Although single skin color model is the normal method of face detection, it often has the disadvantages such as "ultra-detection" and bad
real-time performance. This paper presents a new method of face tracking based on grey prediction model GM(1,1) combined with skin
color model. The method uses grey prediction to minish the region of matching search for skin color model and the matching result to
update the prediction basis of the grey model. In order to acquire the initial position of face, moving information of face is used, and the
moving region is automatically acquired with information entropy. Compared to the α-β-γ filtering with assumption that object in image
sequence makes uniformly accelerated motion, the experiment results of this method show that grey prediction model GM(1,1) can
maintain minor error stably. The result of grey prediction is closer to the real motion trajectory, and better reflects the motion trend of
face. It greatly enhances such two important indexes as robustness and real-time performance under the system tracking process.
In dynamic networks, the failure detection time takes a major part of the convergence time, which is an important network performance index. To detect a node or link failure in the network, traditional protocols, like Hello protocol in OSPF or RSVP, exchanges keep-alive messages between neighboring nodes to keep track of the link/node state. But by default settings, it can get a minimum detection time in the measure of dozens of seconds, which can not meet the demands of fast network convergence and failure recovery. When configuring the related parameters to reduce the detection time, there will be notable instability problems. In this paper, we analyzed the problem and designed a new failure detection algorithm to reduce the network overhead of detection signaling. Through our experiment we found it is effective to enhance the stability by implicitly acknowledge other signaling messages as keep-alive messages. We conducted our proposal and the previous approaches on the ASON test-bed. The experimental results show that our algorithm gives better performances than previous schemes in about an order magnitude reduction of both false failure alarms and queuing delay to other messages, especially under light traffic load.