936 resultados para Data Acquisition Methods.


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Multi-frequency eddy current measurements are employed in estimating pressure tube (PT) to calandria tube (CT) gap in CANDU fuel channels, a critical inspection activity required to ensure fitness for service of fuel channels. In this thesis, a comprehensive characterization of eddy current gap data is laid out, in order to extract further information on fuel channel condition, and to identify generalized applications for multi-frequency eddy current data. A surface profiling technique, generalizable to multiple probe and conductive material configurations has been developed. This technique has allowed for identification of various pressure tube artefacts, has been independently validated (using ultrasonic measurements), and has been deployed and commissioned at Ontario Power Generation. Dodd and Deeds solutions to the electromagnetic boundary value problem associated with the PT to CT gap probe configuration were experimentally validated for amplitude response to changes in gap. Using the validated Dodd and Deeds solutions, principal components analysis (PCA) has been employed to identify independence and redundancies in multi-frequency eddy current data. This has allowed for an enhanced visualization of factors affecting gap measurement. Results of the PCA of simulation data are consistent with the skin depth equation, and are validated against PCA of physical experiments. Finally, compressed data acquisition has been realized, allowing faster data acquisition for multi-frequency eddy current systems with hardware limitations, and is generalizable to other applications where real time acquisition of large data sets is prohibitive.

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The present thesis describes the development of heterogeneous catalytic methodologies using metal−organic frameworks (MOFs) as porous matrices for supporting transition metal catalysts. A wide spectrum of chemical reactions is covered. Following the introductory section (Chapter 1), the results are divided between one descriptive part (Chapter 2) and four experimental parts (Chapters 3–6). Chapter 2 provides a detailed account of MOFs and their role in heterogeneous catalysis. Specific synthesis methods and characterization techniques that may be unfamiliar to organic chemists are illustrated based on examples from this work. Pd-catalyzed heterogeneous C−C coupling and C−H functionalization reactions are studied in Chapter 3, with focus on their practical utility. A vast functional group tolerance is reported, allowing access to substrates of relevance for the pharmaceutical industry. Issues concerning the recyclability of MOF-supported catalysts, leaching and operation under continuous flow are discussed in detail. The following chapter explores puzzling questions regarding the nature of the catalytically active species and the pathways of deactivation for Pd@MOF catalysts. These questions are addressed through detailed mechanistic investigations which include in situ XRD and XAS data acquisition. For this purpose a custom reaction cell is also described in Chapter 4. The scope of Pd@MOF-catalyzed reactions is expanded in Chapter 5. A strategy for boosting the thermal and chemical robustness of MOF crystals is presented. Pd@MOF catalysts are coated with a protecting SiO2 layer, which improves their mechanical properties without impeding diffusion. The resulting nanocomposite is better suited to withstand the harsh conditions of aerobic oxidation reactions. In this chapter, the influence of the nanoparticles’ geometry over the catalyst’s selectivity is also investigated. While Chapters 3–5 dealt with Pd-catalyzed processes, Chapter 6 introduces hybrid materials based on first-row transition metals. Their reactivity is explored towards light-driven water splitting. The heterogenization process leads to stabilized active sites, facilitating the spectroscopic probing of intermediates in the catalytic cycle.

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This report evaluates the use of remotely sensed images in implementing the Iowa DOT LRS that is currently in the stages of system architecture. The Iowa Department of Transportation is investing a significant amount of time and resources into creation of a linear referencing system (LRS). A significant portion of the effort in implementing the system will be creation of a datum, which includes geographically locating anchor points and then measuring anchor section distances between those anchor points. Currently, system architecture and evaluation of different data collection methods to establish the LRS datum is being performed for the DOT by an outside consulting team.

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This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.

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Thesis (Ph.D.)--University of Washington, 2016-08

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One of the most challenging task underlying many hyperspectral imagery applications is the spectral unmixing, which decomposes a mixed pixel into a collection of reectance spectra, called endmember signatures, and their corresponding fractional abundances. Independent Component Analysis (ICA) have recently been proposed as a tool to unmix hyperspectral data. The basic goal of ICA is to nd a linear transformation to recover independent sources (abundance fractions) given only sensor observations that are unknown linear mixtures of the unobserved independent sources. In hyperspectral imagery the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be independent. This paper address hyperspectral data source dependence and its impact on ICA performance. The study consider simulated and real data. In simulated scenarios hyperspectral observations are described by a generative model that takes into account the degradation mechanisms normally found in hyperspectral applications. We conclude that ICA does not unmix correctly all sources. This conclusion is based on the a study of the mutual information. Nevertheless, some sources might be well separated mainly if the number of sources is large and the signal-to-noise ratio (SNR) is high.

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This paper provides an overview of IDS types and how they work as well as configuration considerations and issues that affect them. Advanced methods of increasing the performance of an IDS are explored such as specification based IDS for protecting Supervisory Control And Data Acquisition (SCADA) and Cloud networks. Also by providing a review of varied studies ranging from issues in configuration and specific problems to custom techniques and cutting edge studies a reference can be provided to others interested in learning about and developing IDS solutions. Intrusion Detection is an area of much required study to provide solutions to satisfy evolving services and networks and systems that support them. This paper aims to be a reference for IDS technologies other researchers and developers interested in the field of intrusion detection.

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Neuropeptides affect the activity of the myriad of neuronal circuits in the brain. They are under tight spatial and chemical control and the dynamics of their release and catabolism directly modify neuronal network activity. Understanding neuropeptide functioning requires approaches to determine their chemical and spatial heterogeneity within neural tissue, but most imaging techniques do not provide the complete information desired. To provide chemical information, most imaging techniques used to study the nervous system require preselection and labeling of the peptides of interest; however, mass spectrometry imaging (MSI) detects analytes across a broad mass range without the need to target a specific analyte. When used with matrix-assisted laser desorption/ionization (MALDI), MSI detects analytes in the mass range of neuropeptides. MALDI MSI simultaneously provides spatial and chemical information resulting in images that plot the spatial distributions of neuropeptides over the surface of a thin slice of neural tissue. Here a variety of approaches for neuropeptide characterization are developed. Specifically, several computational approaches are combined with MALDI MSI to create improved approaches that provide spatial distributions and neuropeptide characterizations. After successfully validating these MALDI MSI protocols, the methods are applied to characterize both known and unidentified neuropeptides from neural tissues. The methods are further adapted from tissue analysis to be able to perform tandem MS (MS/MS) imaging on neuronal cultures to enable the study of network formation. In addition, MALDI MSI has been carried out over the timecourse of nervous system regeneration in planarian flatworms resulting in the discovery of two novel neuropeptides that may be involved in planarian regeneration. In addition, several bioinformatic tools are developed to predict final neuropeptide structures and associated masses that can be compared to experimental MSI data in order to make assignments of neuropeptide identities. The integration of computational approaches into the experimental design of MALDI MSI has allowed improved instrument automation and enhanced data acquisition and analysis. These tools also make the methods versatile and adaptable to new sample types.

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The aim of this study was to evaluate the effective dose received by patients undergoing CCTA in both acquisition methods in the period June 1st to October 30th, 2013. Data collection was performed at the Clínica Sabedotti in Ponta Grossa/PR, with General Electric Equipment VCT XT, 64 detections lines. The effective dose was measured from the thirty cases randomly selected of Picture Archival and Communication System – PACS, reported by Dose Lenght Product (DLP) equipment for each examination and the conversion factor (EDLP) set by the European Commission for cardiac region (EDLP = 0.014). The results showed significant differences in radiation dose delivered to the patient according to the employee acquisition method, Retrospective or Prospective of ECG.

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In the context of this work we evaluated a multisensory, noninvasive prototype platform for shake flask cultivations by monitoring three basic parameters (pH, pO2 and biomass). The focus lies on the evaluation of the biomass sensor based on backward light scattering. The application spectrum was expanded to four new organisms in addition to E. coli K12 and S. cerevisiae [1]. It could be shown that the sensor is appropriate for a wide range of standard microorganisms, e.g., L. zeae, K. pastoris, A. niger and CHO-K1. The biomass sensor signal could successfully be correlated and calibrated with well-known measurement methods like OD600, cell dry weight (CDW) and cell concentration. Logarithmic and Bleasdale-Nelder derived functions were adequate for data fitting. Measurements at low cell concentrations proved to be critical in terms of a high signal to noise ratio, but the integration of a custom made light shade in the shake flask improved these measurements significantly. This sensor based measurement method has a high potential to initiate a new generation of online bioprocess monitoring. Metabolic studies will particularly benefit from the multisensory data acquisition. The sensor is already used in labscale experiments for shake flask cultivations.

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Os controladores de caudal, normalmente implementados em sistemas Supervisory control and data acquisition (SCADA), apresentam uma grande relevância no controlo automático de canais de adução. Para garantir que os controladores de caudal sejam fiáveis em todo o seu domínio de funcionamento (em situações de escoamento com ressalto livre ou submerso e de transição entre escoamentos com ressalto livre e ressalto submerso) foram comparados os resultados dos ensaios experimentais com diferentes métodos de cálculo da vazão em comportas e/ou sobre soleiras. O programa de ensaios foi realizado nos canais laboratorial e experimental da Universidade de Évora. Foram realizados ensaios em comportas planas verticais e em soleiras do tipo Waterways Experiment Station (WES) controladas ou não por comportas planas verticais. Em ambos os casos, foram contempladas as situações de escoamento com ressalto livre e submerso. Os resultados obtidos mostram que: a) para as comportas, o método Rajaratnam e Subramanya (1967a) conduz a bons resultados com um erro percentual médio absoluto MAPE < 1% para o escoamento com ressalto livre e MAPE < 4% para o submerso; a transição entre escoamentos foi identificada corretamente por este método; b) para as soleiras, obtiveram-se bons resultados para o escoamento com ressalto livre para o método USACE (1987), com MAPE < 2%, e para o submerso através do método Alves e Martins (2011), com MAPE < 5%; a transição entre escoamentos pode ser considerada adequada de acordo com a curva experimental de Grace (1963); c) para soleiras controladas por comporta, conseguiram-se bons resultados para o escoamento com ressalto livre recorrendo à equação dos orifícios de pequenas dimensões, com MAPE < 1, 5%, e para o submerso com a equação dos orifícios totalmente submersos com MAPE < 1, 6%; em ambos os casos foi necessária calibração do coeficiente de vazão; a transição entre escoamentos foi adequada pelo método de Grace (1963). Com base nos resultados obtidos, foi possível definir um algoritmo de vazão generalizado para comportas e/ou soleiras que permite a determinação da vazão para as situações de escoamento com ressalto livre e submerso incluindo a transição entre escoamentos; ABSTRACT: Flow controllers, usually implemented in Supervisory Control and Data Acquisition (SCADA) systems, are very important in the automatic control of irrigation canal systems. To ensure that flow controllers are reliable for the entire operating range (free or submerged flow and flow transitions) the experimental results were compared with different methods of flow measurement for gates and/or weirs. The test program was conducted in the laboratory flume and in the automatic canal of the University of ´Evora. Tests were carried in sluice gates and in broad-crested weirs controlled or not by sluice gate. In both cases free and submerged flow conditions were analyzed. The results show that: a) for the sluice gates, the method of Rajaratnam e Subramanya (1967a) leads to good results with a mean absolute percentage error (MAPE) < 1% for free flow and MAPE < 4% for submerged flow. The transition between flows is correctly identified by this method; b) for the uncontrolled weir, good results were obtained for free flow with the method USACE (1987) with MAPE < 2%, and for submerged flow by the method Alves e Martins (2011) with MAPE < 5%. The transition between flows can be accurately defined by the experimental curve of Grace (1963); c) for the controlled weir, good results were achieved for the free flow with the small orifice equation with MAPE < 1.5% and for submerged flow with the submerged orifice equation with MAPE < 1.6%; in both cases the calibration of the discharge coefficient is needed. The transition between flows can be accomplished through Grace (1963) method. Based on the obtained results, it was possible to define a generalized flow algorithm for gates and/or weirs that allows flow determination for free and submerged flow conditions including the transition between flows.