how close a process is to exhibiting stochastic resonance. We call them the six measures denoted by $M_1$, $M_2$, $M_3$, $M_4$, $M_5$ and $M_6$. In what follows we will consider so large times, that the relaxation time has effectively elapsed for both the diffusion $X^\epsilon_t$ and Markov Chain $Y^\epsilon_t$, in other words the state probability would have effectively converged to the invariant measure $\overline{\nu}$. This means that over one period $T=2\pi/\Omega$ of the forcing, the invariant measures will have the properties $$\begin{aligned}
\overline{\nu}_\pm(t)=\overline{\nu}_\pm(t+T)
\quad \text{and} \quad 
\overline{\nu}_\pm(t)=\overline{\nu}_\mp(t+T/2)\end{aligned}$$ We obtain the averaged trajectories given by $$\begin{aligned}
\left\langle X^\epsilon_t \right\rangle
=E\left(X^\epsilon_t\right)
\quad \text{and} \quad
\left\langle Y^\epsilon_t \right\rangle
=E\left(Y^\epsilon_t\right)\end{aligned}$$ which are the trajectories obtained after averaging over many realisations. Note that $\left\langle X^\epsilon_t \right\rangle$ is calculated by averaging over many realisation over many periods. The $\left\langle X^\epsilon_t \right\rangle$ is cyclic over one period. Notice that $\left\langle Y^\epsilon_t \right\rangle$ is related to the invariant measures by $$\begin{aligned}
\left\langle Y^\epsilon_t \right\rangle=\overline{\nu}_+(t)-\overline{\nu}_-(t)\end{aligned}$$ We introduce the Out-of-Phase Markov Chain defined by $$\begin{aligned}
\overline{Y}^\epsilon_t=
\left\{
\begin{array}{l}
0 \quad \text{if} \quad Y^\epsilon_t=-1 \quad \text{and} \quad mod(t,T)\leq T/2\\
1 \quad \text{if} \quad Y^\epsilon_t=-1 \quad \text{and} \quad mod(t,T)> T/2\\
1 \quad \text{if} \quad Y^\epsilon_t=+1 \quad \text{and} \quad mod(t,T)\leq T/2\\
0 \quad 