A Coloring Algorithm for Disambiguating Graph and Map Drawings

Drawings of non-planar graphs always result in edge crossings. When there are many edges crossing at small angles, it is often difficult to follow these edges, because of the multiple visual paths resulted from the crossings that slow down eye movements. In this paper we propose an algorithm that disambiguates the edges with automatic selection of distinctive colors. Our proposed algorithm computes a near optimal color assignment of a dual collision graph, using a novel branch-and-bound procedure applied to a space decomposition of the color gamut. We give examples demonstrating the effectiveness of this approach in clarifying drawings of real world graphs and maps

Perceptual organization in user-generated graph layouts

Many graph layout algorithms optimize visual characteristics to achieve useful representations. Implicitly, their goal is to create visual representations that are more intuitive to human observers. In this paper, we asked users to explicitly manipulate nodes in a network diagram to create layouts that they felt best captured the relationships in the data. This allowed us to measure organizational behavior directly, allowing us to evaluate the perceptual importance of particular visual features, such as edge crossings and edge-lengths uniformity. We also manipulated the interior structure of the node relationships by designing data sets that contained clusters, that is, sets of nodes that are strongly interconnected. By varying the degree to which these clusters were ldquomaskedrdquo by extraneous edges we were able to measure observerspsila sensitivity to the existence of clusters and how they revealed them in the network diagram. Based on these measurements we found that observers are able to recover cluster structure, that the distance between clusters is inversely related to the strength of the clustering, and that users exhibit the tendency to use edges to visually delineate perceptual groups. These results demonstrate the role of perceptual organization in representing graph data and provide concrete recommendations for graph layout algorithm

CubeSat electronic power system

Cube Satellites are small satellites used by NASA and other non-governmental space companies as a cost-effective means to get a payload into space to perform research and develop new technologies. The Robotic Systems Lab at Santa Clara University has designed and launched several Cube Satellites over the last ten years. We will be continuing the design of a 3U CubeSat began by a senior design team last year. The goal of this project is to design and build an electronic power system (EPS) for the CubeSat. The EPS must be able to power all system components, including the communication board, the radio and beacon, as well as any additional customer payload. The system is designed to provide power for the satellite throughout the entire orbit, even during periods of eclipse when the satellite will be unable to generate power. In addition, this project is experimenting with a new technology, supercapacitors, to test their potential uses in space. The EPS is a hybrid system utilizing both batteries as a reliable source of power storage and supercapacitors in order to test their capabilities

Diagramming social practice theory:An interdisciplinary experiment exploring practices as networks

Achieving a transition to a low-carbon energy system is now widely recognised as a key challenge facing humanity. To date, the vast majority of research addressing this challenge has been conducted within the disciplines of science, engineering and economics utilising quantitative and modelling techniques. However, there is growing awareness that meeting energy challenges requires fundamentally socio-technical solutions and that the social sciences have an important role to play. This is an interdisciplinary challenge but, to date, there remain very few explorations of, or reflections on, interdisciplinary energy research in practice. This paper seeks to change that by reporting on an interdisciplinary experiment to build new models of energy demand on the basis of cutting-edge social science understandings. The process encouraged the social scientists to communicate their ideas more simply, whilst allowing engineers to think critically about the embedded assumptions in their models in relation to society and social change. To do this, the paper uses a particular set of theoretical approaches to energy use behaviour known collectively as social practice theory (SPT) - and explores the potential of more quantitative forms of network analysis to provide a formal framework by means of which to diagram and visualize practices. The aim of this is to gain insight into the relationships between the elements of a practice, so increasing the ultimate understanding of how practices operate. Graphs of practice networks are populated based on new empirical data drawn from a survey of different types (or variants) of laundry practice. The resulting practice networks are analysed to reveal characteristics of elements and variants of practice, such as which elements could be considered core to the practice, or how elements between variants overlap, or can be shared. This promises insights into energy intensity, flexibility and the rootedness of practices (i.e. how entrenched/ established they are) and so opens up new questions and possibilities for intervention. The novelty of this approach is that it allows practice data to be represented graphically using a quantitative format without being overly reductive. Its usefulness is that it is readily applied to large datasets, provides the capacity to interpret social practices in new ways, and serves to open up potential links with energy modeling. More broadly, a significant dimension of novelty has been the interdisciplinary approach, radically different to that normally seen in energy research. This paper is relevant to a broad audience of social scientists and engineers interested in integrating social practices with energy engineering

Teaching with infographics: practising new digital competencies and visual literacies

This position paper examines the use of infographics as a teaching assignment in the online college classroom. It argues for the benefits of adopting this type of creative assignment for teaching and learning, and considers the pedagogic and technical challenges that may arise in doing so. Data and insights are drawn from two case studies, both from the communications field, one online class and a blended one, taught at two different institutions. The paper demonstrates how incorporating a research-based graphic design assignment into coursework challenges and encourages students' visual digital literacies. The paper includes practical insights and identifies best practices emerging from the authors' classroom experience with the infographic assignment, and from student feedback. The paper suggests that this kind of creative assignment requires students to practice exactly those digital competencies required to participate in an increasingly visual digital culture

FAdo and GUItar: tools for automata manipulation and visualization

Abstract. FAdo is an ongoing project which aims to provide a set of tools for symbolic manipulation of formal languages. To allow highlevel programming with complex data structures, easy prototyping of algorithms, and portability (to use in computer grid systems for example), are its main features. Our main motivation is the theoretical and experimental research, but we have also in mind the construction of a pedagogical tool for teaching automata theory and formal languages. For the graphical visualization and interactive manipulation a new interface application, GUItar, is being developed. In this paper, we describe the main components of the FAdo system as well as the basics of the graphical interface and editor, the export/import filters and its generic interface with external systems, such as FAdo.

Visualization of Large Networks Using Recursive Community Detection

Networks show relationships between people or things. For instance, a person has a social network of friends, and websites are connected through a network of hyperlinks. Networks are most commonly represented as graphs, so graph drawing becomes significant for network visualization. An effective graph drawing can quickly reveal connections and patterns within a network that would be difficult to discern without visual aid. But graph drawing becomes a challenge for large networks. Am- biguous edge crossings are inevitable in large networks with numerous nodes and edges, and large graphs often become a complicated tangle of lines. These issues greatly reduce graph readability and makes analyzing complex networks an arduous task. This project aims to address the large network visualization problem by com- bining recursive community detection, node size scaling, layout formation, labeling, edge coloring, and interactivity to make large graphs more readable. Experiments are performed on five known datasets to test the effectiveness of the proposed approach. A survey of the visualization results is conducted to measure the results

Scalability considerations for multivariate graph visualization

Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations