Joint Contour Net Analysis for Feature Detection in Lattice Quantum Chromodynamics Data

In this paper we demonstrate the use of multivariate topological algorithms to analyse and interpret Lattice Quantum Chromodynamics (QCD) data. Lattice QCD is a long established field of theoretical physics research in the pursuit of understanding the strong nuclear force. Complex computer simulations model interactions between quarks and gluons to test theories regarding the behaviour of matter in a range of extreme environments. Data sets are typically generated using Monte Carlo methods, providing an ensemble of configurations, from which observable averages must be computed. This presents issues with regard to visualisation and analysis of the data as a typical ensemble study can generate hundreds or thousands of unique configurations. We show how multivariate topological methods, such as the Joint Contour Net, can assist physicists in the detection and tracking of important features within their data in a temporal setting. This enables them to focus upon the structure and distribution of the core observables by identifying them within the surrounding data. These techniques also demonstrate how quantitative approaches can help understand the lifetime of objects in a dynamic system.Comment: 30 pages, 19 figures, 4 table

Skeletons for Distributed Topological Computation

Parallel implementation of topological algorithms is highly desirable, but the challenges, from reconstructing algorithms around independent threads through to runtime load balancing, have proven to be formidable. This problem, made all the more acute by the diversity of hardware platforms, has led to new kinds of implementation platform for computational science, with sophisticated runtime systems managing and coordinating large threadcounts to keep processing elements heavily utilized. While simpler and more portable than direct management of threads, these approaches still entangle program logic with resource management. Similar kinds of highly parallel runtime system have also been developed for functional languages. Here, however, language support for higher-order functions allows a cleaner separation between the algorithm and `skeletons' that express generic patterns of parallel computation. We report results on using this technique to develop a distributed version of the Joint Contour Net, a generalization of the Contour Tree to multifields. We present performance comparisons against a recent Haskell implementation using shared-memory parallelism, and initial work on a skeleton for distributed memory implementation that utilizes an innovative strategy to reduce inter-process communication overheads

LiDAR Data Analysis Strategies to Determine Features Indicative of At-Risk Coastal Sites

Light detection and ranging (LiDAR) derived volume changes provide both visual and statistical information for how project shorelines change over time. For beach erosion control (BEC) and coastal storm risk management (CSRM) projects, changes across storm events are fundamental to understanding a project’s progress. The Coastal Systems Portfolio Initiative (CSPI) aims to document and track U.S. Army Corps of Engineers (USACE) projects in a holistic systems-based manner. This web based geographic information system currently lacks numerical metrics beyond fill volumes to represent a project’s progress or reliability. This study aims to identify potential reliability metrics using the Joint Airborne LiDAR and Bathymetry Center for Expertise (JALBTCX) Volume Change Toolbox within ESRI’s ArcGIS software. The toolbox was run on the Haulover and Bal Harbour sections of the BEC project to analyze volume change and identify erosional hotspots. Volume change analysis was done between LiDAR derived digital elevation models (DEMs) for before and after Hurricane Matthew as well as DEMs from project design plans. Single transect profiles were also compared between the post-Matthew LiDAR and the designs to use in determining potential metrics. From these comparisons total volume change, shoreline change, beach width difference, change rates, and composite metrics were discussed to potentially include within the CSPI reliability ratings

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements