Semeadoras autopropelidas adaptadas/transformadas para plantio direto de parcelas experimentais.

bitstream/CNPT-2010/40525/1/p-co160.pd

Semeadura de milho - correção da razão de distribuição de sementes através do ajuste no espaçamento entre linhas da semeadora.

bitstream/CNPT-2010/40573/1/p-co208.pd

Engineered surfaces to control secondary electron emission for multipactor suppression

A significant problem for space-based systems is multipactor - an avalanche of electrons caused by repeated secondary electron emission (SEE). The consequences of multipactor range from altering the operation of radio frequency (RF) devices to permanent device damage. Existing efforts to suppress multipactor rely heavily on limiting power levels below a multipactor threshold [1]. This research applies surface micromachining techniques to create porous surfaces to control the secondary electron yield (SEY) of a material for multipactor suppression. Surface characteristics of interest include pore aspect ratio and density. A discussion is provided on the advantage of using electroplating (vice etching) to create porous surfaces for studying the relationships between SEY and pore aspect ratio & density (i.e. porosity). Preventing multipactor through SEY reduction will allow power level restrictions to be eased, leading to more powerful and capable space-based systems

Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization

Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning