A dynamic complex impedance imaging technique is developed with the aid of the linearized Kalman filter (LKF) for real-time reconstruction of the human chest. The forward problem is solved by an analytical method base...
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A dynamic complex impedance imaging technique is developed with the aid of the linearized Kalman filter (LKF) for real-time reconstruction of the human chest. The forward problem is solved by an analytical method based on the separation of variables and Fourier series. The inverse problem is treated as a state estimation problem. The nonlinear measurement equation is linearized about the best homogeneous impedivity value as an initial guess, and the impedivity distribution is estimated with the aid of the Kalman estimator. The Kalman gain matrix is pre-computed and stored off-line to minimize the on-line computational time. Simulation and phantom experiment are reported to illustrate the reconstruction performances in the sense of spatio-temporal resolution in a simplified geometry of the human chest.
In many noise control applications, the noise is dominated by low frequencies and generated by several independent periodic sources. In such situations the tonal noise may be suppressed by using a narrowband multiple-...
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In many noise control applications, the noise is dominated by low frequencies and generated by several independent periodic sources. In such situations the tonal noise may be suppressed by using a narrowband multiple-reference feedforward controller. The performance characteristics of the control system, e.g., the convergence behavior and noise reduction are directly related to the controller adaptation rate as well as the frequency separation of the tonal components in the noise, i.e., the beat frequency. This paper treats the convergence performance of a complex least-mean-squares (LMS) algorithm using two reference signals. An analysis of its convergence behavior is presented as well as the results from computer simulations validating the convergence behavior. The convergence of the filter weights and the decrease rate of the squared error (the learning curve) for noise control applications are also discussed.
A new program, named ANTIcomplex, has been specially written for searching the optimum conditions of a technological process. The algorithm is based on an iterative procedure with, at each step, a random sampling for ...
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A new program, named ANTIcomplex, has been specially written for searching the optimum conditions of a technological process. The algorithm is based on an iterative procedure with, at each step, a random sampling for a better exploration of the hyperspace of the parameters. Since the process is considered as a 'black box', the program operates by successive reductions of the variation domains with a strategy derived from some function optimization (complex). This approach is very flexible and the experimenter can stop the investigation at any series of experiments according to the desired accuracy for the optimum region. This program has been successfully tested on several real processes. This paper gives a complete description of the approach together with two illustrations on simulated processes.
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