Aiming at the problem of tremendous escalation of energy consumption caused by the dense deployment of micro base stations in heterogeneous cellular networks, the energy efficiency of two-tier heterogeneous cellular networks was analyzed and a new method for maximizing network energy efficiency by adjusting the micro base station transmit power was proposed. Firstly, the heterogeneous cellular network was modeled by using homogeneous Poisson point process, and the coverage probability of base stations at each tier was derived. Secondly, according to the definition of energy efficiency, the total power consumption and total throughput of network were derived respectively, and the closed-form expression of energy efficiency was given. Finally, the impact of the micro base station transmission power on the energy efficiency of network was analyzed, and a micro base station power optimization algorithm was proposed to maximize energy efficiency. The simulation results show that, the transmission power of micro base station has a significant impact on the energy efficiency of heterogeneous cellular network. Furthermore, the energy efficiency of heterogeneous cellular network can be effectively improved by optimizing the transmission power of micro base stations.

In practical application, Modified Gain Extended Kalman Filter (MGEKF) algorithm generally uses erroneous measured values instead of the real values for calculation, so the modified results also contain errors. To solve this problem, an improved MGEKF algorithm based on Back Propagation Neural Network (BPNN), termed BPNN-MGEKF algorithm, was proposed in this paper. At BPNN training time, measured values were used as the input, and modified results by true values as the output. BPNN-MGEKF was applied to single moving station bearing-only position experiment. The experimental results shows that, BPNN-MGEKF improves the positioning accuracy of more than 10% compared to extended Kalman filter, MGEKF and smoothing modified gain extended Kalman filter algorithm, and it is more stable.

Concerning the recognition of closely-connected Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA), a recognition algorithm based on spectral-clustering Recurrent Neural Network (RNN) ensemble was proposed. This algorithm firstly used disagreement measure for distance between two RNNs, thus constructed a graph composed by candidate RNNs. Then, a graph cluster method was used to divide RNNs into clusters. Finally, the best RNN in each cluster was selected. The experimental results reveal that: compared with single candidate RNN, recognition rates of this algorithm is increased by 16%. Compared with the ensemble of all candidate RNNs, ensemble size of this algorithm is much smaller, it is about 23% of the original size.

Combining with the traditional training-categorization framework and feedback,a Chinese text categorization algorithm was proposed.This algorithm made the process of text categorization be a "learn-application-learn again" one,added feedback technique to the construction of categorization machine.The efficiency of the algorithm was analyzed.Experiment results show that the algorithm is feasible.