In recent year, tidal gauge has progressed in temporal resolution or measurement accuracy, so that the quality of observational data tends to stable and reliable. However, setting up tidal gauge in the offshore areas restricts may apply due to many factors such as seabed topography, weather, sea state and leveling survey from land to gauge. Good tidal correction is one of key factors to the accuracy of bathymetric survey and to the area where tidal range is large. This study tried to use tide prediction data derived from the Yu(1993)¡¦s tide numerical model and verified with actual observed tide data, and further establishing a tidal zone of Taiwan Strait by tidal characteristic. Using Taichung and Mailiao tide stations as a reference tidal station, the direct tide station correction, tidal zone correction, nearest model grid correction, and virtual station correction methods were applied to evaluate the accuracy of tide calculating value by amplitude ratio and tidal phase difference. The tidal zone correction is not totally depending on the spatial distance from reference tidal station, and it is found that correction result of this approach is one of the best. However, further improvement in tide correction may need to explore due to different spatial resolution applied in different numerical models. In addition, the boundary condition of a harbor for tidal model is very complicated. This is why it is hard to make a numerical model for a harbor. In this study, two additional high accuracy radar tidal gauge were installed in Kaohsiung harbor and first-class leveling survey was performed in order to maintain tidal measurement accuracy, also to avoid the effect of errors propagation. According to the results from experiment, average tidal phase of second entrance of the Kaohsiung harbor is earlier than that of first entrance about 6 minutes, and average difference of tidal height is approximately 2-3cm. For this reason, we should pay attention to decide a proper reference tide station for tidal correction for dredging hydrographic surveying. And any possible tidal observation errors, such as meteorological tide. If two additional tidal gauges of this study are removed in the future, we still can predict tide height from fixed tidal gauge.

In the field of signal processing, one of the underlying enemies in obtaining a good quality signal is noise. The most common examples of signals that can be corrupted by noise are images and audio signals. Since the early 1980's, a time when wavelet transformations became a modernly defined tool, statistical techniques have been incorporated into processes that use wavelets with the goal of maximizing signal-to-noise ratios. We provide a brief history of wavelet theory, going back to Alfréd Haar's 1909 dissertation on orthogonal functions, as well as its important relationship to the earlier work of Joseph Fourier (circa 1801), which brought about that famous mathematical transformation, the Fourier series. We demonstrate how wavelet theory can be used to reconstruct an analyzed function, ergo, that it can be used to analyze and reconstruct images and audio signals as well. Then, in order to ground the understanding of the application of wavelets to the science of denoising, we discuss some important concepts from statistics. From all of these, we introduce the subject of wavelet shrinkage, a technique that combines wavelets and statistics into a "thresholding" scheme that effectively reduces noise without doing too much damage to the desired signal. Subsequently, we discuss how the effectiveness of these techniques are measured, both in the ideal sense and in the expected sense. We then look at an illustrative example in the application of one technique. Finally, we analyze this example more generally, in accordance with the underlying theory, and make some conclusions as to when wavelets are an effective technique in increasing a signal-to-noise ratio.

Despite the importance of single particle motion in biological systems, systematic inference approaches to analyze particle trajectories and evaluate competing motion models are lacking. An automated approach for robust evaluation of motion models that does not require manual intervention is highly desirable to enable analysis of datasets from high-throughput imaging technologies that contain hundreds or thousands of trajectories of biological particles, such as membrane receptors, vesicles, chromosomes or kinetochores, mRNA particles, or whole cells in developing embryos. Bayesian inference is a general theoretical framework for performing such model comparisons that has proven successful in handling noise and experimental limitations in other biological applications. The inherent Bayesian penalty on model complexity, which avoids overfitting, is particularly important for particle trajectory analysis given the highly stochastic nature of particle diffusion. This thesis presents two complementary approaches for analyzing particle motion using Bayesian inference. The first method, MSD-Bayes, discriminates a wide range of motion models--including diffusion, directed motion, anomalous and confined diffusion--based on mean- square displacement analysis of a set of particle trajectories, while the second method, HMM-Bayes, identifies dynamic switching between diffusive and directed motion along individual trajectories using hidden Markov models. These approaches are validated on biological particle trajectory datasets from a wide range of experimental systems, demonstrating their broad applicability to research in cell biology.

Šiame darbe išnagrinėtas tiesinių sistemų parametrų įvertinimo metodas. Visas darbas susideda iš dviejų dalių: teorinės ir praktinės. Teorinėje dalyje aprašytas modelis, modelio kūrimas bei jo struktūra. Pateiktas Markovo įvertinimas arba įvertinimas apibendrintuoju mažiausiųjų kvadratų metodu, taip pat apibendrintojo modelio aprašymas, jo naudojimas. Praktinėje dalyje atlikti ir aprašyti eksperimentai. Modeliavimo eksperimentai atlikti naudojant programinį paketą MATLAB. Skaičiavimams atlikti buvo sukurtos ir panaudotos funkcijos matrica, period. Eksperimentų rezultatai pateikti lentelėse ir grafikuose. Padarytos išvados apie blokinio parametrų įvertinimo metodo efektyvumą. / In this work a block parameter estimation method for linear time-invariant systems is discussed. The whole work consists of two parts: theoretical and practical. The theoretical part is based on the description of the model, its creation and structure. Furthermore, Markov estimation or an estimation of the least squares generalized method and the description of the experiments and their description. The experiments of modeling have been performed using MATLAB program. In addition, the functions matrica, period were created and used to do the estimations. The results of the experiments are illustrated in charts and diagrams. Finally, the conclusions about the efficiency of the block parameter estimation method are done.

Decaying fossil fuel resources, international relation complexities, and the risks associated with nuclear power have led to an increased demand for alternative energy sources. Renewable energy sources offer adequate solutions to these challenges. Forecasting of solar energy has also increased over the past decade due to its use in photovoltaic (PV) system design, load balance in hybrid systems, and projected potential future PV system feasibility. Artificial neural networks (ANN) have been used successfully for solar energy forecasting. In this work, several meteorological variables from Saudi Arabia as a case study will be used to determine the most effective variables on Global Solar Radiation (GSR) prediction. Those variables will be used as inputs for a proposed GSR prediction model. This model will be applicable in different locations and conditions. This model has a simple structure and offers better results in terms of error between actual and predicted solar radiation values.

Abstract This thesis considers some linear and nonlinear time series models. In the linear case, the analysis of a large number of short time series generated by a first order autoregressive type model is considered. The conditional and exact maximum likelihood procedures are developed to estimate parameters. Simulation results are presented and compare the bias and the mean square errors of the parameter estimates. In Chapter 3, five important nonlinear models are considered and their time series properties are discussed. The estimating function approach for nonlinear models is developed in detail in Chapter 4 and examples are added to illustrate the theory. A simulation study is carried out to examine the finite sample behavior of these proposed estimates based on the estimating functions.

The Gauss circle problem in classical number theory concerns the estimation of N(x) = { (m1;m2) in ZxZ : m1^2 + m2^2 <= x }, the number of integer lattice points inside a circle of radius sqrt(x). Gauss showed that P(x) = N(x)- pi * x satisfi es P(x) = O(sqrt(x)). Later Hardy and Landau independently proved that P(x) = Omega_(x1=4(log x)1=4). It is conjectured that inf{e in R : P(x) = O(x^e )}= 1/4. I. K atai showed that the integral from 0 to X of |P(x)|^2 dx = X^(3/2) + O(X(logX)^2). Similar results to those of the circle have been obtained for regions D in R^2 which contain the origin and whose boundary dD satis fies suff cient smoothness conditions. Denote by P_D(x) the similar error term to P(x) only for the domain D. W. G. Nowak showed that, under appropriate conditions on dD, P_D(x) = Omega_(x1=4(log x)1=4) and that the integral from 0 to X of |P_D(x)|^2 dx = O(X^(3/2)). A result similar to Nowak's mean square estimate is given in the case where only "primitive" lattice points, {(m1;m2) in Z^2 : gcd(m1;m2) = 1 }, are counted in a region D, on assumption of the Riemann Hypothesis.

Background: Individuals with cerebral palsy (CP) are prone to sedentary life with limited recreational- and physical activity. RaceRunning (RR) is a sport for individuals with disabilities such as motor impairment that allow them to move independently and enjoy physical activity. The aim of the study was to investigate muscle fatigue of RR athletes with different classiffcation of CP during six minuteRaceRunning test (6MRRT). The Cerebral Palsy International Sports and RecreationAssociation has the goal to make RR a Paralympic sport. For a sport to be included in the Paralympics a classiffcation system has to be made. Information about muscle function during RR could give vital physical information about athletes with CP and contribute to the development of such classiffcation system. Method: Median frequency of the power spectrum from surface electromyography(sEMG) of four muscles (vastus lateralis, biceps femoris, gluteus medius, gastrocnemius lateralis) was computed along with amplitude changes (root mean square) in order to assess muscle fatigue. Results: Indications of muscle fatigue were observed from one out of five athletes for athlete's less affected side. Further sEMG studies with more subjects have to be conducted on CP RR athletes to give more conclusive results of muscle fatigue.

The Association of Research Libraries (ARL) provides an option for librarians to administer a combination of LibQUAL+® and LibQUAL+® Lite to measure users' perceptions of library service quality. LibQUAL+® Lite is a shorter version of LibQUAL+® that uses planned missing data in its design. The present study investigates the loss of information in commonly administered proportions of LibQUAL+® and LibQUAL+® Lite when compared to administering LibQUAL+® alone. Data from previous administrations of LibQUAL+® protocol (2005, N = 525; 2007, N = 3,261; and 2009, N = 2,103) were used to create simulated datasets representing various proportions of LibQUAL+® versus LibQUAL+® Lite administration (0.2:0.8, 0.4:0.6. 0.5:0.5, 0.6:0.4, and 0.8:0.2). Statistics (i.e., means, adequacy and superiority gaps, standard deviations, Pearson product-moment correlation coefficients, and polychoric correlation coefficients) from simulated and real data were compared. Confidence intervals captured the original values. Root mean square errors and absolute and relative biases of correlations showed that accuracy in the estimates decreased with increase in percentage of planned missing data. The recommendation is to avoid using combinations with more than 20% planned missing data.

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