The use of filter paper plasticized with polyvinyl alcohol-glutaraldehyde in ELISA

F1-antigen purified from Yersinia pestis was covalently linked to 5-mm diameter filter paper discs plasticized with polyvinyl alcohol-glutaraldehyde. These discs were used both for ELISA and dot-ELISA for the detection of anti-F1 IgG in rabbits. The best conditions were achieved using 1.25 µg of F1 antigen/disc, 3% w/v skim milk in PBS as blocking agent, anti-IgG peroxidase conjugate diluted 12,000 times, and serum from rabbits immunized or not against Y. pestis, diluted 6,400 times. The absorbance values obtained from the comparative study between this procedure and conventional ELISA were not significantly different but the low cost of the reagents employed in ELISA using the filter paper discs plasticized with polyvinyl alcohol-glutaraldehyde makes this method economically attractive.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Kalman filter for computational market dynamics

Kalman filter is a recursive mathematical power tool that plays an increasingly vital role in innumerable fields of study. The filter has been put to service in a multitude of studies involving both time series modelling and financial time series modelling. Modelling time series data in Computational Market Dynamics (CMD) can be accomplished using the Jablonska-Capasso-Morale (JCM) model. Maximum likelihood approach has always been utilised to estimate the parameters of the JCM model. The purpose of this study is to discover if the Kalman filter can be effectively utilized in CMD. Ensemble Kalman filter (EnKF), with 50 ensemble members, applied to US sugar prices spanning the period of January, 1960 to February, 2012 was employed for this work. The real data and Kalman filter trajectories showed no significant discrepancies, hence indicating satisfactory performance of the technique. Since only US sugar prices were utilized, it would be interesting to discover the nature of results if other data sets are employed.

Initialization of Continuous Nonlinear Models Using Extended Kalman Filter

The two main objectives of Bayesian inference are to estimate parameters and states. In this thesis, we are interested in how this can be done in the framework of state-space models when there is a complete or partial lack of knowledge of the initial state of a continuous nonlinear dynamical system. In literature, similar problems have been referred to as diffuse initialization problems. This is achieved first by extending the previously developed diffuse initialization Kalman filtering techniques for discrete systems to continuous systems. The second objective is to estimate parameters using MCMC methods with a likelihood function obtained from the diffuse filtering. These methods are tried on the data collected from the 1995 Ebola outbreak in Kikwit, DRC in order to estimate the parameters of the system.

Summa rerum genera esse tantum duo: substantiam nimirum & accidens, ex suffragio ampliss. facult. philos. in florentissimo ad Auram Athenaeo, sub umbone ... dn. mag. Simonis Tålpo, met & log. professoris famigeratissimi, consistorii Acad. assessoris gravissimi nec non annexae Pijkensis pastoris longe fidelissimi. Summos in philosophia honores aditurus dissertatione philosophica ad sobrie philosophantium examen modeste delata statuere conabitur Jeremias Wallenius J. f. Tavastensis. Ad diem 21. Nov. anni redempti orbis M. DC. XC. IV. Horis pomeridianis in auditorio maximo

Invokaatio: A.J.

Compendium metaphysicae, juxta ductum & akribeian clarissimorum in hoc genere philosophorum concinnatum, & tam disputando quàm legendo studiosae juventuti propositum, in Regiâ Academiâ Aboënsi, ab Andrea Thuronio, phys. & log. prof. ordinario. Nunc primâ vice ad usum discentium in lucem prodiens

Dedikaatio: Petrus Brahe.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

Physicochemical and sensory changes in aged sugarcane spirit submitted to filtering with activated carbon filter

Sugarcane spirit is a drink considered as a national symbol of Brazil. It is produced by large producers and by about 30 thousand small and medium home-distilling producers dispersed throughout the country. The copper originating from the home-distillers can become a serious problem since at high concentrations in beverages it may cause serious human health problems. Therefore, the objective of this study was to investigate the influence of the activated carbon used in commercial filters on the physicochemical and sensory characteristics of aged sugarcane spirit. Analyses of copper, dry extract, alcoholic degree, higher alcohols, volatile acids, aldehydes, esters, furfural, and methanol were performed. The sensory evaluation was performed by seven selected trained judges, who analyzed the yellow color, woody aroma and flavor, and intensity of alcoholic aroma and flavor of the cane spirit before and after the filtration process. The sensory tests were carried out using a 9 cm non-structured intensity scale. A reduction was observed in all compounds analyzed physicochemically, except for the esters, which increased after filtration. This increase is probably due to the esterification of the alcohols and acids present. According to the sensory results obtained, a reduction was observed in the intensity of the yellow color, aroma, and wood flavor characteristics, the major characteristics of the aging process.

Fusion of Data from Quadcopter’s Inertial Measurement Unit Using Complementary Filter

A quadcopter is a helicopter with four rotors, which is mechanically simple device, but requires complex electrical control for each motor. Control system needs accurate information about quadcopter’s attitude in order to achieve stable flight. The goal of this bachelor’s thesis was to research how this information could be obtained. Literature review revealed that most of the quadcopters, whose source-code is available, use a complementary filter or some derivative of it to fuse data from a gyroscope, an accelerometer and often also a magnetometer. These sensors combined are called an Inertial Measurement Unit. This thesis focuses on calculating angles from each sensor’s data and fusing these with a complementary filter. On the basis of literature review and measurements using a quadcopter, the proposed filter provides sufficiently accurate attitude data for flight control system. However, a simple complementary filter has one significant drawback – it works reliably only when the quadcopter is hovering or moving at a constant speed. The reason is that an accelerometer can’t be used to measure angles accurately if linear acceleration is present. This problem can be fixed using some derivative of a complementary filter like an adaptive complementary filter or a Kalman filter, which are not covered in this thesis.

Rigid body dynamics and Kalman filtering

X-ray computed log tomography has always been applied for qualitative reconstructions. In most cases, a series of consecutive slices of the timber are scanned to estimate the 3D image reconstruction of the entire log. However, the unexpected movement of the timber under study influences the quality of image reconstruction since the position and orientation of some scanned slices can be incorrectly estimated. In addition, the reconstruction time remains a significant challenge for practical applications. The present study investigates the possibility to employ modern physics engines for the problem of estimating the position of a moving rigid body and its scanned slices which are subject to X-ray computed tomography. The current work includes implementations of the extended Kalman filter and an algebraic reconstruction method for fan-bean computer tomography. In addition, modern techniques such as NVidia PhysX and CUDA are used in current study. As the result, it is numerically shown that it is possible to apply the extended Kalman filter together with a real-time physics engine, known as PhysX, in order to determine the position of a moving object. It is shown that the position of the rigid body can be determined based only on reconstructions of its slices. However, the simulation of the body movement sometimes is subject to an error during Kalman filter employment as PhysX is not always able to continue simulating the movement properly because of incorrect state estimation.

Characterization of the filter media used in the dual media filter

The removal of organics from copper electrolyte solutions after solvent extraction by dual media filtration is one of the most efficient ways to ensure the clean electrolyte flow into the electrowinning. The clean electrolyte will ensure the good quality cathode plate production. Dual media filtration uses two layers of filter media for filtration as anthracite and garnet respectively. The anthracite layer will help the coalescing of the entrained organic droplets which will then float to the top of the filter, and back to the solvent extraction process. The garnet layer will catch any solids left in the electrolyte traveling through the filter media. This thesis will concentrate on characterization of five different anthracites in order to find some differences using specific surface area analysis, particle size analysis, and morphology analysis. These results are compared to the pressure loss values obtained from lab column tests and bed expansion behavior. The goal of the thesis was to find out if there were any differences in the anthracite which would make the one perform better than the other. There were no big differences found on any aspect of the particle characterization, but some found differences should be further studied in order to confirm the meaning of the porosity, surface area, intensity mean and intensity SD (Standard Deviation) on anthracites and their use in dual media filtration. The thesis work analyzed anthracite samples the way that is not found on any public literature sources, and further studies on the issue would bring more knowledge to the electrolyte process.

Photograph - Log cutting

A photograph with a male dressed in a shirt and bow-tie cutting a log and two other men in suits standing on either side of the log. They are surrounded by a large crowd.

Third Annual All-Women's International Air Race Flight Log, 1951

A chart of Dorothy Rungeling's flight landings and departures during the Third Annual All-Women's International Air Race in 1951.

Photograph - Elderly Woman at Log House

A photograph of an elderly female standing on the porch of a log house. A handwritten note is on the reverse.

Reglamento para el régimen interior de la Resp.: Log.: Probidadnum. 76

UANL