314 research outputs found
An Empirical Analysis about Employee Selection Process at Navitor, Inc
https://openriver.winona.edu/urc2018/1010/thumbnail.jp
Model-based 3D micro-navigation and bathymetry estimation for interferometric synthetic aperture sonar
Sub-wavelength navigation information is vital for the formation of all synthetic aperture sonar (SAS) data products. This challenging requirement can be achieved using the redundant phase centre (RPC) or displaced phase centre antenna (DPCA) micro-navigation algorithm, which uses cross-correlation of signals with inter-ping coherence to estimate time delays and hence make navigation estimates. In this paper a new approach to micro- navigation for interferometric synthetic aperture sonar is introduced. The algorithm makes 3D vehicle position estimates for each sonar ping by making use of time delays measured between all possible pairs of redundant phase centre arrays, using both interferometric arrays on each side of the vehicle. Simultaneous estimation of coarse bathymetry allows the SAS images to be projected onto ground-range. The method is based on non-linear minimization of the difference in modelled and measured time delays and surges between redundant phase centre arrays. The approach is demonstrated using data collected by the CMRE MUSCLE AUV using its 270-330 kHz SAS during the MANEXâ14 experiment. SAS images have been projected onto the coarsely estimated bathymetry, and interferograms have been formed. The coarse bathymetry estimate and vehicle navigation estimate are validated by the quality of the image focussing and the near-zero phase of the interferogram. The method has the potential to improve through-the-sensor navigation aiding and to increase the accuracy of single-pass bathymetry estimation. Future development of the algorithm for repeat-pass operation has the potential to enable repeat-pass track registration in three dimensions. The method is therefore an important step towards improved coherent change detection and high resolution bathymetry estimation
Phase wrap error correction by random sample consensus with application to synthetic aperture sonar micro-navigation
Accurate time delay estimation between signals is crucial for coherent imaging systems such as synthetic aperture sonar (SAS) and synthetic aperture radar (SAR). In such systems, time delay estimates resulting from the cross-correlation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery. In this article, an algorithm is introduced for the detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit 1-D and 2-D models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase center (RPC) micronavigation. Phase wrap errors are then corrected by recalculating the phase wrap number using the best-fitting model. The approach is demonstrated using the data collected by the 270&#x2013;330 kHz SAS of the NATO Centre for Maritime Research and Experimentation Minehunting unmanned underwater vehicle for Shallow water Covert Littoral Expeditions. Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using 1-D and 2-D models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2-D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1-D and 2-D RANSAC methods outperform the branch-cuts method, with improvements of 29&#x0025;&#x2013;125&#x0025; and 30&#x0025;&#x2013;150&#x0025;, respectively, compared to 16&#x0025;&#x2013;134&#x0025; for the branch-cuts method for this data set.</p
Phase wrap error correction by random sample consensus with application to synthetic aperture sonar micro-navigation
Accurate time delay estimation between signals is crucial for coherent imaging systems such as synthetic aperture sonar (SAS) and synthetic aperture radar (SAR). In such systems, time delay estimates resulting from the cross-correlation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery. In this article, an algorithm is introduced for the detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit 1-D and 2-D models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase center (RPC) micronavigation. Phase wrap errors are then corrected by recalculating the phase wrap number using the best-fitting model. The approach is demonstrated using the data collected by the 270&#x2013;330 kHz SAS of the NATO Centre for Maritime Research and Experimentation Minehunting unmanned underwater vehicle for Shallow water Covert Littoral Expeditions. Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using 1-D and 2-D models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2-D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1-D and 2-D RANSAC methods outperform the branch-cuts method, with improvements of 29&#x0025;&#x2013;125&#x0025; and 30&#x0025;&#x2013;150&#x0025;, respectively, compared to 16&#x0025;&#x2013;134&#x0025; for the branch-cuts method for this data set.</p
Finding Vegan Poetics In Literature For Nonhumans
In the ever-expanding realm of scholarship discussing nonhuman animals, the question of the animal is experiencing shifts to integrate ethics of care. To explore an extension of ecofeminist writingâvegan poeticsâmy paper enters the debate among eco-writing regarding the eating of nonhuman animals. Currently, there is a small subgroup of authors openly including their politicized abstaining from using nonhuman animal as objects in their diets, as well as in their writing. Through a close reading of Literature for Nonhumans by vegan scholar Gabriel Gudding, this paper follows the motif of rejecting the erasure of factory farm animals from discussions and writings regarding anthropogenic and environmental issues. Literature for Nonhumans encourages future scholarship to both uncover and create more texts embodying elements of vegan poetics, such as confronting human exceptionalism found among the ethical considerations of dominant Western culture. Critically entering Literature for Nonhumans exposes a call for both poetics and scholarship to reassess the consequences of mass consuming nonhuman animals and viewing them as edible objects: âWhen you eat the muscles of animals / your anus is a tunnel to the slaughterhouseâââWe really are ethical misers when it comes / to other beingsâ (Gudding 27-9)
Finding Vegan Poetics: Literature For Nonhumans As An Ecofeminist Response To Carnism
This thesis is my journey to finding and defining vegan poetics, a term I employ to define poetry that exhibits a vegan perspective through witnessing the mass slaughter of nonhuman animals and exposing the connection between the factory farming industry and climate change. As an example of vegan poetics, I present Gabriel Guddingâs 2015 poetry collection Literature for Nonhumans as exhibiting a fusion of vegan ideology and poetic technique. To analyze Guddingâs text, this thesis employs ecofeminist theory along with scholarship from the developing field of vegan studies to foster a discourse on what it means to write poetry in opposition to âcarnism,â a term referring to the culture surrounding meat-eating. In short, Literature for Nonhumans provides entrance into the institution of the slaughterhouse and, in turn, introduces a comprehensive vegan worldview through the genre of poetry
Repeat-pass synthetic aperture sonar micro-navigation using redundant phase center arrays
In this paper, a new algorithm is introduced for high-precision underwater navigation using the coherent echo signals collected during repeat-pass synthetic aperture sonar (SAS) surveys. The algorithm is a generalization of redundant phase center (RPC) micronavigation, expanded to RPCs formed between overlapping pings in repeated passes. For each set of overlapping ping pairs (two intrapass and three interpass), five different RPC arrays can be formed to provide estimates of the vehicle's surge, sway, and yaw. These estimates are used to find a weighted least squares solution for the trajectories of the repeated passes. The algorithm can estimate the relative trajectories to subwavelength precision (on order of millimeters to hundreds of micrometers at typical SAS operating frequencies of hundreds of kilohertz) in a common coordinate frame. This will lead to improved focusing and coregistration for repeat-pass SAS interferometry and is an important step toward repeat-pass bathymetric mapping. The repeat-pass RPC micronavigation algorithm is demonstrated using data collected by the 300-kHz SAS of the NATO Center for Maritime Research and Experimentation (CMRE) Minehunting Unmanned underwater vehicle for Shallow water Covert Littoral Expeditions (MUSCLE)
Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem
<jats:p>In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imageryâimages acquired within and across seasonsâon the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10â20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscapeâs ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas.</jats:p>
Anencephalic Babies and Organ Donation
An exploration of the scientific and ethical issues surrounding Anencephaly
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