Search

This paper deals with the design and applications of digital differentiators. For real time applications it is mandatory that a differentiator should have as small an order as possible. Different procedures available for the design of FIR and IIR type digital differentiators are presented. The IIR Type digital differentiators are obtained by inversion and magnitude stabilization of the existing digital integrators. In some applications like controls, waveshaping, oscillators and communications require a constant 90° phase for differentiators. In this paper an attempt is made to study about the variation of phase angle of digital differentiators with the application of fractional delay. The use of digital differentiators for the detection of edges in an image, QRS detection in an ECG signal is illustrated. It has been observed that the digital differentiators have shown superior performance compared to the well known gradient method. With the proven efciency of the differentiators in various applications, they have been implemented in hardware using Verilog. [ABSTRACT FROM PUBLISHER]

A new and computationally convenient method is used to design the Taylor-Fourier harmonic estimator. This estimator get best approximations (in comparison with that of the FFT) of the harmonics components of a signal under transient conditions. The estimator can be seen as a FIR symmetric filter bank. Each filter of the bank meets maximally flat conditions around the harmonic frequencies. This conditions can be used to solve the least squares problem instead the classical pseudoinverse solution. The maximally flat conditions also ensure the quality of the estimate, as they make that the gains of the filters correspond to the ideal differentiator gains around the harmonic frequencies. An implementation of the proposed estimator using Labview and the cRIO platform is shown. Several experimental tests are used to compare the performance of the estimator with that of the Fourier analysis. [ABSTRACT FROM PUBLISHER]

This paper extends the neural network based algorithm to the equiripple design of FIR digital differentiators in the weighted least-squares (WLS) sense. The error representation reformulated by the Lyapunov error function reflects the difference between the desired amplitude response and the designed response. The optimal filter coefficients are obtained when the neural network is convergent. Furthermore, the proposed method using a weighted updating-function can make a very good approximation of the minimax solution. Simulation results indicate that the proposed approach can achieve a good performance in the parallelism manner without incurring convergence problems. [ABSTRACT FROM PUBLISHER]