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To guarantee the real-time transmission of a video stream, based on the stochastic optimal control method, a frame layer adaptive rate control algorithm for the wireless transcoder is proposed, which is capable of dynamically determining the transcoder's objective bit rate, according to the bandwidth variation of the wireless channel and the buffer occupancy. Then the transient performance, steady performance, and computational complexity of the algorithm are analyzed. Finally, the experiment results demonstrate that the algorithm can improve the synthetic performance of rate control through the compromise between the end-to-end delay and the playout quality.

A category of Distributed Real-Time Systems (DRTS) that has multiprocessor pipeline architecture is increasingly used. The key challenge of such systems is to guarantee the end-to-end deadlines of aperiodic tasks. This paper proposes an end-to-end deadline control model, called Linear Quadratic Stochastic Optimal Control Model (LQ-SOCM), which features a distributed feedback control that dynamically enforces the desired performance. The control system considers the aperiodic task arrivals and execution times' variation as the two external factors of the system unpredictability. LQ-SOCM uses discrete time state space equation to describe the real-time computing system. Then, in the actuator design, a continuous manner is adopted to deal with discrete QoS (Quality of Service) adaptation. Finally, experiments demonstrate that the system is globally stable and can statistically provide the end-to-end deadline guarantee for aperiodic tasks. At the same time, LQ-SOCM is capable of effectively improving the system throughput.

Two quantum search algorithms are proposed for known and unknown number of solutions. The first algorithm begins with an arbitrary rotation phase Grover search algorithm by recursive equations, then a sub-algorithm (G α algorithm) and the corresponding quantum circuits are designed, the probability of success and expected number of iterations of the sub-algorithm to find a solution are analyzed. Based on these results, we design the whole algorithm and estimate the expected number of iterations to search all solutions. The design of the second algorithm follows the same steps. We firstly study a quantum counting algorithm, then design a sub-algorithm (QCG algorithm), and analyze the probability of success and the expected number of iterations to find a solution. Finally the whole algorithm for all solutions is designed and analyzed. The analysis results show that these two algorithms find all solutions in the expected times of $O(t\sqrt{N})$ (t is a number of solutions and N is a total of states).

In this paper we propose a novel approach for optimal path discovery. We consider how to combine the existing optimal path detection tools to achieve better results under the constraint of resource such as energy, in this situation any single method may fail to find the path. Based on the optimal search theory, an optimal path search strategy and an algorithm is derived.

As a representative of hierarchy-based protocol in wireless sensor network, LEACH is an elegant solution for energy efficiency. However, LEACH still have some deficiencies such as 1) some very big clusters and very small clusters may exist in the network at the same time; 2) unreasonable cluster head selection while the nodes have different energy; 3) cluster member nodes deplete energy after cluster head was dead. In this paper, we propose a new energy-efficient LEACH-based protocol that employs cluster member threshold to avoid the very big cluster and the very small cluster existing at the same time; use energy rating to avoid uneven energy distribution; and use energy estimate to avoid unnecessary energy consumption of cluster members. Analysis and simulation results show that our proposed algorithm provides more uniform energy consumption among nodes and can prolong network lifetime compared to LEACH.

This paper studies the aperiodic soft Real-Time Systems (RTS) with random task arrival and unpredictable task execution time, and proposes a dynamic optimal control model based on the queuing theory. Firstly, we construct a dynamic optimal control model for the FCFS (First Come First Served) scheduling strategy; then, we present a more useful model for DM (Deadline Monotonic) scheduling algorithm. This model can statistically guarantee the schedulability of soft RTS, at the same time it can guarantee the QoS (Quality of Service) of incoming tasks and improve the system throughput. Finally, an example shows that the model is capable of improving the systems performance and it is an efficient workload management method.

This paper presents a MPEG-4 video CODEC based watermarking scheme to protect video content. This scheme provides dual protection for intellectual property rights (IPR), utilizing error propagation in temporal MCP. Watermark is embedded into I-VOP, and is adjusted before I-VOP is reconstructed as reference VOP. During decoding, the embedded watermark in I-VOP is adjusted by the same way as in encoding. Only users with authorization can adjust the watermark correctly during decoding to get good video quality. For illegal users the video is also accessible but with bad quality because of the propagation of error caused by adjusted watermark in I-VOP. The watermark in I-VOP exists anytime once it's embedded. This scheme has the virtue of providing dual protection with little expense of time and computation. Some experimental results are given and discussed in the end.

In order to prolong the network lifetime, energy-efficient protocols should be designed to adapt the characteristic of wireless sensor networks. Clustering Algorithm is a kind of key technique used to reduce energy consumption, which can increase network scalability and lifetime. This paper studies the performance of clustering algorithm in saving energy for heterogeneous wireless sensor networks. A new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks is proposed and evaluated. In the new clustering scheme, cluster-heads are elected by a probability based on the ratio between residual energy of node and the average energy of network. The high initial and residual energy nodes will have more chances to be the cluster-heads than the low energy nodes. Simulational results show that the clustering scheme provides longer lifetime and higher throughput than the current important clustering protocols in heterogeneous environments.

Wireless sensor network combines sensing, computation and communication. Due to limited energy, energy efficiency of sensors is a main concern and a most challenging task for the design of wireless sensor networks. This paper proposes a novel algorithm for network topology, namely Dynamic Energy-Efficient Hierarchical clustering algorithm (DEEH). Different from others, DEEH need to know any local information of sensors. The algorithm can be applied to real large-scale sensor networks in which the sensors have different energy levels and different transmission radius. Compared with the classical clustering algorithm LEACH (Low-Energy Adaptive Clustering Hierarchy), the algorithm is better when the nodes are densely distributed. This paper also considers the selfishness of nodes and analyzes its impact, and introduces a trustful mechanism design that is applied to the algorithm. Under this mechanism, the dominant strategy of selfish nodes is to report their energy truthfully. This strategy can prolong the network lifetime and improve the stability of the network topology.