Purpose: The study that has been conducted is a comparative one, where the group compared different innovation management strategies used by three different globally- known Swedish firms that are in the manufacturing industry. The study is aimed at describing, analyzing and making conclusions of the innovation strategies used during the process of product development in the chosen companies bycomparing their similarities and differences. Method: The Study was carried out using a comparative study drawing on the qualitative data. Conclusion: Volvo CE and ABB CRC have similar strategies in internal idea generation because both firms have formalized systems, by using strong online data bases for idea sharing and evaluation. Volvo CE uses a pronounced forum called “Innovation Jams” for online idea sharing among Volvo Group employees whereas ABB CRC uses a strong data base called “ABB Inside” to evaluate ideas within the group. On the other hand, Scania R&D’s internal idea generation process is more informal as it is based on “person-to-person”. When it comes to external idea generation, Scania R&D has a more established strategy of using suppliers and customers for inspiration of ideas. However, ABB CRC generates inspirations from customers through its business centers, whereas Volvo CE has no customer system in place. But one thing that is common in all the three companies is that they are highly collaborating with universities for idea generation and human resource.

Model theory can express properties of algebraic subsets of complex n-space. The constructible subsets are precisely the first order definable subsets, and varieties correspond to maximal consistent collections of formulas, called types. Moreover, the topological dimension of a constructible set is equal to the Morley rank of the formula which defines it.

Ongoing efforts to quantify changes in quality of life attributable to low vision rehabilitation have focused on the utility of a single test instrument to measure this multidimensional concept. It is hypothesized that quality of life is best assessed using multiple instruments to capture some of its component facets, including functional status and psychosocial impact. Low vision devices have a predictably spontaneous impact on functional vision status, but associated psychosocial impact occurs with different magnitudes and over more protracted time intervals. The National Eye Institute Visual Function Questionnaire (NEI VFQ-25) measures the functional status of individuals in key vision areas that are associated with quality of life. The Psychosocial Impact of Assistive Devices Scale (PIADS) is an instrument that measures the psychosocial impact of assistive device intervention in three quality of life domains: competence, adaptability, and self-esteem. 68 participants were obtained from an ongoing parent study. These participants were recruited through the Low Vision Clinic at the University of Waterloo. They had a primary diagnosis of age-related macular degeneration (ARMD) and were obtaining a CCTV system for the first time. Assessments from the parent study used in this thesis included follow-up from 2 weeks, 1 month, 3 months, and 6 months post-adoption of the CCTV. The two tests administered were to measure functional vision status (NEI VFQ-25) and perceived psychosocial impact (PIADS), according the framework outlined by the Consortium for Assistive Technology Outcomes Research (CATOR). Multivariate repeated-measures ANVOA results confirmed that CCTV systems have an immediate and robust effect on the daily visual functioning of their users, and that this effect is stable over long periods of device use. The psychosocial impact of CCTV device use peaks in the shorter term and then seems to wane in the longer term for reasons that are not yet understood. The NEI VFQ-25 and the PIADS appear to have differential sensitivity to important influences on low vision rehabilitation outcomes. This project has demonstrated the value of longitudinal outcomes research in low vision rehabilitation. After obtaining a CCTV, visual function status remains static while psychosocial impact is dynamic during 6-months of follow-up.

Online mining sequential patterns over data streams is an important problem in data mining. There are many applications of using sequential patterns in data streams, such as market analysis, network security, sensor networks and web track- ing. Previous studies have shown mining closed patterns provides more benefits than mining the complete set of frequent patterns, since closed pattern mining leads to compact results. A sequential pattern is closed if the sequential pattern does not have any supersequence which has the same support. Chang et al. proposed a time- based sliding window model. The time-based sliding window has two features, the new item is inserted in front of a sequence, and the obsolete item is removed from of tail of a sequence. For solving the problem of data mining in the time-based sliding window, Chang et al. proposed an algorithm called SeqStream. It uses a data struc- ture IST (Inverse Closed Sequence Tree) to keep the result. IST can incrementally be updated by the SeqStream algorithm. Although the SeqStream algorithm has used the technique of dividing the time-based sliding window to speed up the updating of IST, the SeqStream algorithm still scans the sliding window many times when IST needs to be updated. In this thesis, we propose an obstruction-check approach to maintain the result of closed sequential patterns. Our approach is designed based on the lattice structure. The feature of the lattice structure is that the parent is a supersequence of its children. By utilizing this feature, we decide the obstruction link between the parent and child if their support is the same. If a node does not have any obstruction link parent, the node is a closed sequential pattern. Then we can utilize this feature to locally travel the lattice structure. Moreover, we can fully utilize the features of the time-based sliding window model to locally travel the lat- tice structure. Based on the lattice structure, we propose the EULB (Exact Update based on Lattice structure with Bit stream)-Lattice algorithm. The EULB-Lattice algorithm is an exact method for mining data streams. We record additional informa- tion, instead of scanning the entire sliding window. We conduct several experiments using different synthetic data sets. The simulation results show that the proposed algorithm outperforms the SeqStream algorithm.

For target location and tracking in wireless communication systems, mobile target positioning and tracking play an important role. Since multi-sensor system can be used as an efficient solution to target positioning process, more accurate target location estimation and tracking results can be obtained. However, both the deployment of designed multi-sensor and location algorithm may affect the overall performance of position location. In this thesis, based on the time difference of arrival (TDOA), two closed-form least-square location methods, spherical-interpolation (SI) method and spherical-intersection (SX) method are used to estimate the target location. The two location methods are different from the usual process of iterative and nonlinear minimization. The locations of the target and the designed multiple sensors may yield geometric effects on location performance. The constraints and performance of the two location methods will first be introduced. To achieve real-time target tracking, the Kalman filtering structures are used to combine the SI and SX methods. Because these two positioning and tracking systems have different and complementary performance inside and outside the multi-sensor array, we consider using data fusion to improve location estimation results by using interacting multiple model (IMM) based estimator, in which internal filters running in parallel are designed as the SX-KF1 and the SI-KF2. However, due to the time-varying characteristics of measurement noises, we propose an adjusting scheme for measurement noise variance assignment in the Kalman filters to obtain improved location estimation results. Simulation results are obtained by running Matlab program. In three-dimensional multi-sensor array scenarios, the results of moving target location estimation shows that the IMM-based estimators effectively improve the position performance.

A DNA microarray is a very good tool to study the gene expression level in different situations. Mining association rules in DNA microarray datasets can help us know how genes affect each other, and what genes are usually co-expressed. Mining closed large itemsets can be useful for reducing the size of the mining result from the DNA microarray datasets, where a closed itemset is an itemset that there is no superset whose support value is the same as the support value of this itemset. Since the number of genes stored in columns is much larger than the number of samples stored in rows in a DNA microarray dataset, traditional mining methods which use column enumeration face a great challenge, where the column enumeration means that enumerating itemsets from the combinations of items stored in columns. Therefore, several row enumeration methods, e.g., RERII, have been proposed to solve this problem, where row enumeration means that enumerating itemsets from the combinations of items stored in rows. Although the RERII method saves more memory space and has better performance than the other row enumeration methods, it needs complex intersection operations at each node of the row enumeration tree to generate the closed itemsets. In this thesis, we propose a new method, UMiner, which is based on the union operations to mine the closed large itemsets in the DNA microarray datasets. Our approach is a row enumeration method. First, we add all tuples in the transposed table to a prefix tree, where a transposed table records the information about where an item appears in the original table. Next, we traverse this prefix tree to create a row-node table which records the information about a node and the related range of its child nodes in the prefix tree created from the transposed table. Then we generate the closed itemset by using the union operations on the itemsets in the item groups stored in the row-node table. Since we do not use the intersection operations to generate the closed itemset for each enumeration node, we can reduce the time complexity that is needed at each node of the row enumeration tree. Moreover, we develop four pruning techniques to reduce the number of candidate closed itemsets in the row enumeration tree. By replacing the complex intersection operations with the union operations and designing four pruning techniques to reduce the number of branches in the row enumeration tree, our method can find closed large itemsets very efficiently. In our performance study, we use three real datasets which are the clinical data on breast cancer, lung cancer, and AML-ALL. From the experiment results, we show that our UMiner method is always faster than the RERII method in all support values, no matter what the average length of the closed large itemsets is. Moreover, in our simulation result, we also show that the processing time of our method increases much more slowly than that of the RERII method as the average number of items in the rows of a dataset increases.

Online mining association rules over data streams is an important issue in the area of data mining, where an association rule means that the presence of some items in a transaction will imply the presence of other items in the same transaction. There are many applications of using association rules in data streams, such as market analysis, network security, sensor networks and web tracking. Mining closed frequent itemsets is a further work of mining association rules, which aims to find the subsets of frequent itemsets that could extract all frequent itemsets. Formally, a closed frequent itemset is an frequent itemset which has no superset with the same support as it. Since data streams are continuous, high-speed, and unbounded, archiving everything from data streams is impossible. That is, we can only scan once for the data streams and it is a main-memory database. Therefore, previous algorithms to mine closed frequent itemsets in the traditional database are not suitable for data streams. On the other hand, many applications are interested in the most recent data, and there is a model to deal with the most recent data in data streams, called emph{Sliding Window Model}, which acquires the recent data with a window size meets this characteristic. One of well-known algorithms for mining closed frequent itemsets which based on the sliding window model is the NewMoment algorithm. However, the NewMoment algorithm could not efficiently mine closed frequent itemsets in data streams, since they will generate closed frequent itemsets and many unclosed frequent itemsets. Moreover, when data in the sliding window is incrementally updated, the NewMoment algorithm needs to reconstruct the whole tree structure. Therefore, in this thesis, we propose a sliding window approach, the Subset-Lattice algorithm, which embeds the subset property into the lattice structure to efficiently mine closed frequent itemsets. Basically, Our proposed algorithm considers five kinds of set concepts : (1) equivalent, (2) superset, (3) subset, (4) intersection, (5) empty relation, when data items are inserted. We judge closed frequent itemsets without generating unclosed frequent itemsets by these five kinds of set concepts. Moreover, when data in the sliding window is incrementally updated, our Subset-Lattice algorithm will not reconstruct the whole lattice structure. Therefore, our Subset-Lattice algorithm is more efficient than the Moment algorithm. Furthermore, we use the bit-pattern to represent the itemsets, and use bit-operations to speed up the set-checking. From our simulation results, we show that our Subset-Lattice algorithm needs less memory and less processing time than the NewMoment algorithm. When window slides, the execution time could be saved up to 50\%.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 84-88). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 84-88). Also available on the Internet.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 81-85).