Towards ‘Fourth Paradigm’ Spectral Sensing

Sensors

Forrest Simon Webler¹  Marilyne Andersen¹  Manuel Spitschan² 

[1] Laboratory of Integrated Performance in Design (LIPID), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland;Translational Sensory & Circadian Neuroscience, Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany;

关键词: reconstruction;    symmetric non-negative matrix factorization;    nonlinear dimensionality reduction;    sparse sensor placement;    spectral sensing;   

DOI  :  10.3390/s22062377

来源: DOAJ

【 摘 要 】

Reconstruction algorithms are at the forefront of accessible and compact data collection. In this paper, we present a novel reconstruction algorithm, SpecRA, that adapts based on the relative rarity of a signal compared to previous observations. We leverage a data-driven approach to learn optimal encoder-array sensitivities for a novel filter-array spectrometer. By taking advantage of the regularities mined from diverse online repositories, we are able to exploit low-dimensional patterns for improved spectral reconstruction from as few as $p=2$ channels. Furthermore, the performance of SpecRA is largely independent of signal complexity. Our results illustrate the superiority of our method over conventional approaches and provide a framework towards “fourth paradigm” spectral sensing. We hope that this work can help reduce the size, weight and cost constraints of future spectrometers for specific spectral monitoring tasks in applied contexts such as in remote sensing, healthcare, and quality control.

【 授权许可】

Unknown