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Toward real-time kernel density estimate display for instrumentation

Lee Barford, Ivan Gibbs, Richard Kelley
Measurement Research Laboratory, Agilent Technologies, 561 Keystone Ave. MS 434, Reno, NV 89503 USA
IEEE Instrumentation and Measurement Technology Conference (I2MTC), 2011

@inproceedings{barford2011toward,

   title={Toward real-time kernel density estimate display for instrumentation},

   author={Barford, L. and Gibbs, I. and Kelley, R.},

   booktitle={Instrumentation and Measurement Technology Conference (I2MTC), 2011 IEEE},

   pages={1–6},

   year={2011},

   organization={IEEE}

}

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Histograms are commonly used in instrumentation to produce a visual representation of the probability density of a random signal from repeated measurements. However, histograms have a number of shortcomings as a method of data visualization. We propose using kernel density estimation as a replacement for histograms in instrumentation. Kernel density estimation has a number of advantages as a means of visualizing the probability density of a waveform or derived measurement. However, kernel density estimates have been considered too computationally burdensome for inclusion in instruments and virtual instruments. In this paper, we demonstrate that a graphics processing unit (GPU) can be used to compute and display kernel density estimates of actual measured data at a full video rate.
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