Aiming at the problems of the energy consumption of 802.15.4 network, a channel access mechanism for Media Access Control (MAC) layer based on multi-condition sleep mode was proposed. First, a Markov model of the mechanism was established. Then, the mathematical derivation based on the model of the steady-state probability of the main state, related parameters were given out. Furthermore, the analysis of the node average energy consumption in superframe was carried out. At last, the influence of the protocol parameters such as arrival rate of packets, number of back, superframe order and mininum of backoff exponent to the steady-state probability of the main state, the average energy consumption and the survival time of node was researched. The experimental results show that, compared with 802.15.4 network without node sleep state, the node energy consumption is reduced by 84.4% or so. And compared with the methods of some conditions, node energy consumption is reduced by 62.8% on average; the average survival time of network is increased by 70%. The model describes the proposed channel access mechanism very well, and the reasonable settings for parameters can improve the performance of node energy consumption. It also provides reference for the energy optimization in the practical application of Wireless Sensor Network (WSN).

It has been widely used in computer vision, military investigation, medical care and some other fields. The widely used seamline removal methods include overlappingimage method, wavelet transform method and hardcorrection method. An improved method based on the correctionratio of average gray value of the images on the two sides of the seamline was proposed. This method calculated the rate of the corrected value and the former value. Then the ratio gradually changed from the seamline to the boundary so that the seamline was removed. The experiments have shown that the method proposed above is effective to eliminate seamlines, especially when the difference of the gray level is big.

In this paper, a Self-Organizing Fuzzy Neural Network (SOFNN) based on Unscented Particle Filter (UPF) was designed and developed. The UPF was used to estimate the parameters of the SOFNN and better result was gotten. The generating criterion of fuzzy rules based on the pruning strategy of the error reduction ratio was introduced. The width of membership function was established as the state and the ideal output as the measurement. The UPF was used to learn parameters. The two typical simulations, nonlinear function approximation and system identification, were done to validate the UPF-SOFNN. It can be seen from the results of simulation that the UPF-SOFNN has a more compact structure and better generalization than the other algorithms.

Resources in grid computation environments are heterogeneous and dynamic, and tasks in grid computation environments are executed by computers from different domains or clusters of virtual organization synergistically; the static task scheduling is not fit for tasks execution in grid computing environments. In the paper, a task scheduling model based on the results of resources prediction was proposed. Firstly, a method of weighted least square estimation was given to construct Autoregressive Moving Average (ARMA) model, which would be applied in CPU load prediction of grid computer. After modeling a kind of data parallel grid tasks, the task scheduling model based on the results of resources prediction was presented. Finally the simulations on the proposed model and some other models were designed and accomplished. The simulation results demonstrate that the presented model can run both significantly faster and more stable than other models.

As the main part of resources prediction in the grid computing,host load is particularly important.An improved AR-based model of host load prediction was presented.The improved model not only has the advantages that AR model has,such as,low computing cost and high stability,and has the advantages that AR model has not.It can predict the dynamic interval host load that some job will encounter in the future,and process some characters of the host load variance,for example,self-similarity and long-range dependence.Experiment results show that the improved model has come to an anticipant result.