The purpose of this study was to explore the efficacy of two existing pulse contour analysis (PCA) models for estimating cardiac stroke volume from the arterial pressure waveform during kicking ergometer exercise and head-up tilt manoeuvres. Secondly, one of the existing models was modified in an attempt to enhance its performance. In part I, seven healthy young adults repeated two submaximal exercise sessions on a kicking ergometer, each with three different sets of steady-state cardiac output comparisons (pulsed Doppler vs. pulse contour). Across all exercise trials regression results were found to be PCA = 1. 23 x Doppler-1. 38 with an r2 = 0. 51. In part II, eight young and eight older male healthy subjects participated in a head-up tilt experiment. Cardiac output comparisons were again performed during the supine and tilt conditions using pulsed Doppler and pulse contour cardiac output. Regression results revealed that PCA performed best during supine conditions and preferentially on the older subjects. In all instances, impedance-calibrated pulse contour analysis will provide reasonable beat-by-beat cardiac output within very narrow confines and will result in a progressively more significant bias as cardiovascular dynamics change. In addition, it appears that heart rate variability negatively influences beat-by-beat pulse contour cardiac output results, further limiting application of existing models.

The purpose of this study was to explore the efficacy of two existing pulse contour analysis (PCA) models for estimating cardiac stroke volume from the arterial pressure waveform during kicking ergometer exercise and head-up tilt manoeuvres. Secondly, one of the existing models was modified in an attempt to enhance its performance. In part I, seven healthy young adults repeated two submaximal exercise sessions on a kicking ergometer, each with three different sets of steady-state cardiac output comparisons (pulsed Doppler vs. pulse contour). Across all exercise trials regression results were found to be PCA = 1. 23 x Doppler-1. 38 with an r2 = 0. 51. In part II, eight young and eight older male healthy subjects participated in a head-up tilt experiment. Cardiac output comparisons were again performed during the supine and tilt conditions using pulsed Doppler and pulse contour cardiac output. Regression results revealed that PCA performed best during supine conditions and preferentially on the older subjects. In all instances, impedance-calibrated pulse contour analysis will provide reasonable beat-by-beat cardiac output within very narrow confines and will result in a progressively more significant bias as cardiovascular dynamics change. In addition, it appears that heart rate variability negatively influences beat-by-beat pulse contour cardiac output results, further limiting application of existing models.

Virus recognition by visual examination of electron microscope (EM) images is time consuming and requires highly trained and experienced medical specialists. For these reasons, it is not suitable for screening large numbers of specimens. The objective of this research was to develop a reliable and robust pattern recognition system that could be trained to detect and classify different types of viruses from two-dimensional images obtained from an EM. This research evaluated the use of radial spectra of higher order spectral invariants to capture variations in textures and differences in symmetries of different types of viruses in EM images. The technique exploits invariant properties of the higher order spectral features, statistical techniques of feature averaging, and soft decision fusion in a unique manner applicable to the problem when a large number of particles were available for recognition, but were not easily registered on an individual basis due to the low signal to noise ratio. Experimental evaluations were carried out using EM images of viruses, and a high statistical reliability with low misclassification rates was obtained, showing that higher order spectral features are effective in classifying viruses from digitized electron micrographs. With the use of digital imaging in electron microscopes, this method can be fully automated.

Orientador: Francisco José Teixeira-Neto / Resumo: Introdução e objetivos: Mensurações do débito cardíaco (DC) pela técnica termodiluição transpulmonar (DCTP) e pela análise de contorno de pulso com calibração pela técnica transpulmonar (DCACP) são alternativas menos invasivas em comparação ao DC fornecido pela técnica de termodiluição de artéria pulmonar (DCP). Entretanto, instabilidades hemodinâmicas podem alterar o desempenho destes métodos. A Fase I do estudo objetivou avaliar se a utilização de 10 mL de indicador térmico (solução fisiológica a ≤ 5oC) para o DCTP (artéria femoral) promoveria melhor concordância e habilidade em detectar alterações no DCP em comparação a 5 mL de indicador. Na Fase II, objetivou-se verificar se alterações na resistência vascular sistêmica (RVS) influenciariam a concordância e a habilidade em detectar tendências entre o DCACP e o DCP. Métodos: Em 8 cães adultos (20,8–31,5 kg), mensurações simultâneas em triplicata do DCTP e DCP foram obtidas utilizando 5 e 10 mL de indicador térmico durante anestesia com isoflurano associado ou não com a infusão contínua intravenosa de remifentanil (0,3 e 0,6 μg/kg/min) ou de dobutamina (2,5 e 5,0 μg/kg/min) (Fase I). Durante a Fase II, o DCACP e o DCP foram mensurados simultaneamente (triplicata) antes e durante alterações na RVS induzidas pela infusão contínua de fenilefrina (1,0 μg/kg/min) ou de nitroprussiato (1,0 μg/kg/min). A acurácia e a precisão da concordância entre métodos foram estudadas pela análise de Bland-Altman para medidas múltiplas (Fase I) ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Background and objectives: Cardiac output (CO) measurements by transpulmonary thermodilution (TPTDCO) and by pulse contour analysis calibrated with transpulmonary thermodilution (PCACO) are less invasive alternatives to pulmonary artery thermodilution (PATDCO). However, hemodynamic instability could affect the performance of these methods. The objective of Phase I of the study was to determine if the use of 10 mL of thermal indicator (physiological saline at ≤ 5oC) for TPTDCO (measured in the femoral artery) would improve the agreement and trending ability with PATDCO in comparison to 5 mL of indicator. During Phase II, the aim was to verify if changes in systemic vascular resistance (SVR) would alter the agreement and trending ability between PCACO and PATDCO. Methods: In eight adult dogs (20.8–31.5 kg), simultaneous TPTDCO and PATDCO measurements (averaged from 3 repetitions) using 5 and 10 mL of thermal indicator were obtained during isoflurane anesthesia combined or not with intravenous remifentanil (0.3 e 0.6 μg/kg/min) or dobutamine (2.5 e 5.0 μg/kg/min) (Phase-1). During Phase-2, triplicate PCACO and PATDCO measurements were recorded before and during phenylephrine (1.0 μg/kg/min) or nitroprusside (1.0 μg/kg/min) induced changes in SVR. The accuracy and precision of agreement was evaluated by the Bland-Altman method for multiple measurements (Phase I) and for single measurements per subject (Phase 2). The ability of the test methods (PCACO and TPTDCO) to detect changes... (Complete abstract click electronic access below) / Doutor

Este trabalho apresenta um método para análise do contorno de gotas de chuva artificial em imagem digital, o qual se caracteriza como uma ferramenta para melhor compreensão dos processos agrícolas que envolvem o uso de chuvas artificiais, como a irrigação e a aplicação de defensivos, sendo desenvolvido com o uso de técnicas para análise de formas bidimensionais e processamento de sinais, como representação de formas por contornos paramétricos, análise de Fourier e filtragem gaussiana. Os resultados obtidos demonstram precisão na análise de imagens de aglomerados de gotas, acrescentando as características de descritor apresentadas pela curvatura, assim como a flexibilidade de calibração oferecidas pela abordagem multi-escala adotada, possibilitando a obtenção de erros de medida não maiores que 5%, para os padrões circulares testados com raios entre 10 e 200 pixels / This work presents a method for contour analysis of artificial rain drop based on both digital image and curvature processing. The method is characterized as a tool, which allows a better understanding of the raindrops in irrigation and agrochemicals spraying processes. Its development was based on parametric contours representation of shapes, Fourier analysis, and Gaussian filtering. Results show the suitability of the method, which presents errors smaller than 5% for curvature determination in the range of the radius variation in betwen 10 and 200 pixels as well as the ability for raindrop clusters analysis

Este trabalho apresenta um método para análise do contorno de gotas de chuva artificial em imagem digital, o qual se caracteriza como uma ferramenta para melhor compreensão dos processos agrícolas que envolvem o uso de chuvas artificiais, como a irrigação e a aplicação de defensivos, sendo desenvolvido com o uso de técnicas para análise de formas bidimensionais e processamento de sinais, como representação de formas por contornos paramétricos, análise de Fourier e filtragem gaussiana. Os resultados obtidos demonstram precisão na análise de imagens de aglomerados de gotas, acrescentando as características de descritor apresentadas pela curvatura, assim como a flexibilidade de calibração oferecidas pela abordagem multi-escala adotada, possibilitando a obtenção de erros de medida não maiores que 5%, para os padrões circulares testados com raios entre 10 e 200 pixels / This work presents a method for contour analysis of artificial rain drop based on both digital image and curvature processing. The method is characterized as a tool, which allows a better understanding of the raindrops in irrigation and agrochemicals spraying processes. Its development was based on parametric contours representation of shapes, Fourier analysis, and Gaussian filtering. Results show the suitability of the method, which presents errors smaller than 5% for curvature determination in the range of the radius variation in betwen 10 and 200 pixels as well as the ability for raindrop clusters analysis

The electro-optic technique of measuring the cardiovascular pulse wave known as photoplethysmography (PPG) is clinically utilised for noninvasive characterisation of physiological components by dynamic monitoring of tissue optical absorption. There has been a resurgence of interest in this technique in recent years, driven by the demand for a low cost, compact, simple and portable technology for primary care and community-based clinical settings, and the advancement of computer-based pulse wave analysis techniques. PPG signal provides a means of determining cardiovascular properties during the cardiac cycle and changes with ageing and disease. This thesis focuses on the photoplethysmographic signal for cardiovascular assessment. The contour of the PPG pulse wave is influenced by vascular ageing. Contour analysis of the PPG pulse wave provides a rapid means of assessing vascular tone and arterial stiffness. In this thesis, the parameters extracted from the PPG pulse wave are examined in young adults. The results indicate that the contour parameters of the PPG pulse wave could provide a simple and noninvasive means to study the characteristic change relating to arterial stiffness. The pulsatile component of the PPG signal is due to the pumping action of the heart, and thus could reveal the circulation changes of a specific vascular bed. Heart rate variability (HRV) represents one of the most promising quantitative markers of cardiovascular control. Calculation of HRV from the peripheral pulse wave using PPG, called pulse rate variability (PRV), is investigated. The current work has confirmed that the PPG signal could provide basic information about heart rate (HR) and its variability, and highly suggests a good alternative to understanding dynamics pertaining to the autonomic nervous system (ANS) without the use of an electrocardiogram (ECG) device. Hence, PPG measurement has the potential to be readily accepted in ambulatory cardiac monitoring due to its simplicity and comfort. Noncontact PPG (NPPG) is introduced to overcome the current limitations of contact PPG. As a contactless device, NPPG is especially attractive for physiological monitoring in ambulatory units, NICUs, or trauma centres, where attaching electrodes is either inconvenient or unfeasible. In this research, a prototype for noncontact reflection PPG (NRPPG) with a vertical cavity surface emitting laser (VCSEL) as a light source and a high-speed PiN photodiode as a photodetector is developed. The results from physiological experiments suggest that NRPPG is reliable to extract clinically useful information about cardiac condition and function. In summary, recent evidence demonstrates that PPG as a simple noninvasive measurement offers a fruitful avenue for noninvasive cardiovascular monitoring. Key words: Photoplethysmography (PPG), Cardiovascular assessment, Pulse wave contour analysis, Arterial stiffness, Heart rate (HR), Heart rate variability (HRV), Pulse rate variability (PRV), Autonomic nervous system (ANS), Electrocardiogram (ECG).

This thesis examines an automated method to detect surface deformations on automotive panels for the purpose of quality control along a manufacturing assembly line. Automation in the automotive manufacturing industry is becoming more prominent, but quality control is still largely performed by human workers. Quality control is important in the context of automotive body panels as deformations can occur along the assembly line such as inadequate handling of parts or tools around a vehicle during assembly, rack storage, and shipping from subcontractors. These defects are currently identified and marked, before panels are either rectified or discarded. This work attempts to develop an automated system to detect deformations to alleviate the dependence on human workers in quality control and improve performance by increasing speed and accuracy. Some techniques make use of an ideal CAD model behaving as a master work, and panels scanned on the assembly line are compared to this model to determine the location of deformations. This thesis presents a solution for detecting deformations of various scales without a master work. It also focuses on automated analysis requiring minimal intuitive operator-set parameters and provides the ability to classify the deformations as dings, which are deformations that protrude from the surface, or dents, which are depressions into the surface. A complete automated deformation detection system is proposed, comprised of a feature extraction module, segmentation module, and classification module, which outputs the locations of deformations when provided with the 3D mesh of an automotive panel. Two feature extraction techniques are proposed. The first is a general feature extraction technique for 3D meshes using octrees for multi-resolution analysis and evaluates the amount of surface variation to locate deformations. The second is specifically designed for the purpose of deformation detection, and analyzes multi-resolution cross-sections of a 3D mesh to locate deformations based on their estimated size. The performance of the proposed automated deformation detection system, and all of its sub-modules, is tested on a set of meshes which represent differing characteristics of deformations in surface panels, including deformations of different scales. Noisy, low resolution meshes are captured from a 3D acquisition, while artificial meshes are generated to simulate ideal acquisition conditions. The proposed system shows accurate results in both ideal situations as well as non-ideal situations under the condition of noise and complex surface curvature by extracting only the deformations of interest and accurately classifying them as dings or dents.

This thesis examines an automated method to detect surface deformations on automotive panels for the purpose of quality control along a manufacturing assembly line. Automation in the automotive manufacturing industry is becoming more prominent, but quality control is still largely performed by human workers. Quality control is important in the context of automotive body panels as deformations can occur along the assembly line such as inadequate handling of parts or tools around a vehicle during assembly, rack storage, and shipping from subcontractors. These defects are currently identified and marked, before panels are either rectified or discarded. This work attempts to develop an automated system to detect deformations to alleviate the dependence on human workers in quality control and improve performance by increasing speed and accuracy. Some techniques make use of an ideal CAD model behaving as a master work, and panels scanned on the assembly line are compared to this model to determine the location of deformations. This thesis presents a solution for detecting deformations of various scales without a master work. It also focuses on automated analysis requiring minimal intuitive operator-set parameters and provides the ability to classify the deformations as dings, which are deformations that protrude from the surface, or dents, which are depressions into the surface. A complete automated deformation detection system is proposed, comprised of a feature extraction module, segmentation module, and classification module, which outputs the locations of deformations when provided with the 3D mesh of an automotive panel. Two feature extraction techniques are proposed. The first is a general feature extraction technique for 3D meshes using octrees for multi-resolution analysis and evaluates the amount of surface variation to locate deformations. The second is specifically designed for the purpose of deformation detection, and analyzes multi-resolution cross-sections of a 3D mesh to locate deformations based on their estimated size. The performance of the proposed automated deformation detection system, and all of its sub-modules, is tested on a set of meshes which represent differing characteristics of deformations in surface panels, including deformations of different scales. Noisy, low resolution meshes are captured from a 3D acquisition, while artificial meshes are generated to simulate ideal acquisition conditions. The proposed system shows accurate results in both ideal situations as well as non-ideal situations under the condition of noise and complex surface curvature by extracting only the deformations of interest and accurately classifying them as dings or dents.

This thesis examines an automated method to detect surface deformations on automotive panels for the purpose of quality control along a manufacturing assembly line. Automation in the automotive manufacturing industry is becoming more prominent, but quality control is still largely performed by human workers. Quality control is important in the context of automotive body panels as deformations can occur along the assembly line such as inadequate handling of parts or tools around a vehicle during assembly, rack storage, and shipping from subcontractors. These defects are currently identified and marked, before panels are either rectified or discarded. This work attempts to develop an automated system to detect deformations to alleviate the dependence on human workers in quality control and improve performance by increasing speed and accuracy. Some techniques make use of an ideal CAD model behaving as a master work, and panels scanned on the assembly line are compared to this model to determine the location of deformations. This thesis presents a solution for detecting deformations of various scales without a master work. It also focuses on automated analysis requiring minimal intuitive operator-set parameters and provides the ability to classify the deformations as dings, which are deformations that protrude from the surface, or dents, which are depressions into the surface. A complete automated deformation detection system is proposed, comprised of a feature extraction module, segmentation module, and classification module, which outputs the locations of deformations when provided with the 3D mesh of an automotive panel. Two feature extraction techniques are proposed. The first is a general feature extraction technique for 3D meshes using octrees for multi-resolution analysis and evaluates the amount of surface variation to locate deformations. The second is specifically designed for the purpose of deformation detection, and analyzes multi-resolution cross-sections of a 3D mesh to locate deformations based on their estimated size. The performance of the proposed automated deformation detection system, and all of its sub-modules, is tested on a set of meshes which represent differing characteristics of deformations in surface panels, including deformations of different scales. Noisy, low resolution meshes are captured from a 3D acquisition, while artificial meshes are generated to simulate ideal acquisition conditions. The proposed system shows accurate results in both ideal situations as well as non-ideal situations under the condition of noise and complex surface curvature by extracting only the deformations of interest and accurately classifying them as dings or dents.