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In order to reduce the influence of axial displacement and/or to ease the assembly, certain capacitive angular sensors use symmetric designs limiting their absolute measuring range to (360/spl deg//n) where n is the number of blades. This paper presents a combined sensor that utilizes a Wiegand sensor to obtain additional information needed to resolve 360/spl deg/. Since magnetic and electric fields required by the sensing principles do not interfere, one single rotor design can be used for both fields. When the receiver coil on a Wiegand sensor has to drive a current, this current influences the shape and duration of the pulse generated by the sensor. We found that shape and amplitude of the pulse vary depending on the strength of the applied fields and the location of the initial inverse nucleation. Since most of design optimization for capacitive sensors is done by means of simulations, an adequate model of the combined sensor is needed. For the capacitive part, numerical approaches such as the finite element method are available. For the pulse wire part, an extended Ising model has been developed; results of measurements and numerical simulations are reported.

In reconstruction (i.e., determining the states of a model from measurements of model outputs), one is often forced to search for a regularized solution due to poor sensitivity of model outputs with respect to the model states. The amount of regularization is controlled by the regularization parameter, a scalar value multiplied with the so-called regularization term. The choice of the regularization parameter is crucial for the reconstruction process. In this paper, a new method to estimate the regularization parameter in an adaptive way is proposed. A condition-number based estimate of the regularization parameter for the first iteration step is required to choose the weighting factor for adapting the regularization parameter iteratively. By virtue of controlling the regularization term, a kind of edge preservation can be achieved. The validity of this method will be demonstrated for a capacitance tomography problem, which is solved applying a Gauss-Newton scheme.

Electrical impedance tomography is a noninvasive method, which is particularly useful for patient monitoring. For reconstructing the interior of the human body, a finite-element model is established and the inverse problem is solved by means of a Gauss-Newton method, which requires the Jacobian, describing the mapping between voltage distribution on the body's surface and the conductivity distribution in the interior. The calculation of the Jacobian has to meet an accuracy requirement as well as a minimal processing time requirement. In this paper, the reciprocity principle is applied to meet both requirements leading to good results for the reconstruction.

In most real world optimization problems, one tries to determine the global among some or even numerous local solutions within the feasible region of parameters. Nevertheless, it could be worthwhile to investigate some of the local solutions as well. A most desirable behavior would be that the optimization strategy behaves globally and yields additional information about local minima detected on the way to the global solution. In this paper, a clustering algorithm has been implemented into an extended higher order evolution strategy in order to achieve these goals. Multimodal two-dimensional test problems, namely, Rastrigin's function and the 4-parameter die mold press benchmark problem (Takahashi, 1996), are solved using this approach.

This paper gives an overview of some stochastic optimization strategies, namely, evolution strategies, genetic algorithms, and simulated annealing, and how these methods can be applied to problems in electrical engineering. Since these methods usually require a careful tuning of the parameters which control the behavior of the strategies (strategy parameters), significant features of the algorithms implemented by the authors are presented. An analytical comparison among them is performed. Finally, results are discussed on three optimization problems.