Uncertainty visualization in 3D scalar data

One problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.

Direct visualization of magnetic-field-induced magnetoelectric switching in multiferroic Aurivillius phase thin films

Multiferroic materials displaying coupled ferroelectric and ferromagnetic order parameters could provide a means for data storage whereby bits could be written electrically and read magnetically, or vice versa. Thin films of Aurivillius phase Bi6Ti2.8Fe1.52Mn0.68O18, previously prepared by a chemical solution deposition (CSD) technique, are multiferroics demonstrating magnetoelectric coupling at room temperature. Here, we demonstrate the growth of a similar composition, Bi6Ti2.99Fe1.46Mn0.55O18, via the liquid injection chemical vapor deposition technique. High-resolution magnetic measurements reveal a considerably higher in-plane ferromagnetic signature than CSD grown films (MS = 24.25 emu/g (215 emu/cm3), MR = 9.916 emu/g (81.5 emu/cm3), HC = 170 Oe). A statistical analysis of the results from a thorough microstructural examination of the samples, allows us to conclude that the ferromagnetic signature can be attributed to the Aurivillius phase, with a confidence level of 99.95%. In addition, we report the direct piezoresponse force microscopy visualization of ferroelectric switching while going through a full in-plane magnetic field cycle, where increased volumes (8.6 to 14% compared with 4 to 7% for the CSD-grown films) of the film engage in magnetoelectric coupling and demonstrate both irreversible and reversible magnetoelectric domain switching.

Multi-sensor classification of tennis strokes

In this work, we investigate tennis stroke recognition using a single inertial measuring unit attached to a player’s forearm during a competitive match. This paper evaluates the best approach for stroke detection using either accelerometers, gyroscopes or magnetometers, which are embedded into the inertial measuring unit. This work concludes what is the optimal training data set for stroke classification and proves that classifiers can perform well when tested on players who were not used to train the classifier. This work provides a significant step forward for our overall goal, which is to develop next generation sports coaching tools using both inertial and visual sensors in an instrumented indoor sporting environment.