No abstract

To reduce the computation required in determining the proper scale of salient object, a fast visual saliency based on multi-scale difference of Gaussians fusion in frequency domain (MDF) is proposed. First, based on the phenomenon that the foreground energy is highlighted and densely distributes on certain band of spectrum, the scale coefficients of foreground in an image can be literately approximated on the amplitude spectrum. Next, relying on the linear integration property of Fourier transform, the feature spectrum is obtained through the weighted infinite integral of difference of Gaussian feature maps with respect to the scale of object. Then, the saliency of each channel is obtained from feature spectrum by the inverse Fourier transform and scale filtering. Finally, through the channel integration, the MDF saliency map is obtained. Experiments on Li-Jian data set demonstrate that combined with most appropriate colour space and scale filter, MDF achieves obvious acceleration (5.4 times faster than frequency domain analysis and spatial information) while getting desired accuracy (area under the curve, 0.8814 at Li-Jian data set), which achieves the best accuracy efficiency trade-off.

Adaptive guaranteed-performance formation analysis and design problems for second-order multi-agent systems are studied, where the global information is not required, which means the main results of this article are fully distributed. First, an adaptive guaranteed-performance formation control protocol is presented for second-order multi-agent systems, where the control input is constructed using neighboring state errors and adaptively adjustable interaction weights. Then, an adaptive guaranteed-performance formation control is proposed based on Riccati inequalities. Furthermore, the guaranteed-performance cost is determined and the adjusting approach of the formation control gain is presented in terms of linear matrix inequalities. Finally, a numerical simulation is provided to demonstrate the effectiveness of the theoretical results.

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

To investigate the value of perioperative cytokine levels in predicting the risk for in-stent restenosis in patients with acute myocardial infarction. 452 patients with acute myocardial infarction admitted to our hospital between June 2018 and June 2020 were prospectively selected as subjects. All patients underwent percutaneous coronary intervention. The baseline data of the patients were collected. Venous blood was taken before, 24 hours, and 3 days after the operation to detect the levels of related cytokines. Follow-up was performed for 1 year. The patients were assigned to restenosis and nonrestenosis groups according to the presence and absence of restenosis. Multivariate logistic analysis was used to explore the influencing factors of the risk for in-stent restenosis in patients with acute myocardial infarction. By July 1, 2021, 449 cases had been followed up. Of them, 44 cases suffered from in-stent restenosis and 405 cases did not affect in-stent restenosis. The incidence of in-stent restenosis was 9.80%. Before, 24 hours, and 3 days after the operation, the lipoprotein-associated phospholipase A2 (Lp-PLA2) level was significantly higher in the restenosis group than that in the nonrestenosis group. At 3 days after the operation, the interleukin 6 (IL-6) level was significantly higher in the restenosis group than that in the nonrestenosis group ( P  < 0.05). Multivariate logistic analysis displayed that Lp-PLA2 level preoperatively (OR = 1.048, 95% CI 1.029–1.068), Lp-PLA2 level 24 hours postoperatively (OR = 1.013, 95% CI 1.007–1.019), Lp-PLA2 level 3 days postoperatively (OR = 1.032, 95% CI 1.015–1.048), and IL-6 level 3 days postoperatively (OR = 1.020, 95% CI 1.000–1.040) were risk factors for in-stent restenosis (all P  < 0.05). IL-6 and Lp-PLA2 levels can predict the risk for in-stent restenosis in patients with acute myocardial infarction in the perioperative period.