Circuit Models for Power Bus Structures on Printed Circuit Boards using a Hybrid FEM-SPICE Method

Power bus structures consisting of two parallel conducting planes are widely used on high-speed printed circuit boards. In this paper, a full-wave finite-element method (FEM) method is used to analyze power bus structures, and the resulting matrix equations are converted to equivalent circuits that can be analyzed using SPICE programs. Using this method of combining FEM and SPICE, power bus structures of arbitrary shape can be modeled efficiently both in the time-domain and frequency-domain, along with the circuit components connected to the bus. Dielectric loss and losses due to the finite resistance of the power planes can also be modeled. Practical examples are presented to validate this method

RNA Nanoparticle for Treatment of Gastric Cancer

The presently-disclosed subject matter relates to RNA-based composition and method to treat gastric cancer in a subject. More particularly, the presently disclosed subject matter relates to a RNA nanostructure and composition containing a multiple branched RNA nanoparticle, a gastric cancer targeting module, and an effective amount of a therapeutic agent. Further, the presently disclosed subject matter relates to a method of using the RNA nanoparticle composition to treat gastric cancer in a subject having or at risk of having gastric cancer

Aggregation signature for small object tracking

Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://github.com/bczhangbczhang/.Comment: IEEE Transactions on Image Processing, 201