To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle fi...To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle filter is presented in this paper. The algorithm realizes dynamic combination of multiple model particle filter and joint probabilistic data association algorithm. The rapid expan- sion of computational complexity, caused by the simple combination of the interacting multiple model algorithm and particle filter is solved by introducing model information into the sampling process of particle state, and the effective validation and utilization of echo is accomplished by the joint proba- bilistic data association algorithm. The concrete steps of the algorithm are given, and the theory analysis and simulation results show the validity of the method.展开更多
With the development of Internet and information technology, the digital crimes are also on the rise. Computer forensics is an emerging research area that applies computer investigation and analysis techniques to help...With the development of Internet and information technology, the digital crimes are also on the rise. Computer forensics is an emerging research area that applies computer investigation and analysis techniques to help detection of these crimes and gathering of digital evidence suitable for presentation in courts. This paper provides foundational concept of computer forensics, outlines various principles of computer forensics, discusses the model of computer forensics and presents a proposed model.展开更多
In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Int...In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.展开更多
The rapid development of Internet of Things imposes new requirements on the data mining system, due to the weak capability of traditional distributed networking data mining. To meet the needs of the Internet of Things...The rapid development of Internet of Things imposes new requirements on the data mining system, due to the weak capability of traditional distributed networking data mining. To meet the needs of the Internet of Things, this paper proposes a novel distributed data-mining model to realize the seamless access between cloud computing and distributed data mining. The model is based on the cloud computing architecture, which belongs to the type of incredible nodes.展开更多
基金Supported by the National Natural Science Foundation of China （60634030）, the National Natural Science Foundation of China （60702066, 6097219） and the Natural Science Foundation of Henan Province （092300410158）.
文摘To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle filter is presented in this paper. The algorithm realizes dynamic combination of multiple model particle filter and joint probabilistic data association algorithm. The rapid expan- sion of computational complexity, caused by the simple combination of the interacting multiple model algorithm and particle filter is solved by introducing model information into the sampling process of particle state, and the effective validation and utilization of echo is accomplished by the joint proba- bilistic data association algorithm. The concrete steps of the algorithm are given, and the theory analysis and simulation results show the validity of the method.
文摘With the development of Internet and information technology, the digital crimes are also on the rise. Computer forensics is an emerging research area that applies computer investigation and analysis techniques to help detection of these crimes and gathering of digital evidence suitable for presentation in courts. This paper provides foundational concept of computer forensics, outlines various principles of computer forensics, discusses the model of computer forensics and presents a proposed model.
基金Supported by the National Natural Science Foundation of China (No. 60772154)the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00)
文摘In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.
文摘The rapid development of Internet of Things imposes new requirements on the data mining system, due to the weak capability of traditional distributed networking data mining. To meet the needs of the Internet of Things, this paper proposes a novel distributed data-mining model to realize the seamless access between cloud computing and distributed data mining. The model is based on the cloud computing architecture, which belongs to the type of incredible nodes.
