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A computational aperture stop for optical fiber bundles

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A computational aperture stop for optical fiber bundles

Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

作者： Orth, Antony Ploschner, Martin Wilson, Emma R. Maksymov, Ivan Gibson, Brant C. Computer Vision and Graphics Group National Research Council of Canada OttawaON Canada School of Information Technology and Electrical Engineering University of Queensland BrisbaneQLD Australia ARC Centre of Excellence for Nanoscale BioPhotonics RMIT University MelbourneVIC Australia Centre for Micro-Photonics Swinburne University of Technology HawthornVIC Australia

Optical fiber bundles are flexible image conduits used for endoscopy. We will discuss how the angular sensitivity of the bundle’s waveguiding cores can be leveraged for computational imaging. © 2020 The Authors.

ISBN: (纸本)9781557528209

关键词： Computational Imaging

Feasibility of the J-PET to monitor range of therapeutic proton beams

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arXiv 2023年

作者： Baran, Jakub Borys, Damian Brzeziński, Karol Gajewski, Jan Silarski, Michal Chug, Neha Coussat, Aurélien Czerwiński, Eryk Dadgar, Meysam Dulski, Kamil Eliyan, Kavya V. Gajos, Aleksander Kacprzak, Krzysztof Kaplon, Lukasz Klimaszewski, Konrad Konieczka, Pawel Kopeć, Renata Korcyl, Grzegorz Kozik, Tomasz Krzemień, Wojciech Kumar, Deepak Lomax, Antony J. McNamara, Keegan Niedźwiecki, Szymon Olko, Pawel Panek, Dominik Parzych, Szymon del Rio, Elena Perez Raczyński, Lech Simbarashe, Moyo Sharma, Sushil Shivani Shopa, Roman Y. Skóra, Tomasz Skurzok, Magdalena Stasica, Paulina Stępień, Ewa L. Tayefi, Keyvan Tayefi, Faranak Weber, Damien C. Winterhalter, Carla Wiślicki, Wojciech Moskal, Pawel Rucinski, Antoni Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland Total-Body Jagiellonian-PET Laboratory Jagiellonian University Kraków30-348 Poland Center for Theranostics Jagiellonian University Kraków Poland Silesian University of Technology Department of Systems Biology and Engineering Gliwice Poland Biotechnology Centre Silesian University of Technology Gliwice Poland Institute of Nuclear Physics Polish Academy of Sciences Kraków31-342 Poland CSIC-UV Valencia Spain Department of Complex Systems National Centre for Nuclear Research Otwock-Świerk Poland High Energy Physics Division National Centre for Nuclear Research Otwock-Świerk Poland Department of Radiation Oncology Inselspital Bern University Hospital University of Bern Bern Switzerland Department of Radiation Oncology University Hospital of Zürich Zürich Switzerland Centre for Proton Therapy Paul Scherrer Institute Villigen Switzerland Physics Department ETH Zürich Zürich Switzerland National Oncology Institute National Research Institute Krakow Branch Krakow Poland

Objective: The aim of this work is to investigate the feasibility of the Jagiellonian Positron Emission Tomography (J-PET) scanner for intra-treatment proton beam range monitoring. Approach: The Monte Carlo simulation studies with GATE and PET image reconstruction with CASToR were performed in order to compare six J-PET scanner geometries (three dual-heads and three cylindrical). We simulated proton irradiation of a PMMA phantom with a Single Pencil Beam (SPB) and Spread-Out Bragg Peak (SOBP) of various ranges. The sensitivity and precision of each scanner were calculated, and considering the setup's cost-effectiveness, we indicated potentially optimal geometries for the J-PET scanner prototype dedicated to the proton beam range assessment. Main results: The investigations indicate that the double-layer cylindrical and triple-layer double-head configurations are the most promising for clinical application. We found that the scanner sensitivity is of the order of 10-5 coincidences per primary proton, while the precision of the range assessment for both SPB and SOBP irradiation plans was found below 1 mm. Among the scanners with the same number of detector modules, the best results are found for the triple-layer dual-head geometry. Significance: We performed simulation studies demonstrating that the feasibility of the J-PET detector for PET-based proton beam therapy range monitoring is possible with reasonable sensitivity and precision enabling its pre-clinical tests in the clinical proton therapy environment. Considering the sensitivity, precision and cost-effectiveness, the double-layer cylindrical and triple-layer dual-head J-PET geometry configurations seem promising for the future clinical application. Experimental tests are needed to confirm these findings. © 2023, CC BY.

关键词： Proton beams

Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

Integrated decision support model for selection of industrial wastewater treatment technologies

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Expert Systems with Applications 2025年 287卷

作者： Liu, Zhe Letchmunan, Sukumar Deveci, Muhammet Senapati, Tapan Pamucar, Dragan College of Mathematics and Computer Xinyu University Xinyu338004 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul34942 Turkey Royal School of Mines Imperial College London LondonSW7 2AZ United Kingdom Department of Information Technologies Western Caspian University Baku1001 Azerbaijan School of Mathematics and Statistics Southwest University Chongqing400715 China Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamil Nadu Chennai602105 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade11000 Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan City320315 Taiwan Industrial Engineering Department Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

In this paper, we introduce a novel decision support model based on cubic Fermatean fuzzy sets (CuFFSs), combining stepwise weight assessment ratio analysis (SWARA), vector similarity measures (VSM) and the extended Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), named CuFF-SWARA-VSM-VIKOR method. The proposed method is applied to the selection of industrial wastewater treatment technologies (IWTTs), addressing multi-criteria decision-making (MCDM) problem under uncertainty. We first propose four vector similarity measures (i.e. Tanimoto, Sørensen, cosine and hybrid similarity measures) for CuFFSs and validate their effectiveness through theoretical proofs. Subsequently, the VIKOR method is extended to address the MCDM problem involving CuFFSs, with criteria weights derived from subjective weights via SWARA and objective weights based on proposed measures. Finally, a case study demonstrates the effectiveness of the proposed framework, providing decision-makers with a reliable ranking of alternatives. According to the results, among the six criteria considered, the most suitable alternatives to be selected as IWTTs are Reverse osmosis (0.0000), Membrane bioreactor (0.3372), Electrocoagulation (0.3969), Activated sludge process (0.7621) and Constructed wetlands (1.0000). Additionally, comparisons with existing methods are performed to verify the robustness of the proposed method. © 2025 Elsevier Ltd

关键词： Feedwater treatment

Developing internet of things (IoT) based smart government model for Nepal

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AIP Conference Proceedings 2025年第1期3224卷

作者： Youba Raj Poudyal Gajendra Sharma Lok Bijaya Adhikari Anup Basnet Chetry Sudan Jha Department of Computer Science and Engineering Kathmandu University Kavre Nepal Graduate School of Science and Technology Mid-West University Surkhet Nepal National Earthquake Monitoring and Research Centre Department of Mines and Geology Kathmandu Nepal

The Internet of Things (IoT) is a network of tangible objects or entities that are connected with electronic components, software, sensors, and actuators. These components allow for communication, interaction, and the exchange of data amongst these objects. Governments can use immense volumes of data generated by users, sensors, and networks utilize in IoT systems to develop applications and gain knowledge. Consequently, IoT has the potential to facilitate the creation of important services for individuals, enterprises, and governmental organizations in several industries such areas include transportation, energy, healthcare, education, and public safety. The individual who is visiting essay acknowledges the difficulties associated with implementing and using IoT technology in the public sector, as part of the special edition focusing on the Internet of Things (IoT) for the implementation of efficient and intelligent government systems. Additionally, it proposes a comprehensive research approach that integrates Internet of Things (IoT) components to facilitate the evolution of smart governance. The report examines the difficulties encountered while adopting IoT-based efficient administration in the government sector and suggests a model that includes IoT components. The report also identifies the deficiencies in comprehending the phenomena and proposes potential areas for future investigation, such as domain-specific inquiries, the implementation and assessment of IoT systems, and the resolution of specific obstacles that impede the effective deployment of IoT systems in Nepal.

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Roadmap on Neuromorphic Photonics

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arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations

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arXiv 2024年

作者： Lee, Jae Yong Schotthöfer, Steffen Xiao, Tianbai Krumscheid, Sebastian Frank, Martin Center for Artificial Intelligence and Natural Sciences Korea Institute for Advanced Study Seoul Korea Republic of Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States State Key Laboratory of High Temperature Gas Dynamics Centre for Interdisciplinary Research in Fluids Institute of Mechanics Chinese Academy of Sciences Beijing China School of Engineering Science University of Chinese Academy of Sciences Beijing China Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany

This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a differential advection operator and an integral collision operator, which is a powerful yet expensive mathematical model for interacting particle systems with ample applications, e.g., in radiation transport. We investigate the capabilities of the Deep Operator network (DeepONet) approach to modelling the high dimensional collision operator of the linear kinetic equation. This integral operator has crucial analytical structures that a surrogate model, e.g., a DeepONet, needs to preserve to enable meaningful physical simulation. We propose several DeepONet modifications to encapsulate essential structural properties of this integral operator in a DeepONet model. To be precise, we adapt the architecture of the trunk-net so the DeepONet has the same collision invariants as the theoretical kinetic collision operator, thus preserving conserved quantities, e.g., mass, of the modeled many-particle system. Further, we propose an entropy-inspired data-sampling method tailored to train the modified DeepONet surrogates without requiring an excessive expensive simulation-based data generation. © 2024, CC BY-NC-SA.

关键词： Kinetic energy

eFAirWrite: Bringing energy efficient text entry to next generation smart devices

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Expert Systems with Applications 2025年 289卷

作者： Ghaffar, Muhammad Mohsin Abdullah, Ahmad Younas, Junaid Rybalkin, Vladimir Ney, Jonas Lukowicz, Paul Wehn, Norbert Microelectronic Systems Design Research Group University of Kaiserslautern-Landau Kaiserslautern67663 Germany School of Electrical Engineering & Computer Science National University of Sciences & Technology Islamabad44000 Pakistan German Research Centre for Artificial Intelligence Kaiserslautern67663 Germany

Text entry tasks for emerging wireless Augmented Reality (AR) and Virtual Reality (VR) devices can be realized in many ways, one of the most promising methods is based on an Inertial Measurement Unit (IMU) sensor, which resembles a human writing style and is called air-writing. An existing air-writing Deep Neural Network (DNN) based algorithm called FAirWrite achieves state-of-the-art accuracy. However, this algorithm is optimized only for accuracy without considering the implementation constraints. State-of-the-art implementation executes the algorithm in a cloud, which is associated with large communication latency;privacy issues with respect to data transfer;and a need for reliable internet connection. A solution that tackles all three challenges is executing the algorithm locally at the edge in the closest proximity to the sensor. However, inference at the edge is challenging due to limited memory and computing resources, which can impact accuracy and increase latency. Additionally, battery-powered edge devices must adhere to strict power and energy consumption limits. All these constraints collectively restrict the model size and computational complexity that can be deployed near the sensor. In this work, we explore various optimizations required to enable state-of-the-art FAirWrite algorithm for a real-world deployment scenario, i.e. executing on an edge device. We perform a multi-layer design-space exploration considering multiple levels of design hierarchy spanning from optimizations applied on algorithmic level down to hardware level, considering various deployment run-times and edge platforms including embedded micro-controller, embedded Central Processing Unit (CPU), embedded Graphics Processing Unit (GPU), Neural Processing Unit (NPU), and Field-Programmable Gate Array (FPGA). The complexity reduction optimizations result in a 37× smaller eFAirWrite model without any accuracy degradation. We propose and implement a custom hardware architecture of the alg

关键词： Integrated circuit design

Technologies for Ocean Sensing project developments in imaging and sensing

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Technologies for Ocean Sensing project developments in imagi...

OCEANS

作者： Matthew Mowlem Fillipa Carvalho Rudolf Hanz Ehsan Abdi Camile Catalano Katherine Hartle Mougiou Rana Abualhaija Susan Evans Dan Hayes Ahmed Alrefaey Reuben Forrester Jean-Olivier Irisson Martin Arundel Sarah Giering Kevin Köser Nathan Briggs Electra Gizeli Patricia Lopez-Garcia Wahida Bhuiyan Peter Glynne-Jones Jake Ludgate Jonathan Butement Lionel Guidi Miguel Massot Campos Christopher L. Cardwell Weili Guo Jon McQuillan Marina Montresor Efstathios Papadimitriou Allison Schaap C. Mark Moore Matthew Patey Nina Schuback Hywel Morgan Marc Picheral Blair Thornton Mojtaba Masoudi Fiona Regan Martha Valiadi Caroline Murphy Julie Robidart John Walk Andrew Morris Fabrizio Siracusa Xiangyu Weng David Nakath Dan Spenser Euan Wilson Kevin Oxborough National Oceanography Centre Ocean Technology and Engineering Group Southampon UK National Oceanography Centre Ocean Biogeochemistry and Geoscience Southampon UK Cyprus Subsea Consulting and Services Nicosia Cyprus Laboratoire d'Oceanographie de Villefranche France Foundation for Research and Technology Hellas Institute for Molecular Biology and Biotechnology Heraklion Greece Electronics and Computer Science University of Southampton Southampton UK In Situ and Remote Intelligent Sensing University of Southampton Southampton UK School of Biotechnology Dublin City University Dublin UK Section Integrated Marine Ecology Stazione Zoologica Anton Dohrn Napoli Italy School of Ocean and Earth Science University of Southampton Southampton UK Chelsea Technologies Group London UK School of Chemical Sciences Dublin City University Dublin UK Head of Research Minderoo Foundation Perth Western Australia Australia Helmholtz Centre for Ocean Research Oceanic Machine Vision Group GEOMAR Kiel Germany

The TechOceanS project is developing new remote ocean sensing technology supporting wider ocean measurement and a drive to net zero. The project will deliver 5 new sensor classes for biogeochemistry, biology and ecosystems addressing 10 of 19 EOVs, 31 of 73 subvariables, 6 of 9 MSFD targets together with microplastics and a range of biotoxins and contaminants. It will also develop a new image processing workflow for extracting EOVs (9) and MSFD (6) and litter measurements from images. These innovations concentrate on key capability gaps in ocean observing from non-ship systems with a focus on low-cost per measurement through minimised instrument and deployment costs. This paper gives a brief overview of the technologies, and were possible, because of progress or protection of intellectual property, details of our approaches and early results.

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