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This paper introduces a novel method for accurately estimating the 2D coordinates of hand keypoints from single static images, utilizing a sequential convolutional neural network optimized with Maximum Likelihood Estimation Loss. Unlike traditional heatmap-based techniques, our approach eliminates the need to generate label heatmaps and sidesteps the direct optimization of model parameters based on noisy labels. Instead, it concentrates on modeling the distribution of the discrepancies between predicted results and ground truth, rather than the potential presence of noisy labels, thus enabling the direct prediction of hand keypoint coordinates. Furthermore, we propose a sequential training and inference framework that consists of a deep convolutional backbone network and a multi-stage sequential network. Each stage of this network features similar structures, facilitating the progressive and precise prediction of hand keypoint coordinates. Our extensive experimental results demonstrate that our approach is both highly accurate and robust, outperforming mainstream methods under the experimental conditions detailed in this paper.

In this paper, we present a new shielding and absorbing composite based on carbon nanotubes (CNTs) dispersed inside a polymer dielectric material. The extremely high aspect ratio of CNTs and their remarkable conductive properties lead to good absorbing properties with very low concentrations. A broadband characterization technique is used to measure the microwave electrical properties of CNT composites. It is shown that a conduction level of 1 S/m is reached for only 0.35 weight % of a CNT, while, for a classical absorbing composite based on carbon black, 20% concentration is mandatory. The conductive properties are explained by a phenomenological electrical model and successfully correlated with rheological data aiming at monitoring the dispersion of conductive inclusions in polymer matrices. [ABSTRACT FROM AUTHOR]