The diploma thesis contains knowledge in relatively young information industry branch -- signal monitoring. After reading through this document the reader gains understanding of the essence of signal monitoring, what it is useful for, how it is represented and what the vision of the future related with signal monitoring is. Main part of the thesis is dedicated to the products conveying signal monitoring theory into praxis, their functionality description and the specific software extended functions suggestion. Design of signal monitoring domains connected with particular company is also included.

Obstructive sleep apnea is a potentially life-threatening condition characterized by repetitive episodes of upper airway obstruction that occur during sleep, usually associated with a reduction in blood oxygen saturation. In US population, 9% of women, 24% of men, and 2% of children have been diagnosed with obstructive sleep apnea, suggesting that 18 million people may suffer from the consequences of nightly episodes of apnea. One of the most significant symptoms of obstructive sleep apnea is profound and repeated hypoxia. The analysis of the interaction between cardiovascular and respiratory signals has been a widely-explored area of research due to the significance of the results in describing a functional relationship between the underlying physiologic systems; however, statistical and analytical approaches to analyze the changes in these signals before and after hypoxia are still in their early stages of evolution. A major motivation for this research has been the lack of methodologies to detect mean and/or variance shifts and identify root sources of variation in time-frequency characteristics of multichannel data. The contributions of this thesis are twofold. First, multiscale energy distributions based on wavelet transformations of the analyzed physiological signs are analyzed. This is followed by the development of an online multichannel monitoring approach based on principal curves that detects changes in the wavelet coefficients extracted from the analyzed signals.

Technology development in the sub-field of older adult care has always been on the back-burner compared to other healthcare areas. But with increasing life expectancy, this is poised to change. With the increasing older adult population, the current older adult care facilities and personnel are struggling to keep up with demand. Research conducted in the Netherlands [1] found 33,000 older adults were awaiting admission into a home for the elderly showing that demand far exceeds availability. This huge demand for older adult care has resulted in a decrease in the quality of care being provided. A recent study involving older adults aged 65 and above [2] compared the quality of care given to older adults in nursing homes in the UK and found it to be inadequate. While it is true that giant strides have been made in the field of personal health and fitness [3], we have to acknowledge that these technologies have not found widespread adoption in the elderly communities for a number of reasons which include lack of education, cognitive impediments, low-income and techno-phobia [4]. We believe that older adult care technologies should be approached from a different perspective in order to maximize outcomes. Inventions in the health care space are a moving target and a significant degree of technical aptitude and interest is required to keep up with these changes. My research work will be focused on developing a distributed system infrastructure that will enable large-scale monitoring of vital signals and early detection of emergency situations in nursing homes and assisted living communities. This new approach will increase automation in nursing homes leading to a reduction in running cost and an increase in capacity

Cette thèse porte sur la création d’algorithmes capables d’identifier les meilleures stratégies de contrôle de dépôt de filtres optiques interférentiels. Nous nous intéressons plus particulièrement au contrôle optique qui est, en règle générale, la méthode la plus précise pour définir les critères d’arrêt de dépôt d’une couche de quelques dizaines à centaines de nanomètres et avec une précision de quelques 0,1%. L’objectif de cette thèse vise donc à prédire le résultat expérimental d’un dépôt ainsi que d’en maximiser le taux de réussite.Trois algorithmes sont alors proposés avec pour objectif premier de pouvoir simuler les dépôts de couches minces de manière précise et statistique, de réduire la quantité de stratégies de contrôle possibles à l’aide de critères théoriques, et enfin de sélectionner la solution amenant au meilleur taux de réussite possible. / The aim of this thesis is the creation of algorithms capable of identifying the best strategies for controlling the deposition of optical interference filters. We are particularly interested in optical monitoring, which is, as a general rule, the most accurate method for defining the deposition triggering criteria of a layer from a few tens to hundreds of nanometers and with an accuracy of about 0.1%. The objective of this thesis is thus to predict the result of a deposition and to maximize its success rate by generating an optimal optical monitoring strategy. A virtual deposition process software has then been created to get closer to the experimental conditions and thus allows to select any optical monitoring strategy Two algorithms are then proposed, one with the objective of reducing the quantity of possible solutions for the monitoring of a filter and a second which is used to select the one with the best sucess rate.

One of the most commonly used mechanical systems is the internal combustion engine. Internal combustion engines dominate the automotive industry, and have numerous other applications in generation, transportation, etc. This thesis presents the development of a fault detection and diagnosis (FDD) system for use with an internal combustion engine valve train. A FDD system was developed with a focus on the valve impact amplitudes. Engine cycle averaging and band-pass filtering methods were tuned and utilized for improving the signal to noise ratio. A novel feature extraction method was developed that included a local RMS sliding window method and an adaptive threshold. Faults were seeded in the form of deformed valve springs, as well as abnormal valve clearances. The engine’s manufacturer specifies that a valve spring with 3 mm or more of deformation should be replaced. This thesis investigated the detection of a relatively small 0.5mm spring deformation. Valve clearance values were adjusted 0.1mm above and below the nominal clearance value (0.15mm) to test large clearance faults (0.25mm) and small clearance faults (0.05mm). The performance of the FDD system was tested using an instrumented diesel engine test bed. A comparison of numerous signal processing techniques and classification methods was performed. / Master of Applied Science (MASc)