Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation

A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented. The regression model is used to obtain reliable segmentation results to guide the label propagation for the segmentation. The proposed method has been compared with state-of-the-art multi-atlas based image segmentation methods for segmenting the hippocampus in MR images. The experiment results have demonstrated that our method obtained superior segmentation performance.Comment: Accepted paper in IEEE International Symposium on Biomedical Imaging (ISBI), 201

Dynamics of Bose-Einstein condensates in a one-dimensional optical lattice with double-well potential

We study dynamical behaviors of the weakly interacting Bose-Einstein condensate in the one-dimensional optical lattice with an overall double-well potential by solving the time-dependent Gross-Pitaevskii equation. It is observed that the double-well potential dominates the dynamics of such a system even if the lattice depth is several times larger than the height of the double-well potential. This result suggests that the condensate flows without resistance in the periodic lattice just like the case of a single particle moving in periodic potentials. Nevertheless, the effective mass of atoms is increased, which can be experimentally verified since it is connected to the Josephson oscillation frequency. Moreover, the periodic lattice enhances the nonlinearity of the double-well condensate, making the condensate more "self-trapped" in the $\pi$-mode self-trapping regime.Comment: 5 pages, 7 figure

Dissipation effect in the double-well Bose-Einstein Condensate

Dynamics of the double-well Bose-Einstein condensate subject to energy dissipation is studied by solving a reduced one-dimensional time-dependent Gross-Pitaevskii equation numerically. We first reproduce the phase space diagram of the system without dissipation systematically, and then calculate evolutionary trajectories of dissipated systems. It is clearly shown that the dissipation can drive the system to evolve gradually from the $\pi$-mode quantum macroscopic self-trapping state, a state with relatively higher energy, to the lowest energy stationary state in which particles distribute equally in the two wells. The average phase and phase distribution in each well are discussed as well. We show that the phase distribution varies slowly in each well but may exhibit abrupt changes near the barrier. This sudden change occurs at the minimum position in particle density profile. We also note that the average phase in each well varies much faster with time than the phase difference between two wells.Comment: 7 pages, 7 figures, to be published in Euro. Phys. J.

Detecting Location Fraud in Indoor Mobile Crowdsensing

Mobile crowdsensing allows a large number of mobile devices to measure phenomena of common interests and form a body of knowledge about natural and social environments. In order to get location annotations for indoor mobile crowdsensing, reference tags are usually deployed which are susceptible to tampering and compromises by attackers. In this work, we consider three types of location-related attacks including tag forgery, tag misplacement, and tag removal. Different detection algorithms are proposed to deal with these attacks. First, we introduce location-dependent fingerprints as supplementary information for better location identification. A truth discovery algorithm is then proposed to detect falsified data. Moreover, visiting patterns are utilized for the detection of tag misplacement and removal. Experiments on both crowdsensed and emulated dataset show that the proposed algorithms can detect all three types of attacks with high accuracy.Comment: 6 page