AutoSignal - A timely breakthrough in cutting-edge signal analysis

AutoSignal

A timely breakthrough in cutting-edge signal analysis

Perform complex signal analysis with a mouse click -- no programming required !

AutoSignal™ is the first and only program that completely automates the process of analyzing signals. Save precious time by eliminating the programming time normally required for performing sophisticated signal analysis. AutoSignal takes full advantage of its graphical user intuitive interface to simplify every aspect of operation, from data import to output of results. Choose your analysis techniques from the menu or toolbar. Select the algorithm and options from the interface. You get immediate visual feedback with 2D or 3D graphs of your signal analysis plus numeric summaries for reports.

Quickly locate your signal components

AutoSignal gives researchers the power to rapidly find components of complex signals that normally require extensive programming and mathematical routines. AutoSignal provides a vast array of spectral analysis procedures to help you make intelligent conclusions for any application. Built-in spectral analysis procedures include:

FFT
AutoRegressive
Moving Average
ARMA
Complex exponential modeling
Minimum variance methods
Eigen analysis frequency estimation and Wavelets

Precisely estimate with advanced parametric modeling

With AutoSignal, you get state-of-the-art parametric nonlinear modeling for sinusoid and damped sinusoid models. Non-linear optimization is also available as an independent procedure, or as an adjunct to each of the spectral algorithms. It includes robust maximum-likelihood optimizations as well as automatic parameter constraints. AutoRegressive linear models offer robust models that can quickly handle smaller data sets that FFT cannot accurately analyse.

Easily smooth and process your signals

Only AutoSignal offers so many different user-friendly methods to manipulate signal data. You can inspect your data stream in the Fourier domain and zero higher frequency points -- and see your results immediately in the time domain. This smoothing technique allows for superb noise reduction while maintaining the integrity of the original data stream. AutoSignal also includes eigendecomposition, wavelet, Savitzky-Golay, Loess and detrending for smoothing and denoising. Isolate components and detect signals with powerful filtering and reconstruction techniques with Fourier, eigendecomposition and wavelet methods. For instance, isolate components that appear and disappear with wavelet filtering and reconstruction. Recover the true signal that would have been measured using an ideal sensing system with Gaussian and exponential deconvolution.

Graphically review signal analysis results

As a powerful visualization tool, AutoSignal automatically plots your peaks, contours or 3D surfaces -- so you don't have to perform additional steps to see your results. Change any algorithm or analysis option on the fly through the user interface and see instant results. Isolate components of a signal graphically using eigen decomposition to display and select eigen components in order to find very low frequency oscillatory components or identify paired eigen modes producing a specific oscillation. Analyze your results with residual and root plots, and show statistical significance and probability limits on your output graphs. Clearly present your results with control over titles, fonts, colors, points, scaling, axis scale, labels, grid and plot types.

Identify frequency and power with Fourier Spectrum analysis

AutoSignal lets you see a complete picture of the frequency space using the library of six Fourier Spectrum methods with total flexibility. Solve the leakage problem found with standard FFT by using one of the 30 included data tapering windows. You can even make comparisons of performance of various data tapering windows in a single spectral graph. AutoSignal gives you access to the latest methodologies with techniques such as FFT Multi-taper Spectrum analysis to help you better characterise the power in each signal. Easily handle your unevenly sampled data with Lomb-Scargle Fourier domain analysis with techniques that were originally developed by astrophysicists.

Effortlessly analyze non-stationary data with wavelets

Simultaneously find the time and frequency localisation components of a non-stationary periodic signal with Continuous Wavelet Spectrum analysis techniques. AutoSignal gives you a choice of three adjustable mother wavelets: Morlet, Paul and Gaussian Derivative -- in both real and complex forms to optimise localisation results. You can also perform power analysis in either the time or the frequency range with specialised in-depth analysis techniques to evaluate the signal.

Isolate components by signal strength using eigendecomposition

In addition to FFT and wavelet spectral analysis techniques, you can select from linear and non-linear methods that are right for your application. The eigendecomposition procedures enable you to visually select eigenmodes for signal-noise separation or component isolation. With AutoSignal, you can also recover signal components based on power - the component may be sinusoidal, a square wave, a sawtooth or anharmonic pattern. You can confirm the presence of white noise or isolate red noise by reconstructing only the noise eigenmodes.