Searchable Sky Coverage of Astronomical Observations: Footprints and Exposures

Sky coverage is one of the most important pieces of information about astronomical observations. We discuss possible representations, and present algorithms to create and manipulate shapes consisting of generalized spherical polygons with arbitrary complexity and size on the celestial sphere. This shape specification integrates well with our Hierarchical Triangular Mesh indexing toolbox, whose performance and capabilities are enhanced by the advanced features presented here. Our portable implementation of the relevant spherical geometry routines comes with wrapper functions for database queries, which are currently being used within several scientific catalog archives including the Sloan Digital Sky Survey, the Galaxy Evolution Explorer and the Hubble Legacy Archive projects as well as the Footprint Service of the Virtual Observatory.Comment: 11 pages, 7 figures, submitted to PAS

A Heterogeneous High Performance Computing Framework For Ill-Structured Spatial Join Processing

The frequently employed spatial join processing over two large layers of polygonal datasets to detect cross-layer polygon pairs (CPP) satisfying a join-predicate faces challenges common to ill-structured sparse problems, namely, that of identifying the few intersecting cross-layer edges out of the quadratic universe. The algorithmic engineering challenge is compounded by GPGPU SIMT architecture. Spatial join involves lightweight filter phase typically using overlap test over minimum bounding rectangles (MBRs) to discard majority of CPPs, followed by refinement phase to rigorously test the join predicate over the edges of the surviving CPPs. In this dissertation, we develop new techniques - algorithms, data structure, i/o, load balancing and system implementation - to accelerate the two-phase spatial-join processing. We present a new filtering technique, called Common MBR Filter (CMF), which changes the overall characteristic of the spatial join algorithms wherein the refinement phase is no longer the computational bottleneck. CMF is designed based on the insight that intersecting cross-layer edges must lie within the rectangular intersection of the MBRs of CPPs, their common MBRs (CMBR). We also address a key limitation of CMF for class of spatial datasets with either large or dense active CMBRs by extended CMF, called CMF-grid, that effectively employs both CMBR and grid techniques by embedding a uniform grid over CMBR of each CPP, but of suitably engineered sizes for different CPPs. To show efficiency of CMF-based filters, extensive mathematical and experimental analysis is provided. Then, two GPU-based spatial join systems are proposed based on two CMF versions including four components: 1) sort-based MBR filter, 2) CMF/CMF-grid, 3) point-in-polygon test, and, 4) edge-intersection test. The systems show two orders of magnitude speedup over the optimized sequential GEOS C++ library. Furthermore, we present a distributed system of heterogeneous compute nodes to exploit GPU-CPU computing in order to scale up the computation. A load balancing model based on Integer Linear Programming (ILP) is formulated for this system. We also provide three heuristic algorithms to approximate the ILP. Finally, we develop MPI-cuda-GIS system based on this heterogeneous computing model by integrating our CUDA-based GPU system into a newly designed distributed framework designed based on Message Passing Interface (MPI). Experimental results show good scalability and performance of MPI-cuda-GIS system

The Iray Light Transport Simulation and Rendering System

While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

Simulation environment for testing of planning algorithms

Tato diplomová práce si klade za cíl vytvořit simulační prostředí pro testování plánovacích algoritmů v systému ROS, které umožňuje výzkumníkům ověřit své plánovací algoritmy pro autonomní vozidla. Práce se věnuje rozboru různých zdrojů dat a hodnotí, zda jsou data vhodná pro testování algoritmů. Součástí práce je sada scénářů obsahujících anotace silnic, trajektorie vozidel, a cílů pro autonomní vozidlo. Tyto scénáře jsou vytvořeny na základě záznamů skutečné dopravy. Součástí práce je rovněž simulátor, který je schopen spouštět, hodnotit a vykreslit chování plánovacích algoritmů v různých dopravních situacích. K jeho ověření byl implementován vlastní plánovací algoritmus, s jehož pomocí lze doložit nejen funkčnost simulačního programu, ale i přiblížit čtenáři práci s testovací sadou dat a příslušnými knihovnami.This diploma thesis seeks to create an environment for benchmarking of path planning algorithms for autonomous driving in ROS. The aim is to give researchers the means to validate the performance of their algorithms. We analyze different sources of data to determine, whether they are suitable for the benchmarking. Using this knowledge, we create a set of benchmarks containing vehicle trajectories, road descriptions, and goals for the planning algorithm, all extracted from real traffic. We also provide a simulator able to run multiple planning algorithms, evaluate them and visualize their performance. Furthermore, we provide a planning algorithm to demonstrate the benchmarking process and its outputs, and to provide insight on how to work with the benchmark data set

Screen Capture for Sensitive Systems

Maintaining usable security in application domains such as healthcare or power systems requires an ongoing conversation among stakeholders such as end-users, administrators, developers, and policy makers. Each party has power to influence the design and implementation of the application and its security posture, and effective communication among stakeholders is one key to achieving influence and adapting an application to meet evolving needs. In this thesis, we develop a system that combines keyboard/video/mouse (KVM) capture with automatic text redaction to produce precise technical content that can enrich stakeholder communications, improve end-user influence on system evolution, and help reveal the definition of ``usable security.\u27\u27 Text-redacted screen captures reduce sensitivity of captured material and thus can facilitate timely data sharing among stakeholders. KVM-based capture makes our system both application and operating-system independent because it eliminates software-interface dependencies on capture targets. Thus, our work can be used to instrument closed or certified systems where capture software cannot be installed or documentation and support lack. It can instrument widely-varying platforms that lack standards-compliance and interoperability or redact special document formats while displayed onscreen. We present three techniques for redacting text from screenshots and two redaction applications. One application can capture, text redact, and edit screen video and the other can text redact and edit static screenshots. We also present empirical measurements of redaction effectiveness and processing latency to demonstrate system performance. When applied to our principal dataset, redaction removes text with over 93\% accuracy and simultaneously preserves more than 76\% of image pixels on average. Thus by default, it retains more visual context than a technique such as blindly redacting entire screenshots. Finally, our system redacts each screenshot in 0.1 to 21 seconds depending on which technique it applies

PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras

We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high temporal resolution (order of microseconds), low power consumption (few hundred mW) and wide dynamic range (120 dB) as attractive properties. However, event-based object recognition systems are far behind their frame-based counterparts in terms of accuracy. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional dictionary representation when hardware resources are limited to implement dimensionality reduction. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance and relevance to state-of-the-art algorithms. Additionally, we verified the object detection method and real-time FPGA performance in lab settings under non-controlled illumination conditions with limited training data and ground truth annotations.Comment: Accepted in ACCV 2018 Workshops, to appea