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New York City Research Initiative

Research Projects at Hostos Community College

Go to projects in: 2015 | 2014 | 2013 | 2012

Hostos Community College — 2015

Warps and Morphs of Satellite based Earth Images and Its Impact On The Environment
Team Members

Principal Investigator/Mentor: Dr. Angulo Nieves

Co-Principal Investigator/Mentor: Dr. Tanvir Prince

Educator: Ildefonso D. Salva

Interns: Maria Malik, Ariel Mazor

Final Research Presentation
Screenshot of presentation
Summary

The purpose of this research is to reveal the mathematics and applications of the computer animation techniques of warping and morphing. A warp is a twist or distortion in the form of an object in an image while a morph is the smooth and gradual transformation of an object in one image into the object in another image. Linear algebra makes these computer animation techniques possible; the first phase of this research delves into how those mathematical processes translate into image warps and morphs. The second part of this study requires the analysis and application of image warping and morphing techniques in an array of fields. The team utilized the computer software, Abrosoft Fantamorph, in order to create a series of warps and morphs. The final phase of this research was to identify what uses NASA can have for these computer animation techniques and what further research can be done to expand our knowledge of warps and morphs. By identifying the mechanics of warps and morph, we can discover how they can assist scientists and organization, such as NASA, to create depictions of objects, ideas, places, and events. Ultimately, studying morphing and warping techniques allows us to find better ways to represent visual data - whether it is images of the ozone hole or maps of the ever-changing weather in a region. The limitations that were found during the study can be used to conduct further research about warps and morphs - such as distorting images using quadratics or varying the rate at which each part of a transformation happens.

Hostos Community College — 2014

Image Compression: Mathematical Means
Team Members

Principle Investigator (PI):
Dr. Tanvir Prince

Researchers:
Karina Shah, High School Student
William Ashong, Undergraduate Student
Ildefonso Salva, High School Teacher

Thanks to:
Dr. Nieves Angulo

Final Research Presentation
Summary

A study of the basic fundamentals of image compression was conducted in an effort to inform NASA about the most efficient means of compression. NASA uses images to reveal information, data, and evidence concerning astronomical research. For this reason each NASA image must have the best quality and adequate dimensions. The purpose of this project was to compare the compression ratios resulting from compression by three variations of pixel matrices, and to understand the difference between arithmetic mean and geometric mean compression methods. For this task, a total of 50 planetary images were selected from the NASA website. With the aid of the Mathematica software, the team created a program to compress the images. The team then recorded its findings from the experiment in the form of graphs; visual representation helped to understand the resulting trends.

Hostos Community College — 2013

Mathematics Behind Image Compression
Team Members

Principle Investigator (PI):
Dr. Tanvir Prince

Researchers:
Stefany Franco, Undergraduate Student
Charlie Windolf, High School Student
Ildefonso Salva, High School Teacher

Final Research Presentation
Summary

Image compression is fundamental to NASA and the world's daily operations. Images are transmitted to NASA from satellites and even Mars, making it very important to send data as efficiently as possible through the low-bandwidth links to these locations. This project focuses its studies in three areas: first, a hands-on mathematical analysis of the singular value decomposition (SVD) compression; second, two field experiments that explore the effect of light conditions, shot composition and content, as well as the time of day and other variables on the file sizes of images generated in a digital camera that implements JPEG compression; and third, an in-depth study of the JPEG algorithm.

In the SVD study, the team analyzed mathematically how matrices are manipulated to compress an image. The theory about SVD is reinforced by using the software Wolfram Mathematica to compress images from NASA satellites and Mars rovers. Mathematica analyzed the file size and timing data for the compression process.

In the field experiment, a camera with fixed focus, aperture, and other shooting parameters was used to take pictures at various times of day of the same scene to see how the amount and quality of daylight influenced JPEG's ability to compress images. The same camera with the parameters still fixed was used to shoot various locations, indoors and outdoors, at the same time of day to see how the content of the photo influenced JPEG file sizes.

Finally, the team looked at JPEG's compression algorithm in Wolfram Mathematica to better understand its efficiency and power, since NASA's radiation-hardened computer processors are generally not powerful enough to compress images with JPEG. Loosely, the team found that JPEG is best able to compress images with little variation pixel to pixel in color or brightness, and that it provides better looking images at the same file size than SVD compression.

Hostos Community College — 2012

Image Compression and Image Processing
Team Members

Principle Investigator (PI):
Dr. Tanvir Prince

Researchers:
Maurice Evans, Undergraduate Student
Alyssa Taylor, High School Student
Noam Pillisher, High School Teacher

Final Research Presentation
Summary

Images, both still and moving, are an integral part of our lives. We see images in newspapers, TV, internet, books, and magazines. NASA receives many images from various space telescopes, especially from Hubble space telescope. In this project, we studied how NASA processes and compresses images. Articles published by NASA's Langley Center as well as other academic publications were reviewed. Once the research was completed the team developed a number and quantity unit for a New York State algebra course.

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