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Scale recovery is one of the central problems for monocular visual odometry. Normally, road plane and camera height are specified as reference to recover the scale. The performances of these methods depend on the plane recognition and height measurement of camera. In this work, we propose a novel method to recover the scale by incorporating the depths estimated from images using deep convolutional...
Learning from demonstration (LfD) represents an effective method for generating a trajectory with prior knowledge obtained from demonstrations. The adaption and generalization abilities are crucial to the application of the LfD method. We present a novel trajectory generation method based on spatio-temporal templates. A trajectory is generated by evaluating the similarity between a spatio-temporal...
The rising energy cost has forced the Internet Service Providers to find new ways to reduce the energy consumption of networks. Through resource consolidation, network virtualization can enable energy saving. This paper studies the virtual network (VN) embedding problem with energy awareness for heterogeneous networks, which is a main challenge in network virtualization and refers to mapping multiple...
Network virtualization has been put forward as a promising way to run multiple virtual networks (VNs) simultaneously on a shared physical network. As single physical failure can bring down the services of multiple VNs, the problem of efficiently mapping VNs to a physical network while guaranteeing the survivability of VNs has become an increasingly important issue. In this paper, we propose a novel...
Temporal alignment is an important preprocessing procedure for human action recognition. The challenge of temporal alignment problem is the temporal scale difference between human actions as well as the variability of each subject. Metric learning is the central problem of temporal alignment. This paper presents a nonlinear time alignment method with deep autoencoder. The spatio-temporal features...
Learning from demonstration (LID) is an effective method trying to generate trajectory from the demonstrations for the new specifications. We present a novel LID method by incorporating the trajectory feature metric term with the optimization-based motion planning method. The advantage of our method is that the feature of demonstrated trajectory is kept on the premise of generating feasible and specified...
Learning from demonstration requires reproduction of a movement in the new situation. We present an approach based on dynamic movement primitives (DMP) and Gaussian mixture model (GMM) to learning the movement from demonstration. The original DMP model use only one demonstration to generate the dynamical system of motion primitive. Our work extend the generalization ability by capturing the characteristic...
Problem of intranet security is almost birth with network interconnection, especially when the demand for network interconnection is booming throughout the world. The traditional technology can't guarantee the access terminals to its safe and reliable. In view of this, this paper introduces the trustworthiness evaluation technology in trusted computing. The collection and management model is established...
The study of peer-to-peer network and mobile ad hoc network (MANET) are currently two hotspots in distributed computing and mobile communication researching domain. By building up a P2P overlay network on top of MANET's physical infrastructure, we effectively integrated P2P network's advantage on sustaining highly dynamic network into the design of MANET routing protocol. By deploying passive MANET...
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