Structure and evolution of the Australian continent : insights from seismic and mechanical heterogeneity and anisotropy

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2002.Includes bibliographical references (p. 235-261).In this thesis, I explore the geophysical structure and evolution of the Australian continental lithosphere. I combine insights from isotropic and anisotropic seismic surface-wave tomography with an analysis of the anisotropy in the mechanical properties of the lithosphere, inferred from the coherence between gravity anomalies and topography. With a new high-resolution waveform tomographic model of Australia, I demonstrate that the depth of continental high wave speed anomalies does not universally increase with age, but is dependent on the scale and the tectonic history of the region under consideration. I construct an azimuthally anisotropic three-dimensional model of the Australian upper mantle from Rayleigh-wave waveforms. I compare Bayesian inverse methods with discretely parameterized regularization methods, and explore the use of regular, tectonic and resolution-dependent tomographic grids. I advocate the use of multitaper spectral estimation techniques for coherence analysis of gravity and topography, applied to Australian isostasy. I investigate the importance of internal loading, the directional anisotropy of the gravitational response to loading, and the estimation bias affecting the long wavelengths of the coherence function. I develop a method for non-stationary coherence analysis which enables a complete characterization of continental strength by the dependency of gravity-topography coherence on wavelength, direction and geologic age. Combining high-resolution, depth-dependent anisotropy measurements from surface-wave tomography with the mechanical anisotropy from gravity/topography coherence, I assess the validity of two competing theories regarding the cause of continental anisotropy (vertically coherent deformation or simple asthenospheric flow) quantitatively for the very first time.by Frederik Jozef Maurits Simons.Ph.D

Compressed Sensing Beyond the IID and Static Domains: Theory, Algorithms and Applications

Sparsity is a ubiquitous feature of many real world signals such as natural images and neural spiking activities. Conventional compressed sensing utilizes sparsity to recover low dimensional signal structures in high ambient dimensions using few measurements, where i.i.d measurements are at disposal. However real world scenarios typically exhibit non i.i.d and dynamic structures and are confined by physical constraints, preventing applicability of the theoretical guarantees of compressed sensing and limiting its applications. In this thesis we develop new theory, algorithms and applications for non i.i.d and dynamic compressed sensing by considering such constraints. In the first part of this thesis we derive new optimal sampling-complexity tradeoffs for two commonly used processes used to model dependent temporal structures: the autoregressive processes and self-exciting generalized linear models. Our theoretical results successfully recovered the temporal dependencies in neural activities, financial data and traffic data. Next, we develop a new framework for studying temporal dynamics by introducing compressible state-space models, which simultaneously utilize spatial and temporal sparsity. We develop a fast algorithm for optimal inference on such models and prove its optimal recovery guarantees. Our algorithm shows significant improvement in detecting sparse events in biological applications such as spindle detection and calcium deconvolution. Finally, we develop a sparse Poisson image reconstruction technique and the first compressive two-photon microscope which uses lines of excitation across the sample at multiple angles. We recovered diffraction-limited images from relatively few incoherently multiplexed measurements, at a rate of 1.5 billion voxels per second

IMPROVING THE QUALITY, ANALYSIS AND INTERPRETATION OF BODY SOUNDS ACQUIRED IN CHALLENGING CLINICAL SETTINGS

Despite advances in medicine and technology, Acute Lower Respiratory Diseases are a leading cause of sickness and mortality worldwide, highly affecting countries where access to appropriate medical technology and expertise is scarce. Chest auscultation provides a low-cost, non-invasive, widely available tool for the examination of pulmonary health. Despite universal adoption, its use is riddled by a number of issues including subjectivity in interpretation and vulnerability to ambient noise, limiting its diagnostic capability. Digital auscultation and computerized methods come as a natural aid towards overcoming such imposed limitations. Focused on the challenges, we address the demanding real-life scenario of pediatric lung auscultation in busy clinical settings. Two major objectives lead to our contributions: 1) Can we improve the quality of the delicate auscultated sounds and reduce unwanted noise contamination; 2) Can we augment the screening capabilities of current stethoscopes using computerized lung sound analysis to capture the presence of abnormal breaths, and can we standardize findings. To address the first objective, we developed an adaptive noise suppression scheme that tackles contamination coming from a variety of sources, including subject-centric and electronic artifacts, and environmental noise. The proposed method was validated using objective and subjective measures including an expert reviewer panel and objective signal quality metrics. Results revealed the ability and superiority of the proposed method to i) suppress unwanted noise when compared to state-of-the-art technology, and ii) faithfully maintain the signature of the delicate body sounds. The second objective was addressed by exploring appropriate feature representations that capture distinct characteristics of body sounds. A biomimetic approach was employed, and the acoustic signal was projected onto high-dimensional spaces spanning time, frequency, temporal dynamics and spectral modulations. Trained classifiers produced localized decisions on these breath content features, indicating lung diseases. Unlike existing literature, our proposed scheme is further able to combine and integrate the localized decisions into individual, patient-level evaluation. A large corpus of annotated patient data was used to validate our approach, demonstrating the superiority of the proposed features and patient evaluation scheme. Overall findings indicate that improved accessible auscultation care is possible, towards creating affordable health care solutions with worldwide impact

Characterising Solar Wind Fluctuations at Ion-kinetic Scales

The availability of large in-situ datasets makes the solar wind an excellent astrophysical laboratory to probe kinetic processes in a collisionless plasma. As the solar wind turbulent cascade reaches ion-kinetic scales close to the proton gyro-radius, ρ_p, and inertial length, d_p, collective effects lead to interactions between electromagnetic fluctuations and particle velocity distributions. At these scales, wave-particle interactions can lead to the dissipation of turbulent fluctuations and instability growth, which in turn, moderates the macroscopic properties of the plasma. In this thesis, I use over a decade of magnetic field and ion measurements from the Wind spacecraft to investigate the physical processes ongoing at these scales. I make the first in-flight determination of the magnetometer noise-floor, enabling the most accurate interpretation of magnetic field fluctuations at these scales with Wind to date. I then conduct three detailed statistical analyses of the spectral properties of these fluctuations. I first show that the steepening of the power spectrum and a coherent signature in magnetic helicity at ion-kinetic scales are associated with the cyclotron resonance wave-number, k_c, providing evidence for ongoing wave-particle interactions at these scales. I then use magnetic helicity to characterise the polarisation properties of the fluctuations, identifying three populations at ion-kinetic scales: quasi-parallel propagating Alfvén-ion cyclotron and fast magnetosonic-whistler waves driven by proton temperature anisotropy instabilities, as well as highly-oblique kinetic Alfvén wave-like fluctuations from the turbulent cascade. Finally, I show that the KAW-like fluctuations are associated with steeper spectra and higher proton temperatures, suggesting damping of the turbulence. The results presented in this thesis indicate that wave-particle interactions play an important role in the energy transfer between the turbulent fields and ions in the solar wind, in the absence of collisions. I also show that proton heating in the solar wind depends on the polarisation properties of the fluctuations at ion-kinetic scales and the radial direction in the solar wind, in contradiction to the ergodicity hypothesis. Further investigative work is proposed to confirm these findings and identify specific dissipation mechanisms responsible for turbulent heating

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Full Proceedings, 2018

Full conference proceedings for the 2018 International Building Physics Association Conference hosted at Syracuse University

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity