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«Strengthening the Nation through Diversity, Innovation & Leadership in STEM San Antonio,Texas · October 3-6, 2013 Get Connected! Connect with the ...»

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The vitality and competitiveness of a region is influenced by a reciprocal relationship between economic growth and transportation. The World Bank identifies roads and highways as the back bone of the economy. In the United States, transportation accounts for nearly 11% of the gross domestic product. However, the increasing growth of infrastructure creates a great pressure on the environment and society. The transportation sector accounts for 30% of the global air pollution and greenhouse gases. For that reason, there have been efforts to manage and reduce the negative consequences of transportation infrastructure investments. Nevertheless, many situations can be found where the interests of communities, environment, and economic development are in conflict. The literature presents efforts at different levels and locations. The involvement of communities is a key element of the social component of sustainability; however, information about techniques available to reach an adequate level of community participation is scarce. In that sense, identification of tools for the evaluation of project alternatives based on communities’ preferences was needed. The objective of this project is to evaluate visualization as a tool to assist in a participatory decision making process. It includes three tasks: the identification and classification of sustainability development indicators related to transportation, criteria prioritization based on community preferences obtained through a survey and the analytical hierarchy process, and visualizations design and evaluation. Visualizations reflect a specific transportation project alternative. At the end, the consistency of the preferences in the criteria and the visualizations is evaluated.


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Brian Landron, Nayda Santiago.

University of Puerto Rico at Mayagüez, Mayagüez, PR.

High performance computing power provided by general purpose graphical processing units (GPGPUs) has been widely accepted as a component of hyperspectral image (HSI) analysis due to their ability to efficiently process large amounts of data. Aiming for rapid prototyping of HSI analysis algorithms for GPGPUs, an open source software library prototype, libdect, is being enhanced. This library supports the Reed-Xiaoli (RX) and matched filter (MF) detection algorithms on the NVidia Compute Unified Device Architecture (CUDA). The core of libdect’s infrastructure was built using the CMake development tool in order to incorporate possible cross-platform support. The infrastructure also supports automated testing of coding guidelines with KWStyle and can provide automated compilation reports with CTest. To improve libdect’s ability to process images of various sizes, additional support for NVidia CUDA models with limited memory capacity must be incorporated as well as support for more than one NVidia CUDA model.

Furthermore, code that limits libdect to Linux with a certain version of the CUDA infrastructure has to be replaced. The Graduate

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smaller tasks that a limited NVidia CUDA model can handle. In addition, software that replaces libdect’s dependencies is currently being tested. The resulting libdect will be a cross-platform, HSI analysis software library with support for more than one NVidia CUDA model.

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SWARM Alireza Dirafzoon, Edgar Lobaton.

North Carolina State University, Raleigh, NC.

Mapping and exploration are essential tasks for swarm robotic systems in applications such as search and rescue or surveillance and monitoring. These tasks become extremely challenging when localization information is not available. In this study, we explore how stochastic motion models and weak encounter information can be exploited to learn topological information about an unknown environment. Our system behavior mimics a probabilistic motion model for cockroaches that incorporates random motion as well as wall-following behaviors. We employ tools from algebraic topology to extract spatial information on the environment based on the neighbor-to-neighbor interactions among the biologically inspired agents with no need for positioning data. This information is used to build a map of persistent topological features of the environment. We explore the performance of our estimation based on the existing mathematical tools and propose a switching control mechanism to extract features of complex environments in an effective way.

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ARRAYS Felipe Minotta, Manuel Jimenez.

University of Puerto Rico at Mayagüez, Mayagüez, PR.

The fast Fourier transform (FFT) is the main block in many communication systems and signal processing applications as it provides the frequency spectrum of any signal. Hardware implementations with this operation are highly regarded as they provide improved performance characteristics with respect to software-based sequential implementations. The purpose of this work is to develop a method for scaling the level of parallelism in an FFT core with upper radix butterflies when implemented in hardware. Our approach is mainly based in 2k memory blocks, an address generator, and k radix-r butterflies. The number k of butterflies determines the level of parallelism. The main problem is to develop a block to generate the correct addresses regarding the number of butterflies, radix, size of the FFT, and numeric format. Currently, we are trying to solve the problem using numerical series and studying the different ways of factorization of the original algorithm developed by Fourier. Currently, we have implemented the core with 1, 2, and 4 butterflies independently. The expected high performance of the core lies in the fact it does not need dedicated permutation hardware between stages. Instead, the data flow and order is controlled by an address generator. Using this scheme, the impact on consumed resources is significantly mitigated when the number of points of the FFT core is increased. Our expectation is to develop a fully scalable FFT core including latency, number of points, radix of the butterflies, and numeric format using this approach.

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Juan Tamez, Amar Bhalla, Ruyan Guo.

University of Texas at San Antonio, San Antonio, TX.

Piezoelectric transducer (PZT) design involves computer modeling and experimental verification in order to validate the piezoelectric transducer performance. To construct a precise computer model using COMSOL multiphysics, it is necessary to know the piezoelectric material electrical and mechanical properties. The objective is to analyze and model a noncontact piezoelectric transducer capable of being used in the medical field such as tissue imaging.

Thus, this work experimentally measures the properties of a low frequency (kHz range) PZT transducer composed of hexagonal tube arrays assembled similar to that of a honey comb. The obtained admittance (|Y|), phase (θ) and displacement (d33) measurements are used to calculate the transducer effective coupling coefficient (K33), quality factor (Q) and damping coefficients. These parameters are used to fine tune the computer model to further analyze the sensor under different boundary and electrode conditions to achieve an optimum model. The model and parameter can be further tuned to allow us to design a piezoelectric sensor to desired resonant frequencies and displacement amplitudes.


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Emiliano Morales, Art Grigoryan.

University of Texas at San Antonio, San Antonio, TX.

Methods of digital image processing are widely used in medical imaging when different types of images with cancer require performing a complex processing of images. For many medical images, it is desired to enhance the quality of images; detect, extract, and analyze the areas with tumors; and accomplish accurately the classification of the type of the tumor. Therefore, the main goal of image enhancement is to improve the visual appearance of images.

Existing methods for image enhancement are manifold. Enhancement methods vary with different types of images and noise models. In this proposal, we will develop a novel image filtering approach based on the shape-adaptive will be applied to different transform-based image enhancement algorithms such as ∝-rooting, tensor, and Fourier discreet cosine transform to effectively de-noise grayscale as well as color images. The shape adaptive approach transforms. Enhanced images will be compared to the same altered images presented utilizing MATLAB to verify results. Besides noise removal, this work will investigate if the other transform-based algorithms are also effective in dealing with artifacts that are often encountered in block-DCT compressed images and videos, suppressing blocking artifacts while preserving salient image features. Due to the proven application of the different transform-based algorithms, the visual quality of the estimates is expected to be high with sharp detail preservation, clean edges, and without unpleasant artifacts introduced by the transforms. (This work is partially funded by the NSF LSAMP-BD 1249284.) Ballroom C - 67


Raul Ramos-Garcia, Adam Hoover.

Clemson University, Clemson, SC.

This work is motivated by the growing prevalence of obesity, a health problem affecting over 500 million people.

Measurements of energy intake are commonly used for the study and treatment of obesity. However, the most widely used tools rely upon self-reporting and require a considerable manual effort, leading to underreporting of consumption, non-compliance, and discontinued use over the long term. We are investigating the use of wristworn accelerometers and gyroscopes to automatically recognize eating gestures. In order to improve recognition accuracy, we studied the sequential dependency of actions during eating. Using a set of 4 actions (rest, utensiling, bite, and drink), we developed a hidden Markov model (HMM) and compared its recognition performance against a non-sequential classifier (KNN). Tested on a dataset of 20 meals, the KNN achieved 76% accuracy while the HMM achieved 87% accuracy, showing that knowledge of the sequential nature of activities during eating improves recognition accuracy.


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Ashley Valdez, Carl Chancy, Schwiegerling.

University of Arizona, Tucson, AZ.

Graduate Poster The purpose of our research is to develop an automatic adaptive see-through binocular phoropter. The phoropter will automatically measure spherical and cylindrical error and null this error with a compound sphero-cylindrical fluidic lens. The multi-generation optical system corrects for astigmatism and spherical refractive errors of the eye. Currently, a monocular automated phoropter has been designed and fabricated. The system is comprised of three components required for assembly: a fluidic lens, a relay telescope, and a Shack-Hartmann sensor. The fluidic lens system is a stack of 3 adjustable lenses: a spherical lens and 2 astigmatic lenses. Adjusting the fluid volume within the lenses can modify properties of the fluidic lenses. The modifications change the curvature of the lens, which accommodates for the error by lens power adjustments. Following the fluidic lenses is a relay telescope that directs infrared light toward the final component, the Shack-Hartmann wavefront sensor. The sensor detects scattered infrared light off the eye as a wavefront reconstructing the sphero-cylindrical refractive error. The reconstruction of the error produces a prescription for the participant. Thus far, our monocular system shows the capability of measuring a sphero-cylindrical

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refractive error from -30 diopters to +30 diopters, visibly concluding that the system is capable of accommodating a wide range of astigmatism and sphero-cylindrical errors of the eye.


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Irmarie Cotto, Ingrid Padilla, Perla Torres.

University of Puerto Rico at Mayagüez, Mayagüez, PR.

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