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A phase detector characteristic is a function of phase difference describing the output of the phase detector. For the analysis of Phase detector it is usually considered the models of PD in signal (time) domain and phase-frequency domain. [1] In this case for constructing of an adequate nonlinear mathematical model of PD in phase-frequency ...
The phase detector needs to compute the phase difference of its two input signals. Let α be the phase of the first input and β be the phase of the second. The actual input signals to the phase detector, however, are not α and β, but rather sinusoids such as sin(α) and cos(β).
Conversely, a phase reversal or phase inversion implies a 180-degree phase shift. [2] When the phase difference () is a quarter of turn (a right angle, +90° = π/2 or −90° = 270° = −π/2 = 3π/2), sinusoidal signals are sometimes said to be in quadrature, e.g., in-phase and quadrature components of a composite signal or even different ...
Consider a continuous-time Markov process with m + 1 states, where m ≥ 1, such that the states 1,...,m are transient states and state 0 is an absorbing state. Further, let the process have an initial probability of starting in any of the m + 1 phases given by the probability vector (α 0,α) where α 0 is a scalar and α is a 1 × m vector.
Manasseh Wepundi noted the difference between "the unit of analysis, that is the phenomenon about which generalizations are to be made, that which each 'case' in the data file represents and the level of analysis, that is, the manner in which the units of analysis can be arrayed on a continuum from the very small (micro) to very large (macro ...
Since F=9.3 > 3.68, the results are significant at the 5% significance level. One would not accept the null hypothesis, concluding that there is strong evidence that the expected values in the three groups differ. The p-value for this test is 0.002. After performing the F-test, it is common to carry out some "post-hoc" analysis of the group ...
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Functional data analysis (FDA) is a branch of statistics that analyses data providing information about curves, surfaces or anything else varying over a continuum. In its most general form, under an FDA framework, each sample element of functional data is considered to be a random function.