基于算术傅里叶变换的离散Hartley变换的快速算法

离散Hartley变换是一种有用的实值正交变换。文中对其快速算法进行研究,首先介绍利用算术傅里叶变换（AFT）计算离散傅里叶变换（DFT）可使其乘法计算量仅为O（N）,然后文章根据这一特点,分析离散Hartley变换（DHT）的结构特征,通过DFT将AFT和DHT建立了直接联系,提出了一种新的快速DHT算法。算法的计算复杂度能够达到线性O（N）,且算法结构简单,公式统一且易于实现,并与其他快速算法进行了比较,分析可知在数据长度不是2的幂次方时,文中提出的算法的计算时间明显比其他算法的计算时间要小。实验结果也验证了文中算法的有效性,从而为DHT的快速计算开辟了新的思路和途径。     

遗传算法在周期信号傅立叶变换中的应用

为了克服周期信号进行傅立叶变换时各次谐波幅度值需要进行复杂的理论计算问题,提出一种利用遗传算法进行周期信号傅立叶变换的方法,介绍了周期信号傅立叶变换和遗传算法的基本原理;给出了使用遗传算法对周期信号进行分解适应度函数的实现方法和确定各次谐波幅值计算的方法。并提供使用遗传算法对周期信号进行分解的具体步骤。仿真实验结果表明:该方法能够满足周期信号傅立叶变换的要求,与周期信号傅立叶变换理论计算方法相比,其突出优点是算法简单,易于实现。     

The simple genetic algorithm and the Walsh transform: Part II, The inverse

This paper continues the development, begun in Part I, of the relationship between the simple genetic algorithm and the Walsh transform. The mixing scheme (comprised of crossover and mutation) is essentially "triangularized" when expressed in terms of the Walsh basis. This leads to a formulation of the inverse of the expected next generation operator. The fixed points of the mixing scheme are also determined, and a formula is obtained giving the fixed point corresponding to any starting population. Geiringer's theorem follows from these results in the special case corresponding to zero mutation.     

The speaker identification by using genetic wavelet adaptive network based fuzzy inference system

In this paper, an intelligent speaker identification system is presented for speaker identification by using speech/voice signal. This study includes both combination of the adaptive feature extraction and classification by using optimum wavelet entropy parameter values. These optimum wavelet entropy values are obtained from measured Turkish speech/voice signal waveforms using speech experimental set. It is developed a genetic wavelet adaptive network based on fuzzy inference system (GWANFIS) model in this study. This model consists of three layers which are genetic algorithm, wavelet and adaptive network based on fuzzy inference system (ANFIS). The genetic algorithm layer is used for selecting of the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the eight different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet decomposition, wavelet decomposition – short time Fourier transform, wavelet decomposition – Born–Jordan time–frequency representation, wavelet decomposition – Choi–Williams time–frequency representation, wavelet decomposition – Margenau–Hill time–frequency representation, wavelet decomposition – Wigner–Ville time–frequency representation, wavelet decomposition – Page time–frequency representation, wavelet decomposition – Zhao–Atlas–Marks time–frequency representation. The wavelet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet decomposition and wavelet entropies. The ANFIS approach is used for evaluating to fitness function of the genetic algorithm and for classification speakers. It has been evaluated the performance of the developed system by using noisy Turkish speech/voice signals. The test results showed that this system is effective in detecting real speech signals. The correct classification rate is about 91% for speaker classification.     

并行混合遗传算法在车间调度问题的应用

结合先后表编码和完全活动调度概念,设计了基于先后表的完全活动调度算法PLFA,该算法能将可行解与不可行解转化为完全活动调度。并将PLFA算法与遗传算法结合,提出了一种并行混合遗传算法,初始种群由PLFA G-T算法产生,其产生的解都是完全活动调度,采用LOX的交叉算子与基于PLFA G-T算法的变异算子,并使用主从模型的并行遗传算法模型。最后JSP基准实例验证了算法的有效性。     

A new optimum feature extraction and classification method for speaker recognition: GWPNN

Speech and speaker recognition is an important topic to be performed by a computer system. In this paper, an expert speaker recognition system based on optimum wavelet packet entropy is proposed for speaker recognition by using real speech/voice signal. This study contains both the combination of the new feature extraction and classification approach by using optimum wavelet packet entropy parameter values. These optimum wavelet packet entropy values are obtained from measured real English language speech/voice signal waveforms using speech experimental set. A genetic-wavelet packet-neural network (GWPNN) model is developed in this study. GWPNN includes three layers which are genetic algorithm, wavelet packet and multi-layer perception. The genetic algorithm layer of GWPNN is used for selecting the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the four different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet packet decomposition, wavelet packet decomposition – short-time Fourier transform, wavelet packet decomposition – Born–Jordan time–frequency representation, wavelet packet decomposition – Choi–Williams time–frequency representation. The wavelet packet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet packet decomposition and wavelet packet entropies. The multi-layer perceptron of GWPNN, which is a feed-forward neural network, is used for evaluating the fitness function of the genetic algorithm and for classification speakers. The performance of the developed system has been evaluated by using noisy English speech/voice signals. The test results showed that this system was effective in detecting real speech signals. The correct classification rate was about 85% for speaker classification.     

基于新约束图模型的布图规划和布局算法

布图规划和布局构形的表示是基于随机优化方法的布图规划和布局算法的核心问题.针对Non-slicing结构的布图规划和布局,提出了一种新的基于约束图表示的模型.基于该模型及其性质,可以得到近似O(n)时间复杂度的有效的布局算法.通过引入变形网格的假设,得到了一种新的更加精确的Non-Slicing结构的表示模型:梯形网格模型.其空间复杂度为n(3+lg[n]),时间复杂度为O(n),解空间规模为n!2^3n-7.已经证明,梯形网格模型可以表示所有的Slicing结构的布局,同时又可以有效地表示Non-Slicing结构的布局,而时间复杂度与Slicing表示相同.实验结果表明,该表示优于刚刚发表的O-tree模型.梯形网格模型是一种拓扑模型,而O-tree的表示依赖于模块的尺寸,因而梯形网格能更有效地处理含有软模块的的布图规划问题.     

混沌密钥调制DFRFT旋转因子的视频加密

现有分数阶傅里叶变换（FRFT）由于旋转因子的单一性很少应用于视频实时加密,而当前单纯混沌加密算法的安全性又存在着诸多缺陷。为此,提出一种新的视频实时加密算法——混沌密钥调制DFRFT旋转因子。该算法将混沌加密与分数阶傅里叶变换进行了有机结合。首先将离散分数阶傅里叶变换（DFRFT）的旋转因子用混沌密钥进行调制,然后用调制后的旋转因子对视频数据进行分数阶傅里叶变换,最终完成了对视频数据的加密系统。该加密系统在技术上实现了视频数据在客户端的实时采集、实时加密;密文在网络上的实时传输、密文在接收端的接收、实时解密和播放。实验结果表明,加、解密效果很好,满足了实时性与安全性的要求。对实验结果的理论、安全性分析表明,该算法简单易行、安全性高。该算法的安全性优于单纯的混沌加密算法或单纯傅里叶视频加密算法,且满足了实时性要求。为解决实时性与安全性冲突问题提供了一条新的途径。     

Layer-based representation of polyhedrons for point containment tests

This paper presents the layer-based representation of polyhedrons and its use for point-in-polyhedron tests. In the representation, the facets and edges of a polyhedron are sequentially arranged, and so, the binary search algorithm is efficiently used to speed up inclusion tests. In comparison with conventional representation for polyhedrons, the layer-based representation that we propose greatly reduces the storage requirement because it represents much information implicitly though it still has a storage complexity O(n). It is simple to implement and robust for inclusion tests because many singularities are erased in constructing the layer-based representation. By incorporating an octree structure for organizing polyhedrons, our approach can run at a speed comparable with Binary space partitioning (BSP)-based inclusion tests and, at the same time, greatly reduce storage and preprocessing time in treating large polyhedrons. We have developed an efficient solution for point-in-polyhedron tests, with the time complexity varying between O(n) and O(logn), depending on the polyhedron shape and the constructed representation, and less than O(log³n) in most cases. The time complexity of preprocess is between O(n) and O(n²), varying with polyhedrons, where n is the edge number of a polyhedron.     

基于GASA-FCM混合聚类与霍夫变换的欠定混合矩阵估计

针对模糊C-均值聚类(fuzzy C-means clustering,FCM)算法在欠定混合矩阵估计中精度低、鲁棒性差的缺点,提出一种基于遗传模拟退火优化FCM(GASA-FCM)混合聚类和霍夫变换的欠定混合矩阵估计算法。该算法首先结合了模拟退火算法(simulated annealing algorithm,SA)全局搜索、高精度的优点和遗传算法(genetic algorithm,GA)强大的空间搜索能力,将经遗传模拟退火算法得到的聚类中心点赋给FCM,避免了初值选择的随机性。再利用霍夫变换对聚类得到的每一类数据的中心进行修正,提高混合矩阵的估计精度。实验结果表明,提出的算法明显改善了算法的稳定性和混合矩阵估计精度,具有一定的有效性和可行性。     

Support Function Representation of Convex Bodies, Its Application in Geometric Computing, and Some Related Representations

The importance of thesupport functionin representation, manipulation, and analysis of convex bodies can indeed be compared with that of the Fourier transform in signal processing. The support function, in intuitive terms, is the signed distance of a supporting plane of a convex body from the origin point. In this paper we show that, just as simple multiplication in the Fourier transform domain turns out to be the convolution of two signals, similarly simple algebraic operations on support functions result in a variety of geometric operations on the corresponding geometric objects. In fact, since the support function is areal-valuedfunction, these simple algebraic operations are nothing but arithmetic operations such as addition, subtraction, reciprocal, and max–min, which give rise to geometric operations such as Minkowski addition (dilation), Minkowski decomposition (erosion), polar duality, and union–intersection. Furthermore, it has been shown in this paper that a number of representation schemes (such as the Legendre transformation, the extended Gaussian image, slope diagram representation, the normal transform, and slope transforms), which appear to be very disparate at first sight, belong to the same class of the support function representation. Finally, we indicate some algebraic manipulations of support functions that lead to new and unsuspected geometric operations. Support function like representations for nonconvex objects are also indicated.     

一种抗几何变换的半盲数字水印算法

几何攻击是一种简单、有效的数字水印攻击方法.本文根据Fourier-mellin变换方法的原理,提出了一种改进算法.在水印嵌入过程中,只进行一次全频Fourier变换,改第二次的全频Fourier变换为只对中频部分进行,并对变换过程进行改进.算法既降低了复杂度,又尽量避免了插值所导致的水印图像和水印信号的失真.实验结果表明该水印算法能抵抗RST攻击和JPEG压缩.     

Performance analysis on Lv distribution and its applications

A new signal analysis method, known as Lv distribution (LVD), has been reported recently to provide improved estimation accuracy of centroid frequency and chirp rate. In this paper, performances of the LVD on signal concentration, detection, representation errors and computational complexity are discussed and compared with polynomial Fourier transform (PFT) and fractional Fourier transform (FrFT). Based on the results of our theoretical analysis and Monte Carlo simulations, it is shown that the LVD achieves desirable performance improvement compared with those achieved by other methods. By using the accurate estimation of chirp rate provided by the LVD, the performance of local polynomial periodogram (LPP) is investigated. Comparisons with other time–frequency representations, such as the inverse LVD (ILVD) and the PFT-based LPP, are made on signal concentration in the time–frequency domain.     

A simple derivation of Glassman''s general N fast fourier transform

A simple derivation of Glassman's general N fast Fourier transform, and corresponding FORTRAN program, is presented. This fast Fourier transform is based upon a representation of the discrete Fourier transform matrix as a product of sparse matrices.     

The fractional Fourier transform: theory, implementation and error analysis

The fractional Fourier transform is a time–frequency distribution and an extension of the classical Fourier transform. There are several known applications of the fractional Fourier transform in the areas of signal processing, especially in signal restoration and noise removal. This paper provides an introduction to the fractional Fourier transform and its applications. These applications demand the implementation of the discrete fractional Fourier transform on a digital signal processor (DSP). The details of the implementation of the discrete fractional Fourier transform on ADSP-2192 are provided. The effect of finite register length on implementation of discrete fractional Fourier transform matrix is discussed in some detail. This is followed by the details of the implementation and a theoretical model for the fixed-point errors involved in the implementation of this algorithm. It is hoped that this implementation and fixed-point error analysis will lead to a better understanding of the issues involved in finite register length implementation of the discrete fractional Fourier transform and will help the signal processing community make better use of the transform.     

一种新的网络电话会议混音算法

混音处理在网络电话会议系统中起着举足轻重的作用,怎样解决混音中带来的溢出和噪声问题又是混音处理的核心。通过对现有混音算法中存在问题的分析,提出了一种自适应减谱法,在不发生溢出现象的基础上,对语音信号进行傅里叶变换,从带噪语音的功率谱中减去噪声功率谱,从而得到较为纯净的语音频谱来降低噪声,再进行傅里叶反变换将其与混音权重相适应,使混音后的音质更清晰且流畅,避免了混音过程中的噪声问题,提高了混音后的质量,音质更接近于现场会议,可以应用于多媒体电话会议系统中。     

基于二维FFT的图像滤波方法及实现

二维快速傅立叶变换(FFT)在一个传统概念的处理机上实现时,需要芯片具有更多的逻辑资源。本文给出了基于FPGA的自定义处理机(CCM)的二维FFT算法和实现。在CCM的Splash-2平台上实现了二维FFT,计算速度达到180Mflops,最快速度超过Sparc-10工作站的23倍。同时,对于一个N×N图像,这种实现方法可以满足二维FFT所需要的O(N2log2N)次的浮点算术运算。     

平稳Tetrolet变换算法研究

为了得到有效的图像多尺度几何表达,提出一种有效的基于Haar小波变换的平稳Tetrolet变换算法.平稳Tetrolet变换是一种由四个单位正方形通过边连接起来的新的自适应Haar类小波变换,对应的滤波器组简单而有效.与标准二维小波变换相比,平稳Tetrolet变换是一种新型基于四格拼板的多尺度几何变换工具,能够通过多方向选择有效地捕获图像中各向异性特性.本文对平稳Tetrolet变换的分解和重构算法进行了详细描述,对利用平稳Tetrolet变换对图像的分解进行了仿真与分析.实验结果表明,与传统算法相比,提出的算法在保留原始图像边缘和纹理信息的同时,可以有效地取得较好的稀疏表达,能消除Tetrolet变换算法对图像融合存在方块效应的缺陷.     

基于椭圆傅里叶描述子的形状表示的研究

形状表示是模式识别和计算机视觉中最重要的研究内容之一。针对传统形状表示算法对形状的整体特征和细节信息不能同时描述、通用性不高的问题,提出了一种基于高斯多尺度分析下的椭圆傅里叶描述算子。提出的算法利用高斯函数与目标形状的复坐标函数进行卷积,通过选择高斯曲线的参数,将形状的边界信息呈现到不同的尺度空间之中;利用椭圆傅里叶变换将其展开得到表示该形状的特征向量。实验结果表明,该方法的优点在于描述同类形状时,特征向量之间的相关系数高,具有很好的平移、旋转以及尺度不变性;在描述不同类形状时,相关系数低,有很强的形状区分能力。该方法在形状分类实验中也有较高的检索准确率。