Advanced Materials Research Vols. 756-759

Abstract: Capturing good videos outdoors can be challenging due to harsh lighting, unpredictable scene changes, and most relevant to this work, dynamic weather. Particulate weather, if there were a heavy snow in monitor scene, it would create complex flickering effects that are irritating to people and confusing to computer vision algorithms. The research direction of video image background estimation and background estimation in heavy snow is to improve the visualization of surveillance video, especially to reduce the affection of snowflake in heavy snow. This algorithm can remove snowflakes and retain non-snowflakes moving targets we interested in. We propose a snow removal method of the pixels affected by snowflakes based on probability estimation of snowflakes detected. Firstly, we detect the snowflakes based on the optical of snowflake and time difference method. Then we estimated the coverage rate of snowflakes based on the detecting result. Finally, we build an adaptive estimation method of background pixels value based on the information feedback of snow detection and the model of pixels value. The calculation of this method is simple and the method has good processing results.

Abstract: Building an efficient data structure for range selection problems is considered. While there are several theoretical solutions to the problem, only a few have been tried out, and there is little idea on how the others would perform. The computation model used in this paper is the RAM model with word-size . Our data structure is a practical linear space data structure that supports range selection queries in time with preprocessing time.

Abstract: For estimating two-dimensional direction of arrival (DOA) of local scattering distributed source, the computational complexity of various algorithms for DOA is prohibitively high, induced by high dimension searching manipulation. According to this problem, an ESPRIT-like algorithm is proposed. The proposed algorithm exploits the rotational invariance properties of signal space, the DOA can be obtained without any peak-finding searching, which has significantly reduced the computational complexity. Simulations clearly demonstrate that the algorithm enjoys improved precision, and is more suitable for complicated environment.

Abstract: In order to find information of interest and found valuable information resources in enrich Internet data. This paper describes a personalized recommendation system, personalized recommendation system is an intelligent recommendation system to help e-commerce site for customers to provide complete personalized shopping decision support and information services. for the User Rating data extreme sparseness, This paper presents nearest neighbor collaborative filtering algorithm based on project score predicted ,experiments show that this method can improve the quality of recommendation system.

Abstract: Between each node database there must be different in the distributed heterogeneous database system, so how to resolve these different (It's said that realize the mutual transformation between heterogeneous databases) was an basic problem in the heterogeneous database system. This paper analyzes the system structure and the conversion mechanism for the distributed heterogeneous database and the realization of the conversion module theoretical including the conversion information extraction module, structural conversion module, structure formation module and put forward a concrete realization solutions.

Abstract: Data island exists in most of the information system. And the system platform integration could eliminate the existence of the information island. This paper visited database information system through the EJB component technology, and using the metadata way to extracted data information from the heterogeneous database system, and it realized sharing information between the heterogeneous database system. In this system the B/S mode structure and EJB component technology, improve the system usability and expansibility.

Abstract: Wireless sensor networks are formed by connected sensors that each have the ability to collect, process, and store environmental information as well as communicate with others via inter-sensor wireless communication. The many-to-one communication pattern used by sensor nodes in most of the data gathering applications leads to such unbalanced energy consumption. Cluster-based protocols attempt to solve this problem by load balancing within the cluster and rotating the job of cluster head every few rounds. In this paper, in order to achieve efficient utilization of wireless resources, we propose an opportunistic network-coded cooperative multicast scheme, which can select appropriate relays by synthetically considering location and instantaneous channel state information to improve the network performance than direct multicast with non additional power consumption.

Abstract: Cluster-based protocols attempt to solve this problem by load balancing within the cluster and rotating the job of cluster head every few rounds in wireless sensor networks. In this paper, we derive the formulation of the outage probability for opportunistic network-coded cooperative multicast schemes. Simulation results show that our approach can remarkably improve the performance of outage probability than direct multicast.

Abstract: Currently, FCFS scheduling method is widely used in hospitals for patient admission scheduling, which ignores the impacts of patient length of stay and surgery arrangement on the usage of hospital resources. This paper proposes a more comprehensive mathematical model and evaluation mechanism for the patient admission scheduling of an ophthalmic hospital. A genetic algorithm (GA) is proposed to optimize the model, which can provide detailed scheduling of patient admission in the hospital for everyday. The result is compared with that of the traditional FCFS method, which indicates that the GA helps to reduce the preoperative waiting time for patients. Besides, GA can provide different kinds of scheduling for the hospital to select by adjusting the relative weights of different objectives in the algorithm.

Abstract: in this paper, a computational study of two alternative methods for the path-based logit stochastic user equilibrium model is conducted. The two methods under investigation are the Jacobi gradient projection method and the Gauss-Seidel gradient projection method. We compare the two methods on the Sioux Falls network. Numerical results indicate that for the path-based logit SUE problem, Jacobi gradient projection method is more efficient than Gauss-Seidel gradient projection method.