Analysis

LabVIEW add-on: Advanced Signal Processing

Wavelet and Filter Bank Design
Wavelets are a relatively new signal processing method. A wavelet transform is almost always implemented as a bank of filters that decompose a signal into multiple signal bands. It separates and retains the signal features in one or a few of these subbands. Thus, one of the biggest advantages of using the wavelet transform is that signal features can be easily extracted. In many cases, a wavelet transform outperforms the conventional FFT when it comes to feature extraction and noise reduction. Because the wavelet transform can extract signal features, wavelet transforms find many applications in data compression, echo detection, pattern recognition, edge detection, cancellation, speech recognition, texture analysis, and image compression.

Using the Wavelet and Filter Bank Tools
The key to a creating a successful wavelet application is to select an appropriate wavelet transform, which is equivalent to selecting a good set of filters in the filter bank. The wavelet and filter bank design component of this toolkit provides a unified approach to designing a wavelet transform or filter bank. You can design an arbitrary wavelet transform through an easy-to-use graphical user interface. By interactively selecting a wavelet prototype (equiripple or maxflat) and different FIR filter combinations, you can easily find the best wavelet or filter bank for your application. The end result is a wavelet or filter bank design that works for your application and meets your design specification. The wavelet and filter bank design tools apply to 1D signals and to 2D images as well. The signal or image can be loaded from a data file or acquired and processed in real time using DAQ or IMAQ hardware.

You can graphically design wavelet and filter banks using the stand-alone application. You can save all design results as text files for use in other applications. The toolkit also includes wavelet analysis VIs, such as 1D and 2D analysis and synthesis filters, as well as many other useful functions for use in building custom LabVIEW applications.

Wavelet designs
Orthonormal
Maximum Flat (such as Daubechies wavelets)
Equiripple

Biorthonormal
Maximum Flat (such as B-spline wavelets)