Concerning the problem of low real-time performance of face detection on mobile platform,a FaceYoLo real-time face detection algorithm based on deep learning was proposed. Firstly,based on the YoLov3 detection algorithm,the Rapidly Digested Convolutional Layers (RDCL) were added to reduce the input space size, then Multiple Scale Convolutional Layers(MSCL)were added to enrich the receptive fields of different detection scales,and finally the central loss and densification strategy were added to strengthen the generalization ability and robustness of the model. The experimental results show that,when tested on the GPU,the proposed algorithm improves the speed by nearly 8 times compared with the YoLov3 algorithm,has the processing time of each image reached 0. 002 8 s,and increases the accuracy by 2. 1 percentage points;when tested on the Android platform,the proposed algorithm has the detection rate increased from 5 frame/s to 10 frame/s compared with the best MobileNet model,demonstrating that the algorithm can effectively improve the real-time performance of face detection on mobile platform.

To solve the bottleneck on performance and low reliability of single core node, the cloud monitor system based on Peer-to-Peer (P2P) monitor network was designed and implemented. On the hardware deployment, the monitor nodes were capsulated in application containers and distributed on different racks to build P2P monitor network. By establishing distributed storage clusters with non-relational database, remote data access and backup were supported to improve system reliability. On the software implementation, it was designed hierarchically using combination method of push and pull to collect data, estimating trust degree of data, storing data in distributed nodes and managing hosts in cloud with threshold control and free host estimation strategies. Through the system tests, it was concluded that the system only accounted for 2.17% on average computing resource usage and its average responsive ratio on reading and writing requests per millisecond reached above 93%.The results indicate that the monitor system is superior on low resource consumption and high read/write efficiency.

The Maximum-A-Posteriori-probability (MAP) demodulation of recursive FQPSK-B in the presence of Additive White Gaussian Noise (AWGN) channel was first presented. Required in the iterative detection of Serial Concatenation of Convolutional coded Recursive FQPSK (SCCRFQPSK), the bit extrinsic Log-Likelihood Ratio (ex-LLR) of FQPSK demodulation was also derived. Secondly, aiming at weakening the phenomena of positive feedback during the iterative detection of SCCRFQPSK, the bit ex-LLR of FQPSK demodulation was appropriately adjusted by linear weighted processing. By Monte Carlo simulation, it was concluded that the optimal weighting factor of the weighted SCCRFQPSK system was 0.7, and it got 0.3dB Signal-to-Noise Ratio (SNR) gain at a Bit Error Rate (BRE) of 10-5 at 4 iterations. The simulation results indicate that the proposed method can not only accelerate the decoding convergence and improve the performance of the SCCRFQPSK system, but also reduce the delay. To a certain extent, it can deal with the deep space communication with low SNR caused by long distance.

Towards the large frequency offset caused by Doppler effect in high speed moving environment, a dynamic state space model of Orthogonal Frequency Division Multiplexing (OFDM) was built, and a kind of frequency offset tracking and estimation algorithm in OFDM based on improved Strong Tracking Unscented Kalman Filter (STUKF) was proposed. By combining strong tracking filter theory and UKF together, the fading factor was introduced during the process of calculating the measurement predictive covariance and cross covariance. The frequency offset estimation error covariance was adjusted; meanwhile, the process noise covariance was also controlled, and the gain matrix was adjusted in real-time. So the tracking ability to time-varying frequency offset was enhanced and the estimated accuracy was raised. The simulation test was carried out in time-invariant and time-varying frequency offset models. The simulation results show that the proposed algorithm has better tracking and estimation performance than the UKF frequency offset estimation algorithm, the Signal-to-Noise Ratio (SNR) raises about 1dB under the same Bit Error Rate (BER).

To improve the efficiency of the Bit Flipping (BF), a weighted gradient descent bit-flipping decoding algorithm based on average magnitude was proposed for Low Density Parity Check (LDPC) code. The average magnitude of the information nodes was first introduced as the reliability of the parity checks, which was used to weigh the bipolar syndrome, and then an effective bit-flipping function was obtained. Simulation was conducted at Bit-Error Rate (BER) of 10-5 under an Additive White Gaussian Noise (AWGN) channel, and coding gains of 0.08 and 0.29 dB were achieved in comparison to conventional weighted Gradient Descent Bit-Flipping (GDBF) and Reliability Ratio based Weighted Gradient Descent Bit-Flipping (RRWGDBF) algorithms while the average number of decoding iterations was reduced by 72.6% and 9.3%, respectively. The simulation results show that the improved algorithm outperforms the conventional algorithms while average decoding number is also reduced. It indicates that this new scheme can better balance error-correcting ability, decoding complexity and delay, which can be applied to high-speed communication system with high real-time requirement.

To estimate the frequency offset in Orthogonal Frequency Division Multiplexing (OFDM) system, a novel blind frequency offset estimation algorithm based on Particle Swarm Optimization (PSO) method was proposed. Firstly the mathematical model and cost function were designed according to the principle of minimum reconstruction error of the reconstructed signal and the signal actually received. The powerful random, parallel, global search property of PSO was utilized to minimize the cost function to get the frequency offset estimation. Two inertia weight strategies for PSO algorithm of constant coefficient and differential descending were simulated, and comparison was made with the minimum output variance and gold section methods. The simulation results show that the proposed algorithm performs highly accuracy, about one order of magnitude higher than other similar algorithms in same Signal-to-Noise Ratio (SNR) and it is not restricted by modulation type and frequency estimation range (-0.5,0.5).

The trust management models in Peer-to-Peer (P2P) network mainly have two problems. For one thing, the different influences on value of trust between short-term trading and long-term trading are usually ignored. For another, the lack of the specific risk analysis on trading resources exists. Consequently, focusing on the quality of different nodes and its opposite risk value, this paper introduced the concept of risk factor with setting up its value and proposed a trust management model based on evaluation of value-at-risk with changing time. From the simulation results, a higher efficiency on resisting malicious actions in P2P network is achieved, and it has confirmed to select better traders effectively with a deeply quantitative analysis of trade resources through the model.