Aiming at the problems of premature convergence and easily falling into local optimum in the adaptive large neighborhood search algorithms with single mechanism， a hybrid adaptive large neighborhood search algorithm was proposed to solve Time-Dependent Vehicle Routing Problem （TDVRP） in cold chain logistics. Firstly， the time-varying vehicle speed was described according to the continuous driving time dependent function， the real-time fuel consumption was evaluated by using the comprehensive fuel consumption model， and a routing optimization model with the goal of minimizing the total cost was established. Then， according to the NP （Non-deterministic Polynomial）-hard property and time-dependent characteristics of the problem， a variety of large neighborhood search operators for destroying and repairing solutions were designed， and the destroy-repair large neighborhood search operators were integrated into Artificial Bee Colony （ABC） algorithm to improve the global search ability of the algorithm. Simulation results show that compared with Adaptive Variable Neighborhood Search Elite Ant Colony （AVNS_EAC） algorithm， Adaptive Large Neighborhood Search Elite Ant Colony （ALNS_EAC） algorithm， Adaptive Large Neighborhood Search Elite Genetic （ALNS_EG） algorithm and Adaptive Large Neighborhood Search Simulated Annealing （ALNS_SA） algorithm， the proposed Adaptive Large Neighborhood Search Artificial Bee Colony （ALNS_ABC） algorithm has the optimal fitness values increased by 46.3%， 5.3%， 36.8% and 6% respectively and averagely on multiple test data groups. It can be seen that this algorithm has higher computational performance and stronger stability， and can provide a more reasonable decision-making basis for cold chain logistics enterprises to take into account economic and environmental benefits at the same time.

In massive Multiple-Input Multiple-Output （MIMO） systems， Minimum Mean Square Error （MMSE） detection algorithm has the problems of poor adaptability， high computational complexity and low efficiency on the reconfigurable array structure. Based on the reconfigurable array processor developed by the project team， a parallel mapping method based on MMSE algorithm was proposed. Firstly， a pipeline acceleration scheme which could be highly parallel in time and space was designed based on the relatively simple data dependency of Gram matrix calculation. Secondly， according to the relatively independent characteristic of Gram matrix calculation and matched filter calculation module in MMSE algorithm， a modular parallel mapping scheme was designed. Finally， the mapping scheme was implemented based on Xilinx Virtex-6 development board， and the statistics of its performance were performed. Experimental results show that， the proposed method achieves the acceleration ratio of 2.80， 4.04 and 5.57 in Quadrature Phase Shift Keying （QPSK） uplink with the MIMO scale of \mathrm{128} \times \mathrm{4} ， \mathrm{128} \times \mathrm{8} and \mathrm{128} \times \mathrm{16} ， respectively， and the reconfigurable array processor reduces the resource consumption by 42.6% compared with the dedicated hardware in the \mathrm{128} \times \mathrm{16} massive MIMO system.

In order to increase the efficiency of the line drawing algorithm when the slope of the line is greater than 0.5, a line drawing algorithm based on pixel chain was proposed. A straight line was treated as an aggregation of several horizontal pixel chains or diagonal pixel chains. An algorithm of line drawing in a reverse direction, which was similar to Bresenham algorithm, was introduced, by which the slope between 0.5 and 1 was converted to that between 0 and 0.5 while generating line. One pixel chain was generated by one judgment. The simulation results show the straight line generated by new algorithm is as same as that generated by Bresenham algorithm, but the calculation is greatly reduced. The new algorithm has generated two integer arithmetic: addition and multiplication, and it is suitable for hardware implementation. The generation speed of the new algorithm is 4 times of Bresenham algorithm with the same complexity of design.