CEG477/677: Computer Graphics II

By the end of this quarter, you will be familiar with techniques for generation 3-D scenes and interacting with the generated scenes. You will be introduced to surface rendering techniques, visibility algorithms, illumination models, and geometric modelling

Overcoming Uncertainties in Molecular Visualization

Uncertainties are difficult if not impossible to avoid. Capturing data from the analog world almost always results in some form of uncertainty. The amount of uncertainty depends on the method of measurement and its accuracy. When visualizing data that has some associated uncertainty, it is essential to properly process and convey such uncertainty and especially the amount of uncertainty keeping in mind that additional processing steps can amplify the uncertainty. There are various sources of uncertainty, such as numerical limitations or limitations of the capture device. However, there are other sources of uncertainty. Some of these uncertainties stem from model assumptions or limitations of how we translate natural specimens to 3D representations. Molecular structures are one example of this. This talk will illustrate this further and point to some of the solutions

CEG 433/633-01: Operating Systems

By the end of this quarter, you should be able to apply the learned concepts to the following:•Develop, test and debug programs in Unix.•Improve the performance of programs by tuning virtual memory usage, and file io.•Design and construct device drivers for Unix.•Design and build newer file systems for any OS. During the course we will discuss topics from the following areas:•Operating system structures•Operating system interfaces•Process management and scheduling•Interprocess communication•File systems•Memory managemen

Uncertainty-aware Visualization in Medical Imaging - A Survey

Medical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision-making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state-of-the-art in uncertainty-aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be combined to form uncertainty-aware medical imaging pipelines. Based on our analysis, we are able to point to open problems in uncertainty-aware medical imaging