We exploit the impact of exact frequency modulation on transition time of steering nuclear spin states from theoretical point of view. 1-stage and 2-stage Frequency-Amplitude-Phase modulation (FAPM) algorithms are proposed in contrast with 1-stage an
d 3-stage Amplitude-Phase modulation (APM) algorithms. The sufficient conditions are further present for transiting nuclear spin states within the specified time by these four modulation algorithms. It is demonstrated that transition time performance can be significantly improved if exact frequency modulation is available. It is exemplified that the transition time scale with frequency modulation is about 1/4 of that without frequency modulation. It is also revealed in this research that the hybrid scheme of 1-stage FAPM and APM algorithms is better than all the four modulation algorithms. A simplified hybrid modulation algorithm is also proposed to reduce computational burden.
We propose a new mechanism leading to scale-free networks which is based on the presence of an intrinsic character of a vertex called fitness. In our model, a vertex $i$ is assigned a fitness $x_i$, drawn from a given probability distribution functio
n $f(x)$. During network evolution, with rate $p$ we add a vertex $j$ of fitness $x_j$ and connect to an existing vertex $i$ of fitness $x_i$ selected preferentially to a linking probability function $g(x_i,x_j)$ which depends on the fitnesses of the two vertices involved and, with rate $1-p$ we create an edge between two already existed vertices with fitnesses $x_i$ and $x_j$, with a probability also preferential to the connection function $g(x_i,x_j)$. For the proper choice of $g$, the resulting networks have generalized power laws, irrespective of the fitness distribution of vertices.
