Advanced Analysis and Signal Processing Libraries

Measurement Studio provides powerful analysis classes in Visual Basic .NET and Visual C# to convert raw data into meaningful information. Choose from over 500 analysis functions such as curve fitting, spectral analysis, statistics, and visualization to better understand your data. By performing analysis inside your application, you can integrate more intelligent data processing before storing or presenting the information.

Table of Contents

 Analysis Feature Comparison

Analysis Library Measurement Studio Standard Edition Measurement Studio Professional Edition Measurement Studio Enterprise Edition
Signal Generation
Signal Processing
Linear Algebra
Array and Numeric Operations
Curve Fitting
Special Functions

  No functionality

  Little functionality

  Most functionality

  All functionality

Key Features of Advanced Analysis Libraries

With Measurement Studio analysis functions, you have everything you need to perform the following types of operations:

  • Use advanced signal measurement functions such as peak detection, harmonic analysis, pulse analysis, and spectral analysis.
  • Perform statistical analysis of your data, including one, two, and three-way analysis of variance (ANOVA), histograms, and common statistical functions.
  • Generate complex composite signals for simulation or generation using sine, triangle, square, and sawtooth signals; Gaussian, white, and uniform noise; and arbitrary wavetables.
  • Digitally filter your data using versatile, configurable filters for all common filter designs.
  • Perform time-domain signal processing such as convolution, deconvolution, correlation, decimation, integration, and differentiation.
  • Perform frequency-domain signal processing, such as windowing, and forward and inverse fast Fourier transform (FFT), Hartley, Hilbert, and Laplace transforms.
  • Perform linear algebra operations such as solving equation systems, computing eigenvalues and eigenvectors, decomposition, factorization, and vector and matrix algebra.
  • Fit your data to linear, polynomial, exponential, and arbitrary nonlinear equations.
  • Compute important mathematical functions such as probability distributions, error function, and complementary error function. 

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