Secure Full-Duplex Device-to-Device Communication

This paper considers full-duplex (FD) device-to-device (D2D) communications in a downlink MISO cellular system in the presence of multiple eavesdroppers. The D2D pair communicate sharing the same frequency band allocated to the cellular users (CUs). Since the D2D users share the same frequency as the CUs, both the base station (BS) and D2D transmissions interfere each other. In addition, due to limited processing capability, D2D users are susceptible to external attacks. Our aim is to design optimal beamforming and power control mechanism to guarantee secure communication while delivering the required quality-of-service (QoS) for the D2D link. In order to improve security, artificial noise (AN) is transmitted by the BS. We design robust beamforming for secure message as well as the AN in the worst-case sense for minimizing total transmit power with imperfect channel state information (CSI) of all links available at the BS. The problem is strictly non-convex with infinitely many constraints. By discovering the hidden convexity of the problem, we derive a rank-one optimal solution for the power minimization problem.Comment: Accepted in IEEE GLOBECOM 2017, Singapore, 4-8 Dec. 201

Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition

It is possible to associate a highly constrained subset of relative 6 DoF poses between two 3D shapes, as long as the local surface orientation, the normal vector, is available at every surface point. Local shape features can be used to find putative point correspondences between the models due to their ability to handle noisy and incomplete data. However, this correspondence set is usually contaminated by outliers in practical scenarios, which has led to many past contributions based on robust detectors such as the Hough transform or RANSAC. The key insight of our work is that a single correspondence between oriented points on the two models is constrained to cast votes in a 1 DoF rotational subgroup of the full group of poses, SE(3). Kernel density estimation allows combining the set of votes efficiently to determine a full 6 DoF candidate pose between the models. This modal pose with the highest density is stable under challenging conditions, such as noise, clutter, and occlusions, and provides the output estimate of our method. We first analyze the robustness of our method in relation to noise and show that it handles high outlier rates much better than RANSAC for the task of 6 DoF pose estimation. We then apply our method to four state of the art data sets for 3D object recognition that contain occluded and cluttered scenes. Our method achieves perfect recall on two LIDAR data sets and outperforms competing methods on two RGB-D data sets, thus setting a new standard for general 3D object recognition using point cloud data.Comment: Accepted for International Conference on Computer Vision (ICCV), 201

Kentrophoros sp. H sample 500-19 ciliate cell labels

Kentrophoros sp. H sample 500-19 - label file to mark external boundary of ciliate cell. File in Amira format

Kentrophoros sp. H sample 500-19 pseudotrophosome surface

Kentrophoros sp. H sample 500-19 - surface file for pseudotrophosome containing ectosymbionts. File in Amira format

Kentrophoros sp. H sample 500-10 aligned section stack

Aligned image stack of semithin sections in Amira format. Kentrophoros sp. H collected 6 Nov 2014 from Cavoli, Isola d'Elba, Italy, fixed in 2.5% glutaraldehyde, post-fixed 1% osmium tetroxide, embedded in Epon resin. Serial sections stained with toluidine blue and imaged in greyscale. Image stack automatically aligned with manual refinement

Kentrophoros sp. H sample 500-19 ciliate cell surface

Kentrophoros sp. H sample 500-19 - surface file for external boundary of ciliate cell. File in Amira format

Kentrophoros sp. H sample 500-19 nuclei surface

Kentrophoros sp. H sample 500-19 - surface file for cell nuclei. File in Amira format

Kentrophoros sp. H sample 500-10 pseudotrophosome surface

Kentrophoros sp. H sample 500-10 - surface file for pseudotrophosome containing ectosymbionts. File in Amira format

Kentrophoros sp. H sample 500-19 nuclei labels

Kentrophoros sp. H sample 500-19 - label file to mark cell nuclei. File in Amira format

Kentrophoros sp. H sample 500-10 nuclei labels

Kentrophoros sp. H sample 500-10 - label file to mark cell nuclei. File in Amira format