Tyrone L. Vincent

Analysis of head pose accuracy in augmented reality

A method is developed to analyze the accuracy of the relative head-to-object position and orientation (pose) in augmented reality systems with head-mounted displays. From probabilistic estimates of the errors in optical tracking sensors, the uncertainty in head-to-object pose can be computed in the form of a covariance matrix. The positional uncertainty can be visualized as a 3D ellipsoid. One useful benefit of having an explicit representation of uncertainty is that we can fuse sensor data from a combination of fixed and head-mounted sensors in order to improve the overall registration accuracy. The method was applied to the analysis of an experimental augmented reality system, incorporating an optical see-through head-mounted display, a head-mounted CCD camera, and a fixed optical tracking sensor. The uncertainty of the pose of a movable object with respect to the head-mounted display was analyzed. By using both fixed and head mounted sensors, we produced a pose estimate that is significantly more accurate than that produced by either sensor acting alone.

Integrated real-time and run-to-run control of etch depth in reactive ion etching

Reactive Ion Etching (RIE) is a common process step in semiconductor manufacturing, yet the underlying mechanisms remain poorly understood. Our goal is to reduce the variance of etch characteristics by integrating real-time and run-to-run control of plasma and process variables. The run to run controller suggests plasma variable set-points based on the wafer characteristics of the previous run. The real-time controller maintains the suggested plasma variables by manipulating the process inputs during the etch. We have demonstrated the integrated control architecture for rejecting oxygen and loading disturbances in an Applied 8300 Hexode Reactor during a polysilicon etch.

An Extended Kalman filtering-based method of processing reflectometry data for fast in-situ etch rate measurements

In this paper a new algorithm is presented for determining etch rate from single or multiple wavelength reflectometry data. This algorithm is based on techniques from recursive nonlinear estimation theory-Extended Kalman Filtering. A major advantage of our algorithm is extremely high speed, with computation time less than 1 ms on a Pentium PC. Consequently, it can be used in real-time feedback control applications. The speed advantage also makes it a suitable candidate for full wafer (or multi-point) high-speed etch rate measurement.

Identification of Structured Nonlinear Systems

This paper is concerned with the identification of static nonlinear components in a complex interconnected system. These nonlinear components are treated nonparametrically, in the sense that no natural parameterization is assumed to be available.

Abel's inversion applied to experimental spectroscopic data with off axis peaks

Abstract Many mathematical techniques for solving Abels integral equation have been proposed over the years in the literature. Often these methods handle test functions with known solutions and Gaussian type profiles quite accurately. For experimental data and non-Gaussian shapes, however, these methods are inadequate. An experimental study of the uniformity of the plasma emission on the Gaseous Electronics Conference Reference Cell has as its goal to provide immediate feedback regarding the plasma uniformity. Therefore a rapid, accurate, and sturdy computational method of data analysis is required. The first round of experiments indicate off axis or donut shaped plasmas within the reference cell. The analysis and conclusions are presented herein.

Input design for structured nonlinear system identification

This paper is concerned with the input design problem for a class of structured nonlinear models. This class contains models described by an interconnection of known linear dynamic systems and unknown static nonlinearities. Many widely used model structures are included in this class. The model class considered naturally accommodates a priori knowledge in terms of signal interconnections. Under certain structural conditions, the identification problem for this model class reduces to standard least squares. We treat the input design problem in this situation. An expression for the expected estimate variance is derived. A method for synthesizing an informative input sequence that minimizes an upper bound on this variance is developed. This reduces to a convex optimization problem. Features of the solution include parameterization of the expected estimate variance by the input distribution, and a graph-based method for input generation.

Parametric identification of structured nonlinear systems

In this paper, identification of structured nonlinear systems is considered. Using linear fractional transformations (LFT), the a priori information regarding the structural interconnection is systematically exploited. A parametric approach to the identification problem is investigated, where it is assumed that the linear part of the interconnection is given and the input to the nonlinear part is measurable. An algorithm for the identification of the nonlinear part is proposed. The uniqueness properties of the estimate provided by the algorithm are examined. It is shown that the estimate converges asymptotically to its true value under a certain persistence of excitation condition. Two simulated examples and a real-data example are presented to show the effectiveness of the proposed algorithm.

Parametric and nonparametric curve fitting

We are concerned with convergence issues in the identification of a static nonlinear function. Our investigation focuses on properties of the input signal that ensure convergence of the estimate. Both parametric and nonparametric approaches are considered. In the parametric case, we offer sufficient conditions under which the estimated parameters converge to their true values almost surely. For the nonparametric case, we offer necessary and sufficient conditions under which the estimated function converges almost surely to the true nonlinearity.

Development of closed‐loop control of robotic welding processes

Purpose – The purpose of this research is to develop closed‐loop control of robotic welding processes.Design/methodology/approach – The approach being developed is the creation of three‐dimensional models of the weld pool using stereo imagining. These models will be used in a model‐based feedback control system. Fusion of more than one sensor type in the controller is used.Findings – Three‐dimensional images can be produced from stereo images of GMAW‐p weld pools. This requires coordinating the image capture with the arc pulse to allow observation of the pool.Research limitations/implications – This is a work in progress. The imaging is not being done in real time at this point in time. Future work will address this issue. Also, how the image information is to be used to make corrections within the controller is future work.Practical implications – Closing the loop on GMAW welding will allow robotic automation of welding to proceed to a much broader degree of application.Originality/value – This paper dem...

Discrete-Time Estimators with Guaranteed Peak-To-Peak Performance

Abstract In this paper is considered discrete-time estimation with worst-case peak-to-peak gain as the performance measure, Both linear and a restricted class of nonlinear estimation problems are treated, In the linear case estimators are synthesized that minimize the *-norm, the best upper bound on the induced l∞ norm that one can obtain by bounding the reachable set with inescapable ellipsoids. In the nonlinear case conditions are presented that are sufficient to ensure a given level of peak-to-peak performance.