Matching Items (6)
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- All Subjects: Bayesian
- All Subjects: HMM
- Creators: Presse, Steve
- Creators: Pollat, Scott
Description
Flow measurement has always been one of the most critical processes in many industrial and clinical applications. The dynamic behavior of flow helps to define the state of a process. An industrial example would be that in an aircraft, where the rate of airflow passing the aircraft is used to determine the speed of the plane. A clinical example would be that the flow of a patient's breath which could help determine the state of the patient's lungs. This project is focused on the flow-meter that are used for airflow measurement in human lungs. In order to do these measurements, resistive-type flow-meters are commonly used in respiratory measurement systems. This method consists of passing the respiratory flow through a fluid resistive component, while measuring the resulting pressure drop, which is linearly related to volumetric flow rate. These types of flow-meters typically have a low frequency response but are adequate for most applications, including spirometry and respiration monitoring. In the case of lung parameter estimation methods, such as the Quick Obstruction Method, it becomes important to have a higher frequency response in the flow-meter so that the high frequency components in the flow are measurable. The following three types of flow-meters were: a. Capillary type b. Screen Pneumotach type c. Square Edge orifice type To measure the frequency response, a sinusoidal flow is generated with a small speaker and passed through the flow-meter that is connected to a large, rigid container. True flow is proportional to the derivative of the pressure inside the container. True flow is then compared with the measured flow, which is proportional to the pressure drop across the flow-meter. In order to do the characterization, two LabVIEW data acquisition programs have been developed, one for transducer calibration, and another one that records flow and pressure data for frequency response testing of the flow-meter. In addition, a model that explains the behavior exhibited by the flow-meter has been proposed and simulated. This model contains a fluid resistor and inductor in series. The final step in this project was to approximate the frequency response data to the developed model expressed as a transfer function.
ContributorsHu, Jianchen (Author) / Macia, Narciso (Thesis advisor) / Pollat, Scott (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different illumination intensities or different local environments); and (3) inferring the camera gain. My general theoretical framework utilizes the Bayesian nonparametric Gaussian and beta-Bernoulli processes with a Markov chain Monte Carlo sampling scheme, which I further specify and implement for Total Internal Reflection Fluorescence (TIRF) microscopy data, benchmarking the method on synthetic data. These three frameworks are self-contained, and can be used concurrently so that the fluorescence profile and emitter locations are both considered unknown and, under some conditions, learned simultaneously. The framework I present is flexible and may be adapted to accommodate the inference of other parameters, such as emission photophysical kinetics and the trajectories of moving molecules. My TIRF-specific implementation may find use in the study of structures on cell membranes, or in studying local sample properties that affect fluorescent molecule photon emission rates.
ContributorsWallgren, Ross (Author) / Presse, Steve (Thesis advisor) / Armbruster, Hans (Thesis advisor) / McCulloch, Robert (Committee member) / Arizona State University (Publisher)
Created2019
Description
Many tasks that humans do from day to day are taken for granted in term of appreciating their true complexity. Humans are the only species on the planet that have developed such an in-depth means of auditory communication. Recreating the mechanisms in the brain that recognize speech patterns is no easy task. This paper compares and contrasts various algorithms used in modern day ASR systems, and focuses primarily on ASR systems in resource constrained environments. The Green colored blocks in Figure 1 will be focused on in greater detail throughout this paper, they are the key to building an exceptional ASR system. Deep Neural Networks (DNNs) are the clear and current leader among ASR technologies; all research in this field is currently revolving around this method. Although DNNs are very effective, many older methods of ASR are used often due to the complexities involved with DNNs; these difficulties include the large amount of hardware resources as well as development resources, such as engineers and money, required for this method.
ContributorsPetersen, Casey Alexander (Author) / Csavina, Kristine (Thesis director) / Pollat, Scott (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Single molecule FRET experiments are important for studying processes that happen on the molecular scale. By using pulsed illumination and collecting single photons, it is possible to use information gained from the fluorescence lifetime of the chromophores in the FRET pair to gain more accurate estimates of the underlying FRET rate which is used to determine information about the distance between the chromophores of the FRET pair. In this paper, we outline a method that utilizes Bayesian inference to learn parameter values for a model informed by the physics of a immobilized single-molecule FRET experiment. This method is unique in that it combines a rigorous look at the photophysics of the FRET pair and a nonparametric treatment of the molecular conformational statespace, allowing the method to learn not just relevant photophysical rates (such as relaxation rates and FRET rates), but also the number of molecular conformational states.
ContributorsSafar, Matthew Matej (Author) / Presse, Steve (Thesis director) / Sgouralis, Ioannis (Committee member) / Department of Physics (Contributor) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The Bayesian paradigm provides a flexible and versatile framework for modeling complex biological systems without assuming a fixed functional form or other constraints on the underlying data. This dissertation explores the use of Bayesian nonparametric methods for analyzing fluorescence microscopy data in biophysics, with a focus on enumerating diffraction-limited particles, reconstructing potentials from trajectories corrupted by measurement noise, and inferring potential energy landscapes from fluorescence intensity experiments. This research demonstrates the power and potential of Bayesian methods for solving a variety of problems in fluorescence microscopy and biophysics more broadly.
ContributorsBryan IV, J Shepard (Author) / Presse, Steve (Thesis advisor) / Ozkan, Banu (Committee member) / Wadhwa, Navish (Committee member) / Shepherd, Doug (Committee member) / Arizona State University (Publisher)
Created2023
Description
Electron Multiplying Charge Coupled Device (EMCCD) cameras are widely used for fluorescence microscopy experiments. However, the quantitative determination of biological parameters uniquely depends on characteristics of the unavoidably inhomogenous illumination profile as it gives rise to an image. It is therefore of interest to learn this inhomogenous illumination profiles that can dramatically vary across images alongside the camera parameters though a detailed camera model. In this manuscript we create a detailed model to learn inhomogeneous illumination profiles as well as learn all associated camera parameters. We achieve this within a Bayesian paradigm allowing us to determine full distributions over the unknowns.
ContributorsBryan, Eric (Author) / Presse, Steve (Thesis director) / Fazel, Mohammed (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2022-05