“An algorithm must be seen to be believed.”
RF signal processing
Radio frequency data collected from the ionosphere can get messy and confusing. The multifaceted tool suite I’ve developed agnostically cleans, processes, and intelligently analyzes real time, real world ionogram data.
Some data sets challenge us to find an optimal solution within an N-dimensional parameter space. Nature-inspired algorithms are a first step, like particle swarm (PSO) or Emperor penguin colony (EPC).
Agnostic image processing
Agnostically extracting features in complex and dynamic scenes is challenging. Image processing holds the key to understanding even the most complex scenes, from cloud identification to topological reconstruction.
Discretized signal detection
Although nature tends to be continuous, this isn’t always the case with the data systems collect. Therefore, efficient and intelligent signal detection within discretized data sets becomes a very useful capability.
Reducing data dropout
Similar to data, signals in nature tend to be continuous. Man-made instruments, however, tend to be shoddy and may drop out sporadically. Identifying and correcting these periods can greatly improve data fidelity.
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