In order to improve the efficiency of container port services and reduce the tardiness costs of ship services, a new mathematical model was established with the objective of minimizing the sojourn time of the ships and the total tardiness for the tug-berth joint scheduling problem under the established conditions of port hardwares (berths, tugboats, quay cranes), and a hybrid algorithm was designed for solving it. Firstly, the serial hybrid strategy of Quantum Genetic Algorithm (QGA) and Tabu Search (TS) algorithm was analyzed and determined. Secondly, according to the characteristics of the joint scheduling problem, the update strategy of dynamic quantum revolving gate was adopted when solving key problems in the executing process of the hybrid algorithm (chromosome structure design and measurement, genetic manipulation, population regeneration, etc.). Finally, the feasibility and effectiveness of the algorithm were verified by the production examples. The experimental results show that compared with results of manual scheduling, the sojourn time of the ships and total tardiness of the hybrid algorithm are reduced by 24% and 42.7% respectively; compared with the results of genetic algorithm, they are reduced by 10.9% and 22.5% respectively. The proposed model and algorithm can not only provide optimized operation schemes for berthing, unberthing as well as loading and unloading operations of port ships, but also increase the port competitiveness.

Content offloading technology based on mobile edge computing can effectively reduce the traffic pressure on the backbone network and improve the end user's experience. A content offloading scheme of greedy strategy was designed for the heterogeneous contact rate between end users and small base stations. Firstly, the content optimal offloading problem was transformed into the content maximum delivery rate problem. Secondly, the maximum delivery rate problem was proved to satisfy the submodularity. On this basis, the greedy algorithm was used to deploy the content. The algorithm was able to guarantee its optimality with the probability (1-1/e). Finally, the impacts of content popularity index and cache size on different offloading schemes were analyzed in detail. The experimental results show that the proposed scheme improves the content delivery rate and reduces the content transmission delay at the same time.

In the Device-to-Device (D2D) caching network where mobile user terminals are all cache-enabled, the spatial distributions of all users were modeled as the Homogeneous Poisson Point Processes (HPPP). On this basis, combined with the randomness of content caching and requesting, the network interference was analyzed exactly and then approximately under specific scenarios. Considering that the D2D caching technique has the dual characteristics of both caching at terminals and D2D communications, which means that the content offloadeding includes self-offloading way and D2D-offloading way, and the content transmission has to meet the constraints of received Signal-to-Interference Ratio (SIR) and D2D maximal communication distance simultaneously, the closed-form expressions of Successful Offloading Probability (SOP) of the random D2D caching network was deducted. Simulation results show that the proposed SOP is a general metric and can be reduced to the special cases of existing research results. For example, when the users are distributed densely and the D2D maximal communication distance is relatively large, the SOP is reduced to the Successful Transmission Probability (STP) without considering the D2D distance constraint.

Limited and heterogeneous cache capacity is one of the key parameters which affect the cache efficiency in Device-to-Device (D2D) caching networks. However, most of existing literatures assume all users have homogeneous cache capability. In this regard, cache placement optimization is necessary for practical scenarios with heterogeneous cache capacities. Considering the mobility and random distribution of terminal users, users with different cache capacities were modeled as mutually independent homogeneous Poisson point processes with stochastic geometry. Moreover, the average cache hit ratio was derived with considering both self-offloading and D2D-offloading cases. Finally, a Joint Cache Placement (JCP) algorithm based on coordinate gradient optimization was proposed to obtain the optimal cache placement scheme which can maximize the cache hit ratio. Simulation results show that the proposed JCP can achieve larger cache hit ratio than the existing cache placement schemes.

In order to improve the effectiveness of mobile cloud computing in Device-to-Device (D2D) communication network, a computation offloading scheme based on the time switch policy for energy harvesting was proposed. Firstly, the computational tasks needed to be migrated of a traffic-limited smart mobile terminal were sent to an energy-limited smart mobile terminal in the form of Radio-Frequency (RF) signals through D2D communication, and the time switch policy was used by the energy-limited smart mobile terminal for the energy harvesting of received signals. Then, the extra traffic consumption was paid by the energy-limited terminal for the relay tasks of traffic-limited terminal to the cloud server. Finally, the proposed scheme was modeled as a non-convex optimization problem for minimizing terminal energy and traffic consumption, and the optimal scheme was obtained by optimizing the time switch factor and the harvest energy allocation factor of the energy-limited terminal, and the transmission power of the traffic-limited terminal. The simulation results show that, compared with non-cooperative scheme, the proposed scheme can effectively reduce the terminal's limited resource overhead by the computation offloading through reciprocal cooperation.

In Data Center Network (DCN), multiple tenants may interfere with each other, which leads to unpredictable application performance. Reserving bandwidth resources can hardly guarantee high network utilization, which may result in revenue loss of cloud providers. To address above problems, a Minimum Access Guaranteed Bandwidth Allocation (MAGBA) mechanism for data center networks was proposed. To provide the minimum bandwidth guarantee and make full use of idle bandwidth for tenants, the MAGBA scheduled traffic of Virtual Machine (VM) through Weighted Fairness Queuing at the sending side, and it adjusted TCP flow's receiving window at the receiving side. Through simulations on NS2 (Network Simulation version 2) platform, compared with the static resource reservation method, MAGBA mechanism was more flexible in bandwidth allocation and it could improve the network throughput by 25%. When some tenants sent a lot of TCP flows, the other tenants in the MAGBA obtained higher bandwidth than that in the existing bandwidth allocation mechanism based on TCP flows. The simulation results show that the MAGBA mechanism can provide minimum access guaranteed bandwidth for VMs and it can avoid interferences from other tenants.

To effectively manage the enforcement of secure policies during the cross-domain interoperation among cloud systems, a management technique applied for the verification of multi-domain cloud policies was proposed. First, both the access control policies and security properties under secure inter-operation environments were studied, the intra-domain administration was distinguished from inter-domain administration according to role hierarchies, and a multi-domain Role Based Access Control (domRBAC) model and specifications for the security properties based on Computation Tree Logic (CTL) were formally defined. Next, a role-to-role mapping algorithm derived from the graph theory was proposed, to depict the reasoning for domRBAC hierarchies, and a verification algorithm of security policies for cloud systems was further constructed. The simulation results show that, the time cost of security policy verification for multi-domains increases with the expansion of the size of the system. Multi-process parallel detection mode can reduce the time of policy verification from 70.1% to 88.5%, and compared to the normal mode, the model optimized detection mode fluctuates smaller in time lines, and the time overhead is significantly lower for large-scale systems. Therefore, the proposed technique has stable performance and high efficiency to be used in secure interoperation of large-scale cloud systems.

In view of the efficiency problem of multi-objective recommender systems, this paper utilized the online and offline separation strategy to construct a new frame of recommender system. Aiming at the multi-objective feature of recommender system and current recommendation algorithms' limitations in adaptability, this paper put forward a new multi-objective recommendation algorithm based on the hybrid strategy. Firstly, the algorithm did weighted mix of multiple recommendation algorithms. Secondly, it established a multi-objective optimization model, using the weight sequence as variables and evaluation metrics including F-score, diversity and novelty as objective functions. Then, it optimized the solution through a second version of Strength Pareto Evolutionary Algorithm (SPEA2). Finally, it recommended items to users based on users' shopping preferences and the Pareto set. The experimental results show that: compared with the best single metric sub-recommendation algorithm, the new recommendation algorithm is nearly as well in the F-score, meanwhile increases by 1% in the diversity and increases by 11.5% in the novelty; the distribution of various Pareto solutions of multi-objective forms a dense and neighboring point curve in the solution space. So the recommender algorithm can satisfy the recommend requirements of users with different shopping preferences.

A subband spectral-subtraction method for speech enhancement was proposed. Time series were decomposed into frequency subbands through the filterbank and in each subband, an improved spectral-subtraction technique was applied. This method mainly considered that most environmental noises would affect the speech spectrum nonuniformly within different frequencies and multiband processing would be more accurate and effective. In the speech processing experiments, objective evaluation reveals that the proposed algorithm performs well in noise suppression and speech quality improvement, which makes the speech after processing more comfortable and intelligible.

Since the existing summation aggregation algorithms are almost duplicate-sensitive, an approximate algorithm Flajolet-Martin SUM (FM-S) of distinct summation query for Wireless Sensor Network (WSN) was proposed. In FM-S, each node in WSN combined the FM-S algorithm and the random number sample of binomial distribution to do hash conversion so as to fill a summation sequence of length L, and each node forwarded the generated sequence to the father node in routing tree. Then the root node received the summation sequence of whole network. Finally, according to the sequence of root node, the approximation summation value of distinct records in sensor networks could be obtained. The experimental results show that the distributed algorithm is of low power consumption, high fault tolerance, robustness and scalability.

In order to study the influence of multipath, shadows and the weather variations on meteorological satellite channel, the Suzuki and extension Suzuki channel models were studied according to the analyses on the different weather conditions. Then the two-state Markov model was introduced into satellite channel model, which could describe the transformation between two kinds of channel state models caused by the changes of weather. Finally, the meteorological satellite channel simulator was designed and simulated based on the the shaping filter method. The results show that the proposed meteorological satellite channel simulator can be used in the description of actual meteorological satellite channel propagation characteristics.

Through analysis on the internal structure and the working characteristic of Bluetooth chip, a parallel cryptography algorithm instruction structure model based on Digtal Signal Processor (DSP) coprocessor and the algorithm working procedure was designed. The model was designed by considering two aspects of time overhead and storage space. And the model implemented the high computational and complex cryptography algorithm with DSP coprocessor possible during the Bluetooth communication. The experimental results show that the method can reduce the influence of complex cryptography to Bluetooth transmission.

Existing results of auxiliary word are difficult to use in the automatic annotation of natural language processing. Based on the auxiliary words knowledge base, rule-based method is used in automatic annotation of auxiliary words usage. Contrast to the results of test, it shows that refining, extension and adjusting the matching order of the rules can promote the precision and recall effectively. It is also benefit for improve the quality of Chinese Corpus, deepen the processing depth, and reduce the artificial work.

In view of the current problems that neural network ensemble generates individual networks with low difference degree and its poor generalization ability, a method of evolutionary ensemble of neural network based on niche technique was proposed. Niche technique's good performance was used in increasing population diversity and improving local search capability of evolution, and the similarity degree's sharing function among individuals was adopted to adjust individual network's fitness. Then the individuals were selected according to the new adjusted fitness to get individual network with diversity. Theoretical analysis and experimental results show that this method can generate individual network with great difference degree and can improve the generalization ability and calculation accuracy of neural network ensemble system.