To solve the problem that many improved ant colony algorithms are not efficient to solve the problem of multi-constrained Quality of Service Routing (QoSR), such as slow convergence and local optimization, an Ant Colony Algorithm with Gradient Descent (ACAGD) was proposed. The gradient descent method was introduced into the local search of ant colony, and combined with residual pheromone, the next-hop selection strategy of ants was synthetically determined. Ant colony not only search for the next hop according to the pheromone concentration with certain probability, but also search for the next hop according to the gradient descent method with certain probability, which reduced the possibility that the traditional ant colony algorithm was easy to fall into the local optimum. The Waxman network model was used to randomly generate the network topology with different number of routing nodes. The experimental results show that compared with other improved ACO algorithms, the ACAGD can obtain the route with relatively low comprehensive cost while the convergence rate is not affected, and the stability of the algorithm is better.

To solve the load balance problem of multi-engine intrusion detection system, a dynamic load regulation algorithm of detection engine was proposed. Firstly, load was calculated by monitoring each engine node. Then, the scheduling of the heavy load node was performed by scheduling the overload or no-load node as a scheduling opportunity, and the nodes were traversed to adjust the load balancing. As the session for the scheduling unit, the algorithm was not the absolute average load for the purpose, just to ensure that the engine node does not appear overload or no load to achieve the basic goal. The KDD cup99 data set was used to simulate experiment. The experimental results show that compared with average load allocation algorithm and secure load allocation, the proposed algorithm has a significant effect on session-based load balancing, the running cost is lower, and the packet loss rate under heavy load are lower, which improves the detection rate of intrusion detection system.

In cluster, it is a conventional method to increase attack efficiency for intruder by attacking the specific target, so it is effective to improve the detection efficiency by scheduling the computing resource contrapuntally. A frequency self-adjusting algorithm for Network Intrusion Detection System (NIDS) based on target prediction, named DFSATP, was proposed. By detecting and analyzing the collected data packets, the data packets sent to potentially attacked targets were marked as high risk data and the other packets were marked as low risk data. The efficiency of NIDS was improved by high frequency detection of high risk data packets and low frequency detection of low risk packets, thus the detection rate of abnormal data was also increased to some extent in limited computing resource circumstances. The simulation results show that the detection rate of abnormal data packets is increased because of the detection frequency adjustment of NIDS by using DFSATP.

To deal with the huge spatial and temporal consumption caused by large-scale self-set data, the authors designed a self-set matching mechanism based on artificial immune Network Intrusion Detection System (NIDS). To improve the detection efficiency of the intrusion detection system, an efficient probability matching optimization mechanism was proposed. The authors first proved the relative concentration of the network data, and analyzed the validity of the probability matching mechanism by calculating the Average Search Length (ASL), then verified the fast matching efficiency of the mechanism through simulation experiments. The mechanism has been used in a project application in a new artificial immune network intrusion detection system based on self-set scale simplified mechanism, which has achieved satisfactory matching results.

An effective approach of target extraction based on regional growth and characteristic of speckle in Synthetic Aperture Radar (SAR) images was proposed. In point of characteristic of target in SAR images, the approach fully discussed the key problem (seed selection and homogeneity criterion in regional growth). Then, the corresponding decision and the process of target extraction were given. Simulated result indicates that the algorithm is not only noise-resistant in extracting target in SAR images, but also promising.

In this work, a new run-time reconfiguration multi-DSP system architecture was given. In this system, combining the characteristic of the multi-processor parallel computation and worm arithmetic, a mixed architecture that can be adaptively expanded based on shared bus and linkports was constructed. And the system was designed and discussed from the point of measurability. Self-Adaptive operating system, a feasible project was proposed for the high speed Self-Adaptive signal processing platform. The hardware debugging, system testing and the software loading methods were introduced. These works may offer some meaningful reference and guide for some similar systems developed in the future.