Software Defined Network (SDN) currently is widely used in the implementation of new network technologies owing to its distinctive advantages. In changeable SDN environments, the update performance of SDN switches has significant importance for the overall network performance because packet processing could be interrupted by ruleset updating in SDN switches. In order to guarantee high update performance, we propose a new classification algorithm, SplitTrie, based on trie structures and trie splitting. SplitTrie splits rulesets according to the field type vectors of rules. The splitting can improve the update performance because it reduces the trie structure sizes. Experimental results demonstrated that SplitTrie could achieve 20 times of update speed in the complex rulesets comparing the method without trie splitting.

With the development of SDN, packet classifiers nowadays need to be provided with low update latency besides fast lookup performance because switches need to respond to update control messages from controllers in time to guarantee real-time service in SDN implementations. Classification in this scenario is called online packet classification. In this paper, we put forward an improved trie-based algorithm for online packet classification (ITOC), in which we provide a trie selection strategy to avoid occasional high update latency in the update process of online trie-based algorithms. Experiments are conducted to validate the effectiveness of our optimization and compare the performance of ITOC with the offline methods, DPDK ACL. Experimental results demonstrate that ITOC has the same level of lookup speed with DPDK ACL and greatly decreased the update latency as well. The update latency of ITOC is only 6.85% of DPDK ACL library in the best case.

This paper presents a novel data-driven polytopic approach to event-triggered consensus control of unknown leader-following multi-agent systems (MASs). A distributed data-driven event-triggered consensus control protocol is proposed that utilizes noisy input-state data to enable all followers to track the leader while reducing communication and computational burden. Unlike previous research that relies on quadratic matrix inequalities to characterize system uncertainties, this paper devises a data-based polytopic representation for MASs, which enables addressing the consensus control problem without using explicit system matrices. Based on this representation, a data-based criterion is established, utilizing matrix polytopes to ensure the asymptotic stability of the closed-loop MAS. Moreover, a co-design method is presented for the distributed controller gain and the triggering matrix, using only data and expressed in terms of linear matrix inequalities. Finally, numerical simulations are conducted to demonstrate the validity and effectiveness of the proposed data-driven approach.

This paper has proposed a new baseline deep learning model of more benefits for image classification. Different from the convolutional neural network(CNN) practice where filters are trained by back propagation to represent different patterns of an image, we are inspired by a method called "PCANet" in "PCANet: A Simple Deep Learning Baseline for Image Classification?" to choose filter vectors from basis vectors in frequency domain like Fourier coefficients or wavelets without back propagation. Researchers have demonstrated that those basis in frequency domain can usually provide physical insights, which adds to the interpretability of the model by analyzing the frequencies selected. Besides, the training process will also be more time efficient, mathematically clear and interpretable compared with the "black-box" training process of CNN.

This paper proposes a novel dynamic programming algorithm for nonlinear system optimal control problem, namely Incremental Generalized Policy Iteration (IGPI). The proposed IGPI algorithm combines the advantages of Incremental Control(IC) and Generalized Policy Iteration(GPI). Incremental control can handle the nonlinearity and uncertainty in nonlinear systems without knowing the nonlinear system information, GPI can learn an optimal control law for dynamical systems. Based on the proposed IGPI algorithm, a data-driven adaptive attitude controller is designed for a spacecraft with sloshing liquid fuel. Simulation results demonstrate the effectiveness of the spacecraft attitude controller.