Pros and cons of microwave imaging techniques for the reconstruction of velocity profiles of cylindrical targets in axial motion

In this contribution the strengths and the weaknesses of microwave imaging techniques for the reconstruction of dielectric and velocity profiles of inhomogeneous cylinders in non-uniform axial motion are presented. Some numerical results are presented to support the main conclusions

Development of instrumentation for acoustic monitoring

This thesis describes a source/sink flow acoustic wave sensor for the identification of leaks in gas pipelines. One of the many type of signals associated with the high velocity gas flowing out of a hole in the pipeline is the ramp or step pressure drop.;A large 3-inch diameter diaphragm was installed in an attempt to detect the low frequency waves associated with a leak. This diaphragm with source or sink flow through a 17mm internal diameter pipe acts as a pressure signal amplifier.;The deflection of the diaphragm due to the amplified force can be measured with a strain gage whose voltage is proportional to this deflection. A microphone should be used to record high frequency sound generated by the fluid inside the transmission line. (Abstract shortened by UMI.)

The XMM Cluster Survey: X-ray analysis methodology

The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM-Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling relations. In this paper we describe the data processing methodology applied to the 5,776 XMM observations used to construct the current XCS source catalogue. A total of 3,675 > 4-sigma cluster candidates with > 50 background-subtracted X-ray counts are extracted from a total non-overlapping area suitable for cluster searching of 410 deg^2. Of these, 993 candidates are detected with > 300 background-subtracted X-ray photon counts, and we demonstrate that robust temperature measurements can be obtained down to this count limit. We describe in detail the automated pipelines used to perform the spectral and surface brightness fitting for these candidates, as well as to estimate redshifts from the X-ray data alone. A total of 587 (122) X-ray temperatures to a typical accuracy of < 40 (< 10) per cent have been measured to date. We also present the methodology adopted for determining the selection function of the survey, and show that the extended source detection algorithm is robust to a range of cluster morphologies by inserting mock clusters derived from hydrodynamical simulations into real XMM images. These tests show that the simple isothermal beta-profiles is sufficient to capture the essential details of the cluster population detected in the archival XMM observations. The redshift follow-up of the XCS cluster sample is presented in a companion paper, together with a first data release of 503 optically-confirmed clusters.Comment: MNRAS accepted, 45 pages, 38 figures. Our companion paper describing our optical analysis methodology and presenting a first set of confirmed clusters has now been submitted to MNRA

UAV-Enabled Surface and Subsurface Characterization for Post-Earthquake Geotechnical Reconnaissance

Major earthquakes continue to cause significant damage to infrastructure systems and the loss of life (e.g. 2016 Kaikoura, New Zealand; 2016 Muisne, Ecuador; 2015 Gorkha, Nepal). Following an earthquake, costly human-led reconnaissance studies are conducted to document structural or geotechnical damage and to collect perishable field data. Such efforts are faced with many daunting challenges including safety, resource limitations, and inaccessibility of sites. Unmanned Aerial Vehicles (UAV) represent a transformative tool for mitigating the effects of these challenges and generating spatially distributed and overall higher quality data compared to current manual approaches. UAVs enable multi-sensor data collection and offer a computational decision-making platform that could significantly influence post-earthquake reconnaissance approaches. As demonstrated in this research, UAVs can be used to document earthquake-affected geosystems by creating 3D geometric models of target sites, generate 2D and 3D imagery outputs to perform geomechanical assessments of exposed rock masses, and characterize subsurface field conditions using techniques such as in situ seismic surface wave testing. UAV-camera systems were used to collect images of geotechnical sites to model their 3D geometry using Structure-from-Motion (SfM). Key examples of lessons learned from applying UAV-based SfM to reconnaissance of earthquake-affected sites are presented. The results of 3D modeling and the input imagery were used to assess the mechanical properties of landslides and rock masses. An automatic and semi-automatic 2D fracture detection method was developed and integrated with a 3D, SfM, imaging framework. A UAV was then integrated with seismic surface wave testing to estimate the shear wave velocity of the subsurface materials, which is a critical input parameter in seismic response of geosystems. The UAV was outfitted with a payload release system to autonomously deliver an impulsive seismic source to the ground surface for multichannel analysis of surface waves (MASW) tests. The UAV was found to offer a mobile but higher-energy source than conventional seismic surface wave techniques and is the foundational component for developing the framework for fully-autonomous in situ shear wave velocity profiling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145793/1/wwgreen_1.pd

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Modelling the two-phase plume dynamics of CO2 leakage into open shallow waters

A numerical model of two-phase plume developments in a small scale turbulent ocean is proposed and designed as a fundamental study to predict the near field physicochemical impacts and biological risk to the marine ecosystem from CO2 leakage from potential carbon storage locations around the North Sea. New sub-models are developed for bubble formation and drag coefficients using in-situ measurements from videos of the Quantifying and monitoring potential ecosystem Impacts of geological Carbon Storage (QICS) experiment. Existing sub-models such as Sherwood numbers and plume interactions are also compared, verified and implemented into the new model. Observational data collected from the North Sea provides the ability to develop and verify a large eddy simulation turbulence model, limited to situations where the non-slip boundary wall may be neglected. The model is then tested to assimilate the QICS experiment, before being applied to potential leakage scenarios around the North Sea with key marine impacts from pCO2 and pH changes. The most serious leak is from a well blowout, with maximum pH changes of up to -2.7 and changes greater than -0.1 affecting areas up to 0.23 km2. Other scenarios through geological structures would be challenging to detect with pH changes below -0.27

Selected Papers from the 9th World Congress on Industrial Process Tomography

Industrial process tomography (IPT) is becoming an important tool for Industry 4.0. It consists of multidimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors. This book showcases a selection of papers at the forefront of the latest developments in such technologies

Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI

Simultaneous EEG-fMRI allows multi-parametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner’s static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed.A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post-processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moire Phase Tracker (MPT) markers, and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines.All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post-processing using a multi-channel, recursive least squares (M-RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M-RLS currently provides the best compromise between EEG data quality and practicalities of motion detection

Generative adversarial networks review in earthquake-related engineering fields

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions