Abstract
For applications requiring low power consumption, signal processing in the analog domain is preferable. Approximate implementations of wavelet transforms in analog hardware can be achieved with dynamic translinear circuits. The quality of such implementations depends on the accuracy of the corresponding wavelet approximations. A design trade-off involves the approximation accuracy versus the complexity (model order) of the implemented filter. First we discuss the technique of Padé approximation for obtaining wavelet approximations. Then we present the technique of L2-approximation, which is conceptually more attractive but computationally more demanding. These techniques are compared by means of a worked example, involving Gaussian wavelet approximation and real measurements of an ECG signal. The L2-approximation approach is shown to exhibit superior performance.
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