基于矩阵广义逆递推的自适应滤波算法

本文把自适应滤波算法的优化准则之一最小二乘准则:J(n)= ∑ n i=1 λ^n-i|e(i)|²写为矩阵形式,利用矩阵广义逆递推公式直接对输入信号矩阵而不是自相关矩阵进行递推更新,得到一种新的自适应滤波算法.和其它算法如LMS算法、NLMS算法、FRLS算法、TDNLMS算法、 APA算法、Leaky-LMS算法和RLS算法进行了计算机模拟仿真比较,仿真结果表明该算法有良好的收敛性能,收敛速度快于LMS算法、NLMS算法、FRLS算法、 APA算法、Leaky-LMS算法和RLS算法.     

CS-CIPHER两个变体的线性密码分析

CS-CIPHER是NESSIE公布的17个候选算法之一,它的分组长度为64-比特.本文对CS-CIPHER的两个变体进行了线性密码分析.对第一个变体的攻击成功率约为78.5%,数据复杂度为2⁵²,处理复杂度为2³².对第二个变体的攻击成功率约为78.5%,数据复杂度为2⁵²,处理复杂度为2¹¹².     

SERPENT和SAFER密码算法的能量攻击

SERPENT和SAFER是AES的两个候选算法,本文使用能量攻击方法对它们进行了深入分析,结果表明:对于256、192和128比特密钥的SERPENT算法,能量攻击平均需分别进行2¹⁵⁹、2¹¹⁹和2⁷⁹次试验.虽然所需的试验次数实际没法达到,但是此攻击方法大大地降低了SERPENT的密钥规模,并且发现对于能量攻击,SERPENT有许多弱密钥.经过深入分析和穷尽搜索可知:能量攻击可以获取SAFER的种子密钥.文中还给出了两种抵抗能量攻击的SERPENT的改进密钥方案以及设计密钥方案时需注意的问题.     

WFQ不是PGPS吗？ ——对"一种新的基于GPS的分组公平调度器"一文的评注

邬海涛等^[1]声称"证明了WFQ、WF²Q等算法并不是P-GPS(基于分组的GPS),也就不能保证P-GPS的时延及服务特性".不幸的是,该文对WFQ系统中一个关键概念"阻塞的连接的集合"的理解是错误的,因此导致该文的结论无效.     

基于器官跟踪的人脸实时跟踪方法

头肩序列图像的人脸跟踪有着广泛的应用,但目前的大多数跟踪算法难以同时满足精确与快速的要求.本文提出了一种基于器官跟踪的人脸实时跟踪算法,利用形态学运算对嘴进行跟踪,进而通过对人脸对称性的分析,实现了对头肩序列中人脸的跟踪.由于算法只涉及简单的形态学运算^[1]和局部的旋转运算,速度达到实时要求,同时人脸对称性的分析保证了跟踪结果的精确性.     

一种有效的RGPO干扰鉴别算法及性能分析

本文研究目标跟踪时的距离门后拖(RGPO)干扰鉴别问题,分析了现有χ²检验算法的不足,在χ²检验算法的基础上,提出了一种采用信号似然比预处理和移位寄存器后处理相结合的鉴别算法,称之为Chi+SLR+register算法.该算法在最佳寄存器长度下,能使误判概率最小.在相当宽的寄存器长度范围内,该算法都有很好的鉴别性能.此外,该算法在有SOJ干扰时,也能保持高的鉴别率,因而是一种更有效的RGPO干扰鉴别算法.     

在7级混洗交换网络中实现16×16的可重排性

长期以来,人们猜想(2n-1)级的均匀混洗交换网络Ω对置换2^<em>n×2^<em>n是可重排的.若干论文企图从理论上给出其充分性证明,但都没有成功,包括最近的一次证明^[24],仍然是错误的,但还没有人指出.本文的目的之一是澄清这一点.当n=3时已有学者给出了证明 .本文针对n=4时的7级Ω网络,给出了实现16×16可重排性的构造性证明.论文提出了避免内部冲突的平衡树模型,置换的连接图、回路图表示和对称图形、同解变换等概念,并基于图形压缩、图形剖分等方法,将16×16置换分为五种情况,共给出五种赋值算法.这些算法比较简洁,易于编程实现.本文提出的思想对研究高阶网络的可重排性也有一定参考价值.     

低轮FOX分组密码的碰撞-积分攻击

FOX是最近推出的系列分组密码,它的设计思想基于可证安全的研究结果,且在各种平台上的性能优良.本文利用碰撞攻击和积分攻击相结合的技术分析FOX的安全性,结果显示碰撞-积分攻击比积分攻击有效,攻击对4轮FOX64的计算复杂度是2^45.4,对5轮FOX64的计算复杂度是2^109.4,对6轮FOX64的计算复杂度是2^173.4,对7轮FOX64的计算复杂度是2^237.4,且攻击所需数据量均为2⁹;也就是说4轮FOX64/64、5轮FOX64/128、6轮FOX64/192和7轮FOX64/256对本文攻击是不免疫的.     

高速crossbar控制算法iDRR及其性能分析

DRR(Dual Round-Robin)算法^[6]是一种公平、高效、可扩展性强、硬件实现简单的crossbar控制算法.为了进一步改善算法的时延性能和公平性,文中提出了多重迭代DRR算法,即iDRR算法,它继承了DRR算法所有优点.仿真结果表明iDRR算法可达到100%吞吐量,在时延性能和公平性方面都优于DRR 算法.使用可编程逻辑器件实现了基于iDRR算法的仲裁器,工作频率达80MHz,可支持10Gbps速率的输入端口,可用于超高速、大容量的路由器中.     

一阶相关免疫函数的新构造方法与计数

本文研究了一阶相关免疫函数构造、计数问题,提出了一种新的一阶相关免疫函数的构造方法,由此得到了大量的一阶相关免疫函数;并通过这种构造方法给出了一个目前最好的一阶相关免疫函数个数下界,此下界比现有的结果至少改进了(2^2<em>n-1+2n)/(2ⁿ⁺⁸-2¹⁰).     

The fast Newton transversal filter: an efficient scheme foracoustic echo cancellation in mobile radio

The authors investigate the recently suggested fast Newton family for adaptive filtering in the context of acoustic echo cancellation, with emphasis on the mobile radio case. A distinctive advantage of the fast Newton transversal filter (FNTF) is that it can offer high performance with speech inputs at low computational cost. They discuss possible implementations and compare the FNTF with classical schemes in terms of complexity. A complete numerically stabilized version is presented, and additional features for proper real-time operation with speech are discussed. Experimental comparisons using various signals and real situations show that in all cases, the FNTF behaves similarly to the standard fast RLS transversal filter (FTF) algorithm, whereas its complexity is only slightly higher than that of the normalized LMS (NLMS). Compared with the NLMS, the experiments show that in the context investigated, the latter exhibits inferior performance with respect to convergence and tracking. Thus, they demonstrate that the FNTF is an efficient scheme for acoustic echo cancellation in mobile radio     

Simplified Newton-type adaptive estimation algorithms

A new adaptive estimation algorithm is presented. It is the result of a combination of the LMS and the fast Newton transversal filters (FNTF) class. The main characteristic of the proposed algorithm is its improved convergence rate as compared to LMS, for cases where it is known that LMS behaves poorly. This improved characteristic is achieved in expense of a slight increase in the computational complexity while the overall algorithmic structure is very simple (LMS type). The proposed algorithm seems also to compare relatively well against RLS and FNTF     

A numerically stable fast Newton-type adaptive filter based on order recursive least squares algorithm

This paper presents a numerically stable fast Newton-type adaptive filter algorithm. Two problems are dealt with in the paper. First, we derive the proposed algorithm from an order-recursive least squares algorithm. The result of the proposed algorithm is equivalent to that of the fast Newton transversal filter (FNTF) algorithm. However, the derivation process is different. Instead of extending a covariance matrix of the input based on the min-max and the max-min criteria, the derivation shown in this paper is to solve an optimum extension problem of the gain vector based on the information of the Mth-order forward or backward predictor. The derivation provides an intuitive explanation of the FNTF algorithm, which may be easier to understand. Second, we present stability analysis of the proposed algorithm using a linear time-variant state-space method. We show that the proposed algorithm has a well-analyzable stability structure, which is indicated by a transition matrix. The eigenvalues of the ensemble average of the transition matrix are proved all to be asymptotically less than unity. This results in a much-improved numerical performance of the proposed algorithm compared with the combination of the stabilized fast recursive least squares (SFRLS) and the FNTF algorithms. Computer simulations implemented by using a finite-precision arithmetic have confirmed the validity of our analysis.     

一种基于多尺度小波变换的自适应滤波新算法

将多尺度小波变换的理论引入到LMS自适应滤波器的设计中,分析了基于多尺度正交小波变换的自适应滤波算法的原理;将变步长LMS算法与多尺度小波变换的思想结合,提出了一种新的小波自适应滤波算法(MSWT-MVSS-LMS),新算法既减少了输入向量自相关矩阵条件数,又克服了固定步长LMS算法在收敛速度与收敛精度方面与步长因子μ的矛盾,获得了更好的收敛速度和稳定性.仿真结果表明新算法是有效的和优越的.     

Modified fast exact NLMS adaptive algorithm

A novel method for efficiently implementing the fast exact NLMS (FENLMS) algorithm is proposed. The method is based on partitioning the algorithm into an `updating' part and a `fixed' filtering part, leading to a uniform distribution and a significant reduction in the number of arithmetic operations within the block. Its application is illustrated on the basis of some simulation results dealing with the identification of an acoustic room impulse response     

Set-membership binormalized data-reusing LMS algorithms

This paper presents and analyzes novel data selective normalized adaptive filtering algorithms with two data reuses. The algorithms [the set-membership binormalized LMS (SM-BN-DRLMS) algorithms] are derived using the concept of set-membership filtering (SMF). These algorithms can be regarded as generalizations of the previously proposed set-membership NLMS (SM-NLMS) algorithm. They include two constraint sets in order to construct a space of feasible solutions for the coefficient updates. The algorithms include data-dependent step sizes that provide fast convergence and low-excess mean-squared error (MSE). Convergence analyzes in the mean squared sense are presented, and closed-form expressions are given for both white and colored input signals. Simulation results show good performance of the algorithms in terms of convergence speed, final misadjustment, and reduced computational complexity.     

DSSS通信中基于快速更新子带自适应滤波的窄带干扰抑制

本文面向直接序列扩频(DSSS)通信中的窄带干扰抑制,将分块更新子带自适应滤波的高频谱分隔特性和直接变换自适应滤波的逐点更新特性结合起来,提出了一种快速更新子带自适应(FRSAF)算法,给出了算法的迭代因子收敛界和快速实现结构。理论分析表明：该算法收敛迅速、迭代稳健,其性能明显优于经典子带自适应滤波算法和DCT/DFT-LMS算法,应用于DSSS通信可以得到优良的干扰抑制效果。仿真结果验证了上述结论。     

一种基于LMS滤波的OFDM系统信道估计方法

提出了一种适用于OFDM系统的最小均方(LMS)滤波的信道估计算法,对发送序列中导频位置的信道响应进行LMS滤波,进一步得出所有子载波上的信道响应。仿真结果表明,该方法同基于离散傅里叶变换(DFT)的信道估计算法相比,改善了估计的均方误差(MSE)和误码率(BER)性能。     

一种基于LMS自适应滤波的互相关时延估计优化算法

在多站时差定位系统中使用基于LMS自适应滤波的互相关法进行时延估计时,若采用固定步长因子则会在收敛速度和稳态失调之间存在较大矛盾,从而影响时延估计精度。针对这一问题,文中提出了一种基于分段变步长LMS自适应滤波和希尔伯特差值的互相关时延估计优化算法。该方法首先采用分段变步长LMS自适应滤波对信号进行滤波处理,然后将滤波后的信号作互相关运算,最后通过希尔伯特差值法锐化相关函数的峰值,进一步提高时延估计精度。在相同条件下,文中模拟分析了不同算法的时延估计精度。实验结果表明,新的优化算法时延估计精度更高。在不同信噪比下,新方法相较传统时延估计方法精度提高了2.2%以上,具有良好的抗噪声性能。     

基于小波变换的变步长LMS自适应滤波算法

变步长LMS自适应滤波算法通过构造合适的步长因子有效的解决了传统LMS算法收敛速度和稳态误差相矛盾的问题.变换域LMS自适应滤波算法通过正交变换降低了输入信号矩阵的相关性,提高了算法的收敛速度.将这两种算法相结合,提出了一种新的基于小波变换的变步长LMS自适应滤波算法.仿真结果表明,该算法无论是收敛速度还是稳态误差都有了很大的提高.