Trajectory prediction of objects in moving objects databases (MODs) has garnered wide support in a variety of applications and is gradually becoming an active research area. The existing trajectory prediction algorithms focus on discovering frequent moving patterns or simulating the mobility of objects via mathematical models. While these models are useful in certain applications, they fall short in describing the position and behavior of moving objects in a network-constraint environment. Aiming to solve this problem, a hidden Markov model (HMM)-based trajectory prediction algorithm is proposed, called Hidden Markov model-based Trajectory Prediction (HMTP). By analyzing the disadvantages of HMTP, a self-adaptive parameter selection algorithm called HMTP $\ast$ is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed. In addition, a density-based trajectory partition algorithm is introduced, which helps improve the efficiency of prediction. In order to evaluate the effectiveness and efficiency of the proposed algorithms, extensive experiments were conducted, and the experimental results demonstrate that the effect of critical parameters on the prediction accuracy in the proposed paradigm, with regard to HMTP $\ast$ , can greatly improve the accuracy when compared with HMTP, when subjected to randomly changing speeds. Moreover, it has higher positioning precision than HMTP due to its capability of self-adjustment.

A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models

For intelligent transportation systems, digital military battlefield and driver assistance systems, it is of great practical value to predict the trajectories of moving objects with uncertainty in a real-time, accurate and reliable fashion. Intelligent trajectory prediction can not only provide accurate location-based services, but also monitor and estimate traffic to suggest the best path, and as such becomes an active research direction. Aiming to overcome the drawbacks of the existing methods, a new trajectory prediction model based on Gaussian mixture models called GMTP is proposed. The new model contains the following essential phases: (1) modeling the complex motion patterns based on Gaussian mixture models, (2) calculating the probability distribution of different types of motion patterns by using Gaussian mixture model in order to partition trajectory data into distinct components, and (3) inferring the most possible trajectories of moving objects via Gaussian process regression. The GMTP algorithm is naturally a Gaussian nonlinear statistical probability model and the advantage of the proposed model is that the result is not only a predicted value, but also a whole distribution beyond the future trajectories, therefore making it possible to infer the location in regard to some motion patterns, e.g., uniformly accelerated motion, by using statistical probability distribution. Extensive experiments are conducted on real trajectory data sets and the results show that the prediction accuracy of the GMTP algorithm is improved by 22.2% and 23.8%, and the runtime can be reduced by 92.7% and 95.9% on average, respectively, when compared to the Gaussian process regression model and Kalman filter prediction algorithm with similar parameter setting.

/pdf/trajectory-prediction-algorithm-based-on-gaussian-mixture-3xmbi0ci0f.pdf

Trajectory Prediction Algorithm Based on Gaussian Mixture Model

Development of multi-task embedded software involves very complex modeling process. Based on the data flow diagram (DFD) method, a structure modeling method for multi-task embedded system design is present in this paper. This structure modeling approach mainly depends on the task-based DFD method. As a concrete instance, the development of a project on IMS detector design is discussed using above method. It is true that DFD-based structure model is an effective solution for some real time embedded system design

A structure modeling method for multi-task embedded software design

In order to improve the effectiveness of supervised self-organizing map (SSOM) neural network, a kind of genetic algorithm is designed to optimize it. To improve its classification rate, a real number encoding genetic algorithm is provided and used to optimize the learning rate and neighbor radius of SSOM. To speed up the modeling speed, a binary encoding genetic algorithm is provided to optimize input variables of SSOM and reduce its dimension of input sample. Finally, intrusion detection data set KDD Cup 1999 is used to carry out experiment based on the proposed model. The results show that the optimized model has shorter modeling time and higher intrusion detection rate.

Intrusion Detection Based on Improved SOM with Optimized GA

The algorithm for 3D reconstruction of tree-crown is presented with the UAV aerial images from a mountainous area in China. Considering the fact that the aerial images consist of little tree-crown texture and contour information, a feature area extraction method is proposed based on watershed segmentation, and the local area correlation coefficient is calculated to match the feature areas, in order to fully extract the characteristics that can reflect the structure of tree-crown. Then, the depth of feature points is calculated using the stereo vision theory. Finally, the L-system theory is applied to construct the 3D model of tree. The experiments are conducted with the tree-crown images from UAV aerial images manually. The experiment result showed that the method proposed in this paper can fully extract and match the feature points of tree-crown that can reconstruct the 3D model of the tree-crown correctly.

/pdf/3d-reconstruction-of-tree-crown-based-on-the-uav-aerial-55d8kkdlx0.pdf

3D Reconstruction of Tree-Crown Based on the UAV Aerial Images

This paper proposes a behavior trajectory restoration algorithm for observation sequence state missing problem,which leeds to terminal trajectory restoration inaccurately.The algorithm utilizes base station layout's spatial correlation and revises the partial probability of the solution process of the Hidden Markov Models(HMM) to restore the track sequence without considering the missing observation states.Performance analysis and simulation results show that the greater the degree of state propensity is,the higher the success rate of trajectory restoration is.When the degree of state propensity is 0.8,the success rate of trajectory restoration is about 90 percent.

Behavior Trajectory Restoration Algorithm Based on Hidden Markov Models

In this paper, We discuss what is channelization code and its important function,introduce the allocation rules and the strategies of channelization codes and put forward an applied allocation arithmetic of channelization codes.

Allocation Rules and Arithmetic of Channelization Codes in WCDMA-FDD Mode GUO Shuming

The multitask system in the real-time operating System is introduced in this paper The task decomposition and its general principles of the application software are proposed in details Finally, several kinds of incorrect decomposition and their solutions are discussed

Task Decomposition of Application Design in Real-time Operating System

Since routing is the cornerstone of a network, constructing new routing mechanisms is indispensable to novel network architectures. The present nature of current routing mechanisms in the network layer is based on the so-called single-next-hop. Therefore, a radical solution to network transmission congestion problem is required to change from the single-next-hop mode to something else to allow simultaneous traffic transmission on multiple paths. The Border Gateway Protocol (BGP) is now the unique inter-domain routing protocol used in the Internet. A new scalable hierarchical inter-domain routing architecture (s-idra) is proposed here to deal with the scalability problem posed to the current inter-domain routing. Other than scalability, the routing system is facing other challenges such as security, Quality of Service (QoS), multicast, mobility and adaptability to dynamic network topologies. Researches on routing protocols, especially on those employed in future information network architectures, will shoulder heavy responsibilities.

Guo Yunfei

Papers

Behavior Trajectory Restoration Algorithm Based on Hidden Markov Models

Allocation Rules and Arithmetic of Channelization Codes in WCDMA-FDD Mode GUO Shuming

Task Decomposition of Application Design in Real-time Operating System

Considerations on Novel Network Routing Mechanism