江西省典型水库沉积物碳·氮·磷分布及污染评价

水体富营养化是水库、湖泊水污染问题最为严重的类型之一,为了解江西省水库沉积物的有机物污染水平,笔者测定了江西省15座中型水库沉积物中TP、TN、TOC含量,分析研究其分布特征和来源,并采用有机指数法进行综合评价.成果表明:江西省典型水库沉积物TP、TN总体处于轻度污染状态;在空间分布上,TN、TOC含量总体变幅不大,赣江上游及中游水库沉积物中TP、TN、TOC含量整体较低.TOC/TN比值表明,水生动物和陆地动物是沉积物有机质的主要来源.污染评价成果表明,有机指数IO平均值0.13,总体呈较清洁等级(II级);有机氮WON平均值0.09,总体呈尚清洁等级(III级).     

水工混凝土建筑物补强加固技术综述

对水工混凝土建筑物补强加固中缺陷的成因、加固材料的分类、加固处理技术的分类及应用进行了综述介绍,对加固理论和已有的规范、规程做了归类介绍,并对加固技术进行了展望.     

ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation and Registration Nets” (ASRNet) for the simultaneous estimation of the blood vessel segmentation and the registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills the blood vessel segmentation task, and (ii) A registration module R that estimates the spatial correspondence of an image pair. Based on the segmention-driven registration network, we train the segmentation network using a semi-supervised adversarial learning strategy. Our experimental results show that the proposed ASRNet can achieve state-of-the-art accuracy in segmentation and registration tasks performed with real MSI datasets.     

Image cipher using image filtering with 3D DNA-based confusion and diffusion strategy

Neural Computing and Applications - In this paper, an image cipher is presented based on DNA sequence operations, image filtering and memrisitve chaotic system. Firstly, plain image is preprocessed...     

Exploiting 2D compressed sensing and information entropy for secure color image compression and encryption

Neural Computing and Applications - Compared to 1D compressed sensing (CS), 2D CS is more efficient for compressing the plaintext image from two directions, but security level of current 2D...     

A low distortion image enhancement scheme based on multi-resolutions analysis in next generation network

With the advent of Next Generation Network (NGN), services that are currently provided by multiple specific network-centric architectures. NGN provides AAA (Anytime, Anywhere and Always on) access to users from different service providers with consistent and ubiquitous provision of services as necessary. This special issue of NGN includes pervasive, grid, and peer-to-peer computing to provide computing and communication services at anytime and anywhere. In fact, the application of NGN includes digital image processing, multimedia systems/services, and so on. Here we focus on the digital image processing technology in NGN environments. Low-contrast structure and heavy noise in NGN environments can be found in many kinds of digital images, which makes the images vague and uncertainly, especially in x-ray images. As result, some useful tiny characteristic are weakened—which are difficult to distinguish even by naked eyes. Based on the combination of no-linear grad-contrast operator and multi-resolution wavelet analysis, a kind of image enhancement processing algorithm for useful tiny characters is presented. The algorithm can enhance the tiny characters while confine amplifying noise. The analysis of the results shows that local regions of the image are enhanced by using the concept of the grad contrast to make image clearer adaptively. Experiments were conducted on real pictures, and the results show that the algorithm is flexible and convenient.     

Robust stability and stabilization of discrete singular systems with interval time-varying delay and linear fractional uncertainty

The robust stability and robust stabilization problems for discrete singular systems with interval time-varying delay and linear fractional uncertainty are discussed. A new delay-dependent criterion is established for the nominal discrete singular delay systems to be regular, causal and stable by employing the linear matrix inequality (LMI) approach. It is shown that the newly proposed criterion can provide less conservative results than some existing ones. Then, with this criterion, the problems of robust stability and robust stabilization for uncertain discrete singular delay systems are solved, and the delay-dependent LMI conditions are obtained. Finally, numerical examples are given to illustrate the e?ectiveness of the proposed approach.     

IETM技术在便携式工卡系统中的应用研究

从飞机制造的角度出发,以IETM技术为基础,以带Windows 7操作系统的便携式终端为硬件平台,研究开发应用于某机机载机电设备装配指导的交互式电子工卡系统。文中主要介绍了系统设计的背景知识、开发架构和软硬件实现方法,结合XML和SQL数据库技术创作符合GJB6600标准的应用系统。该系统的成功研制和投入使用可将电子信息技术的最新研究成果用于生产实践,既可有效地减轻生产一线工作人员的劳动强度,又能极大地提高飞机数字化制造效率。     

基于MAK仿真平台的要地防空作战仿真研究

结合要地防空作战样式特性,构建了基于MAK仿真平台的分布交互式作战仿真系统,研究了要地防空CGF框架和仿真实体的探测器、控制器和执行器组件,采用Stealth开发了地空导弹武器系统和飞机视景仿真系统,模拟了地面和空中战场环境,并最终形成了初步仿真案例。研究成果对采用MAK平台开发作战仿真系统有一定借鉴和参考价值。     

城市声环境的综合评价

本文利用模糊数学聚类分析的方法,对城市环境噪声声级进行了分类,得到了与Lehmann的“噪声品级”分类相吻合的结果。