数字图像三点中值去隔行技术研究

对传统三点中值去隔行算法进行改进,采用同步更新迭代的方法,提高了图像的画面质量.实验证明,该算法不仅降低了时间复杂度,保护图像边缘,而且明显改善了噪声滤波效果.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

基于RGBD数据与深度权重的压缩光场显示

使用多层空间光调制器的压缩光场(compressive light field,CLF) 显示技术具有空间带宽利用率高、图像分辨率好等优势,是一种很有前景的光场显示技术。传统的方法将光场分解视为超定问题并使用优化算法求解。随着重建光场的分辨率、深度、视角等性能参数的提升,优化算法的计算效率低、内存消耗大的缺陷被放大,难以实现快速计算。为此,本文提出了一种新型CLF分解算法,该算法将原始光场分解为物点进行存储,仅占用少量内存;利用深度权重和加权平均代替了优化算法,大幅提高了运算效率。在同等条件下,所提出的算法占用内存仅是传统方法的38.8%,计算时间缩短了93.7%,图像质量提高约1 dB。最后通过仿真和实验两种方式对比了该方法和传统方法的显示效果,验证了算法的有效性。     

改进的MEA-WNN图像复原方法

模糊图像复原是计算机视觉和图像处理领域的重要任务。针对思维进化算法(mind evolutionary algorithm,MEA)和小波神经网络(wavelet neural network,WNN)相结合的图像复原模型中,MEA的得分函数相对差别小、选优功能较弱等问题,提出了一种改进的MEA-WNN图像复原方法。该方法采用逻辑回归函数进行幂律变换,增加得分之间的差别,从而增强MEA的选优功能。将改进的模型与传统的基于WNN和MEA-WNN的图像复原模型进行对比,改进的模型把复原图像峰值信噪比(peak signal-to-noise ratio,PSNR)分别提高15%和6.5%、结构相似性(structural similarity,SSIM)提高了6.1%和5%,实验结果证明改进模型的有效性和优越性。     

基于CVMD和DBO-SVM的光纤周界安防信号识别方法

为降低光纤周界安防信号中噪声对分类结果的影响,提升信号分类的准确率和运行效率,提出一种融合了相关变分模态分解(Correlation Variational Mode Decomposition, CVMD)、蜣螂算法(Dung Beetle Optimizer, DBO)和支持向量机(Support Vector Machine, SVM)的分类方法。利用CVMD去除原始信号中的噪声分量,并提取去噪后信号的能量、能量熵和峭度作为特征向量。采用DBO算法优化SVM,得到最佳惩罚因子和核函数参数,并构建DBO-SVM分类模型。搭建了基于相位敏感光时域反射(Φ-OTDR)技术的周界安防系统,采集了攀爬、敲击、踩踏和无入侵四类信号。实验结果表明,CVMD-DBO-SVM的分类准确率相比CVMD-PSO-SVM和CVMD-GA-SVM更高,达到了98.75%,同时运行时间更短,综合性能最优。     

五阶非线性作用下光纤中类明孤子的演化特性

根据含五阶非线性微扰修正项的非线性薛定谔方程,采用变分法,导出了光纤中类明孤子各参数随传输距离演化的方程组,研究了脉宽与距离、啁啾与脉宽之间的关系,并对描述相位和频率的方程组用龙格-库塔算法进行了数值求解.结果表明:五阶非线性使类明孤子的脉宽压缩,可在一定程度上抵消初始啁啾和三阶色散对脉宽的展宽作用;五阶非线性对类明孤子的相位和频率演化特性的影响不明显,因而适度的五阶非线性(γ在0.0001到0.1之间)有利于类明孤子的稳定传输.     

一种电大尺寸组合体散射的快速计算方法

采用自适应多层快速多极子算法分析电大尺寸组合体的雷达散射截面。推导了组合体表面的积分方程,通过将基函数和权函数分别用不同空间位置上的点源函数展开,自适应多层快速多极子算法实现了阻抗积分的快速计算,通过采用射线传播法,远场近似和对称性计算法则使转移因子的计算效率大大提高,所有转移过程可由快速傅里叶变换计算完成。这种算法计算组合体散射时所需的计算时间和内存显著降低,且数值计算结果和实际测试结果吻合良好。     

空间谱估计无线电测向系统

空间谱估计测向方法以其超分辨力、高灵敏度和高准确度的测向性能被广泛应用,并在无线电管理领域扮演主要角色.在对空间谱估计测向理论研究的基础上,给出了具体算法,并对算法进行了仿真,最后通过对比测试论证了空间谱估计测向系统的实用性和优越性.     

光反馈自混合干涉对称折叠算法的精度分析

为了提高对称折叠算法的精度,通过对仿真信号的测量,分析了影响算法精度的几种因素(光反馈水平因子,外腔的非线性运动,抽样速率和转折点的定位),并且给出了改进方法.仿真结果表明,在测量线宽展宽因子之前,需保证光反馈水平因子的准确测量,利用中间干涉条纹进行测量可以减少外腔非线性运动所带来的误差.这一结论对利用对称折叠算法处理实验信号有一定的帮助.     

基于矢量量化的数字视频压缩技术研究

数字视频作为一种重要的信息载体,其应用十分广泛。但是到目前为止,这种大数据量信号的存储和传输仍然受到硬件技术和网络技术发展现状的制约,解决这一问题最现实的方法是设计数字视频压缩算法。矢量量化作为一种十分有效的有损压缩编码技术,具有解码简单、压缩比大等特点,特别适用于电视广播等领域中一次编码多次解码的情况。对各种基于矢量量化器的视频压缩算法进行综述,分析其优缺点,并提出其未来发展研究方向。