Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures

Data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present emerging signal processing challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a highly standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different CWT reconstruction schemas are presented and tested under different SNR conditions. A sparse representation of the Nth order Gabor atoms worked well against a test blast synthetic using the wavelet entropy and a comparable entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for dictionary-based machine learning.