A Haptic System for Depicting Mathematical Graphics for Students with Visual Impairments

When teaching students with visual impairments educators generally rely on tactile tools to depict visual mathematical topics. Tactile media, such as embossed paper and simple manipulable materials, are typically used to convey graphical information. Although these tools are easy to use and relatively inexpensive, they are solely tactile and are not modifiable. Dynamic and interactive technologies such as pin matrices and haptic pens are also commercially available, but tend to be more expensive and less intuitive. This study aims to bridge the gap between easy-to-use tactile tools and dynamic, interactive technologies in order to facilitate the haptic learning of mathematical concepts. We developed an haptic assistive device using a Tanvas electrostatic touchscreen that provides the user with multimodal (haptic, auditory, and visual) output. Three methodological steps comprise this research: 1) a systematic literature review of the state of the art in the design and testing of tactile and haptic assistive devices, 2) a user-centered system design, and 3) testing of the system’s effectiveness via a usability study. The electrostatic touchscreen exhibits promise as an assistive device for displaying visual mathematical elements via the haptic modality.

Slot Film Cooling: A Comprehensive Experimental Characterization

When components of a propulsion system are exposed to elevated flow temperatures there is a risk for catastrophic failure if the components are not properly protected from the thermal loads. Among several strategies, slot film cooling is one of the most commonly used, yet poorly understood active cooling techniques. Tangential injection of a relatively cool fluid layer protects the surface(s) in question, but the turbulent mixing between the hot mainstream and cooler film along with the presence of the wall presents an inherently complex problem where kinematics, thermal transport and multimodal heat transfer are coupled. Furthermore, new propulsion designs rely heavily on CFD analysis to verify their viability. These CFD models require validation of their results, and the current literature does not provide a comprehensive data set for film cooling that meets all the demands for proper validation, namely a comprehensive (kinematic, thermal and boundary condition data) data set obtained over a wide range of conditions. This body of work aims at solving the fundamental issue of validation by providing high quality comprehensive film cooling data (kinematics, thermal mixing, heat transfer). 3 distinct velocity ratios (VR=uc/u∞) are examined corresponding to wall-wake (VR~0.5), min-shear (VR ~ 1.0), and wall-jet (VR~2.0) type flows at injection, while the temperature ratio TR= T∞/Tc is approximately 1.5 for all cases. Turbulence intensities at injection are 2-4% for the mainstream (urms/u∞, vrms/u∞,), and on the order of 8-10% for the coolant (urms/uc, vrms/uc,). A special emphasis is placed on inlet characterization, since inlet data in the literature is often incomplete or is of relatively low quality for CFD development. The data reveals that min-shear injection provides the best performance, followed by the wall-jet. The wall-wake case is comparably poor in performance. The comprehensive data suggests that this relative performance is due to the mixing strength of each case, as well as the location of regions of strong mixing with respect to the wall. Kinematic and thermal data show that strong mixing occurs in the wall-jet away from the wall (y/s>1), while strong mixing in the wall-wake occurs much closer to the wall (y/s<1). Min-shear cases exhibit noticeably weaker mixing confined to about y/s=1. Additionally to these general observations, the experimental data obtained in this work is analyzed to reveal scaling laws for the inlets, near-wall scaling, detecting and characterizing coherent structures in the flow as well as to provide data reduction strategies for comparison to CFD models (RANS and LES).

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.