KOSMOS: An Open Source Underwater Video Lander for Monitoring Coastal Fishes and Habitats.

BACKGROUND: Monitoring the ecological status of coastal ecosystems is essential to track the consequences of anthropogenic pressures and assess conservation actions. Monitoring requires periodic measurements collected in situ, replicated over large areas and able to capture their spatial distribution over time. This means developing tools and protocols that are cost-effective and provide consistent and high-quality data, which is a major challenge. A new tool and protocol with these capabilities for non-extractively assessing the status of fishes and benthic habitats is presented here: the KOSMOS 3.0 underwater video system. METHODS: The KOSMOS 3.0 was conceived based on the pre-existing and successful STAVIRO lander, and developed within a digital fabrication laboratory where collective intelligence was contributed mostly voluntarily within a managed project. Our suite of mechanical, electrical, and software engineering skills were combined with ecological knowledge and field work experience. RESULTS: Pool and aquarium tests of the KOSMOS 3.0 satisfied all the required technical specifications and operational testing. The prototype demonstrated high optical performance and high consistency with image data from the STAVIRO. The project's outcomes are shared under a Creative Commons Attribution CC-BY-SA license. The low cost of a KOSMOS unit (~1400 €) makes multiple units affordable to modest research or monitoring budgets.

Kernel formalism applied to Fourier-based wave-front sensing in presence of residual phases.

In this paper, we describe Fourier-based wave-front sensors (WFSs) as linear integral operators, characterized by their kernel. In the first part, we derive the dependency of this quantity with respect to the WFS's optical parameters: pupil geometry, filtering mask, and tip/tilt modulation. In the second part, we focus the study on the special case of convolutional kernels. The assumptions required to be in such a regime are described. We then show that these convolutional kernels allow to drastically simplify the WFS model by summarizing its behavior in a concise and comprehensive quantity called the WFS impulse response. We explain in particular how it allows to compute the sensor's sensitivity with respect to spatial frequencies. Such an approach therefore provides a fast diagnostic tool to compare and optimize Fourier-based WFSs. In the third part, we develop the impact of the residual phases on the sensor's impulse response, and show that the convolutional model remains valid. Finally, a section dedicated to the pyramid WFS concludes this work and illustrates how the slope maps are easily handled by the convolutional model.

Variation around a pyramid theme: optical recombination and optimal use of photons.

We propose a new type of wave-front sensor (WFS) derived from the pyramid WFS (PWFS). This new WFS, called the flattened pyramid-WFS (FPWFS), has a reduced pyramid angle in order to optically overlap the four pupil images into an unique intensity. This map is then used to derive the phase information. In this Letter, this new WFS is compared to three existing WFSs, namely the PWFS, the modulated PWFS (MPWFS), and the Zernike WFS (ZWFS) following tests about sensitivity, linearity range, and low-photon-flux behavior. The FPWFS turns out to be more linear than a modulated pyramid for the high-spatial order aberrations, but it provides an improved sensitivity compared to the non-modulated pyramid. The noise propagation may even be as low as the ZWFS for some given radial orders. Furthermore, the pixel arrangement being more efficient than for the PWFS, the FPWFS seems particularly well suited for high-contrast applications.

Fast compressive Raman micro-spectroscopy to image and classify microplastics from natural marine environment.

The fast and reliable detection of micron-sized plastic particles from the natural marine environment is an important topic that is mostly addressed using spontaneous Raman spectroscopy. Due to the long (>tens of ms) integration time required to record a viable Raman signal, measurements are limited to a single point per microplastic particle or require very long acquisition times (up to tens of hours). In this work, we develop, validate, and demonstrate a compressive Raman technology using binary spectral filters and single-pixel detection that can image and classify six types of marine microplastic particles over an area of 1 mm2 with a pixel dwell time down to 1.75 ms/pixel and a spatial resolution of 1 µm. This is x10-100 faster than reported in previous studies.