Objective: To test a new software package (Color Quantifier, Kinetic Imaging, Liverpool, United Kingdom) that quantifies power Doppler energy and to determine its reproducibility.
Power modeling based on performance monitoring counters (PMCs) is an effective approach to analyze and quantify power dissipation behaviors on a real computer system.
The present paper proposes the design of a tool to quantify power quality (PQ) parameters using wavelets and fuzzy sets theory.
Many scholars have used different metrics to quantify power grid vulnerability in the literature, but how correlated these metrics are is an interesting topic.
We have developed a framework for researchers to quantify power in the presence of measurement error, which will be applicable to studies of phenotypes in which the measurement is highly variable.
It gives a measure of the spread of a set of data and in case of signals, it quantifies power from the overall amplitudes obtained in the EEG time series.
To quantify power and energy overhead of embedded OS scheduler routines and operations, we have to build test programs containing threads with different priorities, we measure in a first step the average energy consumed by the standalone tasks without scheduling routines, and then with scheduling routines.
However, one can readily quantify power versus the aligned length of the average insert, for example using some τ = δ/ C.
Fast Fourier transformation was utilized to perform power spectral analysis which quantified power spectrum into the standard frequency-domain measurements including VLF (0.003 0.04 Hz), LF (0.04 0.15 Hz), HF (0.15 0.40 Hz), TP, LF/HF ratio, and Var [ 9, 10].
To quantify power spectra of signals and compare differences in spectral composition, we used an unbiased method by calculating the median, first spectral moment and second spectral moment of the acquired power spectra.
