Working Paper Article Version 7 This version is not peer-reviewed

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

Version 1 : Received: 9 July 2020 / Approved: 14 July 2020 / Online: 14 July 2020 (05:49:04 CEST)
Version 2 : Received: 14 July 2020 / Approved: 16 July 2020 / Online: 16 July 2020 (06:07:48 CEST)
Version 3 : Received: 10 August 2020 / Approved: 11 August 2020 / Online: 11 August 2020 (04:21:09 CEST)
Version 4 : Received: 13 August 2020 / Approved: 14 August 2020 / Online: 14 August 2020 (10:14:30 CEST)
Version 5 : Received: 18 August 2020 / Approved: 20 August 2020 / Online: 20 August 2020 (08:27:31 CEST)
Version 6 : Received: 25 August 2020 / Approved: 25 August 2020 / Online: 25 August 2020 (11:43:32 CEST)
Version 7 : Received: 29 August 2020 / Approved: 3 September 2020 / Online: 3 September 2020 (04:28:24 CEST)

A peer-reviewed article of this Preprint also exists.

Garcés, M.A. Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures. Entropy 2020, 22, 936. Garcés, M.A. Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures. Entropy 2020, 22, 936.

Journal reference: Entropy 2020, 22, 936
DOI: 10.3390/e22090936

Abstract

Increased data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present new challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different continuous wavelet transform (CWT) reconstruction formulas are presented and tested under different signal to noise ratio (SNR) conditions. A sparse superposition of Nth order Gabor atoms worked well against a synthetic blast transient using the wavelet entropy and an entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for sparse feature extraction and dictionary-based machine learning across multiple sensor modalities.

Subject Areas

Gabor atoms; wavelet entropy; binary metrics; acoustics; quantum wavelet

Comments (1)

Comment 1
Received: 3 September 2020
Commenter: Milton Garces
Commenter's Conflict of Interests: Author
Comment: With unit correction which should be also in press
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