**4.3 Applications to diagnostics**

As a diagnostic tool, the context switching metric can also detect potential complexities during execution. Since the FFT can easily compute in real-time for typical sample sizes *N*, parallel execution of the entropy algorithm with the context switching data can reveal deviations from expected operation. For example, if an execution profile shows a high regularity of frequent context switches during some interval and then transitions to a more irregular sequence of switches with the same overall density, the expected entropy measure will definitely increase. In that sense, the entropy metric measures an intrinsic property of the signal, and that strictly speaking, density fluctuations such as expected increases in the rate of context switches will not influence the measure. In other words, density alone does not affect the complexity.

By the same token, the multi-scale metric has obvious benefit for detecting long term complexity changes or short-term bursts buried in a nominally sampled signal. The idea of using frequency domain entropy for diagnostics of complex machinery is further explored in (Shen, 2000).
