64 research outputs found

    Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR

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    In the context of climate change, alternatives to fossil energies need to be used as much as possible to produce electricity. Hydroelectric power generation through the utilisation of dams stands out as an exemplar of highly effective methodologies in this endeavour. Various monitoring sensors can be installed with different characteristics w.r.t. spatial resolution, temporal resolution and accuracy to assess their safe usage. Among the array of techniques available, it is noteworthy that ground-based synthetic aperture radar (GB-SAR) has not yet been widely adopted for this purpose. Despite its remarkable equilibrium between the aforementioned attributes, its sensitivity to atmospheric disruptions, specific acquisition geometry, and the requisite for phase unwrapping collectively contribute to constraining its usage. Several processing strategies are developed in this thesis to capitalise on all the opportunities of GB-SAR systems, such as continuous, flexible and autonomous observation combined with high resolutions and accuracy. The first challenge that needs to be solved is to accurately localise and estimate the azimuth of the GB-SAR to improve the geocoding of the image in the subsequent step. A ray tracing algorithm and tomographic techniques are used to recover these external parameters of the sensors. The introduction of corner reflectors for validation purposes confirms a significant error reduction. However, for the subsequent geocoding, challenges persist in scenarios involving vertical structures due to foreshortening and layover, which notably compromise the geocoding quality of the observed points. These issues arise when multiple points at varying elevations are encapsulated within a singular resolution cell, posing difficulties in pinpointing the precise location of the scattering point responsible for signal return. To surmount these hurdles, a Bayesian approach grounded in intensity models is formulated, offering a tool to enhance the accuracy of the geocoding process. The validation is assessed on a dam in the black forest in Germany, characterised by a very specific structure. The second part of this thesis is focused on the feasibility of using GB-SAR systems for long-term geodetic monitoring of large structures. A first assessment is made by testing large temporal baselines between acquisitions for epoch-wise monitoring. Due to large displacements, the phase unwrapping can not recover all the information. An improvement is made by adapting the geometry of the signal processing with the principal component analysis. The main case study consists of several campaigns from different stations at Enguri Dam in Georgia. The consistency of the estimated displacement map is assessed by comparing it to a numerical model calibrated on the plumblines data. It exhibits a strong agreement between the two results and comforts the usage of GB-SAR for epoch-wise monitoring, as it can measure several thousand points on the dam. It also exhibits the possibility of detecting local anomalies in the numerical model. Finally, the instrument has been installed for continuous monitoring for over two years at Enguri Dam. An adequate flowchart is developed to eliminate the drift happening with classical interferometric algorithms to achieve the accuracy required for geodetic monitoring. The analysis of the obtained time series confirms a very plausible result with classical parametric models of dam deformations. Moreover, the results of this processing strategy are also confronted with the numerical model and demonstrate a high consistency. The final comforting result is the comparison of the GB-SAR time series with the output from four GNSS stations installed on the dam crest. The developed algorithms and methods increase the capabilities of the GB-SAR for dam monitoring in different configurations. It can be a valuable and precious supplement to other classical sensors for long-term geodetic observation purposes as well as short-term monitoring in cases of particular dam operations

    Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group

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    This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting

    Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

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    With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

    The Fifteenth Marcel Grossmann Meeting

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    The three volumes of the proceedings of MG15 give a broad view of all aspects of gravitational physics and astrophysics, from mathematical issues to recent observations and experiments. The scientific program of the meeting included 40 morning plenary talks over 6 days, 5 evening popular talks and nearly 100 parallel sessions on 71 topics spread over 4 afternoons. These proceedings are a representative sample of the very many oral and poster presentations made at the meeting.Part A contains plenary and review articles and the contributions from some parallel sessions, while Parts B and C consist of those from the remaining parallel sessions. The contents range from the mathematical foundations of classical and quantum gravitational theories including recent developments in string theory, to precision tests of general relativity including progress towards the detection of gravitational waves, and from supernova cosmology to relativistic astrophysics, including topics such as gamma ray bursts, black hole physics both in our galaxy and in active galactic nuclei in other galaxies, and neutron star, pulsar and white dwarf astrophysics. Parallel sessions touch on dark matter, neutrinos, X-ray sources, astrophysical black holes, neutron stars, white dwarfs, binary systems, radiative transfer, accretion disks, quasars, gamma ray bursts, supernovas, alternative gravitational theories, perturbations of collapsed objects, analog models, black hole thermodynamics, numerical relativity, gravitational lensing, large scale structure, observational cosmology, early universe models and cosmic microwave background anisotropies, inhomogeneous cosmology, inflation, global structure, singularities, chaos, Einstein-Maxwell systems, wormholes, exact solutions of Einstein's equations, gravitational waves, gravitational wave detectors and data analysis, precision gravitational measurements, quantum gravity and loop quantum gravity, quantum cosmology, strings and branes, self-gravitating systems, gamma ray astronomy, cosmic rays and the history of general relativity

    ATHENA Research Book, Volume 2

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    ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of Orléans, University of Siegen, Hellenic Mediterranean University, Niccolò Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-Skłodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers

    Quantum Darwinism and Friends

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    In honor of Wojciech Zurek’s 70th birthday, this Special Issue is dedicated to recent advances in our understanding the emergence of classical reality, and pays tribute to Zurek’s seminal contributions to our understanding of the Universe. To this end, “Quantum Darwinism and Friends” collects articles that make sense of the apparent chasm between quantum weirdness and classical perception, and provides a snapshot of this fundamental, exciting, and vivid field of theoretical physics

    PROBING FUNDAMENTAL PHYSICS WITH GRAVITATIONAL WAVES FROM INSPIRALING BINARY SYSTEMS

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    The mergers of black holes and/or neutron stars in binary systems produce the most extreme gravitational environments in the local universe. The first direct detections of gravitational waves by Advanced LIGO and Virgo provide unprecedented observational access to the highly dynamical, strong-curvature regime of gravity. These measurements allow us to test Einstein’s theory of General Relativity in this extreme regime. This thesis examines how the gravitational-wave signal produced during the inspiral—the earliest phase of a binary’s coalescence—can better inform our understanding of the fundamental nature of gravity. My work addressing this topic is comprised of two major components. First, I examine the behavior of binary black-hole and neutron-star systems in various possible extensions of General Relativity, constructing analytic models of their orbital motion and gravitational waveform—their gravitational-wave signature—during their inspiral. The majority of alternative theories I consider modify General Relativity by introducing a new scalar component of gravity. In many of these theories, standard perturbative techniques are used to model the inspiral of binary systems. However, I also examine in depth the non-perturbative phenomenon of scalarization for which such methods fail. I show that this phenomenon occurs due to a second-order phase transition in the strong-gravity regime and develop an analytic framework to model the effect across a range of alternative theories of gravity. The other component of this thesis is the development of a statistical infrastructure suitable for testing General Relativity using gravitational-wave observations. I adopt a more flexible and modular approach than existing alternatives, allowing this infrastructure to be immediately applied with a wide range of waveform models. In work done in conjunction with the LIGO Scientific and Virgo Collaborations, I use this statistical framework to place bounds on phenomenological deviations from General Relativity using the binary black-hole and neutron-star events detected during LIGO’s first and second observing runs—no evidence for deviations from Relativity is found. These two research directions outlined above are complementary; the type of statistical inference discussed here requires models for the gravitational-wave signal produced by inspiraling systems that allow for deviations from General Relativity, and the analytic models I construct are suitable for this task. In this thesis, I carry out the complete procedure of building and employing analytic models of gravitational waveforms to place constraints on specific alternative theories of gravity with observations by LIGO and Virgo

    Nonlocal noise reduction method based on fringe frequency compensation for SAR interferogram

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    Phase noise reduction is one of the key steps for synthetic aperture radar interferometry data processing. In this article, a novel phase filtering method is proposed. The main innovation and contribution of this research is to 1) incorporate local fringe frequency (LFF) compensation technique into the nonlocal phase filtering method to include more independent and identically distributed samples for filtering; 2) modify the nonlocal phase filter from three aspects: 1) executing nonlocal filtering in the complex domain of the residual phase to avoid gray jumps in phase, 2) adaptively calculating the smoothing parameter based on the LFF and the coherence coefficient, and 3) using the integral image in similarity calculation to improve the efficiency; 3) perform Goldstein filter in high coherence areas to reduce the computation expense. Experiments based on both simulated and real data have shown that the proposed method has achieved a better performance in terms of both noise reduction and edge preservation than some existing phase filtering methods

    Constructing networks of quantum channels for state preparation

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    Entangled possibly mixed states are an essential resource for quantum computation, communication, metrology, and the simulation of many-body systems. It is important to develop and improve preparation protocols for such states. One possible way to prepare states of interest is to design an open system that evolves only towards the desired states. A Markovian evolution of a quantum system can be generally described by a Lindbladian. Tensor networks provide a framework to construct physically relevant entangled states. In particular, matrix product density operators (MPDOs) form an important variational class of states. MPDOs generalize matrix product states to mixed states, can represent thermal states of local one-dimensional Hamiltonians at sufficiently large temperatures, describe systems that satisfy the area law of entanglement, and form the basis of powerful numerical methods. In this work we develop an algorithm that determines for a given linear subspace of MPDOs whether this subspace can be the stable space of some frustration free k-local Lindbladian and, if so, outputs an appropriate Lindbladian. We proceed by using machine learning with networks of quantum channels, also known as quantum neural networks (QNNs), to train denoising post-processing devices for quantum sources. First, we show that QNNs can be trained on imperfect devices even when part of the training data is corrupted. Second, we show that QNNs can be trained to extrapolate quantum states to, e.g., lower temperatures. Third, we show how to denoise quantum states in an unsupervised manner. We develop a novel quantum autoencoder that successfully denoises Greenberger-Horne-Zeilinger, W, Dicke, and cluster states subject to spin-flip, dephasing errors, and random unitary noise. Finally, we develop recurrent QNNs (RQNNs) for denoising that requires memory, such as combating drifts. RQNNs can be thought of as matrix product quantum channels with a quantum algorithm for training and are closely related to MPDOs. The proposed preparation and denoising protocols can be beneficial for various emergent quantum technologies and are within reach of present-day experiments
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