Nanorattles are metallic core-shell particles with core and shell separated by a dielectric spacer. These nanorattles have been identified as a promising class of nanoparticles, due to their extraordinary high electric-field enhancement inside the cavity. Limiting factors are reproducibility and loss of axial symmetry owing to the movable metal core; movement of the core results in fluctuation of the nanocavity dimensions and commensurate variations in enhancement factor. We present a novel synthetic approach for the robust fixation of the central gold rod within a well-defined box, which results in an axisymmetric nanorattle. We determine the structure of the resulting axisymmetric nanorattles by advanced transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS). Optical absorption and scattering cross-sections obtained from UV-vis-NIR spectroscopy quantitatively agree with finite-difference time-domain (FDTD) simulations based on the structural model derived from SAXS. The predictions of high and homogenous field enhancement are evidenced by scanning TEM electron energy loss spectroscopy (STEM-EELS) measurement on single-particle level. Thus, comprehensive understanding of structural and optical properties is achieved for this class of nanoparticles, paving the way for photonic applications where a defined and robust unit cell is crucial.

We generalize the network flow formulation for multiobject tracking to multi-camera setups. In the past, reconstruction of multi-camera data was done as a separate extension. In this work, we present a combined maximum a posteriori (MAP) formulation, which jointly models multicamera reconstruction as well as global temporal data association. A flow graph is constructed, which tracks objects in 3D world space. The multi-camera reconstruction can be efficiently incorporated as additional constraints on the flow graph without making the graph unnecessarily large. The final graph is efficiently solved using binary linear programming. On the PETS 2009 dataset we achieve results that significantly exceed the current state of the art.

Charge-compensation-induced Frenkel defects were proven as the major reason for phonon scattering of p-type half-Heusler ZrCoSb.

In this paper, we present an efficient semantic segmentation framework for indoor scenes operating on 3D point clouds. We use the results of a Random Forest Classifier to initialize the unary potentials of a densely interconnected Conditional Random Field, for which we learn the parameters for the pairwise potentials from training data. These potentials capture and model common spatial relations between class labels, which can often be observed in indoor scenes. We evaluate our approach on the popular NYU Depth datasets, for which it achieves superior results compared to the current state of the art. Exploiting parallelization and applying an efficient CRF inference method based on mean field approximation, our framework is able to process full resolution Kinect point clouds in half a second on a regular laptop, more than twice as fast as comparable methods.