La corrosión de las armaduras es una de las principales causas que afectan a la durabilidad de las estructuras y a su vida útil. La carbonatación del hormigón y la acción de iones agresivos, principalmente los cloruros, son los procesos que mayor riesgo suponen para la corrosión de las armaduras. El control y monitorización mediante técnicas no destructivas es fundamental, de esta forma, se puede obtener información a tiempo real de aquellos factores que pueden favorecer los procesos de corrosión.   En el presente trabajo se exponen los resultados de varios estudios. En primer lugar se detalla el proceso de fabricación, caracterización y evaluación de diferentes tipos de sensores electroquímicos para el control del acceso de iones cloruro y la medida del pH del hormigón. Los sensores se han fabricado en tecnología de microelectrónica híbrida, concretamente en tecnología thick film. A continuación se propone un nuevo modelo de medida de la resistividad en hormigones que permita  valorar de forma indirecta la probabilidad de corrosión de las armaduras. A partir de los resultados obtenidos en un trabajo previo donde se había estudiado la conductividad en una celda electrolítica se desarrolla una metodología alternativa al método directo y al de cuatro puntas que permite monitorizar la evolución de la resistividad de los hormigones endurecidos. Finalmente, se aplican metodologías de análisis multivariante (Principal Component Analysis) en combinación con técnicas electroquímicas dinámicas tradicionales para obtener información del agente que causa la corrosión, por lo que puede ser una herramienta muy útil para el conocimiento fundamental del material metálico objeto de estudio. / Gandía Romero, JM. (2014). Sensores electroquímicos aplicados al estudio de la corrosión en estructuras de hormigón armado [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48516 / TESIS / Premios Extraordinarios de tesis doctorales

High-dimensional data sets can be difficult to visualize and analyze, while data in low-dimensional space tend to be more accessible. In order to aid visualization of the underlying structure of a dataset, the dimension of the dataset is reduced. The simplest approach to accomplish this task of dimensionality reduction is by a random projection of the data. Even though this approach allows some degree of visualization of the underlying structure, it is possible to lose more interesting underlying structure within the data. In order to address this concern, various supervised and unsupervised linear dimensionality reduction algorithms have been designed, such as Principal Component Analysis and Linear Discriminant Analysis. These methods can be powerful, but often miss important non-linear structure in the data. In this thesis, manifold learning approaches to dimensionality reduction are developed. These approaches combine both linear and non-linear methods of dimension reduction.

I evaluated twenty-eight years (1980-2007) of spatial-temporal water quality data from Deer Creek Reservoir in Utah. The data came from three sampling points representing the lotic, transitional and lentic zones. The data included measurements of climatological, hydrological and water quality conditions at four depths; Surface, Above Thermocline, Below Thermocline and Bottom. The time frame spanned dates before and after the completion of the Jordanelle Reservoir (1987-1992), approximately fourteen miles upstream of Deer Creek. I compared temporal groupings and found that a traditional month distribution following standard seasons was not effective in characterizing the measured conditions; I developed a more representative seasonal grouping by performing a Tukey-Kramer multiple comparisons adjustment and a Bonferronian correction of the Student's t comparison. Based on these analyses, I determined the best groupings were Cold (December - April), Semi-Cold (May and November), Semi-Warm (June and October), Warm (July and September) and Transition (August). I performed principal component analysis (PCA) and factor analysis (FA) to determine principal parameters associated with the variability of the water quality of the reservoir. These parameters confirmed our seasonal groups showing the Cold, Transition and Warm seasons as distinct groups. The PCA and FA showed that the variables that drive most of the variability in the reservoir are specific conductivity and variables related with temperature. The PCA and FA showed that the reservoir is highly variable. The first 3 principal components and rotated factors explained a cumulative 59% and 47%, respectively of the variability in Deer Creek. Both parametric and nonparametric approaches provided similar correlations but the evaluations that included censored data (nutrients) were considerably different with the nonparametric approach being preferred.

The Cettiidae, a family of primarily small, insectivorous, Asiatic and Austronesian, mountain birds have been the subject of acoustic analysis in the past. However, until this point, an in-depth review of the songs of the entire family had yet to be undertaken. In an effort to resolve this shortcoming, the songs of 29 Cettiidae species were examined through the usage of acoustic analysis software, with specific factors such as bandwidth, frequency, and strophe duration being statistically recorded. In total 286 individuals and over 800 strophes were analyzed, with the collected data being displayed in various PCA plots. These PCA graphs were then compared to both a dated phylogenetic tree specifically created for this study, and a Mahalanobis distance vs. genetic distance plot, created using the acoustic data as well as Cytochrome b genetic data. Based on these plots, several notable trends could be observed across the entire family. While largescale divergence from the norm was noted in several pairwise comparisons of species, as well as large scale conservation within clades such as the island Horornis species, examples of convergent evolution of their songs was rather scant. It was also noted that despite the strong divergence of certain species, each genus occupied its own area of multivariate space within the PCAs. Strong statistical divergence between island and continental species was also noted in both the PCAs and the Mahalanobis graph. Meanwhile, the statistical analysis of these species unfortunately provided no clues as to the ancestral state of their songs. However, a visual analysis of every species song, mapped on the dated phylogenetic tree, suggested that two distinct linages of simple and complex songs could be traced back approximately 10 million years. This allows for speculation as to the songs of now long extinct Cettiidae species as far back as the Miocene.

Alpha-ketoglutaric acid (AKGA) is an organic acid important for the metabolism of essential amino acids as well as for the transfer of cellular energy. It is a precursor of glutamic acid which is produced by the human body during the Krebs Cycle. AKGA has a specific industrial interest as it can be taken as a dietary supplement and is also widely used as a building block in chemical synthesis. Collectively termed as hydrazine (HZs), hydrazine (HZ) and monomethylhydrazine (MMH) are hypergolic fuels that do not need an ignition source to burn. Because of the particular HZs' characteristics the National Aeronautics and Space Administration (NASA) at Kennedy Space Center (KSC) and the US Air Force at Cape Canaveral Air Force Station (CCAFS) consistently use HZ and MMH as hypergolic propellants. These propellants are highly reactive and toxic, and have carcinogenic properties. The handling, transport, and disposal of HZ waste are strictly regulated under the Resource Conservation and Recovery Act (RCRA) to protect human health and the environment. Significant quantities of wastewater containing residuals of HZ and MMH are generated at KSC and CCAFS that are subsequently disposed off-site as hazardous waste. This hazardous waste is shipped for disposal over public highways, which presents a potential threat to the public and the environment in the event of an accidental discharge in transit. NASA became aware of research done using AKGA to neutralize HZ waste. This research indicated that AKGA transformed HZ in an irreversible reaction potentially leading to the disposal of the hypergols via the wastewater treatment facility located at CCAFS eliminating the need to transport most of the HZ waste off-site. New Mexico Highlands University (NMHU) has researched this transformation of HZ by reaction with AKGA to form stabilized pyridazine derivatives. NMHU's research suggests that the treatment of HZ and MMH using AKGA is an irreversible reaction; once the reaction takes place, HZ and/or MMH cannot re-form from the byproducts obtained. However, further knowledge relating to the ultimate end products of the reaction, and their effects on human health and the environment, must still be addressed. The known byproduct of the AKGA/HZ neutralization reaction is 6-oxo-1,4,5,6-tetrahydro-pyridazine-3-carboxylic acid (PCA), and the byproduct of the AKGA/MMH reaction is 1-methyl-6-oxo-4,5-dihydro-pyridazine-3-carboxylic acid (mPCA). This research addressed several primary areas of interest to further the potential use of AKGA for HZ and MMH neutralization: 1) isolation of the end-product of the MMH-AKGA degradation process, 1-methyl-6-oxo-4,5-dihydro-pyridazine-3-carboxylic acid (mPCA), and determination of several physical properties of this substance, 2) evaluation of the kinetics of the reaction of AKGA with HZ or MMH, 3) verification of the chemical mechanism for the reaction of the individual hypergols with AKGA, 4) determination of whether the addition of a silicone-based antifoaming agent (AF), citric acid (CA) and/or isopropyl alcohol (IPA) to the AKGA and HZ or MMH solution interferes with the degradation reaction, 4) application of laboratory bench scale experiments in field samples, and 5) determination of the reaction enthalpy of these reactions.

The main types of hypergolic propellants used at Kennedy Space Center (KSC) are hydrazine (HZ) and monomethylhydrazine (MMH). HZ and MMH are classified as hazardous materials and they are also known to be potentially carcinogenic to humans; therefore, handling these substances and their waste is strictly regulated. The wastes streams from HZ and MMH have been estimated to be the main hazardous wastes streams at KSC. Currently at KSC these wastes are first neutralized using citric acid and then they are transported on public roads for incineration as hazardous materials. A new method using alpha ketoglutaric acid (AKGA) was proposed to treat HZ and MMH wastes. From the reaction of AKGA with HZ and MMH two stable products are formed, 1,4,5,6-tetrahydro-6-oxo-3-pyridazinecarboxylic acid (PCA) and lmethyl-1,4,5,6-tetrahydro-6-oxo-3-pyridazinecarboxylic acid (mPCA), respectively. The cost of purchasing AKGA is greater than the cost of purchasing citric acid; thus, AKGA can only become a cost effective alternative for the treatment of HZ and MMH wastes if the products of the reactions (PCA and mPCA) can be safely disposed of into the sewage system without affecting the treatment efficiency and effluent quality of the wastewater treatment plant (WWTP). In this research mPCA and PCA were analyzed for acute toxicity using fish and crustaceans as well as their effect on the wastewater treatment efficiency and viability using AS microbes, and their biodegradability by AS organisms. Acute toxicity on fish and crustaceans was investigated according to the methods for acute toxicity by USEPA (USEPA Method EPA- 821-R-02-012) using Ceriodaphnia dubia (96 hours) and Pimephales promelas (96 hours) as the test organisms. The effect of mPCA and PCA in the treatment efficiency and viability were iii estimated from respiration inhibition tests (USEPA Method OCSPP 850.3300) and heterotrophic plate counts (HPCs). Lastly, the biodegradability of mPCA and PCA was assessed using the Closed Bottle Test (USEPA Method OPPTS 835.3110). For mPCA, the 96 hours LC50 for C. dubia was estimated at 0.77 ± 0.06 g/L (with a 95% confidence level) and the NOEC was estimated at 0.5 g/L. For P. promelas, the LC50 was above 1.5 g/L but it was noticed that mPCA had an effect on their behavior. Abnormal behavior observed included loss of equilibrium and curved spine. The NOEC on the fish was estimated at 0.75 g/L. PCA did not exhibit a significant mortality on fish or crustaceans. The LC50 of PCA in P. promelas and C. dubia was > 1.5 g/L and the NOEC was 1.5 g/L for both organisms. An Inhibitory effect on the heterotrophic respiration of activated sludge organisms was not observed after exposing them for 180-min to PCA and mPCA at concentrations of up to 1.5 g/L compared to the blank controls. Overall the impact of PCA and mPCA on total respiration rates was small, and only observed at 1,500 mg/L if at all. The difference was apparently caused by inhibition of nitrification rather than heterotrophic inhibition. However due to the variability observed in the measurements of the replicates, it is not possible to firmly conclude that PCA or mPCA at 1,500 mg/L was inhibitory to nitrification. Based on the results from the HPCs, mPCA and PCA did not affect the viability of heterotrophic organisms at 750 mg/L. In the BOD-like closed bottle test using a diluted activated sludge mixed liquor sample, the AS microorganisms were capable of biodegrading up to 67% of a 2 mg/L concentration of PCA (with respect to its theoretical oxygen demand, or ThOD) in 28 days. No biodegradation was observed in the samples containing 2 and 5 mg/L of mPCA after 28 days of incubation using a diluted activated sludge mixed liquor sample as inoculum. iv The results of this study show that mPCA is more toxic than PCA to Ceriodaphnia dubia and Pimephales promelas. However neither mPCA nor PCA had an effect on the heterotrophic respiration of an AS mixed liquor sample at 1.5 g/L and there was probably no significant inhibition of the nitrification respiration. Samples of PCA and mPCA at 2 and 5 mg/L could not be completely degraded (with respect to their total theoretical oxygen demand) by dilute AS biomass during a 28 day incubation period. mPCA did not show significant degradation in the two different biodegradation tests performed.

In recent years, there has been an increasing interest in face recognition. As a result, many new facial recognition techniques have been introduced. Recent developments in the field of face recognition have led to an increase in the number of available face recognition commercial products. However, Face recognition techniques are currently constrained by three main factors: recognition accuracy, computational complexity, and storage requirements. The problem is that most of the current face recognition techniques succeed in improving one or two of these factors at the expense of the others. In this dissertation, four novel face recognition techniques that improve the storage and computational requirements of face recognition systems are presented and analyzed. Three of the four novel face recognition techniques to be introduced, namely, Quantized/truncated Transform Domain (QTD), Frequency Domain Thresholding and Quantization (FD-TQ), and Normalized Transform Domain (NTD). All the three techniques utilize the Two-dimensional Discrete Cosine Transform (DCT-II), which reduces the dimensionality of facial feature images, thereby reducing the computational complexity. The fourth novel face recognition technique is introduced, namely, the Normalized Histogram Intensity (NHI). It is based on utilizing the pixel intensity histogram of poses' subimages, which reduces the computational complexity and the needed storage requirements. Various simulation experiments using MATLAB were conducted to test the proposed methods. For the purpose of benchmarking the performance of the proposed methods, the simulation experiments were performed using current state-of-the-art face recognition techniques, namely, Two Dimensional Principal Component Analysis (2DPCA), Two-Directional Two-Dimensional Principal Component Analysis ((2D)^2PCA), and Transform Domain Two Dimensional Principal Component Analysis (TD2DPCA). The experiments were applied to the ORL, Yale, and FERET databases. The experimental results for the proposed techniques confirm that the use of any of the four novel techniques examined in this study results in a significant reduction in computational complexity and storage requirements compared to the state-of-the-art techniques without sacrificing the recognition accuracy.

Early detection and mitigation of wildfires can reduce devastating property damage, firefighting costs, pollution, and loss of life. This thesis proposes the method of Principal Component Analysis (PCA) of images in the temporal domain to identify a smoke plume in wildfires. Temporal PCA is an effective motion detector, and spatial filtering of the output Principal Component images can segment the smoke plume region. The effective use of other image processing techniques to identify smoke plumes and heat plumes are compared. The best attributes of smoke plume detectors and heat plume detectors are evaluated for combination in an improved wildfire detection system. PCA of visible blue images at an image sampling rate of 2 seconds per image effectively exploits a smoke plume signal. PCA of infrared images is the fundamental technique for exploiting a heat plume signal. A system architecture is proposed for the implementation of image processing techniques. The real-world deployment and usability are described for this system.

The effects of global warming are well understood. In order to combat this, society must move towards net zero emissions of green house gases, where carbon dioxide (CO2) plays a key role. In several IPCC climate scenarios that meet the Paris agreement, negative emission technologies that effectively remove CO2 from the atmosphere are included. Of several different technologies, bioenergy with carbon capture and storage (BECCS) is one of the most mature. This technology utilises an absorption-desorption process where CO2 is solved in liquid, producing a rich solvent, and later desorbed, resulting in pure CO2. There are, however, still challenges to implement this technology on a large scale, and one such issue is the monitoring of process streams to gain control over process conditions and system parameters.  In this project, the absorption solvent in BECCS processes were mimicked in order to determine if FTIR spectroscopy could be used to produce process parameters that are accurate, sensitive and robust. Accuracy and sensitivity are defined as the ability to correctly predict the presence and amount of species of interest in the absorption liquid. Robustness on the other hand is defined as the ability to produce precise measurements in the presence of pollutants. To evaluate how accurate and sensitive the measurements are, two different numerical models were developed and calibrated using prepared samples mimicking an absorption solvent. One model was solely based on the least square method, whereas the other was based on principal component analysis (PCA). These models were then tested on clean validation samples, as well as pilot plant samples from Stockholm Exergi, in a case study. An analysis of FTIR spectra from simulated absorption liquids showed that it could distinguish between the species of interest. Furthermore, the spectra showed that pollutants did not impact the readings in a major way. The results showed that both models produced accurate predictions of process parameters.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.