793 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

    Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

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    Astronomical imaging technologies are basic tools for the exploration of the universe, providing basic data for the research of astronomy and space physics. The Soft X-ray Imager (SXI) carried by the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) aims to capture two-dimensional (2-D) images of the Earth’s magnetosheath by using soft X-ray imaging. However, the observed 2-D images are affected by many noise factors, destroying the contained information, which is not conducive to the subsequent reconstruction of the three-dimensional (3-D) structure of the magnetopause. The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models. This makes it difficult to establish the mapping relationship between SXI-simulated observation images and target images by using mathematical models. We propose an image restoration algorithm for SXI-simulated observation images that can recover large-scale structure information on the magnetosphere. The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image, whose mapping relationship with the target image is established by the patch estimator. The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator. Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task, according to the peak signal-to-noise ratio and structural similarity. The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques, significantly improving the reconstruction results. Hence, the proposed technology may be feasible for processing SXI-simulated observation images

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Mathematical Problems in Rock Mechanics and Rock Engineering

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    With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

    Quantum imaging and polarimetry with two-color photon pairs

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    Verschränkte Photonenpaare, gemeinhin als Signal- und Idler-Photonen bezeichnet, wurden als Grundlage für Quantum imaging with undetected photons (QIUP) und Quantum ghost imaging (QGI) verwendet. Mit QIUP können wir ein Objekt abbilden, indem wir nur die Signal-Photonen messen, die nie mit dem Objekt wechselwirken, während die Idler-Photonen, die das Objekt beleuchten, undetektiert bleiben. Bei QGI werden die beleuchtenden Idler-Photonen von einem räumlich nicht auflösenden Detektor gemessen, während die nicht wechselwirkenden Signal-Photonen von einer Kamera gemessen werden und das Bild dann nur aus den Koinzidenzen von Signal und Idler rekonstruiert wird. Nennenswert ist hier die Verwendung von zweifarbigen Photonenpaaren, welche es uns ermöglichen, Komplikationen bei der Bildgebung in Wellenlängenbereichen zu überwinden, in denen Kameras nur eine geringe Effizienz aufweisen. Daraus ergibt sich ein enormes Potenzial für die Biosensorik, bei der empfindliche Proben, die für Strahlungsschäden anfällig sind, mit herkömmlichen Einzelphotonen-Kameras abgebildet werden können, wie zum Beispiel im sichtbaren Spektralbereich, während die Probe von Photonen mit viel geringerer Energie beleuchtet wird. In dieser Arbeit wurden drei Lücken in der Literatur zur Quantenbildgebung und Polarimetrie geschlossen: (1) Die fundamentale transversale Auflösungsgrenze von QIUP und QGI, die zweifarbige Photonenpaare verwenden, wurde diskutiert. (2) Ein linsenloses QGI-Verfahren wurde vorgestellt, das sich speziell für die Abbildung in Wellenlängenbereichen eignet, für die weniger Linsen zur Verfügung stehen, wie zum Beispiel im Terahertz-Bereich. Wir haben es Pinhole QGI genannt, da wir gezeigt haben, dass es analog zur klassischen Lochkamera ist. (3) Ein Quantum ghost polarimetry (QGP) Schema wurde vorgeschlagen, bei dem dielektrische Metaoberflächen verwendet werden können, um den Einsatz rekonfigurierbarer optischer Elemente zu vermeiden

    BUILDING RECONSTRUCTION BASED ON A SMALL NUMBER OF TRACKS USING NONPARAMETRIC SAR TOMOGRAPHIC METHODS

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    Nowadays, the synthetic aperture radar (SAR) tomography (TomoSAR) technique plays a notable role in the 3D reconstruction of urban buildings through several SAR acquisitions with slightly different positions. Nonparametric-based TomoSAR spectral estimation algorithms usually work well when a large number of SAR observations. In this study, with a limited number of SAR images, we have assessed the efficiency of the nonparametric spectral estimation methods, including maximum entropy (ME), singular value decomposition (SVD), linear prediction (LP), Capon, minimum norm (MN), and beamforming (BF) in the reconstruction of the third dimension of urban buildings. The experiments are conducted on both simulated and TerraSAR-X stripmap images to indicate the effectiveness of the LP proposed estimation algorithm. The analysis of the results proves that by minimizing the average output signal power over the antenna array elements, the LP spectral estimation achieves the discrimination of distinct scatterers inside an image pixel. In addition, this low computational estimator improves the sidelobe suppression and the height estimates of the scatterers in the complex multiple-scattering urban environment. Compared to SVD, maximum entropy, Capon, minimum norm, and beamforming, the height of the Eskan tower in Tehran, Iran, obtained with the LP technique, is considerably near to field-based measurement

    Marchenko-type focusing functions: Generalisation, modelling and imaging

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    Imaging is a field of mathematics and physics that aims to retrieve information about the internal structure of an object that can only be accessed on its boundary. Many imaging methods are based on the following principle: a source outside of the object emits a wave. The wave propagates through the object. Wherever the physical structure of the object changes, scattered waves are induced. These scattered waves are measured by receivers outside of the object, and these scattered data are used to invert for the interior composition of the medium under investigation. The Marchenko integral was originally introduced for one-dimensional inverse scattering problems in the context of quantum mechanics. It can be related to Green's functions and so-called focusing functions - fields that produce a focus when injected into a medium from a single side. About ten years ago, the Marchenko integral was extended to two and three dimensions. This paved the way for, e.g., the elimination of imaging artefacts due to multiple scattering and Green's function retrieval for virtual source locations. However, many questions about the full potential as well as the accuracy of the Marchenko equation in two and three dimensions remain unanswered. In this thesis we present a new derivation for the multidimensional Marchenko integral. Our derivation is based on a generalised framework for wavefield focusing and circumvents the limiting assumptions of the previous extension. As we use partial differential equations rather than integral equations to define focusing functions, it allows for new physical insights. For instance, our approach indicates that it is possible to model Marchenko-type focusing functions with a conventional wave equation. Ultimately, this enables us to study Marchenko-type focusing in different 2D and 3D media and learn about the accuracy of the concept. We present a straightforward modelling approach for 1D as well as a least-squares modelling approach for 2D and 3D. The latter suggests that the Marchenko integral might be inherently approximative in multiple dimensions. We also discuss Green's function retrieval with our newly derived Marchenko integral, i.e. without wavefield decomposition. This method allows for estimating Green's functions for virtual sources inside of the medium. While it requires single-sided scattering data and an estimate of the first arrival of the desired Green's function there is no need to have an actual source or receiver inside of the medium. Our results demonstrate that we can retrieve good estimates of the full-spectrum Green's functions, involving evanescent and refracted waves, which were believed to not be retrievable with the previously derived Marchenko integral. Ultimately, we discuss imaging with these Marchenko-based Green's functions. Being able to include measurements for virtual sources inside of the medium allows for a natural linearisation of the imaging problem. Thus, we use the Marchenko integral to linearise state-of-the-art imaging approaches, similar to full waveform inversion or least-squares reverse time migration, and estimate the scattering potential. Our Marchenko-based linearisation accounts for all orders of scattering and performs slightly better than a single-scattering approximation

    Abstracts of the 1st GeoDays, 14th–17th March 2023, Helsinki, Finland

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    Deep learning in food category recognition

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    Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8
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