Automated Wavelet Analysis Software

Perform complex signal analysis with a mouse click - no programming
required !
AutoSignalTM 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.
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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:
Moving Average
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
Easily smooth and process your signals
AutoSignal gives researchers the power to rapidly find components of
complex signals that normally require 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
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.
Save precious research time with the production facility
What once took hours now takes seconds - with only a few mouse
clicks. It's so easy - even novice users can learn how to use
AutoSignal in no time. Every procedure is automated. For even more
muscle, streamline your work with the production facility to automate
batch analysis and reporting. With an easy-to-use dialog, set up your
batch import and export options. Link directly to your hardware to
analyse and report on the fly. Already have your data in Microsoft®
Excel? No problem. Process up to 255 Excel worksheets at once. Create
RTF reports with numerical summaries that include publication-quality
graphs or export the data to a new Excel workbook. With AutoSignal,
it's so simple!
Here is some more information about AutoSignal :
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Joel Bondurant
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