Several random measurements may be taken throughout the day.

In this way, many measurements can be added together, allowing some of the random measurement errors to cancel out.

Needless to say, this is a more effective monitoring method than the random measurement method used twice a year.

Directive 96/62 allows the use of random measurement if it can be shown to be sufficiently accurate.

Estimation of both the dictionary matrix and sparse vector from just random measurements only has been done iteratively in.

From this it is argued that, statistically, Bob cannot tell the difference between what Alice did and a random measurement (or whether she did anything at all).

Some more iterative approaches to calculate both dictionary matrix and speech signal from just random measurements of speech signal are shown in.

In 2004, Candès wrote a paper with Terence Tao that kicked off the field of compressed sensing: the recovery of sparse signals from a few carefully constructed, and seemingly random measurements.

In the physical sciences, such variability may result from random measurement errors: instrument measurements are often not perfectly precise, i.e., reproducible, and there is additional inter-rater variability in interpreting and reporting the measured results.

Commenting on this, other writers (such as John von Neumann and David Bohm) hypothesized that consequently there would have to be 'hidden' variables responsible for random measurement results, something which was not expressly claimed in the original paper.

The idea behind CS for speech signals is that can we come up with some algorithms or methods where we only use those random measurements ( to do some application based processing like speaker recognition, speech enhancement etc.

Notice that the error v t in equation (3.7) can be interpreted as a 'measurement error' in the variable Y. We are trying to measure expectations, but can only obtain an indicator which is the true variable plus a random measurement error.