A stacked patch microstrip antenna combined with a stacked ring microstrip antenna is proposed for quadruple band operation. The two antennas (a stacked patch microstrip antenna and a stacked ring microstrip antenna) are excited by an L‐probe feed. The impedance bandwidth is enhanced by using double resonance at all frequency bands. With simulations, the relationships between the antenna's geometrical parameters and operational frequencies are clarified. The operational principle for quadruple band is explained by the simulated electric currents. The antenna is designed for Wi‐Fi (2.45/5.25/5.6 GHz bands) and mobile WiMAX (3.5 GHz band).
Abstract-A dual-band circularly-polarized microstrip antenna is proposed, and the antenna for GPS is designed. The radiation characteristics of the dual-band circular polarization are achieved by installing one pair of L-shaped slits at all edges of the square patch. The impedance matching in the dual band is tuned by an L-probe feed. The proposed antenna is effective and useful in the design for dual-band circularly-polarized operation.
We propose a novel method for joint estimation of shape and pose of rigid objects from their sequentially observed RGB-D images. In sharp contrast to past approaches that rely on complex non-linear optimization, we propose to formulate it as a neural optimization that learns to efficiently estimate the shape and pose. We introduce Deep Directional Distance Function (DeepDDF), a neural network that directly outputs the depth image of an object given the camera viewpoint and viewing direction, for efficient error computation in 2D image space. We formulate the joint estimation itself as a Transformer which we refer to as Trans-Poser. We fully leverage the tokenization and multi-head attention to sequentially process the growing set of observations and to efficiently update the shape and pose with a learned momentum, respectively. Experimental results on synthetic and real data show that DeepDDF achieves high accuracy as a category-level object shape representation and TransPoser achieves state-of-the-art accuracy efficiently for joint shape and pose estimation.
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