3D TerraFly: Quality of service management for online interactive 3D geographic information system

3D geographic information system (GIS) is data and computation intensive in nature. Internet users are usually equipped with low-end personal computers and network connections of limited bandwidth. Data reduction and performance optimization techniques are of critical importance in quality of service (QoS) management for online 3D GIS. In this research, QoS management issues regarding distributed 3D GIS presentation were studied to develop 3D TerraFly, an interactive 3D GIS that supports high quality online terrain visualization and navigation. ^ To tackle the QoS management challenges, multi-resolution rendering model, adaptive level of detail (LOD) control and mesh simplification algorithms were proposed to effectively reduce the terrain model complexity. The rendering model is adaptively decomposed into sub-regions of up-to-three detail levels according to viewing distance and other dynamic quality measurements. The mesh simplification algorithm was designed as a hybrid algorithm that combines edge straightening and quad-tree compression to reduce the mesh complexity by removing geometrically redundant vertices. The main advantage of this mesh simplification algorithm is that grid mesh can be directly processed in parallel without triangulation overhead. Algorithms facilitating remote accessing and distributed processing of volumetric GIS data, such as data replication, directory service, request scheduling, predictive data retrieving and caching were also proposed. ^ A prototype of the proposed 3D TerraFly implemented in this research demonstrates the effectiveness of our proposed QoS management framework in handling interactive online 3D GIS. The system implementation details and future directions of this research are also addressed in this thesis. ^

Truancy reduction: Extension of a county program

The primary purpose of this research was to examine the effect of the Truancy Intervention Program (TIP) on attendance patterns of elementary school students. Longitudinal archival data were used from Miami-Dade County Public School system's data system, ISIS. Data included the students' school information from fifth through ninth grade for attendance, academic grades, referral information, and referral consequences. The sample for this study was drawn from students at TIP-participating M-DCPS elementary schools in Miami-Dade County. Data collected spanned five years for each participant from the fifth grade to the ninth grade. To examine the effect of TIP on attendance, participation in middle school TIP was compared with non-TIP participation. In addition to immediate effects on attendance, the durability of the effects of TIP was studied through an analysis of attendance at the ninth grade level. A secondary purpose was to examine the relation of TIP participation to Grade Point Average (GPA). ^ The data were analyzed using 2 (group) x 3 (grade) Repeated Measures Analysis of Variance (ANOVA) on yearly attendance (number of absences), and grade point average for each year. The interaction between group and grade was significant. Post hoc tests indicated that absences were not significantly different in the two programs in seventh, eighth or ninth grade. Students enrolled in a middle school with TIP showed a significantly higher number of absences in ninth grade than for seventh or eighth grade. There were no differences by grade level for students enrolled in non-TIP middle schools. GPA analysis indicated that students enrolled in a non-TIP middle school had a significantly higher GPA across seventh, eighth, and ninth grades when compared to students enrolled at a TIP middle school. ^ An examination of attendance disciplinary referrals and consequences further revealed that the referral rates for students enrolled at a TIP middle school were higher at the seventh, eighth, and ninth grade level, then for students enrolled at a non-TIP middle school. This pattern was not readily apparent at non-TIP middle schools. Limitations of the research were noted and further research regarding program implementation (process evaluation) was suggested. ^

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Parameter optimization for noise and vibration reduction associated with position and speed sensorless control of permanent magnet machines at low speed

With the advantages and popularity of Permanent Magnet (PM) motors due to their high power density, there is an increasing incentive to use them in variety of applications including electric actuation. These applications have strict noise emission standards. The generation of audible noise and associated vibration modes are characteristics of all electric motors, it is especially problematic in low speed sensorless control rotary actuation applications using high frequency voltage injection technique. This dissertation is aimed at solving the problem of optimizing the sensorless control algorithm for low noise and vibration while achieving at least 12 bit absolute accuracy for speed and position control. The low speed sensorless algorithm is simulated using an improved Phase Variable Model, developed and implemented in a hardware-in-the-loop prototyping environment. Two experimental testbeds were developed and built to test and verify the algorithm in real time.^ A neural network based modeling approach was used to predict the audible noise due to the high frequency injected carrier signal. This model was created based on noise measurements in an especially built chamber. The developed noise model is then integrated into the high frequency based sensorless control scheme so that appropriate tradeoffs and mitigation techniques can be devised. This will improve the position estimation and control performance while keeping the noise below a certain level. Genetic algorithms were used for including the noise optimization parameters into the developed control algorithm.^ A novel wavelet based filtering approach was proposed in this dissertation for the sensorless control algorithm at low speed. This novel filter was capable of extracting the position information at low values of injection voltage where conventional filters fail. This filtering approach can be used in practice to reduce the injected voltage in sensorless control algorithm resulting in significant reduction of noise and vibration.^ Online optimization of sensorless position estimation algorithm was performed to reduce vibration and to improve the position estimation performance. The results obtained are important and represent original contributions that can be helpful in choosing optimal parameters for sensorless control algorithm in many practical applications.^

Sistematización in Urban Disaster Risk Reduction

Purpose – The purpose of this paper is to provide an analysis of sistematización’s use as a research tool in the operationalization of a “neighborhood approach” to the implementation of disaster risk reduction (DRR) in informal urban settlements. Design/methodology/approach – The first section highlights sistematización’s historical origins in Latin America in the fields of popular adult education, social work, and rural development. The second explains why sistematización was made a required component of project implementation. The third section addresses the approach to sistematización used. The final discusses how this experience both contributes to sistematización’s theoretical development and practical application as a methodology. Findings – The introduction of “sistematización” as a research tool facilitated real-time assessment of project implementation, providing timely information that positively influenced decision-making processes. This on-going feedback, collective learning, and open-exchange of know-how between NGOs and partner institutions allowed for the evaluation of existing practices and development of new ways of collaborating to address disaster risk in complex and dynamic urban environments. Practical implications – Sistematización transcends the narrow focus of traditional monitoring and evaluation on final results, emphasizing a comprehensive understanding of processes and contexts. Originality/value – Its use in the implementation of DRR initiatives in informal urban environments is particularly novel, highlighting the capacity of the methodology to be tailored to a variety of needs, in this case, bridging the gap between NGOs, local governments, and vulnerable communities, as well as between urban, development, and disaster risk management planning.