than state-of-the-art algorithms and obtains the largest reduction in the takeoff gross weight of a notional 70-passenger regional jet versus competing design methods.
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    Using magnetic resonance imaging to track inflammatory cells in a murine myocardial infarction model
    (Georgia Institute of Technology, 2009-04-08) Yang, Yidong ; Hu, Tom ; Cho, Sang Hyun ; Wang, Chris ; Yanasak, Nathan ; Mechanical Engineering
    In cellular MRI, micrometer-sized iron oxide particles (MPIO) are a more sensitive contrast agent for tracking inflammatory-cell migration compared to ultra-small superparamagnetic iron oxide particles (USPIO). Inflammation, which promotes adverse tissue remodeling, is known to occur in the viable myocardium adjacent to the necrosed area after a myocardial infarction (MI). This study investigated the temporal relationship between inflammatory cell infiltration and cardiac function during tissue remodeling post-MI using MPIO-enhanced MRI. The MPIO were injected into 7 C57Bl/6 mice (MI+MPIO group) via intravenous administration. The MI was induced 7 days post-MPIO injection. As control groups, 7 mice (Sham+MPIO group) underwent sham-operated surgery without myocardial injury post-MPIO injection and another 6 mice (MI-MPIO group) underwent MI surgery without MPIO injection. MRIs performed post-MI showed a significant signal attenuation at the MI zone in the MI+MPIO group compared to the control groups. The findings suggested that the inflammatory cells containing MPIO infiltrated into the myocardial injury site. Cardiac function was also measured and correlated with the labeled-cell infiltration at the MI site. This study demonstrated a noninvasive technique for monitoring inflammatory cell migration using the MPIO contrast agent. This MPIO-enhanced MRI technique could provide additional insight concerning cardiac disease progression that would improve therapeutic treatment for MI patients.
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    HoneyPhy: A physics-aware CPS honeypot framework
    (Georgia Institute of Technology, 2017-04-28) Litchfield, Samuel Lewis ; Beyah, Raheem A. ; Meliopoulos, Sakis A. P. ; Owen, Henry L. ; Electrical and Computer Engineering
    Cyber Physical Systems (CPS) are vulnerable systems, and attacks are currently being carried out against them. Some of these attacks have never been seen before, and so the first step in defending CPS is to understand what attackers are doing, and how they are doing it. Traditionally, honeypots have been a tool used to gain this information, but honeypots need to be convincing to fool attackers. For CPS, being convincing entails not only ad- dressing networking concerns, but also modeling device actuation fingerprints and how the attached process responds to actuations. In order to create a convincing CPS honeypot, a framework was developed to address the need to present convincing networking, device, and process fingerprints. Two proof of concept systems were developed for this framework, and a set of proof of concept device and process models were implemented.
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    Ultrasonic phased arrays with variable geometric focusing for hyperthermia applications
    (Georgia Institute of Technology, 1991-12) Yoon, Young Joong ; Benkeser, Paul J. ; Electrical Engineering
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    Method of conducting a state-wide highway planning survey
    (Georgia Institute of Technology, 1938-08) Jacobson, Herbert Reinhold ; Civil Engineering
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    The absorption of nitrogen dioxide by condensing water droplets
    (Georgia Institute of Technology, 1977-12) Herrmann, John Patrick ; Matteson, Michael J. ; Chemical Engineering