This paper introduces and extension to our previous papers to handle anomalies in the point based object slicing method. The anomalies handled are point, line and plane touch cases as well as overlaps. These anomalies can cause major problems in any intersection procedure, yet, they are seldom discussed, let alone handled. It turns out that the point based approach is capable of handling these special cases with minor extensions.
Handling Anomalies in Object Slicing for 3-D Printing
W Oropallo, L Piegl, P Rosen, K Rajab
Computer-Aided Design and Applications, 2019
Dimensionality reduction is an integral part of data visualization. It is a process that obtains a structure preserving low-dimensional representation of the high-dimensional data. Two common criteria can be used to achieve a dimensionality reduction: distance preservation and topology preservation. Inspired by recent work in topological data analysis, we are on the quest for a dimensionality reduction technique that achieves the criterion of homology preservation, a specific version of topology preservation. Specifically, we are interested in using topology-inspired manifold landmarking and manifold tearing to aid such a process and evaluate their effectiveness.
Homology-Preserving Dimensionality Reduction via Manifold Landmarking and Tearing
L Yan, Y Zhao, P Rosen, C Scheidegger, B Wang
Visualization in Data Science (VDS at IEEE VIS 2018)
Alan Rodriguez, David Baerg, Jessica Womble, Ryan McBride, and Sara Savitz represented USF College of Engineering at the 2018 Florida-Wide Student Engineering Design Invitational held at UCF on April 19th. The students exhibited their BEST project titled “Mixed Reality C-130 Loadmaster Simulation for CAE USA”. The Mixed Reality C-130 Loadmaster simulator, created by a team of USF Computer Science and Engineering students, uses augmented reality, incorporating both the real world and virtual reality into one view, to achieve an immersive training experience for a fraction of the cost. The Loadmaster trainee is responsible for safely loading and deploying cargo from a C-130 cargo bay.
The project was supervised by Assistant Professor Paul Rosen and was supported by CAE USA.
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Topological data analysis is an emerging area in exploratory data analysis and data mining. Its main tool, persistent homology, has become a popular technique to study the structure of complex, high-dimensional data. In this paper, we propose a novel method using persistent homology to quantify structural changes in time-varying graphs. Specifically, we transform each instance of the time-varying graph into metric spaces, extract topological features using persistent homology, and compare those features over time. We provide a visualization that assists in time-varying graph exploration and helps to identify patterns of behavior within the data. To validate our approach, we conduct several case studies on real world data sets and show how our method can find cyclic patterns, deviations from those patterns, and one-time events in time-varying graphs. We also examine whether persistence-based similarity measure as a graph metric satisfies a set of well-established, desirable properties for graph metrics.
Visual detection of structural changes in time-varying graphs using persistent homology
Mustafa Hajij, Bei Wang, Carlos Scheidegger, Paul Rosen
IEEE Pacific Visualization Symposium (PacificVis) 2018
Parallel coordinates plots (PCPs) are a well-studied technique for exploring multi-attribute datasets. In many situations, users find them a flexible method to analyze and interact with data. Unfortunately, using PCPs becomes challenging as the number of data items grows large or multiple trends within the data mix in the visualization. The resulting overdraw can obscure important features. A number of modifications to PCPs have been proposed, including using color, opacity, smooth curves, frequency, density, and animation to mitigate this problem. However, these modified PCPs tend to have their own limitations in the kinds of relationships they emphasize. We propose a new data scalable design for representing and exploring data relationships in PCPs. The approach exploits the point/line duality property of PCPs and a local linear assumption of data to extract and represent relationship summarizations. This approach simultaneously shows relationships in the data and the consistency of those relationships. Our approach supports various visualization tasks, including mixed linear and nonlinear pattern identification, noise detection, and outlier detection, all in large data. We demonstrate these tasks on multiple synthetic and real-world datasets.
DSPCP: A data scalable approach for identifying relationships in parallel coordinates
H Nguyen, P Rosen
IEEE transactions on visualization and computer graphics 24 (3), 1301-1315
We study the topological construction called Mapper in the context of simply connected domains, in particular on images. The Mapper construction can be considered as a generalization for contour, split, and joint trees on simply connected domains. A contour tree on an image domain assumes the height function to be a piecewise linear Morse function. This is a rather restrictive class of functions and does not allow us to explore the topology for most real world images. The Mapper construction avoids this limitation by assuming only continuity on the height function allowing this construction to robustly deal with a significant larger set of images. We provide a customized construction for Mapper on images, give a fast algorithm to compute it, and show how to simplify the Mapper structure in this case. Finally, we provide a simple procedure that guarantees the equivalence of Mapper to contour, join, and split trees on a simply connected domain.
The Shape of an Image: A Study of Mapper on Images
Alejandro Robles, Mustafa Hajij, and Paul Rosen
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 2018
Scalar fields are used to describe a variety of data from photographs, to laser scans, to x-ray, CT or MRI scans of machine parts and are invaluable for a variety of tasks, such as fatigue detection in parts. Analyzing scalar fields can be quite challenging due to their size, complexity, and the need to understand both local and global details in context. Join trees are a data structure used to capture the geometric properties of scalar fields, including local minima, local maxima, and saddle points. Unfortunately, computing these trees is expensive, and their incremental construction makes parallel computation nontrivial. We introduce an approach that combines three strategies, pruning, spatial-domain parallelization, and value-domain parallelization, to parallelize join tree construction using OpenCL. The resulting implementation shows a significant speedup, making computation of trees on large data practical on even modest commodity hardware.
A hybrid solution to parallel calculation of augmented join trees of scalar fields in any dimension
P Rosen, J Tu, LA Piegl
Computer-Aided Design and Applications 15 (4), 610-618
The advancement of technology and its application to the field of education has caused many to re-examine the merits and pitfalls of cyberlearning environments. Though there is a wealth of research both for and against its mainstream use, there is a consensus that much work remains to be done in key areas such as collaboration, course content, personal learning environments, and engagement. CAD and cyberlearning share a common goal: to communicate information effectively. Unfortunately, many aspects well understood in CAD have been overlooked in online education. In this paper, ten key challenges and their implications for CAD cyber education are discussed. The purpose of this paper is not to provide a dismal outlook for cyberlearning, but to incite discussion, research, and development into these areas with the anticipation of a viable and attractive alternative to traditional classroom education.
Ten challenges in CAD cyber education
ZJ Beasley, LA Piegl, P Rosen
Computer-Aided Design and Applications 15 (3), 432-442
At the IEEE Vast Challenge 2017, held on October 1, 2017 in Phoenix, Arizona, the USF Department of Computer Science and Engineering student team of Sulav Malla, Anwesh Tuladhar, and Ghulam Jilani Quadri received an Honorable Mention. Their submission to the IEEE VAST Challenge was among 56 other entries.
According to the VAST Challenge website, “The Visual Analytics Science and Technology (VAST) Challenge is an annual contest with the goal of advancing the field of visual analytics through competition. The VAST Challenge is designed to help researchers understand how their software would be used in a novel analytic task and determine if their data transformations, visualizations, and interactions would be beneficial for particular analytic tasks. VAST Challenge problems provide researchers with realistic tasks and data sets for evaluating their software, as well as an opportunity to advance the field by solving more complex problems.”
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In visualization education, both science and humanities , the literature is often divided into two parts: the design aspect and the analysis of the visualization. However, we find limited discussion on how to motivate and engage visualization students in the classroom. In the field of Writing Studies, researchers develop tools and frameworks for student peer review of writing. Based on the literature review from the field of Writing Studies, this paper proposes a new framework to implement visualization peer review in the classroom to engage today’s students. This framework can be customized for incremental and double-blind review to inspire students and reinforce critical thinking about visualization.
Leveraging Peer Review in Visualization Education: A Proposal for a New Model
A. Friedman, P. Rosen
IEEE 2017 Pedagogy of Data Visualization Workshop