Earticle

Tennis video’s serve events were detected. In other words, athletes area were obtained to calculate particle of athletes area and particle shift in adjacent frame by using background subtraction algorithm. And then tennis video’s serve events were inferred, according to specific constraints on serve, through establishing ontology and setting SWRL rules.

Developing information systems that are able to present the content that is most appropriate for the interests of the target audience in public spaces is a very challenging task. However, this is also a central concern for advertisers and content publishers who are continuously trying to find appropriate strategies to get the attention of visitors that are potentially interested in their products or services. In this article, we provide an overview of some of the main challenges associated with this task. We also describe a system that combines information from individual user’s interactions with contextual information to infer the general interests of the visitors to support socially aware content recommendations for individuals and groups in public spaces. Evaluation results from experiments performed in two distinct scenarios indicate that the proposed approach can be used to deliver personal and public content in public spaces and that people recognise its advantages over other traditional information systems used in public spaces.

For the decline of the control accuracy of servo system due to the disturbances of load, to design the dimensionality reduction load torque observer, the observed value of load torque after conversion for compensation control. Established the three- loop mathematical model of servo control system, to elicit three- loop control parameter setting formula by the application of optimal second order and third order model theory. Established the simulation model of space vector control AC servo system, by simulation experiment it is verified that three-ring parameters can provide good dynamic performance for system after setting. After the establishment of experimental platform, through the motor load sudden change experiment, it proved the feasibility of the torque compensation control for improving the servo system control precision under load disturbances.

The MR image has provided lots of information used for medical examination. Accurate and robust brain MR image segmentation, feature extraction and feature classification are very important for clinical tumor diagnosis. A new tumor diagnosis method based on brain MR images is hereby put forward. Firstly, detect the deformed area of the images through multi-threshold segmentation morphology, and then, extract the GMM feature used for the classification, and finally, classify the types of tumor images by using decision tree classifier. The whole classification consists of two stages, during training stage extract the different features of tumor images and non-tumor images, and during testing stage conduct the classification of tumor and non-tumor based on knowledge databank. The computing method is appraised according to the three performance index including accuracy, false alarm rate and loss detecting rate, the experiment results show that the computing function is excellent and is helpful for better brain tumor diagnosis.

The demand on fast H.264 video processing technology is highly increasing since the electronics devices with encoding, transmission, and decoding high-resolution video are becoming popular in the consumer market. In this paper, we present a fast H.264 video decoding method by modifying the algorithm dealing with parsing bit-stream of variable-length code (VLC). The key idea of our approach is to reverse the bit order in a fast way through LUT (Look-Up-Table) in order to reduce the bit pattern extraction time for VLC stream. We demonstrated the speedup by employing the proposed idea for H.264 real-time video encryption application. The experimental results show about 11% execution time reduction in the part of video stream parsing function and 3.1% time reduction in the whole video encryption system.

In view of the web service model checking application, the combination mode of traditional finite state machines cannot guarantee the correctness of Web composite service. A web service model diction algorithm of non-centralized automaton based on satisfiability modulo theories (SMT) is proposed. First, SMT is used for bounded model checking of time automaton, and the time automaton model is directly converted into logical formula that can be identified by SMT, to make solution; secondly, the proposed SMT time automaton theory is used to achieve the employee travel arrangements web services for modeling and verification; finally, through an example analysis, the effectiveness of the algorithm on the termination of the path deadlock and the optimization of network parameters.

In recent years, many countries have actively studied wind power generation as a means of realizing low-carbon green growth through a new renewable energy source. The most efficient method of securing the stable operation of wind turbines and reduce maintenance costs is monitoring and analyzing their operational status in realtime through a remote monitoring system. Remote monitoring systems employ various sensor technologies and the Wireless Sensor Network to collect and transmit data on the status of individual parts in realtime, and they diagnose faults through a signal analysis system. Application of the fault analysis method can reduce fault resolution times and minimize losses. In this study, signals collected from wind turbines were analyzed, and their characteristics were extracted through empirical mode decomposition (EMD). In the experiment, EMD learning was carried out using the following fault signals as examples: The back-propagation (BP) neural network algorithm with generator vibration, an unbalanced rotor, and a bearing misalignment fault. This article proposes a method of diagnosing faults through signal analysis and recognition, and it demonstrates the validity of the method through a simulation.

In order to solve the problem that the present logging image reconstruction algorithm has certain staircase effect and ring effect during the discrete Poisson noise image repair process, a noise reduction algorithm based on Schatten matrix operator for discrete Poisson logging image is proposed in this article. Specifically, Schatten norm matrix of Hessian operator is adopted to estimate each pixel value of the image, and meanwhile the non-quadratic regularization function is adopted to eliminate the staircase effect; then Poisson measurement model is integrated with Schatten regularization function to construct the minimum objective function; then norm proximal mapping estimation algorithm is defined and is adopted to decouple the data fidelity item and the regularization matrix in the objective function, thus to recover the image. The simulation result shows: compared with present denoising technology, the algorithm proposed in this article has better repair effect and greater similarity in the aspect of processing the discrete Poisson noise.

It researches the short-term electric load prediction and short-term electric load has the characteristics of time-varying, uncertainty, nonlinearity, etc., so the traditional linear prediction method cannot correctly describe the changing rule of the short-term electric load prediction, and neural network has the deficiencies including local minimum value of neural network, over-fitting and weak generalization ability, and the prediction accuracy is lower. In order to improve the accuracy of the short-term electric load prediction, this paper proposes a short-term electric load prediction model (IQPSO-BPNN) based on optimizing BP neural network. Firstly, it improves Quantum Particle Swarm Optimization to optimize the BP neural network parameters, and then adopts the optimized BP neural network to conduct modeling for the nonlinear change law of the short-term electric load prediction. Finally, it takes simulation test for the model performance. The simulation result shows that IPQPSO solves the problems of the BP neural network, and improve the prediction accuracy of the short-term electric load and reduce the prediction error.

Spectrum scarcity is one of the issue in wireless communication. Dynamic spectrum sharing is the solution for this problem in which the underutilized spectrums are utilized efficiently. Multiple Input Multiple Output (MIMO) based cognitive radio using spatial coding is used to dynamically share the spectrum which allows simultaneous usage of spectrum by more than one user. The drawback of spatial coding is the achievable capacity get reduced with the increased number of cognitive users which limits the number of users to share the spectrum. In order to maximize the number of secondary users sharing the spectrum of the primary user, this paper presents a cluster based multiple primary and secondary user’s MIMO spatial coding which provides higher capacity. The entire network is divided into number of cluster based on the location such that each cluster will be interference free from other. Each cluster is designed with one primary user and many secondary users and a User Scheduling algorithm called Cluster Based Max Signal Power User Scheduling (CBMSPUS) is proposed to select two best secondary users in each cluster. Since the performance of spatial coder depends on the channel matrix, this paper analyze the spatial pre-coder under Rayleigh, Rican and Nakagami fading channels with multiple users and evaluate the capacity of users under CBMSPUS scheduling algorithm. The results show that the Nakagami fading channel achieves high capacity than others. Primary user calculated capacity is 10.8bits/HZ /channel at low SNR region and 14.2 bits/HZ/channel at high SNR region. The secondary user archives maximum capacity of 9.8bits/HZ /channel at low SNR and 13 bits/HZ/channel at high SNR. The Proposed Scheduling method offers maximum of 15dB gain comparing FIFO scheduling algorithm.

In order to solve the problem regarding steel wire defect detection, a gradient vector flow calculation method based on extended neighborhood is proposed in this article and applied to the steel wire defect detection. The new method is used to analyze the gradient vector flow model from the angle of mask film, wherein the original four- neighborhood mask film is replaced by the mask film with a larger neighborhood, thus to obtain the gradient vector flow calculation method based on the extended neighborhood. Actually, the new calculation method only needs less iterations to obtain better effect. Experimentally, the algorithm proposed in this article is feasible and has high algorithm execution efficiency and high complexity.

As a powerful tool for phase transition analysis, the mean-field theory of rumor spreading brings us a new method for research in Online Social Networks (OSN). Based on the theory of complex networks, we simulate the complex process of rumor spreading in OSN. Using numerical simulations with focus on the information spreading threshold as well as critical behaviors, we study the Susceptible-Infected-Recovered- Susceptible (SIRS) information dynamics model in OSN with asymmetric propagation. Basic principle and structure of OSN were analyzed. We present a rumor spreading model, where the influence of the neighbor trees is treated in a more realistic way and the definition of a neighborhood can be tuned by an additional parameter. Based on the mean-field theory approach, a contact network model with scale-free property is built. It is demonstrated that the asymmetry of propagation plays important role: we could redistribute the asymmetry to balance the degree heterogeneity of the network and then to restore the information threshold to a finite value. The relationship of spreading probability, network size, infected fraction and infected ratio are showed respectively in the paper. Our model exhibits a surprisingly sharp phase transition which can be shifted by a redefinition of the neighborhood. The result shows that the SIRS model built in this paper is valid and the simulation of the information propagation is feasible.

JPEG images are almost always self-synchronized after an occurrence of an error in the JPEG file. In spite of the self-synchronization feature, after the synchronization point in the image, the rest of the image is not shown as it has been in the genuine image. Commonly, the blocks are shifted to right or left and usually the tinge is damaged as well. This paper suggests a way how to remedy the block shift and the tinge damage.

This paper adopts a qualitative method to study influencing factors, expands the field research, processes data based on factor analysis, and finally finds out primary factors that influence online buying decision. On the basis of primary factors, it builds up an evaluation system for B2C e-commerce websites to assess the representatives in each industry. The assessment results include current situation of China’s B2C e-commerce websites and their advantages and disadvantages. In terms of those findings, targeted strategies and recommendations are proposed to promote the development of China’s e-commerce websites.

Monitoring events using audio sensed data has become recently an interesting research issue in multimedia based surveillance system, due to the proliferation of very tiny sensors, quite powerful to be deployed in harsh environments and to cooperatively detect critical events. Events detection can be performed through capturing, processing and decision making by an automated centralized system. Adding to this the passive nature of sound, wireless sensor networks based surveillance system may enable event detection without any requirement like line of sight, and so perform well in different application cases. In this paper, we bring out the first research element in building an automated surveillance system for rice field monitoring against pest and grain-eating birds. We study how accurate can be the use of normalized power sequences in detecting bird sounds, which is a kind of harmonic sound type. We proposed a simply detection scheme (which can be executed by sensors), for the purpose of detecting the presence of birds based on the sounds and calls they produce. The scheme is based on pruning audio frame blocks to keep relevant peaks, computing the normalized power of the sequences of captured audio files, and derive the presence of birds by only considering important values of the variances of the input files. The experimental results conducted to test the efficiency of the scheme show that 91,07% of bird calls from our database can be correctly identified.

The multi-sensor information fusion predictive control algorithm for discrete-time linear time-invariant stochastic control system with random time-delay observations is presented in this paper. The algorithm applies the fusion steady-state Kalman filter to the predictive control. It avoids the complex Diophantine equation and it can obviously reduce the computational burden. The algorithm can deal with the multi-sensor discrete-time linear time-invariant stochastic controllable system based on the linear minimum variance optimal information fusion criterion. The fusion method includes the centralized fusion, global optimality weighted measurement fusion. And the two fusion method is completely functionally equivalence. Compared with the single sensor case, the accuracy of the fused filter is greatly improved. A simulation example of the target tracking controllable system with two sensors shows its effectiveness and correctness.

Synthetic aperture sonar (SAS) is a kind of high resolution imaging sonar, but speckle exists in SAS image for that SAS is a coherent imaging system, which makes it very difficult to visually and automatically interpret. In this paper, a fast speckle reduction algorithm for SAS is proposed in GPU environment, which helps to solve the speckle reduction problem of SAS image in real-time. Firstly, the original SAS image is partitioned into small rectangular blocks with partly overlapped and uploaded into the shared memory of GPU by block of threads. Secondly, the Lee filtering process is performed on every blocked SAS image by the multi-core of GPU simultaneously. Finally, the whole processed result is obtained by merging those local filtered images. The feasibility and high efficiency of the method are confirmed by the speckle reduction experiment on the real SAS image.

Most customers of shopping malls get a discount or a rebate with coupons. In order to promote merchandise consumption, businesses issue and distribute coupons. With the coupons, they increase sales by encouraging repeat purchases, by increasing product awareness, by attracting customers with a new product, or by encouraging customers to purchase a large quantity of an over-stocked product. As electronic devices evolved, electronic coupons appeared. Mobile coupons have been widely used and are replacing traditional paper coupons. Nowadays, smartphones with powerful computing capabilities, huge memory capacities, and various sensors are carried by almost everybody. Smart coupons are distributed via short message service, multimedia messaging services, peer-to-peer communications, and so on. Customers can save money with coupons while merchants maximize profits with them. Therefore, this paper reviews existing coupon systems and discusses the design of a smart coupon system.

In urban real-time traffic monitoring, due to the difficult of delay time acquisition at intersection, complicated calculation model, lead to inaccurate traffic discriminant. Aiming at this problem, it proposed a real-time traffic discriminant method based on the simplified network model. In traffic data collection various types of intersections are abstracted as a node in this method, and the delay time at intersection are included in the pasting time of connected roads, then conduct back-stepping calculated according to different types of nodes at monitoring, obtained the traffic conditions of regular road, rotary road and intersection, thus realize real-time monitoring. Finally, to verify the validity of the method through simulation of urban real-time traffic monitoring results.

Aiming at the problem of low accuracy and high computational complexity of the traditional social network community recommendation algorithm, a rapid spanning tree detection algorithm is proposed to independently discriminate social network community with the weak connected edge, in order to improve the accuracy of community recommendation and reduce the complexity of algorithm. Firstly, according to the characteristics of social network community recommendation, the maximum spanning tree algorithm is proposed, which is based on the edge weight distribution node similarity, to realize the effective detection of social network community. Secondly, for the proposed algorithm having the problems of repeated adding and deleting of weakly connected edges and the waste of computing resources, a rapid spanning tree detection algorithm based on the independent discrimination of weakly connected edge is proposed so as to further improve the calculation efficiency of the algorithm. Lastly, the effectiveness of the proposed algorithm is verified by comparing the experimental results in the standard test database.

In an orthogonal frequency-division multiple access passive optical network (OFDMA-PON) using an interleaved polling method, delay is increased by long cycle time, where the cycle time is the time duration between the current and next upstream transmissions. The cycle time is affected by the number of upstream subcarriers for an optical network unit (ONU). Available subcarriers are limited due to the system implementation cost, but the small number of upstream subcarriers increases the cycle time in sending the same amount of data. In this paper, we determine the number of upstream subcarriers to guarantee low delay under a gated service discipline.

Seamless object tracking in wide area is an important and yet challenging issue of intelligent visual surveillance multi-camera collaboration tracking approach has been becoming attractive. In this work, a flexible and integrated system is constructed for object tracking using multiple cameras either with overlapping fields of views (FOVs), non-overlapping FOVs or mixed configurations for wide areas surveillance. The Kalman filter is applied for object tracking in a single camera and object motion prediction is implemented across blind regions for non-overlapping FOVs. Furthermore, a client and server architecture is constituted using TCP/IP network. The FOV lines are used to guide camera switching. In addition, we integrate various features for object matching. Finally, this system can track objects in a wide area no matter the FOVs are overlapping, non-overlapping or mixed situations.

Surface micro cracks are easy to produce in the preparation and service process of metal material, which impacts on the safe operation of metal components. Pulsed laser spot excitation and infrared thermal imaging technology are combined to detect metal surface micro-cracks. The working principle of laser infrared thermal imaging detection technology was described. The three dimensional heat conduction model of pulsed laser excitation flux transfer in metal plate was established, and calculated using finite element method (FEM). The results showed that, thermal flow in the image is a “D” shape. There are temperature differences between the sound regions and defective regions, and the defects experiences the process of obscure, gradually clear, and gradually obscure. Pulsed infrared thermography sequence was processed by polynomial fitting method, and the coefficient images effectively improve the contrast between defective and non-defective areas, which is beneficial to the determination and of recognition micro cracks.

A social network analysis is conducted in an instance in the Mana World, an open source massively on-line role playing game (MMORPG), during a controlled experiment in which data from 181 player characters was gathered during a period of 2 months. The presented results are part of the first phase of the Model MMORPG (Large-Scale Multi-Agent Modeling of Massively On-Line Role-Playing Games) project in which players played a specially designed quest to find patterns of organizational behaviour between players. Results give an insight into the various networks that formed during the experiment based on numerous criteria including in game friendships, parties, and various types of communication.

As a key part of track circuit, the state of insulation joints is related to safe, normal and efficient operation of railway. In order to accurately obtain different degrees of insulation joints, a prediction model based on support vector machines has been proposed to study damage to insulation joints. For unbalanced data sets in the research process, a KNN under-sampling is presented to remove redundant and noise samples. By means of BSMOTE over-sampling method to further take full advantage of the data, KNN-BSMOTE-SVM algorithm of hybrid sampling is given to achieve balanced data sets. The theoretical analysis and simulation results show that the proposed algorithm increases classification performance of SVM classifier. Compared with KNN classifier, the classification results of SVM are better, support vector machines used in insulation damaged joints prediction is feasible and effective.

This study examines the effects of scaffolded video analysis on classroom interaction and professional development among pre-service EFL teachers. Twenty participants recorded their teaching practices for 25 minutes on two separate occasions. Following the first teaching practice, participants reflected on their respective videos during individual meetings with their professor; later, they prepared and completed a second teaching practice. The participants’ first and second practices were subsequently compared to determine how the video analyses affected teachers’ adoption of classroom interaction techniques. The results revealed significant increases in interaction, particularly with respect to teacher talk, questions asked, feedback, and responses.

The forestry investment project has the typical characteristics such as the flexibility, phases, uncertainty, and so on, and needs reasonable applicable decision method and decision support system. The traditional forestry investment project evaluation mostly adopts the net present value method, and ignores option property of the forestry investment project, which is easy to cause the decision-making bias and erroneous judgment. This paper analyzes the real option characteristics of the forestry investment project from the perspective of real option, constructs the analysis model of real option based on the binomial tree theory, and applies the instance to verify. The instance proves that forestry investment project decision will be more scientific based on the real option model. Finally, on the basis of this model, the forestry investment project decision support system is preliminarily studied.

Under the conditions that the target and background are bold and the transmission loss in atmosphere between systems is neglected, the SNR of the thermal imaging system, the radiation flux of the target and the NETD expression are derived. On this basis, the NETD test system of the modern infrared thermal imager is studied. The system is constructed by a Newton type reflecting parallel light tube, and the off-axis parabolic reflector is as the main body, which provides infinity infrared observation target for infrared thermal imager being tested. The target is composed of the infrared reticle located at the focus of off-axis parabolic reflector and the high precision temperature blackbody at the back of the reticle. The image of infinity infrared target is displayed on its own screen by the infrared thermal imager. CCD shoots the screen of infrared thermal imager, and NETD value is obtained by processing the camera's video signal. The experimental results show that the accuracy of NETD synthesis can reach 0.03℃ in this paper.

In a distributed system, from the starting days onwards distribution of load among servers becomes a serious problem in the commercial Internet. The problem in this scenario is? The entire single application oriented server has to engage the entire amount of traffic and if they went down, all commercial activities come down offline result out of business. Running individually the application server couldn’t support start-up budgets. The folks involved in the Web Commercial plan to solve this problem by distributing the load evenly to all the servers running on the web host owned by different agents or organizations; thus new scenario was born named as Load Balancing. In this treatise, we investigate several shapes attenuated by load Balancer and reviewed the algorithms proposed on load balancing. Here we took both static as well as dynamic based algorithms and their performances are formulated by comparison with all other existing scheme. This paper also brings connectivity on green computing with cloud load balancers. By cloud computing we can attain multi tenancy and dynamic resource handling which automatically reduces co2 emission from servers. Without the facility of sharing single resources among thousands of peoples, green computing is not possible. So the nature of cloud load balancer and green computing was illustrated here.