Smart devices with multiple on-board sensors, networked through wired or wireless links, are distributed in physical systems and environments. Broad applications of such sensor networks include manufacturing quality control and wireless sensor systems. In the operation of sensor systems, robust methods for retrieving reliable information from sensor systems are crucial in the presence of potential sensor failures. Existence of sensor redundancy is one of the main drivers for the robustness or fault tolerance capability of a sensor system. The redundancy degree of sensors plays two important roles pertaining to the robustness of a sensor network. First, the redundancy degree provides proper parameter values for robust estimator; second, we can calculate the fault tolerance capability of a sensor network from the redundancy degree. Given this importance of the redundancy degree, this dissertation presents efficient algorithms based on matroid theory to compute the redundancy degree of a clustered sensor network. In the efficient algorithms, a cluster pattern of a sensor network allows us to decompose a large sensor network into smaller sub-systems, from which the redundancy degree can be found more efficiently. Finally, the robustness analysis as well as its algorithm procedure is illustrated using examples of a multi-station assembly process and calibration of wireless sensor networks.

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Because of several advantages, e.g. compact structure, high air-gap flux density, and high torque capability, the PM synchronous motor plays an important role in recent years. The basic principle of controlling a PMSM is based on vector control. The control performance is influenced by factors as the plant parameter variations, the external load disturbances, and the unmodeled or nonlinear dynamics. In the thesis, we apply a recently proposed robust 2DOF configuration to designing controllers for PMSM to achieve the robust asymptotical tracking under perturbations in both the motor and the controllers. Two design methods are adopted to implement the desired controllers, i.e. the linear algebraic method and the design method. The effect of the well-known internal model principle is addressed in the former design method. The merit of the latter design method is that both time and frequency domain design specifications can be easily included in the design procedure. Computer simulation results are displayed to illustrate the advantages of our designs.

Magnetic resonance imaging (MRI) is one of medical images used by doctors for diagnosing diseases. MRI shows higher quality in displaying soft tissues and tumors. Pharmacokinetic dynamic gadolinium-enhanced MR imaging and functional MR imaging (fMRI) were used in this dissertation. Dynamic MR images are obtained using fast spin-echo sequences at consecutive time after the injection of gadolinium-diethylene-triamine penta-acetic (Gd-DTPA) acid. A pharmacokinetic model analyzes time-signal intensity curves of suspected lesions. Functional MR imaging produces images of activated brain regions by detecting the indirect effects of neuronal activity on local blood volume, flow, and oxygen saturation. Thus it is a promising tool for further understanding the relationships between brain structure, function, and pathology. Because of patients' movement during imaging, serially acquired MR images do not correspond in the same pixel position. Therefore, matching corresponding points from MR images is one of fundamental tasks in this dissertation. Least-squares estimation is a standard method for parameter estimation. However, outliers (due to non-Gaussian noise, lesion evolution, motion-related artifacts, etc.) may exist and thus may cause the motion parameter estimation result to deteriorate. In this dissertation, we describe two robust estimation algorithms for the registration of serially acquired MR images. The first estimation algorithm is based on the Newton method and uses the Tukey's biweight objective function. The second estimation algorithm is based on the Levenberg-Marquardt technique and uses a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. Experimental results show the accuracy of the proposed robust estimation algorithms is within subpixel. MR imaging has been used to evaluate nasal papilloma. However, postoperative MR imaging of nasal papilloma becomes more complicated because repair with granulation and fibrosis occurs after surgery. Therefore, it is possible to misclassify recurrences as postoperative changes or to misclassify postoperative changes as recurrences. Recently, dynamic gadolinium-enhanced MR imaging with pharmacokinetic analysis has been successfully used to identify the post-treatment recurrence or postoperative changes in rectal and cervical carcinoma. Nasopharyngeal carcinoma (NPC) comprising malignant tumors is a disease more common in Asia than in other parts of the world. Hence, in this dissertation, we evaluate the feasibility of dynamic gadolinium-enhanced MR imaging with pharmacokinetic analysis in detecting NPC and distinguishing recurrent nasal papilloma from postoperative changes (fibrosis or granulation tissue). In this dissertation, a new approach to differentiate recurrent nasal papilloma from postoperative changes using pharmacokinetic dynamic gadolinium-enhanced MR imaging and robust motion correction is presented. First, a robust estimation technique is incorporated into nonlinear minimization method to robustly register dynamic gadolinium-enhanced MR images. Next, user roughly selects the region of interest (ROI) and an active contour technique is used to extract a more precise ROI. Then, the relative signal increase (RSI) is calculated. We use a three-parameter mathematical model for pharmacokinetic analysis. The pharmacokinetic parameters A (enhancement amplitude) and Tc (tissue distribution time) are calculated by a nonlinear least-squares fitting technique. The calculated A and Tc are used to characterize tissue. Pharmacokinetic analysis shows that recurrent nasal papilloma has faster tissue distribution time (Tc, 41 versus 88 seconds) and higher enhancement amplitude (A, 2.4 versus 1.2 arbitrary units) than do postoperative changes. A cut-off of 65 seconds for tissue distribution time and 1.6 units for enhancement amplitude yields an accuracy of 100% for differentiating recurrent nasal papilloma from postoperative changes. Though the above methods obtained good results, finding the region of interest (ROI) was done in a semi-automatic manner. For diagnosing NPC and improve the drawback, a system that automatically detects and labels NPC with dynamic gadolinium-enhanced MR imaging is presented. This system is a multistage process, involving motion correction, gadolinium-enhanced MR data quantitative evaluation, rough segmentation, and rough segmentation refinement. Three approaches, a relative signal increase method, a slope method and a relative signal change method, are proposed for the quantitative evaluation of gadolinium-enhanced MR data. After the quantitative evaluation, a rough NPC outline is determined. Morphological operations are applied to refine the rough segmentation into a final mask. The NPC detection results obtained using the proposed methods had a rating of 85% in match percent compared with these lesions identified by an experienced radiologist. However, the proposed methods can identify the NPC regions quickly and effectively. In this dissertation, the proposed methods provide significant improvement in correcting the motion-related artifacts and can enhance the detection of real brain activation and provide a fast, valuable diagnostic tool for detecting NPC and differentiating recurrent nasal papilloma from postoperative changes.

A robust design utilizing consecutive orthogonal arrays algorithm is proposed for designing electronic ballasts of fluorescent lamps. By this design method, the variation in the lamp power can be less than 10% under different operating conditions. In the manipulation of the consecutive orthogonal arrays, component values of the ballast circuit and DC-link voltage are used as controllable variables for inner orthogonal arrays; while manufacturers, ambient temperature, used hours, and variation in DC-link voltage are treated as uncontrollable variables for outer orthogonal arrays. The average effects of the output power for each controllable variable are calculated from simulation results, which are served as indexes to find the combination of circuit parameters with a better solution. With consecutive orthogonal arrays, the target values of the circuit parameters are approached step by step. In addition, the effect of the DC-link voltage on the lamp power can be understood from the uncontrollable variable of outer orthogonal arrays. The proposed design tool is implemented on the design of an electronic ballast for a 40W fluorescent lamp. The test results show that the designed electronic ballast can be adopted for the lamps from different manufacturers, with different used hours, and under variation in a wide range of ambient temperature.

In this thesis, an LMI-based pole-clustering characterization for descriptor systems is investigated. A necessary and sufficient condition for checking simultaneously the regularity, impulse immunity, and finite eigenvalues locating in the generalized LMI regions is derived. Since uncertainty exists inevitably in control systems, we propose two sufficient conditions to guarantee the robust pole clustering in the generalized LMI regions for uncertain descriptor systems with two types of uncertainties, i.e. the norm bounded uncertainty and the convex polytopic uncertainty. The LMI-based state feedback controller design methods are developed as well. Finally, the validity and the feasibility of our theoretical results are verified by the numerical simulation results of several examples.

The paper investigates problems of designing controllers to linear time-invariant continuous descriptor systems subject to norm-bounded structured uncertainty so that the closed-loop systems are admissible or D-admissible with their transfer matrices having H-infinite norm bounded by a prescribed value. The constant state feedback and the dynamic output feedback designs are addressed. In both design methods, sufficient LMI conditions are derived to guarantee achievement of the desired specifications, such as robust H-infinite norm and pole-clustering constraints. Finally, two numerical examples are shown for the illustration.

A mixture experiment is an experiments in which the q-ingredients are nonnegative and subject to the simplex restriction on the (q-1)-dimentional probability simplex. In this work , we investigate the robust A-optimal designs for mixture experiments with uncertainty on the linear, quadratic models considered by Scheffe' (1958). In Chan (2000), a review on the optimal designs including A-optimal designs are presented for each of the Scheffe's linear and quadratic models. We will use these results to find the robust A-optimal design for the linear and quadratic models under some robust A-criteria. It is shown with the two types of robust A-criteria defined here, there exists a convex combination of the individual A-optimal designs for linear and quadratic models respectively to be robust A-optimal. In the end, we compare efficiencies of these optimal designs with respect to different A-criteria.

Robustness of a system has been defined in various ways and a lot of work has been done to model the system robustness , but quantifying or measuring robustness has always been very difficult. In this research we consider a simple system of a linear estimator and then attempt to model the system performance and robustness in a geometrical manner which admits an analysis using the differential geometric concepts of slope and curvature. We try to compare two different types of curvatures, namely the curvature along the maximum slope of a surface and the square-root of the absolute value of sectional curvature of a surface, and observe the values to see if both of them can alternately be used in the process of understanding or measuring system robustness. In this process we have worked on two different examples and taken readings for many points to find if there is any consistency in the two curvatures.

Statistical process control (SPC) procedures suitable for autocorrelated processes have been extensively investigated in recent years. The most popular method is the residual-based control chart. To implement this method, a time series model, which is usually an autoregressive moving average (ARMA) model, of the process is required. However, the model must be estimated from data in practice and the resulting ARMA modeling errors are unavoidable. Residual-based control charts are known to be sensitive to ARMA modeling errors and often suffer from inflated false alarm rates. As an alternative, control charts can be applied directly to the autocorrelated data with widened control limits. The widened amount is determined by the autocorrelation function of the process. The alternative method, however, can not be also free from the effects of modeling errors because it relies on an accurate process model to be effective. To compare robustness to the ARMA modeling errors between the preceding two kinds of methods for control charting autocorrelated data, this dissertation investigates the sensitivity analytically. Then, two robust design procedures for residual-based control charts are developed from the result of the sensitivity analysis. The first approach for robust design uses the worst-case (maximum) variance of a chart statistic to guarantee the initial specification of control charts. The second robust design method uses the expected variance of the chart statistic. The resulting control limits are widened by an amount that depends on the variance of chart statistic - maximum or expected - as a function of (among other things) the parameter estimation error covariances.