Measured on Intel i7-11850H, 32 GB RAM, LabVIEW 2024 64-bit.
1. Executive Summary The LabVIEW Advanced Signal Processing (ASP) Toolkit is an extension to the NI LabVIEW environment, designed for engineers and scientists who require sophisticated analysis beyond the standard NI Vision and basic DSP modules. While the base LabVIEW DSP Module handles FFTs, filtering, and basic statistics, the ASP Toolkit focuses on time-frequency analysis , wavelet denoising , non-stationary signal processing , and joint time-frequency domain (JTFA) techniques. labview advanced signal processing toolkit
| Method | Resolution | Computational Cost | LabVIEW VI Name | |--------|------------|--------------------|------------------| | | High | O(p³) | AR Spectrum.vi | | AR (Burg) | Very high, stable | O(Np) | Burg AR Spectrum.vi | | Maximum Entropy (MEM) | Highest | High | MEM Spectrum.vi | | MUSIC | Super-resolution for sinusoids in noise | Very high (eigen-decomposition) | MUSIC Spectrum.vi | | ESPRIT | Direct frequency estimation (no spectrum) | High | ESPRIT Frequencies.vi | Measured on Intel i7-11850H, 32 GB RAM, LabVIEW 2024 64-bit
| Algorithm | Best For | LabVIEW Implementation Detail | |-----------|----------|--------------------------------| | | Stationary segments within non-stationary signals | Hanning/Hamming/Gaussian window, adjustable overlap (0–99%), output as 2D spectrogram array | | Gabor Transform | Optimal time-frequency localization | Uses Gaussian window; computes expansion coefficients via Zak transform | | Wigner-Ville Distribution (WVD) | High resolution for mono-component signals | Includes cross-term reduction (smoothed pseudo WVD) | | Choi-Williams Distribution | Reducing cross-terms while preserving resolution | Exponential kernel; adjustable kernel width parameter | | Scalogram | Wavelet-based time-frequency view | Output from Continuous Wavelet Transform (CWT) | While the base LabVIEW DSP Module handles FFTs,