Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 32 for “"Gaussian mixture model (GMM)"”.
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A Comparative Analysis of Bayesian Nonparametric Variational Inference Algorithms for Speech Recognition
Nonparametric Bayesian models have become increasingly popular in speech recognition tasks such as language and acoustic modeling due to their ability to discover underlying structure in an iterative manner. These methods do not require a priori assumptions about the structure of the data, such as …
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A New Breast Cancer Image Classifier Using Gaussian Mixture Model Based on Histogram and Enhanced Roughness Index
… In this thesis, a new method based on Gaussian Mixture Model (GMM) to perform the breast tumor classification into two different classes (benign class and malignant class) was proposed. Also a new Enhance Roughness Index (ERI) was developed. In the meanwhile, the two different factors …
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Investigating Neuronal Cell Classes and their Role in Cognition
… memory task, and employing an unsupervised Gaussian mixture model (GMM) clustering algorithm, a number of different cell classes and their defining features were distinguished in area 7A, the lateral intraparietal area (LIP), the dorsolateral and ventrolateral prefrontal cortex (PFC) and the …
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Joint appearance and locality image representation by Gaussianization
… for image classification applications. First, we model the feature vectors, from various granularity levels including the corpus level, the image level and image patch level, in a hierarchical Bayesian framework using mixtures of Gaussians. After such a hierarchical Gaussianization, each image is …
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Data-driven Target Tracking and Hybrid Path Planning Methods for Autonomous Operation of UAV
… reliance on a finite number of dynamic motion models. To address this, data-driven target tracking methods were developed based on the statistical model of the Gaussian mixture model (GMM) and deep neural networks of long-short term memory (LSTM) model, to estimate instant and future states of …
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Text-Independent Speaker Identification using Statistical Learning
… applications. The statistical methods used are Gaussian Mixture Models and Support Vector Machines. These methods have become the de facto techniques for designing speaker identification systems. The effectiveness of the support vector machine is dependent on the type of kernel used. Several …
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Clusterización y aplicación del método Newsvendor para optimizar los perfiles de sobreventa en vuelos single leg
La presente tesis desarrolla un modelo sistemático de optimización de sobreventa para la ruta Ezeiza–Madrid de una Aerolínea comercial, con el objetivo de proveer a los analistas de Revenue Management un procedimiento reproducible para determinar la cantidad óptima de asientos a sobrevender a 30 …
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The Tissue-reduced Virtual Family Models for RF-induced Heating Evaluation of Passive Medical Implants at 1.5 T and 3 T
… of patients. The study in ASTM phantom and human models is adopted to evaluate the RF-induced heating near the medical implants. To make the experiment in human models more practical, the tissue-reduced virtual family models were proposed for RF-induced heating simulation and possible experiments …
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Speaker Anonymization using End-to-End Zero-Shot Voice Conversion
… conversion (VC) methods. We first introduce a model for performing end-to-end zero-shot voice conversion by modifying the architecture of a neural vocoder. To the best of our knowledge, this is one of the first end-to-end approaches for zero-shot VC that has ever been proposed. Our model is …
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Applications of missing feature theory to speaker recognition
… is the degradation that occurs when speaker models trained with speech from one type of channel are used to score speech from another type of channel, known as channel mismatch. This thesis investigates various channel compensation techniques and approaches from missing feature theory for …
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PROFILING EFFUSION CELLS BY QUANTITATIVE ANALYSIS OF MORPHOLOGY AND DIFFRACTION IMAGING PATTERNS
… method. Furthermore, realistic optical cell models (OCM) have been developed as virtual PPE cells and used for accurate simulation of diffraction imaging process to obtain calculated p-DI pairs. This approach allows us to correlate p-DI texture feature parameters quantified by the gray-level …
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Comparison of data-driven analysis methods for identification of functional connectivity in fMRI
… paradigms in the absence of a priori model of brain activity. Although ICA and clustering rely on very different assumptions on the underlying distributions, they produce surprisingly similar results for signals with large variation. The main goal of this thesis is to understand the …
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DNA microarray image processing based on advanced pattern recognition techniques
… processing. Following the gridding process, the Gaussian Mixture Model (GMM) and the Fuzzy GMM algorithms were applied to each cell, with the purpose of discriminating foreground from background. In addition, markov random field (MRF), as well as, a proposed wavelet based MRF model (SMRF) were …
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Digital Image Processing via Combination of Low-Level and High-Level Approaches.
… the final new edge. In Face Detection, a skin model is built first, then the boundary condition of this skin model can be extracted to cover almost all of the skin pixels. After skin detection, the knowledge about size, size ratio, locations of ears and mouth is used to recognise the face in …
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Learning joint latent representations for images and language
… (CVAEs). Standard CVAEs with a fixed Gaussian prior yield descriptions with too little variability. Instead, we propose two models that explicitly structure the latent space with K components corresponding to different types of image content, and combine components to create priors for …
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Improving Efficiency and Fairness in Machine Learning: a Discrete Optimization Approach
In recent years, machine learning models are being increasingly deployed in various applications including Education, Finance, Healthcare, Transportation, etc. However, in most practical situations one-size-fits-all solutions suffer from poor predictive performance and/or bias against certain …
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Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction
… mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models used in this …
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Acoustic Emission Signal Classification for Gearbox Failure Detection
… Kohonen Self-organizing Map (SOM), k-mean and Gaussian Mixture Model (GMM). From the clustering iterations, the three cluster criterion algorithms were performed to observe the suggested optimal number of cluster by the criterions. The three criterion algorithms utilized are the Davies-Bouldin, …
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Driver behavior classification and lateral control for automobile safety systems
… A nonlinear extended two-wheel vehicle dynamic model is employed. The sideslip angle and the yaw rate are estimated by discrete Kalman Filter. A time independent piecewise optimization scheme is proposed to provide time-continuous estimates of longitude tire force, which can be transferred to …
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High order stochastic transport and Lagrangian data assimilation
… include the use of approximate measurement models to directly link Lagrangian variables with Eulerian variables, the challenges in respecting the Lagrangian nature of variables, and the assumptions of linearity or of Gaussian statistics during prediction or assimilation. To overcome these, …
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