What are Application Notes?

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Measurement Automation with SigLab

Measurement automation can be a daunting task with outdated software paradigms and "rack and stack" technology. Combining MATLAB GUI tools with DSPT SigLab measurement functions produces a Windows-based automated measurement solution that is powerful and easy to implement.

The three basic steps to creating an automated measurement and test solution are:

1. Define the required measurements
2. Define the measurement limits
3. Write a MATLAB .m file that automatically makes the measurements and compares the results to the limits.

This note details these three steps and shows how to employ examples provided with SigLab.

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Interfacing Telephony Signals to DSPT SigLab

It is often necessary to perform measurements on analog telephone lines. SigLab's differential inputs make it easy to interface to these lines. This application note describes one approach to constructing such an interface.

Analog phone lines have a wide range of signal levels. During the ring, signals of 130 V peak are present. Voice levels are on the order of a few hundred mV peak-to-peak riding on a 10 V DC offset. The line is balanced with a nominal 600 ohm impedance, therefore differential voltage measurements are required.

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Biasing Internally Amplified Accelerometers

Accelerometers with integrated amplifiers are a popular alternative to those requiring external charge amplifiers. To use these integrated devices a constant current bias must be applied to the amplifier and the resulting DC offset component must be removed from the output signal.

SigLab's top hatch access to the input signal conditioning circuitry makes building an internal accelerometer interface easy. The required bias current is injected into the center conductor of the coaxial cable connecting the accelerometer with SigLab and the return path is through the coax shield. This note describes a method of constructing a circuit that supplies the requisite constant current and eliminates the resulting offset output voltage.

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System Identification: A Practical Tool from a Fiddler's Paradise

Measuring the frequency response of a linear system is a typical first step in characterizing its dynamic behavior. Requirements of control system design and signal processing often demand information beyond the frequency response. Underlying differential and/or difference equations provide a concise mathematical description defining all other dynamic behavior.

The task of estimating these equations from measurements of input and output signals is referred to in the control literature as the process of System Identification (SID). Despite the complexity of the signal processing involved, the complete identification process can be integrated into an easy to use GUI interface.

This application note is not intended to illuminate the SID expert, but rather to provide the practicing engineer or researcher with a working knowledge of:

• What the process of SID consists of
• Why SID is useful
• Data acquisition requirements and suggestions
• Some real world examples of SID
• A description of a time-tested SID algorithm used within SigLab
• The integration of the SigLab data acquisition and SID algorithm into an easy-to-use GUI-based application
  

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Estimating Transfer Functions with DSPT SigLab

Accurate transfer function estimation of linear, noise-free dynamic systems is an easy task for SigLab. Often, however, the system to be analyzed is noisy or not perfectly linear. All real world systems suffer from these deficiencies to a degree, but control systems are among the worst offenders. Dealing with the combination of noise and non-linearity requires the user to understand the measurement tradeoffs necessary to assure an accurate transfer function estimate.

The first sections of this applications note address the task of making accurate transfer function measurements on systems which are both noisy and non-linear. The second half of this note focuses specifically on the increased noise and non-linearity problems found in control systems. SigLab's network analyzer and swept-sine network analyzer are ideally suited for such measurements.

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Using SigLab with the Frequency Domain System Identification Toolbox

SigLab makes it easy for users of the Frequency Domain System Identification Toolbox to get high quality measurements for analysis. The most direct measurement that can be used by the toolbox is the transfer function. SigLab's FFT based and Swept Sine Network Analyzers excel in providing accurate transfer function estimates.

SigLab is also optimized to provide alias protected time domain measurements for the MATLAB System Identification Toolbox and the MMLE3 Identification Toolbox. The SigLab itself has a GUI-based time domain SID virtual instrument as well. However, the Frequency Domain identification technique can have advantages over the time domain techniques under any of the following conditions:

1. a model is desired over a strictly prescribed frequency range
2. the device being modeled is part of an operating control system
3. the swept-sine measurement technique is mandated due to noise and non-linearity
4. measurements with non-uniform frequency resolution must be accommodated

This note describes how SigLab transfer function estimates can be analyzed by the FDID toolbox.

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Real Time Processing within MATLAB

Questions often arise as to the viability of using SigLab to continuously acquire data for real time processing by MATLAB. Although SigLab is optimized for dynamic system measurements, surprisingly good results are obtained in such real time applications. This application note provides several examples of real time processing within the MATLAB environment.

For this note the term "real-time processing" will indicate that the data acquisition process in SigLab will not overrun the the data processing operations performed by MATLAB on the host PC.

Although real-time throughput is a function of the acquisition sampling rate, processing complexity and host PC speed, the key to good real-time throughput is the vectorization inherent in both MATLAB and SigLab. When the data is handled in blocks rather than individual samples, the SigLab-MATLAB combination is suitable for operations such as filtering, detection of signals, streaming to disk and continuous graphic display of results.

The note gives some specific examples. Generally, with block sizes in the range of 1000-2000 samples and sample rates in the 12,800 Hz (bandwidth of 5000 Hz) range significant processing (filtering and signal detection) can be accomplished within MATLAB without missing data.

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Using DSPT SigLab for Production Line Audio Test

SigLab is ideal for characterizing audio components. Its input and output subsystems both have low noise, low distortion, and low crosstalk. SigLab's measurement performance is therefore beyond the testing requirements of most any consumer audio device. Its three internal DSP processors and its SCSI interface ensure maximum measurement throughput.

This note covers using SigLab to make three typical audio measurements on a stereo amplifier. The measurements are:

• channel to channel crosstalk
• harmonic distortion
• signal to noise ratio

Many other measurements can be made with SigLab including intermodulation distortion, gain and phase vs. frequency response, step response, etc.

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A Software Lab Notebook Integrating SigLab, MATLAB, and Word 6.0

This Word for Windows document template based on the MATLAB Notebook allows the development of interactive student labs combining Word, MATLAB and measurements made by SigLab.

This application note explains the use and operation of the SigLab Notebook template and provides an example of how to construct an "electronic lab notebook" that automates both the development of university lab course material and the student's lab experiments and reports.

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Characterizing mixed-signal DSP designs with SigLab