| publications-1961 |
Peer reviewed articles |
2023 |
Joan Maso; Alaitz Zabala; Ivette Serral |
Earth Observations for Sustainable Development Goals |
Remote Sensing |
10.3390/rs15102570 |
Uncategorized |
Precipitation & Ecological Systems |
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In 2015, the United Nations adopted the 17 Sustainable Development Goals (SDGs), aiming at ending poverty, protecting the planet, and ensuring peace and prosperity [...] |
101004157 |
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| publications-1962 |
Peer reviewed articles |
2022 |
Fotis Panetsos; Panagiotis Rousseas; George Karras; Charalampos Bechlioulis; Kostas J. Kyriakopoulos |
A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle |
Sustainability |
10.3390/su14116502 |
Simulation & Modeling |
River Basins |
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In this paper, we present a vision-aided motion planning and control framework for the efficient monitoring and surveillance of water surfaces using an Unmanned Aerial Vehicle (UAV). The ultimate goal of the proposed strategy is to equip the UAV with the necessary autonomy and decision-making capabilities to support First Responders during emergency water contamination incidents. Toward this direction, we propose an end-to-end solution, based on which the First Responder indicates visiting and landing waypoints, while the envisioned strategy is responsible for the safe and autonomous navigation of the UAV, the refinement of the way-point locations that maximize the visible water surface area from the onboard camera, as well as the on-site refinement of the appropriate landing region in harsh environments. More specifically, we develop an efficient waypoint-tracking motion-planning scheme with guaranteed collision avoidance, a local autonomous exploration algorithm for refining the way-point location with respect to the areas visible to the droneās camera, water, a vision-based algorithm for the on-site area selection for feasible landing and finally, a model predictive motion controller for the landing procedure. The efficacy of the proposed framework is demonstrated via a set of simulated and experimental scenarios using an octorotor UAV. |
883484 |
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| publications-1963 |
Peer reviewed articles |
2024 |
Leonidas Alagialoglou, Ioannis Manakos, Eleftherios Katsikis, Sergiy Medinets, Yevgen Gazyetov, Volodymyr Medinets, Anastasios Delopoulos |
Machine Learning for Identifying Emergent and Floating Aquatic Vegetation from Space: A Case Study in the Dniester Delta, Ukraine |
SN Computer Science. A Springer Nature Journal |
10.1007/s42979-024-02873-7 |
AI & Machine Learning |
Precipitation & Ecological Systems |
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AbstractMonitoring aquatic vegetation, including both floating and emergent types, plays a crucial role in understanding the dynamics of freshwater ecosystems. Our research focused on the Lower Dniester Basin in Southern Ukraine, covering approximately 1800 square kilometers of steppe plains and wetlands. We applied traditional machine learning algorithms, specifically random forest and boosting trees, to analyze Sentinel-2 satellite imagery for segmenting aquatic vegetation into emergent and floating types. Our methodology was validated against detailed in-situ field measurements collected annually over a 5-year study period. The machine learning classifiers achieved an F1-score of 0.88Ā Ā±Ā 0.03 in classifying floating vegetation, outperforming our previously suggested histogram-based thresholding methodology for the same task. While emergent vegetation and open water were easily identifiable from satellite imagery, the robustness and temporal transferability of our methodology included accurately delineating floating vegetation as well. Additionally, we explored the significance of various features through the Minimum Redundancy - Maximum Relevance algorithm. This study highlights advancements in aquatic vegetation mapping and demonstrates a valuable tool for ecological monitoring and future research endeavors. |
101004157 |
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| publications-1964 |
Peer reviewed articles |
2021 |
Alexandra Canciu; Mihaela Tertis; Oana Hosu; Andreea Cernat; Cecilia Cristea; Florin Graur |
Modern Analytical Techniques for Detection of Bacteria in Surface and Wastewaters |
Sustainability |
10.3390/su13137229 |
Data Management & Analytics |
Groundwater |
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Contamination of surface waters with pathogens as well as all diseases associated with such events are a significant concern worldwide. In recent decades, there has been a growing interest in developing analytical methods with good performance for the detection of this category of contaminants. The most important analytical methods applied for the determination of bacteria in waters are traditional ones (such as bacterial culturing methods, enzyme-linked immunoassay, polymerase chain reaction, and loop-mediated isothermal amplification) and advanced alternative methods (such as spectrometry, chromatography, capillary electrophoresis, surface-enhanced Raman scattering, and magnetic field-assisted and hyphenated techniques). In addition, optical and electrochemical sensors have gained much attention as essential alternatives for the conventional detection of bacteria. The large number of available methods have been materialized by many publications in this field aimed to ensure the control of water quality in water resources. This study represents a critical synthesis of the literature regarding the latest analytical methods covering comparative aspects of pathogen contamination of water resources. All these aspects are presented as representative examples, focusing on two important bacteria with essential implications on the health of the population, namely Pseudomonas aeruginosa and Escherichia coli. |
883484 |
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| publications-1965 |
Peer reviewed articles |
2022 |
Alexandra Canciu; Andreea Cernat; Mihaela Tertis; Silvia Botarca; Madalina Adriana Bordea; Joseph Wang; Cecilia Cristea |
Proof of Concept for the Detection with Custom Printed Electrodes of Enterobactin as a Marker of Escherichia coli |
International Journal of Molecular Sciences, Vol 23, Iss 17, p 9884 (2022) |
10.3390/ijms23179884 |
Uncategorized |
Wastewater Treatment Plants |
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The rapid and decentralized detection of bacteria from biomedical, environmental, and food samples has the capacity to improve the conventional protocols and to change a predictable outcome. Identifying new markers and analysis methods represents an attractive strategy for the indirect but simpler and safer detection of pathogens that could replace existing methods. Enterobactin (Ent), a siderophore produced by Escherichia coli or other Gram-negative bacteria, was studied on different electrode materials to reveal its electrochemical fingerprintāvery useful information towards the detection of the bacteria based on this analyte. The molecule was successfully identified in culture media samples and a future goal is the development of a rapid antibiogram. The presence of Ent was also assessed in wastewater and treated water samples collected from the municipal sewage treatment plant, groundwater, and tap water. Moreover, a custom configuration printed on a medical glove was employed to detect the target in the presence of another bacterial marker, namely pyocyanin (PyoC), that being a metabolite specific of another pathogen bacterium, namely Pseudomonas aeruginosa. Such new mobile and wearable platforms offer considerable promise for rapid low-cost on-site screening of bacterial contamination. |
883484 |
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| publications-1966 |
Peer reviewed articles |
2023 |
L. Alagialoglou, I. Manakos, S. Papadopoulou, R. Chadoulis, A. Kita |
Mapping underwater aquatic vegetation using foundation models with air- and space-borne images: the case of Polyphytos Lake |
Remote Sensing |
10.3390/rs15164001 |
Simulation & Modeling |
Precipitation & Ecological Systems |
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Mapping underwater aquatic vegetation (UVeg) is crucial for understanding the dynamics of freshwater ecosystems. The advancement of artificial intelligence (AI) techniques has shown great potential in improving the accuracy and efficiency of UVeg mapping using remote sensing data. This paper presents a comparative study of the performance of classical and modern AI tools, including logistic regression, random forest, and a visual-prompt-tuned foundational model, the Segment Anything model (SAM), for mapping UVeg by analyzing air- and space-borne images in the few-shot learning regime, i.e., using limited annotations. The findings demonstrate the effectiveness of the SAM foundation model in air-borne imagery (GSD = 3ā6 cm) with an F1 score of 86.5%Ā±4.1% when trained with as few as 40 positive/negative pairs of pixels, compared to 54.0%Ā±9.2% using the random forest model and 42.8%Ā±6.2% using logistic regression models. However, adapting SAM to space-borne images (WorldView-2 and Sentinel-2) remains challenging, and could not outperform classical pixel-wise random forest and logistic regression methods in our task. The findings presented provide valuable insights into the strengths and limitations of AI models for UVeg mapping, aiding researchers and practitioners in selecting the most suitable tools for their specific applications. |
101004157 |
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| publications-1967 |
Peer reviewed articles |
2023 |
Kaian Shahateet, Francisco Navarro, Thorsten Seehaus, Johannes J. FĆ¼rst, Matthias H. Braun |
Estimating ice discharge of the Antarctic Peninsula using different ice-thickness datasets |
Annals of Glaciology |
10.1017/aog.2023.67 |
AI & Machine Learning |
Precipitation & Ecological Systems |
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AbstractThe Antarctic Peninsula Ice Sheet (APIS) has become a significant contributor to sea-level rise over recent decades. Accurately estimating the ice discharge from the outlet glaciers of the APIS is crucial to quantify the mass balance of the Antarctic Peninsula. We here compute the ice discharge from the outlet glaciers of the APIS north of 70${^\circ }$S for the five most widely used ice-thickness reconstructions, using a common surface velocity field and a common set of flux gates, so the differences in ice discharge can be solely attributed to the differences in ice thickness at the flux gates. The total volumetric ice discharge for 2015ā2017 ranges within 45ā141 km3 aā1, depending on the ice-thickness model, with a mean of 87 Ā± 44 km3 aā1. The substantial differences between the ice-discharge results, and a multi-model normalized root-mean-squared deviation of 0.91 for the whole data set, reveal large differences and inconsistencies between the ice-thickness models, giving an indication of the large uncertainty in the current ice-discharge estimates for the APIS. This manifests a fundamental problem of the region: the scarcity of appropriate ice-thickness measurements and the difficulty of the current models to reconstruct the ice-thickness distribution in this complex region. |
948290 |
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| publications-1968 |
Peer reviewed articles |
2022 |
Sotirios Paraskevopoulos; Patrick Smeets; Xin Tian; Gertjan Medema |
Using Artificial Intelligence to extract information on pathogen characteristics from scientific publications |
International Journal of Hygiene and Environmental Health, 245 |
10.1016/j.ijheh.2022.114018 |
Data Management & Analytics |
Precipitation & Ecological Systems |
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No abstract available |
883484 |
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| publications-1969 |
Peer reviewed articles |
2023 |
Fotis Panetsos, George C. Karras, Kostas J. Kyriakopoulos, Odysseas Oikonomides, Panayiotis Kolios, Demetrios Eliades, Christos Panayiotou |
A motion control framework for autonomous water sampling and swing-free transportation of a multirotor UAV with a cable-suspended mechanism |
Journal of Field Robotics |
10.1002/rob.22182 |
Simulation & Modeling |
Precipitation & Ecological Systems |
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AbstractIn this work, we present an endātoāend solution for autonomous water sampling by utilizing an unmanned aerial vehicle (UAV) with a cableāsuspended mechanism. Towards this direction, a sampling mechanism is initially designed in such a manner that the water sampling success ratio is maximized. However, the disturbances, acting on the submerged mechanism due to the water flow during the sampling procedure, impede the stabilization of the vehicle above the desired sampling position. Consequently, to achieve the precise hovering of the UAV, the vehicle's sensor suite is further augmented with a load cell, a depth sensor, an ultrasonic sensor, and a camera. The respective measurements are appropriately fused by employing an extended Kalman filter (EKF). Hence, an estimate of the disturbances is available in realātime and is incorporated into a Model Predictive Control scheme which compensates for the aforementioned disturbances and stabilizes the vehicle above the sampling location. Finally, a complete water sampling mission entails the safe and swingāfree transportation of the mechanism towards the sampling location and, then, to a position where the collected samples are postprocessed by human operators. Consequently, a model predictive controller is employed which ensures the navigation of the vehicle to the desired waypoints while minimizing the swinging motion of the mechanism. The state of the mechanism is obtained by fusing measurements provided by the load cell and the camera with an EKF. The performance of the proposed framework, which aims to address all the aspects of a water sampling mission, is demonstrated through real experiments with an octorotor. |
883484 |
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| publications-1970 |
Peer reviewed articles |
2023 |
Alexandra Canciu, Andreea Cernat, Mihaela Tertis, Florin Graur, Cecilia Cristea |
Tackling the issue of healthcare associated infections through point-of-care devices |
Trends in Analytical Chemistry |
10.1016/j.trac.2023.116983 |
Data Management & Analytics |
Precipitation & Ecological Systems |
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No abstract available |
883484 |
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