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- Title
- Design, Development, and Testing of a Miniature Fixture for Uniaxial Compression of Ceramics Coupled with In-Situ Raman Spectrometer.
- Creator
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Jordan, Ryan, Orlovskaya, Nina, Kwok, Kawai, Ghosh, Ranajay, University of Central Florida
- Abstract / Description
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This thesis is about the design, development and integration of an in-situ compression stage which interfaces through the Leica optical microscope coupled with a Renishaw InVia micro-Raman spectrometer. This combined compression stage and Raman system will enable structural characterization of ceramics and ceramic composites. The in-situ compression stage incorporates a 440C stainless steel structural components, 6061 aluminum frame, a NEMA 23 stepper motor. Two load screws that allow to...
Show moreThis thesis is about the design, development and integration of an in-situ compression stage which interfaces through the Leica optical microscope coupled with a Renishaw InVia micro-Raman spectrometer. This combined compression stage and Raman system will enable structural characterization of ceramics and ceramic composites. The in-situ compression stage incorporates a 440C stainless steel structural components, 6061 aluminum frame, a NEMA 23 stepper motor. Two load screws that allow to apply compressive loads up to 14,137 N, with negligible off axis loading, achieving target stresses of 500 MPa for samples of up to 6.00 mm in diameter. The system will be used in the future to study the structural changes in ceramics and ceramic composites, as well as to study thermal residual stress redistribution under applied compressive loads. A broad variety of Raman active ceramics, including the traditional structural ceramics 3mol%Y2O3-ZrO2, B4C, SiC, Si3N4, as well as exotic materials such as LaCoO3 and other perovskites will be studied using this system. Calibration of the systems load cell was performed in the configured state using MTS universal testing machines. To ensure residual stresses from mounting the load cell did not invalidate the original calibration, the in-situ compression stage was tested once attached to the Renishaw Raman spectrometer using LaCoO3 ceramic samples. The Raman shift of certain peaks in LaCoO3 was detected indicative of the effect of the applied compressive stress on the ceramics understudy.
Show less - Date Issued
- 2019
- Identifier
- CFE0007824, ucf:52809
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007824
- Title
- PATTERNS OF MOTION: DISCOVERY AND GENERALIZED REPRESENTATION.
- Creator
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Saleemi, Imran, Shah, Mubarak, University of Central Florida
- Abstract / Description
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In this dissertation, we address the problem of discovery and representation of motion patterns in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a generic representation, that captures any kind of object motion observable in video sequences. Such motion is a significant source of information typically employed for diverse applications such as tracking, anomaly detection, and action and event recognition. We present statistical...
Show moreIn this dissertation, we address the problem of discovery and representation of motion patterns in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a generic representation, that captures any kind of object motion observable in video sequences. Such motion is a significant source of information typically employed for diverse applications such as tracking, anomaly detection, and action and event recognition. We present statistical frameworks for representation of motion characteristics of objects, learned from tracks or optical flow, for static as well as moving cameras, and propose algorithms for their application to a variety of problems. The proposed motion pattern models and learning methods are general enough to be employed in a variety of problems as we demonstrate experimentally. We first propose a novel method to model and learn the scene activity, observed by a static camera. The motion patterns of objects in the scene are modeled in the form of a multivariate non-parametric probability density function of spatiotemporal variables (object locations and transition times between them). Kernel Density Estimation (KDE) is used to learn this model in a completely unsupervised fashion. Learning is accomplished by observing the trajectories of objects by a static camera over extended periods of time. The model encodes the probabilistic nature of the behavior of moving objects in the scene and is useful for activity analysis applications, such as persistent tracking and anomalous motion detection. In addition, the model also captures salient scene features, such as, the areas of occlusion and most likely paths. Once the model is learned, we use a unified Markov Chain Monte-Carlo (MCMC) based framework for generating the most likely paths in the scene, improving foreground detection, persistent labelling of objects during tracking and deciding whether a given trajectory represents an anomaly to the observed motion patterns. Experiments with real world videos are reported which validate the proposed approach. The representation and estimation framework proposed above, however, has a few limitations. This algorithm proposes to use a single global statistical distribution to represent all kinds of motion observed in a particular scene. It therefore, does not find a separation between multiple semantically distinct motion patterns in the scene. Instead, the learned model is a joint distribution over all possible patterns followed by objects. To overcome this limitation, we then propose a superior method for the discovery and statistical representation of motion patterns in a scene. The advantages of this approach over the first one are two-fold: first, this model is applicable to scenes of dense crowded motion where tracking may not be feasible, and second, it distinguishes between motion patterns that are distinct at a semantic level of abstraction. We propose a mixture model representation of salient patterns of optical flow, and present an algorithm for learning these patterns from dense optical flow in a hierarchical, unsupervised fashion. Using low level cues of noisy optical flow, K-means is employed to initialize a Gaussian mixture model for temporally segmented clips of video. The components of this mixture are then filtered and instances of motion patterns are computed using a simple motion model, by linking components across space and time. Motion patterns are then initialized and membership of instances in different motion patterns is established by using KL divergence between mixture distributions of pattern instances. Finally, a pixel level representation of motion patterns is proposed by deriving conditional expectation of optical flow. Results of extensive experiments are presented for multiple surveillance sequences containing numerous patterns involving both pedestrian and vehicular traffic. The proposed method exploits optical flow as the low level feature and performs a hierarchical clustering to obtain motion patterns; and we observe that the use of optical flow is also an integral part of a variety of other vision applications, for example, as features based representation of human actions. We, therefore, propose a new representation for articulated human actions using the motion patterns. The representation is based on hierarchical clustering of observed optical flow in four dimensional, spatial and motion flow space. The automatically discovered motion patterns, are the primitive actions, representative of flow at salient regions on the human body, much like trajectories of body joints, which are notoriously difficult to obtain automatically. The proposed method works in a completely unsupervised fashion, and in sharp contrast to state of the art representations like bag of video words, provides a truly semantically meaningful representation. Each primitive action depicts the most atomic sub-action, like left arm moving upwards, or right leg moving downward and leftward, and is represented by a mixture of four dimensional Gaussian distributions. A sequence of primitive actions are discovered in the test video, and labelled by computing the KL divergence between mixtures. The entire video sequence containing the human action, is thus reduced to a simple string, which is matched against similar strings of training videos to classify the action. The string matching is performed by global alignment, using the well-known Needleman-Wunsch algorithm. Experiments reported on multiple human actions data sets, confirm the validity, simplicity, and semantically meaningful nature of the proposed representation. Results obtained are encouraging and comparable to the state of the art.
Show less - Date Issued
- 2011
- Identifier
- CFE0003646, ucf:48836
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003646
- Title
- Deposition and characterization studies of boron carbon nitride (BCN) thin films prepared by dual target sputtering.
- Creator
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Prakash, Adithya, Sundaram, Kalpathy, Kapoor, Vikram, Yuan, Jiann-Shiun, Jin, Yier, Chow, Louis, University of Central Florida
- Abstract / Description
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As complementary metal-oxide semiconductor (CMOS) devices shrink to smaller size, the problems related to circuit performance such as critical path signal delay are becoming a pressing issue. These delays are a result of resistance and capacitance product (RC time constant) of the interconnect circuit. A novel material with reduced dielectric constants may compromise both the thermal and mechanical properties that can lead to die cracking during package and other reliability issues. Boron...
Show moreAs complementary metal-oxide semiconductor (CMOS) devices shrink to smaller size, the problems related to circuit performance such as critical path signal delay are becoming a pressing issue. These delays are a result of resistance and capacitance product (RC time constant) of the interconnect circuit. A novel material with reduced dielectric constants may compromise both the thermal and mechanical properties that can lead to die cracking during package and other reliability issues. Boron carbon nitride (BCN) compounds have been expected to combine the excellent properties of boron carbide (B4C), boron nitride (BN) and carbon nitride (C3N4), with their properties adjustable, depending on composition and structure. BCN thin film is a good candidate for being hard, dense, pore-free, low-k dielectric with values in the range of 1.9 to 2.1. Excellent mechanical properties such as adhesion, high hardness and good wear resistance have been reported in the case of sputtered BCN thin films. Problems posed by high hardness materials such as diamonds in high cutting applications and the comparatively lower hardness of c-BN gave rise to the idea of a mixed phase that can overcome these problems with a minimum compromise in its properties. A hybrid between semi-metallic graphite and insulating h-BN may show adjusted semiconductor properties. BCN exhibits the potential to control optical bandgap (band gap engineering) by atomic composition, hence making it a good candidate for electronic and photonic devices. Due to tremendous bandgap engineering capability and refractive index variability in BCN thin film, it is feasible to develop filters and mirrors for use in ultra violet (UV) wavelength region. It is of prime importance to understand process integration challenges like deposition rates, curing, and etching, cleaning and polishing during characterization of low-k films. The sputtering technique provides unique advantages over other techniques such as freedom to choose the substrate material and a uniform deposition over relatively large area. BCN films are prepared by dual target reactive magnetron sputtering from a B4C and BN targets using DC and RF powers respectively. In this work, an investigation of mechanical, optical, chemical, surface and device characterizations is undertaken. These holistic and thorough studies, will provide the insight into the capability of BCN being a hard, chemically inert, low-k, wideband gap material, as a potential leader in semiconductor and optics industry.
Show less - Date Issued
- 2016
- Identifier
- CFE0006378, ucf:51496
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006378
