1,518 research outputs found

    THE APPLICATION OF COMPUTER VISION, MACHINE AND DEEP LEARNING ALGORITHMS UTILIZING MATLAB

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    MATLAB is a multi-paradigm proprietary programming language and numerical computing environment developed by MathWorks. Within MATLAB Integrated Development Environment (IDE) you can perform Computer-aided design (CAD), different matrix manipulations, plotting of functions and data, implementation algorithms, creation of user interfaces, and has the ability to interface with programs written in other languages1. Since, its launch in 1984 MATLAB software has not particularly been associated within the field of data science. In 2013, that changed with the launch of their new data science concentrated toolboxes that included Deep Learning, Image Processing, Computer Vision, and then a year later Statistics and Machine Learning. The main objective of my thesis was to research and explore the field of data science. More specifically pertaining to the development of an object recognition application that could be built entirely using MATLAB IDE and have a positive social impact on the deaf community. And in doing so, answering the question, could MATLAB be utilized for development of this type of application? To simultaneously answer this question while addressing my main objectives, I constructed two different object recognition protocols utilizing MATLAB_R2019 with the add-on data science tool packages. I named the protocols ASLtranslate (I) and (II). This allowed me to experiment with all of MATLAB data science toolboxes while learning the differences, benefits, and disadvantages of using multiple approaches to the same problem. The methods and approaches for the design of both versions was very similar. ASLtranslate takes in 2D image of American Sign Language (ASL) hand gestures as an input, classifies the image and then outputs its corresponding alphabet character. ASLtranslate (I) was an implementation of image category classification using machine learning methods. ASLtranslate (II) was implemented by using a deep learning method called transfer learning, done by fine-tuning a pre-trained convolutional neural network (CNN), AlexNet, to perform classification on a new collection of images

    Gendered Paths to Formal and Informal Resources in Post-Disaster Development in the Ecuadorian Andes

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    The devastating eruptions of Mount Tungurahua in the Ecuadorian highlands in 1999 and 2006 left many communities struggling to rebuild their homes and others permanently displaced to settlements built by state and nongovernmental organizations. For several years afterward, households diversified their economic strategies to compensate for losses, communities organized to promote local development, and the state and nongovernmental organizations sponsored many economic recovery programs in the affected communities. Our study examined the ways in which gender and gender roles were associated with different levels and paths of access to scarce resources in these communities. Specifically, this article contrasts the experiences of men and women in accessing household necessities and project assistance through formal institutions and informal networks. We found that women and men used different types of informal social support networks, with men receiving significantly more material, emotional, and informational support than women. We also found that men and women experienced different challenges and advantages when pursuing support through local and extralocal institutions and that these institutions often coordinated in ways that reified their biases. We present a methodology that is replicable in a wide variety of disaster, resettlement, and development settings, and we advocate an inductive, evidence-based approach to policy, built upon an understanding of local gender, class, and ethnic dynamics affecting access to formal and informal resources. This evidence should be used to build more robust local institutions that can resist wider social and cultural pressures for male dominance and gendered exclusion

    Evaluation of prototype air/fluid separator for Space Station Freedom Health Maintenance Facility

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    A prototype air/fluid separator suction apparatus proposed as a possible design for use with the Health Maintenance Facility aboard Space Station Freedom (SSF) was evaluated. A KC-135 parabolic flight test was performed for this purpose. The flights followed the standard 40 parabola profile with 20 to 25 seconds of near-zero gravity in each parabola. A protocol was prepared to evaluate the prototype device in several regulator modes (or suction force), using three fluids of varying viscosity, and using either continuous or intermittent suction. It was felt that a matrixed approach would best approximate the range of utilization anticipated for medical suction on SSF. The protocols were performed in one-gravity in a lab setting to familiarize the team with procedures and techniques. Identical steps were performed aboard the KC-135 during parabolic flight

    Evaluation of prototype Advanced Life Support (ALS) pack for use by the Health Maintenance Facility (HMF) on Space Station Freedom (SSF)

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    The purpose is to evaluate the prototype Advanced Life Support (ALS) Pack which was developed for the Health Maintenance Facility (HMF). This pack will enable the Crew Medical Officer (CMO) to have ready access to advanced life support supplies and equipment for time critical responses to any situation within the Space Station Freedom. The objectives are: (1) to evaluate the design of the pack; and (2) to collect comments for revision to the design of the pack. The in-flight test procedures and other aspects of the KC-135 parabolic test flight to simulate weightlessness are presented
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