This thesis focuses on the design and analysis of an optimal filter that is capable of making one-step-ahead prediction of a bandlimited signal while attenuating unwanted noise. First, the filter optimization based on the least mean-square-error criterion is presented. Then, an exact expression for the achievable minimum mean square error (MMSE) is derived with the aid of the Toeplitz form method and Szego theory. Based on this MMSE expression, the formulae for estimating the optimal filter¡¦s in-band prediction error and out-of-band noise attenuation are derived. Finally, the optimal filter is applied to sigma-delta modulation. It shows that the modulation performance and stability are intimately related to the filter performance and can be accurately estimated by the derived formulae.

Reversible watermarking techniques extract the watermark and recover the original image from the watermarked image without any distortion. They have been applied for those sensitive fields, such as the medicine and the military. In this thesis, a novel watermarking algorithm using multi-prediction values has been proposed. It exploits the correlation between the original pixel and the neighboring pixels to obtain twelve prediction candidates, and then selects a candidate as the prediction value according to the original pixel and the temporary prediction value. Due to the algorithm use the original pixel as one of the parameters to decide the prediction value, our prediction values are obtained with great precision. The experimental results reveal that the performance of our proposed method outperforms that proposed by Sachnev. For example the variance of the prediction errors histogram obtained by the proposed method is less than that obtained by the algorithm proposed by Sachnev et al. about 44.2%; the mean PSNR greater than about 1.47 dB and 1.1 dB under the watermark capacity 0~0.04 bpp and 0.04~0.5 bpp, respectively. Therefore, the proposed method is especially appropriate for embedding watermark in low or medium capacity. Keyword¡G reversible watermarking, watermarking, prediction, histogram shifting.

Disulfide bonds are special covalent cross links between two cysteines in a protein. This kind of bonding state plays an important role in protein folding and stabilization. For connectivity pattern prediction, it is a very difficult problem because of the fast growth of possible patterns with respect to the number of cysteines. In this thesis, we propose a new approach to address this problem. The method is based on hybrid models with SVM. Via this strategy, we can improve the prediction accuracies by selecting appropriate models. In order to evaluate the performance of our method, we apply the method by 4-fold cross-validation on SP39 dataset, which contains 446 proteins. We achieve accuracies with 70.8% and 65.9% for pair-wise and pattern-wise prediction respectively, which is better than the previous works.

The all-atom protein backbone reconstruction problem (PBRP) is to reconstruct the 3D coordinates of all atoms, including N, C, and O atoms on the backbone, for a protein whose primary sequence and £\-carbon coordinates are given. A variety of methods for solving PBRP have been proposed, such as Adcock¡¦s method, SABBAC, BBQ, Chang¡¦s and Yen¡¦s methods. In a recent work, Yen et al. found that the results of Chang¡¦s method are not always better than SABBAC. So they apply a tool preference classification to determine which tool is more suitable for predicting the structure of the given protein. In this thesis, we involve BBQ (Backbone Building from Quadrilaterals) and Chang¡¦s method as our candidate prediction tools. In addition, the tool preferences of different atoms (N, C, O) are determined separately. We call the preference classification as an atom classifier, which is built by support vector machine (SVM). According to the preference classification of each atom classifier, a proper prediction tool, either BBQ or Chang¡¦s method, is used to construct the atom of the target protein. Thus, the combination of all atom results, the backbone structure of a protein is reconstructed. The datasets of our experiments are extracted from CASP7, CASP8, and CASP9, which consists of 30, 24, and 55 proteins, respectively. The proteins of the datasets contain only standard amino acids. We improve the average RMSDs of Yen¡¦s results from 0.4019 to 0.3682 in CASP7, from 0.4543 to 0.4202 in CASP8, and from 0.4155 to 0.3601 in CASP9.

Prediction systems for College Entrance Examination (CEE) are popular during the graduating season, July every year in Taiwan. These systems give students suggestion according to their examination scores. There are several CEE prediction systems in Taiwan, but most of them are not constructed with rigorous theories. In 2005, Zen et al. constructed a prediction model using statistical method, which was later verified and improved by Lin in 2008. In this thesis, we will introduce the recording mechanism of the College Entrance Examination, and explain how to construct a prediction system under this mechanism. Also, we will compare the previous system with ours. We apply an empirical method and SVR as our first two approaches, and then we propose a new method. In our experiments, we consider the scores published by CEE center from 2004 to 2008. We use the root mean square error (RMSE) value to evaluate the performance of our present method. We also use the value generated by our method to show some information of the schools and the departments.

The objective of this study was to develop near infrared reflectance spectroscopy (NIRS) calibration equations from bamboo and fecal samples to predict diet composition and the physiological status of giant pandas. Discrimination between branch, culm, and leaf parts of bamboo resulted in an Rsquare (R2) of 0.88. The calibration equation for discriminating between 4 species of bamboo had an R2 of 0.47. Calibration equations were created for all bamboo species combined to determine the ability of NIRS to predict the nutrient constituents of CP, NDF, ADF, DM, and OM. No R2 was lower than 0.96, with the exception of DM at 0.63, which was consistently difficult to accurately predict due to variation in factors relating to difference in location of lab work (humidity, shipping, methods, etc.). Giant panda diets vary between seasons from eating primarily leaf to eating almost only culm. When bamboo part samples were compared between March and October, all resulting R2s were above 0.80. The sensitivity analyses for leaf and culm samples within diet season produced inconclusive results, but sensitivity analyses for fecal samples yielded an ability to more greatly discriminate between months that were further apart. For giant panda physiological status calibrations, fecal samples were collected from the Memphis Zoo, Smithsonian's National Zoo, Zoo Atlanta, and San Diego Zoo from 2006 to 2007. One-hundred fecal spectra were used to develop discriminant equations with which to predict between adults and juveniles. The resulting calibration was 100% correct for both age classes. Predictions between 252 male and female fecal spectra were 89% correct for females and 90% correct for males. A small number of samples (N= 60) were used to create a discriminant equation to differentiate between pregnant and non pregnant females. The exercise resulted in an R2 of 0.68 and a prediction of 100% for both pregnant and not-pregnant. It has been determined through these studies that NIRS has the potential to determine nutrient composition of bamboo and giant panda fecals, but increased sampling and equation development is needed before these calibrations are applicable in a captive or wild giant panda setting.

Predicting rock permeability from well logs in uncored wells is an important task in reservoir characterization. Due to the high costs of coring and laboratory analysis, typically cores are acquired in only a few wells. Since most wells are logged, the common practice is to estimate permeability from logs using correlation equations developed from limited core data. Most commonly, permeability is estimated from various well logs using statistical regression. For sandstones, often the logs of permeability can be correlated with porosity, but in carbonates the porosity permeability relationship tends to be much more complex and erratic. For this reason permeability prediction is a critical aspect of reservoir characterization in complex reservoirs such as carbonate reservoirs. In order to improve the permeability estimation in these reservoirs, several statistical regression techniques have already been tested in previous work to correlate permeability with different well logs. It has been shown that statistical regression for data correlation is quite promising in predicting complex reservoirs. But using all the possible well logs to predict permeability is not appropriate because the possibility of spurious correlation increases if you use more well logs. In statistics, variable selection is used to remove unnecessary independent variables and give a better prediction. So we apply variable selection to the permeability prediction procedures in order to further improve permeability estimation. We present three approaches to further improve reservoir permeability prediction based on well logs via data correlation and variable selection in this research. The first is a combination of stepwise algorithm with ACE technique. The second approach is the application of tree regression and cross-validation. The third is multivariate adaptive regression splines. Three methods are tested and compared at two complex carbonate reservoirs in west Texas: Salt Creek Field Unit (SCFU) and North Robertson Unit (NRU). The result of SCFU shows that permeability prediction is improved by applying variable selection to non-parametric regression ACE while tree regression is unable to predict permeability because it can not preserve the continuity of permeability. In NRU, none of these three methods can predict permeability accurately. This is due to the high complexity of NRU reservoir and measurement accuracy. In this reservoir, high permeability is discrete from low permeability, which makes prediction even more difficult. Permeability predictions based on well logs in complex carbonate reservoirs can be further improved by selecting appropriate well logs for data correlation. In comparing the relative predictive performance of the three regression methods, the stepwise with ACE method appears to outperform the other two methods.

A cost-effective, faster and efficient way of screening wheat samples suitable for tortilla production is needed. Hence, we developed prediction models for tortilla quality (diameter, specific volume, color and texture parameters) using grain, flour and dough properties of 16 wheat flours. The prediction models were developed using stepwise multiple regression. Dough rheological tests had higher correlations with tortilla quality than grain and flour chemical tests. Dough resistance to extension was correlated best with tortilla quality, particularly tortilla diameter (r= -0.87, P<0.01). Gluten index was significantly correlated with tortilla diameter (r = -0.67, P less than 0.01) and specific volume (r = -0.73, P less than 0.01). Tortilla diameter was the parameter best predicted. An r2 of 0.87 was obtained when mix-time and dough resistance to extension were entered into the model. This model was validated using another sample set, and an r^2 of 0.91 was obtained. Refined and whole wheat flours, dough and tortillas were compared using five wheat samples. Refined flour doughs were more extensible and softer than whole wheat flour doughs. Whole wheat flour tortillas were larger, thinner and less opaque than refined flour tortillas. Refined wheat flour had much smaller particle size and less fiber than whole wheat flour. These are the major factors that contributed to the observed differences. In general, refined wheat tortillas were more shelf-stable than whole wheat tortillas. However, whole wheat tortillas from strong flours had excellent shelf-stability which must be considered when whole wheat tortillas are processed. . Different objective rheological techniques were used to characterize the texture of refined and whole flour tortillas during storage. Differences in texture between 0, 1 and 4 day-old tortillas were detected by rupture distance from one and two-dimension extensibility techniques. In general, the deformation modulus was not a good parameter to differentiate tortilla texture at the beginning of storage. It detected textural changes of 8 and 14 day-old tortillas. The subjective rollability method detected textural changes after 4 days storage.

Online social networks have quickly become the most popular destination on the World Wide Web. These networks are still a fairly new form of online human interaction and have gained wide popularity only recently within the past three to four years. Few models or descriptions of the dynamics of these systems exist. This is largely due to the difficulty in gaining access to the data from these networks which is often viewed as very valuable. In these networks, members maintain list of friends with which they share content with by first uploading it to the social network service provider. The content is then distributed to members by the service provider who generates a feed for each member containing the content shared by all of the member's friends aggregated together. Direct access to dynamic linkage data for these large networks is especially difficult without a special relationship with the service provider. This makes it difficult for researchers to explore and better understand how humans interface with these systems. This dissertation examines an event driven sampling approach to acquire both dynamics link event data and blog content from the site known as LiveJournal. LiveJournal is one of the oldest online social networking sites whose features are very similar to sites such as Facebook and Myspace yet smaller in scale as to be practical for a research setting. The event driven sampling methodology and analysis of the resulting network model provide insights for other researchers interested in acquiring social network dynamics from LiveJournal or insight into what might be expected if an event driven sampling approach was applied to other online social networks. A detailed analysis of both the static structure and network dynamics of the resulting network model was performed. The analysis helped motivated work on a model of link prediction using both topological and content-based metrics. The relationship between topological and content-based metrics was explored. Factored into the link prediction analysis is the open nature of the social network data where new members are constantly joining and current members are leaving. The data used for the analysis spanned approximately two years.

The new advancement in well monitoring tools have increased the amount of data that could be retrieved with great accuracy. Downhole pressure and temperature could be precisely determined now by using modern instruments. The new challenge that we are facing today is to maximize the benefits of the large amount of data that is being provided by these tools and thus justify the investment of more capital in such gadgets. One of these benefits is to utilize the continuous stream of temperature and pressure data to determine the flow rate in real time out of a multilateral well. Temperature and pressure changes are harder to predict in horizontal laterals compared with vertical wells because of the lack of variation in elevation and geothermal gradient. Thus the need of accurate and high precision gauges becomes critical. The trade-off of high resolution sensors is the related cost and resulting complication in modeling. Interpreting measured data at real-time to a downhole flow profile in multilateral and horizontal wells for production optimization is another challenge. In this study, a theoretical model is developed to predict temperature and pressure in trilateral wells based on given flow conditions. The model is used as a forward engine in the study and inversion procedure is then added to interpret the data to flow profiles. The forward model starts from an assumed well flow pressure in a specified reservoir with a defined well structure. Pressure, temperature and flow rate in the well system are calculated in the motherbore and in the laterals. These predicted temperature and pressure profiles provide the connection between the flow conditions and the temperature and pressure behavior. Then we use an inverse model to interpret the flow rate profiles from the temperature and pressure data measured by the downhole sensors. A gradient-based inversion algorithm is used in this work, which is fast and applicable for real-time monitoring of production performance. In the inverse model, the flow profile is calculated until the one that generates the matching temperature and pressure profiles in the well is identified. The production distribution from each lateral is determined based on this approach. At the end of the study, the results showed that we were able to successfully predict flow rates in the field within 10% of the actual rate. We then used the model to optimize completion design in the field. In conclusion, we were able to build a dependable model capable of predicting flow rates in trilateral wells using pressure and temperature data provided by downhole sensors.