Q-Filter computational structures for paradigm shifts in data engineering

An advanced approach for adaptive nonlinear digital data processing is described in this article. Three primal computational structures referred to as Q-Measures, Q-Metrics, and Q-Aggregates are introduced and utilized in unison as highly adaptive data analysis handlers. The proposed approach relies on universal functionals using few parameters to characterize dynamic system behaviors in broad ranges of unconventional measure, metric, and aggregation spaces. We present this unique approach in application to real-valued signal processing tasks, with suitable optimization algorithms, so that the parameters of the proposed models can be tuned automatically. The new approach is tested on real data sets and the experiments show promising results.