In view of the absence of universality and high consumption of sequence cryptogram adaptation algorithms, a global directional searching algorithm based on AND terms of Non-Linear Boolean Function (NLBF) and truth table was proposed. Firstly, adaptive and reasonable models of Look-Up Table (LUT) were gotten by analyzing the ratio of terms in NLBF. Then a classification algorithm for Boolean function was established which can search all AND terms from high-order ones to lows and "absorb" or "unite" the terms. Finally, a configuration generating algorithm was obtained on the basis of truth table, which can generate the configuration information to fulfill the computation of NLBF by traversing truth tables. The existing NLBF sequence cryptograms can be adapted by the proposed classification algorithm, and it is more easy to adapt to the commonly used algorithms such as ACH-128, Trivium and Grain. At the same time, the resource consumption of LUT is obviously less than the adaptation based on Shannon decomposition theory and genetic algorithm; meanwhile, the consumption results show that the adaptation consumes the most in 4-input look-up tables and the least in 6-input ones.

In order to solve the problem that embedded real-time systems' miss rate of real-time task is too high and the problem of poor stability of real-time scheduling while the load of the system is uncertain, a scheduling model based on feedback control was proposed. This model consisted of an improved multi-level queue scheduler and three controllers, those were access controller, execution level controller and Proportional-Integral-Derivative (PID) controller. The deviation of task miss rate was feedbacked to the PID controller and generated a corresponding adjustment, the adjustment acted on the other two controllers to adjust the level of real-time tasks. Tasks adjusted were executed by the scheduler. After structural adjustment and improvement, the scheduling model was applied in embedded Configurable operating system (eCos). The experimental results show that the proposed method can reduce miss rate of tasks, and solve the overload problem of the system.