Signal Processing
Synthetic Scene Generation
Running a large-scale experiment can be very costly. In place of real-world experiments, radiometrically-accurate synthetic scenes can be generated to produce sensor calibration data.
Image Registration
Seasonal variation in the real-world is a big problem for image analysis. As environments move, shift, and degrade, image analysis algorithms need to be able to align images over time.
Multi-Dimensional Analysis
There are many complex systems in the real world. Understanding the complex nature and relationships between a phenomenon’s defining variables is key to understanding the phenomena iteself.
Image-based audio processing
Noise reduction is a big problem in audio processing. One efficient way to remove noise is to re-interpret the sound as an image. In doing so, efficient image analysis algorithms become a tool for processing audio data.
Pseudo-Random number generation
Satellite communication (SATCOM) security heavily relies on encoding and decoding data in real-time. Generally, the data are encoded using pseudo-random numbers; but validating these can be a chore in itself.
L1CA, L2CM/CL generation
Satellite communication (SATCOM) signal encoding can be accomplished using the Galois or Fibonacci methods. This work focuses on developing the Fibonacci technique to establish pseudo-random number generation.
campaign data collection
Collecting data during a large experiment requires precision, timing, and accurate numerical analysis. This work focuses on real-time multi-sensor data collection and analysis for a large space-based experiment.
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