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Radar-based microwave imaging techniques have been proposed for early stage breast cancer detection. A considerable challenge for the successful implementation of these techniques is the reduction of clutter, or components of the signal originating from objects other than the tumor. In particular, the reduction of clutter from the late-time scattered fields is required in order to detect small (subcentimeter diameter) tumors. In this paper, a method to estimate the tumor response contained in the late-time scattered fields is presented. The method uses a parametric function to model the tumor response. A maximum a posteriori estimation approach is used to evaluate the optimal values for the estimates of the parameters. A pattern classification technique is then used to validate the estimation. The ability of the algorithm to estimate a tumor response is demonstrated by using both experimental and simulated data obtained with a tissue sensing adaptive radar system. Index Terms--Breast cancer, maximum a posteriori (MAP) estimation, microwave imaging, pattern classification.

A multistage identification algorithm for dynamic power system load models is proposed in this paper. The multistage approach is used to address the nonconvexity of the identification problem. Initial stages are used to find good preliminary estimates for the parameters of the model. Specifically, the initial stages are as follows: Equations for dynamic power system loads are discretized using the zero-order hold method and then approximated with a 2nd-order polynomial NARMAX model. Finally, an extended least squares approach is used to estimate the parameters of the NARMAX model, from which initial estimates for the parameters of the original model are obtained. In the final stage, the values found in the initial stages are used as the starting point for a Levenberg-Marquardt optimization routine that computes the optimal parameters. Numerical experiments using data from both simulated and real systems illustrate the computational complexity and accuracy of the proposed algorithm. Curve-fitting experiments are used to justify the polynomial NARMAX approximation. Index Terms--Power system load modeling, system identification.

In a deregulated electricity industry, new concerns have emerged regarding the quality of the power supply as companies tend to focus on their own objectives and interests. One main concern regarding power-supply quality is voltage fluctuation. Voltage fluctuations are defined as repetitive or random variations in the magnitude of the supply voltage. This paper addresses a method for monitoring the voltage flicker using state estimation (FSE). A test system is used to characterize the new algorithm in which parameter inaccuracies have been considered. The new algorithm can be used by operators as well as distribution companies to monitor the flicker severity throughout the network. Index Terms--Power quality (PQ), state estimation, voltage flicker.

This paper considers the use of cubic splines, instead of polynomials, to represent the static nonlinearities in block structured models. It introduces a system identification algorithm for the Hammerstein structure, a static nonlinearity followed by a linear filter, where cubic splines represent the static nonlinearity and the linear dynamics are modeled using a finite impulse response filter. The algorithm uses a separable least squares Levenberg-Marquardt optimization to identify Hammerstein cascades whose nonlinearities are modeled by either cubic splines or polynomials. These algorithms are compared in simulation, where the effects of variations in the input spectrum and distribution, and those of the measurement noise are examined. The two algorithms are used to fit Hammerstein models to stretch reflex electromyogram (EMG) data recorded from a spinal cord injured patient. The model with the cubic spline nonlinearity provides more accurate predictions of the reflex EMG than the polynomial based model, even in novel data. Index Terms--Cubic splines, mean square optimization, nonlinear system identification, separable least squares, stretch reflex dynamics.

We propose a mechanism which can improve the numerical robustness of a subspace based system identification method, the PI scheme [14, Part III], when the unknown system has poles situated close to z = 1, a condition that often arises in applications where the sampling rate is too high. The PI method is capable of solving a deterministic MIMO identification problem in which the output can be corrupted by a very general perturbation including arbitrarily colored noise, transients due to nonzero initial conditions, and a deterministic zero bias. By performing a bilinear transformation on the shift operator we are able to move the poles away from the point z = 1 and a more robust identification results. The implementation of this transformation gives rise to a series of anticausal filters applied to the input/output data. Estimation accuracy is further improved by taking the unknown end conditions of the anticausal filters into account, particularly when only short data records are available. A numerical simulation highlights the improvements realized by our new algorithms.