21 December 2023
This thesis provides insight into methods for estimating blood perfusion, emphasizing the need for accurate modeling in dynamic physiological environments. The thesis critically examines conventional error function solutions used in steady state or gradually changing blood flow scenarios, revealing their shortcomings in accurately reflecting more rapid changes in blood perfusion. To address this limitation, this study introduces a novel prediction model based on the finite-difference method (FDM) specifically designed to produce accurate results under different blood flow perfusion conditions. A comparative analysis concludes that the FDM-based model is consistent with traditional error function methods under constant blood perfusion conditions, thus establishing its validity under dynamic and steady blood flow conditions. In addition, the study attempts to determine whether analytical solutions exist that are suitable for changing perfusion conditions. Three alternative analytical estimation methods were explored, each exposing the common thread of inadequate responsiveness to sudden changes in blood perfusion. Based on the advantages and disadvantages of the error function and FDM estimation, a combination of these two methods was developed. Utilizing the simplicity and efficiency of the error function, the prediction of contact resistance and core temperature along with the initial blood perfusion was first made at the beginning of the data. Then the subsequent blood perfusion values were predicted using the FDM, as the FDM can effectively respond to changing blood perfusion values. / Master of Science / Blood perfusion, the process of blood flowing through our body's tissues, is crucial for our health. It's like monitoring traffic flow on roads, which is especially important during rapid changes, such as during exercise or medical treatments. Traditional methods for estimating blood perfusion, akin to older traffic monitoring techniques, struggle to keep up with these rapid changes. This research introduces a new approach, using a method often found in engineering and physics, called the finite-difference method (FDM), to create more accurate models of blood flow in various conditions. This study puts this new method to the test against the old standards. We discover that while both are effective under steady conditions, the FDM shines when blood flow changes quickly. We also examined three other methods, but they, too, fell short in these fast-changing scenarios. This work is more than just numbers and models; it's about potentially transforming how we understand and manage health. By combining the simplicity of traditional methods for initial blood flow estimates with the dynamic capabilities of the FDM, we're paving the way for more precise medical diagnostics and treatments.
Faez Elias, Feras
The use of underwater systems has grown significantly, and they can be used both for military and civilian purposes. Many of their parts are replaceable. An underwater vehicle can be equipped with different devices depending on the taskit should carry out. This can make the vehicle unbalanced, which means that the demand for balancing systems will increase in line with the increasing use of underwater systems. The goal of the thesis is to deliver a method for balancing based on parameters estimated both in static and dynamic operation. The parameters define a nonlinear physical model that can describe the underwater vehicle in different environments and conditions. The main idea in the proposed method for parameter estimation based on static operation data is to solve equilibrium equations when the on-board control system is used to maintain two different orientations. The balancing can then be done by solving an optimisation problem that gives information about where additional weights or float material should be installed. The static parameter estimation has been evaluated successfully in simulations together with three ways of solving the balancing problem. The dynamic parameter estimation has also been evaluated in simulations. In this case, the estimated parameters seem to have the same sign as the true ones but it seems difficult to obtain accurate estimates of some of the parameters. However, the total dynamic model was good except the prediction of the vertical movements. In particular, the model could explain the rotations of the vehicle well. The reason for the worse performance for the vertical movements might be some difficulties when generating suitable excitation signals. The work done by Feras Faez Elias in connection to this master thesis made a contribution to a patent application that Saab AB has filed where Feras Faez Elias was one of the inventors.
Teka, Kubrom Hisho
Master of Science / Department of Statistics / James Neill / In financial mathematics, asset prices for European options are often modeled according to the Black-Scholes-Merton (BSM) model, a stochastic differential equation (SDE) depending on unknown parameters. A derivation of the solution to this SDE is reviewed, resulting in a stochastic process called geometric Brownian motion (GBM) which depends on two unknown real parameters referred to as the drift and volatility. For additional insight, the BSM equation is expressed as a heat equation, which is a partial differential equation (PDE) with well-known properties. For American options, it is established that asset value can be characterized as the solution to an obstacle problem, which is an example of a free boundary PDE problem. One approach for estimating the parameters in the GBM solution to the BSM model can be based on the method of maximum likelihood. This approach is discussed and applied to a dataset involving the weekly closing prices for the Dow Jones Industrial Average between January 2012 and December 2012.
Intelligent joint channel parameter estimation techniques for mobile wireless positioning applicationsLi, Wei January 2010 (has links)
Mobile wireless positioning has recently received great attention. For mobile wireless communication networks, an inherently suitable approach is to obtain the parameters that are used for positioning estimates from the radio signal measurements between a mobile device and one or more xed base stations. However, obtaining accurate estimates of these location-dependent channel parameters is a challenging task. The focus of this thesis is on the estimation of these channel parameters for mobile wireless positioning applications. In particular, we investigate novel estimators that jointly estimate more than one type of channel parameters. We rst perform a comprehensive critical review on the most recent and popular joint channel parameter estimation techniques. Secondly, we improve a state-of-the-art technique, namely the Space Alternating Generalised Expectation maximisation (SAGE) algorithm by employing adaptive interference cancellation to improve the estimation accuracy of weaker paths. Thirdly, a novel intelligent channel parameter estimation technique using Evolution Strategy (ES) is proposed to overcome the drawbacks of the existing iterative maximum likelihood methods. Furthermore, given that in reality it is di cult to obtain the number of multipath in advance, we propose a two tier Hierarchically Organised ES to jointly estimate the number of multipath as well as the channel parameters. Finally, we extend the proposed ES method to further estimate the Doppler shift in mobile environments. Our proposed intelligent joint channel estimation techniques are shown to exhibit excellent performance even with low Signal to Noise Ratio (SNR) channel conditions as well as robust against uncertainties in initialisations.
Semiparametric methods in generalized linear models for estimating population size and fatality rateLiu, Danping., 劉丹平. January 2005 (has links)
published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy
Xu, Xiaochen., 徐笑晨.
published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
Cai, Kun, 蔡琨
published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy
In today’s modern society, we are surrounded by a multitude of digital devices.The number of available digital devices is set to grow even more. As the trendcontinues, product life-cycle is a major issue in mass production of these devices.Testing and verification is responsible for a significant percentage of the productioncost of digital devices. Time efficient procedures for testing and characterization aretherefore sought for. Moreover, the need for flexible and low-cost solutions in thedesign architecture of radio frequency devices coupled with the demand for highdata rate has presented a challenge caused by interferences from the analog circuitparts. Study of digital signal processing based techniques which would alleviate theeffects of the analog impairments is therefore a pertinent subject. In the first part of this thesis, we address parameter estimation based on wave-form fitting. We look at the sinewave model for parameter estimation which iseventually used to characterize the performance of a device. The underlying goal isto formulate and analyze a set of new parameter estimators which provide a moreaccurate estimate than well known estimators. Specifically, we study the maximum-likelihood (ML) SNR estimator employing the three-parameter sine fit and derivealternative estimator based on its statistical distribution. We show that the meansquare error (MSE) of the alternative estimators is lower than the MSE of the MLestimator for a small sample size and a few of the new estimators are very close tothe Cramér-Rao lower bound (CRB). Simply put, the number of acquired measure-ment samples translate to measurement time, implying that the fewer the numberof samples required for a given accuracy, the faster the test would be. We alsostudy a sub-sampling approach for frequency estimation problem in a dual channelsinewave model with common frequency. Coprime subsampling technique is usedwhere the signals from both channels are uniformly subsampled with coprime pairof sparse samplers. Such subsampling technique is especially beneficial to lower thesampling frequency required in applications with high bandwidth requirement. TheCRB based on the co-prime subsampled data set is derived and numerical illus-trations are given showing the relation between the cost in performance based onthe mean squared error and the employed coprime factors for a given measurementtime. In the second part of the thesis, we deal with the problem of phase-noise (PHN).First, we look at a scheme in orthogonal frequency-division multiplexing (OFDM)system where pilot subcarriers are employed for joint PHN compensation, channelestimation and symbol detection. We investigate a method where the PHN statis-tics is approximated by a finite number of vectors and design a PHN codebook. Amethod of selecting the element in the codebook that is closest to the current PHNrealization with the corresponding channel estimate is discussed. We present simula-tion results showing improved performance compared to state-of-the art techniques.We also look at a sequential Monte-Carlo based method for combined channel im-pulse response and PHN tracking employing known OFDM symbols. Such techniqueallows time domain compensation of PHN such that simultaneous cancellation ofthe common phase error and reduction of the inter-carrier interference occurs. / <p>QC 20150529</p>
Hartz, Andrew Scott
Hydraulic tomography has been tested at the field scale, lab scale and in synthetic experiments. Recently Illman and Berg have conducted studies at the lab scale. Using their data hydraulic tomography can be compared to homogeneous anisotropic solutions using one pumping well or multiple pumping wells. It has been found that hydraulic tomography out performs homogenous methods at predicting hydraulic head for validation pumping experiments. Also it has been shown in this study that homogenous anisotropic tests exhibit scenario dependent behavior. Additional tests performed to further validate the conclusions made in this experiment include spatial moment analysis, response surface analysis, and synthetic hydraulic tomography and show consistent results providing additional validation of these findings. Additional study examining the principle of reciprocity has proven inconclusive.
24 October 2012
A steady-state mathematical model for the stripping section of an industrial EPDM rubber production process was developed for a three-tank process, and two four-tank processes. The experiments that were conducted to determine model parameters such as equivalent radius for EPDM particles, as well as solubility and diffusivity parameters for hexane and ENB in EPDM polymer are described. A single-particle multiple-tank model was developed first, and a process model that accounts for the residence-time distribution of crumb particles was developed second. Plant data as well as input data from an existing steady-state model was used to determine estimates for the tuning parameters used in the multiple-particle, multiple-tank model. Using plant data to assess the model’s predictive accuracy, the resulting three-tank and four-tank process B models provide accurate model predictions with a typical error of 0.35 parts per hundred resin (phr) and 0.12 phr. The four-tank process A model provides less-accurate model predictions for residual crumb concentrations in the second tank and has an overall typical error of 1.05 phr. Additional plant data from the three- and four-tank processes would increase the estimability of the parameter values for parameter ranking and estimations steps and thus, yield increased model predictive accuracy. / Thesis (Master, Chemical Engineering) -- Queen's University, 2012-10-23 21:06:05.509
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