For physical-layer network coding over time-varying bi-directional relay channels, a joint channel coding and non-coherent physical-layer network coded modulation and detection scheme without channel state information was designed in multiple-antenna environment. Firstly, the spatial modulation matrix at the source was designed to achieve physical-layer network coding. Then, a differential spatial modulation was combined with physical-layer network coding and maximum a posteriori probability of superimposed signal was derived at the relay. Moreover, considering the constellation of the superimposed signal, a mapping function to map superimposed signal to broadcasting signal was designed. Lastly, taking advantage of the linear structure of channel coding, and combining bit interleaving, channel decoding and soft-input soft-output detection algorithm, an iterative detection approach for joint channel differential physical-layer network coding was obtained. Simulation results show that the proposed scheme can achieve non-coherent transmission and detection used to physical-layer network coding for two-way relay channels and can effectively enhance the throughput and spectral efficiency of the system.

Focusing on the prediction issue of the price turning point in stock trading process, a prediction algorithm of stock price turning point, named PLR-GPC, was proposed based on Piecewise Linear Representation (PLR) and Gaussian Process Classification (GPC). The algorithm extracted the turning points of the historical stock price series by PLR, and classified the points with different labels. A prediction model of the stock price turning point was built based on GPC, and it was trained with the turning points extracted by PLR. Eventually, the model could predict whether a new price would be a price turning point, and could explain the result with probability. An experiment on the real stock data was carried out among PLR-GPC, PLR-BPN (PLR-Back Propagation Network), and PLR-WSVM (PLR-Weighted Support Vector Machine). It showed that the PLR-GPC had higher forecast accuracy than the other two algorithms, and its rate of return was higher than PLR-BPN, almost equal to PLR-WSVM. The experimental result proves that the PLR-GPC is effective on stock turning point prediction and it can be applied in the actual stock investment trading.

Concerning the high overheads of current channel access mechanisms and frequent collisions in high-density deployed Wireless Local Area Network (WLAN), an enhanced mechanism named as Hybrid Frequency-domain Channel Contention (HFCC) based on frequency-domain channel contention was proposed. Firstly, the subcarriers within Orthogonal Frequency Division Multiplexing (OFDM) symbols which were used for frequency-domain channel contention were classified as contention subcarriers and information subcarriers. Secondly, two rounds of channel contention were performed when station (STA) needs to access channel and the confirmation of successful channel contention was performed if necessary. Finally, a single OFDM symbol was used to acknowledge correct reception of a packet. The theoretical analysis showed that, compared with Distributed Coordination Function (DCF), when many STAs (35 or so) were contending a channel in the network, the collision probability of HFCC declined 99.1%, and the system throughput of it increased 73.2%. In addition, the throughput of HFCC respectively increased by 35.7% and 75.2% compared with Back2F and REPICK. The analysis results show that HFCC can reduce the overhead while improving the robustness, which is suitable for high-density deployed network.

To protect privacy in E-government information resource sharing, a privacy protection model was proposed. The model could classify information resource sharing into two types: one is decision making business based on data mining or statistics and the other is business collaboration. The model adopted data preprocessing method to generalize privacy and a business collaboration simulator to determine the minimum privacy set for business collaboration respectively to protect privacy in the two types of businesses. The analytical results show the proposed method is effective in privacy protection.

To solve the problems of lowautomation in traditional command & decisionmaking system, a workflow based command & decisionmaking system model was proposed. This model evolved from workflow metamodel, and divided the common flow of command & decisionmaking into several key business activities according to some rules. It also divided an activity into several subactivities. The automation of command & decisionmaking system was improved effectively by using workflow. A prototype system was developed based on the model with C/S architecture. It could dominate the activities in client automatically while scheduling missioninstructions. Compared with traditional methods, the model has advantages of generalization, reliability and intelligence.