一种改进的小波阈值高斯噪声图像降噪方法

关于图像传输的优化问题,针对传统小波阈值降噪方法,为了克服硬阈值函数不连续,软阈值函数中估计小波系数与分解小波系数之间存在恒定偏差,构造了一个新的非线性阈值函数,通过调整参数可视化地改变阈值函数的形状,将Dono-ho的硬阈值和软阈值作为两种特殊的情况.算法采用对非重要小波系数的处理,不是均设为0,系数可由多项式调节以接近理想小波系数,并进行仿真.仿真结果表明,算法对图像中的加性噪声能很好地去除,并且较好地克服了传统软、硬阈值存在的振荡和边界模糊偏差缺陷,改善了图像质量.     

基于NDVI阈值法反演地表比辐射率的参数敏感性分析

地表比辐射率是反演地表温度的重要因子,普遍应用于自然界中的水热交换和辐射传输过程。本文通过对基于NDVI阈值法反演地表比辐射率的相关参数进行敏感性研究,确定了影响NDVI阈值法反演地表比辐射率的关键参数,以期通过关键参数获取方法的改进和参数不确定性的降低来提高NDVI阈值法的反演精度。研究以Valor和Caselles在1996年提出的植被指数混合模型为理论基础、湖北省荆门市为研究区域,使用Land-sat TM5数据,采用控制变量法,对NDVI阈值法中影响比辐射率反演精度的大气因子、地形因子、纯像元NDVI阈值、纯像元比辐射率值进行敏感程度的数学分析。研究发现,4大因子敏感性程度:纯像元比辐射率值>纯像元NDVI阈值>地形因子>大气因子,其中纯像元比辐射率值和纯像元NDVI阈值为关键参数;4大因子影响程度:大气因子>纯像元比辐射率值>地形因子>纯像元NDVI阈值。     

基于噪声特性阀值的语音去噪

分析合有噪声的语音信号和噪声信号，对他们分别进行小渡分解，提取各层嗓声小渡系数低频的特性。将提取的各层噪声的特性作为阀值进行去噪。     

高斯白噪声发生器在FPGA中的实现

介绍了一种基于FPGA的可配置高斯白噪声发生器的算法、结构和实现.根据Wallace算法,利用正态分布的可加性直接产生高斯白噪声变量.同时用户可以通过内置异步串行接口对系统进行参数配置.该噪声发生器由Verilog语言编写,可应用于实际无线通信信道模拟器中.     

Stability Region Analysis Using Polynomial and Composite Polynomial Lyapunov Functions and Sum-of-Squares Programming

We propose using (bilinear) sum-of-squares programming for obtaining inner bounds of RoAs for dynamical systems with polynomial vector fields. We search for polynomial as well as composite Lyapunov functions comprised of pointwise maximums of polynomial functions. Results for several examples from the literature are presented using the proposed methods and the PENBMI solver.     

基于Lagrange插值多项式的门限方案的实现

门限方案中,将秘密分割为若干份,需要多个秘密拥有者合作才能恢复秘密。可防止因一部分人原因而泄露秘密,使密钥的管理更加安全灵活。实现了一个基于Lagrange插值多项式的门限方案,包括秘密分割和秘密恢复两方面。并介绍了在基于DSP芯片的加密卡上的应用,实现密钥的管理,如卡内关键数据的备份、恢复等重要操作。     

Improved Approximation of Linear Threshold Functions

We prove two main results on how arbitrary linear threshold functions ${f(x) = {\rm sign}(w \cdot x - \theta)}$ over the n-dimensional Boolean hypercube can be approximated by simple threshold functions. Our first result shows that every n-variable threshold function f is ${\epsilon}$ -close to a threshold function depending only on ${{\rm Inf}(f)^2 \cdot {\rm poly}(1/\epsilon)}$ many variables, where ${{\rm Inf}(f)}$ denotes the total influence or average sensitivity of f. This is an exponential sharpening of Friedgut’s well-known theorem (Friedgut in Combinatorica 18(1):474–483, 1998), which states that every Boolean function f is ${\epsilon}$ -close to a function depending only on ${2^{O({\rm Inf}(f)/\epsilon)}}$ many variables, for the case of threshold functions. We complement this upper bound by showing that ${\Omega({\rm Inf}(f)^2 + 1/\epsilon^2)}$ many variables are required for ${\epsilon}$ -approximating threshold functions. Our second result is a proof that every n-variable threshold function is ${\epsilon}$ -close to a threshold function with integer weights at most ${{\rm poly}(n) \cdot 2^{\tilde{O}(1/\epsilon^{2/3})}.}$ This is an improvement, in the dependence on the error parameter ${\epsilon}$ , on an earlier result of Servedio (Comput Complex 16(2):180–209, 2007) which gave a ${{\rm poly}(n) \cdot 2^{\tilde{O}(1/\epsilon^{2})}}$ bound. Our improvement is obtained via a new proof technique that uses strong anti-concentration bounds from probability theory. The new technique also gives a simple and modular proof of the original result of Servedio (Comput Complex 16(2):180–209, 2007) and extends to give low-weight approximators for threshold functions under a range of probability distributions other than the uniform distribution.     

小波阈值去噪在图像去噪中的应用

小波阈值去噪分为硬阈值去噪和软阈值去噪,它们的去噪思想都是在小波分解后的各层系数中,对模大于和小于某个阈值的系数分别进行处理.本文主要针对图像去噪,将一幅图像分别进行软和硬阈值去噪的方法进行比较,得出无论阈值门限设为何值,软阈值的去噪效果总是比硬阈值的去噪效果好这个结论.     

一种高斯白噪声信号发生器的设计与实现

介绍了高斯白噪声的数学模型,设计了一种高斯白噪声信号发生器,使用FPGA产生m序列,经过数模转换、滤波和放大,实现了特性良好的高斯白噪声;在该高斯白噪声信号发生器中,采用"有限截取源序列"方法,增加了伪随机序列的长度,具有结构简单和实现方便的优点;同时,可通过改变DAC的输出电压对高斯白噪声信号发生器的输出幅值进行调节,满足不同场合的需求,扩大了其应用领域;测试结果表明,输出的高斯白噪声信号形状比较理想,在0～10MHz范围内幅度可调。     

Hermite Interpolation Polynomial for Functions of Several Variables

Cybernetics and Systems Analysis - The Hermite interpolation problem in the Euclidean space is considered, where the value of the function of several variables and its first-order and second-order...     

消除二值化图像干扰方法的研究

在智能车牌识别系统中,对通过图像获取技术得到的复杂灰度图像进行二值化处理后,在图像中存在许多干扰白点。提出一种“高斯滤波”的方法去除这些白点,使二值化图像平滑、少毛刺,并具有较好的连通性。     

散乱数据点多项式插值光顺曲面的构造

为了得到光顺的多项式插值曲面,首先把空间散乱数据点划分为三角形网格,在每个给定数据点处构造C^1连续的分片二次多项式曲面片,针对各数据点的邻接点个数不同,分别利用弯折能量和拉伸能量建立目标函数,极小化目标函数确定插值曲面的未知量,在保持原有的形状特征的同时构造光顺的分片插值曲面,最后用实例说明了文中方法的有效性．     

Reconstruction of Sparse Vectors in White Gaussian Noise

We consider the problem of reconstruction of a sparse vector observed against a background of white Gaussian noise. The sparsity is assumed to be unknown. Two approaches to statistical estimation in this case are discussed, namely, the model selection method and threshold estimators. We propose a method of selecting a threshold estimator based on the principle of empirical complexity minimization with minimal conservative penalization.     

A Survey of Procedural Noise Functions

Procedural noise functions are widely used in computer graphics, from off‐line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This survey is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry and the fact that many recent research developments justify the need for an up‐to‐date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well‐established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modelling and how they are applied to surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation. We finally identify several directions for future work.     

基于高斯曲率和局部方差的去噪模型

用PM(Perona and Malik)模型去除椒盐噪声,使低噪声强度下未受噪的平坦区域的像素值减小,但是不能在有效去噪的同时保护纹理细节,导致图像模糊.为此,用局部方差和高斯曲率代替梯度模值来描述图像局部纹理细节,并定义了噪声度量函数,随之引入扩散方程,得到新去噪模型.实验结果表明:新模型不仅能有效地除去椒盐噪声和解决PM模型的问题,而且信噪比和峰值信噪比均有显著提高.因此新模型优于PM模型.     

Efficiency of Atomic Splittable Selfish Routing with Polynomial Cost Functions

Previous works on the inefficiency of selfish routing have focused on the Wardropian traffic equilibria with an infinite number of infinitesimal players, each controlling a negligible fraction of the overall traffic, but only very limited pseudo-approximation results have been obtained for the atomic selfish routing game with a finite number of players. In this note we examine the price of anarchy of selfish routing with atomic Cournot–Nash players, each controlling a strictly positive splittable amount of flow. We obtain an upper bound of the inefficiency of equilibria with polynomial cost functions, and show that the bound is 1 or there is no efficiency loss when there is only one player, and the bound reduces to the result established in the literature when there are an infinite number of infinitesimal players.     

Representing Probabilistic Rules with Networks of Gaussian Basis Functions

There is great interest in understanding the intrinsic knowledge neural networks have acquired during training. Most work in this direction is focussed on the multi-layer perceptron architecture. The topic of this paper is networks of Gaussian basis functions which are used extensively as learning systems in neural computation. We show that networks of Gaussian basis functions can be generated from simple probabilistic rules. Also, if appropriate learning rules are used, probabilistic rules can be extracted from trained networks. We present methods for the reduction of network complexity with the goal of obtaining concise and meaningful rules. We show how prior knowledge can be refined or supplemented using data by employing either a Bayesian approach, by a weighted combination of knowledge bases, or by generating artificial training data representing the prior knowledge. We validate our approach using a standard statistical data set.     

Neuronal Signal Transduction Aided by Noise at Threshold and at Saturation

A classical model of neuronal signal transmission describing the presence of both a threshold and a saturation in the neuron response is considered. This model is used to analyze the transduction by the neuron of various types of information-carrying input signals in the presence of noise. Improvement by noise of the performance via stochastic resonance is established for transmission in both the threshold and the saturation regimes. Stochastic resonance at saturation is a novel form, expressing that the distortion experienced by large input signals transmitted at saturation, can be reduced by addition of noise.