A theoretical and numerical model of photon and electron–positron pair production in strong-field quantum electrodynamics（QED） is described. Two processes are contained in our QED theoretical model, one is photon e...A theoretical and numerical model of photon and electron–positron pair production in strong-field quantum electrodynamics（QED） is described. Two processes are contained in our QED theoretical model, one is photon emission in the interaction of ultra-intense laser with relativistic electron（or positron）, and the other is pair production by a gamma-ray photon interacting with the laser field.This model has been included in a PIC/MCC simulation code named BUMBLEBEE 1 D, which is used to simulate the laser plasma interaction. Using this code, the evolutions of electron–positron pair and gamma-ray photon production in ultra-intense laser interaction with aluminum foil target are simulated and analyzed. The simulation results revealed that more positrons are moved in the opposite direction to the incident direction of the laser under the charge separation field.展开更多
Aiming at addressing the problem of interactive gesture recognition between lunar robot and astronaut, a novel gesture detection and recognition algorithm is proposed. In gesture detection stage, based on saliency det...Aiming at addressing the problem of interactive gesture recognition between lunar robot and astronaut, a novel gesture detection and recognition algorithm is proposed. In gesture detection stage, based on saliency detection via Graph-Based Manifold Ranking (GBMR) algorithm, the depth information of foreground is added to the calculation of superpixel. By increasing the weight of connectivity domains in graph theory model, the foreground boundary is highlighted and the impact of background is weakened. In gesture recognition stage, Pyramid Histogram of Oriented Gradient (PHOG) feature and Gabor amplitude also phase feature of image samples are extracted. To highlight the Gabor amplitude feature, we propose a novel feature calculation by fusing feature in different directions at the same scale. Because of the strong classification capability and not-easy-to-fit advantage of Adaboosting, this paper applies it as the classifier to realize gesture recognition. Experimental results show that the improved gesture detection algorithm can maintain the robustness to influences of complex environment. Based on multi-feature fusion, the error rate of gesture recognition remains at about 4.2%, and the recognition rate is around 95.8%.展开更多
基金supported by Fundamental Research Funds for the Central Universities(Grant Nos.ZYGX2016J065 and ZYGX2016J066)
文摘A theoretical and numerical model of photon and electron–positron pair production in strong-field quantum electrodynamics（QED） is described. Two processes are contained in our QED theoretical model, one is photon emission in the interaction of ultra-intense laser with relativistic electron（or positron）, and the other is pair production by a gamma-ray photon interacting with the laser field.This model has been included in a PIC/MCC simulation code named BUMBLEBEE 1 D, which is used to simulate the laser plasma interaction. Using this code, the evolutions of electron–positron pair and gamma-ray photon production in ultra-intense laser interaction with aluminum foil target are simulated and analyzed. The simulation results revealed that more positrons are moved in the opposite direction to the incident direction of the laser under the charge separation field.
文摘Aiming at addressing the problem of interactive gesture recognition between lunar robot and astronaut, a novel gesture detection and recognition algorithm is proposed. In gesture detection stage, based on saliency detection via Graph-Based Manifold Ranking (GBMR) algorithm, the depth information of foreground is added to the calculation of superpixel. By increasing the weight of connectivity domains in graph theory model, the foreground boundary is highlighted and the impact of background is weakened. In gesture recognition stage, Pyramid Histogram of Oriented Gradient (PHOG) feature and Gabor amplitude also phase feature of image samples are extracted. To highlight the Gabor amplitude feature, we propose a novel feature calculation by fusing feature in different directions at the same scale. Because of the strong classification capability and not-easy-to-fit advantage of Adaboosting, this paper applies it as the classifier to realize gesture recognition. Experimental results show that the improved gesture detection algorithm can maintain the robustness to influences of complex environment. Based on multi-feature fusion, the error rate of gesture recognition remains at about 4.2%, and the recognition rate is around 95.8%.
