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Dona', Eleonora <1987>. "Dollar Cost Averaging VS Suited Dollar Cost Averaging." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/6960.
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The Dollar Cost Averaging is a strategy based on periodic investment of a fixed amount of money, even small one, into a stock or a portfolio each interval over a given period of time. In this way it may be possible to reduce the volatility effects on the market, since each payments could be done both with positive and with negative conditions. What if the investment is not regular in time, but changes according to the market trend? This thesis project aims to compare the standard Dollar Cost Averaging with a suited plan. The amount of money for each instalment will be kept the same as for the standard plan, what will be changed is the instant of time of each investment, that it will be chosen according to the market trend.
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Ullah, Barkat. "Signal Averaging for Digitizer ADQ214." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143779.
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Signal averaging is a signal processing technique applied in the time domain, intended to increase the strength of a signal relative to noise that is obscuring it. From a very long sequence of data, a number of smaller data sequences called records are collected. The form of averaging performed in this thesis was not among samples within a record, but among samples from different records. For example, let's say a sample x(n, k) which is a sample n from record k, where 1 <= n <= N and N is the record size, and 1 <= k <= K, where K is the total number of records it would perform the averaging. Input signals for multi-record is periodic, typically repeated pulses. These records are stored in the memory of the Signal Processing (SP) Devices Digitizer ADQ214. Averaging is being implemented in two ways: software implementation and hardware implementation. In a software implementation the stored records are read out from a Digitizer to PC over a USB interface and averaging is performed in a PC with Matlab. Averaging in a PC takes a significant amount of time because of reading out data through USB interface. The amount of records and number of samples per record play an important role in transferring a record from the Digitizer on board DDR memory to the PC through a USB interface. A large number of records and long record length increases the time to perform averaging. This limitation is removed by implementing averaging in hardware. Verilog, a hardware description language is being used for designing the averaging unit in one of the Virtex5 FPGAs available on the Digitizer ADQ214. Performing averaging in hardware takes much less time than averaging in software. In a hardware implementation it is required to transfer data, which is the result in this case, only once from the Digitizer board to the PC regardless of the number of records under consideration.
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Chaput, Philippe. "Approximating Markov processes by averaging." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66654.
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We recast the theory of labelled Markov processes in a new setting, in a way "dual" to the usual point of view. Instead of considering state transitions as a collection of subprobability distributions on the state space, we view them as transformers of real-valued functions. By generalizing the operation of conditional expectation, we build a category consisting of labelled Markov processes viewed as a collection of operators; the arrows of this category behave as projections on a smaller state space. We define a notion of equivalence for such processes, called bisimulation, which is closely linked to the usual definition for probabilistic processes. We show that we can categorically construct the smallest bisimilar process, and that this smallest object is linked to a well-known modal logic. We also expose an approximation scheme based on this logic, where the state space of the approximants is finite; furthermore, we show that these finite approximants categorically converge to the smallest bisimilar process.Nous reconsidérons les processus de Markov étiquetés sous une nouvelle approche, dans un certain sens "dual'' au point de vue usuel. Au lieu de considérer les transitions d'état en état en tant qu'une collection de distributions de sous-probabilités sur l'espace d'états, nous les regardons en tant que transformations de fonctions réelles. En généralisant l'opération d'espérance conditionelle, nous construisons une catégorie où les objets sont des processus de Markov étiquetés regardés en tant qu'un rassemblement d'opérateurs; les flèches de cette catégorie se comportent comme des projections sur un espace d'états plus petit. Nous définissons une notion d'équivalence pour de tels processus, que l'on appelle bisimulation, qui est intimement liée avec la définition usuelle pour les processus probabilistes. Nous démontrons que nous pouvons construire, d'une manière catégorique, le plus petit processus bisimilaire à un processus donné, et que ce plus petit object est lié à une logique modale bien connue. Nous développons une méthode d'approximation basée sur cette logique, où l'espace d'états des processus approximatifs est fini; de plus, nous démontrons que ces processus approximatifs convergent, d'une manière catégorique, au plus petit processus bisimilaire.
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Verra, Christina. "Macroeconomic forecasting using model averaging." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/383.
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Recently, there has been a broadening concern on forecasting techniques that are applied on large data sets, since economists in business and management want to deal with the great magnitude of information. In this analysis, the issue of forecasting a large data set by using different model averaging approaches is addressed. In particular, Bayesian and frequentist model averaging methods are considered, including Bayesian model averaging (BMA), information theoretic model averaging (ITMA) and predictive likelihood model averaging (PLMA). The predictive performance of each scheme is compared with the most promising existing alternatives, namely benchmark AR model and the equal weighted model averaging (AV) scheme. An empirical application on Inflation forecasting for five countries using large data sets within the model averaging framework is applied. The average ARX model with weights constructed differently according to each model averaging scheme is compared with both the benchmark AR and the AV model. For the comparison of the accuracy of forecasts several performance indicators have been provided such as the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), the U-Theil’s Inequality Coefficient (U), Mean Square Forecast Error (MSFE) and the Relative Mean Square Forecast Error (RMSFE). Next, within the Granger causality framework through the Diebold & Mariano (DM) test and the Clark & McCracken (CM) test, whether the data-rich models represented by the three different model averaging schemes have made a statistically significant improvement relative to the benchmark forecasts has been tested. Critical values at 5% and at 10% have been calculated based on bootstrap approximation of the finite sample distribution of the DM and CM test statistics. The main outcome is that although the information theoretic model averaging scheme is a more powerful approach, the other two model averaging techniques can be regarded as useful alternatives.
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Rajagopalan, Shreevatsa. "Distributed averaging in dynamic networks." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62315.
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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 39-40).
The question of computing average of numbers present at nodes in a network in a distributed manner using gossip or message-passing algorithms has been of great recent interest across disciplines -- algorithms, control and robotics, estimation, social networks, etc. It has served as a non-trivial, representative model for an important class of questions arising in these disciplines and thus guiding intellectual progress over the past few decades. In most of these applications, there is inherent dynamics present, such as changes in the network topology in terms of communication links, changes in the values of numbers present at nodes, and nodes joining or leaving. The effect of dynamics in terms of communication links on the design and analysis of algorithms for averaging is reasonably well understood, e.g. [14][2][8][4]. However, little is known about the effect of other forms of dynamics. In this thesis, we study the effect of such types of dynamics in the context of maintaining average in the network. Specifically, we design dynamics-aware message-passing or gossip algorithm that maintains good estimate of average in presence of continuous change in numbers at nodes. Clearly, in presence of such dynamics the best one can hope for is a tradeoff between the accuracy of each node's estimate of the average at each time instant and the rate of dynamics. For our algorithm, we characterize this tradeoff and establish it to be near optimal. The dependence of the accuracy of the algorithm on the rate of dynamics as well as on the underlying graph structure is quantified.
by Shreevatsa Rajagopalan.
S.M.
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Li, Lan (Simone). "Disparity averaging mechanisms in stereopsis." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2007. http://wwwlib.umi.com/cr/syr/main.
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Afsari, Bijan. "Means and averaging on riemannian manifolds." College Park, Md. : University of Maryland, 2009. http://hdl.handle.net/1903/9978.
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Thesis (Ph.D.) -- University of Maryland, College Park, 2009.Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Pan, Fei. "Multifrequency Averaging in Power Electronic Systems." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/62.
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Power electronic systems have been widely used in the electrical power processing for applications with power levels ranging from less than one watt in battery-operated portable devices to more than megawatts in the converters, inverters and rectifiers of the utility power systems. These systems typically involve the passive elements such as inductors, capacitors, and resistors, the switching electronic components such as IGBTs, MOSFETS, and diodes, and other electronic circuits. Multifrequency averaging is one of the widely used modeling and simulation techniques today for the analysis and design of power electronic systems. This technique is capable of providing the average behavior as well as the ripple behavior of power electronic systems. This work begins with the extension of multifrequency averaging to represent uniformly sampled PWM converters. A new multifrequency averaging method of solving an observed issue with model stability is proposed and validated. Multifrequency averaging can also be applied to study the instability phenomenon in power electronic systems. In particular, a reduced-order multifrequency averaging method, along with a genetic algorithm based procedure, is proposed in this work to estimate the regions of attraction of power electronic converters. The performance of this method is shown by comparing the accuracy and efficiency with the existing methods. Finally, a new continuous-time multifrequency averaging method of representing discrete-time systems is proposed. The proposed method is applied to model digitally controlled PWM converters. Simulation and hardware results show that the proposed method is capable of predicting the average behavior as well as the ripple behavior of the closed-loop systems. Future research in the area of multifrequency averaging is proposed.
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Amini, Moghadam Shahram. "Model Uncertainty & Model Averaging Techniques." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28398.
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The primary aim of this research is to shed more light on the issue of model uncertainty in applied econometrics in general and cross-country growth as well as happiness and well-being regressions in particular. Model uncertainty consists of three main types: theory uncertainty, focusing on which principal determinants of economic growth or happiness should be included in a model; heterogeneity uncertainty, relating to whether or not the parameters that describe growth or happiness are identical across countries; and functional form uncertainty, relating to which growth and well-being regressors enter the model linearly and which ones enter nonlinearly.
Model averaging methods including Bayesian model averaging and Frequentist model averaging are the main statistical tools that incorporate theory uncertainty into the estimation process. To address functional form uncertainty, a variety of techniques have been proposed in the literature. One suggestion, for example, involves adding regressors that are nonlinear functions of the initial set of theory-based regressors or adding regressors whose values are zero below some threshold and non-zero above that threshold. In recent years, however, there has been a rising interest in using nonparametric framework to address nonlinearities in growth and happiness regressions.
The goal of this research is twofold. First, while Bayesian approaches are dominant methods used in economic empirics to average over the model space, I take a fresh look into Frequentist model averaging techniques and propose statistical routines that computationally ease the implementation of these methods. I provide empirical examples showing that Frequentist estimators can compete with their Bayesian peers. The second objective is to use recently-developed nonparametric techniques to overcome the issue of functional form uncertainty while analyzing the variance of distribution of per capita income. Nonparametric paradigm allows for addressing nonlinearities in growth and well-being regressions by relaxing both the functional form assumptions and traditional assumptions on the structure of error terms.Ph. D.
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Noble, Robert Bruce. "Multivariate Applications of Bayesian Model Averaging." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/30180.
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The standard methodology when building statistical models has been to use one of several algorithms to systematically search the model space for a good model. If the number of variables is small then all possible models or best subset procedures may be used, but for data sets with a large number of variables, a stepwise procedure is usually implemented. The stepwise procedure of model selection was designed for its computational efficiency and is not guaranteed to find the best model with respect to any optimality criteria. While the model selected may not be the best possible of those in the model space, commonly it is almost as good as the best model. Many times there will be several models that exist that may be competitors of the best model in terms of the selection criterion, but classical model building dictates that a single model be chosen to the exclusion of all others. An alternative to this is Bayesian model averaging (BMA), which uses the information from all models based on how well each is supported by the data.
Using BMA allows a variance component due to the uncertainty of the model selection process to be estimated. The variance of any statistic of interest is conditional on the model selected so if there is model uncertainty then variance estimates should reflect this. BMA methodology can also be used for variable assessment since the probability that a given variable is active is readily obtained from the individual model posterior probabilities.
The multivariate methods considered in this research are principal components analysis (PCA), canonical variate analysis (CVA), and canonical correlation analysis (CCA). Each method is viewed as a particular multivariate extension of univariate multiple regression. The marginal likelihood of a univariate multiple regression model has been approximated using the Bayes information criteria (BIC), hence the marginal likelihood for these multivariate extensions also makes use of this approximation.
One of the main criticisms of multivariate techniques in general is that they are difficult to interpret. To aid interpretation, BMA methodology is used to assess the contribution of each variable to the methods investigated. A second issue that is addressed is displaying of results of an analysis graphically. The goal here is to effectively convey the germane elements of an analysis when BMA is used in order to obtain a clearer picture of what conclusions should be drawn.
Finally, the model uncertainty variance component can be estimated using BMA. The variance due to model uncertainty is ignored when the standard model building tenets are used giving overly optimistic variance estimates. Even though the model attained via standard techniques may be adequate, in general, it would be difficult to argue that the chosen model is in fact the correct model. It seems more appropriate to incorporate the information from all plausible models that are well supported by the data to make decisions and to use variance estimates that account for the uncertainty in the model estimation as well as model selection.Ph. D.
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Vela, Patricio Antonio Burdick Joel Wakeman. "Averaging and control of nonlinear systems /." Diss., Pasadena, Calif. : California Institute of Technology, 2003. http://resolver.caltech.edu/CaltechETD:etd-05282003-094253.
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Grappin, Edwin. "Model Averaging in Large Scale Learning." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLG001/document.
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Les travaux de cette thèse explorent les propriétés de procédures d'estimation par agrégation appliquées aux problèmes de régressions en grande dimension. Les estimateurs par agrégation à poids exponentiels bénéficient de résultats théoriques optimaux sous une approche PAC-Bayésienne. Cependant, le comportement théorique de l'agrégat avec extit{prior} de Laplace n'est guère connu. Ce dernier est l'analogue du Lasso dans le cadre pseudo-bayésien. Le Chapitre 2 explicite une borne du risque de prédiction de cet estimateur. Le Chapitre 3 prouve qu'une méthode de simulation s'appuyant sur un processus de Langevin Monte Carlo permet de choisir explicitement le nombre d'itérations nécessaire pour garantir une qualité d'approximation souhaitée. Le Chapitre 4 introduit des variantes du Lasso pour améliorer les performances de prédiction dans des contextes partiellement labélisésThis thesis explores properties of estimations procedures related to aggregation in the problem of high-dimensional regression in a sparse setting. The exponentially weighted aggregate (EWA) is well studied in the literature. It benefits from strong results in fixed and random designs with a PAC-Bayesian approach. However, little is known about the properties of the EWA with Laplace prior. Chapter 2 analyses the statistical behaviour of the prediction loss of the EWA with Laplace prior in the fixed design setting. Sharp oracle inequalities which generalize the properties of the Lasso to a larger family of estimators are established. These results also bridge the gap from the Lasso to the Bayesian Lasso. Chapter 3 introduces an adjusted Langevin Monte Carlo sampling method that approximates the EWA with Laplace prior in an explicit finite number of iterations for any targeted accuracy. Chapter 4 explores the statisctical behaviour of adjusted versions of the Lasso for the transductive and semi-supervised learning task in the random design setting
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Grappin, Edwin. "Model Averaging in Large Scale Learning." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLG001.
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Les travaux de cette thèse explorent les propriétés de procédures d'estimation par agrégation appliquées aux problèmes de régressions en grande dimension. Les estimateurs par agrégation à poids exponentiels bénéficient de résultats théoriques optimaux sous une approche PAC-Bayésienne. Cependant, le comportement théorique de l'agrégat avec extit{prior} de Laplace n'est guère connu. Ce dernier est l'analogue du Lasso dans le cadre pseudo-bayésien. Le Chapitre 2 explicite une borne du risque de prédiction de cet estimateur. Le Chapitre 3 prouve qu'une méthode de simulation s'appuyant sur un processus de Langevin Monte Carlo permet de choisir explicitement le nombre d'itérations nécessaire pour garantir une qualité d'approximation souhaitée. Le Chapitre 4 introduit des variantes du Lasso pour améliorer les performances de prédiction dans des contextes partiellement labélisésThis thesis explores properties of estimations procedures related to aggregation in the problem of high-dimensional regression in a sparse setting. The exponentially weighted aggregate (EWA) is well studied in the literature. It benefits from strong results in fixed and random designs with a PAC-Bayesian approach. However, little is known about the properties of the EWA with Laplace prior. Chapter 2 analyses the statistical behaviour of the prediction loss of the EWA with Laplace prior in the fixed design setting. Sharp oracle inequalities which generalize the properties of the Lasso to a larger family of estimators are established. These results also bridge the gap from the Lasso to the Bayesian Lasso. Chapter 3 introduces an adjusted Langevin Monte Carlo sampling method that approximates the EWA with Laplace prior in an explicit finite number of iterations for any targeted accuracy. Chapter 4 explores the statisctical behaviour of adjusted versions of the Lasso for the transductive and semi-supervised learning task in the random design setting
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Roberts, Rhonda Lareha. "Effects of time averaging versus single trial alignment averaging on the characteristics of the event related potentials /." The Ohio State University, 1988. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487597424136941.
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Eklund, Jana. "Essays on forecasting and Bayesian model averaging." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-490.
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This thesis, which consists of four chapters, focuses on forecasting in a data-rich environment and related computational issues. Chapter 1, “An embarrassment of riches: Forecasting using large panels” explores the idea of combining forecasts from various indicator models by using Bayesian model averaging (BMA) and compares the predictive performance of BMA with predictive performance of factor models. The combination of these two methods is also implemented, together with a benchmark, a simple autoregressive model. The forecast comparison is conducted in a pseudo out-of-sample framework for three distinct datasets measured at different frequencies. These include monthly and quarterly US datasets consisting of more than 140 predictors, and a quarterly Swedish dataset with 77 possible predictors. The results show that none of the considered methods is uniformly superior and that no method consistently outperforms or underperforms a simple autoregressive process. Chapter 2. “Forecast combination using predictive measures” proposes using out-of-sample predictive likelihood as the basis for BMA and forecast combination. In addition to its intuitive appeal, the use of the predictive likelihood relaxes the need to specify proper priors for the parameters of each model. We show that the forecast weights based on the predictive likelihood have desirable asymptotic properties. And that these weights will have better small sample properties than the traditional in-sample marginal likelihood when uninformative priors are used. In order to calculate the weights for the combined forecast, a number of observations, a hold-out sample, is needed. There is a trade off involved in the size of the hold-out sample. The number of observations available for estimation is reduced, which might have a detrimental effect. On the other hand, as the hold-out sample size increases, the predictive measure becomes more stable and this should improve performance. When there is a true model in the model set, the predictive likelihood will select the true model asymptotically, but the convergence to the true model is slower than for the marginal likelihood. It is this slower convergence, coupled with protection against overfitting, which is the reason the predictive likelihood performs better when the true model is not in the model set. In Chapter 3. “Forecasting GDP with factor models and Bayesian forecast combination” the predictive likelihood approach developed in the previous chapter is applied to forecasting GDP growth. The analysis is performed on quarterly economic dataset from six countries: Canada, Germany, Great Britain, Italy, Japan and United States. The forecast combination technique based on both in-sample and out-of-sample weights is compared to forecasts based on factor models. The traditional point forecast analysis is extended by considering confidence intervals. The results indicate that forecast combinations based on the predictive likelihood weights have better forecasting performance compared with the factor models and forecast combinations based on the traditional in-sample weights. In contrast to common findings, the predictive likelihood does improve upon an autoregressive process for longer horizons. The largest improvement over the in-sample weights is for small values of hold-out sample sizes, which provides protection against structural breaks at the end of the sample period. The potential benefits of model averaging as a tool for extracting the relevant information from a large set of predictor variables come at the cost of considerable computational complexity. To avoid evaluating all the models, several approaches have been developed to simulate from the posterior distributions. Markov chain Monte Carlo methods can be used to directly draw from the model posterior distributions. It is desirable that the chain moves well through the model space and takes draws from regions with high probabilities. Several computationally efficient sampling schemes, either one at a time or in blocks, have been proposed for speeding up convergence. There is a trade-off between local moves, which make use of the current parameter values to propose plausible values for model parameters, and more global transitions, which potentially allow faster exploration of the distribution of interest, but may be much harder to implement efficiently. Local model moves enable use of fast updating schemes, where it is unnecessary to completely reestimate the new, slightly modified, model to obtain an updated solution. The last fourth chapter “Computational efficiency in Bayesian model and variable selection” investigates the possibility of increasing computational efficiency by using alternative algorithms to obtain estimates of model parameters as well as keeping track of their numerical accuracy. Also, various samplers that explore the model space are presented and compared based on the output of the Markov chain.Diss. Stockholm : Handelshögskolan, 2006
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Högele, Michael, and Paulo Ruffino. "Averaging along Lévy diffusions in foliated spaces." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6492/.
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We consider an SDE driven by a Lévy noise on a foliated manifold, whose trajectories stay on compact leaves. We determine the effective behavior of the system subject to a small smooth transversal perturbation of positive order epsilon. More precisely, we show that the average of the transversal component of the SDE converges to the solution of a deterministic ODE, according to the average of the perturbing vector field with respect to the invariant measures on the leaves (of the unpertubed system) as epsilon goes to 0. In particular we give upper bounds for the rates of convergence. The main results which are proved for pure jump Lévy processes complement the result by Gargate and Ruffino for Stratonovich SDEs to Lévy driven SDEs of Marcus type.
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Douma, Femke. "Counting and averaging problems in graph theory." Thesis, Durham University, 2010. http://etheses.dur.ac.uk/272/.
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Paul Gunther (1966), proved the following result: Given a continuous function f on a compact surface M of constant curvature -1 and its periodic lift g to the universal covering, the hyperbolic plane, then the averages of the lift g over increasing spheres converge to the average of the function f over the surface M. Heinz Huber (1956) considered the following problem on the hyperbolic plane H: Consider a strictly hyperbolic subgroup of automorphisms on H with compact quotient, and choose a conjugacy class in this group. Count the number of vertices inside an increasing ball, which are images of a fixed point x in H under automorphisms in the chosen conjugacy class, and describe the asymptotic behaviour of this number as the size of the ball goes to infinity. In this thesis, we use a well-known analogy between the hyperbolic plane and the regular tree to solve the above problems, and some related ones, on a tree. We deal mainly with regular trees, however some results incorporate more general graphs.
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Olshevsky, Alexander. "Convergence speed in distributed consensus and averaging." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37927.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (leaves 71-75).
We propose three new algorithms for the distributed averaging and consensus problems: two for the fixed-graph case, and one for the dynamic-topology case. The convergence times of our fixed-graph algorithms compare favorably with other known methods, while our algorithm for the dynamic-topology case is the first to be accompanied by a polynomial-time bound on the worst-case convergence time.
by Alexander Olshevsky.
S.M.
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Rodrigues, Camila Aparecida Benedito. "Método do averaging para sistemas de Filippov." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55135/tde-03072015-090140/.
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Um dos mais investigados problemas na teoria qualitativa dos sistemas dinâmicos no plano é o XVI problema de Hilbert que investiga uma cota superior para o número de ciclos limites em sistemas diferenciais polinomiais e suas posições relativas. Por outro lado, os sistemas diferenciais suaves por partes tem despertado o interesse de muitos pesquisadores recentemente devido a sua estreita relação com outras áreas das ciências como física, biologia, economia e engenharias. Portanto é natural a busca pela extensão das técnicas e ferramentas da teoria qualitativa para essa classe de sistemas. Nessa dissertação apresentamos uma generalização da técnica do averaging para uma classe especial dos sistemas de Filippov, conhecida como sistemas diferenciais contínuos por partes, desenvolvida por Llibre-Novaes-Teixeira e, aplicamos essa técnica na investigação de uma classe particular de sistemas, que chamamos do tipo Kukles generalizado.One of the most investigated problems in the qualitative theory of dynamical systems in the plane is the XVI Hilbert\'s problem which asks for the maximum number and position of limity cycles for all planar polynomial differential systems of degree n. On the other hand, recently piecewise continuous differential systems have attracting the interest of many researches specially because of their close relation with other sciences for instance physics, biology, economy and engineering. These relations motivate extensions of the qualitative tools for this class of systems. In this work we present a generalization of the averaging theory for a class of Filippov systems, namely piecewise continuous differential systems, developed by Llibre-Novaes-Teixeira and, we apply this theory to a particular class of differential systems, which we nominate generalized Kukles type.
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Ma, Genuo. "JACKKNIFE MODEL AVERAGING ON FUNCTIONAL LOGISTIC MODEL." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413059.
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zheng, jiayin. "Calibrated Bayes Factor and Bayesian Model Averaging." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265.
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Volinsky, Christopher T. "Bayesian model averaging for censored survival models /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8944.
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Bhat, Harish Subrahmanya Marsden Jerrold E. "Lagrangian averaging, nonlinear waves, and shock capturing /." Diss., Pasadena, Calif. : California Institute of Technology, 2005. http://resolver.caltech.edu/CaltechETD:etd-05262005-100534.
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TURRI, LUCA. "AVERAGING THEOREMS FOR NLS:PROBABILISTIC AND DETERMINISTIC RESULTS." Doctoral thesis, Università degli Studi di Milano, 2019. http://hdl.handle.net/2434/612979.
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In this thesis, we study the dynamics of NLS, in particular, we deal with
the problem of the construction of prime integrals, either in the probabilistic
or in the deterministic case.
In the first part of the thesis, we consider the non linear Schrödinger equation on the one dimensional torus with a defocusing polynomial nonlinearity and we study the dynamics corresponding to initial data in a set of a large measure with respect to the Gibbs measure. We prove that along the corresponding solutions the modulus of the Fourier coefficients is approximately
constant for long time. The proof is obtained by adapting to the context of Gibbs measure for PDEs some tools of Hamiltonian perturbation theory.
In the second part, we consider the nonlinear Schrödinger equation on the two dimensional torus with a time-dependent nonlinearity starting with cubic terms. In this case, using perturbation theory techniques, we construct an
approximate integral of motion that change slowly for initial data with small H^1-norm, this allows to ensure long time existence of solutions in H^1 on the two dimensional torus.
The main difficulty is that H^1 on the two dimensional torus is not an algebra.
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MADORMO, FILOMENA. "Model Averaging using performance and distance measures." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/88349.
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In this work we introduce the problem of forecast combination using performance and distance measures for binary outcome. The thesis is focused on model averaging for parametric and non parametric approaches, with a special attention on temporal dependent and independent models.
In terms of results, we combine single models using performance measures and we investigate how distance measure based on the Mahalanobis distance can lead to interesting results for model combination.
In order to assess the stability and the predictive capability of the models at hand, we employ different cross-validation techniques: Bootstrap cross-validation, 10-fold cross validation and Leave One Out cross-validation.
Empirical evidence are give on a real application to predict default probabilities of Small and Medium Enterprises.
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Zhang, Xuan. "High Precision Dynamic Power System Frequency Estimation Algorithm Based on Phasor Approach." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/31001.
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An internet-based, real-time, Global Positioning System (GPS) ---synchronized relative to the wide-area frequency-monitoring network (FNET) ---has been developed at Virginia Tech. In this FNET system, an algorithm that employs the relationship between phasor angles and deviated frequency [13] is used to calculate both frequency and its rate of change. Tests of the algorithm disclose that, for non-pure sinusoidal input (as compared to pure sinusoidal input), significant errors in the output frequency will result. Three approaches for increasing the accuracy of the output frequency were compared. The first---increasing the number of samples per cycle N---proved ineffective. The second---using the average of the first estimated frequencies rather than the instant first estimated frequency as the resampling frequency---produces a moderate increase in accuracy of the frequency estimation. The third---multiple resampling---significantly increased accuracy. But both the second and the third become ineffective to the extent the input is not pure sinusoidal.
From a practical standpoint, attention needs to be paid toward eliminating noise in the input data from the power grid so as to make it more purely sinusoidal. Therefore, it will be worthwhile to test more sophisticated digital filters for processing the input data before feeding it to the algorithm.Master of Science
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Cândido, Murilo Rodolfo. "New results in averaging theory and its applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/665999.
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En este trabajo presentamos nuevos resultados en la teoría del promedio para encontrar soluciones periódicas.
Usando reducción de Lyapunov-Schmidt y el grado de Brouwer nosotros elaboramos un teorema del promedio capaz de
detectar la persistencia de soluciones periódicas en sistemas diferenciales cuando este tiene un continuo de
ceros en la primera ecuación promediada no nula. También utilizamos la hiperbolicidad k determinada para
describir la estabilidad de estas soluciones periódicas. Por fin, utilizamos estos resultados para estudiar las
soluciones periódicas de varios sistemas diferenciales.This work presents new results in the averaging theory for finding periodic solutions. Using Lyapunov-Schmidt reduction and Brouwer's degree we elaborate an averaging theorem able to detect the persistence of periodic solutions in differential systems when the first nonvanishing averaged equation has a continuum of zeros. We also used k-determined hyperbolicity to describe the stability of such periodic solutions. Finally, we use these results to study the periodic solutions of several differential systems.
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Aljarrah, Inad A. "Color face recognition by auto-regressive moving averaging." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1174410880.
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Letchford, C. W. "Pneumatic averaging and its application in wind engineering." Thesis, University of Oxford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.233481.
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Howard, Teil Bronwen. "Stochastically Perturbed Dynamics : Escape Rates and Averaging Methods." Thesis, University of Bristol, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525448.
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Boyles, Levi Beinarauskas. "General Purpose MCMC Sampling for Bayesian Model Averaging." Thesis, University of California, Irvine, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3631086.
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In this thesis we explore the problem of inference for Bayesian model averaging. Many popular topics in Bayesian analysis, such as Bayesian nonparametrics, can be cast as model averaging problems. Model averaging problems offer unique difficulties for inference, as the parameter space is not fixed, and may be infinite. As such, there is little existing work on general purpose MCMC algorithms in this area. We introduce a new MCMC sampler, which we call Retrospective Jump sampling, that is suitable for general purpose model averaging. In the development of Retrospective Jump, some practical issues arise in the need for a MCMC sampler for finite dimensions that is suitable for multimodal target densities; we introduce Refractive Sampling as a sampler suitable in this regard. Finally, we evaluate Retrospective Jump on several model averaging and Bayesian nonparametric problems, and develop a novel latent feature model with hierarchical column structure which uses Retrospective Jump for inference.
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Zettas, Spiridon. "Adaptive averaging channel estimation for DVB-T2 systems." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16581.
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In modern communication systems, the rate of transmitted data is growing rapidly. This leads to the need for more sophisticated methods and techniques of implementation in every block of the transmitter-receiver chain. The weakest link in radio communications is the transmission channel. The signal, which is passed through it, suffers from many degrading factors like noise, attenuation, diffraction, scattering etc. In the receiver side, the modulated signal has to be restored to its initial state in order to extract the useful information. Assuming that the channel acts like a filter with finite impulse, one has to know its coefficients in order to apply the inverse function, which will restore the signal back to its initial state. The techniques which deal with this problem are called channel estimation. Noise is one of the causes that degrade the quality of the received signal. If it could be discarded, then the process of channel estimation would be easier. Transmitting special symbols, called pilots with known amplitude, phase and position to the receiver and assuming that the noise has zero mean, an averaging process could reduce the noise impact to the pilot amplitudes and thus simplify the channel estimation process. In this thesis, a novel channel estimation method based on noise rejection is introduced. The estimator takes into account the time variations of the channel and adapts its buffer size in order to achieve the best performance. Many configurations of the estimator were tested and at the beginning of the research fixed size estimators were tested. The fixed estimator has a very good performance for channels which could be considered as stationary in the time domain, like Additive White Gaussian Noise (AWGN) channels or slowly time-varying channels. AWGN channel is a channel model where the only distorting factor is the noise, where noise is every unwanted signal interfering with the useful signal. The properties of the noise are that it is additive, which means that the noise is superimposed on the transmitted signal, it is white so the power density is constant for all frequencies, and it has a Gaussian distribution in the time domain with zero mean and variance σ2=N. A slowly time varying channel refers to channel with coherence time larger than the transmitted symbol duration. The performance of a fixed size averaging estimator in case of fast time-varying channels is subject to the buffering time. When the buffering time is smaller or equal to a portion of the coherence time the averaging process offers better performance than the conventional estimation, but when the buffering time exceeds this portion of the coherence time the performance of the averaging process degrades fast. So, an extension has been made to the averaging estimator that estimates the Doppler shift and thus the coherence time, where the channel could be assumed as stationary. The improved estimator called Adaptive Averaging Channel Estimator (AACE) is capable to adjust its buffer size and thus to average only successive Orthogonal Frequency Division Multiplexing (OFDM) symbols that have the same channel distortions. The OFDM is a transmission method where instead of transmitting the data stream using only on carrier, the stream is divided into parallel sub-streams where the subcarriers conveying the sub-streams are orthogonal to each other. The use of the OFDM increases the symbol duration making it more robust against Inter-Symbol Interference (ISI), which the interference among successive transmitted symbols, and also divides the channel bandwidth into small sub-bandwidths preventing frequency selectivity because of the multipath nature of the radio channel. Simulations using the Rayleigh channel model were performed and the results clearly demonstrate the benefits of the AACE in the channel estimation process. The performance of the combination of AACE with Least Square estimation (AACE-LS) is superior to the conventional Least Square estimation especially for low Doppler shifts and it is close to the Linear Minimum Mean Square Error (LMMSE) estimation performance. Consequently, if the receiver has low computational resources and/or the channel statistics are unknown, then the AACE-LS estimator is a valid choice for modern radio receivers. Moreover, the proposed adaptive averaging process could be used in any OFDM system based on pilot aided channel estimation. In order to verify the superiority of the AACE algorithm, quantitative results are provided in terms of BER vs SNR. It is demonstrated that AACE-LS is 7dB more sensitive than the LS estimator.
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GUTIERREZ, Alex Neri. "Um método de averaging para inclusoes diferenciais fuzzy." Universidade Federal de Goiás, 2012. http://repositorio.bc.ufg.br/tede/handle/tde/1954.
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Made available in DSpace on 2014-07-29T16:02:20Z (GMT). No. of bitstreams: 1
ALEX NERI GUTIERREZ DISSERTACAO.pdf: 1234288 bytes, checksum: ae65a58b7c2fd793b3c15d44001d82d6 (MD5)
Previous issue date: 2012-03-23This work has the main objective in the context of the fuzzy theory. Averaging method, differential inclusions are studied; finally this context of the fuzzy theory.
O trabalho tem como objetivo principal, o estudo de um método de averaging em problemas de valor inicial no contexto fuzzy. Com o intuito de facilitar a compreensão do trabalho, faz-se um estudo do, um método de averaging no contexto determinístico, teoria de inclusões diferencias, teoria dos conjuntos fuzzy, inclusões diferenciais fuzzy e finalmente mostra-se o um resultado da validade do método de averaging no contexto fuzzy.
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Jun, Shi. "Frequentist Model Averaging For Functional Logistic Regression Model." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352519.
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Frequentist model averaging as a newly emerging approach provides us a way to overcome the uncertainty caused by traditional model selection in estimation. It acknowledges the contribution of multiple models, instead of making inference and prediction purely based on one single model. Functional logistic regression is also a burgeoning method in studying the relationship between functional covariates and a binary response. In this paper, the frequentist model averaging approach is applied to the functional logistic regression model. A simulation study is implemented to compare its performance with model selection. The analysis shows that when conditional probability is taken as the focus parameter, model averaging is superior to model selection based on BIC. When the focus parameter is the intercept and slopes, model selection performs better.
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Preston, Anthony. "Diffeomorphism-invariant averaging in quantum gravity and cosmology." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/405469/.
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This thesis concerns research undertaken in two related topics concerning high-energy gravitational physics. The first is the construction of a manifestly diffeomorphisminvariant Exact Renormalization Group (ERG). This is a procedure that constructs effective theories of gravity by integrating out high-energy modes down to an ultraviolet cutoff scale without gauge-fixing. The manifest diffeomorphism invariance enables us to construct a fully background-independent formulation. This thesis will explore both the fixed-background and background-independent forms of the manifestly diffeomorphism-invariant ERG. The second topic is cosmological backreaction, which concerns the effect of averaging over high-frequency metric perturbations to the gravitational field equations describing the universe at large scales. This has been much studied the context of the unmodified form of General Relativity, but has been much less studied in the context of higher-derivative effective theories obtained by integrating out the high-energy modes of some more fundamental (quantum) theory of gravity. The effective stress-energy tensor for backreaction can be used directly as a diffeomorphism-invariant effective stress-energy tensor for gravitational waves without specifying the background metric. This thesis will construct the manifestly diffeomorphism-invariant ERG and compute the effective action at the classical level in two different schemes. We will then turn to cosmological backreaction in higher-derivative gravity, deriving the general form of the effective stress-energy tensor due to inhomogeneity for local diffeomorphism-invariant effective theories gravity. This an exciting research direction, as it begins the construction of a quantum theory of gravity as well as investigating possible implications for cosmology.
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Huang, Guan. "An averaging theory for nonlinear partial differential equations." Palaiseau, Ecole polytechnique, 2014. http://pastel.archives-ouvertes.fr/docs/01/00/25/27/PDF/these.pdf.
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Cette thèse se consacre aux études des comportements de longtemps des solutions pour les EDPs nonlinéaires qui sont proches d'une EDP linéaire ou intégrable hamiltonienne. Une théorie de la moyenne pour les EDPs nonlinéaires est presenté. Les modèles d'équations sont les équations Korteweg-de Vries (KdV) perturbées et quelques équations aux dérivées partielles nonlinéaires faiblementThis Ph. D thesis focuses on studying the long-time behavior of solutions for non-linear PDEs that are close to a linear or an integrable Hamiltonian PDE. An averaging theory for nonlinear PDEs is presented. The model equations are the perturbed Korteweg-de Vries (KdV) equations and some weakly nonlinear partial differential equations
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Zulj, Valentin. "On The Jackknife Averaging of Generalized Linear Models." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412831.
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Frequentist model averaging has started to grow in popularity, and it is considered a good alternative to model selection. It has recently been applied favourably to gen- eralized linear models, where it has mainly been purposed to aid the prediction of probabilities. The performance of averaging estimators has largely been compared to that of models selected using AIC or BIC, without much discussion of model screening. In this paper, we study the performance of model averaging in classification problems, and evaluate performances with reference to a single prediction model tuned using cross-validation. We discuss the concept of model screening and suggest two methods of constructing a candidate model set; averaging over the models that make up the LASSO regularization path, and the so called LASSO-GLM hybrid. By means of a Monte Carlo simulation study, we conclude that model averaging does not necessarily offer any improvement in classification rates. In terms of risk, however, we see that both methods of model screening are efficient, and their errors are more stable than those achieved by the cross-validated model of comparison.
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Maiti, Dipayan. "Multiset Model Selection and Averaging, and Interactive Storytelling." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28563.
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The Multiset Sampler [Leman et al., 2009] has previously been deployed and developed for efficient sampling from complex stochastic processes. We extend the sampler and the surrounding theory to model selection problems. In such problems efficient exploration of the model space becomes a challenge since independent and ad-hoc proposals might not be able to jointly propose multiple parameter sets which correctly explain a new pro- posed model. In order to overcome this we propose a multiset on the model space to en- able efficient exploration of multiple model modes with almost no tuning. The Multiset Model Selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. We show that posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. We also obtain typical Bayesian model averaged estimates for the parameters from MSMS. We apply our algorithm to linear regression where it allows easy moves between parame- ter modes of different models, and in probit regression where it allows jumps between widely varying model specific covariance structures in the latent space of a hierarchical model.
The Storytelling algorithm [Kumar et al., 2006] constructs stories by discovering and con- necting latent connections between documents in a network. Such automated algorithms often do not agree with userâ s mental map of the data. Hence systems that incorporate feedback through visual interaction from the user are of immediate importance. We pro- pose a visual analytic framework in which such interactions are naturally incorporated in to the existing Storytelling algorithm through a redefinition of the latent topic space used in the similarity measure of the network. The document network can be explored us- ing the newly learned normalized topic weights for each document. Hence our algorithm augments the limitations of human sensemaking capabilities in large document networks by providing a collaborative framework between the underlying model and the user. Our formulation of the problem is a supervised topic modeling problem where the supervi- sion is based on relationships imposed by the user as a set of inequalities derived from tolerances on edge costs from inverse shortest path problem. We show a probabilistic modeling of the relationships based on auxiliary variables and propose a Gibbs sampling based strategy. We provide detailed results from a simulated data and the Atlantic Storm data set.Ph. D.
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Yoshimura, Arihiro. "Essays on Semiparametric Model Selection and Model Averaging." Kyoto University, 2015. http://hdl.handle.net/2433/199059.
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Swanton, Dale N. "The role of dopamine in temporal memory averaging." Click here for download, 2009. http://proquest.umi.com/pqdweb?did=1786804491&sid=3&Fmt=2&clientId=3260&RQT=309&VName=PQD.
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Zhu, Zhen. "Averaging correlation for weak Global Positioning System signal processing." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1175015135.
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Vogler, Urs. "Numerical modelling of deep mixing with volume averaging technique." Thesis, University of Strathclyde, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501779.
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The mechanical properties of very soft clays, silts and organic soils can be improved with deep mixing, a soil improvement technique in which stabilising agents, such as lime and/or cement are mixed into the soil in-situ by using auger-type mixing tools. Deep mixed columns are nowadays extensively used to reduce settlements and improve the stability of embankments and foundations constructed on soft soils. The problem analysed involving a regular arrangement of columns under an engineering stmcture, such as embankments or strip footings, is a fiilly three dimensional problem. As 3D analyses are computationally very expensive, an enhanced 2D technique using the so-called volume averaging technique has been developed as part of this research. The basic idea is to describe the column-improved ground as a homogenized composite material and map the the 3D problem into 2D.
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Zanetti, Giulia. "Virus structure studied by cryo-electron tomography and averaging." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.531795.
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Liu, Yingying. "Multifrequency Averaging of Hysteresis-Current-Controlled DC-DC Converters." UKnowledge, 2015. http://uknowledge.uky.edu/ece_etds/67.
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Multifrequency averaging is one of the widely used modeling and simulation techniques today for the analysis and design of power electronic systems. This technique is capable of providing the average behavior as well as the ripple behavior of power electronic systems. Hysteresis current control has fast response and internal current stability through controlling switches to maintain the current within a given hysteresis band of a given current command. However the state space variables in a hysteresis controlled system cannot be directly approached by multifrequency averaging method because of time varing switching frequency. In this thesis, a method of applying multifrequency averaging to hysteresis current controlled dc-dc converters is proposed. A dc-dc converter model with the application of this method has been successfully developed and validated both in simulation and experiment.
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Asterios, Geroukis. "Prediction of Linear Models: Application of Jackknife Model Averaging." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297671.
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When using linear models, a common practice is to find the single best model fit used in predictions. This on the other hand can cause potential problems such as misspecification and sometimes even wrong models due to spurious regression. Another method of predicting models introduced in this study as Jackknife Model Averaging developed by Hansen & Racine (2012). This assigns weights to all possible models one could use and allows the data to have heteroscedastic errors. This model averaging estimator is compared to the Mallows’s Model Averaging (Hansen, 2007) and model selection by Bayesian Information Criterion and Mallows’s Cp. The results show that the Jackknife Model Averaging technique gives less prediction errors compared to the other methods of model prediction. This study concludes that the Jackknife Model Averaging technique might be a useful choice when predicting data.
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Alfadda, Abdullah Ibrahim A. "Temporal Frame Difference Using Averaging Filter for Maritime Surveillance." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/56583.
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Video surveillance is an active research area in Computer Vision and Machine Learning. It
received a lot of attention in the last few decades. Maritime surveillance is the act of effective
detection/recognition of all maritime activities that have impact on economy, security or
the environment. The maritime environment is a dynamic environment. Factors such as
constant moving of waves, sun reflection over the sea surface, rapid change in lightning due
to the sun reflection over the water surface, movement of clouds and presence of moving
objects such as airplanes or birds, makes the maritime environment very challenging. In
this work, we propose a method for detecting a motion generated by a maritime vehicle
and then identifying the type of this vehicle using classification methods. A new maritime
video database was created and tested. Classifying the type of vehicles have been tested by
comparing 13 image features, and two SVM solving algorithms. In motion detection part,
multiple smoothing filters were tested in order to minimize the false positive rate generated
by the water surface movement, the results have been compared to optical flow, a well known
method for motion detection.Master of Science
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Lu, Pingbo. "Calibrated Bayes factors for model selection and model averaging." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343396705.
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Al-Mashat, Alex. "Comparison of Multiple Models for Diabetes Using Model Averaging." Thesis, Uppsala universitet, Institutionen för farmaci, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448168.
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Pharmacometrics is widely used in drug development. Models are developed to describe pharmacological measurements with data gathered from a clinical trial. The information can then be applied to, for instance, safely establish dose-response relationships of a substance. Glycated hemoglobin (HbA1c) is a common biomarker used by models within antihyperglycemic drug development, as it reflects the average plasma glucose level over the previous 8-12 weeks. There are five different nonlinear mixed-effects models that describes HbA1c-formation. They use different biomarkers such as mean plasma glucose (MPG), fasting plasma glucose (FPG), fasting plasma insulin (FPI) or a combination of those. The aim of this study was to compare their performances on a population and an individual level using model averaging (MA) and to explore if reduced trial durations and different treatment could affect the outcome. Multiple weighting methods were applied to the MA workflow, such as the Akaike information criterion (AIC), cross-validation (CV) and a bootstrap model averaging method. Results show that in general, models that use MPG to describe HbA1c-formation on a population level could potentially outperform models using other biomarkers, however, models have shown similar performance on individual level. Further studies on the relationship between biomarkers and model performances must be conducted, since it could potentially lay the ground for better individual HbA1c-predictions. It can then be applied in antihyperglycemic drug development and to possibly reduce sample sizes in a clinical trial. With this project, we have illustrated how to perform MA on the aforementioned models, using different biomarkers as well as the difference between model weights on a population and individual level.
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Dalgic, Meric. "Solutions Of The Equations Of Change By The Averaging Technique." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609525/index.pdf.
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Area averaging is one of the techniques used to solve problems encountered in the transport of momentum, heat, and mass. The application of this technique simplifies the mathematical solution of the problem. However, it necessitates expressing the local value of the dependent variable and/or its derivative(s) on the system boundaries in terms of the averaged variable. In this study, these expressions are obtained by the two-point Hermite expansion and this approximate method is applied to some specific problems, such as, unsteady flow in a concentric annulus, unequal cooling of a long slab, unsteady conduction in a cylindrical rod with internal heat generation, diffusion of a solute into a slab from limited volume of a well-mixed solution, convective mass transport between two parallel plates with a wall reaction, convective mass transport in a cylindrical tube with a wall reaction, and unsteady conduction in a two -layer composite slab. Comparison of the analytical and approximate solutions is shown to be in good agreement for a wide range of dimensionless parameters characterizing each system.
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Lallier, Eric J. P. "Real-time pixel-level image fusion through adaptive weight averaging." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0004/MQ44848.pdf.
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