2,503 research outputs found
Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR
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
Development of a SQUID magnetometry system for cryogenic neutron electric dipole moment experiment
A measurement of the neutron electric dipole moment (nEDM) could hold the key to understanding why the visible universe is the way it is: why matter should predominate over antimatter. As a charge-parity violating (CPV) quantity, an nEDM could provide an insight into new mechanisms that address this baryon asymmetry. The motivation for an improved sensitivity to an nEDM is to find it to be non-zero at a level consistent with certain beyond the Standard Model theories that predict new sources of CPV, or to establish a new limit that constrains them.
CryoEDM is an experiment that sought to better the current limit of cm by an order of magnitude. It is designed to measure the nEDM via the Ramsey Method of Separated Oscillatory Fields, in which it is critical that the magnetic field remains stable throughout. A way of accurately tracking the magnetic fields, moreover at a temperature K, is crucial for CryoEDM, and for future cryogenic projects.
This thesis presents work focussing on the development of a 12-SQUID magnetometry system for CryoEDM, that enables the magnetic field to be monitored to a precision of pT. A major component of its infrastructure is the superconducting capillary shields, which screen the input lines of the SQUIDs from the pick up of spurious magnetic fields that will perturb a SQUID's measurement. These are shown to have a transverse shielding factor of , which is a few orders of magnitude greater than the calculated requirement.
Efforts to characterise the shielding of the SQUID chips themselves are also discussed. The use of Cryoperm for shields reveals a tension between improved SQUID noise and worse neutron statistics. Investigations show that without it, SQUIDs have an elevated noise when cooled in a substantial magnetic field; with it, magnetostatic simulations suggest that it is detrimental to the polarisation of neutrons in transport. The findings suggest that with proper consideration, it is possible to reach a compromise between the two behaviours.
Computational work to develop a simulation of SQUID data is detailed, which is based on the Laplace equation for the magnetic scalar potential. These data are ultimately used in the development of a linear regression technique to determine the volume-averaged magnetic field in the neutron cells. This proves highly effective in determining the fields within the pT requirement under certain conditions
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Design for Accessible Collaborative Engagement: Making online synchronous collaborative learning more accessible for students with sensory impairments.
This thesis looks at the accessibility of collaborative learning and the barriers to engagement experienced by blind/visually impaired (BVI) students and deaf/hard of hearing (DHH) students. It focuses specifically on online synchronous collaborative learning after establishing that this format presented the greatest barriers, and that these student groups were not engaging.
Taking a design-based research (DBR) approach, five studies were undertaken to identify these barriers and determine potential interventions. The product of the research, a result of collaborative design by the participants in the study, is a framework for accessible collaborative engagement represented in the form of an interactive website model, the Model for Accessible Collaborative Engagement (MACE).
The studies involved representatives of all stakeholders in the collaborative learning process at the institution (the Open University): students, tutors, modules teams, academics, support staff, and the student union Disabled Students Group. These studies took the form of an online survey of 327 students, 10 interviews with staff and students, 6 staff workshops and a collaborative design focus group. With significant representation of the target groups (BVI and DHH) in all studies, and taking an iterative approach to the design, evaluation and construction of the framework model, the studies established that barriers existed in four main categories covering different themes:
1. Communications: aural, visual, screen reading and navigation, text and captioning, lip reading and non-verbal communications, interpretation and third-party communications, mode control, and synchronisation.
2. Emotional and Social Factors: familiarisation, support networks, self-advocacy, opting out, cognitive load, and stress and anxiety.
3. Provisioning and Technical Factors: dissemination, speed and pacing of sessions, staff training, participation control, group size, technical provisioning, and recordings.
4. Activity and Session Design: Volume of materials, advance materials, accessible materials, accessible activities, and session formats.
Interventions were designed that could reduce the barriers in each of these categories and themes by adjustments and changes from both the student and institutional standpoints. MACE is designed to be utilised by both students and staff to provide guidance and suggestions on how to identify and acknowledge these barriers and implement interventions to reduce them.
This research represents an original and essential contribution to the field of investigation. As well as informing future research inquiry, the model can be used by all participants and stakeholders in online collaborative learning to help reduce barriers for BVI and DHH students and improve inclusivity in synchronous online events
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields 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 modified Proportional Conflict 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 classifiers, 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, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
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 classification, and hybrid techniques mixing deep learning with belief functions as well
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
Characterising the neck motor system of the blowfly
Flying insects use visual, mechanosensory, and proprioceptive information to control their
movements, both when on the ground and when airborne. Exploiting visual information for
motor control is significantly simplified if the eyes remain aligned with the external horizon.
In fast flying insects, head rotations relative to the body enable gaze stabilisation during highspeed
manoeuvres or externally caused attitude changes due to turbulent air.
Previous behavioural studies into gaze stabilisation suffered from the dynamic properties
of the supplying sensor systems and those of the neck motor system being convolved.
Specifically, stabilisation of the head in Dipteran flies responding to induced thorax roll
involves feed forward information from the mechanosensory halteres, as well as feedback
information from the visual systems. To fully understand the functional design of the blowfly
gaze stabilisation system as a whole, the neck motor system needs to be investigated
independently.
Through X-ray micro-computed tomography (μCT), high resolution 3D data has become
available, and using staining techniques developed in collaboration with the Natural History
Museum London, detailed anatomical data can be extracted. This resulted in a full 3-
dimensional anatomical representation of the 21 neck muscle pairs and neighbouring cuticula
structures which comprise the blowfly neck motor system.
Currently, on the work presented in my PhD thesis, μCT data are being used to infer
function from structure by creating a biomechanical model of the neck motor system. This
effort aims to determine the specific function of each muscle individually, and is likely to
inform the design of artificial gaze stabilisation systems. Any such design would incorporate
both sensory and motor systems as well as the control architecture converting sensor signals
into motor commands under the given physical constraints of the system as a whole.Open Acces
MR in vivo tractography for the reconstruction of cranial nerves course
Aim
The aim of my Ph.D. was to implement a diffusion tensor tractography (DTT) pipeline to reconstruct cranial nerve I (olfactory) to study COVID-19 patients, and anterior optic pathway (AOP, including optic nerve, chiasm, and optic tract) to study patients with sellar/parasellar tumors, and with Leber’s Hereditary Optic Neuropathy (LHON).
Methods
We recruited 23 patients with olfactory dysfunction after COVID-19 infection (mean age 37±14 years, 12 females); 27 patients with sellar/parasellar tumors displacing the optic chiasm eligible for endonasal endoscopic surgery (mean age 53. ±16.4 years, 13 female) and 6 LHON patients (mutation 11778/MT-ND4, mean age 24.9±15.7 years). Sex- and age-matched healthy control were also recruited.
In LHON patients, optical coherence tomography (OCT) was performed. Acquisitions were performed on a clinical high field 3-T MRI scanner, using a multi-shell HARDI (High Angular Resolution Diffusion Imaging) sequence (b-values 0-300-1000-2000 s/mm2, 64 maximum gradient directions, 2mm3 isotropic voxel). DTT was performed with a multi-tissue spherical deconvolution approach and mean diffusivity (MD) DTT metrics were compared with healthy controls using an unpaired t-test. Correlations of DTT metrics with clinical data were sought by regression analysis.
Results
In all 23 hypo/anosmic patients with previous COVID-19 infection the CN I was successfully reconstructed with no DTT metrics alterations, thus suggesting the pathogenetic role of central olfactory cortical system dysfunction.
In all 27 patients with sellar/parasellar tumors the AOP was reconstructed, and in 11/13 (84.7%) undergoing endonasal endoscopic surgery the anatomical fidelity of the reconstruction was confirmed; a significant decrease in MD within the chiasma (p<0.0001) was also found.
In LHON patients a reduction of MD in the AOP was significantly associated with OCT parameters (p=0.036).
Conclusions
Multi-shell HARDI diffusion-weighted MRI followed by multi-tissue spherical deconvolution for the DTT reconstruction of the CN I and AOP has been implemented, and its utility demonstrated in clinical practice
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