弛豫理论对离子交换毛细管电色谱中峰压缩现象的研究

 在毛细管电色谱中,由于溶质在输运过程中所具有的电性质,常会产生一些特殊的现象。这些现象,如离子交换毛细管电色谱中产生超高柱效峰的现象,已经不能用一般的色谱理论加以解释。基于弛豫理论所建立的基本模型,在考虑溶质在两相中皆有可能发生正、反向迁移的情况下,得到了流出曲线一阶原点矩和二阶中心矩的理论表达式,并通过对溶质在两相中电扩散速率与电泳速率、电渗流速率关系的分析结果证实:溶质在固定相表面的电扩散行为可以使其保留变弱,出峰加快;而这种电扩散导致的超常柱效峰的出现具有不稳定性,只有在多方面因素综合影响匹配的情况下才可能出现。     

Highly Sensitive Amperometric Glucose Biosensor Based on Glassy Carbon Electrode with Copper/Palladium Coating

A highly sensitive amperometric glucose biosensor based on immobilizing glucose oxidase in electropolymerized poly(o‐phenylenediamine) film on glassy carbon electrode coated sequentially with copper and palladium layers has been developed. The steady‐state amperometric response to glucose was determined by means of the oxidation of hydrogen peroxide generated by the enzymatic reaction at a potential of either +0.70 or +0.40 V (vs. Ag|AgCl reference). The deposited copper/palladium layer showed great enhancement in the performance of the enzyme electrode, possibly due to its better electrocatalytic activity for hydrogen peroxide oxidation and large surface area. Effects of the relative loading of palladium, enzyme and polymer on the electrode performance were examined in detail. Sensitivity and detection limit for glucose determinations at +0.70 V were about 7.3 μA/mM and 0.1 μM, respectively. A wide linear range up to 6.0 mM glucose could be achieved. Electrode performance was superior to similar works reported in the literature. The response time was less than 2 s and its lifetime was longer than three months. The permeable polyphenylenediamine film also offered good anti‐interference ability to ascorbic acid, uric acid and acetaminophen, especially when a detection potential of +0.40 V was employed.     

准数字示样DRFM对线性调频脉压雷达的干扰和仿真

传统的干扰信号通过线性调频雷达脉冲压缩处理后,能量损失严重,不能很好的完成干扰的任务.本文简述了DRFM的原理、分类和优缺点,提出准数字示样DRFM的干扰方式,分析这种方式对线性调频脉压雷达干扰的有效性并对此进行仿真.     

图像通信业务的现状与发展方向

阐述了图像通信的业务涵盖范围及其技术发展现状，简要介绍了图像通信的发展方向，指出了图像通信产业发展面临的研究课题。     

Point Cloud Geometry Compression Based on Multi-Layer Residual Structure

Point cloud data are extensively used in various applications, such as autonomous driving and augmented reality since it can provide both detailed and realistic depictions of 3D scenes or objects. Meanwhile, 3D point clouds generally occupy a large amount of storage space that is a big burden for efficient communication. However, it is difficult to efficiently compress such sparse, disordered, non-uniform and high dimensional data. Therefore, this work proposes a novel deep-learning framework for point cloud geometric compression based on an autoencoder architecture. Specifically, a multi-layer residual module is designed on a sparse convolution-based autoencoders that progressively down-samples the input point clouds and reconstructs the point clouds in a hierarchically way. It effectively constrains the accuracy of the sampling process at the encoder side, which significantly preserves the feature information with a decrease in the data volume. Compared with the state-of-the-art geometry-based point cloud compression (G-PCC) schemes, our approach obtains more than 70–90% BD-Rate gain on an object point cloud dataset and achieves a better point cloud reconstruction quality. Additionally, compared to the state-of-the-art PCGCv2, we achieve an average gain of about 10% in BD-Rate.     

Lossless Medical Image Compression by Using Difference Transform

This paper introduces a new method of compressing digital images by using the Difference Transform applied in medical imaging. The Difference Transform algorithm performs the decorrelation process of image data, and in this way improves the encoding process, achieving a file with a smaller size than the original. The proposed method proves to be competitive and in many cases better than the standards used for medical images such as TIFF or PNG. In addition, the Difference Transform can replace other transforms like Cosine or Wavelet.     

螺旋压缩弹簧应力松弛特性试验分析及其应用

本文通过应力松弛试验、理论推导及数值模拟研究了高温下螺旋压缩弹簧的应力松弛规律,并利用加速模型对工况下弹簧应力松弛服役寿命做出预测.首先,根据螺旋压缩弹簧的结构特点搭建了弹簧应力松弛连续动态测试装置,该装置不仅避免了传统测试方法存在的缺陷,而且能够保证试验过程中位移载荷恒定,并实时监测载荷变化.本文以某飞机舱门单锁机构...     

基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩

针对基于预测的高光谱图像无损压缩算法压缩比低的问题,该文将聚类算法与高光谱图像预测压缩算法相结合,提出一种基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩算法。首先,对高光谱图像按光谱矢量进行K-均值聚类以提升同类光谱矢量间的相似度。然后,对每一聚类群分别使用传统递归最小二乘法进行预测,消除高光谱图像的空间冗余和谱间冗余。最后,对预测误差图像进行算术编码,完成高光谱图像压缩过程。对AVIRIS 2006高光谱数据进行仿真实验,所提算法对16位校正图像、16位未校正图像和12位未校正图像分别取得了4.63倍,2.82倍和4.77倍的压缩比,优于同类型已报道的各种算法。     

一种高性能低复杂度的基于串匹配的屏幕图像无损压缩算法

传统无损压缩算法对屏幕图像的压缩效果不佳。该文根据典型屏幕图像的特性,以LZ4HC(LZ4 High Compression)算法为具体实现基础,提出一种基于串匹配的高性能低复杂度(String Matching with High Performance and Low Complexity, SMHPLC) 的屏幕图像无损压缩算法。相对于传统字典编码无损压缩算法,新算法提出了以像素为搜索和匹配单位,对未匹配串长度、匹配串长度以及匹配偏移量这3个编码参数进行联合优化编码,并对参数进行映射编码。实验结果表明,SMHPLC具有高性能和低复杂度的综合优势,大幅降低编码复杂度,提高了编码效率。使用移动的文字和图形类的AVS2通用测试序列作为测试对象,对于YUV和RGB两种格式,SMHPLC算法比LZ4HC总体节省码率分别为22.4%,21.2%,同时编码复杂度降低分别为34.6%,46.8%。     

基于Memetic算法的DNA序列数据压缩方法

该文提出一种基于CPMA(Collaborative Particle swarm optimization-based Memetic Algorithm) 算法的DNA序列数据压缩方法,CPMA分别采用综合学习粒子群优化(Comprehensive Learning Particle Swarm Optimization, CLPSO)算法和动态调整的混沌搜索算子(Dynamic Adjustive Chaotic Search Operator, DACSO)进行全局搜索和局部搜索。该文采用CPMA寻找全局最优的基于扩展操作的近似重复矢量(Extended Approximate Repeat Vector, EARV)码书,并用此码书压缩DNA序列数据。实验结果表明,CPMA比其它优化算法有很大的改善,对文中采用的大部分测试函数,其解都非常接近全局最优点;对于DNA基准测序序列,与文中所列的经典DNA序列压缩算法相比,基于CPMA算法的压缩性能得到了显著提升。