Doctor of Philosophy

dissertationWe propose to examine a representation which features combined action and perception signals, i.e., instead of having a purely geometric representation of the perceptual data, we include the motor actions, e.g., aiming a camera at an object, which are al

Human-centered Electric Prosthetic (HELP) Hand

Through a partnership with Indian non-profit Bhagwan Mahaveer Viklang Sahayata Samiti, we designed a functional, robust, and and low cost electrically powered prosthetic hand that communicates with unilateral, transradial, urban Indian amputees through a biointerface. The device uses compliant tendon actuation, a small linear servo, and a wearable garment outfitted with flex sensors to produce a device that, once placed inside a prosthetic glove, is anthropomorphic in both look and feel. The prosthesis was developed such that future groups can design for manufacturing and distribution in India

Lightweight In-Plane Actuated Deformable Mirrors for Space Telescopes

This research focused on lightweight, in-plane actuated, deformable mirrors, with the ultimate goal of developing a 20- meter light gathering aperture for space telescopes. The 0.127 meter diameter deformable mirror small scale testbed was modelled infinite elements using MSC.Nastran software and then used as a basis for a quasi-static controller. Experimental tracking of Zernike tip, tilt, and defocus modes was accomplished. The analytical solutions to plate-membrane and beam-string ordinary differential equations were developed. A simplified approach to modelling the axisymmetric cases was also presented. A novel static control strategy, the Modal Transformation Method, was developed to form Zernike surfaces within an interior, or clear aperture, region using a number of statically-actuated Bessel-based vibration modes. The scaling problem for membrane optics is addressed. Significantly, it is shown linear modelling may correctly explain the behavior of small-scale models, but only non-linear models will account for the important terms which govern the full-scale large aperture membrane telescopes

EVA Glove Research Team

The goal of the basic research portion of the extravehicular activity (EVA) glove research program is to gain a greater understanding of the kinematics of the hand, the characteristics of the pressurized EVA glove, and the interaction of the two. Examination of the literature showed that there existed no acceptable, non-invasive method of obtaining accurate biomechanical data on the hand. For this reason a project was initiated to develop magnetic resonance imaging as a tool for biomechanical data acquisition and visualization. Literature reviews also revealed a lack of practical modeling methods for fabric structures, so a basic science research program was also initiated in this area

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202