贝叶斯融合在SAR图像分类中的应用

本文研究将数据融合理论用于SAR图像分类.通过贝叶斯理论进行多通道SAR图像测量级数据融合,充分利用像素的从属信息并获得单通道分类无法获取的分类结果,有效保留各通道有用信息并抑制图像中的斑点噪声;针对贝叶斯融合涉及到的先验概率的问题采用两种方式进行先验概率估计,对估计引起的马赛克现象提出了三种解决方法;并提出三种先验概率融合方法,比较得出相对最优的SAR图像多通道分类方案.     

非均匀周期采样多率系统的一种辨识方法

本文利用提升技术,推导了非均匀采样多率系统的提升状态空间模型.对于状态可测量的多率系统,利用最小二乘原理,给出了模型参数矩阵辨识方法;对于状态不可测的未知参数多率系统,利用递阶辨识原理,在考虑提升模型的因果约束下,将提升系统分解为子系统进行辨识,形成了状态空间模型递阶辨识方法.仿真例子表明,本文提出的递阶辨识方法是有效的.     

Virtual Screening of Artemisia annua Phytochemicals as Potential Inhibitors of SARS-CoV-2 Main Protease Enzyme

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronaviruses that emerged in China at Wuhan city, Hubei province during December 2019. Subsequently, SARS-CoV-2 has spread worldwide and caused millions of deaths around the globe. Several compounds and vaccines have been proposed to tackle this crisis. Novel recommended in silico approaches have been commonly used to screen for specific SARS-CoV-2 inhibitors of different types. Herein, the phytochemicals of Pakistani medicinal plants (especially Artemisia annua) were virtually screened to identify potential inhibitors of the SARS-CoV-2 main protease enzyme. The X-ray crystal structure of the main protease of SARS-CoV-2 with an N3 inhibitor was obtained from the protein data bank while A. annua phytochemicals were retrieved from different drug databases. The docking technique was carried out to assess the binding efficacy of the retrieved phytochemicals; the docking results revealed that several phytochemicals have potential to inhibit the SARS-CoV-2 main protease enzyme. Among the total docked compounds, the top-10 docked complexes were considered for further study and evaluated for their physiochemical and pharmacokinetic properties. The top-3 docked complexes with the best binding energies were as follows: the top-1 docked complex with a −7 kcal/mol binding energy score, the top-2 docked complex with a −6.9 kcal/mol binding energy score, and the top-3 docked complex with a −6.8 kcal/mol binding energy score. These complexes were subjected to a molecular dynamic simulation analysis for further validation to check the dynamic behavior of the selected top-complexes. During the whole simulation time, no major changes were observed in the docked complexes, which indicated complex stability. Additionally, the free binding energies for the selected docked complexes were also estimated via the MM-GB/PBSA approach, and the results revealed that the total delta energies of MMGBSA were −24.23 kcal/mol, −26.38 kcal/mol, and −25 kcal/mol for top-1, top-2, and top-3, respectively. MMPBSA calculated the delta total energy as −17.23 kcal/mol (top-1 complex), −24.75 kcal/mol (top-2 complex), and −24.86 kcal/mol (top-3 complex). This study explored in silico screened phytochemicals against the main protease of the SARS-CoV-2 virus; however, the findings require an experimentally based study to further validate the obtained results.     

An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function

As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e.g., tiredness detection), the existing algorithms usually consider the estimation of the three angles (i.e., roll, yaw, and pitch) as separate facets, which disregard their interplay as well as differences and thus share the same parameters for all layers; and (2) the discontinuity in angle estimation definitely reduces the accuracy. To solve these two problems, a THESL-Net (tiered head pose estimation with self-adjust loss network) model is proposed in this study. Specifically, first, an idea of stepped estimation using distinct network layers is proposed, gaining a greater freedom during angle estimation. Furthermore, the reasons for the discontinuity in angle estimation are revealed, including not only labeling the dataset with quaternions or Euler angles, but also the loss function that simply adds the classification and regression losses. Subsequently, a self-adjustment constraint on the loss function is applied, making the angle estimation more consistent. Finally, to examine the influence of different angle ranges on the proposed model, experiments are conducted on three popular public benchmark datasets, BIWI, AFLW2000, and UPNA, demonstrating that the proposed model outperforms the state-of-the-art approaches.     

A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation

In order to overcome the spectral interference of the conventional Fourier transform in the International Electrotechnical Commission framework, this paper introduces a Bregman-split-based compressive sensing (BSCS) method to estimate the Taylor–Fourier coefficients in a multi-frequency dynamic phasor model. Considering the DDC component estimation, this paper transforms the phasor problem into a compressive sensing model based on the regularity and sparsity of the dynamic harmonic signal distribution. It then derives an optimized hybrid regularization algorithm with the Bregman split method to reconstruct the dynamic phasor estimation. The accuracy of the model was verified by using the cross entropy to measure the distribution differences of values. Composite tests derived from the dynamic phasor test conditions were then used to verify the potentialities of the BSCS method. Simulation results show that the algorithm can alleviate the impact of dynamic signals on phasor estimation and significantly improve the estimation accuracy, which provides a theoretical basis for P-class phasor measurement units (PMUs).     

一种基于逐点阈值分割的图像边缘检测方法

提出一种对图像中每个点逐一取阈值进行分类的一种新的阈值分割算法.该算法利用图像中像素邻域的灰度值均值统计信息作为该点阈值设置的标准,并引入该点邻域内像素灰度值方差作为附加判断条件,使提取出来的日标点是图像的边缘点.事实上该阈值分割算法起到了边缘提取的效果.实验证明,本文算法起到了良好的边缘检测效果,并且验证了本算法对于以邻域统计信息作为阈值估计标准的合理性.     

一种加权均方误差最小化的鲁棒性干扰对齐算法

线性干扰对齐的一个常见优化目标是总传输速率最大化,但因为和速率函数的非凸特性而难以直接求解。加权均方误差最小化算法借助均方误差与和速率之间的等价关系解决了这一问题。这一方法需要获得准确的信道状态信息,在实际应用中,通道估计误差的存在会导致算法性能的下降。该文提出一种改进算法,在干扰对齐预编码矩阵与接收矩阵的优化求解过程中将通道估计误差的统计特性考虑在内。仿真结果表明,相比以往的加权均方误差最小化算法,该文算法对信道估计误差具有较高的鲁棒性,可以有效提高总的传输速率。     

低信噪比下非数据辅助的OFDM系统信道阶数和噪声方差的估计

在低信噪比情况下,该文提出一种新的针对正交频分复用(OFDM)系统信道阶数和噪声方差的非数据辅助(NDA)估计算法。算法中应用了一种新的基于联合极大几何均值(MGM)的代价函数。新的代价函数不仅利用了循环前缀(CP)冗余性,同时也利用了信道记忆性。对比只利用了CP的方法,该算法可以在低信噪比情况下更准确地估计信道阶数和噪声方差。仿真结果表明,在低信噪比情况下,该算法针对信道阶数的估计得到约10 dB的信噪比增益;同时,对噪声方差的估计,该算法显著提高了估计精度,抑制了信噪比20 dB以下估计性能恶化的现象。     

基于参数估计和感知提升的语音增强降噪算法

为了提高单通道语音增强降噪算法的整体质量,该文从噪声消除和语音感知两个角度出发对传统语音增强算法进行改进,通过引入多种处理手段来达到最佳优化效果。首先在参数估计方面,把基于弱语音出现的平滑算法加入到基于固定先验信噪比的软判决方法中来解决噪声谱过估计问题,并根据语音帧存在概率动态调整平滑因子,从而提高先验信噪比的跟踪效果。其次在语音质量感知提升方面,采用谐波恢复的方法重建语音段的高频谐波分量,并采用相位补偿和增益平滑的方法消除静默段和语音段的音乐噪声。实验结果表明,相比传统算法,该文算法通过引入参数估计改进模块和感知质量提升模块,在消噪效果和语音质量两方面均得到了较大的提高,并适用于多类噪声环境和信噪比条件。     

基于多普勒频偏估计的单帧图像低速运动目标检测方法

常用的图像域运动目标检测跟踪方法对虚警率较敏感,当虚警率较高时,目标检测跟踪的实时性受限。为了降低目标初始检测的虚警率,进而提高目标检测跟踪的实时性,该文提出一种基于多普勒频偏估计的单帧图像低速运动目标检测算法,通过发射多普勒不敏感的LFM脉冲对,忽略多普勒效应对成像结果的影响,但在图像域检测的同时,利用目标回波的多普勒频偏信息进行静目标和杂波亮点的剔除,基于单帧数据,减小运动目标检测的虚警率,实现单帧图像的运动目标检测,从而为目标跟踪奠定良好基础。该算法首先进行图像域的恒虚警检测,再利用宽带时域波束形成和复相关频率测量法,对检测亮点处的波束输出信号进行多普勒测频,仅通过单帧图像就可有效剔除静目标和杂波亮点。同时为了改善宽带时域波束形成的性能,利用2阶锥规划设计滤波器的系数,用9阶FIR滤波器实现了0.01倍采样点的小数时延,提高了多普勒频偏的估计精度。最后通过计算机仿真和水池试验验证了所提算法的有效性。