This thesis presents a method for calibrating regional scale hydrologic models using the upper South Saskatchewan River watershed as a case study. Regional scale hydrologic models can be very difficult to calibrate due to the spatial diversity of their land types. To deal with this diversity, both a manual calibration method and a multi-basin automated calibration method were applied to a WATFLOOD hydrologic model of the watershed. Manual calibration was used to determine the effect of each model parameter on modeling results. A parameter set that heavily influenced modeling results was selected. Each influential parameter was also assigned an initial value and a parameter range to be used during automated calibration. This manual calibration approach was found to be very effective for improving modeling results over the entire watershed. Automated calibration was performed using a weighted multi-basin objective function based on the average streamflow from six sub-basins. The initial parameter set and ranges found during manual calibration were subjected to the optimization search algorithm DDS to automatically calibrate the model. Sub-basin results not involved in the objective function were considered for validation purposes. Automatic calibration was deemed successful in providing watershed-wide modeling improvements. The calibrated model was then used as a basis for determining the effect of altering rain gauge density on model outputs for both a local (sub-basin) and global (watershed) scale. Four de-clustered precipitation data sets were used as input to the model and automated calibration was performed using the multi-basin objective function. It was found that more accurate results were obtained from models with higher rain gauge density. Adding a rain gauge did not necessarily improve modeled results over the entire watershed, but typically improved predictions in the sub-basin in which the gauge was located.

The aim of this thesis was to examine environmental covariate-related issues, the resolution dependency, the contribution of vegetation covariates, and the use of LiDAR data, in the purposive sampling design for fuzzy logic-based digital soil mapping. In this design fuzzy c-means (FCM) clustering of environmental covariates was employed to determine proper sampling sites and assist soil survey and inference. Two subsets of the Laurel Creek Conservation area were examined for the purposes of exploring the resolution and vegetation issues, respectively. Both conventional and LiDAR-derived digital elevation models (DEMs) were used to derive terrain covariates, and a vegetation index calculated from remotely sensed data was employed as a vegetation covariate. A basic field survey was conducted in the study area. A validation experiment was performed in another area. The results show that the choices of optimal numbers of clusters shift with resolution aggregated, which leads to the variations in the optimal partition of environmental covariates space and the purposive sampling design. Combining vegetation covariates with terrain covariates produces different results from the use of only terrain covariates. The level of resolution dependency and the influence of adding vegetation covariates vary with DEM source. This study suggests that DEM resolution, vegetation, and DEM source bear significance to the purposive sampling design for fuzzy logic-based digital soil mapping. The interpretation of fuzzy membership values at sampled sites also indicates the associations between fuzzy clusters and soil series, which lends promise to the applicability of fuzzy logic-based digital soil mapping in areas where fieldwork and data are limited.

The immune system has the ability to discriminate self from non-self proteins and also make appropriate immune responses to pathogens. A fundamental problem is to understand the genomic differences and similarities among the sets of self peptides and non-self peptides. The sequencing of human, mouse and numerous pathogen genomes and cataloging of their respective proteomes allows host self and non-self peptides to be identified. T-cells make this determination at the peptide level based on peptides displayed by MHC molecules.<p>In this project, peptides of specific lengths (k-mers) are generated from each protein in the proteomes of various model organisms. The set of unique k-mers for each species is stored in a library and defines its "immunological self". Using the libraries, organisms can be compared to determine the levels of peptide overlap. The observed levels of overlap can also be compared with levels which can be expected "at random" and statistical conclusions drawn.<p>A problem with this procedure is that sequence information in public protein databases (Swiss-PROT, UniProt, PIR) often contains ambiguities. Three strategies for dealing with such ambiguities have been explored in earlier work and the strategy of removing ambiguous k-mers is used here.<p>Peptide fragments (k-mers) which elicit immune responses are often localized within the sequences of proteins from pathogens. These regions are known as "immunodominants" (i.e., hot spots) and are important in immunological work. After investigating the peptide universes and their overlaps, the question of whether known regions of immunological significance (e.g., epitope) come from regions of low host-similarity is explored. The known regions of epitopes are compared with the regions of low host-similarity (i.e., non-overlaps) between HIV-1 and human proteomes at the 7-mer level. Results show that the correlation between these two regions is not statistically significant. In addition, pairs involving human and human viruses are explored. For these pairs, one graph for each k-mer level is generated showing the actual numbers of matches between organisms versus the expected numbers. From graphs for 5-mer and 6-mer level, we can see that the number of overlapping occurrences increases as the size of the viral proteome increases.<p>A detailed investigation of the overlaps/non-overlaps between viral proteome and human proteome reveals that the distribution of the locations of these overlaps/non-overlaps may have "structure" (e.g. locality clustering). Thus, another question that is explored is whether the locality clustering is statistically significant. A chi-square analysis is used to analyze the locality clustering. Results show that the locality clusterings for HIV-1, HIV-2 and Influenza A virus at the 5-mer, 6-mer and 7-mer levels are statistically significant. Also, for self-similarity of human protein Desmoglein 3 to the remaining human proteome, it shows that the locality clustering is not statistically significant at the 5-mer level while it is at the 6-mer and 7-mer levels.

Proteins are the structural components of living cells and tissues, and thus an important building block in all living organisms. Patterns in proteins sequences are some subsequences which appear frequently. Patterns often denote important functional regions in proteins and can be used to characterize a protein family or discover the function of proteins. Moreover, it provides valuable information about the evolution of species. Grouping protein sequences that share similar structure helps in identifying sequences with similar functionality. Many algorithms have been proposed for clustering proteins according to their similarity, i.e., sequential patterns in protein databases, for example, feature-based clustering algorithms of the global approach and the local approach. They use the algorithm of mining sequential patterns to solve the no-gap-limit sequential pattern problem in a protein sequences database, and then find global features and local features separately for clustering. Feature-based clustering algorithms are entirely different approaches to protein clustering that do not require an all-against-all analysis and use a near-linear complexity K-means based clustering algorithm. Although feature-based clustering algorithms are scalable and lead to reasonably good clusters, they consume time on performing the global approach and the local approach separately. Therefore, in this thesis, we propose hybrid algorithms to find and mark features for feature-based clustering algorithms. We observe an interesting result from the relation between the local features and the closed frequent sequential patterns. The important observation which we find is that some features in the closed frequent sequential patterns can be taken apart to several features in the local selected features and the total support number of these features in the local selected features is equal to the support number of the corresponding feature in the closed frequent sequential patterns. There are two phases, find-feature and mark-feature, in the global approach and the local approach after mining sequential patterns. In our hybrid algorithms of Method 1 (LocalG), we first find and mark the local features. Then, we find the global features. Finally, we mark the bit vectors of the global features efficiently from the bit vector of the local features. In our hybrid algorithms of Method 2 (CLoseLG), we first find the closed frequent sequential patterns directly. Next, we find local candidate features efficiently from the closed frequent sequential patterns and then mark the local features. Finally, we find and mark the global features. From our performance study based on the biological data and the synthetic data, we show that our proposed hybrid algorithms are more efficient than the feature-based algorithm.

We consider the topic of target tracking in this thesis. Target tracking is one of the applications in wireless sensor networks (WSNs). Clustering approach prolongs sensor¡¦s lifetime and provides better data aggregation for WSNs. Most previous researches assumed that cluster regions are disjointed, while others assigned overlapping cluster regions, and utilized them in some applications, including inter-cluster routing and time synchronization. However, in overlapping clustering, processing of redundant sensing data may impair system performance. We present a regular distributed overlapping WSN in this thesis. The network is based on two kinds of sensors: (1) high-capability sensors, which are assigned as cluster heads (CHs), responsible for data processing and inter-cluster communication, (2) normal sensors, which are in a larger number when comparing with the high-capability sensors, the function of normal sensors are to provide data to the CHs. We define several operating modes of CHs and sensors. WSN works more efficient under the settings. Since a target may be located in the overlapping region, redundant data processing problem exists. To solve the problem, we utilize Cholesky decomposition to decorrelate the measurement noise covariance matrices. The correlation will be eliminated during the process. In addition, we modify extended information filter (EIF) and adapt to the decorrelated data. The CHs track the target, fuse the information from other CHs, and implement distributed positioning. The simulations are based on ultra-wideband (UWB) environment, we have verified that the proposed scheme works more efficient under the setting of different modes. The performance with decorrelated measurement is better than that with correlated ones.

The pattern reduction (PR) algorithm we proposed previously, which works by eliminating patterns that are unlikely to change their membership during the convergence process, is obviously one of the most efficient methods for reducing the computation time of clustering algorithms. However, it is limited to problems with solutions that can be binary or integer encoded, such as combinatorial optimization problems. As such, this study is aimed at developing a new pattern reduction algorithm, called pattern reduction over continuous space, to get rid of this limitation. Like the PR, the proposed algorithm consists of two operators: detection and compression. Unlike the PR, the detection operator is divided into two steps. The first step is aimed at finding out subsolutions that can be considered as the candidate subsolutions for compression. The second step is performed to ensure that the candidate subsolutions have reached the final state so that any further computation is eventually a waste and thus can be compressed. To evaluate the performance of the proposed algorithm, we apply it to metaheuristics for clustering.

Users are being embraced as partners in developing computer services in many current computer supported cooperative work systems. Many web-based applications, including collaborative authoring tools like wikis, place users into collaborations with unknown and distant partners. Individual participants in such environments need to identify and understand others' contributions for collaboration to succeed and be efficient. One approach to supporting such understanding is to record user activity for later access. Issues with this approach include difficulties in locating activity of interest in large tasks and the history is often recorded at a system-activity level instead of at a human-activity level. To address these issues, this dissertation introduces CoActIVE, an application-independent history mechanism that clusters records of user activity and extracts keywords in an attempt to provide a human-level representation of history. CoActIVE is integrated in three different software applications to show its applicability and validity. Multiple visualization techniques based on this processing are compared in their ability to improve users' location and comprehension of the activity of others. The results show that filmstrip visualization and visual summarization of user activity show significant improvement over traditional list view interfaces. CoActIVE generates an interpretation of large-scale interaction history and provides the interpretation thorough a variety of visualizations that allow users to navigate the evolution of collaborative work. It supports branching history, with the understanding that asynchronous authoring and design tasks often involve the parallel development of alternatives. Additionally, CoActIVE has the potential to be integrated into a variety of applications with little adjustment for compatibility. Especially, the comparison of visualizations for locating and comprehending the work of others is unique.

Clustering is the unsupervised classification of patterns into groups, which is easy provided the data of patterns are consistent. However, real data are almost always tempered with inconsistencies, which make it a hard problem, and actually, the most widely studied formulations, correlation clustering and hierarchical clustering, are both NP-hard. In the graph representation of data, inconsistencies also frequently present themselves as cycles, also called deadlocks, and to break cycles by removing vertices is the objective of the classical feedback vertex set (FVS) problem. This dissertation studies the three problems, correlation clustering, hierarchical clustering, and disjoint-FVS (a variation of FVS), from a kernelization approach. A kernelization algorithm in polynomial time reduces a problem instance provably to speed up the further processing with other approaches. For each of the problems studied, an efficient kernelization algorithm of linear or sub-quadratic running time is presented. All the kernels obtained in this dissertation have linear size with very small constants. Better parameterized algorithms are also designed based on the kernels for the last two problems. Finally, some concluding remarks on possible directions for future research are briefly mentioned.

Efficient market exist such that financial market make the absence of arbitrage opportunity on intertemporal asset price, There are special existence due to volatility clustering effect provides that the conditional volatility predictor could control, applying on derivative such as option¡Bcurrency exchange¡Bswap¡Bexist possible arbitrage profits ,in this paper involve that forward market efficiency and how to prototype concrete, now we apply parity theory including covered interest parity and uncovered interest parity, then the study of both covered (CIP)and uncovered interest parity (UIP) plus FME are tested in the 30 and 90 forward markets for the NTD/USD exchange rate to examine market efficiency on using GARCH-M,EGARCH models , In the empirical tests, we find the NTS/USD dollar interest rate spread have I(o) property ,Results are provided for interest rate on stationarity indicating that interest differential is stationary ,the result also imply stationary relationship between Taiwan and USA on money policy, Using Taylor(1989) ¡As covered interest arbitrage models, The empirical results show lower positive profit opportunities on NTD or US returns, covered interest parity may hold because NTS/US exchange market after reopening becomes more efficient than market after reopening, the central bank money policy intervention is influential but we test market efficiency hypotheses on basis of Domowitz and Hakkio¡]1985¡^¡As ARCH-M model deeply employing GARCH-M¡BEGARCH models to estimate Risk Premium¡Athen employ Felmingham (2003.2) ¡As regression equation to test forward market efficiency , the empirical results shows that not only CIP¡BUIP theory fail but also Forward Market Efficiency hypotheses cannot hold ,then future spot rates could be predicted by forward rates are worthy of investigate., It may indicate that foreign securities are imperfect substitutes for domestic ones of equivalent maturity and that market participants, implying that there is arbitrage profit opportunity between Taiwan and the USA, there are many arguments to discuss whether forward rates as an unbiased predictor of future spot rate ,Forward Market efficiency give the presence of the time varying premium on different place, Ultimately, therefore, the unbiased nature of forward rates is an empirical, and not a theoretical, issue¡C

As digitisation era will come, a large number of multimedia datas (image, video, etc.) are stored in the database by digitisation, and its retrieval system is more and more important. Video is huge in frames amount, in order to search effectively and fast, the first step will detect and examine the place where the scene changes in the video, cut apart the scene, find out the key frame from each scene, regard as analysis that the index file searches with the key frame. The scene changes the way and divides into the abrupt and the gradual transition. But in the video, even if in the same scene, incident of often violent movements or the camera are moving etc. happens, and obscure with the gradual transition to some extent. Thus this papper gets the main component from every frame in the video using principal component analysis (PCA), reduce the noise to interfere, and classify these feauture points with support vector clustering, it is the same class that the close feature points is belonged to. If the feature points are located between two groups of different datas, represent the scene is changing slowly in the video, detect scene change by this.