Modeling population patterns in New Orleans 2000-2010: a density function approach

Based on the 2000 and 2010 census tract data from the U.S. Census Bureau, this thesis examines the change of population distribution patterns in New Orleans in the pre- and post-Katrina eras by the monocentric and polycentric density functions. The study area is the mostly urbanized part of the New Orleans Metropolitan Statistical Area (MSA), including Orleans, Jefferson and St. Bernard parishes. The post-Katrina New Orleans has experienced an uneven recovery reflected in the geographic disparities in population change. The density function approach investigates what function best captures the population density distribution, how the density patterns have changed over time, how many significant centers can be identified in the study area, and how influential each center has been on the population distribution throughout the area. The regression results of the monocentric model show that New Orleans has experienced suburbanization captured by the negative exponential density function with a lower CBD intercept and a smoother gradient in 2010 than 2000. Two subcenters are identified besides the CBD in New Orleans based on the GIS surface modeling of employment density pattern in combination of field observation. The regression results from the polycentric model indicate that the CBD has significant influence over the citywide population density pattern in both 2000 and 2010, and only one subcenter is significant in 2000 but none in 2010. This indicates that the urban structure in New Orleans has regressed from more of polycentricity in 2010 to monocentricity in 2000

A characteristic-based visual analytics approach to detect subtle attacks from NetFlow records

Security is essentially important for any enterprise networks. Denial of service, port scanning, and data exfiltration are among of the most common network intrusions. It\u27s urgent for network administrators to detect such attacks effectively and efficiently from network traffic. Though there are many intrusion detection systems (IDSs) and approaches, Visual Analytics (VA) provides a human-friendly approach to detect network intrusions with situational awareness functionality. Overview visualization is the first and most important step in a VA approach. However, many VA systems cannot effectively identify subtle attacks from massive traffic data because of the incapability of overview visualizations. In this work, we developed two overviews and tried to identify subtle attacks directly from these two overviews. Moreover, zoomed-in visualizations were also provided for further investigation. The primary data source was NetFlow and we evaluated the VA system with datasets from Mini Challenge 3 of VAST challenge 2013. Evaluation results indicated that the VA system can detect all the labeled intrusions (denial of service, port scanning and data exfiltration) with very few false alerts

GaN Characterization by X-ray Techniques

Gallium nitride (GaN) is a binary III/V direct bandgap semiconductor. In recent years, due to its unique characteristics such as hard, mechanically stable, superior carrier saturation velocity, high heat capacity, and thermal conductivity, GaN is widely applied in the advanced fabrication of bright light-emitting diodes, optoelectronics, high-power and high frequency electronic devices. However, large differences in the coefficient of thermal expansion and lattice constant between GaN epitaxial layers and a substrate exist. These thermal mismatch and lattice mismatch lead to strain and the generation of defects in the GaN and affect the performance of the devices. Techniques of X-ray diffraction are applied for characterization: reciprocal space mapping is applied to analyze strain and relaxation in epitaxial layers; high resolution rocking curves are applied for dislocation density calculation; and X-ray reflectivity is also applied to characterize the thickness and roughness information. For structural simulation of the GaN-based devices, software LEPTOS is applied.Mechanical Engineering, Department o