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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
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    Seasonal monitoring of deep-sea megabenthos in Barkley Canyon cold seep by internet operated vehicle (IOV)
    Public Library of Science (PLoS) ; 2017
    In:  PLOS ONE Vol. 12, No. 5 ( 2017-5-30), p. e0176917-
    Details
    In: PLOS ONE, Public Library of Science (PLoS), Vol. 12, No. 5 ( 2017-5-30), p. e0176917-
    Type of Medium: Online Resource
    ISSN: 1932-6203
    URL: Article
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    DOI: 10.1371/journal.pone.0176917
    DOI: 10.1371/journal.pone.0176917.g001
    DOI: 10.1371/journal.pone.0176917.g002
    DOI: 10.1371/journal.pone.0176917.g003
    DOI: 10.1371/journal.pone.0176917.g004
    DOI: 10.1371/journal.pone.0176917.g005
    DOI: 10.1371/journal.pone.0176917.t001
    DOI: 10.1371/journal.pone.0176917.s001
    DOI: 10.1371/journal.pone.0176917.s002
    DOI: 10.1371/journal.pone.0176917.s003
    DOI: 10.1371/journal.pone.0176917.s004
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2017
    detail.hit.zdb_id: 2267670-3
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  • 3
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    Table_1.DOCX
    Frontiers Media SA ; 2019
    In:  Frontiers in Marine Science Vol. 6 ( 2019-7-23)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 6 ( 2019-7-23)
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    URL: Article
    DOI: 10.3389/fmars.2019.00462
    DOI: 10.3389/fmars.2019.00462.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2019
    detail.hit.zdb_id: 2757748-X
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  • 4
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    Deep learning based deep-sea automatic image enhancement and animal species classification
    Springer Science and Business Media LLC ; 2023
    In:  Journal of Big Data Vol. 10, No. 1 ( 2023-03-24)
    Details
    In: Journal of Big Data, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2023-03-24)
    Abstract: The automatic classification of marine species based on images is a challenging task for which multiple solutions have been increasingly provided in the past two decades. Oceans are complex ecosystems, difficult to access, and often the images obtained are of low quality. In such cases, animal classification becomes tedious. Therefore, it is often necessary to apply enhancement or pre-processing techniques to the images, before applying classification algorithms. In this work, we propose an image enhancement and classification pipeline that allows automated processing of images from benthic moving platforms. Deep-sea (870 m depth) fauna was targeted in footage taken by the crawler “Wally” (an Internet Operated Vehicle), within the Ocean Network Canada (ONC) area of Barkley Canyon (Vancouver, BC; Canada). The image enhancement process consists mainly of a convolutional residual network, capable of generating enhanced images from a set of raw images. The images generated by the trained convolutional residual network obtained high values in metrics for underwater imagery assessment such as UIQM (~ 2.585) and UCIQE (2.406). The highest SSIM and PSNR values were also obtained when compared to the original dataset. The entire process has shown good classification results on an independent test data set, with an accuracy value of 66.44% and an Area Under the ROC Curve (AUROC) value of 82.91%, which were subsequently improved to 79.44% and 88.64% for accuracy and AUROC respectively. These results obtained with the enhanced images are quite promising and superior to those obtained with the non-enhanced datasets, paving the strategy for the on-board real-time processing of crawler imaging, and outperforming those published in previous papers.
    Type of Medium: Online Resource
    ISSN: 2196-1115
    URL: Article
    DOI: 10.1186/s40537-023-00711-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2780218-8
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  • 5
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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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  • 6
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    Innovative Vibrating Hydraulic Dredge for Striped Venus (Chamelea gallina) Fishing
    MDPI AG ; 2022
    In:  AgriEngineering Vol. 4, No. 1 ( 2022-01-06), p. 1-16
    Details
    In: AgriEngineering, MDPI AG, Vol. 4, No. 1 ( 2022-01-06), p. 1-16
    Abstract: This work proposes the experimentation of an innovative hydraulic dredge for clam fishing (Chamelea gallina) in the Adriatic Sea (Italy). This innovative gear aimed at increasing the selectivity of the typical hydraulic dredge used currently, while at the same reducing the impact on benthos through the conception, installation, and experimentation of innovative technological solutions, consisting mainly of a vibrating bottom panel on the dredge and a “warning device” on the dredge mouth. Comparative experiments of the traditional vs. the modified gear, employing two boats fishing in parallel on the northern coast of Abruzzi (Adriatic Sea) and contrasting the catch with both paired comparisons and through modelling, showed that the innovative hydraulic dredge retains fewer undersize clams while yielding similar amounts of commercial product, moreover of higher quality; at the same time, it takes on board less discard, and catches significantly less vagile fauna. In short, the innovative gear is gaining five times over a list of six parameters considered as positive and/or advantageous for the clam fishery. The results allow proposals of potential improvements to clam-fishing instruments to make the selection processes more effective while promoting a lower impacting fishery, which is essential for clam management.
    Type of Medium: Online Resource
    ISSN: 2624-7402
    URL: Article
    DOI: 10.3390/agriengineering4010001
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2946408-0
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  • 7
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    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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  • 8
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    Integrating Diel Vertical Migrations of Bioluminescent Deep Scattering Layers Into Monitoring Programs
    Frontiers Media SA ; 2021
    In:  Frontiers in Marine Science Vol. 8 ( 2021-5-28)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 8 ( 2021-5-28)
    Abstract: The deep sea (i.e., & gt;200 m depth) is a highly dynamic environment where benthic ecosystems are functionally and ecologically connected with the overlying water column and the surface. In the aphotic deep sea, organisms rely on external signals to synchronize their biological clocks. Apart from responding to cyclic hydrodynamic patterns and periodic fluctuations of variables such as temperature, salinity, phytopigments, and oxygen concentration, the arrival of migrators at depth on a 24-h basis (described as Diel Vertical Migrations; DVMs), and from well-lit surface and shallower waters, could represent a major response to a solar-based synchronization between the photic and aphotic realms. In addition to triggering the rhythmic behavioral responses of benthic species, DVMs supply food to deep seafloor communities through the active downward transport of carbon and nutrients. Bioluminescent species of the migrating deep scattering layers play a not yet quantified (but likely important) role in the benthopelagic coupling, raising the need to integrate the efficient detection and quantification of bioluminescence into large-scale monitoring programs. Here, we provide evidence in support of the benefits for quantifying and continuously monitoring bioluminescence in the deep sea. In particular, we recommend the integration of bioluminescence studies into long-term monitoring programs facilitated by deep-sea neutrino telescopes, which offer photon counting capability. Their Photo-Multiplier Tubes and other advanced optical sensors installed in neutrino telescope infrastructures can boost the study of bioluminescent DVMs in concert with acoustic backscatter and video imagery from ultra-low-light cameras. Such integration will enhance our ability to monitor proxies for the mass and energy transfer from the upper ocean into the deep-sea Benthic Boundary Layer (BBL), a key feature of the ocean biological pump and crucial for monitoring the effects of climate-change. In addition, it will allow for investigating the role of deep scattering DVMs in the behavioral responses, abundance and structure of deep-sea benthic communities. The proposed approach may represent a new frontier for the study and discovery of new, taxon-specific bioluminescence capabilities. It will thus help to expand our knowledge of poorly described deep-sea biodiversity inventories and further elucidate the connectivity between pelagic and benthic compartments in the deep-sea.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    DOI: 10.3389/fmars.2021.661809
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2757748-X
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  • 9
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    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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  • 10
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    DataSheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 9 ( 2022-8-29)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 9 ( 2022-8-29)
    Abstract: Scientific, industrial and societal needs call urgently for the development and establishment of intelligent, cost-effective and ecologically sustainable monitoring protocols and robotic platforms for the continuous exploration of marine ecosystems. Internet Operated Vehicles (IOVs) such as crawlers, provide a versatile alternative to conventional observing and sampling tools, being tele-operated, (semi-) permanent mobile platforms capable of operating on the deep and coastal seafloor. Here we present outstanding observations made by the crawler “Wally” in the last decade at the Barkley Canyon (BC, Canada, NE Pacific) methane hydrates site, as a part of the NEPTUNE cabled observatory. The crawler followed the evolution of microhabitats formed on and around biotic and/or abiotic structural features of the site (e.g., a field of egg towers of buccinid snails, and a colonized boulder). Furthermore, episodic events of fresh biomass input were observed (i.e., the mass transport of large gelatinous particles, the scavenging of a dead jellyfish and the arrival of macroalgae from shallower depths). Moreover, we report numerous faunal behaviors (i.e., sablefish rheo- and phototaxis, the behavioral reactions and swimming or resting patterns of further fish species, encounters with octopuses and various crab intra- and interspecific interactions). We report on the observed animal reactions to both natural and artificial stimuli (i.e., crawler’s movement and crawler light systems). These diverse observations showcase different capabilities of the crawler as a modern robotic monitoring platform for marine science and offshore industry. Its long deployments and mobility enable its efficiency in combining the repeatability of long-term studies with the versatility to opportunistically observe rarely seen incidents when they occur, as highlighted here. Finally, we critically assess the empirically recorded ecological footprint and the potential impacts of crawler operations on the benthic ecosystem of the Barkley Canyon hydrates site, together with potential solutions to mitigate them into the future.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    URL: Article
    DOI: 10.3389/fmars.2022.833617
    DOI: 10.3389/fmars.2022.833617.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2757748-X
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