Student-Faculty Programs Office
Summer 2017 Announcements of Opportunity

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Project:  Real-time Verification Software for Hyperspectral Data Compression on System-on-Chip
Disciplines:  Electrical Engineering and Computer Science, Computer Science
Mentor:  Didier Keymeulen, (JPL), Didier.Keymeulen@jpl.nasa.gov, Phone: (818) 354-4280
Background:  Efficient on-board lossless hyperspectral data compression reduces data volume in order to meet NASA limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. A hardware implementation of the compression algorithm was on a Field Programmable Gate Array (FPGA)
Description:  The project consists to develop a real-time data processing to verify the real-time data products of the FPGA data acquisition and compression for spectrometer using the new CHROMA detector with 1280 by 480 pixels at 120 frames per seconds image. The verification software will be written in in C and integrated in the ARM Cortex9 processors in the Zynq System-on-Chip device as a standalone verification tool running in parallel with the data acquisition
References:  Keymeulen et al. (2014) Airborne Demonstration of FPGA Implementation of Fast Lossless Hyperspectral Data Compression System. In NASA/ESA Conference on Adaptive Hardware and Systems, IEEE Proceedings, July 2014.

N. Aranki, D. Keymeulen, M. Klimesh, A. Bakhshi, Hardware Implementation of Lossless Adaptive and Scalable Hyperspectral Data Compression for Space, NASA/ESA Conference on Adaptive Hardware and Systems, pp. 315322, July, 2009, San Francisco, CA, USA

CCSDS, Lossless Multispectral & Hyperspectral Image Compression, Recommendation for Space Data System Standards, Blue Book, vol. 123.0-B-1: CCSDS, May 2012 ( http://www.ccsds.org )

D. Keymeulen, N. Aranki, B. Hopson, A. Kiely, M. Klimesh, K. Benkrid, GPU Lossless Hyperspectral Data Compression System for Space Applications, IEEE aerospace Conference, Mars 2012, Big Sky, MT, USA

N. Aranki, A. Bakhshi, D. Keymeulen, M. Klimesh, Fast and Adaptive Lossless On-Board Hyperspectral Data Compression System for Space Applications, IEEE Aerospace Conference , March, 2009, Big Sky, Mt, USA
Student Requirements:  C, python, data processing and algorithm
Location / Safety:  Project building and/or room locations: . Student will need special safety training: No.
Programs:  This AO can be done under the following programs:

  Program    Available To
       SURF    both Caltech and non-Caltech students 

Click on a program name for program info and application requirements.


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Problems with or questions about submitting an AO?  Call Jen Manglos of the Student-Faculty Programs Office at (626) 395-2885.
 
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