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      MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float

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          Abstract

          At 2000 m depth in the oceans, one can hear biological, seismological, meteorological, and anthropogenic activity. Acoustic monitoring of the oceans at a global scale and over long periods of time could bring important information for various sciences. The Argo project monitors the physical properties of the oceans with autonomous floats, some of which are also equipped with a hydrophone. These have a limited transmission bandwidth requiring acoustic data to be processed on board. However, developing signal processing algorithms for these instruments requires one to be an expert in embedded software. To reduce the need of such expertise, we have developed a programming language, called MeLa. The language hides several aspects of embedded software with specialized programming concepts. It uses models to compute energy consumption, processor usage, and data transmission costs early during the development of applications; this helps to choose a strategy of data processing that has a minimum impact on performances. Simulations on a computer allow for verifying the performance of the algorithms before their deployment on the instrument. We have implemented a seismic P wave detection and a blue whales D call detection algorithm with the MeLa language to show its capabilities. These are the first efforts toward multidisciplinary monitoring of the oceans, which can extend beyond acoustic applications.

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          Most cited references 48

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          Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment

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            The Internet of Things: A survey

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              Guest Editor's Introduction: Model-Driven Engineering

               D.C. Schmidt (2006)
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                26 October 2020
                November 2020
                : 20
                : 21
                Affiliations
                [1 ]Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, IRD, Géoazur, 06560 Valbonne, France; hello@ 123456geoazur.unice.fr (Y.H.); nolet@ 123456princeton.edu (G.N.)
                [2 ]Université Côte d’Azur, CNRS, I3S, 06900 Valbonne, France; bblay@ 123456unice.fr
                [3 ]Lab-STICC, UMR 6285, CNRS, ENSTA Bretagne, 29238 Brest, France; dorian.cazau@ 123456ensta-bretagne.fr
                [4 ]Département d’Informatique, Université du Québec à Montréal, Montréal, QC H3C3P8, Canada; mosser.sebastien@ 123456uqam.ca
                [5 ]Department of Geosciences, Princeton University, Princeton, NJ 08544, USA
                Author notes
                Article
                sensors-20-06081
                10.3390/s20216081
                7672633
                33114608
                00350683-a62b-4d31-acd0-821be90e3d39
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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