三峡建库对上荆江航道影响的略估

在简述三峡水库调度运用基本图形及其所引起的水沙条件变化基础上,通过略估认为,三峡建库后因枯季泄量增大及河床纵向冲刷下切,上荆江沙质河段的航道水深将会明显增加。     

复杂难选矿石深度还原技术应用研究现状

本文介绍了处理复杂难选矿石的新工艺——深度还原技术,并归纳总结了深度还原技术在处理鲕状赤铁矿、白云鄂博氧化矿和红土镍矿几种公认的复杂难选矿石中的应用研究现状,指出了该工艺的发展方向。通过与其他方法的对比分析,得出深度还原技术在对我国复杂难选矿石的开发利用过程中优越性较为明显,为处理复杂难选矿石开辟了一个新的途径。     

基于整体和个体分割融合的双人交互行为识别

在人类交互行为识别领域,基于RGB视频的局部特征往往不能有效区分近似动作,将深度图像（Depth）与彩色图像（RGB）在识别过程中进行融合,提出一种融合Depth信息的整体和个体分割融合的双人交互行为识别算法。该算法首先分别对RGB和Depth视频进行兴趣点提取,在RGB视频上采用3DSIFT进行特征描述,在Depth视频上利用YOLO网络对左右两人兴趣点进行划分,并使用视觉共生矩阵对局部关联信息进行描述。最后使用最近邻分类器分别对RGB特征和Depth特征进行分类识别,进一步通过决策级融合两者识别结果,提高识别准确率。结果表明,结合深度视觉共生矩阵可以大大提高双人交互行为识别准确率,对于SBU Kinect interaction数据库中的动作可以达90%的正确识别率,验证了所提算法的有效性。     

Depth video spatial and temporal correlation enhancement algorithm based on just noticeable rendering distortion model

Spatial and temporal inconsistency of depth video deteriorates encoding efficiency in three dimensional video systems. A depth video processing algorithm based on human perception is presented. Firstly, a just noticeable rendering distortion (JNRD) model is formulated by combining the analyses of the influence of depth distortion on virtual view rendering with human visual perception characteristics. Then, depth video is processed based on the JNRD model from two aspects, spatial and temporal correlation enhancement. During the process of spatial correlation enhancement, depth video is segmented into edge, foreground, and background regions, and smoothened by Gaussian and mean filters. The operations of the temporal correlation enhancement include temporal–spatial transpose (TST), temporal smoothing filter and inverse TST. Finally, encoding and virtual view rendering experiments are conducted to evaluate the proposed algorithm. Experimental results show that the proposed algorithm can greatly reduce the bit rate while it maintains the quality of virtual view.     

Additional deepwater sampling of prey fish in Lake Michigan annual bottom trawl survey reveals new insights for depth distribution dynamics

Beginning in 2013, sites at the 128-m bottom depth contour were added to the sampling design of the annual Lake Michigan bottom trawl survey for prey fish, which has been conducted by the U.S. Geological Survey Great Lakes Science Center (GLSC) each fall since 1973, to better assess fish depth distributions in a changing ecosystem. The standard sampling design included bottom depths from 9 to 110 m, although the GLSC also sporadically conducted bottom trawl tows at the 128-m bottom depth contour during 1973–1988. Enactment of this new sampling design in 2013 revealed that mean biomass density of deepwater sculpins (Myoxocephalus thompsonii) at the 128-m depth exceeded the sum of mean biomass densities at shallower depths, indicating that the bulk of the deepwater sculpin population is residing in waters deeper than 110 m. Thus, our findings supported the hypothesis that the depth distribution of the deepwater sculpin population had shifted to deeper waters beginning in 2007, thereby explaining, at least in part, the marked decline in deepwater sculpin abundance since 2006 based on the standard sampling design. In contrast, our results did not support the hypothesis that the slimy sculpin (Cottus cognatus) population had shifted to deeper waters sometime after 2000. A portion of the burbot (Lota lota) population may have also shifted in depth distribution to waters deeper than 110 m after 2007, based on our results. Our findings have served as an impetus to further expand the range of depths sampled in our bottom trawl survey.     

A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency

With the emerging development of three-dimensional (3D) related technologies, 3D visual saliency modeling is becoming particularly important and challenging. This paper presents a new depth perception and visual comfort guided saliency computational model for stereoscopic 3D images. The prominent advantage of the proposed model is that we incorporate the influence of depth perception and visual comfort on 3D visual saliency computation. The proposed saliency model is composed of three components: 2D image saliency, depth saliency and visual comfort based saliency. In the model, color saliency, texture saliency and spatial compactness are computed respectively and fused to derive 2D image saliency. Global disparity contrast is considered to compute depth saliency. Particularly, we train a visual comfort prediction function to distinguish stereoscopic image pair as high comfortable stereo viewing (HCSV) or low comfortable stereo viewing (LCSV), and devise different computational rules to generate a visual comfort based saliency map. The final 3D saliency map is obtained by using a linear combination and enhanced by a “saliency-center bias” model. Experimental results show that the proposed 3D saliency model outperforms the state-of-the-art models on predicting human eye fixations and visual comfort assessment.     

高斯光束焦深探析

对聚焦激光光束焦深的不同定义作了比较分析，并对基模高斯光束的光强分布作了数值计算，作出了等光强分布曲线。同时对高阶模情况也作了一定分析。对不同情况下激光光束焦深的合理选用进行了讨论。     

具有串行通讯能力的深度测试仪

根据油田测井技术现状，设计了一种以89C52单片机为核心的深度测试仪。单片机对安装在测试滑轮上的光电编码器输出的脉冲进行计数，将计得的脉冲数转换成深度值，运算出测井速度，并显示这两个值；同时也可将深度值通过接口传递给计算机系统。文中介绍了深度测试仪的原理给出了硬件原理图和软件框图。     

5G移动通信室内覆盖解决方案研究

4G方兴未艾,5G已然走来。5G业务的多样化,对网络的要求也越来越高,尤其是室内网络。5G具有高频、高流量、高流量、高可靠性等特征,传统的室内解决方案无法有效满足其业务需求,多样化、组合式室内解决方案等逐步成为主流。文章分析了室内5G业务需求和传统室内解决方案面临的问题,提出4种解决方案,并对其和传统室内解决方案进行组合式分析,最后给出了分场景的适用建议。