Aiming at the shortcoming caused by the lack of prevention measures in the consideration of the existing drug abuse epidemiologic research, an Susceptible-Infected-Treated-Recovered-Susceptible (SITRS) drug abuse epidemiologic model based on prevention and therapy strategy was proposed by introducing prevention mechanism. Firstly, through analyzing the evolution process of the populations correlated with the drug abusers, an autonomous dynamical system was constructed by using ordinary differential equations. Secondly, the existence and local asymptotic stability of the drug-free equilibrium point of the system were proved. Thirdly, the unique existence of endemic equilibrium point was analyzed, and the sufficient conditions for global asymptotic stability of the endemic equilibrium point were obtained. Finally, the necessary conditions of existing backward bifurcation were calculated, the basic reproduction number under comprehensive prevention and therapy strategy and the number under single therapy strategy were compared. The possibility that backward bifurcation may exist and the stability of equilibrium points were verified by the numerical simulations. The study results indicate that, compared with single therapy strategy, comprehensive prevention and therapy strategy can further reduce the basic reproduction number of drug abuse thus more effectively prevent the breeding of drug abuse by increasing publicity coverage rate and education efficiency.

The recovered drug users maybe become susceptible to drug again, but this possibility is neglected in the existing drug abuse epidemic model in which the drug users are assumed to be permanently immune to drugs after recovery. Aiming at the problem, the evolution process of drug abuse population was analyzed with considering both community treatment and isolation therapy, and a drug abuse epidemic model based on temporary immunity was proposed. Furthermore, the basic reproduction number of the proposed model was calculated and the existence and stability of the proposed model equilibrium were discussed. It is shown that the proposed model has a drug free equilibrium which is locally asymptotically stable and a unique endemic equilibrium when the basic reproduction number is less and more than unity respectively. And the global stability of the endemic equilibrium was proved by using a geometric approach. Otherwise, the proposed model has the phenomenon of backward bifurcation under certain conditions when the basic reproduction number is equal to unity. The above results were verified by the numerical simulations. The results indicate that the prevalence of drug abuse can be effectively inhibited by increasing the rate of isolation therapy, improving the effect of community treatment and reducing the infection rate.

The traditional speech-based Parkinson's Disease (PD) prediction method is to predict the motor Unified Parkinson's Disease Rating Scale (motor-UPDRS) and the total Unified Parkinson's Disease Rating Scale (total-UPDRS) respectively. In order to solve the problem that the traditional method could not use the shared information between tasks and the poor prediction performance in the process of single task prediction, a multi-task regression method based on model filtering was proposed to predict the motor-UPDRS and total-UPDRS of Parkinson's disease patients. Firstly, considering the different effects of the subtask speech features on the predicted motor-UPDRS and total-UPDRS, an L1 regularization term was added for feature selection. Secondly, according to different Parkinson's patient objects distributed in different domains, a filtering mechanism was added to improve the prediction accuracy. In the simulation experiments of remote Parkinson data set, the Mean Absolute Error (MAE) of motor-UPDRS is 67.2% higher than that of the Least Squares (LS) method. Compared with the Classification And Regression Tree (CART) in the single task, the motor value increased by 64% and the total value increased by 78.4%. The results of experiment show that multi-task regression based on model filtering is superior to the single task regression algorithm for UPDRS prediction.

Aiming at the problems of scalability, algorithm convergence performance and operational efficiency in building large-scale machine learning systems, the challenges of the large-scale sample, model and network communication to the machine learning system were analyzed and the solutions of the existing systems were also presented. Taking Latent Dirichlet Allocation (LDA) model as an example, by comparing three open source distributed LDA systems-Spark LDA, PLDA+ and LightLDA, the differences in system design, implementation and performance were analyzed in terms of system resource consumption, algorithm convergence performance and scalability. The experimental results show that the memory usage of LightLDA and PLDA+ is about half of Spark LDA, and the convergence speed is 4 to 5 times of Spark LDA in the face of small sample sets and models. In the case of large-scale sample sets and models, the network communication volume and system convergence time of LightLDA is much smaller than PLDA+ and SparkLDA, showing a good scalability. The model of "data parallelism+model parallelism" can effectively meet the challenge of large-scale sample and model. The mechanism of Stale Synchronous Parallel (SSP) model for parameters, local caching mechanism of model and sparse storage of parameter can reduce the network cost effectively and improve the system operation efficiency.

Focusing on the key exchange problem of how to get the higher security for neural cryptography in the short time of the synchronization, a new hybrid algorithm combining the features of "Do not Trust My Partner" (DTMP) and the fast learning rule was proposed. The algorithm could send erroneous output bits in the public channel to disrupt the attacker's eavesdropping of the exchanged bits and reduce the success rate of passive attack. Meanwhile, the proposed algorithm estimated the synchronization by estimating the probability of unequal outputs, then adjusted the change of weights according to the level of synchronization to speed up the process of synchronization. The simulation results show that the proposed algorithm outperforms the original DTMP in the time needed for the partners to synchronize. Moreover, the proposed algorithm is securer than the original DTMP when the partners do not send erroneous output bits at the same time. And the proposed algorithm outperforms the feedback algorithm in both the synchronization time and security obviously. The experimental results show that the proposed algorithm can obtain the key with a high level of security and a less synchronization time.

Multi-scale Quantum Oscillator Harmonic Algorithm (MQHOA) has good characteristics of global convergence and adaptability. For analyzing the specific performance of MQHOA on solution precision and speed, the comparisons of theoretical models and experiments were completed among the MQHOA, the classic Quantum Particle Swarm Optimization (QPSO) algorithm adopting the quantum-behaved model and having been widely used, and the QPSO with Random Mean best position (QPSO-RM) through solving the integer nonlinear programming problems. In simulation experiments, MQHOA achieved 100% success rate in solving seven unconstrained integer nonlinear programming problems, and was faster than QPSO and QPSO-RM in most cases. MQHOA was a little slower than QPSO and QPSO-RM in solving the two constrained integer nonlinear programming problems, but could obtain 100% success rate which was higher than the latter. Through the comparison of the convergence process of QPSO, QPSO-RM, MQHOA was faster and earlier on converging to the global optimal solution. The experimental results show that MQHOA can effectively adapt to solving the integer programming problems, and can avoid falling into the local optimal solution so as to obtain the global optimal solution. MQHOA is better than the contrast algorithms of QPSO and QPSO-RM in accuracy and convergence rate.

In order to lower the computational burden of wireless positioning algorithm with high accuracy, this paper proposed a new 2D beamspace matrix pencil algorithm to jointly estimate Time-Of-Arrival (TOA) and Direction-Of-Arrival (DOA), which can position target accurately with low complexity. This algorithm first transformed the complex data matrix into real and reduced dimensional matrix via Discrete Fourier Transform (DFT) matrix, which significantly reduced the computational burden; then estimated TOA and DOA of Line-of-Sight signal for positioning via singular value decomposition and solving generalized eigenvalues of matrix pencils. Matlab simulation results prove that this positioning method achieves Root Mean Square Error (RMSE) as small as 0.4m with computation cost no more than 1/4 of corresponding algorithm in element space, which makes it a promising positioning method for resource limited environments like battlefield, earthquake-stricken area and rural places.

A watermark algorithm for digital image based on DWT and SVD was proposed.It added gray images as the watermark and increased information capacity of the watermark.The algorithm can satisfy the transparence and robustness of the watermark system. The experiment based on this algorithm demonstrates that the watermark is robust to the common signal processing techniques including JEPG compressing,noise,low pass filter,median filter,contrast enhance.

A multimedia communication terminal based on ADSP-BF533 DSP processor and used in IP network was designed and implemented. μCOS-II embedded operation system was used in the system, with the transplantation of LwIP. The experiments show that by the multimedia communicaiton terminal, the coding and transportation of multimedia information can be realized in real time in LAN.