Reconstruction of Signals From Level-Crossing Samples Using Implicit Information | Zendy

Dominik Rzepka | Zendy; Marek Miskowicz | Zendy; Dariusz Koscielnik | Zendy; Nguyen T. Thao | Zendy

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Reconstruction of Signals From Level-Crossing Samples Using Implicit Information

Author(s) -

Dominik Rzepka,

Marek Miskowicz,

Dariusz Koscielnik,

Nguyen T. Thao

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2839186

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper focuses on the recovery of bandlimited signals from level-crossing samples by exploiting not only the knowledge of when given levels are crossed by the input, but also the implicit information that the signal stays between neighboring levels in the time intervals between the level crossings. We propose to use the technique of projection onto convex sets (POCS) for perfect signal reconstruction from either the level crossings or the associated implicit information. Two POCS algorithms are proposed: iterative POCS and one-step POCS. While the one-step POCS is based on matrix inversion, the iterative projections can be implemented using a chain of standard circuit operations: a low-pass filter and a clipping circuit, respectively. The perfect signal recovery of the infinite projection iteration can be viewed as the completion of the nonperfect input reconstruction achieved in continuous-time digital signal processing from level crossings. The comparative analysis of simulation results for both iterative and one-step POCS algorithms show the importance of a good selection of the initial guess for the POCS reconstruction.

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