Path storage in the particle filter

This article considers the problem of storing the paths generated by a particle filter and more generally by a sequential Monte Carlo algorithm. It provides a theoretical result bounding the expected memory cost by $T + C N \log N$ where $T$ is the time horizon, $N$ is the number of particles and $C$ is a constant, as well as an efficient algorithm to realise this. The theoretical result and the algorithm are illustrated with numerical experiments.Comment: 9 pages, 5 figures. To appear in Statistics and Computin

Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications

Particle filtering has been a very popular method to solve nonlinear/non-Gaussian state estimation problems for more than twenty years. Particle filters (PFs) have found lots of applications in areas that include nonlinear filtering of noisy signals and data, especially in target tracking. However, implementation of high dimensional PFs in real-time for large-scale problems is a very challenging computational task. Parallel & distributed (P&D) computing is a promising way to deal with the computational challenges of PF methods. The main goal of this dissertation is to develop, implement and evaluate computationally efficient PF algorithms for target tracking, and thereby bring them closer to practical applications. To reach this goal, a number of parallel PF algorithms is designed and implemented using different parallel hardware architectures such as Computer Cluster, Graphics Processing Unit (GPU), and Field-Programmable Gate Array (FPGA). Proposed is an improved PF implementation for computer cluster - the Particle Transfer Algorithm (PTA), which takes advantage of the cluster architecture and outperforms significantly existing algorithms. Also, a novel GPU PF algorithm implementation is designed which is highly efficient for GPU architectures. The proposed algorithm implementations on different parallel computing environments are applied and tested for target tracking problems, such as space object tracking, ground multitarget tracking using image sensor, UAV-multisensor tracking. Comprehensive performance evaluation and comparison of the algorithms for both tracking and computational capabilities is performed. It is demonstrated by the obtained simulation results that the proposed implementations help greatly overcome the computational issues of particle filtering for realistic practical problems

Indoor Navigation Using Convolutional Neural Networks and Floor Plans

The goal of this thesis is to evaluate a new indoor navigation technique by incorporating floor plans along with monocular camera images into a CNN as a potential means for identifying camera position. Building floor plans are widely available and provide potential information for localizing within the building. This work sets out to determine if a CNN can learn the architectural features of a floor plan and use that information to determine a location. In this work, a simulated indoor data set is created and used to train two CNNs. A classification CNN, which breaks up the floor plan into 100 discrete bins and achieved 76.1 percent top 5 accuracy on test data. Also, a regression CNN which achieved a distance error of 25.4 meters or less between the truth and predicted position on 80 percent of the test data. The models are further improved by combining them with a filter solution. The best performing classification CNN is evaluated on real world data captured via a TurtleBot 3, demonstrating the potential for this solution to be useful to real world Air Force indoor localization problems

Doctor of Philosophy in Computing

dissertationPhysics-based animation has proven to be a powerful tool for creating compelling animations for film and games. Most techniques in graphics are based on methods developed for predictive simulation for engineering applications; however, the goals for graphics applications are dramatically different than the goals of engineering applications. As a result, most physics-based animation tools are difficult for artists to work with, providing little direct control over simulation results. In this thesis, we describe tools for physics-based animation designed with artist needs and expertise in mind. Most materials can be modeled as elastoplastic: they recover from small deformations, but large deformations permanently alter their rest shape. Unfortunately, large plastic deformations, common in graphical applications, cause simulation instabilities if not addressed. Most elastoplastic simulation techniques in graphics rely on a finite-element approach where objects are discretized into a tetrahedral mesh. Using these approaches, maintaining simulation stability during large plastic flows requires remeshing, a complex and computationally expensive process. We introduce a new point-based approach that does not rely on an explicit mesh and avoids the expense of remeshing. Our approach produces comparable results with much lower implementation complexity. Points are a ubiquitous primitive for many effects, so our approach also integrates well with existing artist pipelines. Next, we introduce a new technique for animating stylized images which we call Dynamic Sprites. Artists can use our tool to create digital assets that interact in a natural, but stylized, way in virtual environments. In order to support the types of nonphysical, exaggerated motions often desired by artists, our approach relies on a heavily modified deformable body simulator, equipped with a set of new intuitive controls and an example-based deformation model. Our approach allows artists to specify how the shape of the object should change as it moves and collides in interactive virtual environments. Finally, we introduce a new technique for animating destructive scenes. Our approach is built on the insight that the most important visual aspects of destruction are plastic deformation and fracture. Like with Dynamic Sprites, we use an example-based model of deformation for intuitive artist control. Our simulator treats objects as rigid when computing dynamics but allows them to deform plastically and fracture in between timesteps based on interactions with the other objects. We demonstrate that our approach can efficiently animate the types of destructive scenes common in film and games. These animation techniques are designed to exploit artist expertise to ease creation of complex animations. By using artist-friendly primitives and allowing artists to provide characteristic deformations as input, our techniques enable artists to create more compelling animations, more easily

Advanced Visualization and Intuitive User Interface Systems for Biomedical Applications

Modern scientific research produces data at rates that far outpace our ability to comprehend and analyze it. Such sources include medical imaging data and computer simulations, where technological advancements and spatiotemporal resolution generate increasing amounts of data from each scan or simulation. A bottleneck has developed whereby medical professionals and researchers are unable to fully use the advanced information available to them. By integrating computer science, computer graphics, artistic ability and medical expertise, scientific visualization of medical data has become a new field of study. The objective of this thesis is to develop two visualization systems that use advanced visualization, natural user interface technologies and the large amount of biomedical data available to produce results that are of clinical utility and overcome the data bottleneck that has developed. Computational Fluid Dynamics (CFD) is a tool used to study the quantities associated with the movement of blood by computer simulation. We developed methods of processing spatiotemporal CFD data and displaying it in stereoscopic 3D with the ability to spatially navigate through the data. We used this method with two sets of display hardware: a full-scale visualization environment and a small-scale desktop system. The advanced display and data navigation abilities provide the user with the means to better understand the relationship between the vessel\u27s form and function. Low-cost 3D, depth-sensing cameras capture and process user body motion to recognize motions and gestures. Such devices allow users to use hand motions as an intuitive interface to computer applications. We developed algorithms to process and prepare the biomedical and scientific data for use with a custom control application. The application interprets user gestures as commands to a visualization tool and allows the user to control the visualization of multi-dimensional data. The intuitive interface allows the user to control the visualization of data without manual contact with an interaction device. In developing these methods and software tools we have leveraged recent trends in advanced visualization and intuitive interfaces in order to efficiently visualize biomedical data in such a way that provides meaningful information that can be used to further appreciate it

Topic Modeling in Theory and Practice

Topic models can decompose a large corpus of text into a relatively small set of interpretable themes or topics, potentially enabling a domain expert to explore and analyze a corpus more efficiently. However, in my work, I have found that theories put forth by topic modeling research are not always borne out in practice. In this dissertation, I use case studies to explore four theories of topic modeling. While these theories are not explicitly stated, I show that they are communicated implicitly, some within an individual study and others more diffusely. I show that this implicit knowledge fails to hold in practice in the settings I consider. While my work is confined to topic modeling research and moreover concentrated on the latent Dirichlet allocation topic model, I argue that these kinds of gaps may pervade scientific research and present an obstacle to improving the diversity of the research community