1. LIDCOM Matt Iverson Greg Jackson Kyle Kopacz David Loughry Chris Routh

2. Table of Contents Project Synopsis Client Overview Current Progress Current Research Next Steps Measures of Success Questions

3. Project Synopsis Develop/find compression algorithms for 4-D LIDAR datasets ▫MIT Lincoln Labs produces vast quantities of LIDAR data ▫Data must be stored and transmitted Produce stable API for evaluating algorithm performance

4. Client Overview Luke Skelly ▫Works at MIT Lincoln Labs ▫Graduated from RHIT in Computer Engineering in 2001 ▫Received a Masters in Computer Science at Boston University through the prestigious Lincoln Scholars award

5. Current Progress Have two compression algorithms in development Have a functional testing API Have definite research direction Have begun evaluating the viability of existing approaches

6. Demonstration

7. Current Algorithms - Cartesian Lossy Divide along one dimension by a compression factor All points along slice can take away the dimension divided by and be described by a single value. Can be called on multiple dimensions. Huffman coding can be used to compress remainder of data.

8. Current Algorithms - HyperCube Lossless Divide the data into 4-d groups of points (of equal 4-d volume) Change the origin of each point in each group to a reference point within the group The size of each dimension of each point has been reduced by a change in the data type

9. Next Steps Delve more into Wavelet Compression ▫Similar to Fourier transformation ▫Haar wavelet as proof-of-concept Implement algorithms on testing API Begin testing algorithms as we complete them

10. Measures of Success One viable compression algorithm fitting the needs of the client Ability to evaluate compression algorithms via API with repeatable results Ability to competently carry out individual and team research Production of a publishable research paper presenting conclusions

11. Comments and Questions?