ADC testing toolbox for MATLAB

March 1, 2023 ADCTest toolbox v5.0 has been released. Version 5.0 is a major technical update. The toolbox has been reformulated to fit MATLAB’s App Designer development environment, instead of the obsolete Matlab GUIDE. It contains minor changes in the user interface and bug fixes.

March 29, 2017 ADCTest toolbox v4.6 has been released. Compared to the latest release of ADCTest toolbox (ver. 4.5), version 4.6 contains minor bug fixes and unified notation regarding approximate maximum likelihood (AML) estimation of quantizer and signal parameters.

January 16, 2017 ADCTest toolbox v4.5 has been released. Compared to the latest release of ADCTest toolbox (ver. 4.4) the main new feature of version 4.5 is the approximate maximum likelihood (AML) estimation of quantizer and excitation signal parameters. This method parameterizes the integral nonlinearity of the quantizer under test and optimizes the ML cost function with respect to the parameters of the excitation signal and the parameters of the quantizer nonlinearity as well.

October 27, 2016 ADCTest toolbox v4.4 has been released. Compared to the latest release of ADCTest toolbox (ver. 4.3), the most important new feature is the extended cost function evaluator (EvaluateCFExtended.m) in maximum likelihood estimation of ADC and signal parameters. This new function calculates the entire gradient (size: 2^b + 4) and Hessian matrix of the ML cost function (size: (2^b + 4) x (2^b + 4)). The Hessian matrix provides the full Fisher information regarding the estimation of code transition levels and signal parameters. This way the toolbox calculates the Cramér-Rao Lower Bound for the covariance of all estimators. The most important values of the CRLB are displayed in the GUI.

March 24, 2015 An ADC testing tool for NI LabVIEW (based on the algorithms developed for the MATLAB toolbox) has been released. Available at the Accessories.

November 25, 2014 ADCTest toolbox v4.3 has been released. The new features are:

Graphical and numerical coherence analysis based on improved frequency estimation.
Suggested selections of measurement record to achieve the best histogram results (using the Carbone-Chiorboli condition)

November 11, 2014 Minor improvements in maximum likelihood estimation have been released.

This page provides access to the latest version of the MATLAB implementation of the standard methods (IEEE standards) of ADC testing. Furthermore, the maximum likelihood method is also available in the same framework.

Well-known problems of measured data testing are also addressed, like

unified handling of all measurement circumstances via descriptors,
graphical user interface based preprocessing of data to prepare the descriptors,
complex data processing implemented in MATLAB functions,
easy handling via Graphical User Interface.

In addition, members and results of the ADC testing and quantization group operating at the Department of Measurement and Information Systems of the Budapest University of Technology and Economics are presented.