Visualizing Quickest Visibility Maps

Non peer reviewe

Maps, fields, and boundary cairns: demarcation and resistance in colonial Cyprus

An important component of the administration and control of a colony by an external power was the demarcation and classification of the land and its people. This was certainly the case in Cyprus under British colonial rule (1878-1960), as three case studies demonstrate: the topographical survey of the island by H. H. Kitchener in 1878-1883; the cadastral survey of 1909-1929; and the work of the forest delimitation commission from 1881 to 1896. This was not achieved without resistance on a variety of levels. Ironically, part of the opposition came from the structure of the colonial demarcation and classification project itself

Evaluation of a Bundling Technique for Parallel Coordinates

We describe a technique for bundled curve representations in parallel-coordinates plots and present a controlled user study evaluating their effectiveness. Replacing the traditional C^0 polygonal lines by C^1 continuous piecewise Bezier curves makes it easier to visually trace data points through each coordinate axis. The resulting Bezier curves can then be bundled to visualize data with given cluster structures. Curve bundles are efficient to compute, provide visual separation between data clusters, reduce visual clutter, and present a clearer overview of the dataset. A controlled user study with 14 participants confirmed the effectiveness of curve bundling for parallel-coordinates visualization: 1) compared to polygonal lines, it is equally capable of revealing correlations between neighboring data attributes; 2) its geometric cues can be effective in displaying cluster information. For some datasets curve bundling allows the color perceptual channel to be applied to other data attributes, while for complex cluster patterns, bundling and color can represent clustering far more clearly than either alone

IMAGE-IN: Interactive web-based multidimensional 3D visualizer for multi-modal microscopy images

Advances in microscopy hardware and storage capabilities lead to increasingly larger multidimensional datasets. The multiple dimensions are commonly associated with space, time, and color channels. Since “seeing is believing”, it is important to have easy access to user-friendly visualization software. Here we present IMAGE-IN, an interactive web-based multidimensional (N-D) viewer designed specifically for confocal laser scanning microscopy (CLSM) and focused ion beam scanning electron microscopy (FIB-SEM) data, with the goal of assisting biologists in their visualization and analysis tasks and promoting digital work-flows. This new visualization platform includes intuitive multidimensional opacity fine-tuning, shading on/off, multiple blending modes for volume viewers, and the ability to handle multichannel volumetric data in volume and surface views. The software accepts a sequence of image files or stacked 3D images as input and offers a variety of viewing options ranging from 3D volume/surface rendering to multiplanar reconstruction approaches. We evaluate the performance by comparing the loading and rendering timings of a heterogeneous dataset of multichannel CLSM and FIB-SEM images on two devices with installed graphic cards, as well as comparing rendered image quality between ClearVolume (the ImageJ open-source desktop viewer), Napari (the Python desktop viewer), Imaris (the closed-source desktop viewer), and our proposed IMAGE-IN web viewer

Intelligent Solar Mapping Tool

The goal of this thesis is to develop an overall cost effective methodology for regional tracking and measuring solar patterns and intensities, for use in solar concentration. The idea is to create a system that is modular, cheap, and easy use. Solar trackers serve many functions in that they allow for the scientist to get an idea of irradiance patterns for studies and other more specific uses such as determining the most efficient orientation of solar capture devices. The thesis focused mainly on raw data collection and visualization, and not on translation so the main challenges were to obtain the quickest and most accurate solar map. This meant deciding between having multiple sensors (photodiodes) versus fewer mobile sensors on movable axes. Using an open-source microcontroller, motors, and photodiodes, different configurations were attempted. It was discovered that better control could be obtained from having fewer sensors mounted on a two-axis system, and then varying the orientation of the sensors based on the region of space to be observed. The final system works scans a hemispherical surface by sweeping the detector head along two polar axes and storing the irradiance (as voltages) at each point. The sensor system consists of a wide-angle (65 degrees) and narrow-angle (10 degrees) photodiode (to allow for compensation of blind spots) connected to variable gain operational amplifiers to prevent saturation during high insolation, and high accuracy during low insolation. This allows for versatility for use during different conditions. The results showed the system built was capable of performing a full run in under 20 seconds and an additional 3 seconds to post-process the data using MATLAB. Complete data analyses is yet to be done, but simple visualization of the data showed that regions of high solar intensity can be identified easily, hence allowing for a solar devices to track maximal intensity. The device built serves as a pedestal for further advancements due to the use of cheap and open-source materials. The future plan is to extend the concept to solve more advanced problems such as determining diffuse versus direct components of solar radiation

Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization

Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of available network parameters to the complex features found in scientific data, leading to a loss in reconstruction quality. We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU systems. We also release an open-source neural volume rendering application that allows plug-and-play rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses multiple spatially adaptive feature grids that learn where to be placed within the domain to dynamically allocate more neural network resources where error is high in the volume, improving state-of-the-art reconstruction accuracy of SRNs for scientific data without requiring expensive octree refining, pruning, and traversal like previous adaptive models. In our domain decomposition approach for representing large-scale data, we train an set of APMGSRNs in parallel on separate bricks of the volume to reduce training time while avoiding overhead necessary for an out-of-core solution for volumes too large to fit in GPU memory. After training, the lightweight SRNs are used for realtime neural volume rendering in our open-source renderer, where arbitrary view angles and transfer functions can be explored. A copy of this paper, all code, all models used in our experiments, and all supplemental materials and videos are available at https://github.com/skywolf829/APMGSRN.Comment: Accepted to IEEE VIS 202

Visualization methods for analysis of 3D multi-scale medical data

[no abstract