Fall among the elderly is an important health issue. Fall detection and movement tracking techniques are therefore instrumental in dealing with this issue. This thesis responds to the challenge of classifying different movement types as a part of a system designed to fulfill the need for a wearable device to collect data for fall and near-fall analysis. Four different fall activities (forward, backward, left and right), three normal activities (standing, walking and lying down) and near-fall situations are identified and detected. Different machine learning algorithms are compared and the best one is used for the real time classification. The comparison is made using Waikato Environment for Knowledge Analysis or in short WEKA. The system also has the ability to adapt to different gaits of different people. A feature selection algorithm is also introduced to reduce the number of features required for the classification problem.

Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it is often very difficult to determine a priori which solving method is best suited to a problem. This work explores the use of machine learning to predict which solving method will be most effective for a given problem. We use four different problem sets to determine the CSP attributes that can be used to determine which solving method should be applied. After choosing an appropriate set of attributes, we determine how well j48 decision trees can predict which solving method to apply. Furthermore, we take a cost sensitive approach such that problem instances where there is a great difference in runtime between algorithms are emphasized. We also attempt to use information gained on one class of problems to inform decisions about a second class of problems. Finally, we show that the additional costs of deciding which method to apply are outweighed by the time savings compared to applying the same solving method to all problem instances.

Stroke rehabilitation is to train the motor function of a patients limb. In this process, functional assessment is of importance, and it is primarily based on a patients task performance. The context of the rehabilitation discussed in this thesis is such that functional assessment is conducted through a computer system and the Internet. In particular, a patient performs the task at home in a haptic virtual environment, and the task performance is transmitted to the therapist over the Internet. One problem with this approach to functional assessment is that a patients mind state is little known to the therapist. This immediately leads to one question, that is, whether an elevated mind state will have some significant effect on the patients task performance? If so, this approach can result in a considerable error. The overall objective of this thesis study was to generate an answer to the aforementioned question. The study focused on a patients elevated fatigue state. The specific objectives of the study include: (i) developing a haptic virtual environment prototype system for functional assessment, (ii) developing a physiological-based inference system for fatigue state, and (iii) performing an experiment to generate knowledge regarding the fatigue effect on task performance. With a limited resource in recruiting patients in the experiment, the study conducted few experiments on patients but mostly on healthy subjects. The study has concluded: (1) the proposed haptic virtual environment system is effective for the wrist coordination task and is likely promising to other tasks, (2) the accuracy of proposed fatigue inference system achieves 89.54%, for two levels of fatigue state, which is promising, (3) the elevated fatigue state significantly affects task performance in the context of wrist coordination task, and (4) the accuracy of the individual-based inference approach is significantly higher than that of the group-based inference approach. The main contributions of the thesis are (1) generation of the new knowledge regarding the fatigue effect on task performance in the context of home-based rehabilitation, (2) provision of the new fatigue inference system with the highest accuracy in comparison with the existing approaches in literature, and (3) generation of the new knowledge regarding the difference between the individual-based inference and group-based inference approaches.

This thesis is focused on investigating the predictability of exchange rate returns on monthly and daily frequency using models that have been mostly developed in the machine learning field. The forecasting performance of these models will be compared to the Random Walk, which is the benchmark model for financial returns, and the popular autoregressive process. The machine learning models that will be used are Regression trees, Random Forests, Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) and Bayesian Additive Regression trees (BART). A characterizing feature of financial returns data is the presence of volatility clustering, i.e. the tendency of persistent periods of low or high variance in the time series. This is in disagreement with the machine learning models which implicitly assume a constant variance. We therefore extend these models with the most widely used model for volatility clustering, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) process. This allows us to jointly estimate the time varying variance and the parameters of the machine learning using an iterative procedure. These GARCH-extended machine learning models are then applied to make one-step-ahead prediction by recursive estimation that the parameters estimated by this model are also updated with the new information. In order to predict returns, information related to the economic variables and the lagged variable will be used. This study is repeated on three different exchange rate returns: EUR/SEK, EUR/USD and USD/SEK in order to obtain robust results. Our result shows that machine learning models are capable of forecasting exchange returns both on daily and monthly frequency. The results were mixed, however. Overall, it was GARCH-extended SVR that shows great potential for improving the predictive performance of the forecasting of exchange rate returns.

The development of machine vision algorithms for inspection and machine guidance has traditionally relied on the knowledge and experience of the developers as most of the techniques are based on heuristics and trial and error. This is especially problematic in the area of natural products where variability of the products is the rule rather than the exception. Humans are particularly good in functioning in this arena and in this thesis we look at the development of techniques derived from the functions of the human visual system (HVS). We first identify the significant processes in the HVS and highlight those that we believe are germane to the problems of interest. We then develop computational techniques using these methods and demonstrate their applicability to practical problems. This thesis uses the knowledge that the HVS is considered to consist of three sequential operations (sensing; encoding/transfer; and image interpretation) as a basis for developing a parallel procedure for a machine vision system. We have found that outputs derived from a simulation of the behaviors of receptive fields in the retina and in the higher levels of the brain can generate useful and robust features. Equivalent processes are then developed for machine applications under the guidance of a human operator to identify the areas of interest in the scene for the problem under consideration. Specifically we use the processes for encoding/transfer of data from the retina to develop techniques to enhance color contrasts, and compute color image features that are useful for defect detection and identification in real world images. This is accomplished by a transformation from image space to a characteristic response space that improves the robustness of classification. In this thesis the approach developed is applied to two industrial problems in the quality monitoring of meat and vegetables. The first problem concerns the quality monitoring of breast butterflies and the other the detection of defects on the surface of citrus. The approach is shown to derive algorithms that are robust and can be implemented at high rates of speed. Additionally we also identify a model within which further developments can be conducted as we learn more about the functioning of the HVS.

The main goal of this thesis is to propose a strategy which can integrate the precision hydrodynamic polishing system with an ordinary CNC machine tool. This integrated CNC machine tool is capable of applying the hydrodynamic polishing process, which is a high-precision machining method, to compensate the form error on a work surface to improve its form precision. With such a compensation capability, a low-cost CNC machine tool may play the function of precision machining as well as an expensive CNC machine tool does. It is hoped that with this function equipped in a CNC machine tool the international competition of the domestic machining industry can be enhanced. The complete integrating scheme is composed of three parts. The first part is the hardware of polishing system, which is required to attach to the CNC machine tool. The second one is the software developed in this thesis. It includes the codes to generate the commands to control the CNC machine tool and the attached polishing system. The final one is coordinating system that is to synchronize the actions of the CNC machine tool and polishing system. It is done by requiring the polishing system to match the actions of machine tool through measuring the configuration of machine tool consistently. Because of the first and third parts, no modification to the machine tool is needed and the requirement to read the internal information of CNC controller is waved. This will significantly reduce the complexity in implementing the integrating job. When properly integrated with the required sensors, the software developed in this thesis can harmonize the actions of the polishing system and machine tool to execute the form error compensation task. The software will automatically generate the commands for the polishing system and machine tool based on the geometric and material data of work. It does not require the user to fully comprehend the function of the CAM software and the details of polishing process. This will obviously reduce the skill requirement of operator and facilitate the use of the integrated system. Since the CNC machine tool only plays the function of offering the three translational motions (in X, Y, and Z directions), the application of this strategy to an ordinary CNC machine tool is straightforward.

Taiwan is surrounded by ocean, thus the ocean transportation has become the necessary support of Taiwan's economy. Due to this fact, this research provides a system based on cloud computing and distributed storage which is applied to compute large amount of data provided by many sensors on the sea in order to diagnose the existence of possible magnetized invaders. We use Hadoop platform from Apache Foundation to proceed distributable K-means clustering computation to process the data collected f rom many sensor nodes containing DGPS and magnetic sensors. With these data, it is possible to diagnose the existence and the moving direction of the possible invader. And the result can be return to remote monitoring terminal. Not only K-means can detect the irregularity of any axis of the magnetic field well, but also this system obtain good reliability and performance by Hadoop platform. The goal system can detect the irregularity of any axis of the magnetic field well enough by deploying K-Means clustering and obtain good reliability and performance by Hadoop platform.

A motion planner finds a sequence of potential motions for a robot to transit from an initial to a goal state. To deal with the intractability of this problem, a class of methods known as sampling-based planners build approximate representations of potential motions through random sampling. This selective random exploration of the space has produced many remarkable results, including solving many previously unsolved problems. Sampling-based planners usually represent the motions as a graph (e.g., the Probabilistic Roadmap Methods or PRMs), or as a tree (e.g., the Rapidly exploring Random Tree or RRT). Although many sampling-based planners have been proposed, we do not know how to select among them because their different sampling biases make their performance depend on the features of the planning space. Moreover, since a single problem can contain regions with vastly different features, there may not exist a simple exploration strategy that will perform well in every region. Unfortunately, we lack quantitative tools to analyze problem features and planners performance that would enable us to match planners to problems. We introduce novel metrics for the analysis of problem features and planner performance at multiple levels: node level, global level, and region level. At the node level, we evaluate how new samples improve coverage and connectivity of the evolving model. At the global level, we evaluate how new samples improve the structure of the model. At the region level, we identify groups or regions that share similar features. This is a set of general metrics that can be applied in both graph-based and tree-based planners. We show several applications for these tools to compare planners, to decide whether to stop planning or to switch strategies, and to adjust sampling in different regions of the problem.

This project investigated a miniature, hourglass-shaped actuator (MHA) and how its geometry affects performance. A custom, self-contained, finite-element simulation code predicts how each MHA deforms when pressurized internally. This analysis describes the MHA geometry's effects on four characteristics: a) work density b) mechanical advantage, c) work advantage and d) percent elongation. The first three characteristics are compared to a traditional actuator operating at the same pressure and elongation. A finite-element modeling code was tailored to study the MHA at 5 MPa internal pressure when 1) MHA height and side-wall thickness are constant and side-wall arc length varies; 2) MHA side-wall arc length and thickness are constant and the height varies; and 3) MHA side-wall thickness varies while height and side-wall arc length are fixed. Case 3 was studied using the MHA geometry with the highest work density found in either condition 1 or 2. Peak mechanical advantage, 6.47, occurs in a constant height MHA-Case 1-when the side-wall arc length is shortest. Highest elongation, 8.67%, occurs in the Case 1 MHA with the longest side-wall arc length. Finally, under Case 3, work density reaches 0.434 MJ/m3 when the side-wall thickness is 1.9 mm. The MHA has potential for active structures because its work density is high-higher than traditional actuators with the same elongation. Their small elongations limit their use; however, much work remains to determine how MHAs might be arranged in a useful array. Never the less, morphing airfoils and other active structures might benefit from embedded MHAs.

With the rapid development of power electronic devices and microprocessor chips, the engineers and researchers have come to pay more attentions to the feasibility of the control and drive for the switched reluctance motor. This motor has lots of advantages of low-cost, high efficiency, high stability and high hot emissive. And, it can be well operated under high temperature environment. In this paper, A newly control and monitor system is proposed with DSP-based driver system and the user-friendly LabVIEW software. The TMS320C240 chip-set is applied to construct the motor-driving system and to produce the PWM signal for the switched motor. The graphic user interface (GUI) is designed by using LabVIEW. The functions of the proposed human/machine interface (MMI) system includes the multi-channel digital I/O acquisition, the voltage/current signal acquisition,and the protocol setting.