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  • 1
    Online Resource
    Online Resource
    Developing technological synergies between deep-sea and space research
    University of California Press ; 2022
    In:  Elementa: Science of the Anthropocene Vol. 10, No. 1 ( 2022-02-08)
    Details
    In: Elementa: Science of the Anthropocene, University of California Press, Vol. 10, No. 1 ( 2022-02-08)
    Abstract: Recent advances in robotic design, autonomy and sensor integration create solutions for the exploration of deep-sea environments, transferable to the oceans of icy moons. Marine platforms do not yet have the mission autonomy capacity of their space counterparts (e.g., the state of the art Mars Perseverance rover mission), although different levels of autonomous navigation and mapping, as well as sampling, are an extant capability. In this setting their increasingly biomimicked designs may allow access to complex environmental scenarios, with novel, highly-integrated life-detecting, oceanographic and geochemical sensor packages. Here, we lay an outlook for the upcoming advances in deep-sea robotics through synergies with space technologies within three major research areas: biomimetic structure and propulsion (including power storage and generation), artificial intelligence and cooperative networks, and life-detecting instrument design. New morphological and material designs, with miniaturized and more diffuse sensor packages, will advance robotic sensing systems. Artificial intelligence algorithms controlling navigation and communications will allow the further development of the behavioral biomimicking by cooperating networks. Solutions will have to be tested within infrastructural networks of cabled observatories, neutrino telescopes, and off-shore industry sites with agendas and modalities that are beyond the scope of our work, but could draw inspiration on the proposed examples for the operational combination of fixed and mobile platforms.
    Type of Medium: Online Resource
    ISSN: 2325-1026
    URL: Article
    DOI: 10.1525/elementa.2021.00064
    Language: English
    Publisher: University of California Press
    Publication Date: 2022
    detail.hit.zdb_id: 2745461-7
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  • 2
    Online Resource
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    Research Trends and Future Perspectives in Marine Biomimicking Robotics
    MDPI AG ; 2021
    In:  Sensors Vol. 21, No. 11 ( 2021-05-29), p. 3778-
    Details
    In: Sensors, MDPI AG, Vol. 21, No. 11 ( 2021-05-29), p. 3778-
    Abstract: Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950–2020), evidencing a sharp research increase in 2003–2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s21113778
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2052857-7
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  • 3
    Online Resource
    Online Resource
    DataSheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 9 ( 2022-9-9)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 9 ( 2022-9-9)
    Abstract: The Norway lobster, Nephrops norvegicus , supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that “1 burrow system = 1 animal”, due to the territorial behavior of N. norvegicus . Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ , as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    URL: Article
    DOI: 10.3389/fmars.2022.969071
    DOI: 10.3389/fmars.2022.969071.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2757748-X
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  • 4
    Online Resource
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    The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms
    MDPI AG ; 2020
    In:  Sensors Vol. 20, No. 6 ( 2020-03-21), p. 1751-
    Details
    In: Sensors, MDPI AG, Vol. 20, No. 6 ( 2020-03-21), p. 1751-
    Abstract: Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs] , and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s20061751
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2052857-7
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  • 5
    Online Resource
    Online Resource
    Video_2.mp4
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 9 ( 2022-8-26)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 9 ( 2022-8-26)
    Abstract: Ocean observatories collect large volumes of video data, with some data archives now spanning well over a few decades, and bringing the challenges of analytical capacity beyond conventional processing tools. The analysis of such vast and complex datasets can only be achieved with appropriate machine learning and Artificial Intelligence (AI) tools. The implementation of AI monitoring programs for animal tracking and classification becomes necessary in the particular case of deep-sea cabled observatories, as those operated by Ocean Networks Canada (ONC), where Petabytes of data are now collected each and every year since their installation. Here, we present a machine-learning and computer vision automated pipeline to detect and count sablefish ( Anoplopoma fimbria ), a key commercially exploited species in the N-NE Pacific. We used 651 hours of video footage obtained from three long-term monitoring sites in the NEPTUNE cabled observatory, in Barkley Canyon, on the nearby slope, and at depths ranging from 420 to 985 m. Our proposed AI sablefish detection and classification pipeline was tested and validated for an initial 4.5 month period (Sep 18 2019-Jan 2 2020), and was a first step towards validation for future processing of the now decade-long video archives from Barkley Canyon. For the validation period, we trained a YOLO neural network on 2917 manually annotated frames containing sablefish images to obtain an automatic detector with a 92% Average Precision (AP) on 730 test images, and a 5-fold cross-validation AP of 93% (± 3.7%). We then ran the detector on all video material (i.e., 651 hours from a 4.5 month period), to automatically detect and annotate sablefish. We finally applied a tracking algorithm on detection results, to approximate counts of individual fishes moving on scene and obtain a time series of proxy sablefish abundance. Those proxy abundance estimates are among the first to be made using such a large volume of video data from deep-sea settings. We discuss our AI results for application on a decade-long video monitoring program, and particularly with potential for complementing fisheries management practices of a commercially important species.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fmars.2022.842946
    DOI: 10.3389/fmars.2022.842946.s001
    DOI: 10.3389/fmars.2022.842946.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2757748-X
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  • 6
    Online Resource
    Online Resource
    Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 8 ( 2022-1-12)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 8 ( 2022-1-12)
    Abstract: Deep-sea ecosystems are reservoirs of biodiversity that are largely unexplored, but their exploration and biodiscovery are becoming a reality thanks to biotechnological advances (e.g., omics technologies) and their integration in an expanding network of marine infrastructures for the exploration of the seas, such as cabled observatories. While still in its infancy, the application of environmental DNA (eDNA) metabarcoding approaches is revolutionizing marine biodiversity monitoring capability. Indeed, the analysis of eDNA in conjunction with the collection of multidisciplinary optoacoustic and environmental data, can provide a more comprehensive monitoring of deep-sea biodiversity. Here, we describe the potential for acquiring eDNA as a core component for the expanding ecological monitoring capabilities through cabled observatories and their docked Internet Operated Vehicles (IOVs), such as crawlers. Furthermore, we provide a critical overview of four areas of development: (i) Integrating eDNA with optoacoustic imaging; (ii) Development of eDNA repositories and cross-linking with other biodiversity databases; (iii) Artificial Intelligence for eDNA analyses and integration with imaging data; and (iv) Benefits of eDNA augmented observatories for the conservation and sustainable management of deep-sea biodiversity. Finally, we discuss the technical limitations and recommendations for future eDNA monitoring of the deep-sea. It is hoped that this review will frame the future direction of an exciting journey of biodiscovery in remote and yet vulnerable areas of our planet, with the overall aim to understand deep-sea biodiversity and hence manage and protect vital marine resources.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    DOI: 10.3389/fmars.2021.797140
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2757748-X
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  • 7
    Online Resource
    Online Resource
    New High-Tech Flexible Networks for the Monitoring of Deep-Sea Ecosystems
    American Chemical Society (ACS) ; 2019
    In:  Environmental Science & Technology Vol. 53, No. 12 ( 2019-06-18), p. 6616-6631
    Details
    In: Environmental Science & Technology, American Chemical Society (ACS), Vol. 53, No. 12 ( 2019-06-18), p. 6616-6631
    Type of Medium: Online Resource
    ISSN: 0013-936X , 1520-5851
    URL: Article
    DOI: 10.1021/acs.est.9b00409
    RVK:
    AR 10100
    Language: English
    Publisher: American Chemical Society (ACS)
    Publication Date: 2019
    detail.hit.zdb_id: 280653-8
    detail.hit.zdb_id: 1465132-4
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