Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation

As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. The proposed network model, which we named Driving Scene Perception Network (DSPNet), uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, depth estimation and image segmentation tasks from a single input image. Hence, the resulting network model uses less than 850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a 1024x512 input image, and both precision and efficiency have been improved over combination of single tasks.Comment: 9 pages, 7 figures, WACV'1

Did the widespread haze pollution over China increase during the last decade? A satellite view from space

Widespread haze layers usually cover China like low clouds, exerting marked influence on air quality and regional climate. With recent Collection 6 MODISDeep Blue aerosol data in 2000–2015, we analyzed the trends of regional haze pollution and the corresponding influence of atmospheric circulation in China. Satellite observations show that regional haze pollution is mainly concentrated in northern and central China. The annual frequency of regional haze in northern China nearly doubles between 2000 and 2006, increasing from30–50 to 80–90 days. Though there is amarked decrease in annual frequency during 2007–2009 due to both reduction of anthropogenic emissions and changes of meteorological conditions, regional pollution increases slowly but steadily after 2009, and maintains at a high level of 70–90 days except for the sudden decrease in 2015. Generally, there is a large increase in the number of regional-scale haze events during the last decade. Seasonal frequency of regional haze exhibits distinct spatial and temporal variations. The increasing winter haze events reach a peak in 2014, but decrease strongly in 2015 due partly to synoptic conditions that are favorable for dispersion. Trends of summer regional haze pollution aremore sensitive to changes of atmospheric circulation. Our results indicate that the frequency of regional haze events is associated not only with the strength of atmospheric circulation, but also with its direction and position, as well as variations in anthropogenic emissions

Did the widespread haze pollution over China increase during the last decade? A satellite view from space

Widespread haze layers usually cover China like low clouds, exerting marked influence on air quality and regional climate. With recent Collection 6 MODISDeep Blue aerosol data in 2000–2015, we analyzed the trends of regional haze pollution and the corresponding influence of atmospheric circulation in China. Satellite observations show that regional haze pollution is mainly concentrated in northern and central China. The annual frequency of regional haze in northern China nearly doubles between 2000 and 2006, increasing from30–50 to 80–90 days. Though there is amarked decrease in annual frequency during 2007–2009 due to both reduction of anthropogenic emissions and changes of meteorological conditions, regional pollution increases slowly but steadily after 2009, and maintains at a high level of 70–90 days except for the sudden decrease in 2015. Generally, there is a large increase in the number of regional-scale haze events during the last decade. Seasonal frequency of regional haze exhibits distinct spatial and temporal variations. The increasing winter haze events reach a peak in 2014, but decrease strongly in 2015 due partly to synoptic conditions that are favorable for dispersion. Trends of summer regional haze pollution aremore sensitive to changes of atmospheric circulation. Our results indicate that the frequency of regional haze events is associated not only with the strength of atmospheric circulation, but also with its direction and position, as well as variations in anthropogenic emissions

Extending the Propagation Distance of a Silver Nanowire Plasmonic Waveguide with a Dielectric Multilayer Substrate

Chemical synthesized silver nanowires have been proved to be the efficient architecture for Plasmonic waveguides, but the high propagation loss prevents their widely applications. Here, we demonstrate that the propagation distance of the plasmons along the Ag NW can be extended if the Ag NW was placed on a dielectric multilayer substrate containing a photonic band gap, but not placed on a commonly used glass substrate. The propagation distance at 630 nm wavelength can reach 16 um even that the Ag NW is as thin as 90 nm in diameter. Experimental and simulation results further show that the polarization of this propagating plasmon mode was nearly parallel to the surface of the dielectric multilayer, so it was excited by a transverse-electric polarized Bloch surface wave propagating along a polymer nanowire with diameter at only about 170 nm on the same dielectric multilayer. Numerical simulations were also carried out and consistent with the experiment results. Our work provides a platform to extend the propagation distance of plasmonic waveguide and also for the integration between photonic and plasmonic waveguides on the nanometre scale.Comment: 5 pages, 4 figure

DiffusionKit: a light one-stop solution for diffusion MRI data analysis

Background Diffusion magnetic resonance imaging (dMRI) techniques are receiving increasing attention due to their ability to characterize the arrangement map of white matter in vivo. However, the existing toolkits for dMRI analysis that have accompanied this surge possess noticeable limitations, such as large installation size, an incomplete pipeline, and a lack of cross-platform support. New method In this work, we developed a light, one-stop, cross-platform solution for dMRI data analysis, called DiffusionKit. It delivers a complete pipeline, including data format conversion, dMRI preprocessing, local reconstruction, white matter fiber tracking, fiber statistical analyses and various visualization schemes. Furthermore, DiffusionKit is a self-contained executable toolkit, without the need to install any other software. Results The DiffusionKit package is implemented in C/C++ and Qt/VTK, is freely available at http://diffusion.brainnetome.org and https://www.nitrc.org/projects/diffusionkit. The website of DiffusionKit includes test data, a complete tutorial and a series of tutorial examples. A mailing list has also been established for update notification and questions and answers. Comparison with existing methods DiffusionKit provides a full-function pipeline for dMRI data analysis, including data processing, modeling and visualization. Additionally, it provides both a graphical user interface (GUI) and command-line functions, which are helpful for batch processing. The standalone installation package has a small size and cross-platform support. Conclusions DiffusionKit provides a complete pipeline with cutting-edge methods for dMRI data analysis, including both a GUI interface and command-line functions. The rich functions for both data analysis and visualization will facilitate and benefit dMRI research