强噪声背景下确定性谐波信号的模糊滤波

文章提出了一种在强背景噪声环境下对被淹没的谐波信号进行模糊滤波，从而恢复微弱目标信号的方法。在MATLAB环境下，先利用相干平均法，除去被测信号中的随机白噪声，对于有色噪声运用自适应神经模糊推理系统（ANFIS）的方法，对被测信号进行模糊滤波，在较低信噪比下，能较好的恢复出原始微弱信号。     

一种新型频域均衡器在水声通信中的应用

在传统频域均衡器的基础上,结合多载波频移键控调制方式的频谱结构,利用接收信号和参考信号的相关性及均衡器的滤波系数,提出了一种兼具均衡与解调功能的新型频域均衡器结构.该均衡器简单、有效地克服了水声信道中多径传播产生的码间干扰和频率选择性衰落等问题,简化了水声通信系统接收机的复杂度. 新型频域均衡器的解调原理以非相关解调算法为基础,但由于利用了均衡器的计算结果,其差错性能优于传统的非相关解调算法,计算机仿真试验表明,改进后的方法使差错性能提高了2 dB;实际水声通信实验表明,该均衡器能够有效地提高水声通信系统的可靠性,具有很好的应用前景.     

Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.     

Chosen plaintext attack on JPEG image encryption with adaptive key and run consistency

A JPEG image encryption with the adaptive key and run consistency of MCUs is proposed. The chosen-plaintext attack (CPA) is given here on this encryption scheme. First, the adaptive key can be reproduced from the encrypted image, so that the plaintext images with the same adaptive key can be constructed. Second, the existence of run consistency of MCUs (RCM) between the original image and the encrypted image facilitates rapid estimation. In addition, the single swap for the runs of MCUs with RCM is designed for more accurate estimation. Detailed cryptanalytic results suggest that this encryption scheme can only be used to realize perceptual encryption but not to provide content protection for digital images. Furthermore, applications of the CPA to break other encryption schemes with RCM are presented.     

基于历史梯度平均方差缩减的协同参数更新方法

随机梯度下降算法(SGD)随机使用一个样本估计梯度,造成较大的方差,使机器学习模型收敛减慢且训练不稳定。该文提出一种基于方差缩减的分布式SGD,命名为DisSAGD。该方法采用历史梯度平均方差缩减来更新机器学习模型中的参数,不需要完全梯度计算或额外存储,而是通过使用异步通信协议来共享跨节点的参数。为了解决全局参数分发存在的“更新滞后”问题,该文采用具有加速因子的学习速率和自适应采样策略：一方面当参数偏离最优值时,增大加速因子,加快收敛速度;另一方面,当一个工作节点比其他工作节点快时,为下一次迭代采样更多样本,使工作节点有更多时间来计算局部梯度。实验表明：DisSAGD显著减少了循环迭代的等待时间,加速了算法的收敛,其收敛速度比对照方法更快,在分布式集群中可以获得近似线性的加速。     

Electrophoresis simulations using Chebyshev pseudo-spectral method on a moving mesh

We present the implementation and demonstration of the Chebyshev pseudo-spectral method coupled with an adaptive mesh method for performing fast and highly accurate electrophoresis simulations. The Chebyshev pseudo-spectral method offers higher numerical accuracy than all other finite difference methods and is applicable for simulating all electrophoresis techniques in channels with open or closed boundaries. To improve the computational efficiency, we use a novel moving mesh scheme that clusters the grid points in the regions with poor numerical resolution. We demonstrate the application of the Chebyshev pseudo-spectral method on a moving mesh for simulating nonlinear electrophoretic processes through examples of isotachophoresis (ITP), isoelectric focusing (IEF), and electromigration-dispersion in capillary zone electrophoresis (CZE) at current densities as high as 1000 A/m. We also show the efficacy of our moving mesh method over existing methods that cluster the grid points in the regions with large concentration gradients. We have integrated the adaptive Chebyshev pseudo-spectral method in the open-source SPYCE simulator and verified its implementation with other electrophoresis simulators.     

环多肽晶体的浮动电荷极化力场模拟

利用原子键电负性均衡结合分子力场方法(ABEEM/MM)对五种环多肽晶体进行了研究. 与传统力场相比, 该方法中的静电势包含了分子内和分子间的静电极化作用, 以及分子内电荷转移影响, 同时加入了化学键等非原子中心电荷位点, 合理地体现了分子中的电荷分布. 相对其他极化力场模型, 具有计算量较小的特点. 该模型下计算得到的环多肽分子单元相对实验测得的结构的原子位置、氢键长度和二面角的均方根偏差分别为0.009 nm、0.013 nm和5.16°, 能够很好地重复实验结果. 总体上, 其结果优于或相当于其他力场模型, 适用于对实际蛋白质体系的模拟和研究.     

联合均衡和多重深度TCM译码技术研究

针对ATSC DTV系统,提出一种多重深度TCM译码作为软判决的联合均衡及译码方案,从而降低误差传播,提高了均衡器的性能.新方案仅增加了译码输出单元,复杂度较深度为1时增加并不大.当采用译码深度为1的TCM译码输出作为反馈,比硬判决反馈时的均衡器收敛门限降低了6 dB.当最大的译码深度增加到6时,均衡器收敛的门限进一步降低了3 dB,从而比硬判决时均衡器的收敛门限降低9 dB.     

大气闪烁对自适应光学校正的影响

 激光在大气湍流中长距离近水平传输时，闪烁加强，限制了常规自适应光学的校正能力。数值研究了大气闪烁对自适应光学校正的影响，拟合得到了在Fresnel数一定时，Strehl比与Rytov方差的表达式，以及Rytov方差一定时，Strehl比与Fresnel数的表达式。结果表明，在Rytov方差较小时，纯相位校正Ｓtrehl比只与Rytov方差有关；随着Rytov方差的增加，Strehl比不仅与Rytov方差有关，还与Fresnel数有关，Fresnel数越大，校正Strehl比越大；大发射和接收孔径有利于提高校正Strehl比；在一定的Rytov方差下，Stregl比随Fresnel数增大而增加，逐步趋于饱和，达到纯相位校正的极限。     

Complexity-performance trade-off of algorithms for combined lattice reduction and QR decomposition

Over the last years, numerous equalization schemes for multiple-input/multiple-output channels have been studied in the literature. New low-complexity approaches based on lattice basis reduction are of special interest, since they achieve the optimum diversity behavior. Although the per-symbol equalization complexity of these schemes is very low, the initial calculation of the required matrices may impose an enormous burden in arithmetic complexity. In this paper, we give a tutorial overview and assess algorithms, which, given the channel matrix, result in the feedforward, feedback, and unimodular matrix required in lattice-reduction-aided decision-feedback equalization or precoding. To this end, via a unified exposition of the Lenstra–Lenstra–Lovász (LLL) algorithm, the LLL with deep insertions, and the reversed Siegel approach similarities and differences of these approaches are enlightened. A modification of the LLL swapping criterion, better matched to the equalization setting, is discussed. It is shown that using lattice-reduction-aided equalization/precoding better performance can be achieved at lower complexity compared to classical equalization or precoding approaches.