KEYWORDS: 3D printing, Consumer electronics, Diagnostics, Digital imaging, Open source software, Integrated optics, Data analysis, Microcontrollers, Software development
There’s a constant need for improvement of optical bio/chemosensoristic devices on key aspects such as portability, cheapness, integration and simplification of experimental protocol. Moreover, new requirements are rapidly gaining ground: connectivity for real time remote access and big data analysis, needs for easy design approaches of customized components, suitability in resource-poor settings or educational context.
According to this scenario, the combination of consumer electronics, open-source 3D printing and microcontrollers running on free software are opening completely new possibilities to develop powerful, low-cost and highly customizable research tools for students, scientists, engineers, and lab personnel
We propose an all-optical fiber-based device able to accomplish both polarization control and OSNR enhancement of an amplitude modulated optical signal, affected by unpolarized additive white Gaussian noise, at the same time. The proposed noise cleaning device is made of a nonlinear lossless polarizer (NLP), that performs polarization control, followed by an ideal polarizing filter that removes the orthogonally polarized half of additive noise. The NLP transforms every input signal polarization into a unique, well defined output polarization (without any loss of signal energy) and its task is to impose a signal polarization aligned with the transparent eigenstate of the polarizing filter. In order to effectively control the polarization of the modulated signal, we show that two different NLP configurations (with counter- or co-propagating pump laser) are needed, as a function of the signal polarization coherence time. The NLP is designed so that polarization attraction is effective only on the "noiseless" (i.e., information-bearing) component of the signal and not on noise, that remains unpolarized at the NLP output. Hence, the proposed device is able to discriminate signal power (that is preserved) from in-band noise power (that is partly suppressed). Since signal repolarization is detrimental if applied to polarization-multiplexed formats, the noise cleaner application is limited here to "legacy" links, with 10 Gb/s OOK modulation, still representing the most common format in deployed networks. By employing the appropriate NLP configurations, we obtain an OSNR gain close to 3dB. Furthermore, we show how the achievable OSNR gain can be estimated theoretically.
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