1,072 research outputs found
Satellite remote sensing of surface winds, waves, and currents: Where are we now?
This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well
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Black holes and how to find them : on the detection of compact objects at several scales
This dissertation is structured in four chapters. First, the reader is introduced to current detection methods for astrophysical black holes at different scales. The following three chapters correspond to different research projects regarding black hole detection. The second chapter presents the kinematic detection of a supermassive black hole at the center of the dwarf spherical galaxy Leo I. Chapter three presents a novel idea for the detection of supermassive black hole binaries using the effect their gravitational waves produce on lower frequency gravitational waves in our vicinity. Finally, chapter four describes the spectroscopic follow-up of LIGO-Virgo-Kagra gravitational wave sources conducted using the Hobby Eberly Telescope at McDonald Observatory.Physic
Polarimetric airborne scientific instrument, mark 2, an iceāsounding airborne synthetic aperture radar for subglacial 3D imagery
Polarimetric Airborne Scientific INstrument, mark 2 (PASIN2) is a 150 MHz coherent pulsed radar with the purpose of deep ice sounding for bedrock, subglacial channels and ice-water interface detection in Antarctica. It is designed and operated by the British Antarctic Survey from 2014. With multiple antennas, oriented along and across-track, for transmission and reception, it enables polarimetric 3D estimation of the ice base with a single pass, reducing the gridding density of the survey paths. The off-line data processing stream consists of channel calibration; 2D synthetic aperture radar (SAR) imaging based on back-projection, for along-track and range dimensions; and finally, a direction of arrival estimation (DoA) of the remaining across-track angle, by modifying the non-linear MUSIC algorithm. Calibration flights, during the Antarctic Summer campaigns in 16/17 and 19/20 seasons, assessed and validated the instrument and processing performances. Imaging flights over ice streams and ice shelves close to grounding lines demonstrate the 3D sensing capabilities. By resolving directional ambiguities and accounting for reflector across-track location, the true ice thickness and bed elevation are obtained, thereby removing the error of the usual assumption of vertical DoA, that greatly influence the output of flow models of ice dynamics
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Mobility classification of cattle with micro-Doppler radar
Lameness in dairy cattle is a welfare concern that negatively impacts animal productivity and farmer profitability. Micro-Doppler radar sensing has been previously suggested as a potential system for automating lameness detection in ruminants. This thesis investigates the refinement of the proposed automated system by analysing and enhancing the repeatability and accuracy of the existing scoring method in cattle mobility scoring, used to provide labels in machine learning. The main aims of the thesis were (1) to quantify the performance of the micro-Doppler radar sensing method for the assessment of mobility, (2) to characterise and validate micro-Doppler radar signatures of dairy cattle with varying degrees of gait impairment, and (3) to develop machine learning algorithms that can infer the mobility status of the animals under test from their radar signatures and support automatic contactless classification.
The first study investigated inter-assessor agreement using a 4-level system and modifications to it, as well as the impact of factors such as mobility scoring experience, confidence in scoring decisions, and video characteristics. The results revealed low levels of agreement between assessors' scores, with kappa values ranging from 0.16 to 0.53. However, after transforming and reducing the mobility scoring system levels, an improvement was observed, with kappa values ranging from 0.2 to 0.67. Subsequently, a longitudinal study was conducted using good-agreement scores as ground truth labels in supervised machine-learning models. However, the accuracy of the algorithmic models was found to be insufficient, ranging from 0.57 to 0.63. To address this issue, different labelling systems and data pre-processing techniques were explored in a cross-sectional study. Nonetheless, the inter-assessor agreement remained challenging, with an average kappa value of 0.37 (SD = 0.16), and high-accuracy algorithmic predictions remained elusive, with an average accuracy of 56.1 (SD =16.58). Finally, the algorithms' performance was tested with high-confidence labels, which consisted of only scores 0 and 3 of the AHDB system. This testing resulted in good classification accuracy (0.82), specificity (0.79), and sensitivity (0.85). This led to the proposal of a new approach to producing labels, testing vantage point changes, and improving the performance of machine learning models (average accuracy = 0.7 & SD = 0.17, average sensitivity = 0.68 & SD = 0.27, average specificity = 0.75 & SD = 0.17).
The research identified a challenge in creating high-confidence diagnostic labels for supervised machine learning-based algorithms to automate the detection and classification of lameness in dairy cows. As a result, the original goals were partially overridden, with the focus shifted to creating reliable labels that would perform well with radar data and machine learning. This point was considered necessary for smooth system development and process automation. Nevertheless, we managed to quantify the performance of the micro-Doppler radar system, partially develop the supervised machine learning algorithms, compare levels of agreement among multiple assessors, evaluate the assessment tools, assess the mobility evaluation process and gather a valuable data set which can be used as a foundation for subsequent studies. Finally, the thesis suggests changes in the assessment process to improve the prediction accuracy of algorithms based on supervised machine learning with radar data
Polarimetric airborne scientific instrument, mark 2, an iceāsounding airborne synthetic aperture radar for subglacial 3DĀ imagery
Polarimetric Airborne Scientific INstrument, mark 2 (PASIN2) is a 150 MHz coherent
pulsed radar with the purpose of deep ice sounding for bedrock, subglacial channels and
iceāwater interface detection in Antarctica. It is designed and operated by the British
Antarctic Survey from 2014. With multiple antennas, oriented along and acrossātrack, for
transmission and reception, it enables polarimetric 3D estimation of the ice base with a
single pass, reducing the gridding density of the survey paths. The offāline data processing
stream consists of channel calibration; 2D synthetic aperture radar (SAR) imaging based
on backāprojection, for alongātrack and range dimensions; and finally, a direction of
arrival estimation (DoA) of the remaining acrossātrack angle, by modifying the nonālinear
MUSIC algorithm. Calibration flights, during the Antarctic Summer campaigns in 16/17
and 19/20 seasons, assessed and validated the instrument and processing performances.
Imaging flights over ice streams and ice shelves close to grounding lines demonstrate the
3D sensing capabilities. By resolving directional ambiguities and accounting for reflector
acrossātrack location, the true ice thickness and bed elevation are obtained, thereby
removing the error of the usual assumption of vertical DoA, that greatly influence the
output of flow models of ice dynamics
BDS GNSS for Earth Observation
For millennia, human communities have wondered about the possibility of observing
phenomena in their surroundings, and in particular those affecting the Earth on which they live.
More generally, it can be conceptually defined as Earth observation (EO) and is the collection of
information about the biological, chemical and physical systems of planet Earth. It can be undertaken
through sensors in direct contact with the ground or airborne platforms (such as weather balloons and
stations) or remote-sensing technologies. However, the definition of EO has only become significant
in the last 50 years, since it has been possible to send artificial satellites out of Earthās orbit.
Referring strictly to civil applications, satellites of this type were initially designed to provide
satellite images; later, their purpose expanded to include the study of information on land
characteristics, growing vegetation, crops, and environmental pollution. The data collected are used
for several purposes, including the identification of natural resources and the production of accurate
cartography. Satellite observations can cover the land, the atmosphere, and the oceans.
Remote-sensing satellites may be equipped with passive instrumentation such as infrared or
cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are
non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the
Earthās equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and
Medium Earth Orbit (MEO), thus covering the entire Earthās surface in a certain scan time (properly
called ātemporal resolutionā), i.e., in a certain number of orbits around the Earth.
The first remote-sensing satellites were the American NASA/USGS Landsat Program;
subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing
satellite), RapidEye, the French SPOT (Satellite Pour lāObservation de laTerre), and the Canadian
RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were
dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed
system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the
Chinese BuFeng-1 and Fengyun-3 series.
Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers
worldwide for a multitude of Earth monitoring and exploration applications. On the other hand,
over the past 40 years, GNSSs have become an essential part of many human activities. As is widely
noted, there are currently four fully operational GNSSs; two of these were developed for military
purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for
civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European
Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning
System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation
Satellite System (IRNSS/NavIC), will become available in the next few years, which will have
enormous potential for scientific applications and geomatics professionals.
In addition to their traditional role of providing global positioning, navigation, and timing (PNT)
information, GNSS navigation signals are now being used in new and innovative ways. Across the
globe, new fields of scientific study are opening up to examine how signals can provide information
about the characteristics of the atmosphere and even the surfaces from which they are reflected before
being collected by a receiver.
EO researchers monitor global environmental systems using in situ and remote monitoring tools.
Their findings provide tools to support decision makers in various areas of interest, from security
to the natural environment. GNSS signals are considered an important new source of information
because they are a free, real-time, and globally available resource for the EO community
The Role of c-Myc in Regulating Cardiac Intermediary Metabolis
Background ā The heart adapts in response to stress by inducing adaptive remodelling pathways leading to cardiac hypertrophy, however this is not sustained and with continued stress, maladaptive remodelling pathways ensue, impairing cell viability and contractile function leading to heart failure. An emerging concept is that cardiac hypertrophy is paralleled by changes in cardiomyocyte metabolism, which may themselves drive cardiac hypertrophy. The proto-oncogene c-Myc is a key regulator of cancer metabolism that promotes anabolic pathways driving tumorigenesis. Interestingly, c-Myc overexpression in the heart causes hypertrophy but how this is accomplished remains unclear.Objective ā Define the functional role of c-Myc during remodelling of the adult heart with a focus on the relationship to intermediary glucose metabolism pathways that support anabolic growth in response to different types of hypertrophic stress stimuli.Methods ā Cardiac-specific c-Myc knock-out (c-Myc KO) mice were generated and subjected to different types of stress stimuli. Pathological stress was induced by abdominal aortic banding (AAB) or transverse aortic constriction (TAC) and compared to sham surgery. Physiological stress was induced by voluntary wheel running (VWR) exercise and compared with sedentary controls. Metabolic pathway activity was comprehensively assessed by optimising a stable isotope resolved metabolomics method, using stable isotope labelled glucose (13C6 glucose) in ex-vivo Langendorff perfused hearts of floxed control and cs-c-Myc KO mice.Results ā The c-Myc KO mice appear phenotypically normal and do not exhibit any changes in cardiac structure or function at baseline. When subjected to chronic pressure overload, c-Myc KO mice show a similar decline in cardiac function and a similar extent of cardiac hypertrophy as their floxed littermates. After chronic pressure overload, there is a significant decrease in 13C label incorporation into TCA metabolites and its related amino acids in the cs-c-Myc KO mice. In parallel, metabolites related to the hexosamine biosynthesis pathway show an increased 13C label incorporation after pressure overload in the floxed mice which is attenuated in the c-Myc KO mice. When subjected to regular exercise, c-Myc KO mice develop cardiac hypertrophy to the same extent as the floxed mice. Exercise causes an increase in the pentose phosphate pathway activity in the floxed mice which is mitigated in the c-Myc KO mice. Exercised floxed mice showed an increased lactate uptake and utilisation into the TCA cycle whereas c-Myc KO mice had decreased reliance on lactate.Conclusion ā Knock-out of c-Myc alters glucose contribution to the TCA cycle and affects the diversion of glycolytic intermediates into pathways of intermediary metabolism important for anabolic growth and adaptation to stress. These results provide new insight into the rewiring of glucose carbon metabolism in the hypertrophied heart that is in part driven by c-Myc. Understanding adaptive remodelling pathways that drive cardiac hypertrophy may help lead to better treatments for preventing heart failure.<br/
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