Current Search: info:fedora/fau:CurrentETDs (x) » Ebook (x)
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Substance of an argument of Samuel F. Vinton, for the defendants, in the case of the Commonwealth of Virginia vs. Peter M. Garner and others, for an alleged abduction of certain slaves : delivered before the General Court of Virginia, at its December term
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6
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Book Title
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Substance of an argument of Samuel F. Vinton, for the defendants, in the case of the Commonwealth of Virginia vs. Peter M. Garner and others, for an alleged abduction of certain slaves : delivered before the General Court of Virginia, at its December term
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5
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Book Title
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Substance of an argument of Samuel F. Vinton, for the defendants, in the case of the Commonwealth of Virginia vs. Peter M. Garner and others, for an alleged abduction of certain slaves : delivered before the General Court of Virginia, at its December term
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4
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Book Title
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Substance of an argument of Samuel F. Vinton, for the defendants, in the case of the Commonwealth of Virginia vs. Peter M. Garner and others, for an alleged abduction of certain slaves : delivered before the General Court of Virginia, at its December term
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3
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Book Title
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Substance of an argument of Samuel F. Vinton, for the defendants, in the case of the Commonwealth of Virginia vs. Peter M. Garner and others, for an alleged abduction of certain slaves : delivered before the General Court of Virginia, at its December term
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2
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Book Title
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Substance of an argument of Samuel F. Vinton, for the defendants, in the case of the Commonwealth of Virginia vs. Peter M. Garner and others, for an alleged abduction of certain slaves : delivered before the General Court of Virginia, at its December term
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1
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Dinner to the Hon. Daniel Webster, of Massachusetts : by the merchants, and other citizens of Philadelphia, December 2, 1846, with Mr. Webster's speech.
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104
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Mr. Webster's speech in defence of the Christian ministry, and in favor of the religious instruction of the young : delivered in the Supreme Court of the United States, February 10, 1844 : in the case of Stephen Girard's will.
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57
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Mr. Webster's speech in defence of the Christian ministry, and in favor of the religious instruction of the young : delivered in the Supreme Court of the United States, February 10, 1844 : in the case of Stephen Girard's will.
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56
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Book Title
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Mr. Webster's speech in defence of the Christian ministry, and in favor of the religious instruction of the young : delivered in the Supreme Court of the United States, February 10, 1844 : in the case of Stephen Girard's will.
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55
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Book Title
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Mr. Webster's speech in defence of the Christian ministry, and in favor of the religious instruction of the young : delivered in the Supreme Court of the United States, February 10, 1844 : in the case of Stephen Girard's will.
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54
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An address delivered at Lenox : on the first of August 1842, the anniversary of emancipation in the British West Indies by William E. Channing.
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37
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An address delivered at Lenox : on the first of August 1842, the anniversary of emancipation in the British West Indies by William E. Channing.
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36
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A Discourse on the Death of William Henry Harrison, Late President of the United States, 1841
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44
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A Discourse on the Death of William Henry Harrison, Late President of the United States, 1841
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44
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"Facts and figures for the hour" : speech of George May Powell, of Wisconsin
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16
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The past and the present : semi-centennial address to the alumni of Yale College, and graduates of 1814, at their annual meeting, July 27, 1864
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48
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The past and the present : semi-centennial address to the alumni of Yale College, and graduates of 1814, at their annual meeting, July 27, 1864
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47
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Counting manatee aggregations using deep neural networks and Anisotropic Gaussian Kernel.
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Creator
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Zhiqiang Wang, Yiran Pang, Cihan Ulus, Xingquan Zhu
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Abstract/Description
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Manatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in groups (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for boaters, divers,...
Show moreManatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in groups (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for boaters, divers, etc., as well as scheduling nursing, intervention, and other plans. In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input. Because manatees have unique shape and they often stay in shallow water in groups, water surface reflection, occlusion, camouflage etc. making it difficult to accurately count manatee numbers. To address the challenges, we propose to use Anisotropic Gaussian Kernel (AGK), with tunable rotation and variances, to ensure that density functions can maximally capture shapes of individual manatees in different aggregations. After that, we apply AGK kernel to different types of deep neural networks primarily designed for crowd counting, including VGG, SANet, Congested Scene Recognition network (CSRNet), MARUNet etc. to learn manatee densities and calculate number of manatees in the scene. By using generic low quality images extracted from surveillance videos, our experiment results and comparison show that AGK kernel based manatee counting achieves minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed method works particularly well for counting manatee aggregations in environments with complex background.
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Date Issued
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2023
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PURL
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http://purl.flvc.org/fau/fd/FAUIR000535
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Format
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Document (PDF)
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Title
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Feeling the beat: a smart hand exoskeleton for learning to play musical instruments.
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Creator
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Maohua Lin, Rudy Paul, Moaed Abd, James Jones, Darryl Dieujuste, Harvey Chim, Erik D. Engeberg
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Abstract/Description
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Individuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by ‘feeling’...
Show moreIndividuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by ‘feeling’ the difference between correct and incorrect versions of the same song. The exoskeleton features piezoresistive sensor arrays with 16 taxels integrated into each fingertip. The hand exoskeleton was created as a single unit, with polyvinyl acid (PVA) used as a stent and later dissolved to construct the internal pressure chambers for the five individually actuated digits. Ten variations of a song were produced, one that was correct and nine containing rhythmic errors. To classify these song variations, Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) algorithms were trained with data from the 80 taxels combined from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the exoskeleton independently and while the exoskeleton was worn by a person. Results demonstrated that the ANN algorithm had the highest classification accuracy of 97.13% ± 2.00% with the human subject and 94.60% ± 1.26% without. These findings highlight the potential of the smart exoskeleton to aid disabled individuals in relearning dexterous tasks like playing musical instruments.
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Date Issued
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2023
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PURL
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http://purl.flvc.org/fau/fd/FAUIR000534
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Format
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Document (PDF)
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