In this paper, we present an object reduction for nonlinear partial differential equations. As a concrete example of its applications in physical problems, this method is applied to the （2＋1）-dimensional Boiti-Leon...In this paper, we present an object reduction for nonlinear partial differential equations. As a concrete example of its applications in physical problems, this method is applied to the （2＋1）-dimensional Boiti-Leon-Pempinelli system, which has the extensive physics background, and an abundance of exact solutions is derived from some reduction equations. Based on the derived solutions, the localized structures under the periodic wave background are obtained.展开更多
The past two decades witnessed a broad-increase in web technology and on-line gaming.Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology.The imme...The past two decades witnessed a broad-increase in web technology and on-line gaming.Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology.The immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud servers.As internet capabilities are enhanced new ways of gaming are being used to improve the gaming experience.In cloud-based video gaming,game engines are hosted in cloud gaming data centers,and compressed gaming scenes are rendered to the players over the internet with updated controls.In such systems,the task of transferring games and video compression imposes huge computational complexity is required on cloud servers.The basic problems in cloud gaming in particular are high encoding time,latency,and low frame rates which require a new methodology for a better solution.To improve the bandwidth issue in cloud games,the compression of video sequences requires an alternative mechanism to improve gaming adaption without input delay.In this paper,the proposed improved methodology is used for automatic unnecessary scene detection,scene removing and bit rate reduction using an adaptive algorithm for object detection in a game scene.As a result,simulations showed without much impact on the players’quality experience,the selective object encoding method and object adaption technique decrease the network latency issue,reduce the game streaming bitrate at a remarkable scale on different games.The proposed algorithm was evaluated for three video game scenes.In this paper,achieved 14.6%decrease in encoding and 45.6%decrease in bit rate for the first video game scene.展开更多
Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems.Motion trajectories provide rich spatiotemporal information about...Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems.Motion trajectories provide rich spatiotemporal information about an object s activity.This paper presents a novel technique for clustering of object trajectory-based video motion clips using basis function approximations.Motion cues can be extracted using a tracking algorithm on video streams from video cameras.In the proposed system,trajectories are treated as time series and modelled using orthogonal basis function representation.Various function approximations have been compared including least squares polynomial,Chebyshev polynomials,piecewise aggregate approximation,discrete Fourier transform (DFT),and modified DFT (DFT-MOD).A novel framework,namely iterative hierarchical semi-agglomerative clustering using learning vector quantization (Iterative HSACT-LVQ),is proposed for learning of patterns in the presence of significant number of anomalies in training data.In this context,anomalies are defined as atypical behavior patterns that are not represented by suffcient samples in training data and are infrequently occurring or unusual.The proposed algorithm does not require any prior knowledge about the number of patterns hidden in unclassified dataset.Experiments using complex real-life trajectory datasets demonstrate the superiority of our proposed Iterative HSACT-LVQ-based motion learning technique compared to other recent approaches.展开更多
基金The project supported by the Natural Science Foundation of Zhejiang Province under Grant No. Y604106 and the Natural Science Foundation of Zhejiang Lishui University under Grant No. FC06001
文摘In this paper, we present an object reduction for nonlinear partial differential equations. As a concrete example of its applications in physical problems, this method is applied to the （2＋1）-dimensional Boiti-Leon-Pempinelli system, which has the extensive physics background, and an abundance of exact solutions is derived from some reduction equations. Based on the derived solutions, the localized structures under the periodic wave background are obtained.
文摘The past two decades witnessed a broad-increase in web technology and on-line gaming.Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology.The immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud servers.As internet capabilities are enhanced new ways of gaming are being used to improve the gaming experience.In cloud-based video gaming,game engines are hosted in cloud gaming data centers,and compressed gaming scenes are rendered to the players over the internet with updated controls.In such systems,the task of transferring games and video compression imposes huge computational complexity is required on cloud servers.The basic problems in cloud gaming in particular are high encoding time,latency,and low frame rates which require a new methodology for a better solution.To improve the bandwidth issue in cloud games,the compression of video sequences requires an alternative mechanism to improve gaming adaption without input delay.In this paper,the proposed improved methodology is used for automatic unnecessary scene detection,scene removing and bit rate reduction using an adaptive algorithm for object detection in a game scene.As a result,simulations showed without much impact on the players’quality experience,the selective object encoding method and object adaption technique decrease the network latency issue,reduce the game streaming bitrate at a remarkable scale on different games.The proposed algorithm was evaluated for three video game scenes.In this paper,achieved 14.6%decrease in encoding and 45.6%decrease in bit rate for the first video game scene.
文摘Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems.Motion trajectories provide rich spatiotemporal information about an object s activity.This paper presents a novel technique for clustering of object trajectory-based video motion clips using basis function approximations.Motion cues can be extracted using a tracking algorithm on video streams from video cameras.In the proposed system,trajectories are treated as time series and modelled using orthogonal basis function representation.Various function approximations have been compared including least squares polynomial,Chebyshev polynomials,piecewise aggregate approximation,discrete Fourier transform (DFT),and modified DFT (DFT-MOD).A novel framework,namely iterative hierarchical semi-agglomerative clustering using learning vector quantization (Iterative HSACT-LVQ),is proposed for learning of patterns in the presence of significant number of anomalies in training data.In this context,anomalies are defined as atypical behavior patterns that are not represented by suffcient samples in training data and are infrequently occurring or unusual.The proposed algorithm does not require any prior knowledge about the number of patterns hidden in unclassified dataset.Experiments using complex real-life trajectory datasets demonstrate the superiority of our proposed Iterative HSACT-LVQ-based motion learning technique compared to other recent approaches.
