Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item Optimization of Signal Routing in Disruption-Tolerant Networks(2021) Singam, Caitlyn; Ephremides, Anthony; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Communication networks are prone to disruption due to inherent uncertainties such as environmental conditions, system outages, and other factors. However, current state-of-the-art communication protocols are not yet optimized for communication in highly disruption-prone environments, such as deep space, where the risk of such uncertainties is not negligible. This work involves the development of a novel protocol for disruption-tolerant communication across space-based networks that avoids idealized assumptions and is consistent with system limitations. The proposed solution is grounded in an approach to information as a time-based commodity, and on reframing the problem of efficient signal routing as a problem of value optimization. The efficacy of the novel protocol was evaluated via a custom Monte Carlo simulation against other state-of-the-art protocols in terms of maintaining both data integrity and transmission speed, and was found to provide a consistent advantage across both metrics of interest.Item Real-time Audio Reverberation for Virtual Room Acoustics(2020) Shen, Justin M; Duraiswami, Ramani; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)For virtual and augmented reality applications, it is desirable to render audio sources in the space the user is in, in real-time without sacrificing the perceptual quality of the sound. One aspect of the rendering that is perceptually important for a listener is the late-reverberation, or "echo", of the sound within a room environment. A popular method of generating a plausible late reverberation in real-time is the use of Feedback Delay Network (FDN). However, its use has the drawback that it first has to be tuned (usually manually) for a particular room before the late-reverberation generated becomes perceptually accurate. In this thesis, we propose a data-driven approach to automatically generate a pre-tuned FDN for any given room described by a set of room parameters. When combined with existing method for rendering the direct path and early reflections of a sound source, we demonstrate the feasibility of being able to render audio source in real-time for interactive applications.Item Model Based Systems Engineering for a Typical Smartgrid(2019) Ninawe, Omkar; Baras, John S; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The complexity and heterogeneity of today’s large Cyber-Physical Systems (CPS) is addressed by model based design. This class of systems is a direct consequence of our entry into the new era of systems characterized by high complexity, increased software dependency, multifaceted support for networking and inclusion of data and services form global networks. Cyber-Physical Power Systems such as SmartGrids provide perfect example to emphasis heterogeneity and complexity of today’s systems. In this thesis we work towards augmenting the creation and demonstration of a framework for developing an integrated CPS modelling hub with powerful and diverse tradeoff analysis methods and tools for design exploration of CPS.Item Distributed Search Method for Teams of Small Unmanned Aircraft Systems(2018) Moschler, Jacob D.; Baras, John S; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We apply Model Based Systems Engineering (MBSE) methods to develop requirements for unmanned aircraft systems (UAS) use cases across industries and create new path planning algorithms for one group of use cases with similar requirements. We then develop and validate models to estimate cost versus data quality for the aforementioned group of use cases. We use our models in conjunction with the MBSE process to plan and execute flights beyond visual line of sight (BVLOS) to scan large areas of remote jungle using small UAS.Item Nonlinear Analysis of Phase Retrieval and Deep Learning(2017) Zou, Dongmian; Balan, Radu V; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Nonlinearity causes information loss. The phase retrieval problem, or the phaseless reconstruction problem, seeks to reconstruct a signal from the magnitudes of linear measurements. With a more complicated design, convolutional neural networks use nonlinearity to extract useful features. We can model both problems in a frame-theoretic setting. With the existence of a noise, it is important to study the stability of the phaseless reconstruction and the feature extraction part of the convolutional neural networks. We prove the Lipschitz properties in both cases. In the phaseless reconstruction problem, we show that phase retrievability implies a bi-Lipschitz reconstruction map, which can be extended to the Euclidean space to accommodate noises while remaining to be stable. In the deep learning problem, we set up a general framework for the convolutional neural networks and provide an approach for computing the Lipschitz constants.Item Subspace Representations for Robust Face and Facial Expression Recognition(2013) Taheri, Sima; Chellappa, Rama; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Analyzing human faces and modeling their variations have always been of interest to the computer vision community. Face analysis based on 2D intensity images is a challenging problem, complicated by variations in pose, lighting, blur, and non-rigid facial deformations due to facial expressions. Among the different sources of variation, facial expressions are of interest as important channels of non-verbal communication. Facial expression analysis is also affected by changes in view-point and inter-subject variations in performing different expressions. This dissertation makes an attempt to address some of the challenges involved in developing robust algorithms for face and facial expression recognition by exploiting the idea of proper subspace representations for data. Variations in the visual appearance of an object mostly arise due to changes in illumination and pose. So we first present a video-based sequential algorithm for estimating the face albedo as an illumination-insensitive signature for face recognition. We show that by knowing/estimating the pose of the face at each frame of a sequence, the albedo can be efficiently estimated using a Kalman filter. Then we extend this to the case of unknown pose by simultaneously tracking the pose as well as updating the albedo through an efficient Bayesian inference method performed using a Rao-Blackwellized particle filter. Since understanding the effects of blur, especially motion blur, is an important problem in unconstrained visual analysis, we then propose a blur-robust recognition algorithm for faces with spatially varying blur. We model a blurred face as a weighted average of geometrically transformed instances of its clean face. We then build a matrix, for each gallery face, whose column space spans the space of all the motion blurred images obtained from the clean face. This matrix representation is then used to define a proper objective function and perform blur-robust face recognition. To develop robust and generalizable models for expression analysis one needs to break the dependence of the models on the choice of the coordinate frame of the camera. To this end, we build models for expressions on the affine shape-space (Grassmann manifold), as an approximation to the projective shape-space, by using a Riemannian interpretation of deformations that facial expressions cause on different parts of the face. This representation enables us to perform various expression analysis and recognition algorithms without the need for pose normalization as a preprocessing step. There is a large degree of inter-subject variations in performing various expressions. This poses an important challenge on developing robust facial expression recognition algorithms. To address this challenge, we propose a dictionary-based approach for facial expression analysis by decomposing expressions in terms of action units (AUs). First, we construct an AU-dictionary using domain experts' knowledge of AUs. To incorporate the high-level knowledge regarding expression decomposition and AUs, we then perform structure-preserving sparse coding by imposing two layers of grouping over AU-dictionary atoms as well as over the test image matrix columns. We use the computed sparse code matrix for each expressive face to perform expression decomposition and recognition. Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. We propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component, which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm, which benefits from the idea of sparsity and morphological diversity. The DCS algorithm uses the data-driven dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition.Item An Optical Density Detection Platform with Integrated Microfluidics for In Situ Growth, Monitoring, and Treatment of Bacterial Biofilms(2012) Mosteller, Matthew Philip; Ghodssi, Reza; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Systems engineering strategies utilizing platform-based design methodologies are implemented to achieve the integration of biological and physical system components in a biomedical system. An application of this platform explored, in which an integrated microsystem is developed capable of the on-chip growth, monitoring, and treatment of bacterial biofilms for drug development and fundamental study applications. In this work, the developed systems engineering paradigm is utilized to develop a device system implementing linear array charge-coupled devices to enable real time, non-invasive, label-free monitoring of bacterial biofilms. A novel biofilm treatment method is demonstrated within the developed microsystem showing drastic increases in treatment efficacy by decreasing both bacterial biomass and cell viability within treated biofilms. Demonstration of this treatment at the microscale enables future applications of this method for the in vivo treatment of biofilm-associated infections.Item Three Dimensional Edge Detection Using Wavelet and Shearlet Analysis(2012) Schug, David Albert; O'Leary, Dianne P; Easley, Glenn R; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Edge detection determines the boundary of objects in an image. A sequence of images records a 2D representation of a scene changing over time, giving 3D data. New 3D edge detectors, particularly ones we developed using shearlets and hybrid shearlet-Canny algorithms, identify edges of complicated objects much more reliably than standard approaches, especially under high noise conditions. We also use edge information to track the position and velocity of objects using an optimization algorithm.