Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item EXPLORING THE RELATIONSHIPS BETWEEN FUEL AND OXIDIZER REACTION OF BIOCIDAL ENERGETIC MATERIALS(2019) Wu, Tao; Zachariah, Michael R.; Chemistry; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Energetic materials are defined as a class of material with extremely high amount of stored chemical energy that can be released when ignited, along with intensive light emission and shock generation. Developing new energetic materials with high efficiency neutralization of biological warfare agents has gained increased attention due to the increased threat of bioterrorism. The objective of this dissertation is to develop new energetic materials with biocidal capabilities and apply them in various nanothermite systems to explore the relationships between fuel and oxidizer reactions. Aerosol techniques offer a convenient route and potentially direct route for preparation of small particles with high purity, and is a method proven to be amenable and economical to scale-up. Here I demonstrate the synthesis of various iodine oxides/iodic acids microparticles by a direct one-step aerosol method from iodic acid. A previously misidentified phase of I4O9 hydrate is in fact a new polymorph of HIO3 which crystalizes in the orthorhombic space group P212121. Various iodine oxides/iodic acids, including I2O5, HI3O8 and HIO3, were employed as oxidizers in thermite systems. Their decomposition behaviors were studied using a home-made time resolved temperature-jump/time-of-flight mass spectrometer (T-Jump/TOFMS). In addition, nano-aluminum (nAl), nano-tantalum and carbon black were adopted as the fuel or additive in order to fully understand how iodine containing oxidizers react with the fuel during ignition. The ignition and reaction process of those thermites were characterized with T-Jump/TOFMS. Carbon black was found to be able to lower both initiation and iodine release temperatures compared to those of Al/iodine oxides and Ta/iodine oxides thermites. Their combustion properties were evaluated in a constant-volume combustion cell and results show that nAl/a-HI3O8 has the highest pressurization rate and peak pressure and shortest burn time. However, an ignition delay was always present in their pressure profiles while combusting. To shorten or eliminate this ignition delay, a secondary oxidizer CuO is incorporated into Al/I2O5 system and four different Al/I2O5/CuO thermites by varying the mass ratio between two oxidizers are prepared and studied in a constant volume combustion cell. Significant enhancement is observed for all four thermites and their peak pressures and pressurization rates are much higher than that of Al/I2O5 or Al/CuO. Two other oxidizers also demonstrate similar effects as to CuO on promoting the combustion performance of Al/I2O5. A novel oxidizer AgFeO2 particles was prepared via a wet-chemistry method and evaluated as an oxidizer in aluminum-based thermite system. Its structure, morphologies and thermal behavior were investigated using X-ray diffraction, scanning electron microscopy, TGA/DSC, and T-Jump/TOFMS. The results indicate the decomposition pathways of AgFeO2 vary with heating rates from a two-step at low heating rate to a single step at high heating rate. Ignition of Al/AgFeO2 at a temperature just above the oxygen release temperature and is very similar to Al/CuO. However, with a pressurization rate three times of Al/CuO, Al/AgFeO2 yields a comparable result to Al/hollow-CuO or Al/KClO4/CuO, with a simpler preparation method. T-Jump/TOFMS was used to study the ignition and decomposition of boron-based thermites. The ignition behaviors of bare boron nanopowders and boron-based nanothermites at various gaseous oxygen pressure were investigated using the T-Jump method. High-heating rate transmission electron microscopy studies were performed on both B/CuO and B/Bi2O3 nanothermites to evaluate the ignition process. I propose a co-sintering effect between B2O3 and the oxidizer play an important role in the ignition process of boron-based nanothermites.Item DICTIONARIES AND MANIFOLDS FOR FACE RECOGNITION ACROSS ILLUMINATION, AGING AND QUANTIZATION(2013) Wu, Tao; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)During the past many decades, many face recognition algorithms have been proposed. The face recognition problem under controlled environment has been well studied and almost solved. However, in unconstrained environments, the performance of face recognition methods could still be significantly affected by factors such as illumination, pose, resolution, occlusion, aging, etc. In this thesis, we look into the problem of face recognition across these variations and quantization. We present a face recognition algorithm based on simultaneous sparse approximations under varying illumination and pose with dictionaries learned for each class. A novel test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. An image relighting technique based on pose-robust albedo estimation is used to generate multiple frontal images of the same person with variable lighting. As a result, the proposed algorithm has the ability to recognize human faces with high accuracy even when only a single or a very few images per person are provided for training. The efficiency of the proposed method is demonstrated using publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms. The problem of recognizing facial images across aging remains an open problem. We look into this problem by studying the growth in the facial shapes. Building on recent advances in landmark extraction, and statistical techniques for landmark-based shape analysis, we show that using well-defined shape spaces and its associated geometry, one can obtain significant performance improvements in face verification. Toward this end, we propose to model the facial shapes as points on a Grassmann manifold. The face verification problem is then formulated as a classification problem on this manifold. We then propose a relative craniofacial growth model which is based on the science of craniofacial anthropometry and integrate it with the Grassmann manifold and the SVM classifier. Experiments show that the proposed method is able to mitigate the variations caused by the aging progress and thus effectively improve the performance of open-set face verification across aging. In applications such as document understanding, only binary face images may be available as inputs to a face recognition algorithm. We investigate the effects of quantization on several classical face recognition algorithms. We study the performances of PCA and multiple exemplar discriminant analysis (MEDA) algorithms with quantized images and with binary images modified by distance and Box-Cox transforms. We propose a dictionary-based method for reconstructing the grey scale facial images from the quantized facial images. Two dictionaries with low mutual coherence are learned for the grey scale and quantized training images respectively using a modified KSVD method. A linear transform function between the sparse vectors of quantized images and the sparse vectors of grey scale images is estimated using the training data. In the testing stage, a grey scale image is reconstructed from the quantized image using the transform matrix and normalized dictionaries. The identities of the reconstructed grey scale images are then determined using the dictionary-based face recognition (DFR) algorithm. Experimental results show that the reconstructed images are similar to the original grey-scale images and the performance of face recognition on the quantized images is comparable to the performance on grey scale images. The online social network and social media is growing rapidly. It is interesting to study the impact of social network on computer vision algorithms. We address the problem of automated face recognition on a social network using a loopy belief propagation framework. The proposed approach propagates the identities of faces in photos across social graphs. We characterize its performance in terms of structural properties of the given social network. We propose a distance metric defined using face recognition results for detecting hidden connections. The performance of the proposed method is analyzed on graph structure networks, scalability, different degrees of nodes, labeling errors correction and hidden connections discovery. The result demonstrates that the constraints imposed by the social network have the potential to improve the performance of face recognition methods. The result also shows it is possible to discover hidden connections in a social network based on face recognition.