View All Items
(1 - 3 of 3)
- Title
- ATMOSPHERIC ENTRY.
- Creator
-
Martin, Dillon A, Elgohary, Tarek, University of Central Florida
- Abstract / Description
-
The development of atmospheric entry guidance methods is crucial to achieving the requirements for future missions to Mars; however, many missions implement a unique controller which are spacecraft specific. Here we look at the implementation of neural networks as a baseline controller that will work for a variety of different spacecraft. To accomplish this, a simulation is developed and validated with the Apollo controller. A feedforward neural network controller is then analyzed and...
Show moreThe development of atmospheric entry guidance methods is crucial to achieving the requirements for future missions to Mars; however, many missions implement a unique controller which are spacecraft specific. Here we look at the implementation of neural networks as a baseline controller that will work for a variety of different spacecraft. To accomplish this, a simulation is developed and validated with the Apollo controller. A feedforward neural network controller is then analyzed and compared to the Apollo case.
Show less - Date Issued
- 2017
- Identifier
- CFH2000354, ucf:45874
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000354
- Title
- BACTERIA THAT RESIST CENTRIFUGAL FORCE.
- Creator
-
Kessler, Nickolas, Moore, Sean, University of Central Florida
- Abstract / Description
-
Our lab discovered that approximately 1 in 10,000 Escherichia coli cells in stationary phase remain in suspension after a high g-force centrifuge event. To establish the mechanism behind this curious phenotype, multiple mutant strains of E. coli were independently evolved such that the majority of their populations resisted migration when exposed to high centrifugal forces. Genomic DNA sequencing of the mutants' revealed unique, isolated mutations in genes involved in capsule synthesis and...
Show moreOur lab discovered that approximately 1 in 10,000 Escherichia coli cells in stationary phase remain in suspension after a high g-force centrifuge event. To establish the mechanism behind this curious phenotype, multiple mutant strains of E. coli were independently evolved such that the majority of their populations resisted migration when exposed to high centrifugal forces. Genomic DNA sequencing of the mutants' revealed unique, isolated mutations in genes involved in capsule synthesis and exopolysaccharide (EPS) production. Each mutant exhibits a novel mechanism that allows them to remain in suspension. The mutants were further characterized by determining their growth rates, strengths of resistance to various centrifugal forces, the phenotype's dependence on a carbon source, and timing of the phenotype's presentation. The results revealed: comparable mutant generation times to the wild-type strain, variable resistance to centrifugal force, phenotype dependence on carbon source, and phenotype presentation during early stationary phase. To interrogate the mechanism by which these cells stay in suspension the production of EPS was quantified, and gene knock-outs were performed. Quantification of the EPS revealed approximately a seventeen-fold increase in EPS in the mutants' compared to the wild-type strain. Gene knock-outs revealed the EPS produced can be attached to the outer-membrane or freely secreted into the media by different mechanisms. In addition, this mechanism was further confirmed to be responsible for the centrifuge resistant trait by attaching extracted EPS to polystyrene microspheres. Experimental results show that mutant extracted EPS treated beads caused increased bead retention in suspension compared to wild-type EPS treated beads. These results reveal that E. coli is using a novel mechanism to adapt to a new environmental factor introduced to remove the bacteria. With the discovery of this mechanism and the transferability to inorganic objects industrial applications are now envisioned where particle sedimentation is controllable and mixtures remain homogenized by attaching optically transparent biomolecules.
Show less - Date Issued
- 2018
- Identifier
- CFH2000332, ucf:45800
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000332
- Title
- A Decision Support Tool for Video Retinal Angiography.
- Creator
-
Laha, Sumit, Bagci, Ulas, Foroosh, Hassan, Song, Sam, University of Central Florida
- Abstract / Description
-
Fluorescein angiogram (FA) is a medical procedure that helps the ophthalmologists to monitor the status of the retinal blood vessels and to diagnose proper treatment. This research is motivated by the necessity of blood vessel segmentation of the retina. Retinal vessel segmentation has been a major challenge and has long drawn the attention of researchers for decades due to the presence of complex blood vessels with varying size, shape, angles and branching pattern of vessels, and non-uniform...
Show moreFluorescein angiogram (FA) is a medical procedure that helps the ophthalmologists to monitor the status of the retinal blood vessels and to diagnose proper treatment. This research is motivated by the necessity of blood vessel segmentation of the retina. Retinal vessel segmentation has been a major challenge and has long drawn the attention of researchers for decades due to the presence of complex blood vessels with varying size, shape, angles and branching pattern of vessels, and non-uniform illumination and huge anatomical variability between subjects. In this thesis, we introduce a new computational tool that combines deep learning based machine learning algorithm and a signal processing based video magnification method to support physicians in analyzing and diagnosing retinal angiogram videos for the first time in the literature.The proposed approach has a pipeline-based architecture containing three phases - image registration for large motion removal from video angiogram, retinal vessel segmentation and video magnification based on the segmented vessels. In image registration phase, we align distorted frames in the FA video using rigid registration approaches. In the next phase, we use baseline capsule based neural networks for retinal vessel segmentation in comparison with the state-of-the-art methods. We move away from traditional convolutional network approaches to capsule networks in this work. This is because, despite being widely used in different computer vision applications, convolutional neural networks suffer from learning ability to understand the object-part relationships, have high computational times due to additive nature of neurons and, loose information in the pooling layer. Although having these drawbacks, we use deep learning methods like U-Net and Tiramisu to measure the performance and accuracy of SegCaps. Lastly, we apply Eulerian video magnification to magnify the subtle changes in the retinal video. In this phase, magnification is applied to segmented videos to visualize the flow of blood in the retinal vessels.
Show less - Date Issued
- 2018
- Identifier
- CFE0007342, ucf:52125
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007342
