Aiming at the problems of long search time and easy to fall into deadlock when using the traditional ant colony algorithm in the path planning process of UAV in two-dimensional space, this paper first introduces the advantages and disadvantages of the traditional algorithm, and puts forward an improved ant colony algorithm. Select A*, potential field method, traditional heuristic search strategy and obstacle avoidance heuristic search, improve pheromone update algorithm, and dynamically modify the dependence of transition probability on pheromone. The simulation results show that the improved ant colony algorithm has better global search ability, the path superiority is more prominent, the number of iterations and calculation time are optimized, and the stability of path search is greatly improved.
Aiming at the 17dB loss problem in the 10Gbps rate transmission channel in SerDes, this article implements an equalizer of SerDes receiver with TSMC 28nm CMOS technology. At first, a structure of continuous time linear equalizer (CTLE) is adopted to compensate for the loss in the channel. However, due to the inter-symbol interference (ISI) of signals passing through channel, the phenomenon of serious tailing occurs. Therefore, a 4-tap finite impulse response (FIR) decision feedback equalizer (DFE) is added to eliminate ISI post-cursor. Based on the circuit schematic, the simulation results show that the proposed equalizer structure can effectively compensate for the 17dB channel loss and eliminate the ISI post-cursor. The equalized eye width reaches 0.95UI at 0.9V supply.
At present, many scholars have conducted extensive research on parameter design in particle swarm optimization algorithms and have achieved many results. The particle swarm algorithm is easy to fall into the local optimum, so the paper modifies the particle swarm parameters to improve the algorithm's performance. To further improve the algorithm, this paper proposes a combination of flower pollination and particle swarm algorithm. The comparison of the optimization accuracy and the convergence speed on the four test functions proves the superiority of the flower pollination and particle swarm algorithm.
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