QDAC products and technology can be applied to a wide range of applications and markets and its architecture has the potential to become the defacto standard for data acquisition systems. Virtually everywhere there is a sensor, QDAC’s solutions can be applied.

End users, OEMs and sensor manufacturers need better performance and less complexity from data acquisition. A key trend is that existing and new applications in many markets are driving the need for increased accuracy, precision, and reliability from data acquisition systems. In addition, new data sources and higher quality data are required for diagnostics, surveying, asset management, manufacturing and efficiencies, automation and control.

Many of the existing and new applications are underserved by current solutions and are plagued by inaccurate and “dirty” data resulting from noise and low data resolution. These issues result in the true data being hidden, poor data quality, and significant engineering time and resources wasted in solving the wrong problem that results from the inaccurate data. With data acquisition systems being pushed to work in areas where higher accuracy is required, these systems are bumping up against technology constraints related to slow sampling rates and aliasing, which cause errors in the data capture.

QDAC solutions solve the problem of inaccurate and “dirty” data that results from aliasing and noise which is particularly prevalent in applications requiring fast sampling and response. QDAC does so by utilizing an innovative and patented data acquisition architecture that integrates signal conditioning with built in intelligence that automatically configures the data acquisition parameters for the best possible performance. This in turn makes it easier for the end user to acquire reliable and usable digital data with performance not available previously.

Example of “Dirty” Data (left – without QDAC) and High Quality Data (right – with QDAC):

Current data acquisition solutions for demanding applications also incorporate multiple individual components that need to be integrated and configured to properly function together. This has resulted in an increase in overall system complexity and corresponding increase in engineering effort in solving these data acquisition problems correctly.

In addition, a paradigm shift is occurring in the sensor market where “smart sensors” with built in signal conditioning, “digitized” data, on-board intelligence, and miniaturization are replacing outdated solutions. Sensor manufacturers and suppliers are looking to address the new application areas with newer technologies and are looking for opportunities to differentiate their product offerings, which have generally become commoditized.

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