SiGe process integrated on-chip dipole antenna on finite size ground plane

This letter investigates the effect of a finite-size ground plane on the radiation pattern and reflection coefficient of a SiGe process integrated on-chip antenna. A flat 77-GHz on-chip strip dipole antenna integrated with a lumped balun circuit is designed and implemented. For increased directivity, the etched silicon substrate is placed on a metal ground plate. The on-chip antenna with the balun circuit is connected to GSG pads for measurement purposes. The antenna is well matched at the original resonance frequency band with 7-12 GHz impedance bandwidth and 4 dBi measured gain at 85 GHz

A77 GHz on-chip dipole antenna with etched silicon substrate

In this paper, a 77 GHz microstrip dipole antenna is integrated on a layered 11.4 m SiO2 and a silicon substrate with thickness of 670 m. The unbalanced microstrip line is balanced by using a lumped LC circuit balun to feed both of the dipole arms. To decrease the substrate loss and hence increase the antenna gain, Localized Backside Etch (LBE) module offered by IHP is utilized to etch the area under the dipole antenna. For mechanical robustness, two walls of silicon substrate are left at the end of the dipole arms inside the etched area. The simulation results show a 3.2 dBi gain and 15 GHz bandwidth at 77 GHz

Implicit Active Model using Radial Basis Function Interpolated Level Sets

Building on recent work by others who introduced RBFs into level sets for structural topology optimisation, we introduce the concept into active models and present a new level set formulation able to handle more complex topological changes, in particular perturbation away from the evolving front. This allows the initial contour or surface to be placed arbitrarily in the image. The proposed level set updating scheme is efficient and does not suffer from self-flattening while evolving, hence it avoids large numerical errors. Unlike conventional level set based active models, periodic re-initialisation is also no longer necessary and the computational grid can be much coarser, thus, it has great potential in modelling in high dimensional space. We show results on synthetic and real data for active modelling in 2D and 3D.

CaloriNet: From silhouettes to calorie estimation in private environments

We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.Comment: 11 pages, 7 figure

Restructured eigenfilter matching for novelty detection in random textures

A new eigenfilter-based novelty detection approach to find abnormalities in random textures is presented. The proposed algorithm reconstructs a given texture twice using a subset of its own eigenfilter bank and a subset of a reference (template) eigenfilter bank, and measures the reconstruction error as the level of novelty. We then present an improved reconstruction generated by structurally matched eigenfilters through rotation, negation, and mirroring. We apply the method to the detection of defects in textured ceramic tiles. The method is over 90 % accurate, and is fast and amenable to implementation on a production line.