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Collusion tolerance is an essential requirement for digital fingerprinting, and existing collusion-resistant encoding and tracking methods are generally based on combinatorics. However, those methods have some deficiencies such as low tracking efficiency and large fingerprint database for matching. Owing to those weaknesses, a hybrid fingerprint encoding method based on residual characters is proposed in this paper. The fingerprint is generated by encoding the user information into a binary image composed of letter, numeral and Chinese character. As a result of the complex structure of Chinese character, more residue characters can be reserved when the fingerprint is damaged. Thus, the fingerprint has a good performance in anti-tampering. Meanwhile, it has the capacity for large users because of the diversity of Chinese character. In addition, the paper proposes a tracking method based on grid feature, which greatly reduces the computational complexity and improves the tracking efficiency. The robustness and collusion tolerance of the digital fingerprinting scheme are confirmed by theoretical analysis and experimental results.

Low link utility, RTT unfairness and unfairness of Multi-Bottleneck network are the existing problems in the present network congestion control algorithms at large. Through the analogy of network congestion control with the "El Farol Bar" problem, we establish a congestion control model based on minority game(MG), and then present a novel network congestion control algorithm based on the model. The result of simulations indicates that the proposed algorithm can make the achievements of link utility closing to 100%, zero packet lose rate, and small of queue size. Besides, the RTT unfairness and the unfairness of Multi-Bottleneck network can be solved, to achieve the max-min fairness in Multi-Bottleneck network, while efficiently weaken the "ping-pong" oscillation caused by the overall synchronization.