Aiming at the deep structure and high computational complexity of current network models based on deep learning for super-resolution image reconstruction, as well as the problem that the networks can not operate effectively on resource-constrained devices caused by the high storage space requirement for the network models, a super-resolution convolutional neural network compression method based on knowledge distillation was proposed. This method utilizes a teacher network with large parameters and good reconstruction effect as well as a student network with few parameters and poor reconstruction effect. Firstly the teacher network was trained; then knowledge distillation method was used to transfer knowledge from teacher network to student network; finally the reconstruction effect of the student network was improved without changing the network structure and the parameters of the student network. The Peak Signal-to-Noise Ratio (PSNR) was used to evaluate the quality of reconstruction in the experiments. Compared to the student network without knowledge distillation method, the student network using the knowledge distillation method has the PSNR increased by 0.53 dB, 0.37 dB, 0.24 dB and 0.45 dB respectively on four public test sets when the magnification times is 3. Without changing the structure of student network, the proposed method significantly improves the super-resolution reconstruction effect of the student network.

Aiming at the lag consensus problem of first-order and second-order hybrid heterogeneous multi-agent systems, a distributed lag consensus control protocol based on pinning control was proposed. Firstly, the lag consensus analysis was transformed into stability verification. Then, the stability of closed loop system was analyzed by using graph theory and Lyapunov stability theory. Finally, the sufficient conditions for solvability of lag consensus based on Linear Matrix Inequality (LMI) were given under fixed and switching topologies respectively, so that leader-follower lag consensus of heterogeneous multi-agent system was achieved. The numerical simulation results show that, the proposed lag consensus control method can make the heterogeneous mult-agent systems achieve leader-follower lag consensus.

The traditional Orthogonal Frequency Division Multiplexing (OFDM) scheduling does not consider the proportional fairness among users' rates while allocating resource. To solve this problem, a new proportional fair scheduling scheme was proposed in multi-user OFDM system for heterogeneous classes of traffic in this paper; its users' queues carry heterogeneous classes of traffic. The scheme maximizes the sum of system's weight capacity under the constraint of the proportion among users' rates; provides different types of packets in users' queues different weight factor, and calculates the user's weight by the packet weight factor; not only defines the channel priority factor, but also allocates users sub-carriers by the factor under the constraint of the proportion among users' rates while allocating sub-carriers; finally derives a linear power allocation pattern. The simulation results and analysis demonstrate that the proposed scheme better satisfies users' requirements of throughput and strictly guarantees the fairness among users' capacity on the basis of improving system's throughput.