基于机器学习的大面积拼接镜倾斜误差探测

为了实现对拼接镜整体幅面的共相位误差的快速检 测,通过Zemax建立相位测量装置,数值模拟拼接镜 的倾斜误差检测过程。使用基于主成 分分析(PCA)、偏最小二乘回归(PLSR)的机器学习算法,替代传统的相位重建方法,从 探测面强度分布图中提取倾斜误差。预测结果表明,在单元情况下对12个样本的倾斜角进行 预测,倾斜角预测值与真实值间的均方根误差(RMSE)约为0.00029;在多元情况下,倾角的 RMSE均维持在0.0003以下。可见,在两种情况下,倾角的RMSE参量值 均小于倾斜步长。因此 ,利用机器学习算法可以实现对倾角步长为0.0005°的倾斜误差的预 测,与相位差波前检测 等传统方法相比,该方法能大幅提高预测速度,明显降低传统波前重建算法复杂度。     

Fuzzy Relation Equations for Compression/Decompression Processes of Colour Images in the RGB and YUV Colour Spaces

We use particular fuzzy relation equations for compression/decompression of colour images in the RGB and YUV spaces, by comparing the results of the reconstructed images obtained in both cases. Our tests are made over well known images of 256×256 pixels (8 bits per pixel in each band) extracted from Corel Gallery. After the decomposition of each image in the three bands of the RGB and YUV colour spaces, the compression is performed using fuzzy relation equations of “min - →_t” type, where “t” is the Lukasiewicz t-norm and “→_t” is its residuum. Any image is subdivided in blocks and each block is compressed by optimizing a parameter inserted in the Gaussian membership functions of the fuzzy sets, used as coders in the fuzzy equations. The decompression process is realized via a fuzzy relation equation of max-t type. In both RGB and YUV spaces we evaluate and compare the root means square error (RMSE) and the consequentpeak signal to noise ratio (PSNR) on the decompressed images with respect to the original image under several compression rates.     

基于上下文和粒子滤波的辅助切换机制

无线通信系统切换机制是支持用户无缝移动服务的一项关键技术,切换成功与否直接影响着用户业务体验和应用。该文提出基于上下文和粒子滤波算法的辅助切换机制,利用粒子滤波算法准确预测移动台下一时刻位置,用户上下文信息对移动台切换起辅助支撑作用。文中提出一种预切换决策机制,为快速、准确和可靠切换判决提供依据,并提前预留资源,显著改善网络服务质量。理论分析和仿真结果表明,该方法位置预测更接近用户真实运动轨迹,位置和速度均方根误差较小,大大提高切换成功率。     

大规模MIMO系统中联合用户分组和联盟博弈的动态导频分配方案

大量研究表明,大规模MIMO系统中的小区边缘用户比中心用户更易遭受导频污染的影响。因此,该文提出一种联合用户分组和联盟博弈(JUG-AG)的动态导频分配方案来减轻系统导频污染。根据用户信号强度将所有用户分为A,B两组,把接收基站信号强度弱的小区边缘用户记为A组,剩余用户则为B组。A组用户使用相互正交的导频,B组用户则借助联盟博弈来重复使用剩余的正交导频。在B组用户的联盟博弈中,用户被分成若干个互不相交的用户子联盟,属于不同子联盟的用户分配不同的相互正交导频序列,而属于同一子联盟中的用户使用相同的导频序列。与已有的导频分配方案相比,该文提出的JUG-AG方案更灵活,可以用于所有用户随机分布的场景。而且,该算法通过循环搜索可以获得整体最优解。仿真结果表明JUG-AG方案能够有效降低上行链路中用户信号检测的平均均方根误差(RMSE),而且可以提高用户的平均服务速率。

     

一种改进的小波阈值函数去噪方法

针对传统小波阈值去噪算法中软阈值函数和硬阈值函数的不足,在现有文献设计的阈值函数的基础上,构造一个新的阈值函数,它不仅能克服软、硬阈值函数的缺点,同时对噪声的处理更具有灵活性。通过Matlab仿真,对软、硬阈值函数以及本文构造阈值函数的去噪效果,在信噪比(SNR)和均方根误差(RMSE)两个方面进行对比。结果表明,本文提出的阈值函数在信号去噪处理中,能获得更高的信噪比以及更小的均方根误差,具有很好的降噪效果。     

基于时空关系和关联规则挖掘的上下文信息缺失插补研究

上下文信息的缺失是上下文信息处理中不可避免的问题,缺失数据插补方法也是数据挖掘中的研究热点。但是,现有的缺失数据的插补方法不太适合上下文信息这一流数据形式,没有充分利用各传感器采集数据之间的关联性,而且在插补的过程中没有考虑传感器数据的时空关系。为了解决现存的缺失数据插补方法的缺陷和不足,该文提出了基于时空关系和关联规则挖掘的上下文信息缺失插补方法(STARM),对传感数据进行空间化和时间序列化,并生成强关联规则对缺失数据进行插补。最后,通过温度传感器采集数据验证了这一算法合理性和高效性。实验证明,该算法在上下文信息缺失估计的准确性要高于简单线性回归算法(SLR)和EM算法等,而且具有较小的时空开销,能够保证实时应用的服务质量(QoS)。     

Extending the working calibration ranges of four hexachlorocyclohexane isomers in gas chromatography-electron capture detector by radial basis function neural network

A radial basis function neural network (RBFNN) method was developed for the first time to model the nonlinear calibration curves of four hexachlorocyclohexane (HCH) isomers, aiming to extend their working calibration ranges in gas chromatography-electron capture detector (GC-ECD). Other 14 methods, including seven parametric curve fitting methods, two nonparametric curve fitting methods, and five other artificial neural network (ANN) methods, were also developed and compared. Only the RBFNN method, with logarithm-transform and normalization operation on the calibration data, was able to model the nonlinear calibration curves of the four HCH isomers adequately. The RBFNN method accurately predicted the concentrations of HCH isomers within and out of the linear ranges in certified test samples. Furthermore, no significant difference (p > 0.05) was found between the results of HCH isomers concentrations in water samples calculated with RBFNN method and ordinary least squares (OLS) method (R² > 0.9990). Conclusively, the working calibration ranges of the four HCH isomers were extended from 0.08-60 ng/ml to 0.08-1000 ng/ml without sacrificing accuracy and precision by means of RBFNN. The outstanding nonlinear modeling capability of RBFNN, along with its universal applicability to various problems as a “soft” modeling method, should make the method an appealing alternative to traditional modeling methods in the calibration analyses of various systems besides the GC-ECD.     

广义自对偶形态学滤波器及其在图像去噪中的应用

针对传统的自对偶形态学滤波器(SMF)是依赖于两个互为对偶的形态学滤波器,虽然能较好地保持图像的细节,但抑制噪声的效果较差。本文基于改进的形态学中值算子(MMO)和均值算子(MAO),提出了广义自对偶中值形态学滤波器(GSSMF)和广义自对偶均值形态学滤波器(GSAMF),分析并证明了两类滤波器均满足形态学滤波器的自对偶特性,分别适用于脉冲噪声和高斯噪声的去除。实验结果表明,GSMF在保持图像亮度不发生偏移的同时有效抑制了图像中的噪声,滤波后的图像具有较高的峰值信噪比(PSNR)和较小的均方根误差(RMSE)。