Hand gesture recognition methods play an important role in human-computer interaction. Among these methods are skeleton-based recognition techniques that seem to be promising. In literature, several methods have been proposed to recognize hand gestures with skeletons. One problem with these methods is that they consider little the connectivity between the joints of a skeleton, constructing simple graphs for skeleton connectivity. Observing this, we built a new model of hand skeletons by adding three types of edges in the graph to finely describe the linkage action of joints. Then, an end-to-end deep neural network, hand gesture graph convolutional network, is presented in which the convolution is conducted only on linked skeleton joints. Since the training dataset is relatively small, this work proposes expanding the coordinate dimensionality so as to let models learn more semantic features. Furthermore, relative coordinates are employed to help hand gesture graph convolutional network learn the feature representation independent of the random starting positions of actions. The proposed method is validated on two challenging datasets, and the experimental results show that it outperforms the state-of-the-art methods. Furthermore, it is relatively lightweight in practice for hand skeleton-based gesture recognition.
The effects of sodium nitroprusside (SNP, a nitric oxide donor), salicylic acid (SA), and their combination on some physiological parameters of cotton (Gossypium hirsutum L.) seedlings grown under saline conditions were studied. 100 mM NaCl resulted in a decrease in photosynthetic attributes and chlorophyll content, and induced an increase in the level of electrolyte leakage, malondialdehyde (MDA) content, O 2 .-generation rate, H 2 O 2 and proline contents. 0.1 mM SNP decreased Na + and Cl -accumulation as well as the lipid peroxidation level and O 2 .-accumulation in leaves, which contributed to alleviate oxidative stress in cotton leaves. 0.1 mM SA alleviated the oxidative stress by increasing K + and Mg 2+ absorption and osmotic regulators accumulation, and decreasing the H 2 O 2 content. However, the protective action of NO and SA (especially 0.05 mM SNP and 0.05 mM SA) against salt-induced oxidative damage was more efficient than the separate effects of SA and SNP.We observed that the accumulation of osmotic regulators was apparently accelerated by 0.05 mM SNP and 0.05 mM SA. As well, it was observed that they had synergistic effects in antioxidant system and ion absorption. Thus, the combination of SNP and SA can be a new indicator of a protective mechanism to lower lipid peroxidation, improve ion absorption, activate the metabolic of osmotic-regulated substances and facilitate the membrane transport to detoxify reactive oxygen species (ROS).
Plant specific NAC (NAM, ATAF1/2 and CUC2) transcription factors (TFs) play important roles in response to abiotic stress. In this study, we identified and characterized a NAC protein, ThNAC7, from Tamarix hispida. ThNAC7 is a nuclear localized protein and has transcriptional activation activity. ThNAC7 expression was markedly induced by salt and osmotic stresses. Transiently transformed T. hispida seedlings overexpressing ThNAC7 (OE) or with RNA interference (RNAi) silenced ThNAC7 were generated to investigate abiotic stress tolerance via the gain- and loss- of function. Overexpressing ThNAC7 showed an increased reactive oxygen species (ROS) scavenging capabilities and proline content, which was accomplished by enhancing the activities of superoxide dismutase (SOD) and peroxidase (POD) in transiently transformed T. hispida and stably transformed Arabidopsis plants. Additionally, ThNAC7 activated these physiological changes by regulating the transcription level of P5CS, SOD and POD genes. RNA-sequencing (RNA-seq) comparison between wild-type and ThNAC7-transformed Arabidopsis showed that more than 40 known salt tolerance genes might regulated by ThNAC7, including stress tolerance-related genes and TF genes. The results indicated that ThNAC7 induces the transcription level of genes associated with stress tolerance to enhance salt and osmotic stress tolerance via an increase in osmotic potential and enhanced ROS scavenging.
A lightweight convolutional (deep) neural networks (CNNs) based modulation format identification (MFI) scheme in 2D Stokes planes for polarization domain multiplexing (PDM) fiber communication system is proposed and demonstrated. Influences of the learning rate of CNN is discussed. Experimental verifications are performed for the PDM system at a symbol rate of 28GBaud. Six modulation formats are identified with a trained CNN from images of received signals. They are PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. By taking advantage of computer vision, the results show that the proposed scheme can significantly improve the identification performance over the existing techniques.
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