Applied Mechanics and Materials Vols. 687-691

Abstract: During the 1990s, with the rapid development of science and technology, the human production and working mode or life-style have the huge transformation. In the sports field, researchers and teachers have the aid of modern science and technology resources to improve various teaching methods , which do help to master the technical action and improve the sports level. In the practical application, these teaching methods is more effectual than conventional method in sports skill learning. Use exploratory study method to develop a suitable and low-price underwater video acquisition system for daily swimming teaching, which is also easy to be operated. The system is with the aid of underwater surveillance cameras to realize the technology of underwater swimming action film and real-time looked, and through the acquisition card timely playback and slow play function to intuitive realistically restore underwater technical movement dynamic change process. This research use the teaching experimental method,mathematical statistics and comparative analysis and so on, the students make two sets of different teaching contrast experiments, verify the effect of the underwater video acquisition system. Underwater video acquisition system in swimming teaching application, change the teacher explain demonstration and strengthen the students single teaching mode, formed by means of image data timely feedback video feedback teaching method.

Abstract: Aiming at improving the uplink sum capacity of the CoMP (Cooperated Multiple Point) system, an adaptive user grouping algorithm is proposed based on the properties of users, in which the uplink CoMP system is regarded as a virtual MIMO system. The proposed algorithm firstly finds out the count of the groups that the users should be divided into, then divides the users into groups by their quantitative properties. Simulation results show that the proposed algorithm increases the sum capacity of uplink, and also proves the algorithm’s effectiveness.

Abstract: The antenna number in distributed MIMO system is much larger than that in distributed antenna system (DAS) and traditional centralized MIMO system. Therefore adopting the existing antenna selection algorithms with excellent performance will make it difficult to realize the system due to the complexity of the algorithms. In order to solve the problem, a novel antenna selection algorithm performed at the base station (BS) is proposed according to the structural characteristics of the system. In the proposed algorithm, the antenna search scope is narrowed down by port selection based on the trace of the sub-channel matrices, and antennas with little contributions to the system capacity are removed gradually by iteratively updating the optimization parameter, which further reduces the complexity. When this algorithm is treated as the transmit antenna selection algorithm, its port selection process is performed by the user equipment, which can reduce the feedback overhead. Simulation results show that the proposed algorithm possesses the similar system capacity with the optimal algorithm.

Abstract: Making use full of multi-source and multi-temporal information to extract richer and interesting information is a tendency in analysis of remote sensing images. In this paper, spatial and temporal contextual classification based on Markov Random Field (MRF) is used to classify ecological function vegetation in Poyang Lake. The results show that spatial and temporal neighborhood complementary information from different images can be used to remove the spectral confusion of different kinds of vegetation on single image and improve classification accuracy compared to MLC method. Building effective spatial and temporal neighborhood model for information extraction in special application is the key of multi-source and multi-temporal image analysis. Although spatial and temporal contextual classification method is computation demanding, it’s promising in the application emphasizing classification accuracy.

Abstract: Time series analysis has been extensively used in many fields, such as system identification, modeling and data predication, and played an important role in system design, planning and performance analysis. The focus of time series application study is how to improve the accuracy and computation speed of the parameter estimation. Many researchers have carried out system modeling study by applying time series analysis and have gained their research results. The traditional methods such as maximum likelihood estimation, moment estimate and least square estimate which exit the defect of low precision, poor convergence and parameter estimation white noises coupling, are mostly utilized in parameter estimation for model. Taking this as basis the data forecasting and anomaly detection are conducted, which is hard to ensure the system’s stability. Different from the traditional algorithm, this paper proposes a new weighted iterative stage parameter estimation algorithm which avoids the coupling with white noise estimation of ARMA model parameter and improves the accuracy of parameter estimation. In theory, this algorithm tends to provide a good convergence performance. The experimental results based on ARIMA model show that the algorithm can improve the accuracy of parameter estimation and provide a good convergence performance.

Abstract: To address the problem that anchor ratio had a strong impact on localization error and coverage in centroid algorithm in wireless sensor network (WSN), an improved algorithm was proposed. This algorithm differentiated the priority of the unknown nodes according to the distance between unknown nodes and anchors. The algorithm was proposed to locate the unknown nodes with the highest priority, and then update them to new anchors. Finally, the rest unknown nodes are located by centroid algorithm. The simulation results show that this improved algorithm can effectively reduce the localization error and enhance the coverage when the anchor ratio is lower.

Abstract: Balancing energy load is a key problem in wireless sensor network (WSN) research. For balancing node energy consumption and prolong the network lifetime, this paper proposes an improved routing algorithm EBRA (Energy Balancing Routing Algorithm) based on energy heterogeneous WSN. To maximize the energy efficiency of network nodes, the EBRA weights the probability of cluster head election. According to the estimate value of the network average remaining energy and the residual energy of network nodes, we can calculate the new cluster head election threshold. The simulation results show that the utilization of energy balance of EBRA algorithm is improved 74%, 30% and 23%, compare with LEACH, SEP and DCHS, respectively.

Abstract: The concentration of suspended sediment is an important parameter for the research of sediment transport. Acoustic backscatter technique has been employed to measure the concentration of suspended sediment recently. It is an inversion problem to measure the concentration from the backscatter signal. In this paper, an improved dual-frequency method is proposed for the concentration inversion of suspension sediment. It is an explicit solution with much lower computational complexity than the commonly used iterative method and with no requirement of known and constant particle size profile compared to the basic dual-frequency method.

Abstract: The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for optimizing the preview model parameters evaluation of RANSAC that has the benefit of offering fast and accurate RANSAC. With guaranteeing the same confidence of the solution as RANSAC, a very large number of erroneous model parameters obtained from the contaminated samples are discarded in the preview evaluation selection. And use local optimization step apply to selected models. The combination of these two strategies results in a robust estimation procedure that provides a significant speed and accuracy RANSAC techniques, while requiring no prior information to guide the sampling process.

Abstract: A real-time predicting trace algorithm is proposed using the process of target tracking, the target’s position in the next frame is estimated by the iterative fitting. Making the predict position as the center, the target is searched within a certain window and the target tracking is accomplished. The target’s trajectory is obtained by fitting the target’s positions using curve. The experiments show that the algorithm reduces the matching complexity, improves the tracking speed and tracking precision.