Science & Engineering Apprenticeship

S&EAP Apprentices

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  • - Must be a graduating high school senior who will attend a senior college in Fall 2017
  • - Must be available to work June 5 through August 10, 2017 (Unpaid time off is allowed for attendance of freshman college orientation, otherwise there are no exceptions to the policy.)
  • - Must have both applied and been admitted to The University of Texas at Austin
  • - Must be a U.S. Citizen

About the Program

The Science & Engineering Apprenticeship Program is a competitive program for graduating high school seniors who plan to attend college in the fall semester following graduation and who have both applied and been admitted to The University of Texas at Austin. The program exposes the selected students to laboratory research and development and gives them an idea of what scientists and engineers do. Local area high schools are notified of the program, and students who have an interest in electrical, mechanical and aerospace engineering, physics, mathematics, and computer science are invited to apply. U.S. citizenship is required.
Each student is supervised by a research scientist or engineer and given a project they can complete during the apprenticeship. In addition to these projects, the program offers students opportunities to learn more about the different types of research done at ARL:UT. These activities include presentations from ARL:UT staff, student "socials" that provide participants a chance to get to know each other and share information about their projects, a tour of ARL:UT's Lake Travis Test Station, and a chance for the students to showcase their research through technical reports and poster presentations.
The Program began as a part of the Department of Defense Science and Engineering Apprenticeship Program for High School Students and encourages students to pursue careers in the science and engineering disciplines, particularly in areas related to the needs of the U.S. Department of Defense. ARL:UT accomplishes this goal by carefully assigning each student to a research project that can be completed during the summer. When the program started the summer of 1982, nine students from five local high schools participated. Now, over 545 students have taken part in the program, and most have gone on to major in science or engineering in colleges throughout the United States. Many participants return to ARL:UT in student and research positions and stay on to contribute for several years.

2016 Student Projects

The ability to synthesize and construct custom sequences of deoxyribonucleic acid (DNA) is one of the central and most practical features of modern synthetic biology. Its applications range from the manufacturing of vaccinations and the synthesis of entire bacterial genomes to biological circuits and even the storage of digital information. Despite its great applicability, high throughput chemical synthesis of long DNA sequences is limited by nucleotide sequence errors introduced in the methods used to synthesize it. Here we present a method utilizing superparamagnetic iron oxide microparticles coated in the protein streptavidin to isolate and amplify error free, full length, biotinylated gene products. We have synthesized and assembled the genes encoding the super-fast green fluorescent protein (sfGFP) and Prancer Purple protein (PP) to test this method. This method is a time and labor reduced alternative to current bound error correction methods that is also amenable to automation.
Rouse High School, Signal & Information Sciences Laboratory
Acoustic measurements of normal incidence, flat interface reflectivity are a reliable method of sensing ocean sediment type. However, seafloor interface roughness causes scattering at non-normal angles, which, if unaccounted for, can significantly alter the measurement from the flat surface value. Simultaneous measurements of seafloor roughness and acoustic reflection can be used to separate the roughness contribution. This paper describes the design of a laser profiling system to measure interface roughness. The system consists of a camera mounted on a remotely operated vehicle (ROV) which records six laser lines projected onto the seafloor. Small deviations of the laser lines reveal microbathymetry to millimeter-scale resolution. A Matlab program to analyze the video isolates each frame’s top laser line and converts its location into spatial coordinates, outputting a bathymetric map of the area. The power spectrum of roughness as a function of spatial wavenumber is also determined, and can be accurately described by a von Karman curve. The program was applied to data from the 2013 Target and Reverberation Experiment (TREX) off the Florida Panhandle. It successfully analyzed short sections of sufficiently high-SNR video, taken when the ROV was near the seafloor. The mean von Karman parameters are consistent with results from a similar experiment off the coast of Elba Island, Italy.

The system can be employed to analyze bathymetry at future test sites. Because inaccuracies greater than 1 meter in the ROV’s GPS sensor make accurate geo-references analysis difficult, future hardware should improve the determination of ROV location from the current resolution
Liberal Arts & Sciences Academy, Advanced Technology Laboratory
In this paper, we describe the development of a Bayesian tracker used for self-localization of a marine vehicle. The objectives of this project are to create a program that would accurately locate a target using fathometer measurement and to create a simulation environment to test the program. The innovation of such a program is the ability for a vehicle to maintain accurate information about its position without the use of GPS. Using bathymetry of the seafloor, the depth of the ocean at the vehicle we are attempting to locate, and the vehicle’s velocity, we can track the vehicle with a grid-based Bayesian tracker and a simple Gaussian error distribution likelihood function. By using a gridded approach, we can break the complex geometry of the seafloor into a finite number of grid cells, each assigned a depth equal to the average of the depths of the four corners of the grid cell. As the vehicle we are tracking takes depth measurements, the tracker calculates the likelihood that the vehicle is in each grid cell given the measured depth using a Gaussian likelihood function. The tracker also processes velocity measurements and incorporates them into a motion model to update the state estimate between measurements. Given a relatively accurate initial prior, the tracker is capable of locating and tracking a vehicle for the duration of the simulation over various terrains, even with measurement error and bias.
Westlake High School, Signal & Information Sciences Laboratory